This dissertation contains three essays in History and Spatial Economics with Big Data. As a part of my dissertation, I develop a modern machine-learning based approach to connect large datasets. Merging several massive databases and matching the records within them presents challenges — some straightforward and others more complex. I employ artificial intelligence and machine learning technologies to link and then analyze massive amounts of historical US federal census, Department of Labor, and Bureau of Labor Statistics data. The transformation of the US economy during this time period was remarkable, from a rural economy at the beginning of the 19th century to an industrial nation by the end. More strikingly, after lagging behind the technological frontier for most of the nineteenth century, the United States entered the twenty-first century as the global technology leader and the richest nation in the world. Results from this dissertation reveal how people lived and how the business operated. It tells us the past that led us to where we are now in terms of people, geography, prices and wages, wealth, revenue, output, capital, numbers, and types of workers, urbanization, migration, and industrialization. As a part of this endeavor, the first chapter studies how the benefits of improving urban mass transit infrastructures in cities are shared across workers with different skills. It exploits a unique historical setting to estimate the impact of urban transportation infrastructure: the introduction of mass-public transit infrastructure in the late nineteenth and twentieth-century New York City. I linked individual-level US census data to investigate how urban transit infrastructure differentially affects the welfare of workers with heterogenous skill. My second chapter measures immigrants' role in the US rise as an economic power. Especially, this chapter focuses on a potential mechanism by which immigrants might have spurred economic prosperity: the transfer of new knowledge. This is the first project to use advances in quantitative spatial theory along with advanced big-data techniques to understand the contribution of immigrants to the process of U.S. economic growth. The key benefit of this approach is to link modern theory with massive amounts of microeconomic data about individual immigrants—their locations and occupations—to address questions that are extremely difficult to assess otherwise. Specifically, the dataset will help the researchers understand the extent to which the novel ideas and expertise immigrants brought to U.S. shores drove the nation’s emergence as an industrial and technological powerhouse. My third chapter exploits advances in data digitization and machine learning to study intergenerational mobility in the United States before World War II. Using machine learning techniques, I construct a massive database for multiple generations of fathers and sons. This allows us to identify “land of opportunities": locations and times in American history where kids had chances to move up in the income ladder. I find that intergenerational mobility elasticities were relatively stable during 1880-1940; there are regional disparities in terms of giving kids opportunities to move up, and the geographic disparities of intergenerational mobility have evolved over time.

Thesis (Ph.D.) -- McMaster University, 1999. / Includes bibliographical references. Also available via World Wide Web.

Thesis (Ph. D.)--Ohio State University, 2008. / Title from first page of PDF file. Includes bibliographical references (p. 168-184).

Mémoire (M.E.S.R.)-- Université du Québec à Chicoutimi, 2002. / Document électronique également accessible en format PDF. CaQCU

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains x, 140 p. Includes bibliographical references (p. 137-140). Available online via OhioLINK's ETD Center

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This dissertation uses original datasets from the U.S. and Japan to explore issues in spatial economics and public finance. In the first chapter, I study how the relocation of inventors affects local and aggregate growth through technological diffusion across U.S. cities. I propose a quantitative spatial theory of growth and knowledge diffusion through internal migration. My model highlights two mechanisms by which productivity growth can be higher in one city than in another: (1) agglomeration forces and (2) knowledge inflows through internal migration. Using data on US cities, I find that knowledge diffusion explains approximately 40 percent of the spatial variation in productivity changes, and agglomeration forces explain the rest. I quantify the dynamic effects of place-based policies and find that reducing the costs of migrating to a small number of cities can improve aggregate efficiency while reducing disparities in productivity across cities. Growing spatial inequality has led policymakers to enact tax breaks to attract corporate investment and jobs to economically peripheral regions. In the second chapter, co-authored with Cameron LaPoint, we demonstrate the importance of multi-plant firms’ physical capital structure for the take-up and efficacy of place-based policies by studying a national bonus depreciation scheme in Japan which altered the relative cost of capital across locations, offering high-tech manufacturers immediate cost deductions from their corporate income tax bill. Combining corporate balance sheets with a registry containing investment by plant location and asset type, we find the policy generated big gains in employment and investment in building construction and in machines at pre-existing production sites, with an implied partial equilibrium fiscal cost per job created of $16,000. The policy produced a welfare gain of $56.72 billion, or roughly 40% of one year’s worth of average annual corporate profits. For eligible firms, plant-level hiring in ineligible areas outstripped that in eligible areas, suggesting reallocation of resources within firms’ internal capital and labor markets mitigates the spatial misallocation inherent in subsidizing low-productivity areas. How governments should choose the frequency of payments has received little attention in the literature on the optimal design of benefits programs. In the third chapter, co-authored with Cameron LaPoint, we propose a simple model in which the government chooses the interval length between payments, subject to a tradeoff between the costs of providing more frequent benefits and welfare gains from mitigating consumption non-smoothing. Using a high-frequency retail dataset that links consumers to their purchase history, we apply the model to the Japanese National Pension System. Our evidence suggests suboptimal intra-cycle consumption patterns with negligible retailer price discrimination. Model calibrations support the worldwide prevalence of monthly payment systems.

This is a comparative study of the spatial patterns of peasant crop farming in Chibombo District between the 1980s and the 1990s. The study lists and discusses the agricultural support system, communication infrastructure and the crop production and patterns of the 1980s within the environment of centralised planning and then compares these to the structures and patterns of the 1990s in an atmosphere of economic liberalisation. This comparison in crop production, cropping patterns, institutional support systems and the communication infrastructure in five sampled farming wards of Chibombo District, leads to the conclusion that there is a marked change in the structures and patterns of the 1990s from those of the 1980s. Thus, in Chibombo District, the state of the communication infrastructure in the 1990s is generally poorer than the communication infrastructure of the 1980s, the agricultural support system of the 1990s is largely privately owned and found in fewer farming areas while the agricultural support system of the 1980s was state controlled and more widely spread, and crop patterns in some farming wards are different in the 1990s from those of the 1980s. In the 1990s, crop production in farming wards with a supportive environment has increased than it was in the 1980s but decreased in those where a conducive environment lacks. In this line, the study makes several recommendations for consideration on how to mitigate the problems that the peasant farmers are facing or how to enhance the positive changes that have occurred in Chibombo District. / Geography / M.A. (Geography)

This is a comparative study of the spatial patterns of peasant crop farming in Chibombo District between the 1980s and the 1990s. The study lists and discusses the agricultural support system, communication infrastructure and the crop production and patterns of the 1980s within the environment of centralised planning and then compares these to the structures and patterns of the 1990s in an atmosphere of economic liberalisation. This comparison in crop production, cropping patterns, institutional support systems and the communication infrastructure in five sampled farming wards of Chibombo District, leads to the conclusion that there is a marked change in the structures and patterns of the 1990s from those of the 1980s. Thus, in Chibombo District, the state of the communication infrastructure in the 1990s is generally poorer than the communication infrastructure of the 1980s, the agricultural support system of the 1990s is largely privately owned and found in fewer farming areas while the agricultural support system of the 1980s was state controlled and more widely spread, and crop patterns in some farming wards are different in the 1990s from those of the 1980s. In the 1990s, crop production in farming wards with a supportive environment has increased than it was in the 1980s but decreased in those where a conducive environment lacks. In this line, the study makes several recommendations for consideration on how to mitigate the problems that the peasant farmers are facing or how to enhance the positive changes that have occurred in Chibombo District. / Geography / M.A. (Geography)