Developing Ensemble Methods for Initial Districting Plan Evaluation

Green graph over Washington State

A dual graph of the 2010 census blocks for Washington State.

Project leads: Daryl DeFord, Assistant Professor of Data Analytics in the Department of Mathematics and Statistics at Washington State University

Data scientists: Bernease Herman and Vaughn Iverson

DSSG fellows: Rowana Ahmed, Katherine Chang, Ryan Goehrung, Michael Souffrant

Participant bios available here.

Project Summary: Gerrymandering, the drawing of legislative boundaries for improper purposes, is a fundamental threat to democracy. While the history of American gerrymandering extends back 200 years, representing a wide variety of specific aims and harms, recent advances in computational methods and data analysis have exacerbated this problem. However, these same circumstances have also allowed us to develop tools for evaluating proposed maps and detecting outliers. Our work this summer aims to create a better understanding of how these methods can be used for the effective design of new maps.

Additionally, in 2018 several states passed bills to reform their redistricting practices, and the For the People Act attempts to address some of these same issues on a national scale. Evaluating the likely impacts of this legislation is a complex problem that calls for detailed analysis of the laws and history of each state, directly incorporating the local political geographies. Since many of the currently implemented analysis techniques were developed and tested in the adversarial court setting, translating these techniques for this broader task will require new, creative ideas.

We intend to use modern computational methods and statistical techniques to evaluate potential tradeoffs between redistricting criteria and proposed methods for challenging newly drawn plans. Recent evidence shows that Markov chain Monte Carlo methods can be successfully implemented to establish baselines of neutrally-constructed maps, and this will be our main analytical tool. The research team will use the GerryChain software in Python to construct ensembles tailored to individual state’s geographies and rules, as well as analyzing the properties of the resulting maps with respect to the relevant legislative and litigation history.

The main objectives of this project are to provide recommendations about effective ways to incorporate sampling methodology into the initial design of redistricting plans and to evaluate the likely success of proposed legislative solutions. These results will be valuable to reform groups, legislators, and commissions and as the 2021 redistricting cycle is currently underway, there will also be opportunities to collaborate directly with many interdisciplinary groups working in this field representing perspectives of reform advocates, legal scholars, and social scientists. Beyond this empirical work, there is also significant space for the investigation of creative theoretical contributions to the mathematical study of this problem.

Geography, equity, and the Seattle $15 minimum wage ordinance

 

Aerial street view

Photo by Domas Mituzas, Creative Commons

Project leads: Jennie Romich, Professor of Social Welfare at the UW School of Social Work and Faculty Director of the West Coast Poverty Center

Data scientists: Jose Hernandez and Valentina Staneva

DSSG fellows: Lamar Foster, Delaney Glass, Christopher Salazar, Mahader Tamene

Participant bios available here.

Project Summary: This project examines the minimum wage as a solution to income inequality – and one potential limitation to this approach. In 2014, Seattle was the first major city to pass a $15 minimum wage. Policymakers hoped that higher wages for low-paid workers would reduce inequality and poverty and make Seattle workers and their families better off.

However, the phase-in of Seattle’s $15 wage coincided with another economic jolt– the rapid influx of tens of thousands of high-paid technology workers. These new arrivals needed housing. Rental prices climbed rapidly, and large swaths of the city quickly gentrified.

As a result, low-wage workers were likely priced out of Seattle housing just as the raise to $15 took effect. While economists argue over the extent to which increasing minimum wages lowers *demand* for low-paid workers, our hot housing market might have also affected the *supply* of people looking for low-paid jobs.

This project will probe the hypothesis that the supply of low-wage labor dropped in Seattle over the years 2014-2016. Our DSSG team will work to understand residential relocation (moves) relative to places of employment (jobs) in the Puget Sound.

Questions include, did lower earners move out of Seattle faster than higher earners? How many workers earning at or near the minimum wage in 2014 moved out of the city over the subsequent years? Was that rate of relocation faster than for earlier cohorts of low-paid workers? How did low-wage workers’ commutes change? For each low-paid position in the city, how many workers of similar wage rates live within reasonable commuting radii?

We will use new and unique data, the Washington Merged Longitudinal Administrative Data (WMLAD) that we have created during the past five years with the help of several state agencies. WMLAD is the most comprehensive state-level geocoded administrative dataset assembled to examine employment and earnings outcomes.