Andrew C. Barr, Kelli A. Bird, Benjamin L. Castleman, William L. Skimmyhorn
Despite generous financial aid, military veterans have high rates of undermatch and generally poor postsecondary outcomes. We conducted a large-scale, multi-arm field experiment with the U.S. Army to investigate whether personalized information about postsecondary options and access to advising affects service members’ postsecondary choices and outcomes. We find no impact of the intervention on whether or where veterans enroll in college or on their college persistence. These results suggest that light touch strategies that have been effective at addressing similar challenges among traditional students, and which we modified for the military context, are not sufficient to improve veterans’ postsecondary outcomes.
{"title":"Can information and advising affect postsecondary participation and attainment for military personnel? Evidence from a large-scale experiment with the U.S. Army","authors":"Andrew C. Barr, Kelli A. Bird, Benjamin L. Castleman, William L. Skimmyhorn","doi":"10.1002/pam.22572","DOIUrl":"10.1002/pam.22572","url":null,"abstract":"<p>Despite generous financial aid, military veterans have high rates of undermatch and generally poor postsecondary outcomes. We conducted a large-scale, multi-arm field experiment with the U.S. Army to investigate whether personalized information about postsecondary options and access to advising affects service members’ postsecondary choices and outcomes. We find no impact of the intervention on whether or where veterans enroll in college or on their college persistence. These results suggest that light touch strategies that have been effective at addressing similar challenges among traditional students, and which we modified for the military context, are not sufficient to improve veterans’ postsecondary outcomes.</p>","PeriodicalId":48105,"journal":{"name":"Journal of Policy Analysis and Management","volume":"44 1","pages":"73-96"},"PeriodicalIF":2.3,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pam.22572","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139835686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Why SNAP Works: A Political History—and Defense—of the Food Stamp Program by Christopher Busso. Oakland, CA: University of California Press, 2023, 257 pp., $24.95 (US) (Hardcover). ISBN 978--0520392816.","authors":"Laura R. Peck","doi":"10.1002/pam.22576","DOIUrl":"10.1002/pam.22576","url":null,"abstract":"","PeriodicalId":48105,"journal":{"name":"Journal of Policy Analysis and Management","volume":"43 2","pages":"644-648"},"PeriodicalIF":3.8,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139788079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Notes from the Editor","authors":"Erdal Tekin","doi":"10.1002/pam.22574","DOIUrl":"https://doi.org/10.1002/pam.22574","url":null,"abstract":"","PeriodicalId":48105,"journal":{"name":"Journal of Policy Analysis and Management","volume":"43 2","pages":"362"},"PeriodicalIF":3.8,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140537720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"AWARD GIVEN BY THE VERNON PRIZE COMMITTEE FOR VOLUME 42 OF JPAM","authors":"","doi":"10.1002/pam.22567","DOIUrl":"10.1002/pam.22567","url":null,"abstract":"","PeriodicalId":48105,"journal":{"name":"Journal of Policy Analysis and Management","volume":"43 2","pages":"649"},"PeriodicalIF":3.8,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139886545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"","authors":"","doi":"10.1002/pam.22570","DOIUrl":"https://doi.org/10.1002/pam.22570","url":null,"abstract":"","PeriodicalId":48105,"journal":{"name":"Journal of Policy Analysis and Management","volume":"43 2","pages":"650"},"PeriodicalIF":3.8,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140537504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Predictive analytics are increasingly pervasive in higher education. However, algorithmic bias has the potential to reinforce racial inequities in postsecondary success. We provide a comprehensive and translational investigation of algorithmic bias in two separate prediction models—one predicting course completion, the second predicting degree completion. We show that if either model were used to target additional supports for “at-risk” students, then the algorithmic bias would lead to fewer marginal Black students receiving these resources. We also find the magnitude of algorithmic bias varies within the distribution of predicted success. With the degree completion model, the amount of bias is over 5 times higher when we define at-risk using the bottom decile than when we focus on students in the bottom half of predicted scores; in the course completion model, the reverse is true. These divergent patterns emphasize the contextual nature of algorithmic bias and attempts to mitigate it. Our results moreover suggest that algorithmic bias is due in part to currently-available administrative data being relatively less useful at predicting Black student success, particularly for new students; this suggests that additional data collection efforts have the potential to mitigate bias.
{"title":"Are algorithms biased in education? Exploring racial bias in predicting community college student success","authors":"Kelli A. Bird, Benjamin L. Castleman, Yifeng Song","doi":"10.1002/pam.22569","DOIUrl":"https://doi.org/10.1002/pam.22569","url":null,"abstract":"Predictive analytics are increasingly pervasive in higher education. However, algorithmic bias has the potential to reinforce racial inequities in postsecondary success. We provide a comprehensive and translational investigation of algorithmic bias in two separate prediction models—one predicting course completion, the second predicting degree completion. We show that if either model were used to target additional supports for “at-risk” students, then the algorithmic bias would lead to fewer marginal Black students receiving these resources. We also find the magnitude of algorithmic bias varies within the distribution of predicted success. With the degree completion model, the amount of bias is over 5 times higher when we define at-risk using the bottom <i>decile</i> than when we focus on students in the bottom <i>half</i> of predicted scores; in the course completion model, the reverse is true. These divergent patterns emphasize the contextual nature of algorithmic bias and attempts to mitigate it. Our results moreover suggest that algorithmic bias is due in part to currently-available administrative data being relatively less useful at predicting Black student success, particularly for new students; this suggests that additional data collection efforts have the potential to mitigate bias.","PeriodicalId":48105,"journal":{"name":"Journal of Policy Analysis and Management","volume":"46 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139904303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Minimum quality regulations are often justified in the childcare market because of the presence of information frictions between parents and providers. However, regulations can also have unintended consequences for the quantity and quality of services provided. In this paper, we merge new data on states’ childcare regulations for maximum classroom group sizes and child-to-staff ratios with the universe of online job postings to study the impact of regulations on the demand for and characteristics of childcare labor. Our identification strategy exploits the unprecedented variation in regulatory reform during the COVID-19 pandemic, relying on changes both within states over time and across children's age groups. We find evidence that these regulations reduce the number of childcare job postings and encourage providers to substitute away from higher-skilled postings, thereby increasing the number of positions that are out-of-compliance with states’ teacher education requirements. In sum, the results imply that childcare regulations may reduce the demand for childcare labor, while simultaneously altering the composition of the workforce.
{"title":"Minimum quality regulations and the demand for childcare labor","authors":"Umair Ali, Chris M. Herbst, Christos A. Makridis","doi":"10.1002/pam.22568","DOIUrl":"10.1002/pam.22568","url":null,"abstract":"<p>Minimum quality regulations are often justified in the childcare market because of the presence of information frictions between parents and providers. However, regulations can also have unintended consequences for the quantity and quality of services provided. In this paper, we merge new data on states’ childcare regulations for maximum classroom group sizes and child-to-staff ratios with the universe of online job postings to study the impact of regulations on the demand for and characteristics of childcare labor. Our identification strategy exploits the unprecedented variation in regulatory reform during the COVID-19 pandemic, relying on changes both within states over time and across children's age groups. We find evidence that these regulations reduce the number of childcare job postings and encourage providers to substitute away from higher-skilled postings, thereby increasing the number of positions that are out-of-compliance with states’ teacher education requirements. In sum, the results imply that childcare regulations may reduce the demand for childcare labor, while simultaneously altering the composition of the workforce.</p>","PeriodicalId":48105,"journal":{"name":"Journal of Policy Analysis and Management","volume":"43 3","pages":"660-695"},"PeriodicalIF":3.8,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140473163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For workers employed in the public and nonprofit sectors, the Public Service Loan Forgiveness (PSLF) program offers the potential for full forgiveness of federal student loans for those with 10 years of full-time work experience. A year-long waiver issued by the Department of Education in 2021 to address administrative problems in program access provided a new path to PSLF relief for many borrowers. We explore the overall impact and distributional implications of potential full participation in loan forgiveness enabled by the PSLF waiver program using the 2018 Survey of Income and Program Participation (SIPP). Our estimates identify more than $100 billion in loan forgiveness available to as many as 3.45 million borrowers through the PSLF waiver program. Potential beneficiaries of this initiative are disproportionately employed in occupations like teaching and health care. Full take-up of the PSLF waiver would lead to a narrowing of the racial gap in student debt burden. However, the distributional impact of the PSLF waiver depends critically on the take-up rate and there is some evidence that those borrowers with relatively high income or advanced degrees have been most likely to access benefits.
{"title":"Waivers for the public service loan forgiveness program: Who could benefit from take-up?","authors":"Diego A. Briones, Nathaniel Ruby, Sarah Turner","doi":"10.1002/pam.22566","DOIUrl":"10.1002/pam.22566","url":null,"abstract":"<p>For workers employed in the public and nonprofit sectors, the Public Service Loan Forgiveness (PSLF) program offers the potential for full forgiveness of federal student loans for those with 10 years of full-time work experience. A year-long waiver issued by the Department of Education in 2021 to address administrative problems in program access provided a new path to PSLF relief for many borrowers. We explore the overall impact and distributional implications of potential full participation in loan forgiveness enabled by the PSLF waiver program using the 2018 Survey of Income and Program Participation (SIPP). Our estimates identify more than $100 billion in loan forgiveness available to as many as 3.45 million borrowers through the PSLF waiver program. Potential beneficiaries of this initiative are disproportionately employed in occupations like teaching and health care. Full take-up of the PSLF waiver would lead to a narrowing of the racial gap in student debt burden. However, the distributional impact of the PSLF waiver depends critically on the take-up rate and there is some evidence that those borrowers with relatively high income or advanced degrees have been most likely to access benefits.</p>","PeriodicalId":48105,"journal":{"name":"Journal of Policy Analysis and Management","volume":"43 4","pages":"1004-1033"},"PeriodicalIF":2.3,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139904294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Most of the world's population lives in countries that ban the self-service sale of gasoline. Causal effects of this regulation can hardly be assessed in these countries due to a lack of policy changes, but a recent quasi-experiment in the state of Oregon allows us to analyze the impact of the ban. From 1992 to 2017, the state of Oregon was one of two U.S. states that banned self-service at gasoline stations. Oregon adjusted regulations at the start of 2018 to allow self-service at gasoline stations in counties with populations below 40,000 individuals. I examine the repeal of this self-service ban and its effects on gasoline prices. I apply a difference-in-differences design using high frequency data of gasoline prices and find that repealing the self-service ban reduced gasoline prices by 4.4 cents per gallon in affected Oregon counties. This effect represents approximately $90 in expected annual savings for a household with three licensed drivers.
{"title":"Self-service bans and gasoline prices: The effect of allowing consumers to pump their own gas","authors":"Vitor Melo","doi":"10.1002/pam.22564","DOIUrl":"10.1002/pam.22564","url":null,"abstract":"<p>Most of the world's population lives in countries that ban the self-service sale of gasoline. Causal effects of this regulation can hardly be assessed in these countries due to a lack of policy changes, but a recent quasi-experiment in the state of Oregon allows us to analyze the impact of the ban. From 1992 to 2017, the state of Oregon was one of two U.S. states that banned self-service at gasoline stations. Oregon adjusted regulations at the start of 2018 to allow self-service at gasoline stations in counties with populations below 40,000 individuals. I examine the repeal of this self-service ban and its effects on gasoline prices. I apply a difference-in-differences design using high frequency data of gasoline prices and find that repealing the self-service ban reduced gasoline prices by 4.4 cents per gallon in affected Oregon counties. This effect represents approximately $90 in expected annual savings for a household with three licensed drivers.</p>","PeriodicalId":48105,"journal":{"name":"Journal of Policy Analysis and Management","volume":"43 3","pages":"804-817"},"PeriodicalIF":3.8,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139436892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Unions play a crucial role in determining wages and employment outcomes. However, union bargaining power may also have important effects on non-pecuniary working conditions. We study the effects of right-to-work laws, which removed agency shop protection and weakened union powers on long hours and non-standard work schedules that may adversely affect workers’ health and safety. We exploit variation in the timing of enactment across U.S. states and compare workers in bordering counties across adopting states and states that did not adopt the laws yet. Using the stacked approach to difference-in-differences estimates proposed by Cengiz et al. (2019), we find evidence that right-to-work laws increased the share of workers working long hours by 6%, while there is little evidence of an impact on hourly wages. The effects on long hours are larger in more unionized sectors (i.e., construction, manufacturing, and transportation). While the likelihood of working non-standard hours increases for particular sectors (education and public administration), there is no evidence of a significant increase in the overall sample.
{"title":"The impact of right-to-work laws on long hours and work schedules","authors":"Rania Gihleb, Osea Giuntella, Jian Qi Tan","doi":"10.1002/pam.22562","DOIUrl":"10.1002/pam.22562","url":null,"abstract":"<p>Unions play a crucial role in determining wages and employment outcomes. However, union bargaining power may also have important effects on non-pecuniary working conditions. We study the effects of right-to-work laws, which removed agency shop protection and weakened union powers on long hours and non-standard work schedules that may adversely affect workers’ health and safety. We exploit variation in the timing of enactment across U.S. states and compare workers in bordering counties across adopting states and states that did not adopt the laws yet. Using the stacked approach to difference-in-differences estimates proposed by Cengiz et al. (2019), we find evidence that right-to-work laws increased the share of workers working long hours by 6%, while there is little evidence of an impact on hourly wages. The effects on long hours are larger in more unionized sectors (i.e., construction, manufacturing, and transportation). While the likelihood of working non-standard hours increases for particular sectors (education and public administration), there is no evidence of a significant increase in the overall sample.</p>","PeriodicalId":48105,"journal":{"name":"Journal of Policy Analysis and Management","volume":"43 3","pages":"696-713"},"PeriodicalIF":3.8,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139436904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}