The Political Economy of Urban Growth

urban politics;political economy;land use;zoning;housing;formal theory;Political Science;Social Sciences;Political Science

Ornstein, JosephPage, Scott E ;

University of Michigan

关键词: urban politics;    political economy;    land use;    zoning;    housing;    formal theory;    Political Science;    Social Sciences;    Political Science;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/145832/ornstein_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Why do some US cities strictly limit the growth of their populations, while others are more accommodating to new housing construction? Though this may seem at first glance like a purely parochial concern, the question is of broad national interest. Regulatory barriers to housing construction slow economic growth by impeding the migration of labor. They exacerbate wealth inequality by privileging incumbent landowners over potential newcomers. And they harm the environment by encouraging auto-dependent urban sprawl and prohibiting dense, walkable communities. Understanding the political motivations behind restrictive municipal zoning regulations is therefore of vital national importance.In my first paper (Chapter II), I show that the timing of city council elections plays an important role in shaping municipal land use policy. Because some residents are deeply involved in municipal politics (e.g. homeowners), while others are not (e.g. renters), the composition of the electorate tends to change depending on the timing of the election. This shapes the reelection incentives of city councilmembers. In an empirical analysis of California cities, I show that cities with off-cycle elections tend to issue fewer new housing permits and have higher home prices than similar cities that hold their elections on-cycle. This result holds in both cross-sectional and difference-in-difference analysis. Cities that shifted their elections from off-cycle to on-cycle subsequently saw a larger increase in permitting, and slower growth in home prices, than comparable cities where elections remained off-cycle. This finding suggests that election timing can have non-trivial effects on both political representation and land use policy. In my second paper (Chapter III), I develop a new method for estimating local area public opinion. This method, called Machine Learning and Poststratification (MLP), improves on current practice by modeling public opinion using machine learning techniques like random forest and k-nearest neighbors. The predictions from these models are then poststratified (i.e. reweighted using demographic information) to produce public opinion estimates for local areas of interest. In a Monte Carlo simulation, I show that this technique outperforms classical multilevel regression and poststratification (MRP) and disaggregated survey estimates, particularly when the data generating process is highly nonlinear. In an empirical application, I show that MLP produces superior county-level estimates of Trump support in the 2016 presidential election than either MRP or disaggregation. In my final paper (Chapter IV), I explore a puzzling feature of US municipal land use politics: cities with more liberal residents tend to enact more restrictive zoning policies than similar conservative cities. In a formal model, I explain this as the result of a public goods provision problem. In liberal cities, where residents value public goods provision more highly, there is a greater incentive to ensure that newcomers do not underinvest in housing, thereby receiving a disproportionate share of public goods relative to property taxes. In an empirical analysis, I show that liberal cities issue fewer new building permits, have higher home prices, and score higher on a survey-based measure of land use policy restrictiveness, a pattern that cannot be explained by differences in geography, demographics, income, or characteristics of the housing stock.