ability to run the new enterprise may not be known.

Thus, we develop and estimate an equilibrium model of household school choice, charter school entry and school interaction in a large urban school district. In the model, a charter entry point is a combination of location (neighborhood), grade level and focus. For some entry points, prospective entrants submit entry applications to the regulator. Charter funding is connected to enrollment and prospective entrants must be financially viable. Hence, the regulator forecasts an applicant’s enrollment and peer characteristics as a function of its entry point and approves those expected to be financially viable.

We estimate the model using a unique and detailed data set from Washington D.C. from 2003 to 2007. The main data set contains information for all public, private and charter schools in the city including enrollment by grade, school demographics, focus and proficiency rates in standardized tests. We supplement these data with neighborhood-level information on charter school attendance and travel distance to charter and public schools. Lacking student-level data, we further augment the school-level data with the block-group level empirical distribution of child age, race, poverty status and family income, and draw from this distribution in order to calculate the model’s predictions. Since market shares for public, private and charter schools vary widely across grades, we define a market as a grade-year combination. We estimate the model in three stages corresponding to student demand, school supply and proficiency rates.

We model schools as differentiated products and estimate the demand side of the model using an approach similar to Berry et al (1995), henceforth BLP. We allow for a school-grade-year quality component (such as teacher quality) observable to households but not to the researcher. The ensuing correlation between school peer characteristics and the unobserved quality component is similar to the correlation between price and unobserved quality in BLP. Unlike price, which is determined by the firm under consideration, peer characteristics are determined by aggregate household choices and are similar to Bayer and Timmins’s (2007) local spillovers. Following Nevo (2000, 2001), we exploit the panel structure of our data and include school, grade and year fixed effects to capture some variation in the unobserved quality component. The school fixed effects are our estimates of school quality; they capture unmeasured factors in household choices such as school climate and culture, length of school day and year and facility characteristics. When estimating parameters of the proficiency rate function we estimate a separate set of school fixed effects that capture the ability of schools to raise passing rates in standardized math tests, and these constitute our measure of school value added.

A single, large urban district allows one to study the behavior of charters that confront the same institutional structure. We chose Washington, D.C. because it has a permissive, well-established 1996 charter law under which the charter school sector has grown to 44 percent of total public school enrollment as of 2013-2014.<sup>1</sup> It has a single

> 1As of 2013-2014, 12 districts had more than a 30 percent charter share. The five largest shares were

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