THREE ESSAYS ON THE LINKS BETWEEN LOCAL GOVERNMENT STRUCTURAL CHANGES AND PUBLIC FINANCE

This dissertation, comprised of three essays, explains and evaluates local government structural changes from a public finance perspective. The first essay examines the determinants of the rapid growth of special districts, while the next two essays estimate the effects on property values of school district consolidation and village dissolution in New York State, respectively. Together, the three essays contribute to our understanding of the causes and consequences of local government structural changes in the United States. By bring together two central trends in state and local public finance, namely, the expansion of state-imposed tax and expenditure limitations (TELs) and the rapid growth of special districts, the first essay looks into the hypothesis that TELs are partly responsible for the increase of special districts over the last several decades. To eliminate the possible omitted variable bias, I employ a combination of fixed effects, regional time trends and approximate measures of fiscal conservativeness. Based on a national data set of counties over the period 1972-2007, I find TELs, on average, increase the use of special districts (circumvention effects), whereas TELs don’t force local governments to cut their intergovernmental fiscal transfer to special districts in the same county area (deterrent effects). The estimation results are robust to multiple tests of common trends assumptions, five alternative measures of TELs, alternative model specifications and different empirical strategies. This results confirm the theory that special districts have been extensively created by local general-purpose governments as an institutional strategy to circumvent the fiscal constraints imposed by TELs. The second essay explores the impacts of school district consolidation on property values in upstate New York from 2000 to 2012. This research, conducted in collaboration with Professors William Duncombe and John Yinger, adds a time dimension to research on the property-value impacts of consolidation. By combining propensity score matching and double-sales data to compare house value changes in consolidating and comparable school districts, we find that it takes time either for the advantages of consolidation to be apparent to homebuyers or for the people who prefer consolidated districts to move in. In addition, the long-run impacts of consolidation on house values are positive in low-income census tracts but negative in high-income census tracts. This result suggests that high-income households are particularly attached to the benefits, such as close contact with teachers, of small districts. Streams of institutional, economic and fiscal factors recently have been converging and substantially changing the landscape of local government in the United States. Dissolution, an old and new approach, has increasingly been used and therefore drawn much public attention nowadays. The third essay provides the first study investigating whether village dissolution, as a form of general-purpose government reorganization, affects the attractiveness of local communities. In New York, voters in several villages voted to dissolve the village and hence to shift all government services to the town government in which the village is located. I show that village dissolution does not alter the amount people are willing to pay inside the (eliminated) village boundaries, but that the price of housing declines in areas of the town outside the village (TOV). Presumably, residents in the TOV areas are upset with the negative externalities of village dissolution

A review and comparison of studies on office window behaviour using engineering and social science methods

With the urgent need of reducing building carbon emissions to ease climate change, it is necessary to have energy efficient buildings. An effective way of achieving this mission is adopting natural ventilation, which is commonly achieved by openable windows controlled by building occupants in most cases. In this kind of buildings, therefore, occupant window behaviour becomes significantly important on the building performance. To better understand this behaviour, researchers from both engineering domain and social science domain have contributed, but it seems like that they have different research aims, methods and findings. To promote transdisciplinary collaboration in this area, this study has carried out a thorough review of studies on window behaviour in office buildings. The review work collected 66 relevant studies, and analysed their aims, methods and main findings to figure out the differences between engineering studies and social science studies. The existing studies were mainly coming from renowned academic journals (91%) and academic conferences (9%). The comparison revealed significant differences between the two scientific domains, with some overlapping between them. To obtain deeper understanding on occupant window behaviour, critical discussions on how to better collaborate between these two domains in the future have been provided as well

Open World Object Detection Combining Graph-FPN and Robust Optimization

Open world object detection (OWOD) requires detecting all known and unknown object categories in the image, and the model must gradually learn new categories to adaptively update knowledge. Aiming at the problems of low recall rate of unknown objects and catastrophic forgetting of incremental learning in ORE (open world object detection) method, this paper proposes adjustable robust optimization of ORE based on graph feature pyramid (GARO-ORE). Firstly, using the superpixel image structure in Graph-FPN and the hierarchical design of context layer and hierarchical layer, rich semantic information can be obtained and the model can accurately locate unknown object. Then, using the robust optimization method to comprehensively consider the uncertainty, a base class learning strategy based on flat minimum is proposed, which greatly ensures that the model avoids forgetting the previously learnt category knowledge while learning new categories. Finally, the classification weights initiali-zation method based on knowledge transfer is used to improve the adaptability of the model to new classes. Experimental results on the OWOD dataset show that GARO-ORE achieves better detection results on the recall rate of unknown categories. In the three types of incremental object detection tasks of 10 + 10, 15 + 5, and 19 + 1, the mAP is increased by 1.38, 1.42 and 1.44 percentage points, respectively. It can be seen that GARO-ORE can improve the recall rate of unknown object detection, and promote the learning of subsequent tasks while effectively alleviating the catastrophic forgetting problem of old tasks