Using Emerging hot Spot Analysis to Explore Spatiotemporal Patterns of Housing Vacancy in Ohio Metropolitan Statistical Areas

We highlight the use of a newer method—emerging hot spot analysis of space-time cubes from defined locations—for examining the spread of housing vacancy in large, Ohio MSAs. Using this method, we discovered that many Ohio MSAs concurrently experienced spread, contraction, and vacancy stabilization in census tracts located adjacent to, or within close proximity of, one another. These results indicate that vacancy proliferation is not solely a matter of geographic determinism, whereby high vacancy in one tract predicts high vacancy in neighboring tracts in future years. We also found that vacancy spread at the tract level is associated with population dynamics at the neighborhood, city, and MSA levels. Our findings suggest that vacancy reduction initiatives should account for population trends at various geographic scales, not just physical conditions within a particular neighborhood or tract.

Municipal Fragmentation and Local Financial Condition

A sizeable literature investigates how intergovernmental competition affects various fiscal outcomes in a fragmented local landscape. However, it remains untested how the fragmentation affects the outcomes simultaneously. This study addresses the issue by condensing individual outcomes into a multifaceted concept of financial condition. Utilizing a pooled cross-sectional time-series approach on the metropolitan statistical areas in the U.S between 1972 and 2017, this study tests how financial condition of municipalities varies by competition among them. The finding exhibit adverse effects on their financial condition when a greater number of municipalities is identified.

Characterizing the sectoral development of cities

Previous research has identified a predictive model of how a nation’s distribution of gross domestic product (GDP) among agriculture (a), industry (i), and services (s) changes as a country develops. Here we use this national model to analyze the composition of GDP for US Metropolitan Statistical Areas (MSA) over time. To characterize the transfer of GDP shares between the sectors in the course of economic development we explore a simple system of differential equations proposed in the country-level model. Fitting the model to more than 120 MSAs we find that according to the obtained parameters MSAs can be classified into 6 groups (consecutive, high industry, re-industrializing; each of them also with reversed development direction). The consecutive transfer (a → i → s) is common but does not represent all MSAs examined. At the 95% confidence level, 40% of MSAs belong to types exhibiting an increasing share of GDP from agriculture. In California, such MSAs, which we classify as part of an agriculture renaissance, are found in the Central Valley.

Development Starts With Historical Endowments: Industrial Policy and Leadership Are Catalysts

Ben Armstrong compares the implementation of regional economic development programs in the Pittsburgh and Cleveland metropolitan statistical areas during the 1980s. He argues that their regional economies and research institutions were then similar. He contends that the transformational policy difference occurred when Pennsylvania’s governor mandated that the presidents of Pittsburgh’s research universities be programmatic leaders while Ohio’s governor did not do the same. Armstrong concludes that Pittsburgh’s Metropolitan Statistical Area became a center of high-tech innovation as a result, while Cleveland’s did not. This author contends that disparities in regional technology resources, as well as institutional self-interest and leadership, were the critical differences. Cleveland’s institutions had to give priority to fixing their business problems. Armstrong and the author agree that economic endowments and industrial policy played roles in both regions’ economic outcomes; where they disagree is on the weights given to each.

Vaccination and three non-pharmaceutical interventions determine the end of COVID-19 at 381 metropolitan statistical areas in the US

The rapid rollout of the COVID-19 vaccine global raises the question of whether and when the ongoing pandemic could be eliminated with vaccination and non-pharmaceutical interventions (NPIs). Despite advances in the impact of NPIs and the conceptual belief that NPIs and vaccination control COVID-19 infections, we lack evidence to employ control theory in real-world social human dynamics in the context of disease spreading. We bridge the gap by developing a new analytical framework that treats COVID-19 as a feedback control system with the NPIs and vaccination as the controllers and a computational and mathematical model that maps human social behaviors to input signals. This approach enables us to effectively predict the epidemic spreading in 381 Metropolitan statistical areas (MSAs) in the US by learning our model parameters utilizing the time series NPIs (i.e., the stay-at-home order, face-mask wearing, and testing) data. This model allows us to optimally identify three NPIs to predict infections actually in 381 MSAs and avoid overfitting. Our numerical results universally demonstrate our approach's excellent predictive power with R2>0.9 of all the MSAs regardless of their sizes, locations, and demographic status. Our methodology allows us to estimate the needed vaccine coverage and NPIs for achieving Re to the manageable level and the required days for disease elimination at each location. Our analytical results provide insights into the debates on the aims for eliminating COVID-19. NPIs, if tailored to the MSAs, can drive the pandemic to an easily containable level and suppress future recurrences of epidemic cycles.