(WP 2010-11) The Benefits of Environmental Improvement: Estimates From Space-time Analysis

This paper develops estimates of environmental improvement based on a two-stage hedonic price analysis of the single family housing market in the Puget Sound region of Washington State. The analysis — which focuses specifically on several EPA-designated environmental hazards and involves 226,918 transactions for 177,303 unique properties that took place between January 2001 and September 2009 — involves four steps: (i) ten hedonic price functions are estimated year-by-year, one for each year of the 2000s; (ii) the hedonic estimates are used to compute the marginal implicit price of distance from air release, superfund, and toxic release sites; (iii) the marginal implicit prices, which vary through time, are used to estimate a series of implicit demand functions describing the relationship between the price of distance and the quantity consumed; and, finally (iv) the demand estimates are compared to those obtained in other research and then used evaluate the potential scale of benefits associated with some basic environmental improvement scenarios. Overall, the analysis provides further evidence that it is possible to develop a structural model of implicit demand within a single housing market and suggests that the benefits of environmental improvement are substantial

General Semiparametric Shared Frailty Model Estimation and Simulation with frailtySurv

The R package frailtySurv for simulating and fitting semi-parametric shared frailty models is introduced. Package frailtySurv implements semi-parametric consistent estimators for a variety of frailty distributions, including gamma, log-normal, inverse Gaussian and power variance function, and provides consistent estimators of the standard errors of the parameters' estimators. The parameters' estimators are asymptotically normally distributed, and therefore statistical inference based on the results of this package, such as hypothesis testing and confidence intervals, can be performed using the normal distribution. Extensive simulations demonstrate the flexibility and correct implementation of the estimator. Two case studies performed with publicly available datasets demonstrate applicability of the package. In the Diabetic Retinopathy Study, the onset of blindness is clustered by patient, and in a large hard drive failure dataset, failure times are thought to be clustered by the hard drive manufacturer and model

The Benefits of Environmental Improvement: Estimates From Space-time Analysis

This paper develops estimates of environmental improvement based on a two-stage hedonic price analysis of the single family housing market in the Puget Sound region of Washington State. The analysis — which focuses specifically on several EPA-designated environmental hazards and involves 226,918 transactions for 177,303 unique properties that took place between January 2001 and September 2009 — involves four steps: (i) ten hedonic price functions are estimated year-by-year, one for each year of the 2000s; (ii) the hedonic estimates are used to compute the marginal implicit price of distance from air release, superfund, and toxic release sites; (iii) the marginal implicit prices, which vary through time, are used to estimate a series of implicit demand functions describing the relationship between the price of distance and the quantity consumed; and, finally (iv) the demand estimates are compared to those obtained in other research and then used evaluate the potential scale of benefits associated with some basic environmental improvement scenarios. Overall, the analysis provides further evidence that it is possible to develop a structural model of implicit demand within a single housing market and suggests that the benefits of environmental improvement are substantial.Hedonic housing model, benefits, environmental improvement

Seismic Risk Analysis of Revenue Losses, Gross Regional Product and transportation systems.

Natural threats like earthquakes, hurricanes or tsunamis have shown seri- ous impacts on communities. In the past, major earthquakes in the United States like Loma Prieta 1989, Northridge 1994, or recent events in Italy like L’Aquila 2009 or Emilia 2012 earthquake emphasized the importance of pre- paredness and awareness to reduce social impacts. Earthquakes impacted businesses and dramatically reduced the gross regional product. Seismic Hazard is traditionally assessed using Probabilistic Seismic Hazard Anal- ysis (PSHA). PSHA well represents the hazard at a specific location, but it’s unsatisfactory for spatially distributed systems. Scenario earthquakes overcome the problem representing the actual distribution of shaking over a spatially distributed system. The performance of distributed productive systems during the recovery process needs to be explored. Scenario earthquakes have been used to assess the risk in bridge networks and the social losses in terms of gross regional product reduction. The proposed method for scenario earthquakes has been applied to a real case study: Treviso, a city in the North East of Italy. The proposed method for scenario earthquakes requires three models: one representation of the sources (Italian Seismogenic Zonation 9), one attenuation relationship (Sa- betta and Pugliese 1996) and a model of the occurrence rate of magnitudes (Gutenberg Richter). A methodology has been proposed to reduce thou- sands of scenarios to a subset consistent with the hazard at each location. Earthquake scenarios, along with Mote Carlo method, have been used to simulate business damage. The response of business facilities to earthquake has been obtained from fragility curves for precast industrial building. Fur- thermore, from business damage the reduction of productivity has been simulated using economic data from the National statistical service and a proposed piecewise “loss of functionality model”. To simulate the economic process in the time domain, an innovative businesses recovery function has been proposed. The proposed method has been applied to generate scenarios earthquakes at the location of bridges and business areas. The proposed selection method- ology has been applied to reduce 8000 scenarios to a subset of 60. Subse- quently, these scenario earthquakes have been used to calculate three system performance parameters: the risk in transportation networks, the risk in terms of business damage and the losses of gross regional product. A novel model for business recovery process has been tested. The proposed model has been used to represent the business recovery process and simulate the effects of government aids allocated for reconstruction. The proposed method has efficiently modeled the seismic hazard using scenario earthquakes. The scenario earthquakes presented have been used to assess possible consequences of earthquakes in seismic prone zones and to increase the preparedness. Scenario earthquakes have been used to sim- ulate the effects to economy of the impacted area; a significant Gross Regional Product reduction has been shown, up to 77% with an earthquake with 0.0003 probability of occurrence. The results showed that limited funds available after the disaster can be distributed in a more efficient way

Optimal Incentives under Moral Hazard and Heterogeneous Agents: Evidence from Production Contracts Data

The objective of this paper is to develop an analytical framework for estimation of the parameters of a structural model of an incentive contract under moral hazard, taking into account agents heterogeneity in preferences. We show that allowing the principal to strategically distribute the production inputs across heterogenous agents as part of the contract design, the principal is able to change what appears to be a uniform contract into individualized contracts tailored to fit agents' preferences or characteristics. Using micro level data on swine production contract settlements, we find that contracting farmers are heterogenous with respect to their risk aversion and that this heterogeneity affects the principal's allocation of production inputs across farmers. Relying on the identifying assumption that contracts are optimal, we obtain the estimates of a lower and an upper bound of agents' reservation utilities. We show that farmers with higher risk aversion have lower outside opportunities because of lower reservation utilities.Agency Contracts, Optimal Incentives, Moral Hazard, Risk Aversion, Heterogeneity, Production Economics, D82, L24, Q12, K32, L51,

High-dimensional Structured Additive Regression Models: Bayesian Regularisation, Smoothing and Predictive Performance

Data structures in modern applications frequently combine the necessity of flexible regression techniques such as nonlinear and spatial effects with high-dimensional covariate vectors. While estimation of the former is typically achieved by supplementing the likelihood with a suitable smoothness penalty, the latter are usually assigned shrinkage penalties that enforce sparse models. In this paper, we consider a Bayesian unifying perspective, where conditionally Gaussian priors can be assigned to all types of regression effects. Suitable hyperprior assumptions on the variances of the Gaussian distributions then induce the desired smoothness or sparseness properties. As a major advantage, general Markov chain Monte Carlo simulation algorithms can be developed that allow for the joint estimation of smooth and spatial effects and regularised coefficient vectors. Two applications demonstrate the usefulness of the proposed procedure: A geoadditive regression model for data from the Munich rental guide and an additive probit model for the prediction of consumer credit defaults. In both cases, high-dimensional vectors of categorical covariates will be included in the regression models. The predictive ability of the resulting high-dimensional structure additive regression models compared to expert models will be of particular relevance and will be evaluated on cross-validation test data