来自低收入家庭的学生

S. Dynarski
{"title":"来自低收入家庭的学生","authors":"S. Dynarski","doi":"10.4135/9781529714395.n373","DOIUrl":null,"url":null,"abstract":"High-achieving, low-income students attend selective colleges at far lower rates than upper-income students with similar achievement. Behavioral biases, intensified by complexity and uncertainty in the admissions and aid process, may explain this gap. In a large-scale experiment we test an early commitment of free tuition at a flagship university. The intervention did not increase aid: rather, students were guaranteed before application the same grant aid that they would qualify for in expectation if admitted. The offer substantially increased application (68 percent vs 26 percent) and enrollment rates (27 percent vs 12 percent). The results suggest that uncertainty, present bias, and loss aversion loom large in students’ college decisions. JEL codes: I0,I21,I22,I23,I24,I28 *Corresponding author: dynarski@umich.edu.735 State St. #5132, Ann Arbor, MI 48109. Author contact: clibassi@collegeboard.org (Libassi); kmmichel@maxwell.syr.edu (Michelmore); srowen@umich.edu (Owen). This project would not have been possible without our collaborators at the University of Michigan, particularly Kedra Ishop, Steve Lonn, and Betsy Brown. We are grateful to the Michigan Department of Education (MDE) and Michigan’s Center for Educational Performance and Information (CEPI) for providing data. Seminar participants at Boston University, Clemson, Cornell, Harvard, Northwestern, University of Illinois, University of Virginia, Princeton, Chicago, Stanford, the National Bureau for Economic Research, and Syracuse provided helpful comments, while Michael Lovenheim and Sarah Turner generously read initial drafts. The Institute of Education Sciences of the U.S. Department of Education (through Grants R305E100008 and R305B110001), Arnold Ventures, the Smith Richardson Foundation, and the University of Michigan Provost’s Office funded this research. This study is registered at the randomized trial registry of the American Economics Association under RCT ID AEARCTR-0001831, with DOI 10.1257/rct.1831. A pre-analysis plan was filed in April 2017 (Dynarski et al., 2017). The code for replicability purposes has been deposited in the AEA Data and Code Repository, openicpsr-130286. This research was approved by the Institutional Review Board at the University of Michigan (ID: HUM00096289 ) and Syracuse University (ID: 16-264). Elizabeth Burland, Meghan Oster, and Shwetha Raghuraman provided outstanding research assistance. This research uses data structured and maintained by the Michigan Education Data Center (MEDC) (Michigan Department of Education 2020a; Michigan Department of Education 2020b). MEDC data is modified for analysis purposes using rules governed by MEDC and are not identical to those data collected and maintained by MDE and/or CEPI. Results, information and opinions solely represent the analysis, information and opinions of the author(s) and are not endorsed by, or reflect the views or positions of, grantors, MDE and CEPI or any employee thereof. Gaps in educational attainment between lowand high-income students are large and have grown in recent decades. Among children born in the 1980s, those from the bottom quartile of family incomes are 50 percentage points less likely to attend college than those from the top quartile. And while 54 percent of children born into the top income quartile earn a bachelor’s degree, only nine percent of those in the lowest quartile do so (Bailey and Dynarski, 2011). These differences stem in part from disparities in academic preparation. But even among well-prepared students, there are substantial gaps in college enrollment and the quality of college attended (Hoxby and Avery, 2012). The under-representation of low-income students at selective colleges likely exacerbates both educational and income inequality.1 While there is no experimental evidence on the effect of college quality, several studies suggest that attending a college of higher quality (e.g., a flagship instead of a less-selective four-year school or a community college) increases both educational attainment and earnings (Hoekstra 2009; Zimmerman 2014; Dillon and Smith 2018). Among high-achieving students, it is application behavior that drives income differences in college quality. Hoxby and Avery (2012) find that the majority of low-income, high-achieving students apply to zero selective schools, even though doing so would likely lower their costs (Cohodes and Goodman, 2014), increase their chances of completing a college degree, and increase their future wages (Hoekstra 2009, Zimmerman 2014; Andrews, Imberman and Lovenheim 2016). Standard models of human capital investment fall short in explaining these behaviors. Though a lack of information about the (net) cost of college or suitability for an elite school could in theory lead low-income students to underinvest in education, previous interventions targeting these information frictions have shown only modest success (Bettinger et al. 2012; Hoxby and Turner 2013; Bergman, Denning and Manoli 2019; Gurantz et al. 2019; Hyman 2020, although see Jensen (2010) for an exception). Insights from behavioral economics suggest that students’ choices deviate from the classical model in predictable ways. Many observed behavioral patterns, such as present bias, overreliance on routine or defaults, and debt aversion, are particularly pronounced for those facing economic scarcity (as are low-income students) and complex decisions (as presented by the higher education and financial aid systems) (Mullainathan and Shafir, 2013). Within such an environment, small changes in choice architecture can lead to large changes in behavior. We use a randomized, controlled trial to test whether targeted, personalized communications, which reframe but do not increase financial aid, can alter the college decisions of low-income students. The intervention, the “HAIL (High Achieving Involved Leader) Scholarship,”2 was designed in the spirit of previous interventions that make small changes to the framework of decision-making. We collaborated with the University of Michigan in Ann Arbor, the state’s most selective college, 1We interchangeably use the terms “high quality”, “selective”, and “elite\" throughout to refer to selective institutions. Such schools tend to spend more per student, as well as enroll high-achieving students who are inputs into the education production function (Black and Smith 2006). 2The acronym “HAIL” is a reference to the University of Michigan’s fight song. “HAIL Michigan” is plastered on t-shirts, bumper stickers, water bottles, tube tops, underwear, beer coolers, dog coats, and billboards across the state and beyond. Go Blue!","PeriodicalId":376217,"journal":{"name":"The SAGE Encyclopedia of Higher Education","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Low-Income Students\",\"authors\":\"S. Dynarski\",\"doi\":\"10.4135/9781529714395.n373\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"High-achieving, low-income students attend selective colleges at far lower rates than upper-income students with similar achievement. Behavioral biases, intensified by complexity and uncertainty in the admissions and aid process, may explain this gap. In a large-scale experiment we test an early commitment of free tuition at a flagship university. The intervention did not increase aid: rather, students were guaranteed before application the same grant aid that they would qualify for in expectation if admitted. The offer substantially increased application (68 percent vs 26 percent) and enrollment rates (27 percent vs 12 percent). The results suggest that uncertainty, present bias, and loss aversion loom large in students’ college decisions. JEL codes: I0,I21,I22,I23,I24,I28 *Corresponding author: dynarski@umich.edu.735 State St. #5132, Ann Arbor, MI 48109. Author contact: clibassi@collegeboard.org (Libassi); kmmichel@maxwell.syr.edu (Michelmore); srowen@umich.edu (Owen). This project would not have been possible without our collaborators at the University of Michigan, particularly Kedra Ishop, Steve Lonn, and Betsy Brown. We are grateful to the Michigan Department of Education (MDE) and Michigan’s Center for Educational Performance and Information (CEPI) for providing data. Seminar participants at Boston University, Clemson, Cornell, Harvard, Northwestern, University of Illinois, University of Virginia, Princeton, Chicago, Stanford, the National Bureau for Economic Research, and Syracuse provided helpful comments, while Michael Lovenheim and Sarah Turner generously read initial drafts. The Institute of Education Sciences of the U.S. Department of Education (through Grants R305E100008 and R305B110001), Arnold Ventures, the Smith Richardson Foundation, and the University of Michigan Provost’s Office funded this research. This study is registered at the randomized trial registry of the American Economics Association under RCT ID AEARCTR-0001831, with DOI 10.1257/rct.1831. A pre-analysis plan was filed in April 2017 (Dynarski et al., 2017). The code for replicability purposes has been deposited in the AEA Data and Code Repository, openicpsr-130286. This research was approved by the Institutional Review Board at the University of Michigan (ID: HUM00096289 ) and Syracuse University (ID: 16-264). Elizabeth Burland, Meghan Oster, and Shwetha Raghuraman provided outstanding research assistance. This research uses data structured and maintained by the Michigan Education Data Center (MEDC) (Michigan Department of Education 2020a; Michigan Department of Education 2020b). MEDC data is modified for analysis purposes using rules governed by MEDC and are not identical to those data collected and maintained by MDE and/or CEPI. Results, information and opinions solely represent the analysis, information and opinions of the author(s) and are not endorsed by, or reflect the views or positions of, grantors, MDE and CEPI or any employee thereof. Gaps in educational attainment between lowand high-income students are large and have grown in recent decades. Among children born in the 1980s, those from the bottom quartile of family incomes are 50 percentage points less likely to attend college than those from the top quartile. And while 54 percent of children born into the top income quartile earn a bachelor’s degree, only nine percent of those in the lowest quartile do so (Bailey and Dynarski, 2011). These differences stem in part from disparities in academic preparation. But even among well-prepared students, there are substantial gaps in college enrollment and the quality of college attended (Hoxby and Avery, 2012). The under-representation of low-income students at selective colleges likely exacerbates both educational and income inequality.1 While there is no experimental evidence on the effect of college quality, several studies suggest that attending a college of higher quality (e.g., a flagship instead of a less-selective four-year school or a community college) increases both educational attainment and earnings (Hoekstra 2009; Zimmerman 2014; Dillon and Smith 2018). Among high-achieving students, it is application behavior that drives income differences in college quality. Hoxby and Avery (2012) find that the majority of low-income, high-achieving students apply to zero selective schools, even though doing so would likely lower their costs (Cohodes and Goodman, 2014), increase their chances of completing a college degree, and increase their future wages (Hoekstra 2009, Zimmerman 2014; Andrews, Imberman and Lovenheim 2016). Standard models of human capital investment fall short in explaining these behaviors. Though a lack of information about the (net) cost of college or suitability for an elite school could in theory lead low-income students to underinvest in education, previous interventions targeting these information frictions have shown only modest success (Bettinger et al. 2012; Hoxby and Turner 2013; Bergman, Denning and Manoli 2019; Gurantz et al. 2019; Hyman 2020, although see Jensen (2010) for an exception). Insights from behavioral economics suggest that students’ choices deviate from the classical model in predictable ways. Many observed behavioral patterns, such as present bias, overreliance on routine or defaults, and debt aversion, are particularly pronounced for those facing economic scarcity (as are low-income students) and complex decisions (as presented by the higher education and financial aid systems) (Mullainathan and Shafir, 2013). Within such an environment, small changes in choice architecture can lead to large changes in behavior. We use a randomized, controlled trial to test whether targeted, personalized communications, which reframe but do not increase financial aid, can alter the college decisions of low-income students. The intervention, the “HAIL (High Achieving Involved Leader) Scholarship,”2 was designed in the spirit of previous interventions that make small changes to the framework of decision-making. We collaborated with the University of Michigan in Ann Arbor, the state’s most selective college, 1We interchangeably use the terms “high quality”, “selective”, and “elite\\\" throughout to refer to selective institutions. 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引用次数: 4

摘要

成绩优异的低收入家庭学生进入名牌大学的比例远低于成绩相似的高收入家庭学生。入学和资助过程的复杂性和不确定性加剧了行为偏见,这可能解释了这种差距。在一项大规模的实验中,我们测试了一所旗舰大学的早期免学费承诺。这种干预并没有增加资助:相反,学生在申请前得到保证,如果他们被录取,他们将有资格获得与预期相同的资助。这一录取通知书大大增加了申请人数(68%对26%)和入学率(27%对12%)。结果表明,不确定性、当前偏见和损失厌恶在学生的大学决策中显得尤为突出。JEL代码:I0,I21,I22,I23,I24,I28 *通讯作者:dynarski@umich.edu.735 State St. #5132, Ann Arbor, MI 48109。作者联系:clibassi@collegeboard.org (Libassi);kmmichel@maxwell.syr.edu(有限公司);srowen@umich.edu(欧文)。如果没有我们在密歇根大学的合作者,特别是Kedra Ishop, Steve Lonn和Betsy Brown,这个项目是不可能完成的。我们感谢密歇根教育部(MDE)和密歇根教育绩效和信息中心(CEPI)提供的数据。波士顿大学、克莱姆森大学、康奈尔大学、哈佛大学、西北大学、伊利诺伊大学、弗吉尼亚大学、普林斯顿大学、芝加哥大学、斯坦福大学、国家经济研究局和锡拉丘兹大学的研讨会参与者提供了有益的评论,而迈克尔·洛文海姆和萨拉·特纳则慷慨地阅读了初稿。美国教育部教育科学研究所(通过拨款R305E100008和R305B110001)、Arnold Ventures、Smith Richardson基金会和密歇根大学教务长办公室资助了这项研究。本研究在美国经济学协会的随机试验注册中心注册,随机对照试验编号aearc -0001831, DOI 10.1257/ RCT .1831。预分析计划于2017年4月提交(Dynarski et al., 2017)。用于可复制目的的代码已存放在AEA数据和代码存储库openicpsr-130286中。这项研究得到了密歇根大学(ID: HUM00096289)和锡拉丘兹大学(ID: 16-264)机构审查委员会的批准。Elizabeth Burland, Meghan Oster和Shwetha Raghuraman提供了杰出的研究协助。本研究使用的数据由密歇根教育数据中心(MEDC) (Michigan Department of Education 2020a;密歇根教育部,20120b)。MEDC数据是根据MEDC管理的规则修改的,用于分析目的,与MDE和/或CEPI收集和维护的数据不同。结果、信息和观点仅代表作者的分析、信息和观点,不受授予人、MDE和CEPI或其任何员工的认可,也不反映其观点或立场。低收入和高收入学生之间的教育程度差距很大,而且在最近几十年里还在扩大。在上世纪80年代出生的孩子中,来自家庭收入最低四分之一家庭的孩子上大学的可能性比来自家庭收入最高四分之一家庭的孩子低50%。收入最高的四分之一家庭中有54%的孩子获得学士学位,而收入最低的四分之一家庭中只有9%的孩子获得学士学位(Bailey和Dynarski, 2011)。这些差异部分源于学术准备方面的差异。但即使在准备充分的学生中,在大学入学率和大学就读质量方面也存在巨大差距(Hoxby和Avery, 2012)。低收入家庭学生在名牌大学中的代表性不足可能会加剧教育和收入的不平等虽然没有关于大学质量影响的实验证据,但一些研究表明,上高质量的大学(例如,旗舰大学而不是选择性较差的四年制学校或社区大学)可以提高教育程度和收入(Hoekstra 2009;齐默尔曼2014;狄龙和史密斯2018)。在成绩优异的学生中,正是申请行为导致了大学质量的收入差异。Hoxby和Avery(2012)发现,大多数低收入,成绩优异的学生申请零选择性学校,即使这样做可能会降低他们的成本(Cohodes和Goodman, 2014),增加他们完成大学学位的机会,并增加他们未来的工资(Hoekstra 2009, Zimmerman 2014;Andrews, Imberman和Lovenheim 2016)。人力资本投资的标准模型无法解释这些行为。虽然缺乏关于大学(净)成本或是否适合精英学校的信息在理论上可能导致低收入家庭的学生在教育上投资不足,但之前针对这些信息摩擦的干预措施只显示出适度的成功(Bettinger等人)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Low-Income Students
High-achieving, low-income students attend selective colleges at far lower rates than upper-income students with similar achievement. Behavioral biases, intensified by complexity and uncertainty in the admissions and aid process, may explain this gap. In a large-scale experiment we test an early commitment of free tuition at a flagship university. The intervention did not increase aid: rather, students were guaranteed before application the same grant aid that they would qualify for in expectation if admitted. The offer substantially increased application (68 percent vs 26 percent) and enrollment rates (27 percent vs 12 percent). The results suggest that uncertainty, present bias, and loss aversion loom large in students’ college decisions. JEL codes: I0,I21,I22,I23,I24,I28 *Corresponding author: dynarski@umich.edu.735 State St. #5132, Ann Arbor, MI 48109. Author contact: clibassi@collegeboard.org (Libassi); kmmichel@maxwell.syr.edu (Michelmore); srowen@umich.edu (Owen). This project would not have been possible without our collaborators at the University of Michigan, particularly Kedra Ishop, Steve Lonn, and Betsy Brown. We are grateful to the Michigan Department of Education (MDE) and Michigan’s Center for Educational Performance and Information (CEPI) for providing data. Seminar participants at Boston University, Clemson, Cornell, Harvard, Northwestern, University of Illinois, University of Virginia, Princeton, Chicago, Stanford, the National Bureau for Economic Research, and Syracuse provided helpful comments, while Michael Lovenheim and Sarah Turner generously read initial drafts. The Institute of Education Sciences of the U.S. Department of Education (through Grants R305E100008 and R305B110001), Arnold Ventures, the Smith Richardson Foundation, and the University of Michigan Provost’s Office funded this research. This study is registered at the randomized trial registry of the American Economics Association under RCT ID AEARCTR-0001831, with DOI 10.1257/rct.1831. A pre-analysis plan was filed in April 2017 (Dynarski et al., 2017). The code for replicability purposes has been deposited in the AEA Data and Code Repository, openicpsr-130286. This research was approved by the Institutional Review Board at the University of Michigan (ID: HUM00096289 ) and Syracuse University (ID: 16-264). Elizabeth Burland, Meghan Oster, and Shwetha Raghuraman provided outstanding research assistance. This research uses data structured and maintained by the Michigan Education Data Center (MEDC) (Michigan Department of Education 2020a; Michigan Department of Education 2020b). MEDC data is modified for analysis purposes using rules governed by MEDC and are not identical to those data collected and maintained by MDE and/or CEPI. Results, information and opinions solely represent the analysis, information and opinions of the author(s) and are not endorsed by, or reflect the views or positions of, grantors, MDE and CEPI or any employee thereof. Gaps in educational attainment between lowand high-income students are large and have grown in recent decades. Among children born in the 1980s, those from the bottom quartile of family incomes are 50 percentage points less likely to attend college than those from the top quartile. And while 54 percent of children born into the top income quartile earn a bachelor’s degree, only nine percent of those in the lowest quartile do so (Bailey and Dynarski, 2011). These differences stem in part from disparities in academic preparation. But even among well-prepared students, there are substantial gaps in college enrollment and the quality of college attended (Hoxby and Avery, 2012). The under-representation of low-income students at selective colleges likely exacerbates both educational and income inequality.1 While there is no experimental evidence on the effect of college quality, several studies suggest that attending a college of higher quality (e.g., a flagship instead of a less-selective four-year school or a community college) increases both educational attainment and earnings (Hoekstra 2009; Zimmerman 2014; Dillon and Smith 2018). Among high-achieving students, it is application behavior that drives income differences in college quality. Hoxby and Avery (2012) find that the majority of low-income, high-achieving students apply to zero selective schools, even though doing so would likely lower their costs (Cohodes and Goodman, 2014), increase their chances of completing a college degree, and increase their future wages (Hoekstra 2009, Zimmerman 2014; Andrews, Imberman and Lovenheim 2016). Standard models of human capital investment fall short in explaining these behaviors. Though a lack of information about the (net) cost of college or suitability for an elite school could in theory lead low-income students to underinvest in education, previous interventions targeting these information frictions have shown only modest success (Bettinger et al. 2012; Hoxby and Turner 2013; Bergman, Denning and Manoli 2019; Gurantz et al. 2019; Hyman 2020, although see Jensen (2010) for an exception). Insights from behavioral economics suggest that students’ choices deviate from the classical model in predictable ways. Many observed behavioral patterns, such as present bias, overreliance on routine or defaults, and debt aversion, are particularly pronounced for those facing economic scarcity (as are low-income students) and complex decisions (as presented by the higher education and financial aid systems) (Mullainathan and Shafir, 2013). Within such an environment, small changes in choice architecture can lead to large changes in behavior. We use a randomized, controlled trial to test whether targeted, personalized communications, which reframe but do not increase financial aid, can alter the college decisions of low-income students. The intervention, the “HAIL (High Achieving Involved Leader) Scholarship,”2 was designed in the spirit of previous interventions that make small changes to the framework of decision-making. We collaborated with the University of Michigan in Ann Arbor, the state’s most selective college, 1We interchangeably use the terms “high quality”, “selective”, and “elite" throughout to refer to selective institutions. Such schools tend to spend more per student, as well as enroll high-achieving students who are inputs into the education production function (Black and Smith 2006). 2The acronym “HAIL” is a reference to the University of Michigan’s fight song. “HAIL Michigan” is plastered on t-shirts, bumper stickers, water bottles, tube tops, underwear, beer coolers, dog coats, and billboards across the state and beyond. Go Blue!
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