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Michael F. Lovenheim (Cornell University and NBER) and Patrick Walsh (St. Michael’s College) April 26, 2014 Conference on Subnational Government Competition.

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Presentation on theme: "Michael F. Lovenheim (Cornell University and NBER) and Patrick Walsh (St. Michael’s College) April 26, 2014 Conference on Subnational Government Competition."— Presentation transcript:

1 Michael F. Lovenheim (Cornell University and NBER) and Patrick Walsh (St. Michael’s College) April 26, 2014 Conference on Subnational Government Competition Does Choice Increase Information? Evidence from Online Search Behavior

2 Introduction School choice policies have grown dramatically in past several decades.  Open enrollment  Charter schools  Vouchers for private school enrollment By decoupling link between residence location and school location, choice policies induce more competition between schools and districts. For choice to be effective, a critical mass of parents must:  Value academic quality of schools  Be able to accurately assess academic quality of schools

3 Introduction Until recently, lack of data on parental information has hampered research on these assumptions We examine a unique question in this literature: does parental school quality search information respond to the choice environment?  Lends insight into whether there is “full information” in this market.  Part of the benefits of school choice may be due to more accurate parental decision-making, especially for low SES families.

4 What We Do We link search data at the city- and county-level from Greatschools Inc. over a 4-year period to changes in local school choice policies. We examine 5 school choice policies:  State open enrollment  State tuition vouchers  State charitable scholarship tax credits  State tuition tax credits  NCLB-induced choice at the district level. Main Results: expansions in all but one of these choice policies leads to significant increases in searches for local school quality information.

5 Greatschools Search Data is a website that allows users to compare school quality measures in a local area.  Information on test scores and a ranking from 1-10 based on these scores  Student demographics  “Community Ratings”: 1-5 stars for teacher quality, leadership and parent involvement  User reviews of schools. The test score information has been shown in other settings to lead parents to choose schools that increase test scores (Hastings and Weinstein, 2008). The reviews and ratings are unique to this setting.




9 Greatschools Search Data We have the universe of search terms from 1/1/2010 – 10/31/2013.  102,616,862 individual searches that contain over 3 million unique search terms  Can search for city, district, school, zip code or address.  Match each search term toCBSA for cities and to county for the others. Can match about 60% of unique terms, 80% of searches.  A large portion of unmatched searches are due to errors that would not lead to search results. Calculate total searches by month and searches for specific terms, like “elementary” and “charter.”

10 School Choice Policies Collected information on 4 types of state policies and when they are announced and enacted:  Open enrollment, vouchers, charitable tax deductions and tuition tax credits.  Policies consist of multiple laws. We construct indices that count the number of laws in a state-month. NCLB requires any Title I school that fails AYP 2 years in a row to offer students the opportunity to transfer to a “non- failing school.”  In 24 states, we calculate proportion of schools in each district- year that are subject to such sanctions.  Also have an indicator for whether a waiver granted that exempts state from NCLB sanctions.

11 Variation in Choice Environment

12 Methodology Difference-in-Difference models that relate changes in policies to changes in search prevalence:  Model includes month, year and “Search Unit” fixed effects.  Search Units are the city or county. Main identification assumptions:  Changes in the choice environment are uncorrelated with prior trends in search prevalence  The timing of changes in the choice environment are uncorrelated with unobserved local shocks that independently influence search behavior.

13 Baseline Results A 1 standard deviation increase in  open enrollment increases search by 2.7%  tuition vouchers increases search by 6.2%  tuition tax credits increases search by 25.5% A 1 standard deviation increase in NCLB choice percentage increases search by 8%.

14 Identification Questions NCLB results biased by trends in SES composition of localities?  If so, results would be biased downwards – results are lower bound  But NCLB wavier would not be negative & significant if choice policies didn’t drive search  Searches interacted with NCLB choice peak in June/July “Other choice policy” results driven by trends in SES composition?  High-SES families move to area, lobby for choice, and also search?  High-choice areas attract high-SES families, who also search?  These choice policies vary at state level: families move states?  Could either effect operate in 4 years?

15 NCLB Choice Estimates by Month

16 Results for Specific Search Terms

17 Conclusion Online school quality search prevalence is strongly influenced by changes in school choice policies.  Suggests incomplete latent school quality information amongst parents.  Online search tools can help overcome school quality information asymmetries  An added benefit of school choice policies is to increase parental knowledge of local schooling options. Policymakers could use these types of online search tools explicitly to increase effectiveness of choice policies. Ultimately want to know how student outcomes are affected by this information.

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