Stock Markets and Household Wealth: Can a Stock Market Crash Cause a Recession in The U.S. Economy?

Stock market wealth effects on the level of consumption in the United States economy have been constantly debated; there is evidence for arguments for and against its prominence and its symmetry. This paper seeks to investigate the strength of its negative effect by creating models to analyze unexpected shocks to the Standard and Poor's 500 index. First, a transmission mechanism between the stock market and GDP is established through the use of second-order vector autoregressive models. Following which, theory from the life cycle model and adaptations of previous researchers' models are used to create a structural model. This paper finds that stock market wealth effects are small, but important to consider, especially if markets are overpriced; this claim is corroborated by evidence from simulation of 'alternative scenarios' and the historical experiences of 1987 and 2001.

Household water use behavior : an integrated model

Water authorities are dealing with the challenge of ensuring that there is enough water to meet demand in the face of drought, population growth and predictions of reduced supply due to climate change. In order to develop  effective household demand management programs, water managers need to understand the factors that influence household water use. Following an examination and re-analysis of current water consumption behavioral models we propose a new model for understanding household water consumption. We argue that trust plays a role in household water consumption, since people will not save water if they feel others are not minimizing their water use (inter-personal trust). Furthermore, people are less likely to save water if they do not trust the water authority (institutional trust). This paper proposes that to fully understand the factors involved in determining household water use the impact of trust on water consumption needs investigation.

Consumption smoothing in Latin America: an empirical assessment of present value models

The study aims to assess the empirical adherence of the permanent income theory and the consumption smoothing view in Latin America. Two present value models are considered, one describing household behavior and the other open economy macroeconomics. Following the methodology developed in Campbell and Schiller (1987), Bivariate Vector Autoregressions are estimated for the saving ratio and the real growth rate of income concerning the household behavior model and for the current account and the change in national cash ‡ow regarding the open economy model. The countries in the sample are considered separately in the estimation process (individual system estimation) as well as jointly (joint system estimation). Ordinary Least Squares (OLS) and Seemingly Unrelated Regressions (SURE) estimates of the coe¢cients are generated. Wald Tests are then conducted to verify if the VAR coe¢cient estimates are in conformity with those predicted by the theory. While the empirical results are sensitive to the estimation method and discount factors used, there is only weak evidence in favor of the permanent income theory and consumption smoothing view in the group of countries analyzed.

Multilevel Analysis in Household Survey: An Application to Health Condition Data

The aim of this thesis is to apply multilevel regression model in context of household surveys. Hierarchical structure in this type of data is characterized by many small groups. In last years comparative and multilevel analysis in the field of perceived health have grown in size. The purpose of this thesis is to develop a multilevel analysis with three level of hierarchy for Physical Component Summary outcome to: evaluate magnitude of within and between variance at each level (individual, household and municipality); explore which covariates affect on perceived physical health at each level; compare model-based and design-based approach in order to establish informativeness of sampling design; estimate a quantile regression for hierarchical data. The target population are the Italian residents aged 18 years and older. Our study shows a high degree of homogeneity within level 1 units belonging from the same group, with an intraclass correlation of 27% in a level-2 null model. Almost all variance is explained by level 1 covariates. In fact, in our model the explanatory variables having more impact on the outcome are disability, unable to work, age and chronic diseases (18 pathologies). An additional analysis are performed by using novel procedure of analysis :"Linear Quantile Mixed Model", named "Multilevel Linear Quantile Regression", estimate. This give us the possibility to describe more generally the conditional distribution of the response through the estimation of its quantiles, while accounting for the dependence among the observations. This has represented a great advantage of our models with respect to classic multilevel regression. The median regression with random effects reveals to be more efficient than the mean regression in representation of the outcome central tendency. A more detailed analysis of the conditional distribution of the response on other quantiles highlighted a differential effect of some covariate along the distribution.

Semiparametric Latent Variable Regression Models for Spatio-temporal Modeling of Mobile Source Particles in the Greater Boston Area

Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic particles, with some recording only outdoor concentrations of black or elemental carbon, some recording indoor concentrations of black carbon, and others recording both indoor and outdoor concentrations of black carbon. A joint model for outdoor and indoor exposure that specifies a spatially varying latent variable provides greater spatial coverage in the area of interest. We propose a penalised spline formation of the model that relates to generalised kringing of the latent traffic pollution variable and leads to a natural Bayesian Markov Chain Monte Carlo algorithm for model fitting. We propose methods that allow us to control the degress of freedom of the smoother in a Bayesian framework. Finally, we present results from an analysis that applies the model to data from summer and winter separately

Human and embodied energy analysis applied to water source protection and household water treatment interventions used in Mali, West Africa

As water quality interventions are scaled up to meet the Millennium Development Goal of halving the proportion of the population without access to safe drinking water by 2015 there has been much discussion on the merits of household- and source-level interventions. This study furthers the discussion by examining specific interventions through the use of embodied human and material energy. Embodied energy quantifies the total energy required to produce and use an intervention, including all upstream energy transactions. This model uses material quantities and prices to calculate embodied energy using national economic input/output-based models from China, the United States and Mali. Embodied energy is a measure of aggregate environmental impacts of the interventions. Human energy quantifies the caloric expenditure associated with the installation and operation of an intervention is calculated using the physical activity ratios (PARs) and basal metabolic rates (BMRs). Human energy is a measure of aggregate social impacts of an intervention. A total of four household treatment interventions – biosand filtration, chlorination, ceramic filtration and boiling – and four water source-level interventions – an improved well, a rope pump, a hand pump and a solar pump – are evaluated in the context of Mali, West Africa. Source-level interventions slightly out-perform household-level interventions in terms of having less total embodied energy. Human energy, typically assumed to be a negligible portion of total embodied energy, is shown to be significant to all eight interventions, and contributing over half of total embodied energy in four of the interventions. Traditional gender roles in Mali dictate the types of work performed by men and women. When the human energy is disaggregated by gender, it is seen that women perform over 99% of the work associated with seven of the eight interventions. This has profound implications for gender equality in the context of water quality interventions, and may justify investment in interventions that reduce human energy burdens.

Household Hardships, Public Programs, and Their Associations with the Health and Development of Very Young Children: Insights from Children’s HealthWatch

America’s low-income families struggle to protect their children from multiple threats to their health and growth. Many research and advocacy groups explore the health and educational effects of food insecurity, but less is known about these effects on very young children. Children’s HealthWatch, a group of pediatric clinicians and public health researchers, has continuously collected data on the effects of food insecurity alone and in conjunction with other household hardships since 1998. The group’s peer reviewed research has shown that a number of economic risks at the household level, including food, housing and energy insecurity, tend to be correlated. These insecurities alone or in conjunction increase the risk that a young child will suffer various negative health consequences, including increases in lifetime hospitalizations, parental report of fair or poor health,1 or risk for developmental delays.2 Child food insecurity is an incremental risk indicator above and beyond the risk imposed by household-level food insecurity. The Children’sHealthwatch research also suggests public benefits programs modify some of these effects for families experiencing hardships. This empirical evidence is presented in a variety of public venues outside the usual scientific settings, such as congressional hearings, to support the needs of America’s most vulnerable population through policy change. Children’s HealthWatch research supports legislative solutions to food insecurity, including sustained funding for public programs and re-evaluation of the use of the Thrifty Food Plan as the basis of SNAP benefits calculations. Children’s HealthWatch is one of many models to support the American Academy of Pediatrics’ call to “stand up, speak up, and step up for children.”3 No isolated group or single intervention will solve child poverty or multiple hardships. However, working collaboratively each group has a role to play in supporting the health and well-being of young children and their families. 1. Cook JT, Frank DA, Berkowitz C, et al. Food insecurity is associated with adverse health outcomes among human infants and toddlers. J Nutr. 2004;134:1432-1438. 2. Rose-Jacobs R, Black MM, Casey PH, et al. Household food insecurity: associations with at-risk infant and toddler development. Pediatrics. 2008;121:65-72. 3. AAP leader says to stand up, speak up, and step up for child health [news release]. Boston, MA: American Academy of Pediatrics; October 11, 2008. http://www2.aap.org/pressroom/nce/nce08childhealth.htm. Accessed January 1, 2012.

Using spatial linear models with SAR and CAR structure to examine Texas lung cancer incidence rates

Scholars have found that socioeconomic status was one of the key factors that influenced early-stage lung cancer incidence rates in a variety of regions. This thesis examined the association between median household income and lung cancer incidence rates in Texas counties. A total of 254 individual counties in Texas with corresponding lung cancer incidence rates from 2004 to 2008 and median household incomes in 2006 were collected from the National Cancer Institute Surveillance System. A simple linear model and spatial linear models with two structures, Simultaneous Autoregressive Structure (SAR) and Conditional Autoregressive Structure (CAR), were used to link median household income and lung cancer incidence rates in Texas. The residuals of the spatial linear models were analyzed with Moran's I and Geary's C statistics, and the statistical results were used to detect similar lung cancer incidence rate clusters and disease patterns in Texas.^

A Community Analysis of the Implementation for Household Curbside Recycling Programs: A Diversionary Method to Control Municipal Solid Waste

The Municipality of Anchorage (MOA) is required to better manage, operate and control municipal solid waste (MSW) after the Anchorage Assembly instituted a Zero Waste Policy. Two household curbside recycling programs (CRPs), pay-as-you-throw (PAYT) and single-stream, were compared and evaluated to determine an optimal municipal solid waste diversion method for households within the MOA. The analyses find: (1) a CRP must be designed from comprehensive analysis, models and data correlation that combine demographic and psychographic variables; and (2) CRPs can be easily adjusted towards community-specific goals using technology, such as Geographic Information System (GIS) and Radio Frequency Identification (RFID). Combining resources of policy-makers, businesses, and other viable actors are necessary components to produce a sustainable, economically viable curbside recycling program.

The analysis and prediction of the quantity and composition of household refuse

This thesis describes the development of a simple and accurate method for estimating the quantity and composition of household waste arisings. The method is based on the fundamental tenet that waste arisings can be predicted from information on the demographic and socio-economic characteristics of households, thus reducing the need for the direct measurement of waste arisings to that necessary for the calibration of a prediction model. The aim of the research is twofold: firstly to investigate the generation of waste arisings at the household level, and secondly to devise a method for supplying information on waste arisings to meet the needs of waste collection and disposal authorities, policy makers at both national and European level and the manufacturers of plant and equipment for waste sorting and treatment. The research was carried out in three phases: theoretical, empirical and analytical. In the theoretical phase specific testable hypotheses were formulated concerning the process of waste generation at the household level. The empirical phase of the research involved an initial questionnaire survey of 1277 households to obtain data on their socio-economic characteristics, and the subsequent sorting of waste arisings from each of the households surveyed. The analytical phase was divided between (a) the testing of the research hypotheses by matching each household's waste against its demographic/socioeconomic characteristics (b) the development of statistical models capable of predicting the waste arisings from an individual household and (c) the development of a practical method for obtaining area-based estimates of waste arisings using readily available data from the national census. The latter method was found to represent a substantial improvement over conventional methods of waste estimation in terms of both accuracy and spatial flexibility. The research therefore represents a substantial contribution both to scientific knowledge of the process of household waste generation, and to the practical management of waste arisings.

Breaks in the UK household sector money demand function

We use non-parametric procedures to identify breaks in the underlying series of UK household sector money demand functions. Money demand functions are estimated using cointegration techniques and by employing both the Simple Sum and Divisia measures of money. P-star models are also estimated for out-of-sample inflation forecasting. Our findings suggest that the presence of breaks affects both the estimation of cointegrated money demand functions and the inflation forecasts. P-star forecast models based on Divisia measures appear more accurate at longer horizons and the majority of models with fundamentals perform better than a random walk model.

Female employment from the household. Towards a comprehensive analysis of work-life articulation

Research on women’s employment has proliferated over recent decades, often under a perspective that conceptualizes female labour market activity as independent of male presences and absences in the productive and reproductive spheres. In the face of these approaches, the article argues the need to focus on the couple as the unit of analysis of work-life articulation. After referring to the main theoretical arguments that, from a gender perspective within labour studies, have pointed out the relevance of placing the household as the central space for the analysis of the sexual division of labour, the article reviews different empirical contributions that have incorporated such perspective in the international literature. Next, the state of the art in the Spanish literature is presented, before arguing the desirability of applying such framework of analysis to the study of employment and care work in Spanish households, which are at present undergoing major transformations.

Influence of household remittance on childhood stunting in Nepal

Thesis (Master's)--University of Washington, 2016-08

LUNG CANCER IN NEPAL: THE ROLE OF TRADITIONAL TOBACCO PRODUCTS AND COMBUSTION RELATED HOUSEHOLD AIR POLLUTION

The burden of chronic diseases such as cancer is increasing in low and middle income countries around the globe. Nepal, one of the world’s poorest countries, is no exception to this trend, with lung cancer as the leading causes of cancer deaths. Despite this, limited data is available on the environmental and behavioral risk factors that contribute to the lung cancer etiology in Nepal. The objectives of this dissertation are to: 1) investigate the ethnic differences in consumption of local tobacco products and their role in lung cancer risk in Nepal; 2) evaluate urinary metabolite of 1,3-butadiene as a biomarker of exposure to combustion related household air pollution (CRHAP); 3) investigate the association between CRHAP exposure and lung cancer risk using urinary metabolite of 1,3-butadiene as a biomarker of exposure; 4) investigate the association between CRHAP exposure and lung cancer risk using questionnaire based measure of exposure. Lung cancer cases (n=606) and frequency matched controls (N=606) were recruited from B.P. Koirala Memorial Cancer Hospital. We obtained biological samples and information on lifestyles including cooking habits and type of fuels used. We used liquid chromatograph tandem mass spectrometer (LC-MS/MS) to quantify urinary metabolites of 1,3-butadiene in urine samples. We employed a combination of logistic and linear regression models to detect any exposure-disease associations while controlling for known confounding variables. Overall, we found that ethnic groups in Nepal use different tobacco products that have different differing cancer potency -we observed the highest odds ratios for the traditional tobacco products. The biomarker analysis showed strong evidence that monohydroxybutyl mercapturic acid is associated with biomass fuel use among participants. However, we did not find significant association between urinary MHMBA and lung cancer risk. When we used questionnaire based measure of exposure to household air pollution, we observed significant, dose-response associations between CRHAP exposure and lung cancer risk, particularly among never-smokers. Our results show that important role of local tobacco products in lung cancer risk in Nepal. Furthermore, we demonstrate that CRHAP exposure is a risk factor for lung cancer risk, independent of tobacco smoking.

STATISTICAL METHODS FOR ADVANCED ECONOMETRIC MODELS WITH APPLICATIONS TO VEHICLE HOLDING AND SPEED QUANTILES DISTRIBUTION

This dissertation proposes statistical methods to formulate, estimate and apply complex transportation models. Two main problems are part of the analyses conducted and presented in this dissertation. The first method solves an econometric problem and is concerned with the joint estimation of models that contain both discrete and continuous decision variables. The use of ordered models along with a regression is proposed and their effectiveness is evaluated with respect to unordered models. Procedure to calculate and optimize the log-likelihood functions of both discrete-continuous approaches are derived, and difficulties associated with the estimation of unordered models explained. Numerical approximation methods based on the Genz algortithm are implemented in order to solve the multidimensional integral associated with the unordered modeling structure. The problems deriving from the lack of smoothness of the probit model around the maximum of the log-likelihood function, which makes the optimization and the calculation of standard deviations very difficult, are carefully analyzed. A methodology to perform out-of-sample validation in the context of a joint model is proposed. Comprehensive numerical experiments have been conducted on both simulated and real data. In particular, the discrete-continuous models are estimated and applied to vehicle ownership and use models on data extracted from the 2009 National Household Travel Survey. The second part of this work offers a comprehensive statistical analysis of free-flow speed distribution; the method is applied to data collected on a sample of roads in Italy. A linear mixed model that includes speed quantiles in its predictors is estimated. Results show that there is no road effect in the analysis of free-flow speeds, which is particularly important for model transferability. A very general framework to predict random effects with few observations and incomplete access to model covariates is formulated and applied to predict the distribution of free-flow speed quantiles. The speed distribution of most road sections is successfully predicted; jack-knife estimates are calculated and used to explain why some sections are poorly predicted. Eventually, this work contributes to the literature in transportation modeling by proposing econometric model formulations for discrete-continuous variables, more efficient methods for the calculation of multivariate normal probabilities, and random effects models for free-flow speed estimation that takes into account the survey design. All methods are rigorously validated on both real and simulated data.