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- Creators: Barrett, The Honors College
- Creators: Schoellman, Todd
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Description
This dissertation consists of three essays that broadly deal with the international economics and development. The first chapter provides empirical evidence of the prevalence and importance of intangible capital transfer within multinational corporations (MNCs). Using a unique data set of Korean multinational foreign affiliates, I find that most of the foreign affiliates have managers transferred from their parent, while almost half are isolated from the parent in terms of physical trade. Furthermore, the transferred managers are positively associated with labor productivity, while physical trade from the parent is less so. I consider two possibilities for this productivity effect: (1) the managers transferred from the parent are simply more efficient than native managers; and (2) they provide knowledge that increases the productivity of all inputs. I find that the latter is consistent with the data. My findings provide evidence that transferring managers from the parent is a main source of benefit from foreign direct investment (FDI) to foreign affiliates because the managers transfer firm-specific knowledge. The second chapter analyzes importance role of service or other sectors for economic growth of manufacturing. Productivity in agriculture or services has long been understood as playing an important role in the growth of manufacturing. In this paper we provide an endogenous growth model in which manufacturing growth is stimulated by the non-manufacturing sector that provides goods used for both research and final consumption. The model permits to evaluatation of two policy options for stimulating manufacturing growth: (1) a country imports more non-manufacturing goods from a foreign country with a higher productivity; or (2) the country increases productivity of domestic non-manufacturing. We find that both policies increase welfare of the economy, but depending on the policy the manufacturing sector responses differently. Specifically, employment and value added in manufacturing rise with policy (1), but contract with policy (2). Therefore, specialization through importing non-manufacturing goods explains how some Asian economies experience fast growth in the manufacturing sector without progress in the other sectors. The third chapter tests for the importance of composition effects in affecting levels and changes of education wage premiums. In this paper I revisit composition effects in the context of Korea. Korea's large and rapid expansion of education makes it an ideal place to look for composition effects. A large, policy-induced increase in attainment in the 1980s offers additional scope for identifying composition effects. I find strong evidence that the policy-induced expansion of education lowered education wage premiums for the affected cohorts, but only weak evidence that the trend expansion of education lowered education wage premiums.
ContributorsCho, Jaehan (Author) / Silverman, Daniel (Thesis advisor) / Prescott, Edward C. (Committee member) / Schoellman, Todd (Committee member) / Arizona State University (Publisher)
Created2014
Description
This dissertation consists of two essays. The first measures the degree to which schooling accounts for differences in industry value added per worker. Using a sample of 107 economies and seven industries, the paper considers the patterns in the education levels of various industries and their relative value added per worker. Agriculture has notably less schooling and is less productive than other sectors, while a group of services including financial services, education and health care has higher rates of schooling and higher value added per worker. The essay finds that in the case of these specific industries education is important in explaining sector differences, and the role of education all other industries are less defined. The second essay provides theory to investigate the relationship between agriculture and schooling. During structural transformation, workers shift from the agriculture sector with relatively low schooling to other sectors which have more schooling. This essay explores to what extent changes in the costs of acquiring schooling drive structural transformation using a multi-sector growth model which includes a schooling choice. The model is disciplined using cross country data on sector of employment and schooling constructed from the IPUM International census collection. Counterfactual exercises are used to determine how much structural transformation is accounted for by changes in the cost of acquiring schooling. These changes account for small shares of structural transformation in all economies with a median near zero.
ContributorsSchreck, Paul (Author) / Herrendorf, Berthold (Committee member) / Lagakos, David (Committee member) / Schoellman, Todd (Committee member) / Arizona State University (Publisher)
Created2011
Description
This dissertation studies two wide ranging phenomena and their socio-economic impacts: urban divergence in terms of geographical skill sorting and fast rising housing prices. The first essay explores the empirical pattern as well as the driving forces behind the American cities’ diverging path over the past forty years. Compared to the rest of the U.S. cities, the top 20 largest cities have been growing faster in several aspects, such as city-average wage, housing price, and measured innovation intensity (e.g., patents, venture capital). In addition, this geographical divergence has contributed substantially to the rising inequality in America. To explore the causes of this divergence, this paper constructs a spatial sorting model where entrepreneurs with different talents can freely move across cities. The key idea is that cities with advantages in innovation attract more productive entrepreneurs and more workers, thereby driving up wages and housing prices. Two things distinguish my models from others: 1. Large cities are having endogenous innovation advantage in equilibrium; 2. I can freely explore the driving forces behind the divergence, with an emphasis on how technology changes can reinforce the spatial sorting mechanism. Specifically, three types of technological changes have increased the benefits of skill clustering in innovative cities: general productivity increases; improvements in communications technologies; and declines in trade costs.
The second essay studies how heterogeneous households respond to the fast rising housing prices through their life-cycle behaviors. Chinese housing market has been undergoing a rapid booming period since 1998, causing the house prices increasing significantly. As a result, households endured severe financial burdens to buy homes at price-to-income ratios of around six. Along with the rising house prices, household savings rate has been increasing consistently since 1998. Can the rising house prices be an important factor to explain the increase in household saving rate? This paper develops a life cycle dynastic model with endogenous choice on housing, coresidence and intergenerational transfer, then quantitatively analyze the effect of housing price on household saving. It shows that housing is an important motive for saving, and it accounts for about 35% of the increase in household savings rate. The housing situation affects households’ saving behavior through three channels. First, households are financially constrained due to the down payment requirement and they choose to limit their consumption in order to buy houses. Second, young adults live in their parents’ houses for a long time and save more intensively, since they get to pay less for the housing expenses under coresidence. Thirdly, older parents make large sum of intergeneration transfer in aid of the children’s housing purchase, indicating the housing affordability issue also has influence on old parents’ saving decisions.
The second essay studies how heterogeneous households respond to the fast rising housing prices through their life-cycle behaviors. Chinese housing market has been undergoing a rapid booming period since 1998, causing the house prices increasing significantly. As a result, households endured severe financial burdens to buy homes at price-to-income ratios of around six. Along with the rising house prices, household savings rate has been increasing consistently since 1998. Can the rising house prices be an important factor to explain the increase in household saving rate? This paper develops a life cycle dynastic model with endogenous choice on housing, coresidence and intergenerational transfer, then quantitatively analyze the effect of housing price on household saving. It shows that housing is an important motive for saving, and it accounts for about 35% of the increase in household savings rate. The housing situation affects households’ saving behavior through three channels. First, households are financially constrained due to the down payment requirement and they choose to limit their consumption in order to buy houses. Second, young adults live in their parents’ houses for a long time and save more intensively, since they get to pay less for the housing expenses under coresidence. Thirdly, older parents make large sum of intergeneration transfer in aid of the children’s housing purchase, indicating the housing affordability issue also has influence on old parents’ saving decisions.
ContributorsSun, Minjuan (Author) / Schoellman, Todd (Thesis advisor) / Ventura, Gustavo (Committee member) / Vereshchagina, Galina (Committee member) / Arizona State University (Publisher)
Created2018
Description
This dissertation consists in two chapters. In the first chapter I collected and digitized historical tax records from the Spanish colonial regime in Ecuador to estimate the long-run effects of a forced labor institution called concertaje on today’s economic performance. This institution allowed landlords to retain indigenous workers due to unpaid debts, and forced them to work as peasants in rural estates known as haciendas. In order to identify the causal effects of concertaje, I exploit variation in its intensity caused by differences in labor requirements from the crops a region could grow. I first report that an increase in 10 percentage points in concertaje rates is associated with a 6 percentage points increase in contemporary poverty. I then explore several channels of persistence. Districts with higher concertaje rates have been historically associated with higher illiteracy rates, lower school enrollment, and populations with fewer years of education. I also report that concertaje is associated with a higher fraction of people working nowadays in the agricultural sector.
In the second chapter I use administrative data on the ownership, management, and taxes for the universe of all firms in Ecuador to study the role of family management in firm dynamics and its implications for aggregate productivity. A novel finding I document is that family-managed firms grow half as quickly as externally-managed firms. This growth differential implies that family-managed firms account for half of employment, despite comprising 80% of firms. I construct a general equilibrium model of firm dynamics that is consistent with these facts. Entrepreneurs choose whether to utilize family members as managers or hire external managers. External managers allow firms to scale up production, but their efficiency is a affected due to contractual frictions. Changes in the contractual environment that lead to a drop in the presence of family-managed firms by half could increase output on the order of 6%, as firms that abandon family management enjoy rapid growth.
In the second chapter I use administrative data on the ownership, management, and taxes for the universe of all firms in Ecuador to study the role of family management in firm dynamics and its implications for aggregate productivity. A novel finding I document is that family-managed firms grow half as quickly as externally-managed firms. This growth differential implies that family-managed firms account for half of employment, despite comprising 80% of firms. I construct a general equilibrium model of firm dynamics that is consistent with these facts. Entrepreneurs choose whether to utilize family members as managers or hire external managers. External managers allow firms to scale up production, but their efficiency is a affected due to contractual frictions. Changes in the contractual environment that lead to a drop in the presence of family-managed firms by half could increase output on the order of 6%, as firms that abandon family management enjoy rapid growth.
ContributorsRivadeneira Acosta, Alex Pierre (Author) / Ventura, Gustavo (Thesis advisor) / Vereshchagina, Galina (Committee member) / Schoellman, Todd (Committee member) / Arizona State University (Publisher)
Created2019
Leveraging Machine Learning and Wireless Sensing for Robot Localization - Location Variance Analysis
Description
Object localization is used to determine the location of a device, an important aspect of applications ranging from autonomous driving to augmented reality. Commonly-used localization techniques include global positioning systems (GPS), simultaneous localization and mapping (SLAM), and positional tracking, but all of these methodologies have drawbacks, especially in high traffic indoor or urban environments. Using recent improvements in the field of machine learning, this project proposes a new method of localization using networks with several wireless transceivers and implemented without heavy computational loads or high costs. This project aims to build a proof-of-concept prototype and demonstrate that the proposed technique is feasible and accurate.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
ContributorsChang, Roger (Co-author) / Kann, Trevor (Co-author) / Alkhateeb, Ahmed (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients.
ContributorsRahman, Farhan Nadir (Co-author) / Nawar, Afra (Co-author) / Turaga, Pavan (Thesis director) / Krishnamurthi, Narayanan (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.
ContributorsRichards, Nicholas Giovanni (Author) / Miller, Phillip (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The passage of 2007's Legal Arizona Workers Act, which required all new hires to be tested for legal employment status through the federal E-Verify database, drastically changed the employment prospects for undocumented workers in the state. Using data from the 2007-2010 American Community Survey, this paper seeks to identify the impact of this law on the labor force in Arizona, specifically regarding undocumented workers and less educated native workers. Overall, the data shows that the wage bias against undocumented immigrants doubled in the four years studied, and the wages of native workers without a high school degree saw a temporary, positive increase compared to comparable workers in other states. The law did not have an effect on the wages of native workers with a high school degree.
ContributorsSantiago, Maria Christina (Author) / Pereira, Claudiney (Thesis director) / Mendez, Jose (Committee member) / School of International Letters and Cultures (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This paper is intended to identify a correlation between the winning percentage of sports teams in the four major professional sports leagues in the United States and the GDP per capita of their respective cities. We initially compiled fifteen years of franchise performance along with economic data from the Federal Reserve Bank of St. Louis to analyze this relationship. After converting the data into a language recognized by Stata, the regression tool we used, we ran multiple regressions to find relevant correlations based off of our inputs. This paper will show the value of the economic impact of strong or weak performance throughout various economic cycles through data analysis and conclusions drawn from the results of the regression analysis.
ContributorsAndl, Tyler (Co-author) / Shirk, Brandon (Co-author) / Goegan, Brian (Thesis director) / Eaton, John (Committee member) / School of Accountancy (Contributor) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12