Browsing by Subject "Economics"

Social attitudes, attitudes toward financial risk and attitudes toward deferred gratification are thought to influence many important economic decisions over the life-course. In economic theory, these attitudes are key components in diverse models of behavior, including collective action, saving and investment decisions and occupational choice. The relevance of these attitudes have been confirmed empirically. Yet, the factors that influence them are not well understood. This research evaluates how these attitudes are affected by large disruptive events, namely, a natural disaster and a civil conflict, and also by an individual-specific life event, namely, having children.

By implementing rigorous empirical strategies drawing on rich longitudinal datasets, this research project advances our understanding of how life experiences shape these attitudes. Moreover, compelling evidence is provided that the observed changes in attitudes are likely to reflect changes in preferences given that they are not driven just by changes in financial circumstances. Therefore the findings of this research project also contribute to the discussion of whether preferences are really fixed, a usual assumption in economics.

In the first chapter, I study how altruistic and trusting attitudes are affected by exposure to the 2004 Indian Ocean tsunami as long as ten years after the disaster occurred. Establishing a causal relationship between natural disasters and attitudes presents several challenges as endogenous exposure and sample selection can confound the analysis. I take on these challenges by exploiting plausibly exogenous variation in exposure to the tsunami and by relying on a longitudinal dataset representative of the pre-tsunami population in two districts of Aceh, Indonesia. The sample is drawn from the Study of the Tsunami Aftermath and Recovery (STAR), a survey with data collected both before and after the disaster and especially designed to identify the impact of the tsunami. The altruistic and trusting attitudes of the respondents are measured by their behavior in the dictator and trust games. I find that witnessing closely the damage caused by the tsunami but without suffering severe economic damage oneself increases altruistic and trusting behavior, particularly towards individuals from tsunami affected communities. Having suffered severe economic damage has no impact on altruistic behavior but may have increased trusting behavior. These effects do not seem to be caused by the consequences of the tsunami on people’s financial situation. Instead they are consistent with how experiences of loss and solidarity may have shaped social attitudes by affecting empathy and perceptions of who is deserving of aid and trust.

In the second chapter, co-authored with Ryan Brown, Duncan Thomas and Andrea Velasquez, we investigate how attitudes toward financial risk are affected by elevated levels of insecurity and uncertainty brought on by the Mexican Drug War. To conduct our analysis, we pair the Mexican Family Life Survey (MxFLS), a rich longitudinal dataset ideally suited for our purposes, with a dataset on homicide rates at the month and municipality-level. The homicide rates capture well the overall crime environment created by the drug war. The MxFLS elicits risk attitudes by asking respondents to choose between hypothetical gambles with different payoffs. Our strategy to identify a causal effect has two key components. First, we implement an individual fixed effects strategy which allows us to control for all time-invariant heterogeneity. The remaining time variant heterogeneity is unlikely to be correlated with changes in the local crime environment given the well-documented political origins of the Mexican Drug War. We also show supporting evidence in this regard. The second component of our identification strategy is to use an intent-to-treat approach to shield our estimates from endogenous migration. Our findings indicate that exposure to greater local-area violent crime results in increased risk aversion. This effect is not driven by changes in financial circumstances, but may be explained instead by heightened fear of victimization. Nonetheless, we find that having greater economic resources mitigate the impact. This may be due to individuals with greater economic resources being able to avoid crime by affording better transportation or security at work.

The third chapter, co-authored with Duncan Thomas, evaluates whether attitudes toward deferred gratification change after having children. For this study we also exploit the MxFLS, which elicits attitudes toward deferred gratification (commonly known as time discounting) by asking individuals to choose between hypothetical payments at different points in time. We implement a difference-in-difference estimator to control for all time-invariant heterogeneity and show that our results are robust to the inclusion of time varying characteristics likely correlated with child birth. We find that becoming a mother increases time discounting especially in the first two years after childbirth and in particular for those women without a spouse at home. Having additional children does not have an effect and the effect for men seems to go in the opposite direction. These heterogeneous effects suggest that child rearing may affect time discounting due to generated stress or not fully anticipated spending needs.

How households and individuals respond to adverse and unanticipated shocks is an important concern for both economists and policy makers. This is especially true in developing countries where poverty, weak infrastructure, and a lack of social safety nets often exacerbate the effects of adverse shocks on household welfare. My research addresses these issues in the context of three economic outcomes and behaviors - early life health and the accumulation of human capital, willingness to take on financial risk, and behavior in the labor market. The results of this research project both adds to our understanding of how life experiences shape individuals' well-being and behavior and how policy can help individuals achieve long-term improvements in the lives following adverse events.

My research focuses on households and individuals affected by a large-scale natural disaster, the 2004 Indian Ocean tsunami. I utilize data from the Study of the Tsunami Aftermath and Recovery (STAR), a unique longitudinal survey of individuals and households living in coastal communities in Aceh and North Sumatra, Indonesia, at the time of the tsunami. The STAR surveys were conducted annually for five years after the disaster and include a wide range of demographic, economic, and health measures.

In the first chapter, Child Height after a Natural Disaster, co-authored with Elizabeth Frankenberg, Duncan Thomas, and Jed Friedman, we investigate the immediate and long-run impacts on child health of in utero exposure to stress induced by the tsunami. We investigate whether in utero exposure to stress, as measured by tsunami-induced maternal posttraumatic stress, affected the growth of children born in the aftermath of the tsunami in the critical first five years of their lives. Although previous studies suggest that in utero exposure to stress is related to a number of adverse birth outcomes such as prematurity and lower birth weight, there is little evidence of the impact on linear growth, a strong correlate of later life income. We find evidence that children exposed to high levels of stress beginning in the second trimester experienced reduced growth in the first two years of their lives. We also find evidence that growth reductions largely disappear by age five. This suggests that significant catch-up growth is possible, particularly in the context of pronounced post-disaster reconstruction and economic rehabilitation.

In the second chapter, The Impact of a Natural Disaster on Observed Risk Aversion, I investigate the short and long-term impacts of the 2004 Indian Ocean tsunami on attitudes toward risk. Attitudes toward risk are important determinants of economic, demographic, and health-related behaviors, but how these attitudes evolve after an event like a natural disaster remains unclear because past research has been confounded by issues of selective exposure, mortality, and migration. My study is the first to directly address these problems by utilizing exogenous variation in exposure to a disruptive event in a sample of individuals that is representative of the population as it existed at the time of the event. In addition, intensive efforts were made to track migrants in the sample population, which is important for this study because migration is common following events like natural disasters and is likely related to attitudes toward risk. I find that physical exposure to the tsunami (e.g., seeing or hearing the tsunami or being caught up in the tsunami) causes significant short-term decreases in observed aversion to risk, especially for the poor, but few longer-term differences. This finding has important implications for the design of effective post-disaster assistance policies. In particular, it implies that post-disaster assistance programs should include aid that is consistent with the observed risk attitudes of the survivors such as job training and capital to start-up businesses.

In the last chapter, Labor Market Outcomes following the 2004 Indian Ocean Tsunami, I investigate how labor market outcomes changed in coastal communities in Aceh and North Sumatra following the tsunami and the post-disaster recovery efforts. Although restoring the livelihoods of survivors of adverse events is critical for their long-term recovery, there is little evidence from developing countries of how labor market outcomes change after such events. Using the STAR data, I find a significant and persistent increase in paid employment for younger women in urban communities. The increase occurred in communities that were heavily damaged by the tsunami and those that were not, suggesting that the impacts of the disaster on livelihoods are likely long-lasting and extend beyond the communities that were directly stuck by the disaster.

Pro-urban bias in policy is a common phenomenon in many developing countries. Bates (1981) has famously argued the wish to industrialize paired with the political clout of urban residents results in distinctly anti-rural policies in many developing countries. At the same time, empirical reality is much more varied than the standard urban bias argument suggests. Many government have actively supported agricultural producers and rural citizens at early stages of development. Building on Bates' argument, this paper develops a theory that identifies conditions under which politicians will institute pro urban or pro rural policies, by considering the threat of a rural insurgency. Specifically, the direction of urban-rural bias is a function of the asymmetric political threat geographically distinct groups pose to the survival of the central government.

According to IPCC estimates, sea levels will rise between .18 and .6 meters by 2100. More recent estimates indicate that actual amounts of sea level rise may be much more, and that 1 meter of sea level rise by 2100 is likely a conservative estimate. These rising sea levels will result not only in more flooding during storm events, but also increased erosion and gradual inundation of coastal property. At the same time, coastal populations in the United States continue to increase rapidly: over half of all Americans live in coastal counties, and at least 25 million more people are expected to move to the coast by 2015. The end result is that human populations, coastal infrastructure, and coastal ecosystems will become increasingly vulnerable to the impacts of climate change. This study examines the political and legal constraints to and opportunities for adaptation to rising sea levels. Using legal and policy analysis and case studies from California, North Carolina and Texas, this study explores the ability of governments to use market tools, land use regulations, and property acquisition to promote adaptation to rising sea levels. Because of market dynamics and political factors including flaws in public risk perception, I conclude that governments who wish to avoid extensive coastal engineering, , can address coastal community vulnerability through a combination of regulations and incentives that spur state and local governments to engage in forward land use planning and other measures to reduce their exposure to sea level rise impacts.

In this dissertation, I investigate the impact of administrative burdens on the US health care sector. Using observational data---particularly medical claims from Medicare----and policy variation in the administrative burdens to which health care providers are exposed, I use causal inference methods to understand the effects of various administrative burdens on economic, health, and fiscal outcomes in multiple contexts within the US health care system. I also use theoretical and structural modeling techniques to highlight and quantify the trade-offs faced by economic agents in the health care system and the impact of policy choices. Using turnover in the identity of private contractors that administer Traditional Medicare, I first find that exposing providers to the increased administrative burden imposed by higher-denial contractors does not reduce Medicare spending despite increasing claim denials. The increased administrative burden also leads providers to invest in billing effort, consolidate into larger firms, and earn lower profits. Next, I use similar variation across contractors to show that Medicare coverage restrictions slow the adoption of new medical procedures. Furthermore, I find that the diffusion patterns induced by these administrative burdens are consistent with social learning by providers about the value of the innovations, motivating a structural model of provider learning that indicates that coverage restrictions slow the learning process of the medical community. Finally, I use the staggered roll out of a prior authorization regulation along with criminal and civil lawsuits to identify the effects of ``pay-and-chase" litigation and an administrative burden regulation on the prevalence of health care fraud. I find that prior authorization was extremely effective at reducing health care spending without causing any adverse patient health outcomes, while litigation was much less effective. In conclusion, this dissertation finds that the administrative burdens that permeate the US health care sector have major impacts on market structure, innovation, and health care fraud, with the benefits and costs of these burdens being highlight context dependent.

While there is a common understanding that artists find it hard to make a living through their artistic activities, research is silent on how artists view and understand their lived experiences. “Creative wealth”, which is accumulated through artistic activities, is part of an untapped, unmeasured, and invisible economy (Kabanda, 2018). This qualitative, exploratory study examines the complexities of creative wealth through the lens of fifteen African and Afro-descendant performance, visual, literary, digital, and applied artists. Using a thematic analysis of semi-structured interview data, I present two conceptual themes: artistic challenges and artistic opportunities. The challenges focus on (1a) the trade-offs involved in the production and distribution of one’s art, and (1b) the lack resources to produce art, lack ownership of art, and lack of avenues to display or investigate art. The opportunities focus on (2a) how artistic activity can foster innovation and (2b) how artistic activity can be used as an individual-level and a communal-level tool of expression or "exchange of experience". The findings of this study indicate that artists perceive their artistic endeavors to be simultaneously impeded and elevated by conditions of scarcity, and they understand that these conditions are unique to their African ancestral identity. Drawing on insights and recommendations from the artists themselves, this research sheds light on how creative wealth can improve lives – both monetarily and non-monetarily.

This paper examines whether deferred tax asset valuation allowance growth, as a measure of expected future performance, aggregated at the macroeconomy level, conveys information about future GDP growth. Using hand-collected tax footnote data from publicly traded firms over the 1993 to 2019 period, I find that quarterly aggregate valuation allowance growth is negatively associated with future GDP growth up to four quarters ahead. This relationship is incremental to existing accounting and macroeconomic GDP growth indicators, especially for forecast horizons longer than one quarter when other indicators are uninformative. Additionally, the findings suggest that aggregate valuation allowance growth provides unique information that cannot be obtained from other sources of management information, such as management forecasts, the allowance for doubtful accounts, banks’ loan loss provision, and goodwill impairment loss. The findings further indicate that the documented association of GDP growth and aggregate valuation allowance growth is driven by the corporate profit growth component of GDP growth. Collectively, the evidence indicates that aggregate valuation allowance growth provides incremental forward-looking information about GDP growth.

Despite the threats to morbidity, mortality, and human capital accumulation posed by environmental risks, investments in environmental health technologies remain low. This is especially evident in low- and middle-income countries, which disproportionately shoulder the burden of environmental risk exposure and consequence. Households face competing risks associated with poor air and water quality, necessitating choices about how to invest in technologies to reduce the consequences associated with their exposures. Yet, even in areas where access to environmental health technologies such as improved cookstoves, latrines, and insecticide-treated bednets has expanded and products are subsidized to make them more affordable, adoption and use of these technologies often lag. This dissertation examines some of the conditions that impact environmental health technology adoption and use decisions as well as the health implications of low investment.

In Chapter 1, I ask how air pollution exposure drives consumption behaviors and impacts health outcomes. I examine this question in the short term--asking how behavior and health respond to a large, yet transitory, spike in ambient air pollution--as well as over time--considering the responses to average ambient air pollution levels over a period of 19 years. I leverage variation in air pollution resulting from forest fire emissions in Indonesia between 1996 and 2015 to generate short-term exposure spikes and average exposures over time, and I combine these exposures with four waves household and individual-level survey data. I implement a cross-sectional, difference-in-difference analysis to estimate the immediate effects of an unexpectedly severe forest fire season in 2015, finding increased fuel demands among the most-exposed households as well as declines in lung capacity among emissions-affected children. I extend my analysis across the panel using an instrumental variables approach to estimate consequences of average exposure over time. I find that households facing higher average ambient air pollution exposures are more likely to utilize clean cooking fuels such as LPG. Even with these behavioral adjustments, more-exposed individuals face significant reductions in lung capacity. In line with existing literature, I find negative health implications resulting from short-term exposure shocks; however, my analysis demonstrates that these respiratory consequences are not fleeting, particularly in areas that experience elevated average ambient air pollution levels.

In Chapter 2, I turn to environmental risks posed by limited access to improved sanitation technologies to examine how social influences impact household sanitation decisions. Using three waves of data collected immediately before, a few months after, and a few years after a randomized latrine promotion campaign in rural Orissa, India, I evaluate the extent to which social influences impact sanitation choices. I find that a ten percentage point decrease in neighbors' open defecation reduces a household's likelihood of open defecation by 3-4 percentage points. The sanitation intervention decreased open defecation in the short term; however, this treatment was less effective in neighborhoods with higher rates of open defecation due to strong social effects. Disaggregating social effects by gender, I find that both women and men respond to sanitation behaviors among male neighbors in the short term and female neighbors in the longer term, perhaps because men have more control over initial latrine purchasing decisions while women are more influential in sustaining latrine use over time.

Finally, in Chapter 3, I expand on my analysis of social influences and sanitation practices and examine how households make decisions to contribute to collective action for sanitation. In this chapter, I analyze data from an experimental public goods game I designed and implemented among over 1500 households in rural Bihar and Orissa, India. I randomly assigned each of the 70 villages in the sample into groups that are either homogeneous or heterogeneous by gender for game play. In the context of rural India, individuals are more likely to frequently interact with and make decisions in front of others of the same gender. Thus, splitting the groups in this way provides a proxy for peer or social groups. Participants chose how much to contribute to improved sanitation by making decisions in the game that are associated with actual sanitation and hygiene choices they face every day. Payoffs were awarded after each round, and payoff amounts were dependent on both individual contributions and aggregated group contributions, generating a setting in which the benefits participants received were connected. Comparing the game behavior among participants in groups that were homogeneous and heterogeneous by gender, I find evidence that contributions to collective action for sanitation are higher in gender homogenous groups. Female participants drive this difference, and it is more distinct in the first round of game play. I also find evidence that preferences for improved sanitation as elicited during the experimental games are reflective of actual improved sanitation practices at the household level.

This paper intends to present my summary of internship and some academic individual and team projects, including a quantitative and statistical analysis of some important Macro factors and financial models, and a data analysis project in drug cost reduction. The first chapter discusses the mechanism and impact of pass-through from the dynamics of RMB exchange in China, and the method I used here is the basic econometrics regression analysis. The result is significant and coincides with our common sense when we make investment decisions. The second chapter is about the revised CCAPM model. Through a modified distribution of error terms, CCAPM model will show an improved explanation power. The third chapter is a data analysis project of drug cost reduction. I used Bayesian method to explore the relationship between drug cost and other predictors, and the result gives us advice on designing health plans to minimize the cost.

This dissertation consists of three essays on asymmetric correlations in financial markets. In the first essay, I have two main contributions. First, I show that dividend growth rates have symmetric correlations. Second, I show that asymmetric correlations are different than correlations being counter-cyclical. The correlation asymmetry I study in this dissertation should not be confused with correlations being counter-cyclical, i.e. being higher during recessions than during booms. I show that while counter-cyclical correlations can simply be explained by counter-cyclical aggregate market volatility, the correlation asymmetry with respect to joint upside and downside movements of returns are not just due to the heightened market volatility during those times.

In the second essay I present a model in order to explain the correlation asymmetry observed in the data. This is the first paper to offer an explanation for observed correlation asymmetry. I formalize the explanation using an equilibrium model. The model is useful to understand both the cross-section and time-series of correlation asymmetry. By the means of my model, we can answer questions about why some stocks have higher correlation asymmetry, and why the correlation asymmetry was higher during 1990s? In the model asset prices respond the realization of dividends and news about the future. However, price responses to news are asymmetric and this asymmetry is endogenous. Price responses are endogenously stronger conditional on bad news than conditional on good news. This asymmetry also generates the observed correlation asymmetry. The price responses are asymmetric due to the ambiguity about the news quality. Information about the quality of the signal is incomplete in the sense that the exact precision of the signal is unknown; it is only known to be in an interval, which makes the representative agent treat news as ambiguous. To model ambiguity aversion, I use Gilboa and Schmeidler (1989)'s max-min expected utility representation. The agent has a set of beliefs about the quality of signals, and the ambiguity-averse agent behaves as if she maximizes expected utility under a worst-case scenario. This incomplete information about the news quality, together with ambiguity-averse agents, generates an asymmetric response to news. Endogenous worst-case scenarios differ depending on the realization of news. When observing ``bad" news, the worst-case scenario is that the news is reliable and the prices of trees decrease strongly. On the other hand, when ``good news" is observed, under the worst-case scenario the news is evaluated as less reliable, and thus the price increases are mild. Therefore, price responses are stronger conditional on a negative signal and this asymmetry creates a higher correlation conditional on a negative signal than conditional on a positive signal. I also show that the results are robust to the smooth ambiguity aversion representation.

Motivated by the model, I uncover a new empirical regularity that is unknown in the literature. I show that correlation asymmetry is related to idiosyncratic volatility: the higher the idiosyncratic volatility, the higher the correlation asymmetry. This novel empirical finding is also useful to understand the time-series and cross-sectional variation in correlation asymmetry. Stocks with smaller market capitalizations have greater correlation asymmetry compared to stocks with higher market capitalization. However, an explanation for this finding has been lacking. According to the explanation offered in this paper, smaller size stocks have greater correlation asymmetry compared to bigger size stocks because small size stocks tend to have higher idiosyncratic volatilities compared to bigger size stocks. In the time-series, correlation asymmetry shows quite significant variation as well. The average correlation asymmetry is especially high for the 1990s and decreases significantly at the beginning of the 2000s. This pattern in times-series can also be explained in terms of the time-series behavior of idiosyncratic volatilities. Several papers including Brandt et al. (2010), document higher idiosyncratic volatilities during 1990s while the aggregate volatility stays fairly stable. Basically, the high idiosyncratic volatilities during the 1990s also caused greater correlation asymmetry.

In the third essay, I study the correlation of returns in government bond markets. Similar to the findings in equity markets, I show that there is some evidence for asymmetric correlations in government bond markets. First, I show that the maturity structure matters for correlation asymmetry in bonds markets: Unlike long-maturity bonds, shorter-maturity bonds tend to have asymmetric correlations. Second, I show that the correlation asymmetry observed in European bond markets disappears with the formation of a common currency area. Lastly, I study the correlation between equity and bond returns in different countries. For long-maturity bonds, correlations with the domestic equity returns are asymmetric for half of the countries in the sample, including the U.S. These findings show that results on asymmetric correlations from equity markets can generalize, at least to some extent, to other financial markets.

We design algorithms for markets consisting of multiple items, and agents with budget constraints on the maximum amount of money they can afford to spend. This problem can be considered under two broad frameworks. (a) From the standpoint of Auction Theory, the agents valuation functions over the items are private knowledge. Here, a "truthful auction" computes the subset of items received by every agent and her payment, and ensures that no agent can manipulate the scheme to her advantage by misreporting her valuation function. The question is to design a truthful auction whose outcome can be computed in polynomial time. (b) A different, but equally

important, question is to investigate if and when the market is in "equilibrium",

meaning that every item is assigned a price, every agent gets her utility-maximizing subset of items under the current prices, and every unallocated item is priced at zero.

First, we consider the setting of multiple heterogeneous items and present approximation algorithms for revenue-optimal truthful auctions. When the items are homogeneous, we give an efficient algorithm whose outcome defines a truthful and Pareto-optimal auction. Finally, we focus on the notion of "competitive equilibrium", which is a well known solution concept for market clearing. We present efficient algorithms for finding competitive equilibria in markets with budget constrained agents, and show that these equilibria outcomes have strong revenue guarantees.

In the first chapter, motivated by a model used to analyze spike train data, we present a method for learning multiple probability vectors by using information from large samples to improve estimates for smaller samples. The method makes use of Polya-gamma data augmentation to construct a conjugate model whose posterior can estimate the weights of a mixture distribution. This novel method successfully uses borrows information from large samples to increase the precision and accuracy of estimates for smaller samples.

In the second chapter, data from the Federal Communications Commission spectrum auction number 73 is analyzed. By analyzing the structure of the auctions bounds are placed on the valuations that govern the bidders' decisions in the auction. With these bounds, common models are estimated by imputing valuations and the results are compared with the estimates from standard methods used in the literature. The comparison shows some important differences between the approaches. A second model that accounts for the geographic relationship between the licenses sold finds strong evidence of a correlation between the value of adjacent licenses, as expected by economic theory.

This paper investigates Bayesian instrumental variable models with many instruments. The number of instrumental variables grows with the sample size and is allowed to be much larger than the sample size. With some sparsity condition on the coefficients on the instruments, we characterize a general prior specification where the posterior consistency of the parameters is established and calculate the corresponding convergence rate.

In particular, we show the posterior consistency for a class of spike and slab priors on the many potentially weak instruments. The spike and slab prior shrinks the number of instrumental variables, which avoids overfitting and provides uncertainty quantifications on the first stage. A simulation study is conducted to illustrate the convergence notion and estimation/selection performance under dependent instruments. Computational issues related to the Gibbs sampler are also discussed.

This dissertation examines the social and financial activities of Buddhist nuns to demonstrate how and why they deployed Buddhist doctrines, rituals, legends, and material culture to interact with society outside the convent. By examining the activities of the nuns of the Daihongan convent (one of the two administrative heads of the popular pilgrimage temple, Zenkōji) in Japan’s early modern period (roughly 1550 to 1868) as documented in the convent’s rich archival sources, I shed further light on the oft-overlooked political and financial activities of nuns, illustrate how Buddhist institutions interacted with the laity, provide further nuance to the discussion of how Buddhist women navigated patriarchal sectarian and secular hierarchies, and, within the field of Japanese history, give voice to women who were active outside of the household unit around which early modern Japanese society was organized.

Zenkōji temple, surrounded by the mountains of Nagano, has been one of Japan’s most popular pilgrimage sites since the medieval period. The abbesses of Daihongan, one Zenkōji’s main sub-temples, traveled widely to maintain connections with elite and common laypeople, participated in frequent country-wide displays of Zenkōji’s icon, and oversaw the creation of branch temples in Edo (now Tokyo), Osaka, Echigo (now Niigata), and Shinano (now Nagano). The abbesses of Daihongan were one of only a few women to hold the imperially sanctioned title of eminent person (shōnin 上人) and to wear purple robes. While this means that this Pure Land convent was in some ways not representative of all convents in early modern Japan, Daihongan’s position is particularly instructive because the existence of nuns and monks in a single temple complex allows us to see in detail how monastics of both genders interacted in close quarters.

This work draws heavily from the convent’s archival materials, which I used as a guide in framing my dissertation chapters. In the Introduction I discuss previous works on women in Buddhism. In Chapter 1, I briefly discuss the convent’s history and its place within the Zenkōji temple complex. In Chapter 2, I examine the convent’s regular economic bases and its expenditures. In Chapter 3, I highlight Daihongan’s branch temples and discuss the ways that they acted as nodes in a network connecting people in various areas to Daihongan and Zenkōji, thus demonstrating how a rural religious center extended its sphere of influence in urban settings. In Chapter 4, I discuss the nuns’ travels throughout the country to generate new and maintain old connections with the imperial court in Kyoto, confraternities in Osaka, influential women in the shogun’s castle, and commoners in Edo. In Chapter 5, I examine the convent’s reliance upon irregular means of income such as patronage, temple lotteries, loans, and displays of treasures, and how these were needed to balance irregular expenditures such as travel and the maintenance or reconstruction of temple buildings. Throughout the dissertation I describe Daihongan’s inner social structure comprised of abbesses, nuns, and administrators, and its local emplacement within Zenkōji and Zenkōji’s temple lands.

Exploring these themes sheds light on the lives of Japanese Buddhist nuns in this period. While the tensions between freedom and agency on the one hand and obligations to patrons, subordination to monks, or gender- and status-based restrictions on the other are important, and I discuss them in my work, my primary focus is on the nuns’ activities and lives. Doing so demonstrates that nuns were central figures in ever-changing economic and social networks as they made and maintained connections with the outside world through Buddhist practices and through precedents set centuries before. This research contributes to our understanding of nuns in Japan’s early modern period and will participate in and shape debates on the roles of women in patriarchal religious hierarchies.

Developing countries have long worried about the prospects of their “best and brightest” moving to the developed world. Some scholars have argued that massive emigration of highly-educated labor deprives these countries of much-needed human capital, leaving them “forever destitute.” However, other scholars have questioned this argument, pointing out that high wages in migrant-receiving countries can serve as an incentive for potential migrants to invest more in human capital than they would otherwise. Some of these high-skilled workers will end up staying, raising the overall level of human capital in developing countries. This phenomenon is referred to as “brain gain.” One key underlying assumption of existing brain gain models is that migrant-receiving countries cannot distinguish high-skilled workers from low-skilled workers when deciding whether to grant them entry. This dissertation argues that while this assumption may have been valid in the past, it no longer reflects today’s world where high-income countries have developed a rigorous screening process and select only the best qualified immigrants. Thus, we need a new theoretical framework to reflect this new reality. Building a model of brain gain based on the “tournament model” introduced by Lazear and Rosen (1979), this dissertation shows that the wage difference between migrant-sending and migrant-receiving countries is the main motivation for potential migrants to acquire more education and compete for higher paying jobs in migrant-receiving countries. This dissertation argues that two key factors determining this income gap are 1) the intensity of screening by migrant-receiving countries and 2) the wage level in migrant-sending countries. Utilizing two exogenous shocks including an increase in screening by the United States following the 9/11 terrorist attacks and a series of affirmative action policies implemented in Malaysia, the dissertation finds that the intensity of screening by migrant-receiving countries has a positive effect on human capital accumulation in migrant-sending countries and a decrease in the wage level in migrant-sending countries has a positive impact on the educational outcomes of potential migrants.

Young people living with HIV (YPLWH) constitute a growing proportion of the global population of people living with HIV but have less access to HIV testing, diagnosis, treatment, and face heightened mental health challenges. To address these challenges, targeted mental health and medication adherence interventions have been developed, including in Tanzania, which is home to 6% of the world's YPLWH. This study proposes a mathematical model to estimate the health and economic outcomes of mental health HIV adherence interventions targeting YPLWH in Tanzania.We developed a Markov model to predict the long-term health (Disability-Adjusted Life Years (DALY)) and economic outcomes (Value of a Statistical Life Year (VLSY)) of mental health HIV adherence interventions targeting YPLWH. We parameterized the model using outcomes data from the 2016-2020 Sauti ya Vijana randomized control trial (RCT) conducted in Moshi, Tanzania. Cost data were retrieved from a cost analysis of the same RCT and supplemented with data from published literature. The study is conducted from a health payer’s perspective, and the Willingness-To-Pay (WTP) per DALY averted was set to the 2021 Tanzanian GDP per capita (USD 1099.3). Costs and outcomes were modeled for ten years and discounted at an annual rate of 3%. The findings suggest that the Sauti ya Vijana intervention modeled in this study is cost-effective at a WTP of USD 1099.3. The Incremental cost-effectiveness ratio for the intervention compared to the standard of care was USD 637.06 per DALY averted at a 3% discount rate. The benefit-to-cost ratio of the intervention was USD 26.54 in economic productivity for the intervention arm for every dollar spent on the intervention, and the net economic productivity benefit was USD 17,174.74 over a decade. Mental health adherence interventions hold the promise of improving health outcomes amongst YPLWH. The mathematical model developed in this study is a valuable decision-making tool for policymakers regarding mental health adherence interventions targeting YPLWH in Tanzania. The model contributes to the global goal of achieving the UNAIDS 95-95-95 targets for YPLWH.

Background: An estimated 225 million women globally have an unmet family planning need, three-quarters of whom live in low and middle-income countries. Addressing this need requires new and innovative approaches, such as digital health solutions. We examined the impact of a new phone-based screening and referral service on the take-up of family planning as part of a pilot study to prepare for a full trial of the intervention.

Methods: This pilot study tested the procedures for a randomized encouragement trial. We recruited 112 women with an unmet need for family planning from local markets in Western Kenya, conducted an eligibility screening, and randomized half of the women to receive an encouragement to try the investigational intervention. Four months after sending an encouraging to the treatment group, we attempted to conduct a follow-up survey with all enrolled participants.

Results: The encouragement sent via text message to the treatment group led to differential rates of intervention uptake between the treatment and control groups, but take-up among the group was lower than anticipated (33.9% vs 1.8% in the control group). Study attrition was also substantial. We obtained follow-up data from 44.6% of enrolled participants. Among those in the treatment group who tried the intervention, however, the instrumental variables estimate of the Local Average Treatment Effect was an increase of 41 percentage points in the probability of contraceptive take-up.

Conclusion: This randomized encouragement design and study protocol is feasible but requires modifications to the encouragement and follow-up data collection procedures. The investigational intervention appears to have a positive impact on contraceptive take-up among women with an unmet need despite a number of contextual challenges.

Empirical studies of education programs and systems, by nature, rely upon use of student outcomes that are measurable. Often, these come in the form of test scores. However, in light of growing evidence about the long-run importance of other student skills and behaviors, the time has come for a broader approach to evaluating education. This dissertation undertakes experimental, quasi-experimental, and descriptive analyses to examine social, behavioral, and health-related mechanisms of the educational process. My overarching research question is simply, which inside- and outside-the-classroom features of schools and educational interventions are most beneficial to students in the long term? Furthermore, how can we apply this evidence toward informing policy that could effectively reduce stark social, educational, and economic inequalities?

The first study of three assesses mechanisms by which the Fast Track project, a randomized intervention in the early 1990s for high-risk children in four communities (Durham, NC; Nashville, TN; rural PA; and Seattle, WA), reduced delinquency, arrests, and health and mental health service utilization in adolescence through young adulthood (ages 12-20). A decomposition of treatment effects indicates that about a third of Fast Track’s impact on later crime outcomes can be accounted for by improvements in social and self-regulation skills during childhood (ages 6-11), such as prosocial behavior, emotion regulation and problem solving. These skills proved less valuable for the prevention of mental and physical health problems.

The second study contributes new evidence on how non-instructional investments – such as increased spending on school social workers, guidance counselors, and health services – affect multiple aspects of student performance and well-being. Merging several administrative data sources spanning the 1996-2013 school years in North Carolina, I use an instrumental variables approach to estimate the extent to which local expenditure shifts affect students’ academic and behavioral outcomes. My findings indicate that exogenous increases in spending on non-instructional services not only reduce student absenteeism and disciplinary problems (important predictors of long-term outcomes) but also significantly raise student achievement, in similar magnitude to corresponding increases in instructional spending. Furthermore, subgroup analyses suggest that investments in student support personnel such as social workers, health services, and guidance counselors, in schools with concentrated low-income student populations could go a long way toward closing socioeconomic achievement gaps.

The third study examines individual pathways that lead to high school graduation or dropout. It employs a variety of machine learning techniques, including decision trees, random forests with bagging and boosting, and support vector machines, to predict student dropout using longitudinal administrative data from North Carolina. I consider a large set of predictor measures from grades three through eight including academic achievement, behavioral indicators, and background characteristics. My findings indicate that the most important predictors include eighth grade absences, math scores, and age-for-grade as well as early reading scores. Support vector classification (with a high cost parameter and low gamma parameter) predicts high school dropout with the highest overall validity in the testing dataset at 90.1 percent followed by decision trees with boosting and interaction terms at 89.5 percent.

Causal inference methods are commonly used across domains to aid high-stakes decision-making. The validity of causal studies often relies on strong assumptions that might not be realistic in high-stakes scenarios. Inferences based on incorrect assumptions frequently result in sub-optimal decisions with high penalties and long-term consequences. Unlike prediction or machine learning methods, it is particularly challenging to evaluate the performance of causal methods using just the observed data because the ground truth causal effects are missing for all units. My research presents frameworks to enable validation of causal inference methods in one of the following three ways: (i) auditing the estimation procedure by a domain expert, (ii) studying the performance using synthetic data, and (iii) using placebo tests to identify biases. This work enables decision-makers to reason about the validity of the estimation procedure by thinking carefully about the underlying assumptions. Our Learning-to-Match framework is an auditable-and-accurate approach that learns an optimal distance metric for estimating heterogeneous treatment effects. We augment Learning-to-Match framework with pharmacological mechanistic knowledge to study the long-term effects of untreated seizure-like brain activities in critically ill patients. Here, the auditability of the estimator allowed neurologists to qualitatively validate the analysis via a chart-review. We also propose Credence, a synthetic data based framework to validate causal inference methods. Credence simulates data that is stochastically indistinguishable from the observed data while allowing for user-designed treatment effects and selection biases. We demonstrate Credence's ability to accurately assess the relative performance of causal estimation techniques in an extensive simulation study and two real-world data applications. We also discuss an approach to combines experimental and observational studies. Our approach provides a principled approach to test for the violations of no-unobserved confounder assumption and estimate treatment effects under this violation.