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Measuring the Impacts of Teachers II: Teacher Value-added and Student Outcomes in Adulthood

Issue/Topic: Teaching Quality--Evaluation and Effectiveness
Author(s): Chetty, Raj; Rockoff, Jonah; Friedman, John
Organization(s): National Bureau of Economic Research
Publication: National Bureau of Economic Research Working Paper
Published On: 9/1/2013

Background:
One method of measuring the quality of teaching is to evaluate teachers based on their impacts on students' test scores, commonly termed the value-added ”(VA) approach. Advocates argue that selecting teachers on the basis of their VA can generate substantial gains in achievement, while critics contend that VA measures are poor proxies for teacher quality. Do teachers who raise test scores improve their students' outcomes in adulthood or are they simply better at teaching to the test?

Purpose:
To estimate the long-term impacts on a student of being assigned to a high VA teacher

Findings/Results:
  • Teacher VA has substantial impacts on a broad range of long-term outcomes, including:

    • increased college attendance rates
    • increased quality of the colleges students attend, as measured by average earnings of prior graduates
    • steeper earnings trajectories for students in their 20s
    • reduction in the possibility of having a child while a teenager
    • increased quality of the neighborhood in which the student lives in adulthood
    • increased participation rates in 401(k) retirement savings plans

  • Teachers have significant impact in grades 4-8.

  • The authors estimate that replacing a teacher whose current VA is in the bottom 5 percent with an average teacher would increase the mean present value of students' lifetime income by $250,000 per classroom over a teacher's career.

Policy Implications/Recommendations:
Replacing low VA teachers may be a more cost effective strategy to increase teacher quality in the short run than paying bonuses to retain high-VA teachers. The authors evaluated the expected gains from policies that pay bonuses to high-VA teachers to increase retention rates. The gains from such policies were only slightly larger than their costs because most bonus payments end up going to high-VA teachers who would have stayed even without the additional payment. They suggest that, in the long run, higher salaries could attract more high VA teachers to the teaching profession, though they don't measure that impact.

The authors caution that, although their findings are encouraging for the use of value-added metrics, two important issues must be resolved before determining how VA should be used for policy:
  • First, using VA measures could induce responses such as teaching to the test or cheating, eroding the signal in VA measures. If behavioral responses substantially reduce the signal quality of VA, policymakers may need to develop metrics that are more robust to such responses.

  • Second, the long-term impacts of evaluating teachers on the basis of VA should be compared to other metrics, such as principal evaluations or classroom observation. When a teacher who is rated highly by principals enters a school, do subsequent cohorts of students have higher college attendance rates and earnings? What fraction of a teacher’s long-term impact is captured by test-score VA versus other measures of teacher quality? By answering these questions, one could ultimately estimate the optimal weighting of available metrics to identify teachers who are most successful in improving students' long-term outcomes.
The authors explain that, more generally, there are many aspects of teachers' long-term impacts that remain to be explored and would be helpful in designing education policy. For example, are teachers' impacts additive over time? Do good teachers complement or substitute for each other across years?

Research Design:
Regression analysis and quasi-experimental

Population/Participants/Subjects:
Linked information from an administrative data set on students and teachers in grades 3-8 from a large urban school district spanning 1989-2009 with selected data from United States tax records spanning 1996-2011.

Year data is from:
1989-2011

Setting:
District

Data Collection and Analysis:
The authors matched approximately 90 percent of the observations in the school district data to the tax data, allowing them to track approximately one million individuals from elementary school to early adulthood. They measured outcomes such as earnings, college attendance and teenage births.

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