Essays on Bank Performance, Strategic Behavior, and Community Development with a Focus on Minority Depository Institutions and the Political Economy of Forgiving Student Loans

I explore questions related to financial institutions, the accessibility of financial services, community development, and the political economy of forgiving student loans using the ideas from spatial economics, industrial organization, and public choice. Increased access to financial services improves the economic outcomes of individuals and firms in the community. Understanding sources leading to a lower accessibility of financial services is integral for an effective endeavor to lower barriers to financial services with far-reaching policy and economic implications. The economic literature provides evidence of the beneficial effects of increased access to financial services as well as the adverse effects of diminished access to credit and banking services. These effects are particularly pronounced for low-income individuals, minorities, and small businesses. The research also shows that, despite the technological advances, credit and depository service markets are local. Hence considering the geographic aspects of these services is essential. My research explores the role of geography in bank exit and entry, which directly affects the spatial accessibility of financial services within a community. Similarly, a rapid increase in student-loan debt has drawn much attention form public, scholars, and politicians. The sharp increase of defaults on student loans accompanied by growing student loan debt during the Great Recession led to proposals of forgiving student loans and making some public higher education tuition-free. To understand the political economy of such proposals, I explore the circumstances that motivate the implementation of the student-loan debt forgiveness policy in a two-period model of schooling and unemployment insurance with search costs

Advances in the sociology of trust and cooperation: theory, experiments, and field studies

The problem of cooperation and social order is one of the core issues in the social sciences. The key question is how humans, groups, institutions, and countries can avoid or overcome the collective good dilemmas that could lead to a Hobbesian war of all against all. Using the general set of social dilemmas as a paradigmatic example, rigorous formal analysis can stimulate scientific progress in several ways. The book, consisting of original articles, provides state of the art examples of research along these lines: theoretical, experimental, and field studies on trust and cooperation. The theoretical work covers articles on trust and control, reputation formation, and paradigmatic articles on the benefits and caveats of abstracting reality into models. The experimental articles treat lab based tests of models of trust and reputation, and the effects of the social and institutional embeddedness on behavior in cooperative interactions and possibly emerging inequalities. The field studies test these models in applied settings such as cooperation between organizations, informal care, and different kinds of collaboration networks. The book will be exemplary for rigorous sociology and social sciences more in general in a variety of ways: There is a focus on effects of social conditions, in particular different forms of social and institutional embeddedness, on social outcomes. Theorizing about and testing of effects of social contexts on individual and group outcomes is one of the main aims of sociological research. Modelling efforts include formal explications of micro-macro links that are typically easily overlooked when argumentation is intuitive and impressionistic Extensive attention is paid to unintended effects of intentional behavior, another feature that is a direct consequence of formal theoretical modelling and in-depth data-analyses of the social processe

Advances in the Sociology of Trust and Cooperation

The book identifies conditions for trust and cooperation. It highlights unintended consequences of individually rational behavior, and shows how trust and cooperation change dependent on social embeddedness. Such analyses inspire experimental tests in lab conditions, but also tests through empirical applications in field studies. The results of this mixed-method approach can in turn be used to inspire further theoretical work

Robustness, Heterogeneity and Structure Capturing for Graph Representation Learning and its Application

Graph neural networks (GNNs) are potent methods for graph representation learn- ing (GRL), which extract knowledge from complicated (graph) structured data in various real-world scenarios. However, GRL still faces many challenges. Firstly GNN-based node classification may deteriorate substantially by overlooking the pos- sibility of noisy data in graph structures, as models wrongly process the relation among nodes in the input graphs as the ground truth. Secondly, nodes and edges have different types in the real-world and it is essential to capture this heterogeneity in graph representation learning. Next, relations among nodes are not restricted to pairwise relations and it is necessary to capture the complex relations accordingly. Finally, the absence of structural encodings, such as positional information, deterio- rates the performance of GNNs. This thesis proposes novel methods to address the aforementioned problems: 1. Bayesian Graph Attention Network (BGAT): Developed for situations with scarce data, this method addresses the influence of spurious edges. Incor- porating Bayesian principles into the graph attention mechanism enhances robustness, leading to competitive performance against benchmarks (Chapter 3). 2. Neighbour Contrastive Heterogeneous Graph Attention Network (NC-HGAT): By enhancing a cutting-edge self-supervised heterogeneous graph neural net- work model (HGAT) with neighbour contrastive learning, this method ad- dresses heterogeneity and uncertainty simultaneously. Extra attention to edge relations in heterogeneous graphs also aids in subsequent classification tasks (Chapter 4). 3. A novel ensemble learning framework is introduced for predicting stock price movements. It adeptly captures both group-level and pairwise relations, lead- ing to notable advancements over the existing state-of-the-art. The integration of hypergraph and graph models, coupled with the utilisation of auxiliary data via GNNs before recurrent neural network (RNN), provides a deeper under- standing of long-term dependencies between similar entities in multivariate time series analysis (Chapter 5). 4. A novel framework for graph structure learning is introduced, segmenting graphs into distinct patches. By harnessing the capabilities of transformers and integrating other position encoding techniques, this approach robustly capture intricate structural information within a graph. This results in a more comprehensive understanding of its underlying patterns (Chapter 6)

Stories from different worlds in the universe of complex systems: A journey through microstructural dynamics and emergent behaviours in the human heart and financial markets

A physical system is said to be complex if it exhibits unpredictable structures, patterns or regularities emerging from microstructural dynamics involving a large number of components. The study of complex systems, known as complexity science, is maturing into an independent and multidisciplinary area of research seeking to understand microscopic interactions and macroscopic emergence across a broad spectrum systems, such as the human brain and the economy, by combining specific modelling techniques, data analytics, statistics and computer simulations. In this dissertation we examine two different complex systems, the human heart and financial markets, and present various research projects addressing specific problems in these areas. Cardiac fibrillation is a diffuse pathology in which the periodic planar electrical conduction across the cardiac tissue is disrupted and replaced by fast and disorganised electrical waves. In spite of a century-long history of research, numerous debates and disputes on the mechanisms of cardiac fibrillation are still unresolved while the outcomes of clinical treatments remain far from satisfactory. In this dissertation we use cellular automata and mean-field models to qualitatively replicate the onset and maintenance of cardiac fibrillation from the interactions among neighboring cells and the underlying topology of the cardiac tissue. We use these models to study the transition from paroxysmal to persistent atrial fibrillation, the mechanisms through which the gap-junction enhancer drug Rotigaptide terminates cardiac fibrillation and how focal and circuital drivers of fibrillation may co-exist as projections of transmural electrical activities. Financial markets are hubs in which heterogeneous participants, such as humans and algorithms, adopt different strategic behaviors to exchange financial assets. In recent decades the widespread adoption of algorithmic trading, the electronification of financial transactions, the increased competition among trading venues and the use of sophisticated financial instruments drove the transformation of financial markets into a global and interconnected complex system. In this thesis we introduce agent-based and state-space models to describe specific microstructural dynamics in the stock and foreign exchange markets. We use these models to replicate the emergence of cross-currency correlations from the interactions between heterogeneous participants in the currency market and to disentangle the relationships between price fluctuations, market liquidity and demand/supply imbalances in the stock market.Open Acces

RESOURCE ALLOCATION IN SUBSIDY WELFARE PROGRAMS: MANAGERIAL INSIGHTS FOR NONPROFITS, GOVERNMENTS, AND SERVICE PROVIDERS

Subsidy welfare programs provide financial assistance to economically disadvantaged individuals and families to access essential and life-altering services (e.g., education, child care, and housing) that they might not otherwise have access to. Access to these services is considered critical to achieving a better and more sustainable future for all. As such, these high-quality services are directly related to several United Nations Sustainable Development Goals, which were adopted as a universal call to action to end poverty, save the planet and improve the lives and prospects of everyone, everywhere. In particular, the need for these affordable and high-quality services has been underscored during the COVID-19 pandemic to facilitate a safe and robust reopening of the economy. Inspired by this, in this dissertation, we construct, analyze, and analytically solve a spectrum of resource allocation problems involving different participants within subsidy welfare programs, including local nonprofit organizations, government agencies, private service providers, and individuals and families. The models we construct aim to help different participants make better operational decisions that enable the generation of the most effective and/or equitable social outcomes under these programs. The dissertation consists of three studies addressing these decisions. In the first study, we consider operational challenges faced by a local nonprofit organization that administers and manages the operations of a subsidy voucher program within its service area. Specifically, motivated by a child care subsidy voucher program, we develop an analytical model that incorporates details of the subsidy voucher offer process and that captures the challenges faced by a Child Care Resource and Referral Agency (CCR&R, a local nonprofit organization) when allocating funds for its outreach and provider services activities. We analyze how a CCR&R should allocate its limited funds between these two types of activities to ensure equitable access to child care across the different regions of its service area. We show that it might be optimal for the CCR&R to invest more funds in outreach in the region with a lower proportion of income-eligible families. This is especially true when: the external considerations (e.g., public transportation and infrastructure) in that region have a greater impact on a family’s acceptance propensity; the marginal return of investment in outreach in that region is higher and abundant funds are available; the socioeconomic distress experienced by families in that region is significantly higher; or a large amount of funds is earmarked for outreach in that region. We contextualize our study for a CCR&R in Massachusetts and conclude that the proposed investment decisions can improve equity outcomes by 7.0%. In the second study, we examine operational challenges faced by a government agency in a subsidy voucher program. Specifically, we delve deeper into another important complexity within the subsidy voucher programs by studying how a government agency should allocate funds among several local nonprofit organizations. In a typical subsidy voucher program, a government agency (say, the funding agency) provides funds to multiple local nonprofit organizations (say, the service agencies) in order to enhance the accessibility and quality of subsidized services for beneficiaries residing in their local service areas. These service agencies invest in activities within their areas to generate social impact for beneficiaries by enhancing the quantity and quality of services at local providers. The funds allocation decisions in such a program are complicated by consideration of equity in social impact generated across different areas, intricate relationships among contextual factors in social impact generation, and information asymmetry between different entities. Considering that additional funds may become available for only one area, we develop a model to analyze how the funding agency’s funds allocation decisions lead to the most overall social impact in an equitable manner. Our analysis shows how the funding agency should incorporate the within-area factors in addition to the between-area factors in its optimal allocation decisions. For instance, the funding agency should allocate more funds toward an area when it has a relatively balanced mix of subsidy-accepting and non-accepting providers, or outreach activity is more likely to yield a higher investment return and it has fewer non-accepting providers. Also, comparing the resulting outcomes under the equity-ensuring method with those under different funding methods, we find that: While an efficiency-focused method leads to a higher total social impact, it could lead to significantly high levels of inequity across the areas. Further, although a simple formula-based method could achieve greater total social impact while not severely sacrificing equity in certain situations, the equity-ensuring method always eliminates inequity while not severely sacrificing the total social impact under a wide range of values of contextual factors. Finally, using a case study based on Massachusetts’ child care subsidy program, we illustrate that the proposed optimal decisions achieve equity while enhancing overall social impact by approximately 3% versus current allocation decisions. In the third study, we investigate operational challenges faced by a government agency in designing subsidy welfare programs and program participation decisions faced by service providers. Specifically, we help a government agency make a selection decision between two types of subsidy welfare programs--the subsidy voucher programs and the contracted slot programs. Although both programs are service-based and rely on the involvement of service providers for service delivery to the beneficiaries, they create social impact through different mechanisms. Under the subsidy voucher programs, beneficiaries have access to services from a large number of service providers (including a mix of high- and low-quality providers). Whereas, under the contracted slot programs, beneficiaries have access to services only from high-quality providers (even if at a fewer number of service providers). Since the government\u27s goal is to deliver high levels of quantity and quality of services to the beneficiaries, which are both influenced by the service providers\u27 participation decisions (based on their payoff-driven objectives), the government\u27s mechanism selection decision becomes non-trivial. We develop a game-theoretical model setup to analyze how contextual factors impact the service providers\u27 participation decisions in these two programs. Considering the interrelationship between service providers\u27 decisions and contextual factors under each type of subsidy program, our analysis shows that providers are more willing to participate in the program when: the reimbursement rate is relatively high and the cost of managing the program is relatively low. Further, we compare the two programs in terms of the societal outcomes generated for the beneficiaries of the programs. To do so, we conduct numerical analysis using the child care context in Massachusetts and identify conditions under which the level of societal outcomes under a contracted slot program outperforms a subsidy voucher program or vice versa. For example, a contracted slot program generates higher societal outcomes than a subsidy voucher program when: there are relatively more high-quality providers, and they have a relatively high capacity; the reimbursement rate in the contracted slot program is relatively high, and the demand for the high-quality providers\u27 services in the private market is relatively low; or low-quality providers\u27 capacity is relatively high, and the demand for the high-quality providers\u27 services in the private market is relatively low. However, we find that the subsidy voucher program generally outperforms the contracted slot program when we evaluate the two programs based on the societal outcomes per total reimbursement expenditure by the government. As one of the first few research studies to consider resource allocation in subsidy welfare programs, we help nonprofit organizations, government agencies, and for-profit service providers improve their operational decisions in order to benefit the beneficiaries of the programs. The results of this dissertation are expected to offer managerial insights to the participants within the subsidy welfare programs, increase equity, efficiency, and sustainability of the programs, and benefit society at large

The Legacy of Car-Share and Light-Rail Transit for Mobility and Accessibility Improvements in Economically Marginalized Neighborhoods in the New York Metro Area

This dissertation explores the value of the car-share program and a new light rail system with respect to their impact on mobility and accessibility improvements in economically and transportation access-wise marginalized neighborhoods in NYC and its adjacent communities. It consists of three main essays that took deep dives into how each new service or system altered mobility or accessibility for those in need. The first essay (Chapter 2) investigates car-share vehicle utilization rates of the Zipcar across NYC. It assesses the utilization rates by vehicle type, service location, time period, and weekday usage compared to weekend activity. With a multivariate regression model, the study found the presence of previously unmet mobility needs in low-income neighborhoods and a positive impact of various pricing incentives to create a successful car-share program implementation. The result suggests that urban policymakers may want to consider different pricing incentives and subsidies to develop a potential public-private partnership car-share program in NYC focusing on mobility and accessibility instead of just the competitive marked incentives of a private operator. The second and third essays, presented here as Chapters 3 and 4 investigate the accessibility benefit of a new light rail system – the Hudson Bergen Light Rail (HBLR), that runs along the Hudson County Gold Coast waterfront in New Jersey. Chapter 3 applies a hedonic approach, a traditional quantitative methodology for economic impact analysis, to measure the accessibility gain of a transit system deployed in a developed urban zone, in general. The study estimates the accessibility gain with longitudinal home sales records, capitalized in home values near the HBLR stations. The gain was generally higher in neighborhoods with less choice of public transportation options in a pre-HBLR period and also in ones far from a major regional job center, Manhattan. The study also identifies that the premium of the accessibility gain dissipated within a quarter-mile of the HBLR stations, which is smaller than the generally accepted allowable half-mile walking distance for users of rail service. Chapter 4 explored changes in commuting flows between the vicinity of the HBLR neighborhoods and Manhattan. The study utilizes two methods of analysis to understand these issues. First, it explores the change in jobs by wage groups and workplace locations for residents in economically disadvantaged neighborhoods near HBLR stations. Second, it develops a regression model to see the impact on residents\u27 job change by proximity to the HBLR stations. The study hinted at how the new light rail system affected commuting patterns in the low-income neighborhoods, especially in areas with inferior public transportation access prior to the development of the HBLR. Chapter 2 and Chapter 3 were published in peer-reviewed journals, and an earlier version of Chapter 4 was presented at the Transportation Research Board 91st annual meeting in Washington D.C. and the Association of American Geographers 2012 annual meeting in New York in 2012. As such, below the full abstracts for each of these papers were included as provided in their original presentation: Abstract for Chapter 2 - For decades, car-sharing has become an attentive dialogue among transportation planners and civic groups who have long supported, and business owners and government officials who see car-sharing as a means to realize their interests, i.e., another market for revenue generation and replacement of government vehicles for municipal government use with car-share units. It has particularly drawn attention in New York City (NYC). NYC is the largest car-sharing market in the United States, accounting for about one-third of all North American car-sharing members. In addition to market-driven forces, the City government has pronounced pro-carsharing policies. However, car-sharing is still considered as an exclusive program to middle-income, white, and young populations. The purpose of this study is to see if car-sharing can help meet the mobility demand for urban residents, especially in marginalized neighborhoods. By investigating a leading car-sharing program – Zipcar\u27s vehicle utilization pattern in NYC, I attempt to disentangle how areas with different socio-demographics are associated with car-sharing usage. The study results revealed a high demand for midsize (standard) vehicles on weekdays and weeknights. Besides, car-sharing use was positively correlated with the number of total vehicles, not the number of Zipcar parking lots, if the cars are accessible within walking distances. More importantly, car-sharing in low-income neighborhoods did not differ from the typical car-sharing locations. What matters is affordability. Hence, there is no reason not to consider new services or expanding existing service boundaries to the outer boroughs in the future. Abstract for Chapter 3 - This paper analyses the Hudson-Bergen Light Rail (HBLR) impact on residential property prices. Unlike similar studies that use a hedonic model with cross-sectional data, this one uses repeat-sales data of properties sold at least twice between 1991 and 2009. It shows how proximity to the nearest HBLR station, relative accessibility gains across stations, and anticipation of the commencement date of the HBLR station influenced home price change. The results show that properties near the two commuting stations farthest from the revitalized central business district experienced high appreciation. The study also reveals different accessibility gains across areas based on the availability of existing public transportation options. Using a negative–exponential gradient, we find that these higher appreciation rates tended to dissipate about 1/4 mile (402 m) from stations. It supports that properties around urban commuting stations enjoy higher marginal benefits through improved transit accessibility and reduced transportation costs, as Alonso\u27s model predicts. Abstract for Chapter 4 - This study examines if the HBLR has affected commuting flows in economically disadvantaged Environment Justice (EJ) neighborhoods. It hypothesized that the low-income and working-age population in the northern and southern neighborhoods of Hudson County, New Jersey, had taken advantage of the new light rail access. Thanks to Hudson county\u27s unique geographic location, where Manhattan, the most prominent employment center in the region, is located across the Hudson River, the most significant accessibility benefit of the new transit service would be the improvement of Manhattan-bound job commute. This study\u27s key data source is the U.S. Census Bureau\u27s time-series Longitudinal Employer‐Household Dynamics Origin-Destination Employment Statistics (LODES). The investigation disclosed that residents in EJ neighborhoods enjoyed more job opportunities in Manhattan thanks to the enhanced, reliable transit accessibility. Moreover, the HBLR influenced neighborhoods differently in which different levels of public transportation accessibility existed before the inauguration of the HBLR. The study demonstrated that the benefits of the HBLR are not limited to a typical property appreciation but the provision of enhanced employment opportunities to residents in EJ neighborhoods

Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

Financial contagion from the US structured finance market: evidence from international markets and asset pricing perspectives

Given the growing importance of securitisation to financial stability, it is surprising that empirical studies on the role of the US structured finance market in the recent crisis have been relatively sparse. To fill this gap, this thesis studies the US structured finance market (tracked by the ABX indices) and addresses various important research questions specific to the recent 2007 to 2009 financial crisis. First, I contribute to the contagion literature by extending Longstaff’s (2010) investigation to an international market perspective. Evidence of contagion from the ABX indices to the G5 international equity and government bond markets via the funding illiquidity and credit risk channels during the subprime crisis is documented. Second, I formulate a multifactor model with crisis interaction effects and document significant increases in the ABX AAA factor loadings during the subprime crisis, which is consistent with contagion. My cross-sectional pricing tests show that the ABX AAA factor significantly explains the cross-section of expected returns during the subprime crisis; that is, the impact of contagion on the US equity market was reasonably systematic. I compute a simple statistic that gauges the degree of the stocks’ exposure to the ABX innovations in each month and find that the exposure spiked in February, July and October 2007 and in February, July and November 2008. Third, I investigate whether the US bank holding companies’ fundamental characteristics determine bank equity risks during the recent crisis. I depart from prior studies and consider bank equity risks relating to the banks’ exposure to the ABX innovations, the asset-backed money market and the market wide default risk in a variance decomposition. My study establishes the link between the banks’ fundamental and equity risks, and shows that banks’ regulatory capital requirement is an effective means to limit banks’ exposure to systemic risks in relation to funding illiquidity. Lastly, I document compelling evidence of quarterly bank stock return predictability based on variables relating to banks’ profitability, loan asset credit quality, capital adequacy and equity risks over the 2006 to 2011 period. By studying the turnover ratios and order flows, I show that bank stocks with weaker fundamentals and smaller size were traded more intensely in the following quarter while the higher trading activity was dominated by selling pressure. The evidence lends support to my ‘fire sale’ or ‘flight-to-safety’ hypothesis and reveals that the banks’ fundamental variables and size were the major criteria used by investors in formulating their ‘flight’ decisions during the recent crisi