Moral Hazard, Adverse Selection and Health Expenditures: A Semiparametric Analysis

Theoretical models predict asymmetric information in health insurance markets may generate inefficient outcomes due to adverse selection and moral hazard. However, previous empirical research has found it difficult to disentangle adverse selection from moral hazard in health care. We empirically study this question by using data from the Health and Retirement Study to estimate a structural model of the demand for health insurance and medical care. Using a two-step semi-parametric estimation strategy we find significant evidence of moral hazard, but not of adverse selection.

Convertible bond announcement effects: why is Japan different?

U.S. and Japanese firms dominate global convertible bond issuance. Previous research documents more favorable convertible bond announcement effects in Japan than in the U.S. and other developed countries. Using a global sample of convertible bonds issued from 1982 to 2012, we find that the more favorable announcement effects of Japanese convertibles are driven by their stated uses of proceeds. Japanese convertibles more often include capital expenditure as an intended use, while U.S. firms tend to mention general purposes to motivate their offering. Our findings illustrate the value to firms of being more explicit when disclosing the intended use of proceeds of security offerings

Regression Discontinuity Designs with an Endogenous Forcing Variable and an Application to Contracting in Health Care

Regression discontinuity designs (RDDs) are a popular method to estimate treatment effects. However, RDDs may fail to yield consistent estimates if the forcing variable can be manipulated by the agent. In this paper, we examine one interesting set of economic models with such a feature. Specifically, we examine the case where there is a structural relationship between the forcing variable and the outcome variable because they are determined simultaneously. We propose a modi…ed RDD estimator for such models and derive the conditions under which it is consistent. As an application of our method, we study contracts between a large managed care organization and leading hospitals for the provision of organ and tissue transplants. Exploiting "donut holes" in the reimbursement contracts we estimate how the total claims filed by the hospitals depend on the generosity of the reimbursement structure. Our results show that hospitals submit significantly larger bills when the reimbursement rate is higher, indicating informational asymmetries between the payer and hospitals in this market.

Estimating Static Models of Strategic Interaction

We propose a method for estimating static games of incomplete information. A static game is a generalization of a discrete choice model, such as a multinomial logit or probit, which allows the actions of a group of agents to be interdependent. Unlike most earlier work, the method we propose is semiparametric and does not require the covariates to lie in a discrete set. While the estimator we propose is quite flexible, we demonstrate that in most cases it can be easily implemented using standard statistical packages such as STATA. We also propose an algorithm for simulating the model which finds all equilibria to the game. As an application of our estimator, we study recommendations for high technology stocks between 1998-2003. We find that strategic motives, typically ignored in the empirical literature, appear to be an important consideration in the recommendations submitted by equity analysts.

Finite element based surface roughness study for ohmic contact of microswitches

Finite element method (FEM) is used to model ohmic contact in microswitches. A determinist approach is adopted, including atomic force microscope (AFM) scanning real contact surfaces and generating rough surfaces with three-dimensional mesh. FE frictionless models are set up with the elastoplastic material and the simulations are performed with a loading-unloading cycle. Two material properties, gold and ruthenium, are studied in the simulations. The effect of roughness is investigated by comparing the models with several smoothing intensities and asperity heights. The comparison is quantitatively analyzed with relations of force vs. displacement, force vs. contact area and force vs. electrical contact resistance (ECR); further the evolution of spots in contact during a loading-unloading cycle is studied

Electron correlation and Fermi surface topology of Nax_xCoO2_2

The electronic structure of Na$_x$CoO$_2$ revealed by recent photoemission experiments shows important deviations from band theory predictions. The six small Fermi surface pockets predicted by LDA calculations have not been observed as the associated $e_g^\prime$ band fails to cross the Fermi level for a wide range of sodium doping concentration $x$. In addition, significant bandwidth renormalizations of the $t_{2g}$ complex have been observed. We show that these discrepancies are due to strong electronic correlations by studying the multi-orbital Hubbard model in the Hartree-Fock and strong-coupling Gutzwiller approximation. The quasiparticle dispersion and the Fermi surface topology obtained in the presence of strong local Coulomb repulsion are in good agreement with experiments.Comment: 5 pages, 4 figures, revtex4; minor changes, to be published in Phys. Rev. Let

Cross-Dimensional Refined Learning for Real-Time 3D Visual Perception from Monocular Video

We present a novel real-time capable learning method that jointly perceives a 3D scene's geometry structure and semantic labels. Recent approaches to real-time 3D scene reconstruction mostly adopt a volumetric scheme, where a Truncated Signed Distance Function (TSDF) is directly regressed. However, these volumetric approaches tend to focus on the global coherence of their reconstructions, which leads to a lack of local geometric detail. To overcome this issue, we propose to leverage the latent geometric prior knowledge in 2D image features by explicit depth prediction and anchored feature generation, to refine the occupancy learning in TSDF volume. Besides, we find that this cross-dimensional feature refinement methodology can also be adopted for the semantic segmentation task by utilizing semantic priors. Hence, we proposed an end-to-end cross-dimensional refinement neural network (CDRNet) to extract both 3D mesh and 3D semantic labeling in real time. The experiment results show that this method achieves a state-of-the-art 3D perception efficiency on multiple datasets, which indicates the great potential of our method for industrial applications.Comment: Accpeted to ICCV 2023 Workshops. Project page: https://hafred.github.io/cdrnet