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Urban blight is a complex problem that has been challenging for cities in the United States "Rust Belt" region for many decades. However, in the wake of the real estate and financial crisis, it is also a growing challenge for urban communities in many states such as California, Arizona, Nevada, and Texas. Detroit was particularly hit hard, where more than 40,000 blighted structures were identified in 2014. To curb blight, the city mobilized the most extensive demolition program in the country. Funded through the Federal Hardest Hit Fund (HHF), the Detroit Demolition Program began in 2014 and has demolished more than 20,800 properties at the cost of over $250 million. Furthermore, during 2009–2015, the city demolished 11,400 structures with hazardous materials such as asbestos. In this article, we assess the impacts of demolitions on the value of neighboring properties using a Repeat Sales (RS) regression approach. Specifically, we use housing sales price information from 2009 through 2019 to construct real estate price indices. We also rely on information from the 2009 Detroit Residential Survey to differentiate by dilapidation and blight levels before the start of the program. On average, blight removal through the demolition program does not appear to have been capitalized into the residential property prices. However, when considering the effect of ex‐ante program property characteristics, we find a modest positive effect of demolitions on property prices in areas with a low level of blight before the demolitions. Given the magnitude of the observed effect and the high costs associated with the demolition program, our results highlight the need for further research on alternative blight removal strategies that might provide a more cost‐effective solution to this urban challenge. [ABSTRACT FROM AUTHOR]

Fiscal stress is a common condition in shrinking cities such as Detroit. Among the contributing factors, property tax delinquency is a significant consideration due to the unwillingness or inability to pay property taxes. The literature shows that improvement in the quality of public goods and services can increase tax compliance and strengthen the social contract between taxpayers and the local government. Nonetheless, this relationship is unclear in the context of low-income shrinking cities. This article estimates the effects of new publicly provided transportation infrastructure on property tax compliance in Detroit, Michigan. Specifically, we use the announcement of the Qline in Detroit. We compute the effects of the Qline announcement on tax delinquency behavior using a Spatial Difference-in-Difference approach. The results indicate that the announcement of the Qline had an economically and statistically significant impact on tax delinquency for properties within one mile of the nearest Qline station. [ABSTRACT FROM AUTHOR]

The purpose of this article is to evaluate the degree to which the demolition of low-quality structures affects property tax compliance in Detroit. During the real estate crisis, the rate of property tax delinquency in the city exceeded 50%. The demolition of dilapidated structures could lead to improvements in tax compliance in two ways. The first is through an amenity effect: removal of blighted structures improves the neighborhood and thus may increase the likelihood that neighbors pay their property taxes. The second is via the existence of a peer effect in property tax compliance decisions. We use a 6-year panel database to examine the degree to which demolitions affect nearby residential property tax delinquency. Even though we are unable to disentangle the amenity and peer effects, our analysis shows that when a dilapidated property is demolished, tax delinquency within the neighborhood is reduced, providing evidence that demolitions improve tax compliance. [ABSTRACT FROM AUTHOR]

The β-convergence model is based on the neoclassical framework in which the spatial level of analysis is not relevant. These levels will result in decreasing returns. However, local processes of agglomeration, spillover effects, or other forces could operate differently depending on the level of spatial disaggregation. The primary objective of this article is to observe whether different local, regional, and national convergence behaviors are possible in the European Union (EU). To capture the differences among spatial scales, a multilevel and spatial effects extension of the Solow–Swan theoretical growth model is proposed. The estimations of this model are made using consistent information from Eurostat and Cambridge Econometrics for the period of 2000–2014. A multilevel spatial Durbin model estimator is applied to evaluate the spatial interactions of the regions. The results indicate that a general process of convergence in the EU coexists with intranational processes of divergence, highlighting the relevance of the spatial level of analysis. A final analysis of the impact of the economic crisis over the general behavior has been included verifying that the economic crisis strongly reinforce the weakness of the EU convergence. [ABSTRACT FROM AUTHOR]

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The fly‐in/fly‐Out (FIFO) or, drive‐in/drive‐out (DIDO) labour system is a long‐distance commuting work arrangement to attract workers towards remote mineral or fossil fuel extraction areas, where they work in shifts and then return to their usual place of residence located in a different region. Along with more and cheaper transportation alternatives, the use of FIFO/DIDO systems have importantly increased in last decades around the world, which has translated to FIFO/DIDO systems operating even when already established cities are present in extractive regions. This paper uses the case of Chile, one of the most important mining countries in the world, to explore whether and in what extent these labor systems influence wage compensations. We find that FIFO/DIDO commuters obtain an average wage compensation of 2.4 per cent per commuted hour. [ABSTRACT FROM AUTHOR]

Rural leaders can point to low housing costs as a reason that their area should be competitive for business attraction. To what extent do rural housing costs offset transportation and other locational disadvantages in cost structures? The United States lacks information to systematically answer the question. We adapt a strategy employed by The Economist in exploring purchasing power parity: the Big Mac index. We gather information on Big Mac prices with a random sample of restaurants across the contiguous United States. We find that core metro counties exhibit slightly higher Big Mac prices than other counties, but that differences across the balance of the rural–urban continuum code are not significant, implying that costs in a metroadjacent county are not different than areas that are much more rural. We show that some groups of states exhibit lower prices, especially in the southeast. Furthermore, we test for the presence of spatial monopoly and find that distance to other MacDonald’s restaurants has some influence on price. Stores at a greater distance from their competitors tend to charge more, ceteris paribus. We also show our results are consistent with other localized estimates of living costs. Our general findings could help rural decision makers determine whether their area truly holds cost advantages for firms looking to relocate. [ABSTRACT FROM AUTHOR]