Proposed changes to the federal racial and ethnic classification system in the United States offer a unique opportunity to understand how the general public thinks about Asian American identity and how Asian populations should be classified in federal data. The Improving Asian Classification Typologies (ImpACT) project analyzes public comments submitted in response to two Federal Register Notices: (1) the OMB’s proposed revisions to Statistical Policy Directive 15, 2023, and (2) the U.S. Census Bureau’s draft race and ethnicity coding guidelines, 2024.

Using a mixed deductive-inductive coding framework, six coders working in pairs analyzed comments to develop key themes. Overall, we found the boundaries of the Asian category are contested in the comments, particularly at the intersections with the Middle Eastern and North African, White, and Native Hawaiian and Pacific Islander categories. The geographic subgroups used to define Asian communities, including East Asian, Southeast Asian, and South Asian, are similarly disputed, particularly for communities with identities that traverse regional boundaries. We also found disagreement over whether Census’ pre-defined regional categories should be retained or eliminated, with some commenters arguing that standardized groupings are essential for longitudinal research and reporting, while others contending that pre-defined categories introduce misclassification and can undermine community self-identification. Additional themes examine debates over terminology, data collection practices, and how classification decisions shape community visibility and health equity.

On Wednesday April 15, from 10 – 11:30 AM, CSDE will host a workshop that provides a basic introduction to Agent-Based Modeling (ABM). The workshop will be divided into three sections. During the first third of the course we will review and discuss the basic elements of ABMs and their applications in a variety of fields including demography, sociology, anthropology, political science and public health. In the second section of the course we will work through one or two seminal examples of ABMs and reproduce the models in base R. Due to the limited time available, the R code to build these models will be provided to participants in advance. Finally, we will walk through an example of a complex ABM using the statnet and EpiModel R packages. Students will not need these packages to complete the workshop.

By the end of the workshop participants will be able to describe the unique features of ABM that make them distinct from other modeling approaches, write R functions to produce a simple ABM, and be familiar with additional R packages that provide functionality for ABMs.

On April 15th from 10 – 11 AM PT, the UW Computational Demography Working Group will host CSDE Affiliate Dr. Andrew Messamore (Sociology). Dr. Messamore will deliver a talk titled “The Emergence of Ownership Opacity in Landed Capitalism: Consolidation, Adaptation, Evasion The talk will be held in person at Raitt 223.

Declining sole proprietorship rates among landlords are viewed as indicators of growing corporate control of rental housing. However, declines in sole proprietorship may reflect the popularization of investment vehicles across amateur landlords, causing studies to overestimate the ownership share of firms. Moving beyond political economy, this presentation conceptualizes landed capitalism as a complex and adaptive housing system, and proposes declines in sole proprietorship reflect the emergence of ownership opacity across the landlord population. Evaluating this perspective through an enumeration of landownership in Austin, Texas, results from longitudinal analysis show that the ownership share of small landlords is stable, but that processes of portfolio consolidation, investor adaptation, and regulatory evasion are encouraging opaque ownership structures across landlords of nearly all sizes. These findings suggest ownership opacity is a form of emergent complexity created by population evolution among landlords, and demonstrate the utility of housing systems theory for both the field of comparative landownership studies and policies that aim to address disparities in housing ownership and tenure.

On Wednesday, April 15, CSSS will host Dr. Charles Lanfear (Cambridge University) who will deliver a seminar titled, “Reciprocal Relationships, Reverse Causality, and Temporal Ordering: Testing Theories with Cross-lagged Panel Model.” Sign-ups for meeting with Dr. Lanfear are available here. CSSS is also sponsoring a lunch for students. Those interested can add their name/email to the this google sheet.

Abstract: Reciprocal causal relationships are a common feature of criminological theories. For example, stable employment may reduce offending while offending may lead to job loss, and perceived disorder may increase fear of crime while fear of crime may increase sensitivity to signs of disorder. When multiple observations over time are available, cross-lagged panel models are commonly used to estimate these reciprocal effects. Yet this is often done without careful attention to how they map on to the theoretical process they are meant to capture or whether key assumptions of the models are satisfied. This may result in estimates that are not substantively meaningful or are biased or even reversed in sign. Reciprocal relationships also pose challenges for causal assumptions based on graphical tools; theories that posit reciprocal causation often rely on underlying macro–micro mechanisms not explicitly represented in empirical models. We provide guidance on how to align theory, model specification, and choice of estimator, illustrated with two motivating examples: the relationship between employment and offending and the relationship between perceived disorder and fear of crime. We distinguish theoretical reciprocality—in which feedback processes are of substantive interest—from reverse causality—in which reciprocal processes are a nuisance threatening identification of an effect of interest—and discuss three common issues for cross-lagged panel models: unobserved time-stable heterogeneity, improperly specified temporal order, and insufficient variation over time. We conclude by reminding researchers of the importance of aligning methods with theory in longitudinal research.

Charles Lanfear is an Associate Professor and Director of the MPhil in Criminology at the Institute of Criminology in the University of Cambridge. He conducts theory-driven research on how crime, victimization, and social control are related to large-scale changes in communities and society as a whole using methods from econometrics, epidemiology, and demography.