The Marginal Labor Supply Disincentives of Welfare: Evidence from Administrative Barriers to Participation

Moffitt, Robert A.

doi:10.3386/w26028

“…(Kochanek & Buka, 1998), tutoring programs (Nickow et al, 2020), and extracurricular opportunities (Houser, 2016). Scholars in the broader literature on social and antipoverty policy often use the term administrative burden to describe the barriers that prevent families from accessing socially provided resources and services (Herd & Moynihan, 2020;Ko & Moffitt, 2022;Moffitt & Zahn, 2019). School-based service provision efforts during the pandemic sought to minimize administrative burden, often substantially altering the organizational structure of schools to do so.…”

Section: Framing the Problem Of Resource Uptakementioning

confidence: 99%

“…Achieving successful uptake is no trivial task for school officials overseeing myriad programs such as nutrition, counseling, social work, and other student-wellness services. Social service and support providers in a wide range of contexts struggle with low rates of program participation (Moffitt & Zahn, 2019). For example, less than a quarter of U.S. families in poverty receive the cash assistance for which they are qualified under the federally funded Temporary Assistance for Needy Families (TANF) program (Floyd et al, 2017); and in 2019, less than 60% of those eligible for the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) received program benefits (Gray et al, 2022).…”

mentioning

confidence: 99%

Offer It and They Will Come? An Investigation of the Factors Associated With the Uptake of School-Sponsored Resources

Kearney,

Nidia Garza,

Perez

et al. 2023

American Educational Research Journal

2

0

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In response to economic distress, schools are increasingly serving as providers and distributors of social service resources. However, even when schools offer resources that respond to needs, they struggle to attain high levels of uptake. We examine the family-level correlates of participation in school-sponsored resources during the early months of the COVID-19 pandemic and find that uptake increases with economic need. In addition, net of need, families who report maintaining communication with parents of their children’s classmates take up more resources; and take-up of key meal and digital technology resources is associated with higher levels of take-up of other resources. These findings contribute to efforts to reposition schools as social service hubs by highlighting promising practices to improve resource uptake.

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“…1 Introduction Heckman and Vytlacil (1999) introduced the marginal treatment effect (MTE) as a unifying concept for program and policy evaluation. 1 Since then, MTE methods have become a fundamental tool for empirical work, and have been applied in a variety of different settings including the returns to schooling (Moffitt, 2008;Carneiro, Heckman, and Vytlacil, 2011;Carneiro, Lokshin, and Umapathi, 2016;Nybom, 2017) and its impacts on wage inequality (Carneiro and Lee, 2009), discrimination (Arnold, Dobbie, and Yang, 2018;Arnold, Dobbie, and Hull, 2020), the effects of foster care (Doyle Jr., 2007), the impacts of welfare (Moffitt, 2019) and disability insurance (Maestas, Mullen, and Strand, 2013;French and Song, 2014;Autor, Kostøl, Mogstad, and Setzler, 2019) programs on labor supply, the performance of charter schools (Walters, 2018), health care (Kowalski, 2018;Depalo, 2020), the effects of early childhood programs (Kline and Walters, 2016;Cornelissen, Dustmann, Raute, and Schönberg, 2018;Felfe and Lalive, 2018), the efficacy of preventative health products (Mogstad, Santos, and Torgovitsky, 2017), the quantity-quality theory of fertility (Brinch, Mogstad, and Wiswall, 2017), and the effects of incarceration (Bhuller, Dahl, Løken, and Mogstad, 2020), among many others. Mogstad and Torgovitsky (2018) provide a recent review of the MTE methodology and its connection to other instrumental variable (IV) approaches.…”

mentioning

confidence: 99%

Policy Evaluation with Multiple Instrumental Variables

Mogstad¹,

Torgovitsky²,

Walters³

2020

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We thank Vishal Kamat and Ed Vytlacil for helpful comments. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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“…1 Introduction Heckman and Vytlacil (1999) introduced the marginal treatment effect (MTE) as a unifying concept for program and policy evaluation. 1 Since then, MTE methods have become a fundamental tool for empirical work, and have been applied in a variety of different settings including the returns to schooling (Moffitt, 2008;Carneiro, Heckman, and Vytlacil, 2011;Carneiro, Lokshin, and Umapathi, 2016;Nybom, 2017) and its impacts on wage inequality (Carneiro and Lee, 2009), discrimination (Arnold, Dobbie, and Yang, 2018;Arnold, Dobbie, and Hull, 2020), the effects of foster care (Doyle Jr., 2007), the impacts of welfare (Moffitt, 2019) and disability insurance (Maestas, Mullen, and Strand, 2013;French and Song, 2014;Autor, Kostøl, Mogstad, and Setzler, 2019) programs on labor supply, the performance of charter schools (Walters, 2018), health care (Kowalski, 2018;Depalo, 2020), the effects of early childhood programs (Kline and Walters, 2016;Cornelissen, Dustmann, Raute, and Schönberg, 2018;Felfe and Lalive, 2018), the efficacy of preventative health products (Mogstad, Santos, and Torgovitsky, 2017), the quantity-quality theory of fertility (Brinch, Mogstad, and Wiswall, 2017), and the effects of incarceration (Bhuller, Dahl, Løken, and Mogstad, 2020), among many others. Mogstad and Torgovitsky (2018) provide a recent review of the MTE methodology and its connection to other instrumental variable (IV) approaches.…”

mentioning

confidence: 99%

Policy Evaluation with Multiple Instrumental Variables

Mogstad

¹

,

Torgovitsky

²

,

Walters

³

2020

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Marginal treatment effect methods are widely used for causal inference and policy evaluation with instrumental variables. However, they fundamentally rely on the well-known monotonicity (threshold-crossing) condition on treatment choice behavior. Recent research has shown that this condition cannot hold with multiple instruments unless treatment choice is effectively homogeneous. Based on these findings, we develop a new marginal treatment effect framework under a weaker, partial monotonicity condition. The partial monotonicity condition is implied by standard choice theory and allows for rich heterogeneity even in the presence of multiple instruments. The new framework can be viewed as having multiple different choice models for the same observed treatment variable, all of which must be consistent with the data and with each other. Using this framework, we develop a methodology for partial identification of clearly stated, policy-relevant target parameters while allowing for a wide variety of nonparametric shape restrictions and parametric functional form assumptions. We show how the methodology can be used to combine multiple instruments together to yield more informative empirical conclusions than one would obtain by using each instrument separately. The methodology provides a blueprint for extracting and aggregating information about treatment effects from multiple controlled or natural experiments while still allowing for rich heterogeneity in both treatment effects and choice behavior.

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The Marginal Labor Supply Disincentives of Welfare: Evidence from Administrative Barriers to Participation

Cited by 6 publications

References 23 publications

Offer It and They Will Come? An Investigation of the Factors Associated With the Uptake of School-Sponsored Resources

Offer It and They Will Come? An Investigation of the Factors Associated With the Uptake of School-Sponsored Resources

Policy Evaluation with Multiple Instrumental Variables

Policy Evaluation with Multiple Instrumental Variables

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