Behavioral Responses to Teacher Transfer Incentives: Results from a Randomized Experiment INVALSI Conference on Improving Education through Accountability and Evaluation: Lessons from Around the World Rome, Italy October 4, 2012 Steven Glazerman Ali Protik Bing-ru Teh Julie Bruch Neil Seftor
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Behavioral Responses to Teacher Transfer Incentives: Results from a Randomized Experiment
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Behavioral Responses to Teacher Transfer Incentives: Results from a Randomized Experiment
INVALSI Conference on Improving Education through Accountability and Evaluation: Lessons from Around the World
Rome, Italy
October 4, 2012
Steven Glazerman Ali Protik Bing-ru Teh Julie Bruch Neil Seftor
Best teachers may not be working with the students who need them the most
Shift focus from improving productivity of the teacher workforce to composition
Big gaps in knowledge– Weak documentation of the policy problem– Lack of data on teacher transfer behavior– Lack of data on whether skills transfer– Controversy about teacher quality measures
(value added)
Policy Problem
2
Policy Response: Talent Transfer Initiative
$20,000 transfer incentive
Identify highest-performing (HP) teachers– Use value-added analysis, three years of data– Three pools: elementary, MS math, MS language arts– Top 20% are “highest performing”
Identify potential “receiving schools”
Recruit transfer candidates, arrange interviews
Support transfer teachers, issue payments
HP teachers already in potential receiving schools get retention stipend of $10,000
3
1. How do HP teachers respond to a monetary transfer incentive?
2. How do hard-to-staff schools respond to the opportunity to hire a HP teacher?
3. What impact do transfer teachers have in their new settings?– Did their skills transfer, i.e. were they portable?– Was “value added” the right metric?
Research Questions
4
Summary of Findings to Date
Implementation– Filling vacancies was feasible
– Large pool of candidates needed
– Meaningful contrast achieved
Intermediate impacts– Increased experience and credentials slightly
– No significant impact on climate or collegiality
– No change in how students assigned to teachers
– TTI transfers used less & provided more mentoring
Impact on test scores and retention– Will be public in the final report (2013)
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Study Design
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Identify potential receiving schools with a vacancy in a targeted grade/subject
Unit of randomization = teacher team– Team types can be:
• Elementary self-contained math and reading• Middle school math• Middle school English/language arts (ELA)
Experimental Design
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School A School B
Study Design, Illustration
8
Randomization Block
School A School BFocal Teachers
Randomly assign teacher teams (grade within school) to treatment or control
Study Design, Illustration
9
Data
10
Ten Large, Diverse Districts in the Study
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Cohort 1: seven districts in five states Cohort 2: three districts in two more states
Primary Data Collection: Surveys– Candidates– Receiving school teachers in study grades– Receiving school principals
Secondary Data– District-provided test scores and demographics– School-provided teacher rosters
Data
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7 districts – Large, diverse– 5 county, 2 city
1,012 transfer candidates– 63 transfers from 51 sending schools