![Page 1: Strategies for estimating the effects of teacher credentials Helen F. Ladd Based on joint work with Charles Clotfelter and Jacob Vigdor CALDER Conference,](https://reader036.vdocuments.net/reader036/viewer/2022082816/56649f445503460f94c65700/html5/thumbnails/1.jpg)
Strategies for estimating the effects of teacher credentials
Helen F. Ladd Based on joint work with Charles Clotfelter
and Jacob Vigdor
CALDER Conference, Oct. 4, 2007
![Page 2: Strategies for estimating the effects of teacher credentials Helen F. Ladd Based on joint work with Charles Clotfelter and Jacob Vigdor CALDER Conference,](https://reader036.vdocuments.net/reader036/viewer/2022082816/56649f445503460f94c65700/html5/thumbnails/2.jpg)
Basic value-added model • Definition:
Ait = a Ait-1 + b TQit + c Xit + errorit
where A = student achievement (i.e. test score) ; and TQ = teacher qualifications
X = control variables
• Justification: Education is a cumulative process a = estimate of persistence of knowledge from one year to the next.
a =1 => complete persistence (no decay)a = 0 => no persistence (100 percent decay)
b = estimate of the effects of the qualifications of the student’s teacher in year t on her achievement in year t.
(Model assumes a and b are constant across years)
![Page 3: Strategies for estimating the effects of teacher credentials Helen F. Ladd Based on joint work with Charles Clotfelter and Jacob Vigdor CALDER Conference,](https://reader036.vdocuments.net/reader036/viewer/2022082816/56649f445503460f94c65700/html5/thumbnails/3.jpg)
Three papers – NC data
• Cross sectional data – fifth graders “Teacher-Student Matching and the Assessment of Teacher Effectiveness”
• Longitudinal data – fourth and fifth graders, multiple cohorts of students“How and Why Do Teacher Credentials Matter for Student Achievement?””
• Course-specific achievement in high school courses – multiple cohorts “Teacher Credentials and Student Achievement in High School: A Cross-Subject Analysis with Student Fixed Effects”
Note. Student achievement is normalized by grade, year and subject so that the mean is 0 and the SD = 1.
![Page 4: Strategies for estimating the effects of teacher credentials Helen F. Ladd Based on joint work with Charles Clotfelter and Jacob Vigdor CALDER Conference,](https://reader036.vdocuments.net/reader036/viewer/2022082816/56649f445503460f94c65700/html5/thumbnails/4.jpg)
Challenges for all three papers
Data – Identification of each student’s teacher Elementary schools – EOG tests
High schools – E0C tests In both cases, we start with proctor of the test but we keep the observation only if we are quite confident that the proctor is the relevant teacher. `(> 75 % match rate in both elementary schools and high schools)
Middle schools – identification not feasible Estimation – Non-random sorting of teachers and students among class rooms.
“Positive” sorting => upward biased coefficients of teacher credentials
![Page 5: Strategies for estimating the effects of teacher credentials Helen F. Ladd Based on joint work with Charles Clotfelter and Jacob Vigdor CALDER Conference,](https://reader036.vdocuments.net/reader036/viewer/2022082816/56649f445503460f94c65700/html5/thumbnails/5.jpg)
Cross sectional model – 5th graders
Strategies to reduce bias of estimates:
• Add an extensive set of student covariates Rich set available in NC data – e.g. education level of parents, T.V. watching
• Include school fixed effects Rules out bias from teacher-student sorting across schools
• Restrict sample to schools with evenly balanced classroom
Reduces bias from sorting across classrooms within schools.
![Page 6: Strategies for estimating the effects of teacher credentials Helen F. Ladd Based on joint work with Charles Clotfelter and Jacob Vigdor CALDER Conference,](https://reader036.vdocuments.net/reader036/viewer/2022082816/56649f445503460f94c65700/html5/thumbnails/6.jpg)
Coefficients of teacher experience - Math (all coefficients are statistically significant.)
Years of experience
(Base = 0 years)
Student covariates
School fixed effects
Restricted sampleWith school fixed effects
1-2 0.058 0.051 0.066
3-5 0.082 0.078 0.080
6-12 0.086 0.076 0.085
13-20 0.077 0.089 0.113
20-27 0.093 0.096 0.103
> 27 0.104 0.090 0.130
Observations 60,656 60,656 25,711
![Page 7: Strategies for estimating the effects of teacher credentials Helen F. Ladd Based on joint work with Charles Clotfelter and Jacob Vigdor CALDER Conference,](https://reader036.vdocuments.net/reader036/viewer/2022082816/56649f445503460f94c65700/html5/thumbnails/7.jpg)
Longitudinal – grades 4 and 5Achievement levels (Ait)
or achievement gains (Ait- Ai,t-1 )
Models 1-3 (of 5) No fixed effects 1. Levels (with prior year achievement) . Upward
biased coefficients because of teacher student matching; potential bias from lagged achievement
With school fixed effects2. Levels. Better but problem of matching within schools
remains and potential bias from lagged achievement; direction of bias unclear (see earlier paper)
3. Gains. Downward bias from misspecified persistence variable
![Page 8: Strategies for estimating the effects of teacher credentials Helen F. Ladd Based on joint work with Charles Clotfelter and Jacob Vigdor CALDER Conference,](https://reader036.vdocuments.net/reader036/viewer/2022082816/56649f445503460f94c65700/html5/thumbnails/8.jpg)
Longitudinal Data (cont.) Models 4 and 5 (preferred)
Full use of the longitudinal aspect of the data With student fixed effects
4. Levels (but no lagged achievement). Lower bound estimates of teacher credentials
5. Gains. Upward bound estimates of teacher credentials
![Page 9: Strategies for estimating the effects of teacher credentials Helen F. Ladd Based on joint work with Charles Clotfelter and Jacob Vigdor CALDER Conference,](https://reader036.vdocuments.net/reader036/viewer/2022082816/56649f445503460f94c65700/html5/thumbnails/9.jpg)
Teacher experienceCoefficients from models 4 and 5
All are statistically significant
Base= no experience
Math Reading
1-2 years 0.057 / 0.072 0.032 / 0.043
3-5 years 0.072 / 0.091 0.046 / 0.064
6-12 years 0.079 / 0.094 0.053 / 0.071
13-20 years 0.082 / 0.102 0.062 / 0.820
21-27 years 0.092 / 0.118 0.067 / 0.096
28+ 0.084 / 0.109 0.062 / 0.089
![Page 10: Strategies for estimating the effects of teacher credentials Helen F. Ladd Based on joint work with Charles Clotfelter and Jacob Vigdor CALDER Conference,](https://reader036.vdocuments.net/reader036/viewer/2022082816/56649f445503460f94c65700/html5/thumbnails/10.jpg)
High school cross-subject analysis Subjects – algebra 1, English I, biology, geometry, ELP
Strategy – at least three test scores for every student; include student fixed effects
Equivalent to estimating:
(Ais-Ai*) = b (TQis-TQi*) + error terms.
Where A* is the mean for the student.
Consider one potentially problematic error term: (eis-ei*).
Think of e as unmeasured student ability. Potential concern if ability for a given student differs by subject AND teachers are distributed in a systematic way by the relative ability of students
Based on empirical tests reported in the paper, we have reasonable confidence in our approach.
![Page 11: Strategies for estimating the effects of teacher credentials Helen F. Ladd Based on joint work with Charles Clotfelter and Jacob Vigdor CALDER Conference,](https://reader036.vdocuments.net/reader036/viewer/2022082816/56649f445503460f94c65700/html5/thumbnails/11.jpg)
Coefficients of teacher experiencein high school courses
Years of experience
(base = 0 years)
Model with student fixed effects
1-2 0.050
3-5 0.061
6-12 0.061
13-20 0.059
21-27 0.062
More than 27 0.043
Cf. rising coefficients with with teacher fixed effects