Empirical studies of financial and labor economics

This dissertation consists of three essays in financial and labor economics. It provides empirical evidence for testing the efficient market hypothesis in some financial markets and for analyzing the trends of power couples’ concentration in large metropolitan areas. The first chapter investigates the Bitcoin market’s efficiency by examining the correlation between social media information and Bitcoin future returns. First, I extract Twitter sentiment information from the text analysis of more than 130,000 Bitcoin-related tweets. Granger causality tests confirm that market sentiment information affects Bitcoin returns in the short run. Moreover, I find that time series models that incorporate sentiment information better forecast Bitcoin future prices. Based on the predicted prices, I also implement an investment strategy that yields a sizeable return for investors. The second chapter examines episodes of exuberance and collapse in the Chinese stock market and the second-board market using a series of extended right-tailed augmented Dickey-Fuller tests. The empirical results suggest that multiple “bubbles” occurred in the Chinese stock market, although insufficient evidence is found to claim the same for the second-board market. The third chapter analyzes the trends of power couples’ concentration in large metropolitan areas of the United States between 1940 and 2010. The urbanization of college-educated couples between 1940 and 1990 was primarily due to the growth of dual-career households and the resulting severity of the co-location problem (Costa and Kahn, 2000). However, the concentration of college-educated couples in large metropolitan areas stopped increasing between 1990 and 2010. According to the results of a multinomial logit model and a triple difference-in-difference model, this is because the co-location effect faded away after 1990

Advancing large-scale analysis of human settlements and their dynamics

Due to the importance for a range of sustainability challenges, it is important to understand the spatial dynamics of human settlements. The rapid expansion of built-up land is among the most extensive global land changes, even though built-up land occupies only a small fraction of the terrestrial biosphere. Moreover, the different ways in which human settlements are manifested are crucially important for their environmental and socioeconomic impacts. Yet, current analysis of human settlements heavily relies on land cover datasets, which typically have only one class to represent human settlements. Consequently, the analysis of human settlements does often not account for the heterogeneity within urban environment or their subtle changes. This simplistic representation severely limits our understanding of change processes in human settlements, as well as our capacity to assess socioeconomic and environmental impacts. This thesis aims to advance large-scale analysis of human settlements and their dynamics through the lens of land systems, with a specific focus on the role of land-use intensity. Chapter 2 explores the use of human settlement systems as an approach to understanding their variation in space and changes over time. Results show that settlement systems exist along a density gradient, and their change trajectories are typically gradual and incremental. In addition, results indicate that the total increase in built-up land in village landscapes outweighs that of dense urban regions. This chapter suggests that we should characterize human settlements more comprehensively to advance the analysis of human settlements, going beyond the emergence of new built-up land in a few mega-cities only. In Chapter 3, urban land-use intensity is operationalized by the horizontal and vertical spatial patterns of buildings. Particularly, I trained three random forest models to estimate building footprint, height, and volume, respectively, at a 1-km resolution for Europe, the US, and China. The models yield R2 values of 0.90, 0.81, and 0.88 for building footprint, height, and volume, respectively. The correlation between building footprint and building height at a pixel level was 0.66, illustrating the relevance of mapping these properties independently. Chapter 4 builds on the methodological approach presented in chapter 3. Specifically, it presents an improved approach to mapping 3D built-up patterns (i.e., 3D building structure), and applies this to map building footprint, height, and volume at a global scale. The methodological improvement includes an optimized model structure, additional explanatory variables, and updated input data. I find distance decay functions from the centre of the city to its outskirts for all three properties for major cities in all continents. Yet, again, the height, footprint (density), and volume differ drastically across these cities. Chapter 5 uses built-up land per person as an operationalization for urban land-use intensity, in order to investigate its temporal dynamics at a global scale. Results suggest that the decrease of urban land-use intensity relates to 38.3%, 49.6%, and 37.5% of the built-up land expansion in the three periods during 1975-2015, but with large local variations. In the Global South, densification often happens in regions where human settlements are already used intensively, suggesting potential trade-offs with other living standards. These chapters represent the recent advancements in large-scale analysis of human settlements by revealing a large variation in urban fabric. Urban densification is widely acknowledged as one of the tangible solutions to satisfy the increased land demand for human settlement while conserving other land, suggesting the relevance of these findings to inform sustainable development. Nevertheless, local settlement trajectories towards intensive forms should also be guided in a large-scale context with broad considerations, including the quality of life for inhabitants, because these trade-offs and synergies remain largely unexplored in this analysis