Mother's Education and Child Health: Is There a Nurturing Effect?

In this paper, we examine the effect of maternal education on the health of young children by using a large sample of adopted children from China. As adopted children are genetically unrelated to the nurturing parents, the educational effect on them is most likely to be the nurturing effect. We find that the mother's education is an important determinant of the health of adopted children even after we control for income, the number of siblings, health environments, and other socioeconomic variables. Moreover, the effect of the mother's education on the adoptee sample is similar to that on the own birth sample, which suggests that the main effect of the mother's education on child health is in post-natal nurturing. Our work provides new evidence to the general literature that examines the determinants of health and that examines the intergenerational immobility of socioeconomic status.

Does Health Insurance Coverage Lead to Better Health and Educational Outcomes? Evidence from Rural China

Using 2006 China Agricultural Census (CAC), we examine whether the introduction of the New Cooperative Medical System (NCMS) has affected child mortality, maternal mortality, and school enrollment of the 6-16 years olds. Our data cover 5.9 million people living in eight low-income rural counties, of which four adopted the NCMS by 2006 and four did not adopt it until 2007. Raw data suggest that enrolling in NCMS is associated with better school enrollment and lower mortality of young children and pregnant women. However, using a difference-in-difference propensity score method, we find most of these differences are driven by the endogenous introduction and take-up of NCMS, and out method overcomes classical propensity score matching's failure to address the selection bias. While the NCMS does not affect child mortality and maternal mortality, it does help improve the school enrollment of six-year-olds.

Observational Learning: Evidence from a Randomized Natural Field Experiment

We present results about the effects of observing others' choices, called observational learning, on individuals' behavior and subjective well-being in the context of restaurant dining from a randomized natural field experiment. Our experimental design aims to distinguish observational learning effect from saliency effect (because observing others' choices also makes these choices more salient). We find that, depending on specifications, the demand for the top 5 dishes was increased by an average of about 13 to 18 percent when these popularity rankings were revealed to the customers; in contrast, being merely mentioned as some sample dishes did not significantly boost their demand. Moreover, we find that, consistent with theoretical predictions, some modest evidence that observational learning effect was stronger among infrequent customers. We also find that customers' subjective dining experiences were improved when presented with the information about the top choices by other consumers, but not when presented with the names of some sample dishes.

Microinsurance, Trust and Economic Development: Evidence from a Randomized Natural Field Experiment

We report results from a large randomized natural field experiment conducted in southwestern China in the context of insurance for sows. Our study sheds light on two important questions about microinsurance. First, how does access to formal insurance affect farmers' production decisions? Second, what explains the low takeup rate of formal insurance, despite substantial premium subsidy from the government? We find that providing access to formal insurance significantly increases farmers' tendency to raise sows. We argue that this finding also suggests that farmers are not previously insured efficiently through informal mechanisms. We also provide several pieces of evidence suggesting that trust, or lack thereof, for government-sponsored insurance products is a significant barrier for farmers' willingness to participate in the insurance program.Microinsurance; Trust, Natural Field Experiment

A Cost-effective Shuffling Method against DDoS Attacks using Moving Target Defense

Moving Target Defense (MTD) has emerged as a newcomer into the asymmetric field of attack and defense, and shuffling-based MTD has been regarded as one of the most effective ways to mitigate DDoS attacks. However, previous work does not acknowledge that frequent shuffles would significantly intensify the overhead. MTD requires a quantitative measure to compare the cost and effectiveness of available adaptations and explore the best trade-off between them. In this paper, therefore, we propose a new cost-effective shuffling method against DDoS attacks using MTD. By exploiting Multi-Objective Markov Decision Processes to model the interaction between the attacker and the defender, and designing a cost-effective shuffling algorithm, we study the best trade-off between the effectiveness and cost of shuffling in a given shuffling scenario. Finally, simulation and experimentation on an experimental software defined network (SDN) indicate that our approach imposes an acceptable shuffling overload and is effective in mitigating DDoS attacks

Robust Risk Aggregation Techniques and Applications

Risk aggregation, which concerns the statistical behaviors of an aggregation position S(X) associated with a random vector X = (X1, . . . , Xn), is an important research topic in risk management, economics, and statistics. The distribution of S(X) is determined by both the marginal behaviors and the joint dependence structure of X. In general, it is challenging to obtain an accurate estimation of the dependence structure of X compared with the estimation of the marginal distributions. Given the marginal distributions of X, this thesis focuses on studying the aggregation position S(X) with different dependence assumptions in different contexts. We will assume that X has a specific dependence structure (e.g., independence), or its dependence structure is (partially) unknown. In particular, for the case that the dependence structure is (partially) unknown, we are interested in the worst-case and the best-case scenarios of S(X). In Chapter 2, we show the surprising inequality that the weighted average of iid ultra heavy-tailed Pareto losses (with infinite mean) is larger than a standalone loss in the sense of first-order stochastic dominance. This result is further generalized to allow for random total number and weights of Pareto losses and for the losses to be triggered by catastrophic events. We discuss several important implications of these results via an equilibrium analysis of a risk exchange market. First, diversification of ultra heavy-tailed Pareto losses leads to increases in portfolio risk, and thus diversification penalty exists. Second, agents with ultra heavy-tailed Pareto losses will not share risks in a market equilibrium. Third, transferring losses from agents bearing Pareto losses to external parties without any losses may arrive at an equilibrium which benefits every party involved. In Chapter 3, we focus on aggregation sets, which represent model uncertainty due to unknown dependence structure of random vectors. We investigate ordering relations between two aggregation sets for which the sets of marginals are related by two simple operations: distribution mixtures and quantile mixtures. Intuitively, these operations “ho- homogenize” marginal distributions by making them similar. As a general conclusion from our results, more “homogeneous” marginals lead to a larger aggregation set, and thus more severe model uncertainty, although the situation for quantile mixtures is much more complicated than that for distribution mixtures. We proceed to study inequalities on the worst-case values of risk measures in risk aggregation, which represent the conservative calculation of regulatory capital. Among other results, we obtain an order relation on VaR under quantile mixture for marginal distributions with monotone densities. Numerical results are presented to visualize the theoretical results. Finally, we provide applications on portfolio diversification under dependence uncertainty and merging p-values in multiple hypothesis testing, and discuss the connection of our results to joint mixability. In Chapter 4, we study the aggregation of two risks when the marginal distributions are known and the dependence structure is unknown, with the additional constraint that one risk is smaller than or equal to the other. Risk aggregation problems with the order constraint are closely related to the recently introduced notion of the directional lower (DL) coupling. The largest aggregate risk in concave order (thus, the smallest aggregate risk in convex order) is attained by the DL coupling. These results are further generalized to calculate the best-case and worst-case values of tail risk measures. In particular, we obtain analytical formulas for bounds on Value-at-Risk. Our numerical results suggest that the new bounds on risk measures with the extra order constraint can greatly improve those with full dependence uncertainty. In Chapter 5, we study various methods for combining p-values from multiple hypothesis testing into one p-value, under different dependence assumptions of p-values. We say that a combining method is valid for arbitrary dependence if it does not require any assumption on the dependence structure of the p-values, whereas it is valid for some dependence if it requires some specific, perhaps realistic, but unjustifiable, dependence structures. The trade-off between the validity and efficiency of these methods is studied by analyzing the choices of critical values under different dependence assumptions. We introduce the notions of independence-comonotonicity balance (IC-balance) and the price for validity. In particular, IC-balanced methods always produce an identical critical value for independent and perfectly positively dependent p-values, a specific type of insensitivity to a family of dependence assumptions. We show that among two very general classes of merging methods commonly used in practice, the Cauchy combination method and the Simes method are the only IC-balanced ones. Simulation studies and a real-data analysis are conducted to analyze the size and power of various combining methods in the presence of weak and strong dependence