Research and Projects

In the US, elections are often administered by directly elected local officials who run as members of a political party. Do these officials use their office to give their party an edge in elections? Using a newly collected dataset of nearly 5,900 clerk elections and a close-election regression discontinuity design, we compare counties that narrowly elect a Democratic election administrator to those that narrowly elect a Republican. We find that Democrats and Republicans serving equivalent counties oversee similar election results, turnout, and policies. We also find that reelection is not the primary moderating force on clerks. Instead, clerks may be more likely to agree on election policies across parties than the general public and selecting different election policies may only modestly affect outcomes. While we cannot rule out small effects that nevertheless tip close elections, our results imply that clerks are not typically and noticeably advantaging their preferred party. 

Journal link here; ungated version available here.

Presented at Southern Political Science Association (SPSA) Annual Meeting as well as meeting of the Election Sciences, Reform, and Administration (ESRA) conference.

Press coverage:

• https://podcasts.apple.com/be/podcast/how-concerned-should-we-be-about-partisan-election/id1496412542?i=1000554200694

• https://www.niskanencenter.org/partisan-election-administrators-dont-tip-the-scales/

• https://goodauthority.org/news/partisan-election-officials-dont-advantage-their-own-party/ 

We come up with causally valid estimates of the effect of education on voter turnout, donation to political campaigns, and other measures of political participation. The main approach is a regression discontinuity (RD) design on administrative data on nearly 16 million applicants to the Cal Grant program in California. Leveraging as-if random assignment of Cal Grant receipt, we estimate the effect of enrolling in a California higher ed institution (UC, CSU, private college, trade school, etc.) on the aforementioned outcomes. We also offer evidence about colleges' effect on applicants' political affiliation and provide suggestive analyses about the mechanisms that undergird these effects.

Journal link here; ungated version available here.

• Awarded a summer research award from the California Policy Lab (CPL) (Summer 2022).

• Presented at 2023 Midwest Political Science Association (MPSA) Annual Meeting, 2024 Southern California Conference in Applied Microeconomics, and 2024 annual meeting of the Association for Public Policy Analysis and Management.

We examine the current state and some historical details of election administration in the U.S., which is to say local election administration in the U.S. We characterize states--and, where relevant, municipalities--in terms of various election administration characteristics, including who holds final authority, how many individuals are tasked with responsibility for overseeing elections, nuances of jurisdictions with shared power, and so on.  We also describe various personal characteristics of local election officials, for example finding that they are on average older and more likely to identify as white.

Publication link here; ungated version available here.

An assessment of California's supply of primary care providers (MD physicians, PAs, and advance practice RNs) across a multitude of data sources. Dimensions that were analyzed include geographic distribution, racial and ethnic composition, and years of clinical experience.

A forecast of primary care providers using projections built on report 1 as well as estimates of demand from the California Health Interview Study (CHIS) and privately obtained data on clinician trainees from individual programs.

An analysis of the current and expected future supply of behavioral health professionals (e.g., therapists and psychologists) with a particular focus on areas with disparities between need and supply (e.g., California's San Joaquin Valley).

Using a survey of hundreds of federal government employees — including technical experts — I wrote up an analysis of the federal government's readiness with regards to artificial intelligence (AI). The report analyzes the full survey findings, including breakouts by demographic group and professional function in the government.

Sponsored by Dell EMC and Vion Corporation.

Winter 2018-2019 saw the longest federal government shutdown in U.S. history, shuttering agencies, national parks, and salaries to furloughed employees for 35 days. Using a survey fielded to a diverse sample of federal government employees, my GBC colleagues and I wrote up public opinion around both the shutdown itself and the underlying drivers. 

Quick highlights: While some federal government respondents supported both the construction of a wall along the U.S.-Mexico border and the shutdown, a majority of respondents both. Support among Republicans and active duty/civilian military respondents was higher for both the wall and the shutdown. At one point during the shutdown, just one-quarter of respondents were not confident or only slightly confident that they would receive backpay for the furlough period.

An analysis combining public opinion polls and election predictions based on leading economic indicators (LEIs) to assess the impact of coronavirus on President Trump's reelection prospects. Using local economic data from the Bureau of Labor Statistics, I synthesize an LEI-based perspective with one simply based on favorability for a POTUS whose favorability has remained remarkably consistent (albeit low).

An analytical opinion piece that contextualizes Florida's Amendment 4 / SB 7066 in a broader historical frame of Jim Crow voter suppression. Combines independent analysis and synthesis from existing literature.

Using data from the Office of Juvenile Justice and Delinquency Prevention (OJJDP) for 2017, I present summary statistics for demographics (race, gender, etc.) and criminal penalty distribution, and offer commentary on trends for youth in juvenile detention.

During the first iteration of what is commonly referred to as President Trump's first travel ban, and prompted by work done in support of a blog post from the California Health Care Foundation (CHCF), I used ACS data to estimate the number of foreign-born medical clinicians in the U.S. and assess their importance for health care in California.

During the Trump administration's efforts to repeal and replace the ACA (Obamacare) in early 2017, former Senator John McCain made national headlines for a dramatic vote that ultimately sank the effort and dealt an early blow to the administration. I took a look at the data to see what polling told us about ACA public opinion at the time.

Does copartisanship between executive-level politicians (particularly, the President of the United States) and members of Congress produce political bias in disaster funding? Existing work suggests bias in Presidents’ decisions to declare disasters at the county level, but scholars have not credibly examined whether copartisanship is also associated with bias in downstream measures (e.g., funding). I study this question using a regression discontinuity (RD) design where as-if random assignment of party at the Congressional district-year level determines copartisanship with the President. Looking across all disasters that received FEMA funding between 1990 and 2022, I find that, while there is a substantial and statistically significant effect of electing a copartisan on disaster relief funding, this effect is negative across several specifications and depending on modeling choices. Preliminary heterogeneity analyses examine when the bias is more or less likely to occur, looking across Presidential terms, party, and timing (i.e. early or later in a President’s tenure) as well as across disaster type and other measures.

• Accepted at Southern Political Science Association (SPSA) Annual Meeting, 2023.

Abstract

TBD.

Abstract

TBD.