On the extent of the market: a Monte Carlo study and an application to the United States egg market

This paper investigates the extent of the market, using a switching regimes model similar to those used in stochastic frontiers estimations. We started by performing a Monte Carlo simulation on our model, seeking to evaluate its performance in terms of correctly estimating the probability of integration of two markets. Our Monte Carlo results under the assumption of half-normal and exponential distribution of the errors, revealed that these two distributions predict almost correctly the probability of integration of two markets. The half-normal error distribution model tends to slightly underestimate the true probability of integration, while the exponential error distribution model tends to slightly overestimate the true probability of integration. We, finally, applied the model to the United States egg market using data from three highly productive states and one less productive state. The model predicts that, the markets pairs considered are integrated. That is, the four markets studied belong to the same economic market in the sense of Marshall. Further, based on our Monte Carlo study, we find that the true probability of integration of two given markets lies in between the half-normal model estimates and the exponential distribution model estimates.Industrial Organization,

Integrated Economic-Epidemic Modeling of Avian Influenza Mitigation Options: A Case Study of an Outbreak in Texas

Recent World Animal Health Organization (OIE) reports on Avian Influenza (AI) outbreaks in Asia, Europe and Canada suggest that there is a nonzero probability that an outbreak may occur anywhere in the world, including the US. To help evaluate possible policy in the face of such an event, this dissertation does an economic evaluation of the implications of using two mitigation strategies: one corresponding to the currently response strategy; and the other an OIE recommended one utilizing vaccination. To do this, the dissertation develops and uses an integrated economic-epidemic model. In this effort, I first estimate the cost of an AI outbreak under a deterministic disease spread assumption where a new vaccination strategy and the current strategy are compared. Subsequently, I introduce risk in the model and construct 95 percent confidence intervals for the outbreak costs, and I rank the outcomes of the alternative strategies using stochastic dominance criteria. In addition, during both phases, I develop and estimate the breakeven probability for an event where ex-ante fixed costs of vaccine stockpiling are justified by the reduction in disease event damages. Results under deterministic disease spread assumption suggest that the vaccination strategy lowers the cost of outbreaks as opposed to the current strategy. This happens because vaccination reduces the number of culled and quarantined flocks. The study is conducted in three locations, yielding the finding that the costs of an outbreak vary depending on the densities of poultry flocks. I also find that when consumer demand shifts due to the outbreak, the costs are much larger. Finally, I find that ex-ante vaccine stockpiling is justified for all the sub-regions if the probability of outbreak exceeds 0.07. The stochastic disease spread assumption results also show that the vaccination strategy dominates in first degree stochastic dominance sense. Consistent with stochastic dominance results, the 95 percent confidence intervals have narrower ranges under the vaccination strategy than without it. Finally, the distribution of the breakeven probability for vaccine stocking has a mode of 0.07 and that the probability is accurate with 82 percent likelihood. However, the threshold varies with the disease transmission parameters and could reach up to 0.32

Can Dispersed Biomass Processing Protect the Environment and Cover the Bottom Line for Biofuel?

This paper compares environmental and profitability outcomes for a centralized biorefinery for cellulosic ethanol that does all processing versus a biorefinery linked to a decentralized array of local depots that pretreat biomass into concentrated briquettes. The analysis uses a spatial bioeconomic model that maximizes predicted profit from crop and energy products, subject to the requirement that the biorefinery must be operated at full capacity. The model draws upon biophysical crop input-output coefficients simulated with the EPIC model, as well as input and output prices, spatial transportation costs, ethanol yields from biomass, and biorefinery capital and operational costs. The model was applied to 82 cropping systems simulated across 37 sub-watersheds in a 9-county region of southern Michigan in response to ethanol prices simulated to rise from $1.78 to$3.36 per gallon. Results show that the decentralized local biomass processing depots lead to lower profitability but better environmental performance, due to more reliance on perennial grasses than the centralized biorefinery. Simulated technological improvement that reduces the processing cost and increases the ethanol yield of switchgrass by 17% could cause a shift to more processing of switchgrass, with increased profitability and environmental benefits.Biomass production, bioenergy supply, cellulosic ethanol, environmental trade-off analysis, bioeconomic modeling, EPIC, spatial configuration, local biomass processing, Crop Production/Industries, Environmental Economics and Policy, Production Economics, Resource /Energy Economics and Policy, Q16, Q15, Q57, Q18,

Biomass Supply from Alternative Cellulosic Crops and Crop Residues: A Preliminary Spatial Bioeconomic Modeling Approach

This paper introduces a spatial bioeconomic model for study of potential cellulosic biomass supply at regional scale. By modeling the profitability of alternative crop production practices, it captures the opportunity cost of replacing current crops by cellulosic biomass crops. The model draws upon biophysical crop input-output coefficients, price and cost data, and spatial transportation costs in the context of profit maximization theory. Yields are simulated using temperature, precipitation and soil quality data with various commercial crops and potential new cellulosic biomass crops. Three types of alternative crop management scenarios are simulated by varying crop rotation, fertilization and tillage. The cost of transporting biomass to a specific demand location is obtained using road distances and bulk shipping costs from geographic information systems. The spatial mathematical programming model predicts the supply of biomass and implied environmental consequences for a landscape managed by representative, profit maximizing farmers. The model was applied and validated for simulation of cellulosic biomass supply in a 9-county region of southern Michigan. Results for 74 cropping systems simulated across 39 sub-watersheds show that crop residues are the first types of biomass to be supplied. Corn stover and wheat straw supply start at $21/Mg and$27/Mg delivered prices. Perennial bioenergy crops become profitable to produce when the delivered biomass price reaches $46/Mg for switchgrass,$118/Mg for grass mixes and $154/Mg for Miscanthus giganteus. The predicted effect of the USDA Biomass Conversion Assistance Program is to sharply reduce the minimum biomass price at which miscanthus would become profitable to supply. Compared to conventional crop production practices in the area, the EPIC-simulated environmental outcomes with crop residue removal include increased greenhouse gas emissions and reduced water quality through increased nutrient loss. By contrast, perennial cellulosic biomass crops reduced greenhouse gas emissions and improved water quality compared to current commercial cropping systems.biomass production, bioenergy supply, biofuel policy, bioenergy, cellulosic ethanol, agro-ecosystem economics, ecosystem services economics, agro-environmental trade-off analysis, mathematical programming, EPIC, Agricultural and Food Policy, Crop Production/Industries, Environmental Economics and Policy, Land Economics/Use, Production Economics, Resource /Energy Economics and Policy, Q16, Q15, Q57, Q18,

Analysis of Effects of Meteorological Factors on Dengue Incidence in Sri Lanka Using Time Series Data

In tropical and subtropical regions of eastern and South-eastern Asia, dengue fever (DF) and dengue hemorrhagic fever (DHF) outbreaks occur frequently. Previous studies indicate an association between meteorological variables and dengue incidence using time series analyses. The impacts of meteorological changes can affect dengue outbreak. However, difficulties in collecting detailed time series data in developing countries have led to common use of monthly data in most previous studies. In addition, time series analyses are often limited to one area because of the difficulty in collecting meteorological and dengue incidence data in multiple areas. To gain better understanding, we examined the effects of meteorological factors on dengue incidence in three geographically distinct areas (Ratnapura, Colombo, and Anuradhapura) of Sri Lanka by time series analysis of weekly data. The weekly average maximum temperature and total rainfall and the total number of dengue cases from 2005 to 2011 (7 years) were used as time series data in this study. Subsequently, time series analyses were performed on the basis of ordinary least squares regression analysis followed by the vector autoregressive model (VAR). In conclusion, weekly average maximum temperatures and the weekly total rainfall did not significantly affect dengue incidence in three geographically different areas of Sri Lanka. However, the weekly total rainfall slightly influenced dengue incidence in the cities of Colombo and Anuradhapura

On the extent of the market: a Monte Carlo study and an application to the United States egg market

This paper investigates the extent of the market, using a switching regimes model similar to those used in stochastic frontiers estimations. We started by performing a Monte Carlo simulation on our model, seeking to evaluate its performance in terms of correctly estimating the probability of integration of two markets. Our Monte Carlo results under the assumption of half-normal and exponential distribution of the errors, revealed that these two distributions predict almost correctly the probability of integration of two markets. The half-normal error distribution model tends to slightly underestimate the true probability of integration, while the exponential error distribution model tends to slightly overestimate the true probability of integration. We, finally, applied the model to the United States egg market using data from three highly productive states and one less productive state. The model predicts that, the markets pairs considered are integrated. That is, the four markets studied belong to the same economic market in the sense of Marshall. Further, based on our Monte Carlo study, we find that the true probability of integration of two given markets lies in between the half-normal model estimates and the exponential distribution model estimates

Attributes Affecting Preferences for Traffic Safety Camera Programs

With just a few notable exceptions, research supports the concept that red light cameras (RLCs) improve safety. However, many communities that have implemented RLC programs have faced a firestorm of public opinion associated with the use of RLCS, with many communities having to remove the cameras. What makes or breaks a red light camera program? Because of the experimental design process, stated choice is recognized as a tool that can resemble a laboratory experiment for the public policy arena. In this research, a stated choice model was developed and used to explore public preferences for a RLC program through an internet survey and a convenience sample drawn from a typical college town. The results suggest while independently the opposite is true, that when there is an increase in both the fine for violators and the number of cameras together (i.e., the interaction of these two) there is a perceived public safety gain. The interacted variable positively increases utility from the selected RLCS program we analyzed and could be key in generating public support for RLC programs. The results suggest some important deterrence theory implications for improving accident prevention through the use of RLC programs that are designed to avoid unnecessary public scrutiny

Red-light Cameras at Intersections: Estimating Preferences Using a Stated Choice Model

Red-light cameras placed at intersections have the potential to increase safety, but they are often viewed as an invasion of privacy. Preferences for these cameras were explored using a stated choice model that presents key attributes of camera placements. Stated choice models involve careful experimental design, akin to experimental control in laboratory settings. A variety of design approaches were used, settling on a composition of the choice sets people face in the survey. To illustrate the approach, an internet survey was used with a convenience sample containing a high percentage of college students. The results show that while not the case independently, as the number of cameras and fines for violators are simultaneously increased, the preferences for one particular red light cameras program are likely to improve