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The Lifetime Impacts of a Kindergarten Classroom

The Lifetime Impacts of a Kindergarten Classroom. Diane Whitmore Schanzenbach Northwestern University and National Bureau of Economic Research. Introduction. What are the long-term impacts of high quality early childhood education ?

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The Lifetime Impacts of a Kindergarten Classroom

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  1. The Lifetime Impacts of a Kindergarten Classroom Diane Whitmore Schanzenbach Northwestern University and National Bureau of Economic Research

  2. Introduction • What are the long-term impacts of high quality early childhood education? • We link data from a widely studied education experiment to administrative records on earnings for the first time  How does kindergarten classroom assignment affect you 25 years later?

  3. Research Context • Important policy question but very limited evidence to date • Why? Few datasets link information on early childhood test scores with data on adult outcomes • Perry Preschool • High quality preschool intervention in 1962 • Long term positive impacts across a wide range of outcomes • 123 participants, all low income

  4. Design of STAR Experiment • 11,571 Children • 79 Schools • 1 Cohort, Started School in 1985-86 • Random Assignment • Small (13-17 Students) • Regular, Aide (22-25 Students) • Teachers Randomly Assigned • KG-3rd Grade • 45% of cohort entered after KG • ~25% of cohort in STAR all 4 years • 4th grade all returned to regular classes • Most children born in 1980 -> 30 years old today

  5. Students in small classes scored higher on standardized tests, K-8 Black students Switch from SAT to CTBS and return to regular-size classes Free-lunch students Full sample Note: Effect of class size after controlling for student’s race, gender, free-lunch status and initial school-by-entry-wave fixed effects.

  6. Students randomly assigned to small classes more likely to take a college entrance exam • Percent Taking SAT or ACT Notes: Figure shows percent of students who took the ACT or the SAT exam, by their initial class-type assignment. Students initially assigned to regular classes with and without a teacher aide are combined in the figure. Sample consists of 9,397 STAR students who were H.S. seniors in 1998. Source: Krueger and Whitmore (1999).

  7. Other benefits of small classes • African American boys less likely to have juvenile criminal record • Girls less likely to give birth as teens • More likely to graduate from high school • Students today are ~30 years old • Opportunity to investigate a wider range of adult outcomes

  8. United States Tax Data • Access to selected variables in anonymous U.S. tax records to conduct research on behavioral responses to economic policies • Dataset covers full U.S. population from 1996-2008 • Approximately 90% of working age adults file tax returns • Third-partyreports yield data on many outcomes even for non-filers • Employer and wage earnings from W-2 forms • College attendance from 1098-T forms • 95% of STAR records were linked to tax data • Match rate unrelated to treatment

  9. Table 1: Summary Statistics

  10. Outline • Test scores and adult outcomes in the cross-section • Re-evaluate validity of STAR experimental design • Impacts of observable classroom characteristics • Impacts of unobservable classroom characteristics • Fade-out, re-emergence, and non-cognitive skills • Cost-benefit analysis

  11. 1. Cross-sectional correlations • Begin by correlating KG test scores with adult outcomes • Useful to benchmark estimates from randomized interventions • Show both raw correlations and OLS regressions with controls • Quartic in parental household income interacted with marital status • Mother’s age at child’s birth • Parent 401K contributions, home ownership • Child gender, free-lunch status, race and age • Test score: percentile score on the KG Stanford Achievement Test (average of math & reading)

  12. What is a kindergarten test? • Instructions: • You should be on the part of the page with the keys across the top. Look at the cups in the first row. How many cups are there? Fill in the circle under the number that tells how many cups there are. Mathematics 4 5 6

  13. What is a kindergarten test? • Instructions: • You should be on the part of the page with the strawberries across the top. Put your marker under the first row. In this box there are a sentence and three words. Read the sentence and then read the three words under the sentence. Decide which word makes the most sense in the sentence. Fill in the circle under the word that goes best in the sentence. Reading The dog likes to _________. can jump big

  14. Figure 1a: Wage Earnings in 2007 vs. KG Test Score $25K $20K Mean Wage Earnings from Age 25-27 $15K $10K 0 20 40 60 80 100 KG Test Score Percentile

  15. Test scores and Earnings in the Cross-Section

  16. Figure 1b: College Attendance Rates vs. KG Test Score 80% 60% Attended College before Age 27 40% 20% 0% 0 20 40 60 80 100 KG Test Score Percentile

  17. An Earnings-Based Index of College Quality • We construct an index of college quality using tax data • Tuition paid to any higher ed. institution automatically generates a 1098-T form linking student and institution • Find everyone age 20 enrolled in college in 1999 • Calculate average wage earnings in 2007 (from W-2s) by college • For those who do not attend college, define college quality index as mean earnings for those not in college in 1999

  18. An Earnings-Based Index of College Quality

  19. College Mean Wage Earnings by US News Ranking $80K $70K $60K Mean Earnings at Age 28 $50K $40K 0 25 50 75 100 125 US News Rank of Colleges

  20. Figure 1c: College Quality vs. KG Test Score $28K $26K $24K Earnings-Based College Quality Index $22K $20K $18K 0 20 40 60 80 100 KG Test Score Percentile

  21. Figure 2a: Home Ownership vs. KG Test Score 40% Owned a Home by Age 27 30% 20% 0 20 40 60 80 100 KG Test Score Percentile

  22. Figure 2b: Retirement Savings vs. KG Test Score 45% 40% 35% Made a 401(k) Contribution by Age 27 30% 25% 20% 0 20 40 60 80 100 KG Test Score Percentile

  23. Figure 2c: Marriage by Age 27 vs. KG Test Score 55% 50% 45% Married by Age 27 40% 35% 30% 25% 0 20 40 60 80 100 KG Test Score Percentile

  24. Figure 2d: Cross-State Mobility vs. KG Test Score 35% 30% Lived Outside TN before Age 27 25% 20% 0 20 40 60 80 100 KG Test Score Percentile

  25. Figure 2e: Percent College Grads in ZIP code vs. KG Test Score 22% 20% 18% Percent College Graduates in 2008 ZIP 16% 14% 0 20 40 60 80 100 KG Test Score Percentile

  26. From now on, concentrate on 4 outcomes: • 1. College attendance • 2. College quality • 3. Mean earnings (ages 25-27) • 4. Summary index of other outcomes: • Index = 401K + Home Owner + Married + Moved (Leave TN) + Pct. College Grads. in Zip

  27. Figure 2f: Summary Outcome Index vs. KG Test Score .4 .2 Outcome Summary Index 0 -.2 -.4 0 20 40 60 80 100 KG Test Score Percentile

  28. Part 2: Validity of the STAR Experimental Design • Experimental analysis rests on two assumptions: • Assumption #1: Successful Randomization • All pre-determined variables (e.g. parent characteristics) are balanced across classrooms • Assumption #2: No Differential Attrition • 95% match rate  little attrition here • No evidence of differences in match rates across classrooms • No evidence of differences in death rates across classrooms

  29. Part 2: Validity of the STAR Experimental Design • Threat #1: Failure of Randomization • We test for balance across class types with an expanded set of parent/sibling characteristics in two ways: • 1. Class size (type) intervention • Students were assigned to small vs. large class sizes • Do characteristics vary across small vs. large class types? • 2. Class room intervention • Students were assigned to specific classroom within class size • Do characteristics vary across classrooms within schools?

  30. Table 2: Randomization Tests

  31. Validity of the STAR Experimental Design • Threat #2: Selective Attrition • Much less attrition than in prior studies of STAR because we follow 95% of the sample • Test for selective attrition through two channels: • Does match rate vary across treatment groups? • Does death rate vary across treatment groups (Muennig et al. 2010)? • Find no difference in rate of follow-up observation

  32. Part 3: Class Size Impacts • Replicate specifications in previous studies • Independent variable: dummy for small class assignment (ITT) • Focus on four outcomes: • 1. College attendance in 2000 • 2. College quality index • 3. Mean earnings (ages 25-27) • 4. Standardized (SD = 1) summary index of other outcomes: • Index = 401K + Home Owner + Married + Moved (Leave TN) + Pct. College Grads. in Zip

  33. Figure 2a: Effect of Class Size on College Attendance by Year 30% 25% Percent Attending College 20% 15% 10% 2000 2002 2004 2006 Year Large Class Small Class

  34. Figure 2b: College Earnings Quality by Class Size Frequency $20K $30K $40K $50K $60K Earnings-Based Index of College Quality Large Class Small Class

  35. Figure 2c: Effect of Class Size on Wage Earnings by Year $18K $16K $14K $12K Wage Earnings $10K $8K $6K 2000 2002 2004 2006 Year Large Class Small Class

  36. Table 5: Impacts of Class Size on Adult Outcomes Note: All specifications control for school-by-entry-wave effects, class size, and student and parent demographics, and standard errors are clustered on initial school.

  37. Part 3: Teacher/Peer Effects • Students randomly assigned to classrooms that differ in teacher and peer quality • For example, a school has two small classes • One small class taught by experienced teacher, one by inexperienced teacher • By luck of draw, students assigned to one or other teacher • Can tease out impact of being assigned to more experienced teacher • After accounting for class size • Similarly, by luck of draw some classes have more/fewer • Girls • African American students • Students on free lunch

  38. Do Teachers & Peers Affect Adult Outcomes? First test: does random assignment to a more experienced KG teacher improve adult outcomes? • Not necessarily causal effect of teacher experience per se • Experienced teachers not only have more experience but may also be different along other dimensions like dedication to teaching

  39. Figure 3a: Causal Effect of Teacher Experience on Test Scores 56 54 52 KG Test Score Percentile 50 48 0 5 10 15 20 Kindergarten Teacher Experience (Years)

  40. Figure 3b: Causal Effect of Teacher Experience on Earnings $19K $18K Mean Wage Earnings, 2005-2007 $17K $16K 0 5 10 15 20 Kindergarten Teacher Experience (Years)

  41. Figure 3c: Effect of Teacher Experience on Earnings by Year $20K $1104 $18K $16K $14K Wage Earnings $12K $10K $8K 2000 2002 2004 2006 Year Teacher Experience <=10 Years Teacher Experience > 10 Years

  42. Table 6: Observable Teacher vs. Peer Effects Note: All specifications control for school fixed effects and class size, as well as student and parent demographics.

  43. Part 4: Unobserved Class Effects • Many elements of teacher and peer quality (e.g. clarity of instruction, enthusiasm) not captured by observable measures • Well known problem in literature on teacher effects • Modern literature captures unobserved teacher characteristics using analysis of variance • In other words, does across-class difference in outcomes vary by more than normal statistical variation? • Other studies have estimated whether certain teachers usually have classes that show larger-than-average achievement gains • Large differences across individual teachers

  44. Part 4: Unobserved Class Effects • We do not observe the same teacher multiple times • Only one cohort of students • Cannot isolate “teacher effects” • We can test for “classroom effects” on adult outcomes • Is there significant intra-class correlation in student’s outcomes? • Note that this “class effect” includes effect of teachers, peers, and any class-level shocks such as noise outside classroom (barking dog on test day)

  45. A Statistical Model of Class Effects • Test scores and earnings for individual iin class c in school n: • zcn = class-level intervention (e.g. better teaching) that affects scores and earnings • zYcn = intervention that affects earnings but not scores • aicn = academic ability • nicn = earnings ability orthogonal to academic ability • b + g= impacts of interventions on earnings • b= covariance of class effects on scores and earnings

  46. A Statistical Model of Class Effects • Test scores and earnings for individual iin class c in school n: • Thus far, we have estimated bdirectly by using observable z’s that affect test scores (e.g. teacher experience) • How can we estimate bwhen z is unobserved?

  47. Strategy 1: Analysis of Variance • Test for class effects on earnings (β + γ > 0) using ANOVA • Do earnings vary across classes by more than what would be predicted by random variation in student abilities? • F-test for significance of class fixed effects • Random effects estimate of class-level SD for outcomes

  48. Table 7: F-Tests for Kindergarten Class Effects Note: All specifications control for school fixed effects and class size as well as student and parent demographics.

  49. Measuring class quality: the intuition • We see that there is variation across classrooms from the same schools in adult outcomes • Want to know: is it same classrooms that have higher test score gains that produce higher earnings? • Because of random assignment, class “pre-test” scores should on average be the same • Difference in end of year scores across classrooms is measure of “class quality” • Includes teacher effects, peer effects, common shocks • Each student’s class quality is the difference between classmates’ scores and schoolmates’ scores • How did classmates score, relative to the classmates you could have had? • Higher scores -> better class quality

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