Student Mobility Patterns and Achievement in Wisconsin

1. Student Mobility Patterns and Achievement in Wisconsin Prepared for the Wisconsin Department of Public Instruction Selina Eadie Ryan Eisner Bryan Miller Leni Wolf Monday, May 13, 2013

2. Overview of Presentation • ● Overview of Previous Findings • ● Methodology of Analysis • ● Description of Mobility in Wisconsin • ● Association Between Mobility and Achievement • ● Policy Implications and Ideas for Future Research

3. Research Questions ● What is the state of student mobility in WI? ● What is mobility’s association with achievement?

4. Previous Findings ● Moves associated with lower academic performance ● Different mobility rates for different groups of students ●Timing matters (both time of year and time of academic career) ●Inter- versus intra-district moves

5. Methodology • ● First research using Wisconsin Student Number Locator System (WSLS) • ●Created a cohort of students for analysis • ●Start: September 15, 2006 • ●End: May 1, 2011 • ●Students in Cohort: 319,230

6. Mobility Rates by Ethnicity (2006-07 through 2010-11) Number of moves

7. Mobility Rates by Economic Status (2006-07 through 2010-11)

8. Mobility Rates by Homeless Status (2006-07 through 2010-11)

9. Moves by Time of Year (2006-07 through 2010-11)

10. Moves in the Five Largest Districts (2006-07 through 2010-11)

11. Top Receiving Districts of Milwaukee Public Schools Students

12. Mobility and 2010-11 Graduation Status

13. Reading and Math WKCE Scores for 10th Graders in 2010-11

14. Reading and Math WKCE Scores for 10th Graders in 2010-11

15. Regression Confirmation ● Ran an OLS regression to test these relationships ● Controlled for: ● Race, Economic Status, Homelessness,District,Ever received English Language Learner (ELL) services Includes fixed effects for district ● Findings ● Coefficients on moves consistently negative and significant at .01 level ● Coefficients on other variables consistently of greater magnitude

16. Summary of Findings ● Black and Hispanic students appear to move much more frequently than their White and Asian-American peers ●Students who qualify for free or reduced-priced lunch move more than full-price students ●Students in largest districts are more likely to move than the state average ● Especially Milwaukee ●Consistent relationship between mobility and achievement

17. Future Research Considerations ● Detect residential moves ● Moves by time in academic career ●Type of destination school ● Prior year test score controls

18. Policy Implications ● Information and record-keeping systems ● Funding mechanisms and sources ● Implications for school choice policies

19. Acknowledgements ● Jared Knowles, Wisconsin Department of Public Instruction ● Russell Dimond, University of Wisconsin-Madison ● Jed Richardson, Wisconsin Center for Education Research ● Professor Andrew Reschovsky, University of Wisconsin- Madison

20. Thank you! Questions?

21. Reading Regression Results

22. Math Regression Results