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Journal Issue: The Next Generation of Antipoverty Policies Volume 17 Number 2 Fall 2007

Reducing Poverty through Preschool Interventions
Greg J. Duncan Jens Ludwig Katherine A. Magnuson

Endnotes

  1. Charles A. Nelson, “Neural Plasticity and Human Development: The Role of Early Experience in Sculpting Memory Systems,” Developmental Science 3, no. 2 (2000): 115–36; Jack P. Shonkoff and Deborah A. Phillips, eds., From Neurons to Neighborhoods: The Science of Early Childhood Development (Washington: National Academy Press, 2000); Lynn Karoly, “Investing in the Future: Reducing Poverty through Human Capital Programs,” in Understanding Poverty in America: Progress and Problems, edited by Sheldon H. Danziger and Robert H. Haveman (Harvard University Press, 2002).
  2. Eric I. Knudsen and others, “Economic, Neurobiological and Behavioral Perspectives on Building America’s Future Workforce,” Proceedings of the National Academy of Sciences of the United States of America 103, no. 27 (July 2006): 10155–62.
  3. Pedro Carneiro and James J. Heckman, “Human Capital Policy,” in Inequality in America: What Role for Human Capital Policies? edited by Heckman and Alan B. Krueger (MIT Press, 2004), 77–240; Flavio Cunha and others, “Interpreting the Evidence on Life Cycle Skill Formation,” Working Paper 11311 (Cambridge, Mass.: National Bureau of Economic Research, 2005).
  4. Valerie E. Lee and David T. Burkham, Inequality at the Starting Gate (Washington: Economic Policy Institute, 2002), pp. 25–28.
  5. Betty Hart and Todd Risley, Meaningful Differences in the Everyday Experiences of Young American Children (Baltimore: Brookes, 1995).
  6. Christopher Jencks and Meredith Phillips, eds., The Black-White Test Score Gap (Brookings, 1998); Roland Fryer and Steven D. Levitt, “Understanding the Black-White Test Score Gap in the First Two Years of School,” Review of Economics and Statistics 86, no. 2 (2004): 447–64; Lee and Burkham, Inequality at the Starting Gate (see note 4); Cecilia Rouse, Jeanne Brooks-Gunn, and Sara McLanahan, “Introducing the Issue,” Future of Children 15, no. 1 (2005): 5–14; Donald A. Rock and A. Jackson Stenner, “Assessment Issues in the Testing of Children at School Entry,” Future of Children 15, no. 1 (2005): 15–34; J. Brooks- Gunn and L. B. Markman, “The Contribution of Parenting to Ethnic and Racial Gaps in School Readiness,” Future of Children 15, no. 1 (2005).
  7. Nelson, “Neural Plasticity and Human Development” (see note 1); National Scientific Council on the Developing Child, “Excessive Stress Disrupts the Architecture of the Developing Brain,” Working Paper 3 (2005), www.developingchild.net/pubs/wp/excessive_stress.pdf (February 2007).
  8. J. LeDoux, “Emotion Circuits in the Brain,” Annual Review of Neuroscience 23 (2000): 155–84.
  9. Richard Tremblay and others, “Physical Aggression during Early Childhood: Trajectories and Predictors,” Pediatrics 114, no. 1 (2004): e43–50; Cunha and others, “Interpreting the Evidence on Life Cycle Skill Formation” (see note 3).
  10. Greg Duncan and others, “School Readiness and Later Achievement,” Developmental Psychology (forthcoming); Cybele C. Raver and others, “Self-Regulation across Differing Risk and Sociocultural Contexts: Preliminary Findings from the Chicago School Readiness Project,” paper presented at the biennial meeting of the Society for Research in Child Development, Atlanta, April 2005.
  11. Duncan and others, “School Readiness and Later Achievement” (see note 10).
  12. Rouse and others, “Introducing the Issue” (see note 6).
  13. Albert J. Reiss and Jeffrey A. Roth, Understanding and Preventing Violence (Washington: National Academies Press, 2003).
  14. Meredith Phillips, James Crouse, and John Ralph, “Does the Black-White Test Score Gap Widen after Children Enter School?” in The Black-White Test Score Gap, edited by Jencks and Phillips (see note 6), pp. 229–72. For a discussion of measurement issues, see Jens Ludwig, “Educational Achievement and Black- White Inequality: The Great Unknown,” Education Next 3, no. 3 (2003): 79–82.
  15. According to U.S. Budget, Fiscal Year 2005, the United States now spends more than $530 billion a year on elementary and secondary schooling for children aged five and older, including $13 billion in extra federal funding through the Title I program for schools serving poor children. In contrast, the federal government spends only about $18 billion on the Head Start program and child care subsidies, most of which go to preschoolers; see testimony of Douglas J. Besharov before the Subcommittee on 21st Century Competitiveness of the Committee on Education and the Workforce, February 27, 2002, www.welfareacademy.org/ pubs/testimony-022702.pdf (February 2007).
  16. Standard deviation units are a common way of expressing effect sizes. For comparison, the standard deviation for a typical IQ test is 15–16 points, and for the SAT, 100 points.
  17. Lawrence Schweinhart and others, Lifetime Effects: The High/Scope Perry Preschool Study through Age 40 (Ypsilanti, Mich.: High/Scope Press, 2005).
  18. Frances A. Campbell and others, “Early Childhood Education: Young Adult Outcomes from the Abecedarian Project,” Applied Developmental Science 6, no. 1 (2002): 42–57; Steven Barnett and Leonard Masse, “Comparative Benefit-Cost Analysis of the Abecedarian Program and Its Policy Implications,” Economics of Education Review (2007, forthcoming); Craig Ramey and Frances Campbell, “Compensatory Education for Disadvantaged Children,” School Review 87, no. 2 (1979): 171–89.
  19. The cost estimate is in 2005 dollars. See Janet Currie, “Early Childhood Education Programs,” Journal of Economic Perspectives 15, no. 2 (2001): 213–38.
  20. Craig T. Ramey and Frances A. Campbell, “Preventive Education for High-Risk Children: Cognitive Consequences of the Carolina Abecedarian Project,” American Journal of Mental Deficiency 88, no. 5 (1984): 515–23.
  21. Campbell and others, “Early Childhood Education: Young Adult Outcomes from the Abecedarian Project” (see note 18).
  22. In addition, criminal involvement was less common for treatments than controls (14 percent versus 18 percent for misdemeanor convictions, and 8 percent versus 12 percent for felony convictions), although the absolute numbers of those arrested in the two Abecedarian groups were small enough that it is impossible to prove statistically that this particular difference did not result from chance.
  23. Barnett and Masse, “Comparative Benefit-Cost Analysis of the Abecedarian Program and Its Policy Implications” (see note 18).
  24. Michael Puma and others, Head Start Impact Study: First Year Findings (U.S. Department of Health and Human Services, Administration for Children and Families, 2005). Note that the point estimates we report in the text are larger than those in this report. The Head Start report presents the difference between average outcomes for all children assigned to the treatment group and all children assigned to the control group, known in the program evaluation literature as the “intent to treat” (ITT) effect. But not all children assigned to the experimental group participated in Head Start (the figure is around 84 percent), while some children (18 percent) assigned to the control group enrolled in the program. If we divide the ITT effect by the difference between the treatment and control groups in Head Start participation (66 percent), the implied effect of Head Start participation on participants is around 1.5 times as large as the ITT effects presented in Puma and others’ report. For a discussion of this methodology, see H. S. Bloom, “Accounting for No-Shows in Experimental Evaluation Designs,” Evaluation Review 8 (1984): 225–46. If we define the “treatment” more broadly, as participation in any center-based care, the effects of Head Start participation may be up to 2.5 times as large as the ITT impacts reported by Puma and others, since more than 96 percent of the treatment group receives some sort of center-based care in the experiment but so does around 53–60 percent of the control group (see exhibits 3.2 and 3.3 in Puma and others’ report). For more on our calculations, see Jens Ludwig and Deborah Phillips, “The Benefits and Costs of Head Start,” Working Paper 12973 (Cambridge, Mass.: National Bureau of Economic Research, 2007).
  25. The Early Head Start initiative serves children under age three in a mix of home and center-based programs. A rigorous evaluation of the Early Head Start program found some evidence that the program had positive effects on some aspects of children’s development and of parenting practices, but in general the effects were smaller than those produced by the Head Start program. See John M. Love and others, Making a Difference in the Lives of Infants and Toddlers and Their Families: The Impacts of Early Head Start (U.S. Department of Health and Human Services, Administration for Children and Families, 2002).
  26. Ludwig and Phillips, “The Benefits and Costs of Head Start” (see note 24).
  27. Janet Currie and Duncan Thomas, “Does Head Start Make a Difference?” American Economic Review 85, no. 3 (1995): 341–64; Eliana Garces, Duncan Thomas, and Janet Currie, “Longer-Term Effects of Head Start,” American Economic Review 92, no. 4 (2002): 999–1012; Jens Ludwig and Douglas L. Miller, “Does Head Start Improve Children’s Life Chances? Evidence from a Regression-Discontinuity Design,” Quarterly Journal of Economics 122, no. 1 (2007): 159–208.
  28. W. Steven Barnett, Cynthia Lamy, and Kwanghee Jung, “The Effects of State Prekindergarten Program on Young Children’s School Readiness in Five States” (Rutgers University, National Institute for Early Education Research, 2005); William T. Gormley and Ted Gayer, “Promoting School Readiness in Oklahoma: An Evaluation of Tulsa’s Pre-K Program,” Journal of Human Resources 40, no. 3 (2005): 533–58; William T. Gormley Jr. and others, “The Effects of Universal Pre-K on Cognitive Development,” Developmental Psychology 41, no. 6 (2005): 872–84.
  29. Barnett, Lamy, and Jung, “The Effects of State Prekindergarten Program on Young Children’s School Readiness in Five States” (see note 28).
  30. Gormley and others, ”The Effects of Universal Pre-K” (see note 28).
  31. Barnett, Lamy, and Jung, “The Effects of State Prekindergarten Program on Young Children’s School Readiness in Five States” (see note 28); Katie Hamm and Danielle Ewen, “Still Going Strong: Head Start Children, Families, Staff, and Programs in 2004,” Head Start Series Policy Brief 6 (Center for Law and Policy, November 2005), www.clasp.org/publications/headstart_brief_6.pdf (February 2007); Gormley and others, “The Effects of Universal Pre-K” (see note 28).
  32. Thomas Cook, Northwestern University, PowerPoint presentation, www.northwestern.edu/ipr/events/ briefingdec06-cook/slide16.html.
  33. Specifically, these recent studies all use a regression discontinuity design that compares fall semester tests for kindergarten children who participated in pre-K the previous year and have birthdates close to the previous year’s enrollment cutoff with fall tests of children who are currently starting pre-K because their birthdates just barely excluded them from participating the previous year. The key assumption behind these studies is that the selection of children into pre-K does not change dramatically by child age around the birthday enrollment cutoff (that is, it changes “smoothly” with child age). But this need not be the case, because there is a discrete change at the birthday threshold in terms of the choice set that families face in making the decision to select pre-K. Suppose, for instance, that among the children whose birthdays just barely excluded them from enrolling in pre-K during the previous year, those with the most motivated parents were instead sent to private programs that are analogous to the public pre-K program that year and are then enrolled in private kindergarten programs in the fall semester when the pre-K study outcome measures are collected. This type of selection would reduce the share of more motivated parents among the control group in the pre-K studies and lead them to overstate the benefits of pre-K participation.
  34. Evaluations do not produce definitive evidence on the importance of the parental outreach component of early childhood education programs. The outreach we propose is more modest than Perry’s, which involved home visits, although it does build the connections between classroom teachers and parents.
  35. A variety of developmental and academic curricula have been developed for preschool programs, but since few have been evaluated rigorously it is hard to compare the relative effectiveness of these programs. The Institute for Educational Sciences is currently sponsoring a number of evaluations, the findings of which should help guide the selection of the national curriculum for our proposed program.
  36. Geoffrey D. Borman and others, “The National Randomized Field Trial of Success for All: Second-Year Outcomes,” American Educational Research Journal 42, no. 4 (2005): 673–96.
  37. David M. Blau and Janet Currie, “Preschool, Daycare and Afterschool Care: Who’s Minding the Kids?” Working Paper W10670 (Cambridge, Mass.: National Bureau of Economic Research, August 2004).
  38. State and federal governments spend about $9 billion a year on child care subsidies through the Child Care and Development Fund (CCDF), and about 25 percent of these subsidies go to children of ages three and four, about 70 percent of whom attend center-based care; see www.acf.hhs.gov/programs/ccb/data/ index.htm (February 8, 2007). Our take-up rates assume that every child whose family income is below three times the poverty line and who is currently in center-based child care will participate in our program. In this case, CCDBG expenditures would decline by (0.25 x 0.7 x $9 billion) = $1.6 billion. Similarly, TANF programs currently spend about $2.5 billion in funding each year for child care subsidies, and under our assumed take-up rates for low-income children a portion of these expenditures would no longer be necessary; see www.clasp.org/publications/childcareassistance2004.pdf. Current Head Start program expenditures are on the order of $7 billion a year; if our take-up rate assumptions are correct, then more than four-fifths of Head Start participants would switch over to the program we propose, either because their current Head Start providers would take over operation of the program or because these participants would move to another provider, who was providing our program. In either case, spending on Head Start in its current form would decline by around $5.8 billion. Similarly, states are now spending more than $2 billion on pre-K programs, and under our assumed take-up rates about three-quarters of these children would switch over to the new program we propose, saving $1.5 billion in government spending.
  39. Specifically, we assume that Head Start’s impact on children’s cognitive achievement test scores will be on the order of 0.2 of a standard deviation, effects of pre-K programs will be around 0.3 of a standard deviation, and effects of center-based care will be about 0.1 of a standard deviation. The “intent to treat” effects in the recent national randomized experimental impact of Head Start are on the order of 0.1 to 0.25, where the effects are statistically significant (Puma and others, Head Start Impact Study; see note 24); the implied effect of treatment on the treated will be more on the order of 0.15 to 0.35 standard deviation, and so averaging across relevant outcome domains (including those where standard errors did not allow for detectable impacts) suggests that 0.2 is a reasonable assumption for this program. Our assumption of 0.3 for state pre-K programs is the average across five states found by Barnett, Lamy, and Jung, “The Effects of State Prekindergarten Program on Young Children’s School Readiness in Five States” (see note 28), for the PPVT vocabulary and Woodcock-Johnson early math tests. Because our program entails the same level of spending per child on early childhood intervention as Perry Preschool (about $16,000 in current dollars for two years of half-day high-quality early education), if “scale-up” of our program does not reduce the intervention’s effectiveness by more than half compared with the Perry model demonstration result, then our program’s effect would be around 0.4 of a standard deviation. School-based research suggests that the benefits of two years of instruction are roughly twice those of a single year; D. Card, “The Causal Effect of Education on Earnings,” in Handbook of Labor Economics: Volume 3A, edited by. O. Ashenfelter and D. E. Card (New York: Elsevier, 1999), pp. 1801–63. Because our intervention is about equal to twice the early childhood instruction provided by current high-quality, one-year, state pre-K programs, our proposed program could generate impacts of about 0.6 of a standard deviation (twice the single-year pre-K impact). We also note that the average impact per participant of our program would be higher if more children than we have assumed stick with Head Start and pre-K. The reason is that both of these programs have larger impacts on children’s cognitive skills compared with either “regular” center-based or other types of child care. If there is an increase in the fraction of children who switch into our program from center-based, parental, or informal care and a decline in the fraction who switch over from Head Start or pre-K, then the average effect of participating in our program rather than the alternative care arrangements they would have experienced will increase as well.
  40. Schweinhart and others, Lifetime Effects (see note 17).
  41. Clive R. Belfield and others, “The High/Scope Perry Preschool Program: Cost-Benefit Analysis Using Data from the Age 40 Follow-up,” Journal of Human Resources 41, no.1 (2006): 162–90.
  42. The standard cost estimate for two years of Perry Preschool is around $16,000, less than the $24,000 figure for our proposed program. The difference in costs is due in part to the wraparound child care that we propose. However, as discussed in the text, we think that offsets from reduced spending on other programs will reduce the costs of our proposal by up to one-third, which would make our per-child costs about the same as Perry Preschool.
  43. Robert Haveman and Barbara Wolfe, “The Determinants of Children’s Attainments: A Review of Methods and Findings,” Journal of Economic Literature 23 (December 1995): 1829–78; Alan B. Krueger, “Economic Considerations and Class Size,” Economic Journal 113 (2003): 34–63.
  44. Jill S. Cannon, Allison Jacknowitz, and Gary Painter, “Is Full Better than Half? Examining the Longitudinal Effects of Full-Day Kindergarten Attendance,” Journal of Policy Analysis and Management 25, no 2. (2006): 299–321; John R. Cryan and others, “Success Outcomes of Full-Day Kindergarten: More Positive (1992): 187–203; Amy Rathburn, Jerry West, and Elvira Germino Hauskens, From Kindergarten through Third Grade: Children’s Beginning School Experiences (U.S. Department of Education, National Center for Educational Statistics, 2004).
  45. James Elicker and Sangeeta Mathur, “What Do They Do All Day? Comprehensive Evaluation of a Full- School-Day Kindergarten,” Early Childhood Research Quarterly 12 (1997): 459–80; Nancy Karweit, “The Kindergarten Experience,” Educational Leadership 49 (1992): 82–86.
  46. “The Science of Early Childhood Development: Closing the Gap between What We Know and What We Do,” National Scientific Council on the Developing Child, January 2007, www.developingchild.net/pubs/ persp/pdf/science_of_development.pdf.
  47. “Child Care Eligibility and Enrollment Estimates for Fiscal Year 2003,” Policy Issue Brief (Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation, April 2005), http://aspe.hhs.gov/hsp/05/cc-elig-est03/index.htm (February 2007).
  48. Jens Ludwig and Susan E. Mayer, “ ‘Culture’ and the Intergenerational Transmission of Poverty: The Prevention Paradox,” Future of Children 16, no. 2 (2006): 175–96..
  49. Gordon Dahl and Lance Lochner, “The Effects of Family Income on Child Achievement,” Working Paper 1305-05 (University of Wisconsin Institute for Research on Poverty, 2005); Pamela Morris, Greg Duncan, and Christopher Rodrigues, “Does Money Really Matter? Estimating Impacts of Family Income on Children’s Achievement with Data from Random-Assignment Experiments,” paper presented at the Chicago Workshop on Black-White Inequality, University of Chicago, 2006.
  50. W. Steven Barnett, Kristy Brown, and Rima Shore, “The Universal versus Targeted Debate: Should the United States Have Preschool for All?” (Rutgers University, National Institute for Early Education Research, 2004).