nep-edu New Economics Papers
on Education
Issue of 2016‒09‒04
thirty-four papers chosen by
João Carlos Correia Leitão
Universidade da Beira Interior

  1. Teacher Expectations Matter By Papageorge, Nicholas W.; Gershenson, Seth; Kang, Kyungmin
  2. Lost in Transition: The Influence of Locus of Control on Delaying Educational Decisions By Katharina Jaik; Stefan C. Wolter
  3. Interrogating a Paradox of Performance in the WCED: A Provincial and Regional Comparison of Student Learning By Gabrielle Wills; Debra Shepherd; Janeli Kotze
  4. Empirical Analysis of Informative School Outreach on Home-based Parental Involvement By Midori Otani
  5. Education Curriculum and Student Achievement: Theory and Evidence By Andrietti, Vincenzo; Su, Xuejuan
  6. The Heterogeneous Impacts of Business Cycles on Educational Attainment By Boffy-Ramirez, Ernest
  7. The Impact of Fundamentalist Terrorism on School Enrolment: Evidence from North-Western Pakistan, 2004-09 By Khan, Sarah; Seltzer, Andrew
  8. Does it Matter who your Parents are? Findings on Economic Mobility from the Survey of Household Economics and Decisionmaking By Jeff Larrimore
  9. Match or Mismatch? Automatic Admissions and College Preferences of Low- and High-Income Students By Jane Arnold Lincove; Kalena E. Cortes
  10. Birth order and college major in Sweden By Kieron Barclay; Martin Hällsten; Mikko Myrskylä
  11. The Economic Impact of Universities: Evidence from Across the Globe By Valero, Anna; Van Reenen, John
  12. Low Test Scores in Latin America: Poor Schools, Poor Families, or Something Else? By Theodore R. Breton; Gustavo Canavire-Bacarreza
  13. Does Temporary Interruption in Postsecondary Education Induce a Wage Penalty? Evidence from Canada By Fortin, Bernard; Ragued, Safa
  14. The Alma Mater Effect. Does Foreign Education of Political Leaders Influence Foreign Policy? By Dreher, Axel; Yu, Shu
  15. Understanding District-Charter Collaboration (Issue Brief) By Alyson Burnett; Moira McCullough; Christina Clark Tuttle
  16. Macroeconomic and School Variables to Reveal Country Choices of General and Vocational Education: A Cross-Country Analysis with focus on Arab Economies By Driouchi, Ahmed; Harkat, Tahar
  17. Estimating the size and impact of Affirmative Action at the University of Cape Town By Andrew Kerr; Patrizio Piraino; Vimal Ranchhod
  18. How Do Hurricanes Impact Achievement in School? A Caribbean Perspective By Spencer, Nekeisha; Polachek, Solomon; Strobl, Eric
  19. Student Loans and Homeownership Trends By Alvaro A. Mezza; Kamila Sommer; Shane M. Sherlund
  20. Settling for Academia? H-1B Visas and the Career Choices of International Students in the United States By Amuedo-Dorantes, Catalina; Furtado, Delia
  21. Measuring Principals' Effectiveness: Results from New Jersey's First Year of Statewide Principal Evaluation By Mariesa Herrmann; Christine Ross
  22. Intergenerational wealth mobility and the role of inheritance: Evidence from multiple generations By Adermon, Adrian; Lindahl, Mikael; Waldenström, Daniel
  23. Employment Protection, Investment in Job-Specific Skills, and Inequality Trends in the United States and Europe By Ruben Gaetani; Matthias Doepke
  24. Understanding District-Charter Collaboration Grants (Final Report) By Christina Tuttle; Moira McCullough; Scott Richman; Kevin Booker; Alyson Burnett; Betsy Keating; Michael Cavanaugh
  25. Efectos de la provisión universal de educación preescolar sobre la asistencia y la participación laboral femenina. Evidencia para el caso uruguayo. By Natalia Nollenberger; Ivone Perazzo
  26. A Trillion Dollar Question : What Predicts Student Loan Delinquency Risk? By Alvaro A. Mezza; Kamila Sommer
  27. Predicting Experimental Results: Who Knows What? By Stefano DellaVigna; Devin Pope
  28. The Scandinavian Fantasy: The Sources of Intergenerational Mobility in Denmark and the U.S. By Rasmus Landerso; James J. Heckman
  29. Measuring and profiling financial literacy in South Africa By Elizabeth Lwanga Nanziri; Murray Leibbrandt
  30. "Effectiveness of Animated Spokes Character in Advertising Targeted to Kids" By Shuja, Komal; Ali, Mazhar; Mehak Anjum, Munazzah; Rahim, Abdul
  31. Signaling to Experts By Florian Scheuer; Pablo Kurlat
  32. Development of Labour Market Participation Until 2030 With Respect to Changes in Education Participation and Recent Pension Reforms. Update By Thomas Horvath; Helmut Mahringer
  33. Human Capital, Public Debt, and Economic Growth: A Political Economy Analysis By Tetsuo Ono; Yuki Uchida
  34. Financial Aid, Debt Management, and Socioeconomic Outcomes: Post-College Effects of Merit-Based Aid By Judith Scott-Clayton; Basit Zafar

  1. By: Papageorge, Nicholas W. (Johns Hopkins University); Gershenson, Seth (American University); Kang, Kyungmin (Johns Hopkins University)
    Abstract: We develop and estimate a joint model of the education and teacher-expectation production functions that identifies both the distribution of biases in teacher expectations and the impact of those biases on student outcomes via self-fulfilling prophecies. The identification strategy leverages insights from the measurement-error literature and a unique feature of a nationally representative dataset: two teachers provided their educational expectations for each student. We provide novel, arguably causal evidence that teacher expectations affect students' educational attainment. Estimates suggest that the elasticity of the likelihood of college completion with respect to teachers' expectations is about 0.12. On average, teachers are overly optimistic about students' ability to complete a four-year college degree. However, the degree of over-optimism of white teachers is significantly larger for white students than for black students. This highlights a nuance that is frequently overlooked in discussions of biased beliefs: unbiased (i.e., accurate) beliefs can be counterproductive if there are positive returns to optimism or if there are socio-demographic gaps in the degree of teachers' over-optimism, both of which we find evidence of. We use the estimated model to assess the effects of two policies on black students' college completion: hiring more black teachers and "de-biasing" white teachers so that they are similarly optimistic about black and white students.
    Keywords: education, educational attainment, teachers, subjective expectations, human capital accumulation
    JEL: I2 D84 J15
    Date: 2016–08
  2. By: Katharina Jaik (Department of Business Administration, University of Zurich); Stefan C. Wolter (University of Bern; Swiss Coordination Center for Research in Education; CESifo and IZA)
    Abstract: The transition from compulsory schooling to upper-secondary education is a crucial and frequently difficult step in the educational career of young people. In this study, we analyze the impact of one non-cognitive skill, locus of control, on the intention and the decision to delay the transition into post-compulsory education in Switzerland. We find that locus of control, measured at ages 13–14, has a significant impact on the intention to delay the transition into upper-secondary education. Furthermore, we find that the intention to delay the transition is strongly correlated with the actual delay, measured one and a half years after the intention. Finally, students with the initial intention to delay but successfully continuing into upper-secondary education show a stronger internal locus of control than comparable students who do delay their transition.
    Keywords: Locus of control, school-to-school transition
    JEL: I21 J24
    Date: 2016–08
  3. By: Gabrielle Wills (Department of Economics, University of Stellenbosch); Debra Shepherd (Department of Economics, University of Stellenbosch); Janeli Kotze (Department of Economics, University of Stellenbosch)
    Abstract: The Western Cape, one of South Africa’s better performing provinces in terms of educational outcomes, has a relatively well-run education bureaucracy when compared not only within South Africa but also with other middle-income country education systems. Nevertheless, questions have been raised about whether bureaucratic competence has translated into higher levels of student learning in the province. In this paper, we consider how well primary school students perform in the Western Cape when compared with their peers in other systems within and across Southern and Eastern Africa after we control for differences in the socio-economic profiles of students and schooling inputs. Primarily relying on SACMEQ 2007 data, we use both descriptive and multivariate estimation with propensity score matching to explore performance differentials. In particular, we use an internationally calibrated measure of socio-economic status to compare test scores across equally poor students in different systems before drawing naïve conclusions about performance differentials. We find that while the Western Cape is a relatively efficient education system within South Africa, particularly in serving the poorest students, a less-resourced country such as Kenya produces higher levels of grade 6 student achievement across the student socio-economic profile. We also identify that observed differences in resourcing, teacher and other school inputs are typically not able to explain away performance differentials across different systems.
    Keywords: Student achievement, Western Cape, Southern and Eastern Africa, comparative education
    JEL: I20 I21 I24
    Date: 2016
  4. By: Midori Otani (Ph.D. Candidate, Osaka School of International Public Policy (OSIPP))
    Abstract: Parental involvement is essential for children's education. Several studies have examined relationships between parental involvement and parents' socioeconomic status. However, less attention has been placed on school influences on parental involvement even though schools play an important role in children's education, and can also, in turn, affect the parents as well. This study addressed the question: how informative school outreach influence parents of children in different school levels to get involved in their children's education? The present study examined a nationally represented sample of elementary and middle school children in Japan (3,939 fourth grade students from 140 schools and 4,143 eighth grade students from 133 schools) from Trends International Mathematics and Science Study (TIMSS) 2011. Findings revealed that different types of informative school outreach have different effects depending on the school level.
    Keywords: Parental Involvement, School Outreach, TIMSS
    JEL: I21 I24
    Date: 2016–08
  5. By: Andrietti, Vincenzo (University of Chieti-Pescara); Su, Xuejuan (University of Alberta, Department of Economics)
    Abstract: This paper proposes a theory of education curriculum and analyzes its distributional impact on student learning outcomes. Different curricula represent horizontal differentiation in the education technology, thus a curriculum change has distributional effects across students. We test the model using the quasi-natural experiment of the G8 reform in Germany. We find evidence of heterogeneous reform effects consistent with our theory. While the reform improves student test scores on average, such benefits are more pronounced for well-prepared students. In contrast, less-prepared students do not benefit from the reform.
    Keywords: Education curriculum; horizontal differentiation; distributional effects; difference-in-differences; quantile analysis
    JEL: D04 I21 I28
    Date: 2016–08–29
  6. By: Boffy-Ramirez, Ernest (University of Colorado Denver)
    Abstract: In this study I examine the impact of fluctuations in the unemployment rate before high school graduation on educational attainment measured 30 years later. I find evidence that educational attainment is countercyclical, as found in other studies, but also find that the impact of the unemployment rate varies across the ability distribution. Using data from the 1979 National Longitudinal Survey of Youth, this analysis identifies individuals who are on the boundary between pursuing and not pursuing additional education. Exposure to a higher unemployment rate at age 17 is associated with higher educational attainment for men in the 60-80th quintile of the ability distribution. There is little to no evidence of an effect beyond this quintile – highlighting the heterogeneous impacts of higher unemployment on educational attainment.
    Keywords: educational attainment, unemployment, heterogeneous impacts, ability distribution
    JEL: I2 I22 J1 J18 J24
    Date: 2016–08
  7. By: Khan, Sarah (University of Göttingen); Seltzer, Andrew (Royal Holloway, University of London)
    Abstract: Islamist groups in Afghanistan, Pakistan, and elsewhere have sought to remove females from public life. This paper uses data from Pakistan Social and Living Standards Measurement and the Global Terrorism Database to examine the impact of the Pakistani Taliban's terror campaign in the north-western province of Khyber Pukhtoonkhwa aimed at removing girls from school from the age of 10. Using a difference-in-difference-indifference approach, we show that low levels of exposure to terrorism had little effect on school enrolment. High levels of exposure reduced the enrolment rate for boys by about 5.5 percent and girls by about 10.5 percent. This decline in enrolment, although strongly significant, is far smaller than has commonly been portrayed in the media. Finally, although the Taliban warned students to enrol in madrassas rather than secular schools, we find no evidence that this led to increased madrassa enrolment in the affected regions.
    Keywords: education, terrorism, Pakistan
    JEL: O15 I25 D74 O53
    Date: 2016–08
  8. By: Jeff Larrimore
    Abstract: July 20, 2015{{p}}Does it matter who your parents are? Findings on economic mobility from the Survey of{{p}}Household Economics and Decisionmaking{{p}}Jeff Larrimore{{p}}Coming out of the Great Recession, there are renewed concerns about the level of economic opportunity throughout the income{{p}}distribution and the extent to which economic advancement is a realistic goal for all American families. Recognizing this growing interest{{p}}in economic mobility, this note outlines the findings on several dimensions of mobility for young adults from the Federal Reserve's 2014{{p}}Survey of Household Economics and Decisionmaking (SHED).1{{p}}Economic mobility over the past generation{{p}}The SHED data covers a wide array of topics, including information on the highest level of education that each of the respondent's{{p}}parents completed. Since education is a strong determinant of income and socioeconomic status (see e.g. Autor 2014; Oreopoulos and{{p}}Petronijevic 2013), responses to this question can be used in conjunction with other aspects of the survey to observe the level of{{p}}economic mobility from one generation to the next. In doing so, we focus on individuals aged 25-44 (born between 1970 and 1989) to{{p}}get a picture of mobility for the latest generation of Americans entering adulthood.2{{p}}Figure 1 considers mobility across generations by exploring the relationship between parental education and their child's income as an{{p}}adult. Among individuals whose parents both completed a bachelor's degree, 21 percent had a household income of under $50,000 in{{p}}2014 and 45 percent had over $100,000 of income. In contrast, among individuals for whom neither parent attended college this{{p}}relationship is reversed. Just 17 percent of these individuals had a household income of over $100,000 while 47 percent made less than{{p}}$50,000. This strong relationship between parental education and one's own income reflects the extent to which one's family{{p}}background has a lasting impact on economic outcomes into adulthood.{{p}}Figure 1: Household income, by parents' educational attainment{{p}} Note: Black lines represent the 95% confidence intervals. Sample limited to respondents age 25-44 who report at least one parent's education. N=1,641{{p}} Source: 2014 Survey of Household Economics and Decisionmaking{{p}}Accessible version{{p}}A large portion of this difference in incomes can be attributed to the intergenerational correlation of education between parents and{{p}}children. Consistent with that seen in the previous literature (Choy 2001, Reardon 2011), the SHED finds that children of parents who{{p}} FRB: FEDS Notes: Does it matter who your parents are? Findings on ec...{{p}}1 of 6 7/20/2015 3:33 PM{{p}}completed a bachelor's degree are substantially more likely to complete a bachelor's degree themselves (Figure 2). However, the SHED{{p}}data is not limited to simply comparing educational attainment. The survey also asks respondents where they went to college, which{{p}}enables an additional analysis of how individuals from different family backgrounds differ in the type of institution that they attended.{{p}}Figure 2: Educational attainment, by parents' educational attainment{{p}} Note: Black lines represent the 95% confidence intervals. Sample limited to respondents age 25-44 who report at least one parent’s education. N=1,641{{p}} Source: 2014 Survey of Household Economics and Decisionmaking{{p}}Accessible version{{p}}Young adults who went to college, but whose parents obtained an associate degree or less, were more likely to have attended a{{p}}for-profit school, and were less likely to have attended a traditional 4-year public or non-profit school, than were children of at least one{{p}}bachelor's degree recipient (Table 1). Children of parents with lower levels of education were also much more likely to have attended a{{p}}2-year, rather than a 4-year, institution. Since there is evidence that the rates of return to education differ by the type of institution{{p}}attended (Cellini and Chaudhary 2012; Deming, Goldin, and Katz 2012), this may represent a further obstacle towards upward mobility{{p}}beyond that which comes from just the highest level of education attained.{{p}}Table 1: Type of institution attended, by parents' educational attainment{{p}}Sector of{{p}}institution{{p}}Both parents have high{{p}}school degree or less{{p}}At least one parent with some{{p}}college or associate degree{{p}}One parent received{{p}}bachelor's degree{{p}}Both parents received{{p}}bachelor's degree{{p}}Public, 4-year 38.7 39.7 51.5 62.1{{p}}(3.9) (3.3) (3.8) (3.9){{p}}Non-profit{{p}}4-year 10.6 14.9 25.9 31.5{{p}}(2.3) (2.4) (3.4) (3.8){{p}}For-profit, 4-year 12.2 5.9 5.4 0.3{{p}}(2.9) (1.6) (1.5) (0.2){{p}}Public, 2-year or{{p}}less 28.3 32.4 15.7 4.7{{p}}(3.5) (3.3) (2.8) (1.4){{p}}For-profit, 2-year{{p}}or less 9.1 5.9 1.4 0.3{{p}}(2.4) (1.9) (1) (0.3){{p}} Note: Standard errors in parentheses. Sample limited to respondents age 25-44 who report at least one parent's education and who completed at least some{{p}} FRB: FEDS Notes: Does it matter who your parents are? Findings on ec...{{p}}2 of 6 7/20/2015 3:33 PM{{p}}education beyond high school. N=1,021{{p}} Source: 2014 Survey of Household Economics and Decisionmaking{{p}}To better understand the underlying mechanism for these differences in college attendance and completion decisions, the SHED also{{p}}asks respondents who did not attend college or did not complete their degree what factors influenced that decision. The most commonly{{p}}cited reasons vary by the education level of one's parents. In particular, respondents whose parents either did not attend or did not{{p}}complete college themselves are disproportionately likely to say they did not complete college because it was too expensive (41{{p}}percent) or because they had family responsibilities that prevented them from continuing their education (40 percent) (Table 2). In{{p}}contrast, among those respondents with at least one parent who obtained a bachelor's degree, a lack of interest is the most common{{p}}reason provided for not attending or completing college (38 percent).{{p}}Table 2: Reason for not attending or not completing college, by parents' educational attainment{{p}}Reason for not starting or{{p}}completing College degree{{p}}Both parents have HS{{p}}degree or less{{p}}At least one parent with some{{p}}college or associate degree{{p}}At least one parent received{{p}}bachelor's degree{{p}}Too expensive 41 43.2 30.2{{p}}(3.6) (4.7) (6.1){{p}}Family responsibilities 39.5 35.8 27.8{{p}}(3.6) (4.7) (5.7){{p}}Wanted to work 30.6 23.2 21.1{{p}}(3.5) (4) (5.5){{p}}Wasn't interested 33.1 25 37.6{{p}}(3.7) (4) (6.7){{p}}Low grades / not admitted 2.6 3.2 10.5{{p}}(1.1) (1.1) (3.9){{p}}Didn't think worth the cost 15.1 17.6 19.4{{p}}(2.7) (3.7) (5.5){{p}} Note: Responses are aggregated responses to questions "Which of the following are reasons why you did not attend college" and "Which of the following are{{p}}reasons why you did not complete your college degree?" Respondents who had one parent or both parent complete their bachelor’s degree are combined due{{p}}to the relatively small number of respondents whose parents have this level of education but did not complete a college degree themselves. Standard errors in{{p}}parentheses. Sample limited to respondents age 25-44 who report at least one parent's education and who did not attend college or completed some college{{p}}but did not complete any degree and are not currently enrolled. N=603.{{p}} Source: 2014 Survey of Household Economics and Decisionmaking{{p}}The relationships considered thus far emphasize how individual outcomes and educational decisions are influenced by one's starting{{p}}point in the distribution. They illustrate that one's starting point in life matters, as individuals whose parents were economically better off{{p}}are more likely to have higher levels of education, are more likely to have attended traditional 4-year non-profit or public institutions, and{{p}}tend to have higher incomes. This supports findings elsewhere that individuals who start off from a disadvantaged background are less{{p}}likely to get ahead economically than those whose parents were better off.{{p}}However, the observation that one's background growing up is correlated with adult economic outcomes does not preclude the{{p}}possibility that individuals from a range of backgrounds are still advancing economically relative to their parents. It is possible that{{p}}individuals' relative positions in the distribution are highly correlated with that of their parents but that the level of well-being is improving{{p}}for individuals throughout the distribution. To assess this type of absolute mobility from one generation to the next, the SHED asks{{p}}individuals how they feel they are doing financially relative to how their parents were faring at the same age. Figure 3 shows the{{p}}responses to this question based the education level of each respondent's parents. As before, the sample is restricted to respondents{{p}}aged 25-44 to focus on the most recent generation entering adulthood.{{p}}Figure 3: Financial well-being compared to parents, by parents' educational attainment{{p}} FRB: FEDS Notes: Does it matter who your parents are? Findings on ec...{{p}}3 of 6 7/20/2015 3:33 PM{{p}} Notes: Responses are to the question: "Think of your parents when they were your age. Would you say you (and your family living with you) are better, the{{p}}same, or worse off financially than they were?" Responses do not add to 100% due to rounding and non-response. Sample limited to respondents age 25-44{{p}}who report at least one parent's education. N=1,641{{p}} Source: 2014 Survey of Household Economics and Decisionmaking{{p}}Accessible version{{p}}Irrespective of the education of one's parents, a plurality of young adults feel that they are better off than their parents were at the same{{p}}age. Overall, 51 percent of all individuals aged 25-44 report that they are better off than their parents, while only 24 percent indicate that{{p}}they are worse off. Additionally, the frequency with which respondents feel that they are better off than their parents is largely consistent{{p}}across socioeconomic starting points, with the exception of individuals for whom both parents completed their bachelor's degree. Among{{p}}those young adults with two parents who obtained a bachelor's degree, only 40 percent believe that they are doing somewhat or much{{p}}better off than their parents at the same age. This is a significantly lower fraction feeling that they are better off than is observed among{{p}}those for whom neither parent had a college education (54 percent).{{p}}These results seem to suggest that individuals from lower socioeconomic backgrounds perceive themselves to be advancing in absolute{{p}}terms at a similar or greater frequency than those from higher socioeconomic backgrounds. However, it is important to qualify this{{p}}observation by noting the lower threshold that individuals from lower socioeconomic backgrounds must pass in order to improve{{p}}financially relative to their parents. Additionally, this measure also provides only limited information on the magnitude of economic{{p}}advancement, so we cannot say with certainty how much better or worse off respondents are than their parents and whether this differs{{p}}by one's socioeconomic background.{{p}}Expectations for economic mobility in the next generation{{p}}Just as we explored whether young adults coming from different economic starting points feel that they are better off than their parents,{{p}}we can explore how young adults expect that their children will fare based on the economic starting point being provided to them. Figure{{p}}4 shows the results of asking young adults whether they expect their children (or the next generation of their family) to be better or worse{{p}}off financially than they are. It separates the responses based on each respondent's level of education, so similar to Figure 3, it{{p}}assesses the expected economic advancement of an individual (now the respondent's children) based on the education of their parent{{p}}(now the respondents themselves).{{p}}Figure 4: Expectations of the financial well-being of the next generation of your family, by own educational{{p}}attainment{{p}} FRB: FEDS Notes: Does it matter who your parents are? Findings on ec...{{p}}4 of 6 7/20/2015 3:33 PM{{p}} Notes: Responses are to the question: "Think about the next generation of your family (e.g. your children, nieces, nephews, etc.). When they are your age, do{{p}}you think that they will be better off, the same, or worse off financially than you are today?" Responses do not add to 100% due to rounding and non-response.{{p}}Sample limited to respondents age 25-44. N=1,743{{p}} Source: 2014 Survey of Household Economics and Decisionmaking{{p}}Accessible version{{p}}Overall, when thinking about the next generation of their families, respondents are largely optimistic. Just 20 percent feel that their{{p}}children will be worse off than they are, and almost half expect their children to be better off financially. Additionally, when viewed{{p}}through the lens of the socioeconomic background that individuals grow up with, the expectations that young adults have for their{{p}}children are remarkably similar to the experiences of their own generation. Respondents who have less than a college degree are{{p}}generally more optimistic that their children will be better off than they are than those whose graduated from college. Fifty-two percent of{{p}}young adults who have a high school education or less believe that their children will be better off. This compares to 54 percent of this{{p}}population whose parents did not have education beyond high school feeling that they are better off than their parents were. Similarly, 44{{p}}percent of respondents who have at least a bachelor's degree think their children will be better off than they are, while 40 percent of{{p}}respondents whose parents both had a bachelor's degree think that they are better off than their parents were.{{p}}Conclusion{{p}}The results from the 2014 Survey of Household Economics and Decisionmaking provides evidence that one's starting point matters in{{p}}determining economic outcome on a wide range of measures, including educational attainment, institution attended, and income as a{{p}}young adult. However, despite the strong relationship between parental education and one's own outcomes, the survey suggests that{{p}}individuals from a range of backgrounds feel that they are improving financially when compared to their parents. Additionally, there{{p}}remains a general level of optimism throughout the socioeconomic distribution that this advancement will continue into the next{{p}}generation.{{p}}References{{p}}Autor, David. 2014. "Skills, Education, and the Rise of Earning Inequality Among the `Other 99 Percent.'" Science 344(6186): 843-851.{{p}}Cellini, Stephanie R., and Latika Chaudhary. 2012. "The Labor Market Returns to a for-Profit College Education." NBER Working Paper{{p}}18343.{{p}}Choy, Susan. 2001. "Whose Parents Did Not Go to College: Postsecondary Access, Persistence, and Attainment" In The Condition of{{p}}Education 2001, NCES 2001-072, edited by U.S. Department of Education, National Center for Educational Statistics, XVIII-XLIII.{{p}}Washington, DC: U.S. Government Printing Office.{{p}}Deming, David J., Claudia Goldin, and Lawrence F. Katz. 2012. "The For-Profit Postsecondary School Sector: Nimble Critters or Agile{{p}}Predators?" Journal of Economic Perspectives 26(1):139-164.{{p}}Reardon, Sean F. "The Widening Academic Achievement Gap between the Rich and the Poor: New Evidence and Possible{{p}}Explanations." In Whither Opportunity? Rising Inequality, Schools, and Children's Life Chances, edited by Duncan, Greg J. and Richard{{p}}J. Murnane, 91-116. Washington, DC: Russell Sage Foundation.{{p}}Oreopoulos, Philip and Uros Petronijevic. 2013. "Making College Worth It: A Review of Research on the Returns to Higher Education"{{p}}NBER Working Paper 19053.{{p}}1. SHED microdata is publicly available via: Return to text{{p}}2. The SHED survey interviewed 5,896 respondents, including 1,743 individuals aged 25-44. Of these respondents aged 25-44, 1,641 reported the education{{p}} FRB: FEDS Notes: Does it matter who your parents are? Findings on ec...{{p}}5 of 6 7/20/2015 3:33 PM{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}}level of at least one parent. Return to text{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Last update: July 20, 2015{{p}}Home | Economic Research & Data{{p}} FRB: FEDS Notes: Does it matter who your parents are? Findings on ec...{{p}}6 of 6 7/20/2015 3:33 PM
    Date: 2015–07–20
  9. By: Jane Arnold Lincove; Kalena E. Cortes
    Abstract: We examine the role of information in the college matching behavior of low- and high-income students, exploiting a state automatic admissions policy that provides some students with perfect a priori certainty of college admissions. We find that admissions certainty encourages college-ready low-income students to seek more rigorous universities. Low-income students who are less college-ready are not influenced by admissions certainty and are sensitive to college entrance exams scores. Most students also prefer campuses with students of similar race, income, and high school class rank, but only highly-qualified low-income students choose institutions where they have fewer same-race and same-income peers.
    JEL: I21 I23 J15
    Date: 2016–08
  10. By: Kieron Barclay (Max Planck Institute for Demographic Research, Rostock, Germany); Martin Hällsten; Mikko Myrskylä (Max Planck Institute for Demographic Research, Rostock, Germany)
    Abstract: Previous research on birth order has consistently shown that later-borns have lower educational attainment than first-borns, however it is not known whether there are birth order patterns in college major. Given empirical evidence that parents disproportionately invest in first born children, there are likely to be birth order patterns attributable to differences in both opportunities and preferences, related to ability, human capital specialization through parent-child transfers of knowledge, and personality. Birth order patterns in college major specialization may shed light on these explanatory mechanisms, and may also account for long-term birth order differences in educational and labour market outcomes. Furthermore, given that within-family differences in resource access are small compared to between-family differences, the explanatory potential of these mechanisms has the potential to say much more about inequality production mechanisms in society at large. Using Swedish population register data and sibling fixed effects we find large birth order differences in university applications. First-borns are more likely to apply to, and graduate from, medicine and engineering programs at university, while later-borns are more likely to study journalism and business programs, and to attend art school. We also find that these birth order patterns are stronger in high SES families. These results indicate that early life experiences and parental investment shapes sibling differences in ability, preferences, and ambitions even within the shared environment of the family.
    Keywords: Sweden, birth order
    JEL: J1 Z0
    Date: 2016–08
  11. By: Valero, Anna; Van Reenen, John
    Abstract: We develop a new dataset using UNESCO source materials on the location of nearly 15,000 universities in about 1,500 regions across 78 countries, some dating back to the 11th Century. We estimate fixed effects models at the sub-national level between 1950 and 2010 and find that increases in the number of universities are positively associated with future growth of GDP per capita (and this relationship is robust to controlling for a host of observables, as well as unobserved regional trends). Our estimates imply that doubling the number of universities per capita is associated with 4% higher future GDP per capita. Furthermore, there appear to be positive spillover effects from universities to geographically close neighboring regions. We show that the relationship between growth and universities is not simply driven by the direct expenditures of the university, its staff and students. Part of the effect of universities on growth is mediated through an increased supply of human capital and greater innovation (although the magnitudes are not large). We find that within countries, higher historical university presence is associated with stronger pro-democratic attitudes.
    Keywords: growth; Human Capital; innovation; Universities
    JEL: I23 I25 J24 O10 O31
    Date: 2016–08
  12. By: Theodore R. Breton; Gustavo Canavire-Bacarreza
    Abstract: Latin American students consistently score low on international tests of cognitive skills. In the PISA 2012 results, students in seven Latin American countries had an average score of 395, or about 100 points lower than the average score of 497 in four Scandinavian countries. We examine why Latin American scores are lower and conclude that 50 points are explained by Latin American families’ lower average educational and socioeconomic characteristics, 25 points are explained by Latin America’s weak cultural orientation toward reading books, and the remaining 25 points are explained by the lower effectiveness of educational systems in teaching cognitive skills.
    Keywords: Latin America; test scores; PISA; books; school quality
    Date: 2016–06–19
  13. By: Fortin, Bernard (Université Laval); Ragued, Safa (Laval University)
    Abstract: Data from the Youth in Transition Survey reveal that almost 40% of Canadian youth who left post-secondary education in 1999 had returned two years later. This paper investigates the extent to which schooling discontinuities affect post-graduation starting real wages and whether the latter are differently influenced by the reasons behind these discontinuities. We analyse this issue using data from the 2007 National Graduate Survey. We take covariates endogeneity into account using Lewbel's (2012) generated instrument approach. The source of identification is a heteroscedastic covariance restriction of the error terms that is a feature of many models of endogeneity. To allow for individual heterogeneity in the causal effect of various reasons for schooling interruption, we also provide results from two-stage quantile regressions using Lewbel's generated instruments. Conditional on the levels of schooling and experience, we find a positive effect on wages of temporary schooling interruption for men who had held a full-time job during their out-of-school spell(s). Both men and women witness a wage decrease if their interruption is associated with health issues. Women also bear a wage penalty if their interruption is due to a part-time job, to lack of money, or is caused by reasons other than health, work, and money.
    Keywords: schooling interruption, wages, temporary attrition, delayed graduation, Lewbel IV, two-stage quantile regression, Box-Cox
    JEL: C21 C26 C31 I21 I23 I26
    Date: 2016–08
  14. By: Dreher, Axel; Yu, Shu
    Abstract: We study whether national leaders' foreign education influences their foreign policy, measured by voting behavior at the United Nations General Assembly (UNGA). We hypothesize that "affinity"' - pre-existing or developed while studying abroad - makes leaders with foreign education more likely to vote with their host country. At the same time, such leaders need to show sufficient distance to their host country and demonstrate "allegiance"' to their own one, which will reduce voting coincidence. To test this theory we make use of data on the educational background of 831 leaders and the voting affinity between the countries they govern and those in which they studied. Over the 1975-2011 period, we find that foreign-educated leaders are less likely to vote in line with their host countries but more likely to vote in line with (other) G7 countries. We identify the causal effect of "allegiance" by investigating the differential effect of foreign education on voting in pre-election years compared to other years. The difference-in-difference-like results show that G7-educated leaders vote less in line with their host countries when facing an election. Overall, both "allegiance" and "affinity" affect foreign policy.
    Keywords: Foreign Education; leaders; United Nations General Assembly voting
    JEL: D78 F51 F53
    Date: 2016–08
  15. By: Alyson Burnett; Moira McCullough; Christina Clark Tuttle
    Abstract: This issue brief summarizes findings from our examination of seven innovative partnerships between traditional public school districts and the charter school sector.
    Keywords: charter schools, education, leadership, principals, teachers, districts
    JEL: I
  16. By: Driouchi, Ahmed; Harkat, Tahar
    Abstract: Abstract This research focuses on the analysis of the determinants of general and vocational educational choices in Arab economies with comparisons to other regions of the world. The selected framework considers educational choices as influenced by macroeconomic and education variables. The empirical investigation is based on regression analysis as inspired by the above model. Time series analysis is also used for Arab countries. The results indicate that education in the groups of countries analyzed is generally driven by unemployment, economic growth, and the schooling results. Arab countries do show that vocational educational accounts for the schooling performance, only. Comparisons with other groups indicate that Arab countries need to strengthen the links between general and professional education as this allows for a more balanced educational and employment systems, not accounting only for the performance of general education.
    Keywords: Keywords: Vocational education, Arab world, Comparisons
    JEL: I25 J68 M51
    Date: 2016–08–31
  17. By: Andrew Kerr (DataFirst, University of Cape Town); Patrizio Piraino (School of Economics, University of Cape Town); Vimal Ranchhod (SALDRU, University of Cape Town)
    Abstract: In this paper we estimate the extent and targeting of affirmative action at the University of Cape Town, a large public university in South Africa. To do this we use admissions data from the University of Cape Town (UCT), as well as South African population census data and administrative enrolment and graduation data from the South African Department of Higher Education. We find that affirmative action does have a significant effect on the racial distribution of who is made an offer by the university. We also find that affirmative action is well targeted, with those who we estimate to be beneficiaries being of much lower socioeconomic status than those who we estimate are displaced by affirmative action. Beneficiaries of affirmative action have low graduation rates on average, with those beneficiaries who attend UCT being less likely to graduate than those beneficiaries who enrol at other public universities.
    Keywords: Affirmative action, University of Cape Town, Graduation rates, South Africa
    Date: 2016
  18. By: Spencer, Nekeisha (University of the West Indies, Mona); Polachek, Solomon (Binghamton University, New York); Strobl, Eric (Aix-Marseille University)
    Abstract: This study examines whether hurricanes have any impact on performance in standardized examinations. The analysis uses a panel of thirteen Caribbean countries and over 800 schools for the period 1993 through 2010. In particular, the effect on subjects in the humanities and sciences are examined. A generalized difference-in-difference technique is utilized to study the relationship at the school, parish, year and country level. The results show a negative and significant effect on performance in the sciences if hurricanes strike when school is in session and a positive or no effect when school is not in session. In addition, subjects in the humanities remain unaffected.
    Keywords: human capital, rate of return, hurricanes
    JEL: I2 Q54
    Date: 2016–08
  19. By: Alvaro A. Mezza; Kamila Sommer; Shane M. Sherlund
    Abstract: October 15, 2014{{p}}Student Loans and Homeownership Trends{{p}}{{p}}Alvaro Mezza, Kamila Sommer, and Shane Sherlund{{p}}{{p}}The increases in student loan debt and delinquencies over the past few years have raised concerns about whether heavy student loan debt burdens are making it more difficult for young households to become homeowners. For example, using a large dataset of borrowers with credit records, Brown and Caldwell (2013) find that (i) during the financial crisis, homeownership rates--approximated by the presence of home-secured debt in credit files--for young individuals with student loan debt fell relatively more than for those without student loan debt, and that (ii) by the end of 2011, young individuals with student loan debt were associated with lower homeownership rates than those without such debt. However, due to data limitations, those researchers were unable to separate individuals with no student loan debt into groups with and without college education. Yet there are a number of reasons to think that individuals who went to college (albeit without incurring student debt) are quite different in homeownership attainment from those who never attended college (and, therefore, do not have any student loan debt). As such, combining individuals with no student loan debt and with or without college education into a single group could confound statistical findings related to the relationship between homeownership and student loan debt. In this note, we separate individuals with no student loan debt into groups with and without post-secondary education, and explore the patterns in homeownership attainment once college education is considered.{{p}}{{p}}Using a nationally representative sample of young individuals with credit records who were between the ages of 29 and 31 in years 2004-2010, panel A in figure 1 shows that--similar to Brown and Caldwell's (2013) findings--the homeownership rate declined relatively more for those with student loan debt (the green line) than for those without student loan debt (the purple line).1 Thus, while homeownership rates were generally higher for those with student loan debt during the entire period, the difference between the rates became much smaller by the end of 2010, suggesting that student debt may have made ownership more difficult.{{p}}{{p}}In contrast, panel B separates the group of individuals with no student debt (the purple line) into two subgroups: (i) those with no college education (the blue line) and (ii) those with some college education (the red line). The panel illustrates that the homeownership rates between the two groups differ substantially, with an average homeownership rate gap of about 13 percentage points over the full period. Moreover, the evolution of homeownership rate across time varies between the two groups, suggesting that combining individuals with no student loan debt and with or without college education into a single group could indeed confound statistical findings related to the relationship between homeownership and student loan debt.{{p}}{{p}}Panel C compares the changes in homeownership between those with college education with and without student debt (the green and the red lines, respectively). The panel suggests that the observed declines in homeownership are not dissimilar between those with and without debt and college education.{{p}}Figure 1: Home Ownership Rate: 2004-2010{{p}}Figure 1: Home Ownership Rate: 2004-2010. Three panels. Panel A compares the home ownership rates of individuals between ages 29 and 31 with no student loan debt versus individuals with college education and student loan debt. The panel has one y-axis on the right. The y-axis shows home ownership rate, spanning from 0.10 to .050, and represents the rate at which individuals own a home or not. The x-axis shows the year from 2004 to 2010. The home ownership rate for individuals with no student loan debt (the purple line) is concave down, with a slight bent. The rate increases slightly between 2004 and 2007, from 0.27 to 0.29. The rate subsequently falls from 0.29 in 2007 to 0.28 in 2008 and then to 0.22 in 2010. The home ownership rate for individuals with student loan debt (the green line) is also concave down, with a slight bent. The rate increases slightly between 2004 and 2007, from 0.33 to 0.34. The rate also subsequently falls from 0.34 in 2007 to 0.32 in 2008 and then to 0.25 in 2010. In addition to these two curves in Panel A, there are also dashed lines around each curve, denoting the 95 percent confidence intervals. The upper and lower bounds of the confidence intervals are about 0.01 above and below the main curves, respectively. Panel B decomposes the home ownership rate of individuals between ages 29 and 31 with no student loan debt (the purple line; previously shown in Panel A) into home ownership rates for individuals with no college education (the blue line) and with college education (the red line). The panel has one y-axis on the right. The y-axis shows home ownership rate, spanning from 0.10 to .050, and represents the rate at which individuals own a home or not. The x-axis shows the year from 2004 to 2010. The home ownership rate for students with no student loan (the purple line) is exactly as described in Panel A. The home ownership rate for individuals with college education and no student loan debt (the red line) is concave down but sharply bent. The rate increases between 2004 and 2008, from 0.35 to 0.37. The rate subsequently declines from 0.37 in 2008 to 0.28 in 2010. The home ownership rate for individuals with no college education (and, consequently, no student loan debt; the blue line) is also concave down. The rate increases between 2004 and 2007, from 0.23 to 0.23. The rate subsequently declines from 0.23 in 2007 to 0.20 in 2008 and then to 0.17 in 2010. In addition to these three curves in Panel B, there are also dashed lines around each curve, denoting the 95 percent confidence intervals. The upper and lower bounds of the confidence intervals range from about +/- 0.01 to +/- 0.02 above and below the main curves. Panel C compares the home ownership rates of individuals between ages 29 and 31 with college education and student loan debt (the green line; previously shown in Panel A) with that of individuals with college education and no student loan debt (the red line; previously shown in Panel B). The panel has one y-axis on the right. The y-axis shows home ownership rate, spanning from 0.10 to .050, and represents the rate at which individuals own a home or not. The x-axis shows the year from 2004 to 2010. See accessible link for underlying data.{{p}}{{p}} Source: TransUnion, LLC and National Student Clearinghouse.{{p}}{{p}}* Includes individuals both with and without post-secondary education.{{p}}{{p}} Notes: Individuals with no college denote those with no post-secondary education. Individuals with college denote those with at least some post-secondary education. Dotted lines represent 95 percent confidence intervals.{{p}}{{p}}Accessible Version{{p}}{{p}}Some important patterns underlying these relationships are summarized in Figure 2, which shows homeownership-age profiles for the same three groups in Panel B. The chart shows that individuals with no college education (the blue line) enter homeownership earlier, on average, than those with college education and with and without student loan debt (the green and red lines, respectively). However, by age 26 the homeownership rate of those with college education catches up with the homeownership rate of those with no college education and, subsequently, rises appreciably above it. For those with college education, having no student loan debt is associated with somewhat higher homeownership rates at younger ages. However, by age 30 the homeownership rates of those who attended college with and without student loan debt are not statistically significantly different from each other and by the age of 34 the rates are essentially identical. Taken together, our results suggest that for those with college education student loan debt more likely affects the timing of homeownership than people's eventual attainment of it.{{p}}Figure 2: Home Ownership Rate: Ages 23-35{{p}}Figure 2: Home Ownership Rate: Ages 23-35. One panel. The figure compares the age profile of home ownership rates of individuals with (i) no college (the blue line), (ii) individuals with college education and student loan debt (the green line), and (iii) individuals with college education and no student loan debt (the red line). The panel has one y-axis on the right. The y-axis shows home ownership rate, spanning from 0.00 to .050, and represents the rate at which individuals own a home or not. The x-axis shows ages from 23 to 35. The homeownership rate for individuals with no college education (the blue line) rises steadily with age, from 0.08 for 23-year olds to 0.20 for 28-year olds. The rate is flat at 0.20 between ages 28 and 29, before increasing again to 0.31 by the age of 35. The home ownership rate for individuals with college education and student loan debt (the green line) and individuals with college education and no student loan debt (the red line) have steeper slopes than the one for individuals with no college (the blue line). The rate for individuals with college education and student loan debt (the green line) increases roughly monotonically from 0.04 at the age 23 to 0.42 by the age of 35. The rate for individuals with college education and no student loan debt (the red line) increases from 0.06 at the age 23 to 0.43 by the age of 35. Note that the homeownership rates of individuals with (i) college education and student loan debt (the green line) and individuals with college education and no student loan debt (the red line) converge to each other by the age of 35. In addition to these three curves in Figure 2, there are also dashed lines around each curve, denoting the 95 percent confidence intervals. The upper and lower bounds of the confidence intervals are about 0.01 above and below the main curves, respectively. See accessible link for underlying data.{{p}}{{p}}{{p}} Source: TransUnion, LLC and National Student Clearinghouse.{{p}}{{p}} Notes: Individuals with no college denote those with no post-secondary education. Individuals with college denote those with at least some post-secondary education. Dotted lines represent 95 percent confidence intervals.{{p}}{{p}}Accessible Version{{p}}{{p}}A few caveats should be taken into consideration when interpreting our results. First, our results are based on relative movements in summary statistics and should not be interpreted as speaking to any causal relationship between student loans and homeownership. For example, the relatively higher homeownership rates among those who went to college but did not have any student loans might be caused by lower overall debt burdens but potentially also by other factors that we have not explicitly accounted for, such as the ability of one's family to provide funds for a down payment. Second, our analysis is limited to the period from 2004 to 2010 (due to data limitations). As such, our results cannot address how the homeownership trends in recent years may have been affected by student loan balances and take-up rates that continued to rise or by any impacts of post-crisis mortgage regulation.{{p}}{{p}}Despite the aforementioned limitations, we believe that our results convey an important direction for future research, namely that college attendance is an important correlate of homeownership that, among other things, presents a challenge for isolating the role of changes in student debt on homeownership.{{p}}{{p}}References{{p}}{{p}}Brown, Meta and Sydnee Caldwell (2013). "Young Student Loan Borrowers Retreat from Housing and Auto Markets." Liberty Street Economics, Federal Reserve Bank of New York.{{p}}{{p}}Appendix{{p}}{{p}}The analysis in the note is based on a nationally representative, anonymous sample of credit bureau records randomly drawn by TransUnion, LLC, for a cohort of 34,890 young individuals who were between ages 23 and 31 in 2004. The data spans the period 1997 through 2010. Individuals are followed biannually between June 1997 and June 2003, and then in December 2004, June 2007, and December 2008 and 2010. The dataset contains several major credit bureau variables, including credit scores, tradeline debt levels, and delinquency and severe derogatory records. The National Student Clearinghouse merged their educational records anonymously onto the TransUnion credit bureau data. All personally identifiable information was removed from the data before it was provided to the Federal Reserve Board.{{p}}{{p}}1. See the Appendix for a description of the underlying data. Return to text{{p}}{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Search Working Papers{{p}}{{p}}{{p}}{{p}}Skip Meet Economists Section{{p}}{{p}}Meet the Economists{{p}}All Economists{{p}}By Field of Interest{{p}}Financial Economics{{p}}International Economics{{p}}Macroeconomics{{p}}Mathematical and Quantitative Methods{{p}}Microeconomics{{p}}{{p}}Skip stay connected section{{p}}{{p}}Stay Connected{{p}}Twitter{{p}}YouTube{{p}}RSS Feeds{{p}}Subscribe{{p}}{{p}}{{p}}Last update: October 16, 2014
    Date: 2014–10–15
  20. By: Amuedo-Dorantes, Catalina (San Diego State University); Furtado, Delia (University of Connecticut)
    Abstract: For the first time since the inception of the H-1B visa, yearly caps became binding in 2004, making it harder for most foreign-born students to secure employment in the United States. However, since the year 2000, institutions of higher education and related non-profit research institutes had been exempt from the cap. We explore how immigrant employment choices were impacted by the binding visa cap, exploiting the fact that citizens of five countries (Canada, Mexico, Chile, Singapore and Australia) had access to alternate work visas. Our estimates suggest that international students from H-1B dependent countries became more likely to work in academic institutions if they graduated after 2004 than immigrants from the five countries with substitute work visas. Within academia, foreign-born graduates affected by the visa cap became more likely to work in a job unrelated to their field of study, while no such change occurred in the private sector –a finding consistent with the notion of workers "settling for academia." We conclude with an analysis of workforce compositional changes in the academic versus private sectors as a result of the binding visa caps.
    Keywords: H-1B visas, foreign-born workers, academic market, United States
    JEL: F22 J61 J68
    Date: 2016–08
  21. By: Mariesa Herrmann; Christine Ross
    Abstract: This report examines the principal evaluation measures used in the first year of statewide implementation of New Jersey’s principal evaluation system. Variation in ratings across principals, the year-to-year stability, and correlations among these measures and with school populations can inform system improvements and district guidance.
    Keywords: educator performance evaluation, school leaders evaluation or effectiveness, school leaders, education professionals, descriptive statistics, correlational, New Jersey
    JEL: I
  22. By: Adermon, Adrian; Lindahl, Mikael; Waldenström, Daniel
    Abstract: This study estimates intergenerational correlations in mid-life wealth across three generations, and a young fourth generation, and examines how much of the parent-child association that can be explained by inheritances. Using a Swedish data set we find parent-child rank correlations of 0.3-0.4 and grandparents-grandchild rank correlations of 0.1-0.2. Conditional on parents' wealth, grandparents' wealth is weakly positively associated with grandchild's wealth and the parent-child correlation is basically unchanged if we control for grandparents' wealth. Bequests and gifts strikingly account for at least 50 per cent of the parent-child wealth correlation while earnings and education are only able to explain 25 per cent.
    Keywords: Income mobility; inequality; Inheritance
    JEL: D31 J62
    Date: 2016–08
  23. By: Ruben Gaetani (Northwestern University); Matthias Doepke (Northwestern University)
    Abstract: Since the 1980s, the United States economy has experienced a sharp rise in education premia in the labor market, with the college premium going up by more than 30 percent. In contrast, most European economies witnessed a much smaller rise in the return to education, and in Germany, Italy, and Spain the college premium actually fell. In this paper, we argue that differences in employment protection can account for a substantial part of these diverging trends. We consider an environment where firms can invest in technologies that are complementary to experienced workers with long tenure, and workers can make corresponding investments in firm-specific skills. The incentive to undertake such investments interact with employment protection. Incentives are particularly strong if employment protection favors older workers and workers with long tenure, as is the case in the European countries where the college premium fell. We use a calibrated dynamic model that allows for different education levels, labor-market search, and investment in relationship-specific capital and skills to quantify the ability of this affect to account for diverging inequality trend in the United States and Europe.
    Date: 2016
  24. By: Christina Tuttle; Moira McCullough; Scott Richman; Kevin Booker; Alyson Burnett; Betsy Keating; Michael Cavanaugh
    Abstract: In November 2012, the Bill & Melinda Gates Foundation invested in seven innovative district-charter partnerships with “the potential capacity and commitment to accelerate student college ready rates through deep collaboration and sharing of best practices†. This report synthesizes findings across multiple data collection sources and offers broad findings from across the three-year grant period.
    Keywords: charter schools, education, leadership, principals, teachers, districts
    JEL: I
  25. By: Natalia Nollenberger (IE Business School- IE University); Ivone Perazzo (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía)
    Abstract: The provision of public preschool is expected to facilitate families, particularly mothers, their link with the labor market. However, empirical evidence on its effects is inconclusive. This research estimates the effect of an expansion in the provision of public preschool, held in Uruguay in the mid-1990s, on the attendance of children 4 and 5 years old to preschool and on the participation of mothers in the labor market. Following Duflo (2001) and Berlinski and Galiani (2007), the identification strategy exploits the differences in the number of new places available across regions, produced by the timing and priorities of the program. The results indicate that the expansion of places increased the preschool’s attendance although the take up rate in Uruguay was relatively low in comparison with the result of the same policy in similar countries (as in the case of Argentina). This was partially because the expansion of public places crowded out the attendance to private schools, particularly among children of high-skill mothers. The policy was much more effective in increasing the attendance of children of lowskill mothers. For this group of mothers, it would be expected to find a positive effect on employment or activity. However, we did not find any effect of the policy on their labor market outcomes.
    Keywords: Pre-primary education; Female labor supply
    JEL: J13 J22 I28
    Date: 2016–08
  26. By: Alvaro A. Mezza; Kamila Sommer
    Abstract: Print{{p}}October 16, 2015{{p}}A Trillion Dollar Question: What Predicts Student Loan Delinquency Risk?{{p}}Alvaro Mezza and Kamila Sommer{{p}}Over the past ten years, the real amount of student debt owed by American households more than doubled, from about $450 billion to{{p}}more than $1.1 trillion. As a result of this increase, in 2010 student loan debt surpassed credit card debt as the largest class of{{p}}non-housing consumer debt. Currently, about 42.5 million borrowers hold student debt, nearly double the number from ten years ago,{{p}}with average real debt per borrower increasing from about $19,000 to $27,000. A potential consequence of the higher reliance on{{p}}student debt to finance higher education, coupled with the adverse effects of the Great Recession, is difficulty in meeting these debt{{p}}obligations. As a possible reflection, the share of student loan balances 90 or more days delinquent increased from 6.7 percent to 11.7{{p}}percent.1{{p}}Following the rapid increase in student debt and delinquencies, a number of initiatives have been put forth by the Department of{{p}}Education (DoEd) to help borrowers to manage their debt. For example, new plans tied to borrowers' incomes (the so-called "income-driven"{{p}}repayment plans) were introduced to help borrowers to lower monthly payments to manageable levels relative to their incomes.2{{p}}While income-driven repayment plans are a potentially promising way to alleviate student loan burdens for borrowers, efficient targeting{{p}}of this at-risk population appears to be a challenge, in part due to existing data limitations.3{{p}}Using a unique data set that combines student loan debt and other individual credit variables with individual post-secondary education{{p}}records, in a new research paper (Mezza and Sommer (2015)4 ) we study predictors of student loan delinquency and, thus, identify{{p}}variables that could be used to more effectively target borrowers for enrollment in programs designed to mitigate delinquency risk.5{{p}}For illustrative purposes, we initially summarize our main findings in the form of bivariate tables. However, the arguments made based{{p}}on tabulations also hold in a multivariate analysis.6{{p}}Student loan delinquencies7 do not appear to be driven by high levels of student loan debt, but rather by other factors that affect{{p}}borrowers' ability to repay it.8 As shown in Table 1, borrowers who leave school without a college degree are disproportionately more{{p}}likely to become delinquent on their student loans, although their student loan burdens are on average relatively low. In marked{{p}}contrast, graduate degree holders, while generally associated with sizable student loan debt, rarely become delinquent on their student{{p}}loan debt.9 In particular, not controlling for other factors, the average delinquency rate and student loan balance among those who did{{p}}not earn a degree are 43.5 percent and $12,524, compared to 6.8 percent and $48,260 for those with earning a Master's or higher{{p}}degree.10{{p}}Table 1: Average Student Loan Balance and Delinquency Rates by Highest Attained Degree{{p}}Max. Degree Attained Avg. Student Loan Balances ($) Delinquency Rate{{p}}No Degree 12,524 0.435{{p}}Certificate/Associate's Degree 12,307 0.228{{p}}Bachelor's Degree 24,133 0.111{{p}}Master's or Above 48,260 0.068{{p}} Note: Tabulations reflect the highest reported attained degree in the sample.{{p}}Attending a for-profit institution, with or without completing a degree, is associated with disproportionately greater risk of future student{{p}}loan delinquency (Table 2).11 Most notably, not controlling for other factors, student loan borrowers with a degree from a private{{p}}for-profit institution are on average 2.6 times more likely to become delinquent on their student loan debt than borrowers from public{{p}}4-year schools.12 However, even in the for-profit and 2-year public sectors, where delinquencies are prevalent, there is still significant{{p}}heterogeneity in student delinquency outcomes across specific institutions.13{{p}}Table 2: Delinquency Rate by School Sector and Degree Completion{{p}} FRB: FEDS Notes: A Trillion Dollar Question: What Predicts Student L...{{p}}1 of 4 10/20/2015 10:56 AM{{p}}Sector Type With Degree With No Degree{{p}}Public 4-year 0.103 0.409{{p}}Public 2-year 0.166 0.464{{p}}Private 4-year. not-for-profit 0.116 0.328{{p}}Private, for-profit 0.265 0.543{{p}}Total 0.119 0.435{{p}} Note: Tabulations are based on the most recent school sector affiliation. Individuals most recently affiliated with private, 2-year institutions are dropped from the{{p}}analysis due to limited number of observations.{{p}}A borrower's credit score (even when measured at a time that precedes the borrower's entry into student loan repayment) is highly{{p}}predictive of future student loan delinquencies and is correlated with both degree non-completion and for-profit attendance.14 In part,{{p}}this might reflect that borrowers with low credit scores at the time of their entry into repayment tend to be less likely to have a degree{{p}}and are more likely to have attended a for-profit institution. Table 3 shows that while student loan balances on net rise with borrowers'{{p}}credit scores, delinquency rates fall. This finding challenges the notion that credit histories of young student loan borrowers are not{{p}}necessarily well established and, consequently, less likely to be predictive of future credit behavior. Instead, our analysis suggests that{{p}}borrowers' credit scores observed at or shortly before school exit, if made available to program administrators, could be very effectively{{p}}used to target borrowers for enrollment in programs designed to mitigate delinquency risk.{{p}}Table 3: Average Student Loan Balance and Delinquency Rate by Borrowers’ Credit Score Measured Prior to the{{p}}School Exit{{p}}Credit Score Avg. Student Loan Balances ($) Delinquency Rate{{p}}270-499 18,927 0.592{{p}}500-599 22,504 0.301{{p}}600-679 23,704 0.175{{p}}680-729 27,454 0.090{{p}}730-900 25,540 0.041{{p}}Missing Score 11,372 0.341{{p}} Note: Tabulations are based on borrowers' credit scores that are on average lagged by one year relative to borrowers' school exit.{{p}}To illustrate the three points made above in a multivariate analysis framework, we build a series of statistical models designed to predict{{p}}the probability that borrowers become delinquent on their student loans within the first 5 years after entering repayment. The models{{p}}vary in the explanatory variables used to predict delinquency risk, and all of them produce a predicted probability of future student{{p}}delinquency for every individual in our data set. With these predicted probabilities (as well as data on actual delinquencies experienced{{p}}by these individual borrowers) in hand, we assess which combination of variables is the most effective in identifying borrowers who{{p}}eventually became delinquent.15{{p}}To assess the relative performance of these models, we construct cumulative delinquency curves --an analytical tool commonly used in{{p}}the mortgage industry to gauge performance of statistical models predicting mortgage loan delinquency risk. In a nutshell, to construct a{{p}}cumulative delinquency curve, for each borrower in our data set, we first compute her individual probability of future student loan{{p}}delinquency based on each model specification. Second, we use these probabilities to rank borrowers from the individual who is{{p}}associated with the largest risk of future student loan delinquency to the one who is associated with the smallest risk. Thus, the 10{{p}}percent riskiest borrowers (as predicted by each model) are located in the bottom decile of the distribution on the X-axis in the{{p}}cumulative delinquency curve graph in Figure 1. Third, using the actual delinquency data, on the Y-axis, we plot the cumulative portion of{{p}}the actual realized delinquencies for each percentile of student loan borrowers ranked by their student loan delinquency risk. As such, a{{p}}point with coordinates (X=10,Y=30) on the graph in Figure 1 implies that the 10 percent of the riskiest borrowers (as predicted by a{{p}}model) account for 30 percent of all actual student loan delinquencies in the sample.{{p}}The black line in Figure 1 shows what a perfect prediction for our sample would look like--about 25 percent of borrowers have ever been{{p}}delinquent on their student loans in our sample, and these would be interpreted by the best-fitting model as the "riskiest" borrowers{{p}}based on their observable characteristics. In practice, an estimated model is unlikely to fit the perfect prediction line exactly. However,{{p}}the model's fit relative to the perfect prediction provides a gauge for assessing how well the model separates borrowers in a high risk of{{p}}student loan delinquency from their lower-risk counterparts.{{p}}Figure 1: Cumulative Delinquency Curves by Model Specification{{p}} FRB: FEDS Notes: A Trillion Dollar Question: What Predicts Student L...{{p}}2 of 4 10/20/2015 10:56 AM{{p}}Accessible version{{p}}The red line in figure 1 shows the cumulative delinquency curve for our fully-specified model that includes a full set of explanatory{{p}}variables, many of which might not be readily available to policy makers. By way of summary, these variables include borrower's age{{p}}when entering repayment, whether a borrower ever received Pell Grants and their average amount, the highest degree attained{{p}}(including an indicator for those with non-completed college degrees), degree major for those with attained degrees, school sector{{p}}controls, school-level cohort default rates, credit scores and indicators for missing credit scores, indicators for borrowers with other types{{p}}of debt (mortgage, auto, credit card), and time dummy variables. 16{{p}}The red line shows that our fully-specified model captures 60 percent of all student loan delinquencies among the riskiest 25 percent of{{p}}student loan borrowers ranked by the model-predicted delinquency risk. This compares quite favorably to a "perfect" model that would in{{p}}theory capture 100 percent of all student loan delinquencies in the riskiest borrower quartile. In marked contrast, a model that uses only{{p}}student loan balances (the blue line) captures only about 35 percent of all student loan delinquencies for the riskiest model-predicted{{p}}quartile. Notably, the fact that the blue line is not far from a 45-degree line indicates that the ability of a model that identifies delinquency{{p}}risk based on student loan balances is quite limited.{{p}}The green line shows the cumulative delinquency curve for a model that includes student loan balances, but also controls for school{{p}}sectors and the highest attained degree. As these two sets of additional controls are added, the predictive power of the models{{p}}improves; however, it is still a far cry from the predictive power of the fully-specified model (the red line).{{p}}The purple and yellow lines capture the models where borrowers' credit scores (measured prior to borrowers' entry into repayment) are{{p}}added. The predictive power of these models improves markedly, and converges almost to our fully-specified model (the red line). In{{p}}other words, the inclusion of credit scores as a predictor of future student loan delinquencies gives even the simplest model a mighty{{p}}boost, in terms of sample fit. In particular, a simple model that includes only student loan balances and credit scores (the purple line){{p}}captures about 57 percent of all student loan delinquencies among the riskiest model-predicted quartile, essentially the same fraction as{{p}}the fully specified model (red line) and nearly double the fraction of delinquencies captured by its analog that does not employ credit{{p}}scores (the blue line). In all, our findings suggest that credit scores measured prior to the borrower's entering repayment (unlike student{{p}}loan balances) are highly effective as a predictor of future delinquency events.{{p}}All told, our finding that student loan balances are only a poor predictor of future student loan delinquencies challenges aspects of the{{p}}popular narrative that frequently link borrowers with high student loan burdens (and often advanced degrees) to student loan debt{{p}}repayment difficulties. While such anecdotes undoubtedly capture the challenges facing some borrowers, the data show that they are{{p}}not generally representative of the typical student loan borrower experiencing repayment difficulties. The result also calls into question{{p}}the efficacy of using student loan balances as a tool to target borrowers for enrollment in income-driven (or other risk-mitigating){{p}}programs. Instead, when devising plans for loan modification or enrollment in income-driven repayment plans, targeting could be based{{p}}on broader credit information. To be sure, our analysis is not designed nor should be interpreted as suggesting that credit scores be{{p}}used for student loan underwriting; doing so could undermine the objective of equalizing college access opportunities.{{p}}For details of this analysis, please, see Mezza and Sommer (2015).{{p}} References:{{p}}Dynarski, Susan and Daniel Kreisman. "Loans for Educational Opportunity: Making Borrowing Work for Today's Students," Hamilton{{p}}Project Discussion Paper, 2013.{{p}}Hylands, Thomas. "Student Loan Trends in the Third Federal Reserve District," Cascade Focus, 2014.{{p}} FRB: FEDS Notes: A Trillion Dollar Question: What Predicts Student L...{{p}}3 of 4 10/20/2015 10:56 AM{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}}Looney, Adam and Constantine Yannelis. "A Crisis in Student Loans? How Changes in the Characteristics of Borrowers and in the{{p}}Institutions They Attended Contributed to Rising Loan Defaults? Brookings Papers on Economic Activity, 2015.{{p}}Mezza, Alvaro and Kamila Sommer. "A Trillion Dollar Question: What Predicts Student Loan Delinquencies?" Forthcoming in the FEDS{{p}}Working Series paper, 2015.{{p}}1. Figures based on author's calculations using the NYFed CCP/Equifax data set for 2005:Q2 and 2015:Q2. Nominal amounts are deflated by CPI-U into{{p}}constant 2015:Q2 dollars. Return to text{{p}}2. The two new plans are the Income-Based Repayment (IBR) plan--available since 2009--and the (ii) Pay-As-You-Earn (PAYE) repayment plan--available{{p}}since 2012. While the two plans vary in some of the eligibility requirements, they both offer low income-based payments tied to discretionary income over a{{p}}long amortization periods (from 20 to 25 years, depending on the specific plan). Additionally, the Income-Contingent Repayment (ICR) plan has been available{{p}}for Direct Loan Program (DLP) loan borrowers since the inception of the DLP in 1994. However, the take-up rate of this plan has been historically low and the{{p}}plan is less generous than the other two new plans recently implemented. Return to text{{p}}3. As of 2015:Q2, about 19 percent of borrowers owing about 33 percent of outstanding federal Direct student loan balances are enrolled in income-driven{{p}}repayment plans ( These figures include those enrolled in ICR, IBR, and PAYE plans.{{p}}Interestingly, the enrollment figures indicate that those currently enrolled have higher balances, on average, than the average DLP loan borrower (about{{p}}$50,000 versus $28,000), suggesting that a significant number of borrowers taking advantage of these plans are borrowers with high balances. As we will{{p}}show, these are not the borrowers that are most frequently associated with delinquencies and defaults. Return to text{{p}}4. The paper includes a detailed description of all the data sources and statistical methods used. Return to text{{p}}5. Income-driven repayment plans are intended to make student loan debt more manageable by reducing required monthly payments. While we are not able to{{p}}measure debt manageability in our data per se, there is likely a link between borrowers' ability to manage their student loan debt in this sense and their{{p}}delinquency risk. Return to text{{p}}6. For the multivariate results, see Mezza and Sommer (2015). Return to text{{p}}7. To this end, we define a delinquent borrower as one who ever becomes 120 or more days past due on their student debt payments within five years of{{p}}entering repayment. Student loan defaults are included in this definition of student loan delinquency. Return to text{{p}}8. For corroborating evidence, see Dynarski and Kreisman (2013) or Hylands (2014). Return to text{{p}}9. We are not the first to point this out: see, for example, "Student Loan and Defaults: The Facts" by Susan Dynarski, New York Times, June 11, 2015. Return{{p}}to text{{p}}10. Given the non-causal nature of our analysis, this result does not necessarily imply that pushing non-completers to finish their degrees will help them{{p}}repaying their debt. Return to text{{p}}11. For additional evidence on the correlation between the for-profit sector (and, to a lesser degree, the public 2-year sector) and Federal student loan defaults{{p}}in particular, see Looney and Yannelis (2105). Return to text{{p}}12. As was the case with degree non-completers, the positive relationship between delinquency risk and attending a for-profit institution is not necessarily{{p}}causal. However, for the purpose of identifying characteristics predicting future credit risk, for-profit institution attendance is a relevant variable to consider.{{p}}Return to text{{p}}13. This heterogeneity is better captured by the school-level 2-year cohort default rate (CDR)--a metric constructed by the DoEd that is mainly used to sanction{{p}}schools with high student loan default rates. The CDR reflects the percentage of borrowers at a given school who enter repayment on federal loans during a{{p}}particular federal fiscal year and default on their student loan(s) prior to the end of the next fiscal year. For an illustration of this heterogeneity, see Figure 3 in{{p}}Mezza and Sommer (2015). Return to text{{p}}14. To avoid the confounding effects of student loan repayment behavior on credit scores, a lagged credit score measure relative to school exit is used in the{{p}}analysis. In particular, scores are lagged on average by one year relative to school exit, depending on when we observe credit records and when the school{{p}}exit occurs for each individual in our sample. More timely credit scores (like those accessed at the time of a borrower's school exit or entry into repayment) are{{p}}likely to be even more predictive of delinquency risk. The credit score used in this analysis is the TU TransRisk AM Score. Return to text{{p}}15. In our analysis, we estimate a probability model (probit). The binary dependent variable--our student loan delinquency measure--takes a value of one if a{{p}}borrower was ever 120 or more days delinquent on her student loans within five years after entering repayment; zero otherwise. Return to text{{p}}16. To be consistent with the CDR information that might be available to the DoEd at the moment when the borrower enters repayment, we lagged the{{p}}school-level CDR by three years with respect to the year when the borrower entered repayment. Return to text{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Last update: October 16, 2015{{p}}Home | Economic Research & Data{{p}} FRB: FEDS Notes: A Trillion Dollar Question: What Predicts Student L...{{p}}4 of 4 10/20/2015 10:56 AM
    Date: 2015–10–16
  27. By: Stefano DellaVigna; Devin Pope
    Abstract: Academic experts frequently recommend policies and treatments. But how well do they anticipate the impact of different treatments? And how do their predictions compare to the predictions of non-experts? We analyze how 208 experts forecast the results of 15 treatments involving monetary and non-monetary motivators in a real-effort task. We compare these forecasts to those made by PhD students and non-experts: undergraduates, MBAs, and an online sample. We document seven main results. First, the average forecast of experts predicts quite well the experimental results. Second, there is a strong wisdom-of-crowds effect: the average forecast outperforms 96 percent of individual forecasts. Third, correlates of expertise---citations, academic rank, field, and contextual experience--do not improve forecasting accuracy. Fourth, experts as a group do better than non-experts, but not if accuracy is defined as rank ordering treatments. Fifth, measures of effort, confidence, and revealed ability are predictive of forecast accuracy to some extent, especially for non-experts. Sixth, using these measures we identify `superforecasters' among the non-experts who outperform the experts out of sample. Seventh, we document that these results on forecasting accuracy surprise the forecasters themselves. We present a simple model that organizes several of these results and we stress the implications for the collection of forecasts of future experimental results.
    JEL: C9 C91 C93 D03
    Date: 2016–08
  28. By: Rasmus Landerso (Aarhus Universitet); James J. Heckman (The University of Chicago)
    Abstract: This paper examines the sources of differences in social mobility between the U.S. and Denmark. Measured by income mobility, Denmark is a more mobile society, but not when measured by educational mobility. There are pronounced nonlinearities in income and educational mobility in both countries. Greater Danish income mobility is largely a consequence of redistributional tax, transfer, and wage compression policies. While Danish social policies for children produce more favorable cognitive test scores for disadvantaged children, these do not translate into more favorable educational outcomes, partly because of disincentives to acquire education arising from the redistributional policies that increase income mobility.
    Keywords: Inequality, education, social mobility, comparative analysis of systems
    JEL: I24 I28 I32 P51
    Date: 2016–08
  29. By: Elizabeth Lwanga Nanziri (SALDRU, University of Cape Town); Murray Leibbrandt (SALDRU, University of Cape Town)
    Abstract: Microeconomic theories of financial behaviour tend to assume that consumers possess financial skills necessary to undertake related financial decisions. We investigate this assumption by exploring the distribution of financial literacy among South Africans. In the absence of a standard measure, a financial literacy index is constructed for the country using data collected on attitudes (towards), access to and use of financial services over the period 2005 – 2009. We use the index to examine the extent to which differences in financial literacy correlate with demographic and economic characteristics. The Index reveals substantial variation in financial literacy by age, education, province and race. Overall, demographic characteristics contribute up to 10% of the financial literacy differences among individuals in South Africa. These results can be used to guide policy makers where to place more emphasis in terms of financial education for South Africans.
    Keywords: financial literacy, index, South Africa
    JEL: D14 G19 O55
    Date: 2016
  30. By: Shuja, Komal; Ali, Mazhar; Mehak Anjum, Munazzah; Rahim, Abdul
    Abstract: The purpose of the study was to find the effectiveness of using animated characters in advertising targeted to kids. The research design was quantitative and its research type was causal. The respondents of the study were ‘Pre-primary school going kids’ from nine different schools belonging to different areas of Karachi, Pakistan. Data was analyzed through Classification Regression Tree (CRT).The findings of this research study reveal that liking of the animated spokes character has a significant effect on product and brand character recognition, Product-Brand Character Association and brand preference. The majority of earlier related studies have been descriptive in nature. This study has used relatively advanced measurement technique like CRT thereby making methodical contribution. It is especially useful considering the paucity of research studies on advertising targeted at kids in Pakistan.
    Keywords: Children Buying Behavior, Animated Character , Advertising Effectiveness, Brand Preference, Brand Association, Brand Recall, Brand Recognition
    JEL: M37
    Date: 2016–08–27
  31. By: Florian Scheuer (Stanford University); Pablo Kurlat (Stanford University)
    Abstract: We study an otherwise standard education signaling economy with one additional feature: some of the potential employers can (imperfectly) observe workers' types directly. We propose a definition of competitive equilibrium for such an economy. The separating allocation is an equilibrium, in which employer's direct observation is irrelevant. However, there is another equilibrium where more informed employers hire high type workers with no education and pay lower wagers.
    Date: 2016
  32. By: Thomas Horvath (WIFO); Helmut Mahringer (WIFO)
    Abstract: The Austrian population will continue to grow over the next decades. At the same time the working age population is projected to increase until 2020 before declining again until 2030. In how far this demographic change will translate into changes in the actual labour force will mainly depend on the labour market attachment of the persons involved. This project analyses the development of labour force participation rates, explicitly accounting for changes in the educational structure, long-term trends in participation rates and recent tightening in pension law. These factors substantially affect labour force participation rates. Taking account of new population forecasts, this project works out new projections of labour force participation until 2030.
    Keywords: Arbeitskräfteangebot, Demographie, Bildungsbeteiligung, Pensionsreform
    Date: 2016–08–29
  33. By: Tetsuo Ono (Graduate School of Economics, Osaka University); Yuki Uchida (Graduate School of Economics, Osaka University)
    Abstract: This study considers public education policy and its impact on growth and wel- fare across generations. In particular, the study compares two scal perspectives| tax nance and debt nance|and shows that in a competitive equilibrium context, the growth and utility in the debt- nance case could be higher than those in the tax- nance case in the long run. However, the opposite occurs when the policy is shaped by politics. When the degree of parents' altruism is low, they choose debt nance in their voting, despite its long-run worse performance because a current generation can pass the cost of debt repayment to future generations.
    Keywords: Economic growth, Human capital, Public debt, Political equilib- rium
    JEL: D70 E24 H63
    Date: 2016–01
  34. By: Judith Scott-Clayton; Basit Zafar
    Abstract: Prior research has demonstrated that financial aid can influence both college enrollments and completions, but less is known about its post-college consequences. Even for students whose attainment is unaffected, financial aid may affect post-college outcomes via reductions in both time to degree and debt at graduation. We utilize two complementary quasi-experimental strategies to identify causal effects of the WV PROMISE scholarship, a broad-based state merit aid program, up to 10 years post-college-entry. This study is the first to link college transcripts and financial aid information to credit bureau data later in life, enabling us to examine important outcomes that have not previously been examined, including homeownership, neighborhood characteristics, and financial management (credit risk scores, defaults, and delinquencies). We find that even as graduation impacts fade out over time, impacts on other outcomes emerge: scholarship recipients are more likely to earn a graduate degree, more likely to own a home and live in higher-income neighborhoods, less likely to have adverse credit outcomes, and are more likely to be in better financial health than similar students who did not receive scholarships.
    JEL: I22 I26 J24
    Date: 2016–08

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