Dissertations

This dissertation quantitatively studies the impact of land policy on welfare in China. There are two specific nationwide land policies of importance on which I focus: 1) The red-line policy that imposes a minimum 1.2 million square kilometers for agricultural use only 2) The zoning policy for urban land that strictly regulates the amount of industrial and residential land usages respectively; In the first chapter, I give an introduction about the land policy and institutional background in China and the related literature to my dissertation. Second chapter builds a two-sector(two-region) model to examine how the red-line policy interacts with land misallocation within agricultural-sector. Third chapter gives a spatial equilibrium with internal urban structure to quantify to what extent the zoning policy for urban land accounts for the empirically observed high price ratio of residential over industrial land and how it affects welfare.

In Chapter 1, we examine whether various anomalies can be driven by two common behavioral forces, namely, "subjective" sentiment (representing investors' subjective biased beliefs) and "objective" limited attention (representing investors' objective cognitive constraints). While sentiment explains well many anomalies that are more speculative on the short-leg, it fails to explain anomalies that are equally speculative on the long and short-leg, including momentum and post-earnings announcement drift. Market-wide attention shifts, proxied by number of news averaged across stocks, significantly attenuates underreaction-driven anomalies, beyond the effect of sentiment. Our findings suggest that increase in market-wide attention can temporarily reduce the cost of attending to market and improve price efficiency.

In Chapter 2, we use systematic methods to solve factor timing problem and to improve the performance of factor investing. Past factor returns predict the cross section of factor returns, and this predictability is at its strongest at the one-month horizon (Arnott et al. 2019). We find that factor momentum is pervasive in international stock markets. We show that factor momentum can be captured by trading almost any set of factors. Industry momentum and size-B/M momentum stem from factor momentum. We further find that stock factor momentum, stock factor IVOL, and cross-assets factor momentum can generate alphas. These alphas cannot be explained by current asset pricing models.

In Chapter 3, We take an optimal portfolio approach on investing in multiple anomalies. We find that a variety of estimated optimal rules outperform substantially investing in any single anomaly. In addition, although it has been documented that the publication of a given anomaly may significantly reduce its standalone economic value, we show that these anomalies are still valuable collectively in the optimal portfolio even after they are published.

This dissertation examines digital strategies used by online retailers to engage customers and increase sales. The first essay investigates the impact of offering incentives to recapture lost sales from abandoned online shopping carts (i.e., customers adding items to their carts, but leaving without purchasing). Collaborating with an online fashion retailer, I conducted a randomized field experiment by manipulating the presence of incentives in recovery interventions. Findings reveal that incentives facilitate purchase conversion, but responsiveness to the incentives differs across various customer and cart characteristics.

In the second essay, I explore how retailers can leverage Augmented Reality (AR), a technology that helps users visualize how virtual objects fit into their physical reality, to enable customers to try products virtually. Using data from an international cosmetics retailer who incorporated AR in their mobile app, I find that customers who use AR display higher levels of in-app engagement, and are more inclined to explore products and brands they have never purchased before. Furthermore, AR benefits lower-priced products and less-popular brands and could potentially level the playing field for brands or products at the long tail of the product sales distribution.

The findings uncovered in these two essays contribute to the literature on digital marketing and retailing, and would benefit retailers who are planning to implement these strategies.

My dissertation consists of three essays which contribute new theoretical results to nonstationary time-series analysis and network dynamics.

Chapter 2 examines the limit properties of information criteria (such as AIC, BIC, HQIC) for distinguishing between the unit root model and the various kinds of explosive models. The explosive models include the local-to-unit-root model, the mildly explosive model and the regular explosive model. Initial conditions with different orders of magnitude are considered. Both the OLS estimator and the indirect inference estimator are studied. It is found that BIC and HQIC, but not AIC, consistently select the unit root model when data come from the unit root model. When data come from the local-to-unit-root model, both BIC and HQIC select the wrong model with probability approaching 1 while AIC has a positive probability of selecting the right model in the limit. When data come from the regular explosive model or from the mildly explosive model in the form of 1 + n^a/n with a in the range of (0,1), all three information criteria consistently select the true model. Indirect inference estimation can increase or decrease the probability for information criteria to select the right model asymptotically relative to OLS, depending on the information criteria and the true model. Simulation results confirm our asymptotic results in finite sample.

Chapter 3 studies a continuous time dynamic system with a random persistence parameter. The exact discrete time representation is obtained and related to several discrete time random coefficient models currently in the literature. The model distinguishes various forms of unstable and explosive behavior according to specific regions of the parameter space that open up the potential for testing these forms of extreme behavior. A two-stage approach that employs realized volatility is proposed for the continuous system estimation, asymptotic theory is developed, and test statistics to identify the different forms of extreme sample path behavior are proposed. Simulations show that the proposed estimators work well in empirically realistic settings and that the tests have good size and power properties in discriminating characteristics in the data that differ from typical unit root behavior. The theory is extended to cover models where the random persistence parameter is endogenously determined. An empirical application based on daily real S&P 500 index data over 1928-2018 reveals strong evidence against parameter constancy over the whole sample period leading to a long duration of what the model characterizes as extreme behavior in real stock prices.

Chapter 4 develops a dynamic covariate-assisted spectral clustering method to uniformly estimate the latent group membership of cryptocurrencies consistently. We show that return inter-predictability and crypto characteristics, including hashing algorithms and proof types, jointly determine the crypto market segmentation. Based on this classification result, it is natural to employ eigenvector centrality to identify a cryptocurrency’s idiosyncratic risk. An asset pricing analysis finds that a cross-sectional portfolio with a higher centrality earns a higher risk premium. Further tests confirm that centrality serves as a risk factor well and delivers valuable information content on cryptocurrency markets.

This dissertation comprises three papers that separately study product quality in international trade, the governance’ effect on FDI and the agency problems in firms’ exporting decisions.

The first chapter quantifies the contribution of differences in quality preferences to the differences in gains from trade across countries. The quantification demonstrates that variations in the strength of quality preferences across countries add to heterogeneities across countries in market competitiveness. If the quality channel is shut down, countries with stronger preferences for quality have larger degrees of underestimations in their losses from the trade barrier. Finally, gains from a universal rise in quality preference are unequal among countries, with larger economies generally gaining more than smaller economies.

The second chapter proposes a theoretical model to micro-found firms’ optimal choice of FDI location, and sourcing and production, allowing for many countries, industries, and heterogeneous firms. We arrive at the main hypothesis that predicts an institutional complementarity pattern across countries in bilateral FDI flows at both the firm and country levels. We conduct an extensive test of the theory using worldwide bilateral FDI data at the firm level and at the country level. The results indicate a statistically significant assortative matching pattern in the institutional qualities of FDI origins and destinations.

The third chapter incorporates financial constraints into agency problems of firms. We show that under the same conditions, managers of potential exporting firms around the export threshold exert more efforts in financially under-developed countries to induce their owners to export. This finding has very positive policy implications, as firms in financially under-developed countries can compete with their peers in financially developed countries by exerting more managerial efforts. We find clear empirical evidence for this theoretical prediction using World Management Survey data for more than ten thousands firms from around 20 countries during 1999 – 2010.

Objective: This study aimed to distinguish between daily experiences of gratitude and indebtedness through three stages - emotional appraisals, motivations to reciprocate and behavioural tendencies. Through these three stages, I aimed to gain a better insight into the emotional process involved before and after receiving favours. Method: 196 participants were recruited from Singapore Management University to take part in a 14-day diary study. Every two days, participants were asked to report a favour they received over the past two days and evaluate the favour based on their appraisals of the experience. They were also asked to report their motivation to reciprocate each favour received. Upon completing the diary study, participants attended a follow-up session where were asked to report their behavioural tendencies over the past week. Results: Gratitude and indebtedness were associated with varying extents with different benefit appraisals. For instance, gratitude was positively associated with perceived benevolence and indebtedness with perceived expectations of repayment. Gratitude was also found to motivated reciprocity via the desire to affiliate, while indebtedness motivated reciprocity through the desire to adhere to the norm of reciprocity. Finally, gratitude was associated with increased reports of affiliative behaviours while indebtedness was associated with the likelihood of repaying the favour. The theoretical implications, practical implications, and future directions of these findings were discussed.

Corporate acquisition is among the most important strategic tools wielded by managers to achieve competitive advantage. Acquisition may create strategic values for the acquirer by gaining market power through industry consolidation, diversifying into rapidly growing industries, entering into emerging markets, and most importantly by combining unique valuable resources from acquirer and target. Despite the many appealing aspects of the motive for acquisition, however, meta-analytical studies suggest that though highly beneficial for the target firm’s shareholders, acquisition on average destroys acquirer shareholder values. to understand the determinants of acquisition performance success, it becomes imperative to study the antecedents as well as consequences of acquisition. Existing literature has found scores of potential culprits including agency behavior, position in merger wave, managerial hubris, poor acquisition capability in post-acquisition integration, and loss of valuable human capital resources among others. Beyond acquisition performance, acquisition also results in many other intended and unintended consequences such as employee turnover and CEO departures. What may affect various acquisition outcomes? How do these outcomes affect future acquisition decisions?

In my dissertation, I look at two additional factors that may affect the success of acquisition by conducting two empirical studies on the antecedents as well as consequences of acquisition using behavioral and resource based theories. I further explore the connections between the antecedents and outcomes of acquisitions. In the second chapter, incorporating risk taking, I focus on the influences of alliance and acquisition performance feedback on the rate of future acquisitions. The Behavioral Theory of the Firm (BTF) and performance feedback theory have been used to explain corporate acquisition behaviors both at the organizational and deal levels. However, extant literature does not agree on the relationship between acquisition performance feedback and

likelihood of subsequent acquisition. Firms may search in different directions in response to acquisitions failures. Moreover, we still lack a clear understanding of the behavioral influences on the various sourcing mechanisms. To address these questions, we built on both the BTF and prospect theory to propose a search hierarchy among corporate sourcing methods. We posit that the general search direction in corporate development follows from simple to complex and from economical to costly. We hypothesize that, under the duo influences of problemistic search and risk taking, deal performance feedback is negatively related to future acquisitions. We further investigate the interaction between alliance and acquisition performance feedback on future acquisitions.

In the third chapter, given the importance of human capital retention and turnover in the success of acquisition, I examine how target firm’s knowledge base may affect acquirer’s decision to retain the target’s CEO. Drawing upon the firm-specific resources and strategic human capital literatures, we develop the argument that the level of firm-specific knowledge in an acquisition target may affect the likelihood of the target’s CEO being retained after an acquisition. Specifically, due to the important role of target CEOs in preserving the value of and integrating firm-specific knowledge, we expect a positive relationship between a target’s pre-acquisition firm-specific knowledge level and the likelihood that its CEO is retained. Furthermore, we argue that this relationship is strengthened by the target’s pre-acquisition performance, which signals a higher value of firm-specific knowledge, and the target CEO’s tenure, which is positively associated with both the benefit of retaining and the cost of replacing the CEO. Using a sample of acquisitions involving US target firms acquired between 1995 and 2006, we find support for our hypotheses.

IT innovations disrupt traditional business models and challenge conventional thinking. Thus, industry incumbents face fierce competition from start-ups with new business models and new ways of engaging customers. Digital entertainment goods and personalized services have become a lucrative market, which has undergone a transformation enabled by seamless Internet connections. Meanwhile, social networks and other online platforms have brought people and business even closer.

Negative stereotypes concerning females’ inferior quantitative abilities continue to hinder females’ preference and success in science, technology, engineering, and mathematics (STEM) fields. Studies on multiple identities show that priming females with a favorable identity, a social identity they possess that is associated with superior quantitative abilities, can reduce the aversive effects of stereotype threat. However, this line of research overlooked the fact that females manage their multiple identities in different ways and therefore respond to identity cues differently. This paper examined the role of gender-professional identity integration (G-PII), an individual difference on perceived compatibility of gender and professional identities, in influencing how women cope with stereotype threat when a favorable identity is primed. Study 1 examined how female professionals with varying levels of G-PII react to identity cues differently. Results show that only Low G-PIIs were sensitive to the identity cues and behaved in accordance to the primed identity. In contrast, High G-PIIs were not significantly influenced by the identity cues. Moreover, performance differences were only observed in a domain where females are stereotyped against (i.e., in a math test). Study 2 investigated how G-PII influences the effects of stereotype threat when a favorable identity is made salient during stereotype threat and the underlying mechanism that accounts for the performance difference observed amongst females with different levels of identity integration. The findings of Study 2 were not significant but were consistent with the prediction that Low G-PIIs spend more cognitive effort in processing identity cues, depleting those that could have been use for subsequent performance task. The theoretical implications, practical implications, and future directions of this paper will then be discussed.

Despite a huge number of studies examining bilingual advantages in executive functions (EFs), the research findings with regards to the relations between bilingualism and EFs are mostly inconsistent and mixed. In order to shed light on these inconsistent findings, the current research aimed to tackle on both conceptual and methodological limitations that are prevalent in previous studies, namely: (a) failure to consider bilingual experiences in assessing bilingual advantages, and (b) task impurity due to substantial influence of non-EFs processes on EFs task performance. Based on Adaptive Control Hypothesis and Control Process Model of Code-switching, a theory-driven multisession study coupled with a latent variable approach was conducted to systematically examine the relations between bilingual interactional contexts and EFs, measured by nine different EFs tasks. The study found that dual-language context significantly predicted latent variable of task-switching, while dense code-switching context significantly predicted latent variable of inhibitory control and goal maintenance. The findings remained robust even taking into account potential confounds of demographics, socioeconomic status, intelligence, and unintended language-switching tendency. The current study identified bilingual interactional contexts as the key language experiences that could modulate the manifestation of bilingual advantages in EFs.

Research has shown that gratitude towards a benefactor positively predicts subjective well-being and other outcomes such as reciprocity and helping behaviours. However, previous research has not examined whether this effect is consistent or will differ across benefactor type (i.e., individual versus group). Research has also not examined the potential effects of accompanying thoughts related to the benefit assessment.

Through two experimental studies, the hypotheses that gratitude towards benefactor is lower for group benefactor as compared to individual benefactor, that self-entitlement thoughts and downward counterfactual thoughts will have main effects on gratitude as well as moderate the effect of benefactor type on gratitude, were tested. Results showed that the hypothesised main effect of benefactor type on gratitude was supported in one of the two studies (Study 2) but the other hypotheses were not supported.

Contrary to the hypothesised stronger positive effect, Study 2 found that there was no difference in effect between downward counterfactual thoughts and neutral thoughts that focused on recalling about benefiting experiences. Study 2 found that participants in the individual benefactor condition reported higher intent to help than participants in the group benefactor condition, and this effect of benefactor type on intent to help was partially mediated by gratitude.

In addition, trait gratitude was a moderator. When trait gratitude was high, those who reflected upon the benefits brought about by group benefactor experienced lower gratitude than those who reflected upon the benefits brought about by individual benefactor. However, when trait gratitude was low, the difference in the level of gratitude across benefactor type was not significant. The findings also showed that gratitude and indebtedness, as measured in both studies, were distinct constructs. Limitations of the current research, as well as future research directions and potential contributions were discussed.

This chapter discusses the main problem and motivation of this dissertation. It also discusses a quantification of various research issues directly related to the dissertation. A summary of works done will also be presented along with the structure of the dissertation.

This thesis proposes a general solution framework that integrates methods in machine learning in creative ways to solve a diverse set of problems arising in urban environments. It particularly focuses on modeling spatiotemporal data for the purpose of predicting urban phenomena. Concretely, the framework is applied to solve three specific real-world problems: human mobility prediction, trac speed prediction and incident prediction. For human mobility prediction, I use visitor trajectories collected a large theme park in Singapore as a simplified microcosm of an urban area. A trajectory is an ordered sequence of attraction visits and corresponding timestamps produced by a visitor. This problem has two related subproblems: (spatial) bundle prediction and trajectory prediction. In the first problem, I apply the framework to predict a bundle (i.e., an unordered set) of attractions that a given visitor would visit given a time budget. In the second problem, the framework is applied to predict the visitor's actual trajectory given the current partial trajectory and time budget. In both problems, I apply the methods of trajectory clustering, hidden Markov model, revealed preference learning and (inverse) reinforcement learning in the integrated framework. In trac speed prediction, I wish to predict the spatiotemporal distribution of trac speed over urban road networks. To this end, I propose local Gaussian processes which combine non-negative matrix (NMF) factorization with Gaussian process (GP) in order to enhance the efficiency of model training such that the solution could be deployed in real-time use cases. NMF is essentially a spatiotemporal clustering technique. The solution is extensively evaluated using real-world trac data collected in two U.S. cities. The incident prediction problem is about predicting the distribution of the number of crime incidents over urban areas in future time periods. Because of its similarity to the trac prediction problem above, its solution greatly benefits from the GP model developed earlier. Particularly, the GP kernel function is inherited and extended to model the distribution of incidents in urban areas and their features. The proposed solution is evaluated using real-world incident data collected in a large Asian city. Conceptually, this thesis uses machine learning techniques to solve three separate urban problems, whose contribution belongs to the large category of urban computing. At the core, its technical contribution lies in the unification of separate solutions tailored to those problems into an integrated framework that reasons with spatiotemporal data and, thus, is highly generalizable to other problems of similar nature.

We uncover a novel stock return predictor from the options market, the volume-weighted strike-spot price ratio (VWKS) across all traded option contracts. High (low) VWKS indicates that the mass of options volume on an underlying stock centers at the out-of-the-money region of call (put) options. Empirically, VWKS has positive and robust predictive ability for underlying returns after controlling for a long list of variables including known return predictors from the options market, stock illiquidity, and past stock returns, and has more persistent and stronger predictive power for stocks with higher information asymmetry and arbitrage costs. We also find that VWKS exhibits abnormal run-ups and becomes more informative before permanent but not transitory price jumps, suggesting that options traders exploit only fundamental information. Finally, VWKS significantly predicts the merger premium in an event study.

The rapid increase in online social networking services over the last decade has pre- sented an unprecedented opportunity to observe users’ behaviour both on a societal and individual level. The insight gained from analysing such data can help foster a deeper understanding of social media users and the flow of information, while also offering valuable business applications. User relationships are among the most studied aspects of online behaviour. These relationships are not homogeneous. Past research has shown that people use social networks to both socialize and source in- formation. Hence, different types of links – used to socialize, gain information, or both – are formed among users. While much research has focused on how users are connected online in general, it is crucial to explore how users interact with those present in offline social networks on the online social networks. Questions such as, ”What would speed up the diffusion of a piece of information?” can be better answered from an integrated offline-online perspective. My thesis explores the be- haviour of offline friends on the social information network in three main areas. I especially focus on social information networks, and use Twitter as a case study. In the first study, I explore and compare network characteristics on Twitter among of- fline friends and online friends. In the second study, I explore information diffusion in the same setting. In the last study, I investigate whether we can use the measure- ment of tie strength among friends on Twitter as a substitute for, or a complement to the measurement of tie strength among friends in the offline world.

Softwares are constantly evolving. This evolution has been made easier through the use of Application Programming Interfaces (APIs). By leveraging APIs, developers reuse previously implemented functionalities and concentrate on writing new codes. These APIs may originate from either third parties or internally from other compo- nents of the software that are currently developed. In the first case, developers need to know how to find and use third party APIs. In the second case, developers need to be aware of internal APIs in their own software. In either case, there is often too much information to digest. For instance, finding the right APIs may require sifting through many different APIs and learning them one by one, which can easily cost a large amount of time. Also, as the software becomes bigger and more complex, developers may not be aware of all functionalities available in their software.

One critical deficiency of traditional online kernel learning methods is their increasing and unbounded number of support vectors (SV’s), making them inefficient and non-scalable for large-scale applications. Recent studies on budget online learning have attempted to overcome this shortcoming by bounding the number of SV’s. Despite being extensively studied, budget algorithms usually suffer from several drawbacks. First of all, although existing algorithms attempt to bound the number of SV’s at each iteration, most of them fail to bound the number of SV’s for the final averaged classifier, which is commonly used for online-to-batch conversion. To solve this problem, we propose a novel bounded online kernel learning method, Sparse Passive Aggressive learning (SPA), which is able to yield a final output kernel-based hypothesis with a bounded number of support vectors. The idea is to attain the bounded number of SV’s using an efficient stochastic sampling strategy which samples an incoming training example as a new SV with a probability proportional to its loss suffered. Since the SV’s are added wisely and no SV’s are removed during the learning, the proposed SPA algorithm achieves a bounded final averaged classifier. We theoretically prove that the proposed SPA algorithm achieves an optimal regret bound in expectation, and empirically show that the new algorithm outperforms various bounded kernel-based online learning algorithms. Secondly, existing budget learning algorithms are either too simple to achieve satisfactory approximation accuracy, or too computationally intensive to scale for large datasets. To overcome this limitation and make online kernel learning efficient and scalable, we explore two functional approximation based online kernel machine learning algorithms, Fourier Online Gradient Descent (FOGD) and Nystr¨om Online Gradient Descent (NOGD). The main idea is to adopt the two methods to approximate the kernel model with a linear classifier, so that the efficiency is highly improved. The encouraging results of our experiments on large-scale datasets validate the effectiveness and efficiency of the proposed algorithms, making them potentially more practical than the family of existing budget online kernel learning approaches Thirdly, we also extend the proposed algorithms to solve the online multiple kernel learning (OMKL) problem, in which the goal is to significantly reduce the learning complexity of Online Multiple Kernel Classification (OMKC) while achieving satisfactory accuracy as compared with existing unbounded OMKC algorithms. We theoretically prove that the proposed algorithms achieve an optimal regret bound, and empirically show that the new algorithms outperform various bounded kernel-based online learning algorithms. In conclusion, this work presents several novel solutions for scaling up online kernel learning algorithms for large scale applications. To facilitate other researchers, we also provide an open source software tool-box that includes the implementation of all proposed algorithms in this paper, as well as many state-of-the-art online kernel learning algorithms.

Streaming music and social networks offer an easy way for people to gain access to a massive amount of music, but there are also challenges for the music industry to design for promotion strategies via the new channels. My dissertation employs a fusion of machine-based methods and explanatory empiricism to explore music popularity, diffusion, and promotion in the social network context.

In the first quarter of 2018, 75.66% of smartphones sales were devices running An- droid. Due to its popularity, cyber-criminals have increasingly targeted this ecosys- tem. Malware running on Android severely violates end users security and privacy, allowing many attacks such as defeating two factor authentication of mobile bank- ing applications, capturing real-time voice calls and leaking sensitive information. In this dissertation, I describe the pieces of work that I have done to effectively de- tect malware on Android platform, i.e., ICC-based malware detection system (IC- CDetector), multi-layer malware detection system (DeepRefiner), and self-evolving and scalable malware detection system (DroidEvolver) for Android platform.

Policy analytics are essential in supporting more informed policy-making in environmental management. This dissertation employs a fusion of machine methods and explanatory empiricism that involves data analytics, math programming, optimization, econometrics, geospatial and spatiotemporal analysis, and other approaches for assessing and evaluating current and future environmental policies.

Essay 1 discusses household informedness and its impact on the collection and recycling of household hazardous waste (HHW). Household informedness is the degree to which households have the necessary information to make utility-maximizing decisions about the handling of their waste. Such informedness seems to be influenced by HHW public education and environmental quality information. This essay assesses the effects of household informedness on HHW collection and recycling using public agency data, community surveys, drinking water compliance re-ports, and census data for California from 2004 to 2012. The results enable the calculation of the elasticity of the output quantities of HHW collected and recycled for differences in household informedness at the county level.

Essay 2 considers the pro-environmental spatial spillovers, based on agency actions and waste collection behavior that is occurring in other counties, that represent the influence of HHW-related practices in close-by regions. Using county-level spatio-temporal datasets that consist of economic, demographic, and HHW data in California from 2004 to 2015, I evaluate the impact of grants on the HHW collection activities using a research design that emphasizes spatial variations and controls for confounding factors. A random effects panel data model with instrumental variables is then developed to measure the effects of HHW grant on HHW collection activities while considering the spatial effects from the influence of the waste collection activities among close-by counties or regions.

Essay 3 assesses transition pathways in electricity generation and their future water impacts using an electricity generation capacity expansion model. Scenarios that do or do not comply with the U.S. Environmental Protection Agency's proposed carbon pollution standards – the New Source Performance Standards and Clean Power Plan – are considered. Using the Electric Reliability Council of Texas region as an illustration, the scenarios with the carbon regulations are shown to have lower water use from the power sector than the continuation of the status quo with more electricity generation from coal than natural gas. This is due to an increase in electricity generation from renewable sources and natural gas combined cycle plants that is influenced by the CO2 allowance price. Water withdrawal limits affect electricity generation, decreasing it from power plants with once-through cooling, but this will increase water consumption.

These essays demonstrate the use of a variety of data analytics and management science methods that represent advances in policy analytics to overcome the research challenges, such as the data limitations, the uncertainties associated with the analysis of energy futures, and best practices establishing causal estimates in empirical research designs. This dissertation contributes to the growing body of research on policy analytics for environmental sustainability and improves our understanding of how to craft policies that enhance sustainability for the future.

This dissertation examines the impact of three distinct marketing actions from three different perspectives, i.e., customer, investor, and operational. Specifically, the first essay examines investors’ evaluation of firms’ price-increase preannouncements, thereby responding to recent calls for exploring investors’ evaluation of a firm’s pricing actions which have been predominantly examined from consumers’ perspective. The second essay adopts an operations lens to present the first empirical examination about the impact of customer satisfaction on the future costs of selling and producing for a firm. The essay, therefore, is of direct importance to CEOs as they consider costs as their top priority. In addition, it is responsive to recent calls for more research on the cost implications of marketing actions. Finally, the third essay integrates the customer, investor and operational perspectives, to explore the consequences of mergers and acquisitions using a stakeholder-specific approach. Specifically, using a longitudinal dataset, this essay examines how mergers and acquisitions in the airline industry have an impact on key stakeholders – consumers, employees, senior managers, and investors. Taken together, this dissertation seeks to contribute to existing literature by exploring, for different stakeholders, the outcomes of marketing actions that have high managerial relevance, but have received little attention in current literature.

This dissertation studies the impact of credit rating on firms’ financing behavior and investigates insider trading activities.

The first essay documents how firms’ concerns about credit rating change affect their choice between the use of debt and lease. Firms approaching a credit rating change tend to use less debt relative to operating leases to finance their new projects. In this paper, I propose a new method of measuring the potential of a credit rating change. Using the new measures, I find that not only the concerns about being downgraded but also the at- tempts to get upgraded have significant impacts. The result is surprising because rating agencies are fully aware of firms’ use of off-balance-sheet finance and make correspond- ing adjustments when they assess firms’ creditworthiness. There are two possible reasons for the result. First, the operating lease obligations are usually underestimated. Second, auditors tolerate more misstatement for disclosed off-balance-sheet items than they do for recognized balance sheet items.

This dissertation has three essays in corporate finance. In the first chapter, We investigate whether a CEO’s experience with mergers matter when her firm becomes a takeover target? We find that shareholders receive higher premiums when their CEO has experience. The evidence suggests this is due to learning rather than innate skills or selection. Consistent with superior negotiation of salient features of takeover offers, experienced target CEOs obtain either safer cash payments or higher premiums as the fraction of cash in the offer decreases. These benefits do not come at the cost of other contractual concessions or inefficiencies in takeover negotiations. Overall, the results suggest that M&A experience is valuable when the CEO’s firm becomes a takeover target.

This thesis consists of three chapters. In Chapter 1, I show that returns to currency carry and momentum strategies are compensations for the risk of US monetary policy uncertainty (MPU), with risk exposures explaining 96% of their cross-sectional return variations. The findings are consistent with an intermediary-based exchange rate model. Higher MPU triggers position unwinding by the intermediary, which decreases there turns of currency with high-interest rate or appreciation, while that with low-interest rate or depreciation earns positive returns. Different responses stem from the long and short behavior of the intermediary. The explanatory power of US MPU risk is robust and unrelated to commonly used risk factors.

Chapter 1: Structural Changes in Functional Curves: Estimation and Testing

Abstract: This paper considers the estimation and testing of structural changes in functional curves that occurs at an unknown date. The functional principal component analysis is applied to the random functional curves, decomposing them into interpretable simple latent functions and random scalars. We model the random scalars using a simple autoregressive model and test for a change in parameters that occur at an unknown date. This method is applied to the crude oil futures market to estimate and date possible structural breaks during OPEC announcement periods from 1984 to 2017.

Synergistic interactions between task/resource allocation and multi-agent coordinated planning/assignment exist in many problem domains such as trans- portation and logistics, disaster rescue, security patrolling, sensor networks, power distribution networks, etc. These domains often feature dynamic environments where allocations of tasks/resources may have complex dependencies and agents may leave the team due to unforeseen conditions (e.g., emergency, accident or violation, damage to agent, reconfiguration of environment).

This dissertation consists of three chapters on human capital development in Bangladesh. The first chapter provides microeconometric evidence that access to electricity has a positive impact on the nutritional status of children under five in rural Bangladesh.

Vulnerability becomes a major threat to the security of many systems, including computer systems (e.g., Windows and Linux) and mobile systems (e.g., Android and iOS). Attackers can steal private information and perform harmful actions by exploiting unpatched vulnerabilities. Vulnerabilities often remain undetected for a long time as they may not affect the typical functionalities of systems. Thus, it is important to detect and repair a vulnerability in time. However, it is often difficult for a developer to detect and repair a vulnerability correctly and timely if he/she is not a security expert. Fortunately, automatic repair approaches significantly assist developers to deal with different types of vulnerabilities. There are lots of work to detect different vulnerabilities, and only few vulnerability repair approaches are proposed to repair certain types of vulnerabilities.

A number of virtualization based systems have been proposed in the literature as an effective measure against the adversaries with the kernel privilege. However, under a systematic analysis, such systems exhibit vulnerabilities that can still be exploited by such an attacker with the kernel privilege. The fundamental reason is that there is an inherent incompatibility between the tamper-proof requirement and the complete mediation requirement of the reference monitor model. The incompatibility manifests in the virtualization based systems in the form of a discrepancy between the enforcement capability demanded by the high-level policy and the one achievable through the system design approach mandated by the low-level hardware enforcement mechanism.

Due to the increasing population and lack of coordination, there is a mismatch in supply and demand of common resources (e.g., shared bikes, ambulances, taxis) in urban environments, which has deteriorated a wide variety of quality of life metrics such as success rate in issuing shared bikes, response times for emergency needs, waiting times in queues etc. Thus, in my thesis, I propose efficient algorithms that optimise the quality of life metrics by proactively redistributing the resources using intelligent operational (day-to-day) and strategic (long-term) decisions in the context of urban transportation and health & safety. For urban transportation, Bike Sharing System (BSS) is adopted as the motivating domain. Operational decisions are crucial for BSS, because the stations of BSS are often not balanced due to uncoordinated movements of resources (i.e., bikes) by customers. The imbalanced stations lead to significant loss in demand and increase the usage of private transportation and therefore, defeat the primary objective of BSS which is to reduce carbon footprint. In order to reduce the carbon footprint, I contribute three operational decision making approaches for sequential redistribution of bikes: (i) Optimising lost demand through dynamic redistribution; (ii) Optimising lost demand through robust redistribution; and (iii) Optimising lost demand through incentives. In the first approach, I consider the expected demand for multiple time steps to find a redistribution solution and provide novel decomposition and abstraction mechanisms to speed up the solution process. This approach is useful for BSS with consistent demand patterns. Therefore, the second approach proposes a robust redistribution solution using the notion of two-player adversarial game to address the scenarios where the demand has high variance. For the third approach, within the central budget constraints of the operators, a mechanism is designed to incentivise the customers for executing the bike redistribution tasks by themselves. The experimental results on two real-world data sets of Capital Bikeshare (Washington, DC) and Hubway (Boston, MA) BSS demonstrate that our approaches significantly reduce the average and worse case lost demand over the current practices. For health & safety, Emergency Medical System (EMS) is adopted as the motivating domain. EMS is an extremely sensitive and critical domain for public healthcare services, because reducing the response times for emergency incidents by a few seconds can save a human life. In order to reduce the response times, I propose strategic decision making approach for EMS so as to place base stations at “right” location and allocate “right” number of ambulances on those bases. An accelerated version of greedy algorithm on top of an existing data-driven optimisation formulation is proposed to jointly consider the placement of bases and allocation of ambulances. Subsequently, I provide insights to improve the operational decisions of EMS for dynamic redistribution of ambulances by incorporating the exact real-world dynamics of EMS into the existing data-driven optimisation formulation. Experimental results on real-world data sets demonstrate that both our strategic and operational decisions improve the efficacy of EMS over the existing approaches.

This dissertation addresses the modeling of latent characteristics of locations to describe the mobility of users of location-based social networking platforms. With many users signing up location-based social networking platforms to share their daily activities, these platforms become a gold mine for researchers to study human visitation behavior and location characteristics. Modeling such visitation behavior and location characteristics can benefit many use- ful applications such as urban planning and location-aware recommender sys- tems. In this dissertation, we focus on modeling two latent characteristics of locations, namely area attraction and neighborhood competition effects using location-based social network data. Our literature survey reveals that previous researchers did not pay enough attention to area attraction and neighborhood competition effects. Area attraction refers to the ability of an area with mul- tiple venues to collectively attract check-ins from users, while neighborhood competition represents the need for a venue to compete with its neighbors in the same area for getting check-ins from users.

Along with the regular review content, there is a new type of user-generated content arising from the prevalence of mobile devices and social media, that is micro-review. Micro-reviews are bite-size reviews (usually under 200 char- acters), commonly posted on social media or check-in services, using a mobile device. They capture the immediate reaction of users, and they are rich in information, concise, and to the point. Both reviews and micro-reviews are useful for users to get to know the entity of interest, thus facilitating users in making their decision of purchasing or dining. However, the abundant number of both reviews and micro-reviews makes it increasingly difficult to go through them and extract the useful information. In this dissertation, we propose to summarize reviews and micro-reviews to ease users in understanding entity (or a set of entities).

This thesis proposes a general solution framework that integrates methods in machine learning in creative ways to solve a diverse set of problems arising in urban environments. It particularly focuses on modeling spatiotemporal data for the purpose of predicting urban phenomena. Concretely, the framework is applied to solve three specific real-world problems: human mobility prediction, trac speed prediction and incident prediction. For human mobility prediction, I use visitor trajectories collected a large theme park in Singapore as a simplified microcosm of an urban area. A trajectory is an ordered sequence of attraction visits and corresponding timestamps produced by a visitor. This problem has two related subproblems: (spatial) bundle prediction and trajectory prediction. In the first problem, I apply the framework to predict a bundle (i.e., an unordered set) of attractions that a given visitor would visit given a time budget. In the second problem, the framework is applied to predict the visitor's actual trajectory given the current partial trajectory and time budget. In both problems, I apply the methods of trajectory clustering, hidden Markov model, revealed preference learning and (inverse) reinforcement learning in the integrated framework. In trac speed prediction, I wish to predict the spatiotemporal distribution of trac speed over urban road networks. To this end, I propose local Gaussian processes which combine non-negative matrix (NMF) factorization with Gaussian process (GP) in order to enhance the efficiency of model training such that the solution could be deployed in real-time use cases. NMF is essentially a spatiotemporal clustering technique. The solution is extensively evaluated using real-world trac data collected in two U.S. cities. The incident prediction problem is about predicting the distribution of the number of crime incidents over urban areas in future time periods. Because of its similarity to the trac prediction problem above, its solution greatly benefits from the GP model developed earlier. Particularly, the GP kernel function is inherited and extended to model the distribution of incidents in urban areas and their features. The proposed solution is evaluated using real-world incident data collected in a large Asian city. Conceptually, this thesis uses machine learning techniques to solve three separate urban problems, whose contribution belongs to the large category of urban computing. At the core, its technical contribution lies in the unification of separate solutions tailored to those problems into an integrated framework that reasons with spatiotemporal data and, thus, is highly generalizable to other problems of similar nature.

This dissertation comprises three papers that study the welfare impact of GATT/WTO, the effects of preference bias on trade flows and welfare, and the optimal trade policy with strategic interactions under a Ricardian model. The first chapter provides a comprehensive evaluation of the welfare impact of GATT/WTO in its entire history of 1950-2015 for 180 countries. The analysis embeds non-parametric matching methods in structural quantitative simulations. The results indicate substantial (but highly heterogeneous) welfare gains created by GATT/WTO at the global level and across more than six decades of its history. An extensive set of robustness checks with respect to model specifications, parameter values, and matching estimations are provided. We also characterize the effects of GATT/WTO on global income disparity, its interaction with preferential trade agreements, and the effects of China‘s WTO entry.

The second chapter estimates the effects of bilateral and time-varying preference bias on trade flows and welfare. We use a unique dataset from the BBC World Opinion Poll that surveys (annually during 2005-2017 with some gaps) the populations from a wide array of countries on their views of whether an evaluated country is having a mainly positive or negative influence in the world. We identify the effects on bilateral preference parameters due to shifts in these country image perceptions, and quantify their general equilibrium effects on bilateral exports and welfare (each time for an evaluated exporting country, holding the exporting country's own preference parameters constant). We consider five important shifts in country image: the George W. Bush effect, the Donald Trump effect, the Senkaku Islands Dispute effect, the Brexit effect, and the Good-Boy Canadian effect. We find that such changes in bilateral country image perceptions have quantitatively important trade and welfare effects. The negative impact of Donald Trump’s “America First" campaign rhetorics on the US' country image might have cost the US as much as 3% of its total exports and gains from trade. In contrast, the consistent improvement of Canadian country image between 2010 and 2017 has amounted to more than 8% of its total welfare gains from trade.

The third chapter incorporates strategic interactions into a canonical Ricardian model where two countries choose their optimal trade taxes. We show that in a Nash equilibrium, a country's optimal import tariffs are zero, whereas the optimal export taxes are weakly increasing with respect to its comparative advantage. Compared with Costinot, Donaldson, Vogel, and Werning (2015) where Foreign is passive, the structure of optimal trade policy with strategic interactions remains the same, but the welfare gain from trade policy for each country is lower. When Foreign is active, the welfare gain for Home from its trade policy is smaller because Foreign also exerts its market power on its exported goods over Home's consumers.

This dissertation addresses context recovery in Location-Based Social Networks (LBSN), which are platforms where users post content from various locations. With this general LBSN definition, many existing social media platforms that support user-generated location relevant content using mobile devices could also qualify as LBSNs. Context recovery for such user posts refers to recovering the venue and the semantic contexts of these user posts. Such information is useful for user profiling and to support various applications such as venue recommendation and location- based advertising.

This dissertation develops several econometric techniques to address the unobserved heterogeneity in nonstationary panels, namely identifying latent group structures in cointegrated panels, studying nonstationary panels with both cross-sectional dependence and latent group structures, and estimating panel error-correction model with unobserved dynamic common factors.

Chapter 1 considers a panel cointegration model with latent group structures that allows for heterogeneous long-run relations across groups. We extend Su et al. (2013) classifier-Lasso (C-Lasso) method to the nonstationary panels and allow for the presence of endogeneity in both the stationary and nonstationary regressors in the model. In addition, we allow the dimension of the stationary regressors to diverge with the sample size. We show that we can identify the individuals’ group membership and estimate the group-specific long-run cointegrated relationships simultaneously. We demonstrate the desirable property of uniform classification consistency and the oracle properties of both the C-Lasso estimators and their post-Lasso versions. The special case of dynamic penalized least squares is also studied. Simulations show superb finite sample performance in both classification and estimation. In an empirical application, we study the potential heterogeneous behavior in testing the validity of long-run PPP hypothesis in the post-Bretton Woods period from 1975-2014 covering 99 countries. We identify two groups in the period 1975-1998 and three ones in the period 1999-2014. The results confirm that at least some countries favor the long-run PPP hypothesis in the post-Bretton Woods period.

Chapter 2 proposes a novel approach, based on Lasso, to handle unobserved parameter heterogeneity and cross-sectional dependence in nonstationary panel models. We propose a penalized principal component method to jointly estimate group-specific long-run relationships, unobserved common factors and to identify unknown group membership. Our Lasso-type estimators are consistent and efficient. We provide a bias-correction procedure under which our estimators are centered around zero as both dimensions of the panel tend to infinity. We establish a mixed normal asymptotic distribution for our estimators, which permit inference using standard test statistics. Finally, we apply our approach to study the international R&D spillovers model with unobserved group patterns. The results shed new light on growth convergence puzzle though the channel of technology diffusions.

Chapter 3 proposes a novel econometric model that accounts for both long-run and short-run co-movements in panel error correction models. By imposing latent group structures, we achieve efficient estimation for long-run cointegration vectors in the presence of unobserved heterogeneity. The short-run co-movements are driven by unobserved dynamic common factors, which can be consistently estimated by principal components. We propose a penalized generalized least squares method that jointly estimates long-run cointegration vectors and infers unobserved group structures. We establish asymptotic properties for two Lasso-type estimators. In an empirical application, we estimate longrun cointegration relationships between bid and ask quotes in stock market. We introduce a new measure for efficient price, which is weighted average of bid and ask quotes.

In panel structure models, individuals can be classified into different groups with the slope parameters being homogeneous within the same group but heterogeneous across groups, both the number of groups and each individual’s group membership are unknown. This dissertation proposes some methods to identify the panel structure models under different specifications, namely, developing a Lasso-type Panel-CARDS method in the linear panel, constructing two sequential binary segmentation algorithms in the nonlinear panel, and using K-means algorithm in the spatial panel.

Chapter 2 studies the estimation of a linear panel data model with latent structures. To identify the unknown group structure of vector parameters, we design an algorithm called Panel-CARDS which is a systematic extension of the CARDS procedure proposed by Ke, Fan, and Wu (2015) in a cross-sectional framework. We show that it can identify the true group structure asymptotically and estimate the model parameters consistently at the same time. Simulations evaluate performance and corroborate the asymptotic theory in several practical design settings. The empirical economic application considers the effect of income on democracy by using cross-country data over the period 1961-2000. It reveals the presence of latent groupings in this panel data.

Chapter 3 proposes a procedure to identify latent group structures in nonlinear panel data models. To identify the group structures, we consider the order statistics for the preliminary unconstrained consistent estimators of the regression coefficients and translate the problem of classification into the problem of break detection. Then we extend the sequential binary segmentation algorithm of Bai (1997) for break detection from the time series setup to the panel data framework. We demonstrate that our method can identify the true latent group structures with probability approaching one and the post-classification estimators are oracle-efficient. The method has the advantage of more convenient implementation compared with some alternative methods, which is a desirable feature in nonlinear panel applications. To improve the finite sample performance, we also consider an alternative version based on the spectral decomposition of certain estimated matrix and link the group identification issue to the community detection problem in the network literature. Simulations show that our method has good finite sample performance. We apply this method to explore how individuals’ portfolio choices respond to their financial status and other characteristics using the Netherlands household panel data from year 1993 to 2015, and find three latent groups.

Chapter 4 considers the identification of latent group structures in dynamic spatial models with interactive fixed effects. The model treats three kinds of heterogeneity at the same time, namely, the spatial heterogeneity, individuals’ heterogeneous responses to the same time factors, and heterogeneous slope coefficients. To identify the latent group structures, we adopt quasi-maximum likelihood estimation to get preliminary unconstrained estimators of the slope coefficients. Then we use the K-means algorithm on slope coefficients’ consistent preliminary estimators to get the clusters. The asymptotic analysis shows that this method can identify the true group structure consistently. Therefore, the post-classification estimators have the oracle property. Monte Carlo simulations demonstrate that it has good finite sample performance.

My dissertation consists of three essays which contribute new theoretical results to Bayesian econometrics.

Chapter 2 proposes a new Bayesian test statistic to test a point null hypothesis based on a quadratic loss. The proposed test statistic may be regarded as the Bayesian version of the Lagrange multiplier test. Its asymptotic distribution is obtained based on a set of regular conditions and follows a chi-squared distribution when the null hypothesis is correct. The new statistic has several important advantages that make it appealing in practical applications. First, it is well-defined under improper prior distributions. Second, it avoids Jeffrey-Lindley’s paradox. Third, it always takes a non-negative value and is relatively easy to compute, even for models with latent variables. Fourth, its numerical standard error is relatively easy to obtain. Finally, it is asymptotically pivotal and its threshold values can be obtained from the chi-squared distribution.

Chapter 3 proposes a new Wald-type statistic for hypothesis testing based on Bayesian posterior distributions. The new statistic can be explained as a posterior version of Wald test and have several nice properties. First, it is well-defined under improper prior distributions. Second, it avoids Jeffreys-Lindley’s paradox. Third, under the null hypothesis and repeated sampling, it follows a c2 distribution asymptotically, offering an asymptotically pivotal test. Fourth, it only requires inverting the posterior covariance for the parameters of interest. Fifth and perhaps most importantly, when a random sample from the posterior distribution (such as an MCMC output) is available, the proposed statistic can be easily obtained as a by-product of posterior simulation. In addition, the numerical standard error of the estimated proposed statistic can be computed based on the random sample. The finite-sample performance of the statistic is examined in Monte Carlo studies.

Chapter 4 proposes a quasi-Bayesian approach for structural parameters in finitehorizon life-cycle models. This approach circumvents the numerical evaluation of the gradient of the objective function and alleviates the local optimum problem. The asymptotic normality of the estimators with and without approximation errors is derived. The proposed estimators reach the efficiency bound in the general methods of moment (GMM) framework. Both the estimators and the corresponding asymptotic covariance are readily computable. The estimation procedure is easy to parallel so that the graphic processing unit (GPU) can be used to enhance the computational speed. The estimation procedure is illustrated using a variant of the model in Gourinchas and Parker (2002).

This dissertation consists of three chapters related to international trade and industrial policies.

The first chapter establishes that international trade and the market size affect institutional quality positively. Institutions, such as contract enforcements and rule of law, are arguably one of the most important determinants of economic development. I adopt an incomplete-contract approach to model institutions. Due to contract incompleteness, a firm can hold up its suppliers and distort production. When the effective market size facing firms is larger, due to trade liberalization, or increases in population or numbers of trading partners, benevolent governments have incentive to improve institutional quality to facilitate production to meet the larger demand. Interestingly, in my multiple-country framework, the competition in institutional quality also matters in a Nash-equilibrium sense. Institutional quality increases in trade-liberalized countries whereas those in the non-liberalized ones may decrease. This chapter also empirically shows the positive impact of real effective market size on institutional quality, supporting the model.

The second chapter finds that foreign direct investment (FDI) affects China’s industrial agglomeration negatively by utilizing the differential effects of FDI deregulation in 2002 in China on different industries. This result is somewhat counter-intuitive, as the conventional wisdom tends to think that FDI attracts domestic firms to cluster around them for various agglomeration benefits, technological spillovers in particular. To reconcile our empirical findings and the conventional wisdom, we develop a theory of FDI and agglomeration based on two counter-veiling force. Technology diffusion from FDI attracts domestic firms to be around them, but fiercer competition drives firms away. Our theory indicates that which force dominates depends on the scale of the economy. When the scale of the economy is sufficiently large, FDI discourages agglomeration. We find various evidence on this competition mechanism.

The third chapter studies the Chinese industrial subsidy policy from 1998 to 2007. Our industry equilibrium model establishes that the optimal policy should be positively correlated with various input distortions confronting firms. Based on this prediction, we evaluate the effectiveness of subsidy policy in China and document four stylized facts: (1) The efficiency of subsidy policy in China has grown by around 50% over the ten years, with a notable increase at the ascendance of Hu Jingtao into presidency; (2) Subsidy policy tends to have differential efficiency effect on the sector level, with more downstream sectors experiencing higher efficiency; (3) Provinces in the ‘Western Development Program’ received more subsidies compared to their eastern counterparts; (4) Labour and materials distortions have been properly corrected in the western regions, and materials distortion can explain most of the variation of subsidies in China. Finally we quantify the effect of the policy on welfare.

This dissertation consists of three chapters on the economics of social insurance. Each chapter explores an aspect of the evaluation and design of social insurance in terms of nutrition, healthcare and unemployment.

The first chapter, Kim, Fleisher and Sun (2016) report evidence of long-term adverse health impacts of fetal malnutrition exposure of middle-aged survivors of the 1959-1961 China Famine using data from the China Health and Retirement Longitudinal Study. We find that fetal exposure to malnutrition has large and long lasting impacts on both physical health and cognitive abilities, including the risks of suffering a stroke, physical disabilities in speech, walking and vision, and measures of mental acuity even half a century after a tragic event. Our findings imply that policies and programs that improve the nutritional status of pregnant women yield benefits on the health of a fetus that extend through the life cycle in the form of reduced physical and mental impairment.

In Chapter 2, I evaluate the welfare benefits of the New Cooperative Medical Scheme (NCMS), the main public health insurance plan for the rural population in China. I find that the value of the NCMS to recipients is slightly higher than the government’s costs of implementation. Household benefits from the insurance through its value in transfer and insurance function. The estimated moral hazard costs are small compared to the total benefits. The findings suggest that behavioral changes due to health insurance (i.e. increase of medical service utilization) are in large welfare improving among low-income households.

In Chapter 3, I examine the effect of a two-tiered unemployment insurance system, combining both the UISA and the current unemployment insurance. Unemployment insurance savings account (UISA) is a mandatory individual savings accounts that can be used only during unemployment or retirement. Different from unemployment insurance, UISA does not lead to moral hazard problem but also provide no public insurance to workers. Workers are mandated to save when employed and can withdraw from the account when unemployed. Once the account is exhausted, the unemployed worker receives the usual unemployment benefits. The two-tiered unemployment insurance works more efficiently than an unemployment insurance system since it provides government benefits only to individuals who are not capable of consumption smoothing themselves. Fitting the model to the US economy, I find that, relative to the existing unemployment insurance system, the proposed two-tiered unemployment insurance leads to a welfare gain of 1% and reduce unemployment duration for younger workers.

This dissertation comprises four papers that evaluate the effectiveness of public policies in Singapore. The first paper studies the non-tangible effect of a new non-contributory pension in Singapore on subjective well-being. The second investigates the effect of the same pension on labour supply, work expectations, private cash transfers and expenditure. The third evaluates a pragmatic randomised controlled trial of a new transitional care programme in a large quaternary hospital, while the fourth examines the effect of a large-scale housing upgrading programme on the resale prices of upgraded flats.

Chapter 2 looks at the non-tangible effect of a new non-contributory pension (the Silver Support Scheme, or SSS) on subjective well-being. This chapter separately estimates the announcement and disbursement effects, and finds that SSS recipients experienced greater life satisfaction at announcement, and the difference between announcement and disbursement effects is not statistically different from zero. The improvement in life satisfaction appears to be driven by social, household income, and economic satisfaction. These effects are heterogeneous, with less financially prepared individuals exhibiting larger increases in well-being.

Chapter 3 examines the effect of SSS on labour supply, work expectations, private cash transfers and expenditure, one year after its implementation. There is no evidence that receiving SSS payouts led to a fall in labour supply, work expectations or the receipt of private cash transfers – these outcome estimates are statistically insignificant and are either close to zero or positive. The expenditure estimates are positive, but unfortunately too imprecise for definitive conclusions. These results support Chapter 2’s findings that SSS improves the welfare of recipients as it has not led to substantial crowding out of private transfers or changes in labour market behaviour.

Chapter 4 evaluates a pragmatic randomised controlled trial of a new transitional care programme (TCP), CareHub, in a large quaternary hospital. CareHub merges an assortment of existing transitional care services to protocolise post-discharge patient encounters, with the goal of improving patient outcomes and containing costs. In the six months after the index hospital admission, CareHub reduced the number of unplanned cardiac-related readmissions and total unplanned cardiac-related days in hospital by 39% and 56% respectively, which translate into a decrease of 0.23 readmissions and 2.2 days of hospitalisation. Net costs were reduced by about SGD1,300 per CareHub patient. It also reduced patient anxiety/depression and improved the quality of care transition.

Chapter 5 estimates heterogeneous hedonic prices for different levels of housing space, by exploiting a unique space-adding project in Singapore that added a uniform amount of 6m2 of space to each existing housing unit regardless of the original size of the unit. This space adding was part of a large scale urban renewal and housing upgrading policy. The resale price of a housing unit increased by 7% on average, and the absolute extent of price appreciation varied significantly across the original size of the units. The total house price appreciation can be attributed to the combined effect of changes in housing space and average price per unit housing space.

Crowdfunding is a method of raising funds to support a venture, typically by raising small amounts from a large number of investors (backers or patrons). This whole process is conducted on an online platform that facilitates interactions between project creators and potential contributors. We explore in the dissertation, the determinants of the success of crowdfunding projects. The first essay, using data from Kickstarter (the leading crowdfunding platform), explores how backers to a project are interconnected with other backers through their backing of common projects thus forming an implicit backer network. We find that backers that are in central positions within the network have an impact on other backers and, through them, affect the outcomes of projects by increasing the likelihood of project success, increasing funding and decreasing the time taken to reach the funding goal. The second essay explores the unique phenomenon of patronage. Unlike the one-time contribution that backers make in Kickstarter, patrons fund the creator and their projects in a recurring manner. We use data from a leading patronage crowdfunding platform to explore what project characteristics lead to changes in patterns of patron growth and recurring contributions in crowdfunding. We find that several project characteristics not only have an impact on the change in patron and contribution functions but also in the velocity and acceleration of these functions. Both essays uncover determinants that have not been considered thus far in their respective crowdfunding context and provide recommendations for project creators and platforms to maximize the funding generated within each specific context.

This dissertation consists of four papers in applied microeconomics / the economics of health and ageing that analyse the causal effect of public policies, or of events / issues amenable to policy intervention. Chapter 1 provides an overview of the papers in this dissertation.

Chapter 2 investigates whether a major and growing environmental disamenity – dengue fever – leads to protective behavior that increases residential electricity consumption. Being near a dengue cluster leads to a persistent increase in electricity consumption in 4-room and 5-room/bigger flats (by 1.7% and 1.1% respectively). In addition, electricity consumption rises discontinuously when a dengue cluster’s risk classification is upgraded from yellow to red. This increased electricity consumption cost $11.9 to 16.3 million per annum (in 2015 Singapore dollars), or 7% – 12% of the overall costs of dengue in Singapore.

Chapter 3 studies the effect of in-utero exposure to mild nutritional shocks during Ramadan on an individual’s later-life outcomes. In-utero exposure to Ramadan leads to poorer subjective well-being across a broad range of domains (overall life, social and family life, daily activities, economic, and health satisfaction), self-rated health condition, and poorer mental well-being. In addition, exposed individuals report higher rates of diagnosed cardiovascular conditions and higher body mass index (among women). We find no evidence that these results are driven by selective timing of pregnancies, differing survey participation rates, or seasonal effects.

Chapter 4 examines the effect of an exogenous permanent income shock on subjective well-being. This permanent income shock is the introduction of Singapore’s first national non-contributory pension, the Silver Support Scheme. The pension improved the life satisfaction of recipients; this effect appears to be driven by social, household income, and economic satisfaction. Consistent with the predictions of the permanent income hypothesis, well-being improved at announcement, but did not improve significantly further at disbursement of the pension. Lastly, we find evidence that the marginal utility of income varies – recipients who reported being less financially prepared for retirement exhibited larger increases in well-being.

Lastly, Chapter 5 reports results from a pragmatic, randomized controlled trial of CareHub, a new transitional care program (TCP) in Singapore’s National University Hospital that aims to contain costs, reduce re-hospitalizations, and improve patient quality of life. CareHub reduced unplanned cardiac-related readmissions by 39% and unplanned cardiac-related days in hospital by 56%. In addition, we found suggestive evidence that CareHub reduced patient anxiety and depression, and improved the quality of transitional care. In all, CareHub achieved net cost savings of about S$1,300 per patient over six months, suggesting that a carefully designed TCP can reduce resource utilization while improving quality of life.

This dissertation studies patterns of dispersion in density forecasts as reported in surveys of professional forecasters. We pay special attention to the role of uncertainty in explaining dispersion in professional forecasters’ density forecasts of real output growth and inflation. We also consider the relationship between survey design and forecaster behavior. The last chapter describes future research exploring the characteristics of forecaster expectations using probability integral transforms.

As a starting point, chapter one gives a summary of the literature that tries to answer, using data from survey of forecasters, the following three key questions: Why do forecasters disagree? What do density forecasts reveal in addition to point forecasts? Does disagreement serve as a good proxy for forecaster uncertainty? This chapter provides an overview of the studies and briefly discusses how this dissertation can make contributions to the literature.

Chapter two explores the role of uncertainty in explaining dispersion in professional forecasters’ density forecasts of real output growth and inflation. We consider three separate notions of uncertainty: general macroeconomic uncertainty (the fact that macroeconomic variables are easier to forecast at some time than others), policy uncertainty, and forecaster uncertainty. The main finding is that dispersion in individual density forecasts is related to overall macroeconomic uncertainty and policy uncertainty, while forecaster uncertainty (which we define as the average in the uncertainty expressed by individual forecasters) appears to have little role in forecast dispersion.

Chapter three examines the relationship between survey design and forecasters’ behavior by exploiting changes to the probability bins provided to forecasters at the solicitation of density forecasts. We consider three important surveys, namely Survey of Professional Forecasters by the Philadelphia Fed (USSPF), Survey of Professional Forecasters by the European Central Bank (ECBSPF), and Survey of External Forecasters by Bank of England (SEF). While the adjustment of forecast bins can reasonably arise from the fluctuation of underlying macroeconomic variable, there are also cases where the modification is neutral to the economic environment. Our analysis examines how disagreement and forecaster uncertainty respond to these two different categories of survey changes. The results suggest that disagreement only responds to changes caused by real economy. Uncertainty responds to both and the effect is more persistent. These empirical facts highlight the importance of behavioral perspective when inferences are drawn from professional forecasts.

I summarize our conclusion in Chapter four, and describe future research plan exploring the features of forecaster uncertainty using probability integral transforms (“z-statistics”), a commonly-used test for density forecast optimality. We focus on the shape of the distribution of z-statistics, which is informative about the confidence level as well as bias (optimism or pessimism) of forecasters. There is evidence of significant hysteresis and that survey scheme greatly affects the performance of density forecasts.

My dissertation aims to better understand managers and financial analysts’ behavior, incentives, and constraints, as well as their impacts on firm decisions and financial markets. In Chapter 1, I show that peer firms play an important role in determining U.S. corporate cash saving decisions. Using an instrument variable identification strategy, I find that one standard deviation change in peer firms average cash savings leads to a 2.63% same-direction change in firm’s own cash savings, which exceeds the marginal effects of many previously identified determinants. The economic implications of such peer effects are large, which can significantly alter cash savings in a representative industry by 7.2%. In cross-sectional tests, I find that peer effects are stronger when the product market is highly competitive and when the economy is in recession. In addition, less powerful, smaller, and financially constrained firms respond more actively to their peers’ cash saving decisions. Finally, I provide evidence that such peer effects are asymmetric — cash-rich firms, who already hold enough cash, are less likely to mimic peers’ cash policies compared to cash-starved firms.

Debugging programs and writing formal specifications are essential but expensive processes to maintain quality and reliability of software systems. Developers often have to debug and create specifications manually, which take a lot of their time and effort. Recently, several automated solutions have been proposed to help developers alleviate the cost of manual labor in the two processes. In particular, fault localization techniques help developer debug by accepting textual information in bug reports or program spectra (i.e., a record of which program elements are executed for each test case).

Their output is a ranked list of program elements that are likely to be faulty. Developers then inspect the ranked list from beginning of the ranked list until root causes of the fault are found. On the other hand, many systems have no or lack of high quality formal specifications. To deal with the issue, researchers have proposed techniques to automatically infer specifications in a variety of formalism, such as nite state automation (FSA). The inferred specifications can be used for many manual software processes, including debugging.


 

In this thesis, we are broadly interested in solving real world problems that involve decision support for coordinating agent movements in dynamic urban environments, where people are agents exhibiting different human behavior patterns and preferences. The rapid development of mobile technologies makes it easier to capture agent behavioral and preference information. Such rich agent specific information, coupled with the explosive growth of computational power, opens many opportunities that we could potentially leverage, to better guide/influence the agents in urban environments.

The purpose of this thesis is to investigate how we can effectively and efficiently guide and coordinate the agents with a personal touch, which entails optimized resource allocation and scheduling at the operational level.

More specifically, we look into the agent coordination from three specific aspects with different application domains: (a) crowd control in leisure environments by providing personalized guidance to individual agents to smooth the congestions due to the crowd; (b) mobile crowdsourcing by distributing location-based tasks to part-time crowd workers on-the-go to promote the platform efficiency; (c) workforce scheduling by better utilizing full-time workforce to provide location-based services at customers' homes.

For each, we propose models and efficient algorithms, considering agent-level preferences and problem-specific requirements. The proposed solution approaches are shown to be effective through various experiments on real-world and synthetic datasets.

In this dissertation, I strived to understand the role of individuals’ affect in the development processes of interpersonal trust within organizations. To achieve the goal, I conducted two studies, one conceptual framework and one empirical investigation. Trust scholars have long recognized the affective component of trust experience. However, previous theoretical arguments and empirical findings are not well integrated to provide a cohesive understanding on various dynamic roles that emotions and moods can play in the trust development. Recognizing that the trustor and trustee may face diverse relational problems at various stages of their trust relationship, I first suggested a Phase Model based on well-recognized trust development models.

In the phase model, a trust development process encompasses pre-encounter, first impression, trust interaction, trust maintenance, and trust disruption/deterioration phases. I also recognized that individuals’ affect may impact trust development through multiple ways based on two perspectives on the roles of affect: The Affect Cognitive perspective and the Social Functional perspective. I delineated various mechanisms that emotions and moods can play in each phase and whether the mechanism is based on the Affect Cognitive perspective or the Social Functional perspective. In addition, I suggested that the trust development can go back from a latter phase to an earlier phase and that various affective mechanisms can phase in, out, and back in again as relationships are initiated, develop, and perhaps are even disrupted and restored.

As a result, the conceptual framework could help guide future research on affect and trust development. After delineating the conceptual paper, I conducted an empirical investigation on how newcomers develop trust in their supervisors. The literature on leader behaviors and employee trust in leader has suggested that interactional justice could promote employee trust through impacting the social exchange processes between employees and their leaders. Integrating the Social Exchange theory and findings from affect literature, I investigated how supervisor interactional justice and newcomer agreeableness impact the development of newcomer trust in supervisor through influencing newcomer anxiety level and anxiety reduction. Findings of an experience sampling study suggested that high supervisor interactional justice could lead to high levels of newcomer trust through low levels of newcomer anxiety.

Newcomer anxiety reduction (i.e., negative change over the encounter period of two weeks) could promote newcomer trust improvement (i.e., positive change over the encounter period), which in turn impacted the final levels of newcomer trust in supervisor at the end of encounter stage. In addition, supervisor interactional justice and newcomer agreeableness interacted to impact newcomer anxiety reduction. For low agreeable individuals, higher supervisor interactional justice led to more newcomer anxiety reduction in the encounter stage. Taken together, the empirical study offers insights into the process of interpersonal trust development starting from the first day at work, and uncovers the role of affective mechanisms underlying initial trust development.

This dissertation addresses three topics in corporate cash holdings. The first paper provides a new determinant of cash holdings by examining the impact of earnings transparency on corporate cash holdings. Motivated by Barth et al. (2013), who show that firms with less earnings transparency tend to have higher cost of equity, this paper shows that the cross-section differences in earnings transparency cause variations in firm cash holdings because firms with less earnings transparency have more incentives to hold cash in order to avoid costly external financing.

Using data of US firms from 1980 to 2013, it is found that earnings transparency is significantly negatively associated with cash reserves. This impact remains significant when corporate governance measures, accounting-based earnings quality, geography diversity and other information asymmetry measures are accounted for. And this impact is more pronounced in firms with more growth opportunities, more R&D expenses and more financial constraints. It is further found that firm with lower earnings transparency have a higher value of cash holdings, suggesting that cash held by firms with lower earnings transparency are expected to be used to invest, which is also a verification that firms with less earnings transparency hold more cash for precautionary motivation.

The second paper studies on the channel of the relation between corporate cash holdings and stock return. Corporate cash holding is found to be able to predict stock return. Some scholars attribute this to the association of cash with systematic risk with respect to growth options. Others find that the relation is a mispricing effect. I try to test whether the relation between cash and return is driven by systematic risk that captured by cash. The empirical results do not support the risk explanation of cash-return relation. First, the risk loading on CASH factor cannot predict returns, which is not consistent with rational frictionless asset pricing models. Second, CASH factor cannot reflect future GDP growth. Third, CASH and its factor loading exhibit no association with implied cost of capital derived from analysts’ earnings forecasts. Additionally, I find institutional investors tend to buy in more stocks of firms with more cash, and the cash-return relation is less pronounced in firms with more institutional investors, providing evidence supporting the mispricing explanation. Overall, this study casts doubt on the argument that cash can serve as a proxy of systematic risk in the explanation of cross sectional variation in stock returns while finds evidence of the mispricing story.

The third paper studies the monitoring role of sovereign wealth funds on corporate cash holding policy and uses Temasek Holdings as the case. We find that Temasek’s presence has a negative effect on cash for companies with poor governance quality while its cash effect becomes positive for well-governed firms. Temasek’s discerning effect on cash policies highlights the effective monitoring role of sovereign funds.

The financial services sector has seen dramatic technological innovations in the last several years associated with the “fintech revolution.” Major changes have taken place in channel management, credit card rewards marketing, cryptocurren-cy, and wealth management, and have influenced consumers’ banking behavior in different ways. As a consequence, there has been a growing demand for banks to rethink their business models and operations to adapt to changing consumer be-havior and counter the competitive pressure from other banks and non-bank play-ers. In this dissertation, I study consumer behavior related to different aspects of financial innovation by addressing research questions that are motivated by theo-ry-focused research literature and managerial considerations in business practice. I seek to understand how technology is reshaping financial services, and how finan-cial institutions can leverage big data analytics to create deep insights about con-sumer behavior for decision support.

The first essay studies credit card-based partnerships between banks and re-tailers. I test a number of hypotheses that assess the indirect effects related to the impacts of credit card programs in my research setting. I use publicly-available data, together with proprietary data from a large financial institution, to examine the impact of card-based promotions on consumer behavior and merchant perfor-mance. The results show that such promotions create positive indirect effects, leading to increased purchases from customers of other banks, in addition to the bank running the promotion. This research creates insights that banks can leverage to optimize their card-based reward programs, and paves the way forward for strengthening credit card merchant partnerships.

The second essay emphasizes the importance of investigating bank branch network changes, including branch openings and closures, and their impacts on customers’ omni-channel banking behavior. I find that branch openings create customer awareness and lead to synergetic increases in transactions across chan-nels, and branch closures result in a migration pattern from alternative channels to online banking due to the joint effects of negative perceptions and substitution. This essay contributes to our knowledge of multi-channel services in the IS and Management literature, and provides strategic implications on branch network re-structuring in omni-channel financial services.

In the third essay, I draw on social contagion theory and a spatiotemporal per-spective to explore the global penetration of bitcoin, and how security events have been influential in this process. This essay uses transaction data from Mt.Gox, one of the largest bitcoin exchange platforms in the world before its bankruptcy in 2014, and other publicly-available data sources for county-level information. The results suggest that the global penetration of bitcoin is jointly influenced by the economy, technology and regulatory situation of a country. And news about the occurrence of security incidents related to the cryptocurrency has a negative im-pact on its cross-country diffusion. This study contributes to the literature on the diffusion of emerging financial technologies, as well as business practices at the early stage of the development of a digital currency.