The effect of school capital investments on local housing markets and household sorting in California

ABSTRACT

In this paper, I investigate what effect school capital investments have on housing values and household location choice in the context of the Tiebout model. This research identifies an exogenous variation in school capital investments by exploiting the lottery allocation of entitlement to an interest-free construction bond among districts in California. Although the lottery is exogenous, additional non-lottery allocation complicates identification. This paper develops an empirical model based on sample selection methods in order to create a counterfactual state in which additional non-lottery allocation would not have existed. I find that receiving the interest-free construction bond increases school capital expenditure and housing values at the district level. I view the increase in housing values as the capitalization of school capital investments. I find little evidence for the effect of the interest-free construction bond on household sorting and student’s academic outcomes.

KEYWORDS:

• School capital investments
• tiebout model
• housing market
• household sorting
• sample selection

JEL CLASSIFICATION:

• H73
• R21

Acknowledgments

I would like to thank Thomas Mroz for his advice on the development of an empirical model and thank Sally Wallace, Kyle Mangum, and Chris Cunningham for comments on this paper. Any errors are my own.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

¹ The Qualified School Construction Bond (QSCB) was created by the American Recovery and Reinvestment Act of 2009 and nationally provided to school districts through state education agencies in 2009 and 2010. Under the program, selective districts could issue interest-free bonds for the construction and renovation of school facilities and the purchase of land.

² For example, for the effect of student performance on housing prices, see Ries and Somerville (2010) and Black (1999). For the relationship between school finance equalization and Tiebout sorting, see Chakrabarti and Roy (2015), Hilber and Mayer (2004), and Aaronson (1999). For the effect of the school choice program on housing values, see Reback (2005).

³ Although previous studies on this topic suggested convincing evidence, their empirical designs are not perfect. This makes this present paper’s evidence worthwhile. One limitation of Cellini, Ferreira, and Rothstein (2010)’s dynamic discontinuity regression design is that it requires to condition on referendum outcomes and a dummy for bond measures which might be endogenous to local unobserved factors. One limitation of Neilson and Zimmerman (2014) is that they only look at schools in one district, so that their estimates might be easily susceptible to spillovers from neighbouring areas within a district.

⁴ The number of school districts is the result of my calculation based on enrollment data from the California Department of Education. I exclude the county offices of education, other special schools from the number of school districts for the purpose of this study.

⁵ The CDE required that the first round QSCBs must be issued by July 2010. According to federal regulations, at least 10% of QSCB proceeds must be spent within 6 months of the issuance of QSCBs, and 100% must be spent within 3 years.

⁶ Data indeed shows that only 3 districts out of 40 lottery-won districts applied for the second round, whereas 71 districts out of 174 lottery-lost districts applied for the second round.

⁷ Treatment is defined here as winning the QSCB lottery. If treatment was alternatively defined as the actual issue of QSCBs, $\gamma$ would be an estimate for the intention-to-treat effect. QSCB-awarded districts could refuse to issue QSCBs due to the failure of the bond referenda passage or other uncertain reasons. Data shows that 31 districts out of 43 lottery-won districts issued QSCBs.

⁸ It would lead to equivalent results regardless of which dummy is used between $Pt{2}_i$ and $\sim Pt{2}_i$. Existing literature derives the additive correction terms for the sample selection model in terms of participation. In order to lead to equivalent results regardless of the use between $Pt{2}_i$ and $\sim Pt{2}_i$, one needs to newly derive the additive correction terms in terms of non-participation. The use of $\sim Pt{2}_i$ in lieu of $Pt{2}_i$ allow us to avoid such complication with no harm.

⁹ See Greene (2012), Ch. 17.5.5 and Wooldridge (2010), Ch. 15.7.3.

¹⁰ The webpage about the QSCB allocations was closed and became no longer publicly available since 27 January 2016.

¹¹ The inverse hyperbolic sine function is the approximation of the log function but can take zero and negative values. This function is sometimes used for the transformation of variables with extreme values as well as zeros and negative values in the literature. For the further discussion about the inverse hyperbolic sine function, see MacKinnon and Magee (1990) and Burbidge, Magee, and Leslie Robb (1988).

¹² CAASPP test is a standardized test in English language, arts/literacy, and mathematics for students enrolled in public schools in California. The test began in 2014. The academic outcomes that I use in this paper are based on the average of scores over all grades in public schools within each district.

Additional information

Funding

The author received no specific funding for this work.