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Journal Issue: Opportunity in America Volume 16 Number 2 Fall 2006

U.S. Elementary and Secondary Schools: Equalizing Opportunity or Replicating the Status Quo?
Cecilia Elena Rouse Lisa Barrow

Endnotes

  1. Based on authors' calculations using March Current Population Survey data available from Unicon. We limit the sample to individuals twenty-five to sixty-five years of age who worked at least one week in the past year.
  2. Based on authors' calculations using 2004 Current Population Survey March Outgoing Rotations Group data available from Unicon. We limit the sample to individuals aged twenty-five to sixty-five, omitting those with wages of less than one-half of the minimum wage or above the 99th percentile of the wage distribution.
  3. See, for example, Derek Neal and William R. Johnson, “The Role of Pre-Market Factors in Black-White Wage Differences,” Journal of Political Economy 104, no. 5 (1996): 869–95; and Orley Ashenfelter and Cecilia Elena Rouse, “Income, Schooling, and Ability: Evidence from a New Sample of Twins,” Quarterly Journal of Economics 113, no. 1 (1998): 253–84.
  4. Bruce Sacerdote, “What Happens When We Randomly Assign Children to Families?” Working Paper 10894 (Cambridge, Mass.: National Bureau of Economic Research, 2004).
  5. See William T. Dickens, “Genetic Differences and School Readiness,” Future of Children 15, no. 1 (2005): 55–69.
  6. See Mike Stoolmiller, “Implications of the Restricted Range of Family Environments for Estimates of Heritability and Nonshared Environment in Behavior-Genetic Adoption Studies,” Psychological Bulletin 125, no. 4 (1999): 392–409.
  7. Pamela Morris, Greg J. Duncan, and Christopher Rodrigues, “Does Money Really Matter? Estimating Impacts of Family Income on Children's Achievement with Data from Random-Assignment Experiments,” unpublished manuscript, MDRC and Northwestern University, 2004.
  8. Alan B. Krueger and Diane M. Whitmore, “The Effect of Attending a Small Class in the Early Grades on College Test Taking and Middle School Test Results: Evidence from Project STAR,” Economic Journal 111 (2001): 1–28. This calculation assumes that per pupil costs increase 47 percent per year for 2.3 years, on average, and that per pupil costs equal the average U.S. total expenditure per pupil in average daily attendance in 1997–98 in 2001–02 dollars ($8,487). Digest of Education Statistics 2003, table 166.
  9. Gordon B. Dahl and Lance Lochner, “The Impact of Family Income on Child Achievement,” Working Paper 11279 (Cambridge, Mass.: National Bureau of Economic Research, 2005).
  10. Based on calculations by the authors using data received through personal correspondence with Dahl.
  11. As Jacob Mincer shows, if forgone earnings are the only cost of school attendance, this is the private marginal benefit (or “return”) to the investment in a year of schooling. See Jacob Mincer, Schooling, Experience, and Earnings (Columbia University Press, 1974).
  12. Based on a regression of the natural logarithm of hourly wages on years of completed education, a quadratic in potential experience controls for sex, race/ethnicity, marital status, and nine regions using the 2004 March Current Population Survey. The regression was weighted using the earnings weight.
  13. Gary Becker, Human Capital (Columbia University Press, 1964).
  14. Michael Spence, “Job Market Signaling,” Quarterly Journal of Economics 87, no. 3 (1973): 355–74.
  15. For example, Joshua D. Angrist and Alan B. Krueger, “Does Compulsory Schooling Affect Schooling and Earnings?” Quarterly Journal of Economics 106, no. 4 (1991): 979–1014; Thomas J. Kane and Cecilia Elena Rouse, “Labor Market Returns to Two- and Four-Year Colleges,” American Economic Review 83, no. 3 (1993): 600–13; Jeffrey Kling, “Interpreting Instrumental Variables Estimates of the Returns to Schooling,” Journal of Business and Economics Statistics 19, no. 3 (2001): 358–64; David Card, “Using Geographic Variation in College Proximity to Estimate the Return to Schooling,” Working Paper 4483 (Cambridge, Mass.: National Bureau of Economic Research, 1993); and Philip Oreopoulos, “Average Treatment Effects of Education When Compulsory School Laws Really Matter,” American Economic Review (forthcoming). Angrist and Krueger use an individual's quarter of birth as the natural experiment; Kane and Rouse, Card, and Kling use proximity to a two- or four-year college as the natural experiment.
  16. For studies using siblings, see, for example, Orley Ashenfelter and David Zimmerman, “Estimates of the Returns to Schooling from Sibling Data: Fathers, Sons, and Brothers,” Review of Economics and Statistics 79, no. 1 (1997): 1–9; and Joseph Altonji and Thomas Dunn, “The Effects of Family Characteristics on the Return to Education,” Review of Economics and Statistics 78, no. 4 (1996): 692–704. For studies using twins, see Jere R. Behrman, Mark R. Rosenzweig, and Paul Taubman, “Endowments and the Allocation of Schooling in the Family and in the Marriage Market: The Twins Experiment,” Journal of Political Economy 102, no. 6 (1994): 1131–74; Orley Ashenfelter and Cecilia Elena Rouse, “Income, Schooling, and Ability: Evidence from a New Sample of Twins,” Quarterly Journal of Economics 113, no. 1 (1998): 253–84; and Cecilia Elena Rouse, “Further Estimates of the Economic Return to Schooling from a New Sample of Twins,” Economics of Education Review 18, no. 2 (1999): 149–57.
  17. Unfortunately, the measurement error in reported schooling poses an econometric challenge for these models. The reason is that classical measurement error is exacerbated in within-sibling (or within-twin) estimators because sibling education levels are so highly correlated. Zvi Griliches, “Estimating the Returns to Schooling: Some Econometric Problems,” Econometrica 45, no. 1 (1977): 1–22. As a result, much of the more recent literature using this approach has focused on addressing the measurement error bias as well as the ability bias.
  18. Pedro Carneiro and James J. Heckman, “Human Capital Policy,” in Inequality in America: What Role for Human Capital Policies? edited by Benjamin M. Friedman (MIT Press, 2003), pp. 148–49.
  19. Ashenfelter and Rouse, “Income, Schooling, and Ability” (see note 16).
  20. Lisa Barrow and Cecilia Elena Rouse, “Do Returns to Schooling Differ by Race and Ethnicity?” American Economic Review 95, no. 2 (2005): 83–87; and Altonji and Dunn, “The Effects of Family Characteristics” (see note 16).
  21. John Cawley and others, “Understanding the Role of Cognitive Ability in Accounting for the Recent Rise in the Economic Return to Education,” in Meritocracy and Economic Inequality, edited by Kenneth Arrow, Samuel Bowles, and Steven Durlauf (Princeton University Press, 2000), pp. 230–65; Carneiro and Heckman, “Human Capital Policy” (see note 18); and Christopher Taber, “The Rising College Premium in the Eighties: Return to College or Return to Unobserved Ability?” Review of Economic Studies 68, no. 3 (2001): 665–91.
  22. U.S. Department of Education, National Center for Education Statistics, The Condition of Education 2005, NCES-2005-094 (2005), appendix 1, table 2-2, “Trends in Private School Enrollments.”
  23. Robert Rosenthal, and Lenore Jacobson, Pygmalion in the Classroom: Teacher Expectation and Pupils' Intellectual Development (New York: Holt, Reinhart, and Winston, 1968).
  24. Lee Jessim and Kent D. Harber, “Teacher Expectations and Self-Fulfilling Prophecies: Knowns and Unknowns, Resolved and Unresolved Controversies,” Personality and Social Psychology Review 9, no. 2 (2005): 131–55.
  25. David Figlio, “Names, Expectations and the Black-White Test Score Gap,” Working Paper 11195 (Cambridge, Mass.: National Bureau of Economic Research, 2005).
  26. For example, see Christopher Avery and Thomas J. Kane, “Student Perceptions of College Opportunities,” in College Choices: The Economics of Where to Go, When to Go, and How to Pay for It, edited by Caroline M. Hoxby (University of Chicago Press, 2004), pp. 355–91; and Cecilia Elena Rouse, “Low-Income Students and College Attendance: An Exploration of Income Expectations,” Social Science Quarterly 85, no. 5 (2004): 1299–317.
  27. Clearly differences in information costs may be much more important in the transition from high school to college, when students need information about where and how to apply to college and how to go about getting financial aid. Children with college-educated parents have an advantage over other children in having parents who have “been there before.” See the article by Robert Haveman and Timothy Smeeding in this volume.
  28. See, for example, James J. Heckman and Lance Lochner, “Rethinking Education and Training Policy: Understanding the Sources of Skill Formation in a Modern Economy,” in Securing the Future: Investing in Children from Birth to College, edited by Sheldon Danziger and Jane Waldfogel (New York: Russell Sage Foundation, 2000), pp. 47–83; and David T. Ellwood and Thomas J. Kane, “Who Is Getting a College Education? Family Background and the Growing Gaps in Enrollment,” in Securing the Future, edited by Danziger and Waldfogel, pp. 283–324.
  29. See John T. Warner and Saul Pleeter, “The Personal Discount Rate: Evidence from Military Downsizing Programs,” American Economic Review 91, no. 1 (2001): 33–53; and David B. Gross and Nicholas Souleles, “Consumer Response to Changes in Credit Supply: Evidence from Credit Card Data,” mimeo, University of Pennsylvania (2000).
  30. See Helen Ladd, “Evidence on Discrimination in Mortgage Lending,” Journal of Economic Perspectives 12, no. 2 (1998): 41–62, for a nice review of the evidence on discrimination in mortgage lending.
  31. Alicia H. Munnell and others, “Mortgage Lending in Boston: Interpreting HMDA Data,” American Economic Review 86, no. 1 (1996): 25–53.
  32. T. D. Snyder, A. G. Tan, and C. M. Hoffman, Digest of Education Statistics, 2003, NCES-2005-025 (U.S. Department of Education, National Center for Education Statistics, 2004), table 164 (www.nces.ed.gov/pubs2005/2005025.pdf [February 26, 2005]).
  33. Authors' calculations from the 2003 CCD.
  34. See Michael A. Boozer and Cecilia Elena Rouse, “Intraschool Variation in Class Size: Patterns and Implications,” Journal of Urban Economics 50, no. 1 (2001): 163–89, for a more complete discussion of this issue.
  35. Daniel P. Mayer, John E. Mullins, and Mary T. Moore, Monitoring School Quality: An Indicators Report, NCES 2001-030 (U.S. Department of Education, National Center for Education Statistics, 2000) (http://nces.ed.gov/pubs2001/2001030.pdf [September 5, 2005]), figure 2.3.
  36. Hamilton Lankford, Susanna Loeb, and James Wyckoff, “Teacher Sorting and the Plight of Urban Schools: A Descriptive Analysis,” Educational Evaluation and Policy Analysis 24, no. 1 (Spring 2002): 37–62, table 6, p. 47.
  37. Snyder, Tan, and Hoffman, Digest of Education Statistics, 2003, table 101 (see note 32). The building features considered are roofs; framing, floors, and foundations; exterior walls, finishes, windows and doors; interior finishes and trim; plumbing; heating, ventilation, air conditioning; electric power; electrical lighting; and life safety features.
  38. Lisa Barrow and Cecilia Elena Rouse, “Using Market Valuation to Assess Public School Spending,” Journal of Public Economics 88, no. 9–10 (2004): 1749–71.
  39. Eric A. Hanushek, “Measuring Investment in Education,” Journal of Economic Perspectives 10, no. 4 (1996): 9.
  40. See Larry V. Hedges, Richard Laine, and Robert Greenwald, “Does Money Matter? A Meta-Analysis of Studies of the Effects of Differential School Inputs on Student Outcomes,” Education Researcher 23, no. 33 (1994): 5–14; and Alan B. Krueger, “Economic Considerations and Class Size,” Economic Journal 113 (2003): F34–63.
  41. Hedges, Laine, and Greenwald, “Does Money Matter?” (see note 40); and Eric A. Hanushek, “The Impact of Differential Expenditures on School Performance,” Educational Researcher 18, no. 4 (1989): 45–65.
  42. Barrow and Rouse, “Using Market Valuation to Assess Public School Spending” (see note 38); Jonathan Guryan, “Does Money Matter? Regression-Discontinuity Estimates from Education Finance Reform in Massachusetts,” Working Paper 8269 (Cambridge, Mass.: National Bureau of Economic Research, 2001); Geoffrey D. Borman and Jerome V. D'Agostino, “Title I and Student Achievement: A Meta-Analysis of Federal Evaluation Results,” Educational Evaluation and Policy Analysis 18, no. 4 (1996): 309–26; and David Card and A. Abigail Payne, “School Finance Reform, the Distribution of School Spending, and the Distribution of Student Test Scores,” Journal of Public Economics 83, no. 1 (2002): 49–82.
  43. Other recent papers on the effect of class size use “quasi-experimental” designs. For example, Joshua D. Angrist and Victor Lavy, “Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement,” Quarterly Journal of Economics 114, no. 2 (1999): 533–75, use the nonlinearity in the determination of class size in Israel to identify an effect of class size, finding effects on the same order of magnitude as those reported by Boozer and Rouse, “Intraschool Variation” (see note 34); and Caroline Minter Hoxby, “The Effects of Class Size on Student Achievement: New Evidence from Population Variation,” Quarterly Journal of Economics 115, no. 4 (2000): 1239–85, exploits variation in the size of the school-aged population in Connecticut to identify an effect of class size, finding that small class sizes have no effect on student achievement.
  44. Alan B. Krueger, “Experimental Estimates of Education Production Functions,” Quarterly Journal of Economics 114, no. 2 (1999): 497–531.
  45. See, for example, Jeremy D. Finn and Charles M. Achilles, “Answers and Questions about Class Size: A Statewide Experiment,” American Educational Research Journal 27, no. 3 (1990): 557–77; and Alan B. Krueger, “Experimental Estimates of Education Production Functions,” Quarterly Journal of Economics 114, no. 2 (1999): 497–531.
  46. Krueger and Whitmore, “The Effect of Attending a Small Class in the Early Grades” (see note 8). Others believe the evidence on a positive impact of school quality on subsequent educational attainment and earnings is not very strong. See, for example, the volume edited by Gary Burtless, Does Money Matter? The Effect of School Resources on Student Achievement and Adult Success (Brookings, 1996), for differing viewpoints.
  47. David Card and Alan Krueger, “Does School Quality Matter? Returns to Education and the Characteristics of Public Schools in the United States,” Journal of Political Economy 100, no. 1 (1992): 1–40.
  48. Card and Krueger, “Labor Market Effects of School Quality: Theory and Evidence,” in Does Money Matter? edited by Burtless, pp. 97–140 (see note 46).
  49. For example, see Daniel Aaronson, Lisa Barrow, and William Sander, “Teachers and Student Achievement in the Chicago Public High Schools,” unpublished manuscript, Federal Reserve Bank of Chicago (2005); and Eric A. Hanushek, Steve G. Rivkin, and John F. Kain, “Teachers, Schools, and Academic Achievement,” Econometrica 73, no. 20 (2005): 417–58.
  50. Another form of accountability targets the student. In this case, students are not permitted to advance to the next grade until they have demonstrated a predetermined level of proficiency in academic subjects. Evidence on these so-called no social promotion policies, however, is mixed. The best evidence comes from Brian A. Jacob and Lars Lefgren, “Remedial Education and Student Achievement: A Regression- Discontinuity Analysis,” Review of Economics and Statistics 86, no. 1 (2004): 226–44, who study the introduction of such a policy in the Chicago public schools. They find that retention increases achievement for third graders but not for sixth graders.
  51. See Martin Carnoy and Susanna Loeb, “Does External Accountability Affect Student Outcomes? A Cross- State Analysis,” Education Evaluation and Policy Analysis 24, no. 4 (2002): 305–31; Melissa Clark, “Education Reform, Redistribution, and Student Achievement: Evidence from the Kentucky Education Reform Act,” mimeo, Princeton University (2002); David Figlio and Cecilia Elena Rouse, “Do Accountability and Voucher Threats Improve Low-Performing Schools?” Journal of Public Economics 90, nos. 1–2 (2006): 239–55; Eric A. Hanushek and Margaret E. Raymond, “Does School Accountability Lead to Improved Student Performance?” Journal of Policy Analysis and Management 24, no. 2 (2005): 297–327; Walt Haney, “The Myth of the Texas Miracle in Education,” Education Policy Analysis Archives 8, no. 41 (2000); and Brian A. Jacob, “Accountability, Incentives and Behavior: Evidence from School Reform in Chicago,” Journal of Public Economics 89, nos. 5–6 (2005): 761–96.
  52. Brian A. Jacob and Steven D. Levitt, “Rotten Apples: An Investigation of the Prevalance and Predictors of Teacher Cheating,” Quarterly Journal of Economics 118, no. 3 (2003): 843–77.
  53. Julie Berry Cullen and Randall Reback, “Tinkering toward Accolades: School Gaming under a Performance Accountability System,” mimeo, University of Michigan (2003); David Figlio and Lawrence Getzler, “Accountability, Ability and Disability: Gaming the System?” Working Paper 9307 (Cambridge, Mass.: National Bureau of Economic Research, 2002); and Jacob, “Accountability, Incentives and Behavior” (see note 51).
  54. David Figlio, “Testing, Crime and Punishment,” Journal of Public Economics, forthcoming; and Jacob, “Accountability, Incentives and Behavior” (see note 51). It is worth noting that while these unintended consequences may have short-run benefits, it is unclear whether any of them would persist in the long run. If a school increases its average test scores by reclassifying students, for example, it is unclear whether the school will continue to experience large gains in the future, as it can only gain by reclassifying new students.
  55. Robert Bifulco and Helen F. Ladd, “The Impacts of Charter Schools on Student Achievement: Evidence from North Carolina,” Working Paper SAN04-01 (Durham, N.C.: Terry Sanford Institute of Public Policy, 2004); Tim R. Sass, “Charter Schools and Student Achievement in Florida,” mimeo, Florida State University (2004); and Eric A. Hanushek and others, “Charter School Quality and Parental Decision Making with School Choice,” Working Paper 11252 (Cambridge, Mass.: National Bureau of Economic Research, 2005).
  56. William Howell and Paul Peterson (with Patrick Wolf and David Campbell), The Education Gap: Vouchers and Urban Schools (Brookings, 2002).
  57. Alan B. Krueger and Pei Zhu, “Another Look at the New York City Voucher Experiment,” American Behavioral Scientist 47, no. 5 (2004): 658–98.
  58. Cecilia Elena Rouse, “Private School Vouchers and Student Achievement: An Evaluation of the Milwaukee Parental Choice Program,” Quarterly Journal of Economics 113, no. 2 (1998): 553–602; and John Witte, “Achievement Effects of the Milwaukee Voucher Program,” mimeo, University of Wisconsin (1997).
  59. Kim K. Metcalf and others, “Evaluation of the Cleveland Scholarship and Tutoring Program, Summary Report, 1998–2003,” mimeo, Indiana University (2004).
  60. Figlio and Rouse, “Do Accountability and Voucher Threats Improve Low-Performing Schools?” (see note 51).
  61. Janet Quint and others, “The Challenge of Scaling up Educational Reform: Findings and Lessons from First Things First, Final Report,” monograph (New York: MDRC, 2005); Jacob and Lefgren, “Remedial Education and Student Achievement” (see note 50).