- A Sampling, recruitment and treatment assignment A.1 Sampling To construct our initial middle school sampling frame, we began with two school-level datasets from the NYCDOE: the 2014-15 Demographic Snapshot, and the LCGMS extract from March 22, 2015. The latter is a file updated daily showing all NYC schools in operation, allowing us to identify school status changes since the Demographic Snapshot was released.
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- • The 2015-16 NYC High School Directory, which includes (among other things) graduation rates, program interest areas, and admissions methods. The graduation rate pertained to the cohort graduating in 2013-14, the most recent available at the time of printing.
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Castleman, B. L., & Page, L. C. (2015). Summer nudging: Can personalized text messages and peer mentor outreach increase college going among low-income high school graduates? Journal of Economic Behavior & Organization, 115, 144—160.
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- Corcoran, S. P., Jennings, J. L., Cohodes, S. R., & Sattin-Bajaj, C. (2017). Administrative complexity as a barrier to school choice: Evidence from New York City. Unpublished working paper, New York University.
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Deming, D. J., Hastings, J. S., Kane, T. J., & Staiger, D. O. (2014). School choice, school quality, and postsecondary attainment. American Economic Review, 104(3), 991—1013.
- Disare, M. (2017, June 6). City to eliminate high school admissions method that favored families with time and resources. Chalkbeat. Retrieved from https://www.chalkbeat.org/posts/ny/2017/06/06/city-to-eliminate-high-school-admissionsmethod -that-favored-families-with-time-and-resources/ Fryer Jr., R. G. (2016). Information, non-financial incentives, and student achievement: Evidence from a text messaging experiment. Journal of Public Economics, 144, 109—121.
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- Figure 2: School-level variation in the percent of choices from Fast Facts Notes: each point is a school. The top row shows the percent of choices (either top three or all) that appeared on Fast Facts in 2015-16. The bottom row shows the 2014-15 to 2015-16 change in the percent of choices that appeared on the 2015-16 Fast Facts sheet.
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- Geocoded student residential addresses from 2012-13 were used to calculate for each middle school the percent of 8th graders living in low-income Census tracts, defined using the population share with income below 150% of the poverty line from the American Community Survey. (2012-13 was the most recent address file available at the time this sampling frame was produced).32 The working sample of 438 schools was split into quartiles based on this poverty measure. Schools in the top two quartiles of poverty comprised our “high-poverty†recruitment pool (N=217). We sorted these in random order and began recruiting from the top of this list (see the following subsection for details on recruitment). When it became apparent we would need schools beyond this list, we created a “mid-poverty†recruitment pool consisting of the next quartile of schools (N=108).
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Glazerman, S., & Dotter, D. (2017). Market signals: Evidence on the determinants and consequences of school choice from a citywide lottery. Educational Evaluation and Policy Analysis, 39(4), 593—619.
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Hastings, J. S., & Weinstein, J. M. (2008). Information, school choice, and academic achievement: Evidence from two experiments. Quarterly Journal of Economics, 123(4), 1373—1414.
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Jackson, C. K. (2010). Do students benefit from attending better schools? Evidence from rulebased student assignments in Trinidad and Tobago. The Economic Journal, 120(549), 1399—1429.
Jochim, A., DeArmond, M., Gross, B., & Lake, R. (2014). How parents experience public school choice. Seattle: Center for Reinventing Public Education. Retrieved from http://www.crpe.org/publications/how-parents-experience-public-school-choice Johnson, E. J., Hassin, R., Baker, T., Bajger, A. T., & Treuer, G. (2013). Can consumers make affordable care affordable? The value of choice architecture. PloS One, 8(12), e81521.
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- Lareau, A., Adia Evans, S., & Yee, A. (2016). The rules of the game and the uncertain transmission of advantage. Sociology of Education, 89(4), 279—299.
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- Page, L. C., Castleman, B., & Meyer, K. (2016). Customized nudging to improve FAFSA completion and income verification. SSRN Electronic Journal. Retrieved from https://doi.org/10.2139/ssrn.2854345 Page, L. C., & Gehlbach, H. (2017). How an artificially intelligent virtual assistant helps students navigate the road to college. Working paper.
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- PeÃŒÂrez, P. A., & McDonough, P. M. (2008). Understanding Latina and Latino college choice: A social capital and chain migration analysis. Journal of Hispanic Higher Education, 7(3), 249—265.
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Perna, L. W. (2000). Differences in the decision to attend college among African Americans, Hispanics, and Whites. The Journal of Higher Education, 71(2), 117—141.
Pop-Eleches, C., & Urquiola, M. (2013). Going to a better school: Effects and behavioral responses.
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- Sample sizes vary from 18,019 (participation in supplemental round conditional on first round match) to 19,109 (match to 1st choice). All regression models include the following controls: school randomization block, student race/ethnicity, female, free lunch eligible, reduced-price lunch eligible, special education, EL, foreign born, quadratic in 7th grade ELA and mathematics z-scores, missing indicators for z-scores and other covariates, and indicator for students in schools that received a treatment in our 2014-15 pilot study. Schoollevel controls include a charter indicator, 8th grade enrollment, percent female, percent by race/ethnicity, percent with disabilities, percent EL, and mean 8th grade math and ELA scores. All school controls are measured in the year prior to treatment. Standard errors in parentheses, adjusted for clustering at the school level. + p < 0.10 * p < 0.05 ** p < 0.01 *** p < 0.001.
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- Sattin-Bajaj, C. (2014). Unaccompanied minors: Immigrant youth, school choice, and the pursuit of equity. Cambridge: Harvard Education Press.
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- Shafir, E., Simonson, I., & Tversky, A. (1993). Reason-based choice. Cognition, 49(1), 11—36.
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- Table 1: Regression-adjusted differences in the graduation rates of high school choices and matches, by student background, 2014-15 Without test score controls: With test score controls: (1) (2) (3) (4) (5) (6) (7) (8) Graduation rate % Low graduation rate Graduation rate %
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- Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New York: Yale University Press.
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- Valant, J., & Loeb, S. (2014). Information, choice, and decision-making: Field experiments with adult and student school choosers. Unpublished working paper.
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- Venezia, A., & Kirst, M. W. (2005). Inequitable opportunities: How current education systems and policies undermine the chances for student persistence and success in college. Educational Policy, 19(2), 283—307.
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Wiswall, M., & Zafar, B. (2015). Determinants of college major choice: Identification using an information experiment. The Review of Economic Studies, 82(2), 791—824. Figure 1: Impact of informational intervention on students’ propensity to choose Fast Facts schools Notes: each point estimate comes from a separate regression where the outcome is an indicator equal to one if the student chose a Fast Facts school as their kth choice (k=1 to 12). For clarity of presentation, a 95% confidence interval is shown for the FF1 point estimate only.