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Benefits Cliffs and Effective Marginal Tax Rates

I study how the structure of the U.S. tax and transfer system - featuring phase-outs as income rise - create disincentives for economic mobility of low- and moderate-income families.

Below is the full list of my work that explores this issue.

Mitigating Benefits Cliffs for Low-Income Families: District of Columbia Career Mobility Action Plan as a Case Study
With Alvaro Sanchez

The structure of the United States social safety net features the phaseout of public assistance as household income increases, which functions as an effective marginal tax on wage gains and is commonly referred to as a "benefits cliff." This presents a disincentive for some low-income workers, especially those with children, to accept higher-paying jobs or promotions. Workforce development programs focused on helping low-income workers must contend with the challenges that benefits cliffs present to the career advancement of their clients. In this paper, we describe the overall structure of the public assistance benefits system in the District of Columbia (DC) and describe how benefits cliffs affect the financial resources of a single adult with one child. Afterward, we introduce the DC Career Mobility Action Plan (Career MAP), a five-year pilot program (2022–27), as a case study for implementing benefits cliff mitigation strategies for workers seeking to find employment and increase their earnings. Our findings suggest that Career MAP’s policies, which function as rental assistance and cash payments to offset benefits losses, reduce the effective marginal tax rates families experience below 100 percent, helping households to avoid experiencing benefits cliffs.

Estimating Public Assistance Program Participation
With Alvaro Sanchez and Victor Ye

Many low-income families are eligible for public assistance but do not “take up” the benefit. In this paper, we develop a novel, machine learning-based method to estimate take-up rates for seven major U.S. social safety net programs. Our method consists of four steps. First, we determine the universe of respondents to the 2019 Annual Social and Economic Supplement (ASEC) of the Current Population Survey (CPS) that are eligible for social safety net programs of interest. Second, we contrast three alternative algorithms for binary classification (gradient-boosted trees, random forests, and logistic regression) and evaluate their performance on predicting out-of-sample program take-up in the ASEC. Specifically, we select XGboost (Chen and Guestrin 2016), a gradient-boosted classifier, for its relative efficiency and high performance of 70 to 90 percent out-of-sample accuracy. Third, to address underreporting of program participation, for each program-eligible observation in ASEC that does not report participation, we utilize XGboost to assign participation indicators to match national statistics on take-up rates. Finally, we apply a similar algorithm to the adjusted ASEC data, and re-estimate the model to relate social-economic factors to public assistance take-up. Importantly, the imputation method developed in this paper can be applied to any dataset with a detailed family-level demographic and income information. We demonstrate how the method can be used, by applying it to the 2019 American Community Survey (ACS) and describing patterns of the U.S. social safety net program participation. We find that take-up rates vary considerably by program, geographical location, and demographic characteristics.

Marginal Net Taxation of American Labor Supply
With David Altig, Alan Auerbach, Laurence Kotlikoff, and Victor Ye

The U.S. has a plethora of federal and state tax and benefit programs, each with its own work incentives and disincentives. This paper uses the Fiscal Analyzer (TFA) to assess how these policies, in unison, impact work incentives. TFA is a life-cycle, consumption-smoothing program that incorporates household borrowing constraints and all major federal and state fiscal policies.

Media Mentions
Benefits Cliffs and the Financial Incentives for Career Advancement: A Case Study of the Health Care Services Career Pathway
With David Altig, Alexander Ruder, and Ellie Terry

Benefits cliffs, which occur when earnings gains are offset by the loss of public benefits, have long been recognized to create financial disincentives for low-income individuals to earn more income. In this paper, we develop a new methodology to study benefits cliffs in the context of career advancement. We illustrate the change in net financial resources for an individual pursuing the health care services career pathway from certified nursing assistant (CNA) to licensed practical nurse (LPN) to registered nurse (RN).

Restructuring the Eligibility Policies of the Child Care and Development Fund to Address Benefits Cliffs and Affordability
With Brittany Birken, Alexander Ruder, and Ellie Terry

This paper explores how the current eligibility policies of the federal Child Care and Development Fund (CCDF) create affordability challenges and benefits cliffs that act as barriers to economic self-sufficiency. By examining Florida data and policies, the authors demonstrate how the program’s existing co-payment schedule affects the same hypothetical family living in two contrasting Florida counties: one with state median living costs and one with high living costs.

Guaranteed Income (GI) Dashboard: Visualizing Public Benefits Loss for Participants in GI Pilot Programs
With Alexander Ruder and Ellie Terry

We have developed the Guaranteed Income Dashboard tool as part of our Advancing Careers initiative. Partners Update discusses the tool and how practitioners, policymakers, and GI recipients are using it in GI pilot programs across the country.

Policy Rules Database: Visualizing the US Social Safety Net
With Ellie Terry

We have developed the Policy Rules Database to provide clarity on the complexity of the US social safety net. This article provides an overview of the database and visualization tool.

The Racial Income Gap and Benefits Cliffs
With Alexander Ruder and Ellie Terry

Income gaps along racial lines are the result of long-standing structurally discriminatory policies across U.S. institutions. Although laws have changed, the legacy of these policies still affects people today. This article examines how benefits cliffs differentially affect minority populations.

Amid the COVID-19 Crisis, a Tale of Two Southeastern Cities
With John Robertson, Alexander Ruder, and Ellie Terry

Congress passed the CARES Act and other legislation to help workers who have lost their jobs during the pandemic. This article considers a case study of a hypothetical laid-off restaurant worker in Birmingham and Miami and the potential financial assistance he’d receive in each city.

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