In a 1969 issue of the Journal of Political Economy, Wallace Oates published an important paper in the field of urban public finance – ‘The Effects of Property Taxes and Local Public Spending on Property Values: An Empirical Study of Tax Capitalization and the Tiebout Hypothesis‘.
The paper was the first quantitative test of Charles Tiebout’s now celebrated 1956 paper entitled ‘A Pure Theory of Local Expenditures’.
As described by Professor Oates, in the Tiebout world:
…the consumer shops among different communities offering varying packages of local public services and selects as a residence the community which offers the tax-expenditure program best suited to his tastes.
In common parlance, we often speak of this dynamic as households “voting with their feet” for the community that best suits their preferences. Additional depth is provided:
…In terms of the Tiebout model, we can conceive of a utility maximizing consumer who weighs the benefits stemming from the program of local public services against the cost of his tax liability and chooses as a residence that locality which provides him with the greatest surplus of benefits over costs.
His hypothesis is:
If consumers, in their choice of locality of residence, do consider the available program of public services, we would expect to find that, other things being equal (including tax rates), gross rents (actual or imputed) and therefore property values would be higher in a community the more attractive its package of public goods.
To test this relationship, Oates gathered data on a handful of New Jersey “residential” communities within close proximity of New York City. His aim was to determine, other things being equal, the relationship between property values and local property taxes and expenditures. Given that all things are never equal, his dataset includes various quality controls for homes in a given town and the distance from each town to the major employment center.
Effective tax rates are used instead of nominal tax rates because it’s generally a more representative gauge of a property owner’s tax liability.
Oates suggests that by far the largest single item in local public budgets…is primary and secondary education, but given that he was unable to find a direct measure of education quality, he includes data on expenditure per pupil. He suggests that although this is by no means a perfect variable for my purposes…there is some reason to expect that…the quality of local school systems should vary directly with expenditure per pupil.
Some years later, in a book written in his honor, Oates admitted that he “never set out to test the Tiebout model,” and that he “had assembled a small body of fiscal data on 52 New Jersey municipalities…as an exercise to learn how to do multiple regression analysis.”
This is quite fitting as this dataset was also created to teach introductory regression modeling to the students of an advanced spatial analysis class at the University of Pennsylvania.
It recreates Oates’ dataset using data from 2011 compiled from the State of New Jersey, the National Center for Education Statistics and the US Census. Data is included for 550 of the 565 towns and cities listed by the state (look out for outliers in the data).
Included are average home prices, effective tax rates, and two measures of education provision. Like the Oates dataset, this one has a variable for expenditure per pupil but it also has a direct measure of school quality – the student/teacher ratio. Additional controls are also included.
The data is in shapefile format, which is a map file specifically for use with the ArcGIS (Geographic Information System) software package. One of the seven accompanying files that makes up the shapefile is a ‘.dbf’ file which can be opened in Excel and converted for use in any standard statistical package.
The variable list can be found below as well as in the shapefile metadata.
The data which can be downloaded here is free to distribute as long as the below citation is used:
Ken Steif (2013). A Recreation of Wallace Oates’ Tiebout Capitalization Dataset in New Jersey, 2011.
Ken Steif is a Doctoral Candidate in the Graduate Group of the City & Regional Planning Program at the University of Pennsylvania. You can follow him on Twitter @KenSteif