Essays on the determinants of private investment: The effects of relative price uncertainty and political instability

The objective of this study was to provide empirical evidence on the effects of relative price uncertainty and political instability on private investment. My effort is expressed in a single-equation model using macroeconomic and socio-political data from eight Latin American countries for the period 1970–1996. Relative price uncertainty is measured by the implied volatility of the exchange rate and political instability is measured by using indicators of social unrest and political violence. ^ I found that, after controlling for other variables, relative price uncertainty and political instability are negatively associated with private investment. Macroeconomic and political stability are key ingredients for the achievement of a strong investment response. This highlights the need to develop the state and build a civil society in which citizens can participate in decision-making and express consent without generating social turmoil. At the same time the government needs to implement structural policies along with relative price adjustments to eliminate excess volatility in price movements in order to provide a stable environment for investment. ^

Quantifying the effects of temperature and concentration on variable-density flow in numerical modeling of groundwater systems: Implications for predictive uncertainty and data collection

Groundwater systems of different densities are often mathematically modeled to understand and predict environmental behavior such as seawater intrusion or submarine groundwater discharge. Additional data collection may be justified if it will cost-effectively aid in reducing the uncertainty of a model's prediction. The collection of salinity, as well as, temperature data could aid in reducing predictive uncertainty in a variable-density model. However, before numerical models can be created, rigorous testing of the modeling code needs to be completed. This research documents the benchmark testing of a new modeling code, SEAWAT Version 4. The benchmark problems include various combinations of density-dependent flow resulting from variations in concentration and temperature. The verified code, SEAWAT, was then applied to two different hydrological analyses to explore the capacity of a variable-density model to guide data collection. ^ The first analysis tested a linear method to guide data collection by quantifying the contribution of different data types and locations toward reducing predictive uncertainty in a nonlinear variable-density flow and transport model. The relative contributions of temperature and concentration measurements, at different locations within a simulated carbonate platform, for predicting movement of the saltwater interface were assessed. Results from the method showed that concentration data had greater worth than temperature data in reducing predictive uncertainty in this case. Results also indicated that a linear method could be used to quantify data worth in a nonlinear model. ^ The second hydrological analysis utilized a model to identify the transient response of the salinity, temperature, age, and amount of submarine groundwater discharge to changes in tidal ocean stage, seasonal temperature variations, and different types of geology. The model was compared to multiple kinds of data to (1) calibrate and verify the model, and (2) explore the potential for the model to be used to guide the collection of data using techniques such as electromagnetic resistivity, thermal imagery, and seepage meters. Results indicated that the model can be used to give insight to submarine groundwater discharge and be used to guide data collection. ^

Development of an integrated surface and subsurface model of Everglades National Park

An integrated surface-subsurface hydrological model of Everglades National Park (ENP) was developed using MIKE SHE and MIKE 11 modeling software. The model has a resolution of 400 meters, covers approximately 1050 square miles of ENP, includes 110 miles of drainage canals with a variety of hydraulic structures, and processes hydrological information, such as evapotranspiration, precipitation, groundwater levels, canal discharges and levels, and operational schedules. Calibration was based on time series and probability of exceedance for water levels and discharges in the years 1987 through 1997. Model verification was then completed for the period of 1998 through 2005. Parameter sensitivity in uncertainty analysis showed that the model was most sensitive to the hydraulic conductivity of the regional Surficial Aquifer System, the Manning's roughness coefficient, and the leakage coefficient, which defines the canal-subsurface interaction. The model offers an enhanced predictive capability, compared to other models currently available, to simulate the flow regime in ENP and to forecast the impact of topography, water flows, and modifying operation schedules.

Decision-making under extreme *uncertainty: Rethinking hazard -related perceptions and action

Understanding who evacuates and who does not has been one of the cornerstones of research on the pre-impact phase of both natural and technological hazards. Its history is rich in descriptive illustrations focusing on lists of characteristics of those who flee to safety. Early models of evacuation focused almost exclusively on the relationship between whether warnings were heard and ultimately believed and evacuation behavior. How people came to believe these warnings and even how they interpreted the warnings were not incorporated. In fact, the individual seemed almost removed from the picture with analysis focusing exclusively on external measures. ^ This study built and tested a more comprehensive model of evacuation that centers on the decision-making process, rather than decision outcomes. The model focused on three important factors that alter and shape the evacuation decision-making landscape. These factors are: individual level indicators which exist independently of the hazard itself and act as cultural lenses through which information is heard, processed and interpreted; hazard specific variables that directly relate to the specific hazard threat; and risk perception. The ultimate goal is to determine what factors influence the evacuation decision-making process. Using data collected for 1998's Hurricane Georges, logistic regression models were used to evaluate how well the three main factors help our understanding of how individuals come to their decisions to either flee to safety during a hurricane or remain in their homes. ^ The results of the logistic regression were significant emphasizing that the three broad types of factors tested in the model influence the decision making process. Conclusions drawn from the data analysis focus on how decision-making frames are different for those who can be designated “evacuators” and for those in evacuation zones. ^

Development, validation and uncertainty analysis of quantitative structure and activity relationship models for Log P of disinfection by -products

Hydrophobicity as measured by Log P is an important molecular property related to toxicity and carcinogenicity. With increasing public health concerns for the effects of Disinfection By-Products (DBPs), there are considerable benefits in developing Quantitative Structure and Activity Relationship (QSAR) models capable of accurately predicting Log P. In this research, Log P values of 173 DBP compounds in 6 functional classes were used to develop QSAR models, by applying 3 molecular descriptors, namely, Energy of the Lowest Unoccupied Molecular Orbital (ELUMO), Number of Chlorine (NCl) and Number of Carbon (NC) by Multiple Linear Regression (MLR) analysis. The QSAR models developed were validated based on the Organization for Economic Co-operation and Development (OECD) principles. The model Applicability Domain (AD) and mechanistic interpretation were explored. Considering the very complex nature of DBPs, the established QSAR models performed very well with respect to goodness-of-fit, robustness and predictability. The predicted values of Log P of DBPs by the QSAR models were found to be significant with a correlation coefficient R2 from 81% to 98%. The Leverage Approach by Williams Plot was applied to detect and remove outliers, consequently increasing R 2 by approximately 2% to 13% for different DBP classes. The developed QSAR models were statistically validated for their predictive power by the Leave-One-Out (LOO) and Leave-Many-Out (LMO) cross validation methods. Finally, Monte Carlo simulation was used to assess the variations and inherent uncertainties in the QSAR models of Log P and determine the most influential parameters in connection with Log P prediction. The developed QSAR models in this dissertation will have a broad applicability domain because the research data set covered six out of eight common DBP classes, including halogenated alkane, halogenated alkene, halogenated aromatic, halogenated aldehyde, halogenated ketone, and halogenated carboxylic acid, which have been brought to the attention of regulatory agencies in recent years. Furthermore, the QSAR models are suitable to be used for prediction of similar DBP compounds within the same applicability domain. The selection and integration of various methodologies developed in this research may also benefit future research in similar fields.