Household food expenditures in the United States: A Bayesian MCMC approach to censored equation systems

We apply a Bayesian Markov Chain Monte Carlo (MCMC) technique, along with data augmentation to accommodate censoring in the dependent variables, to the estimation of a large expenditure system of food expenditures. Our finding of significant error covariance estimates justifies estimation of the system in improving statistical efficiency. Income, household composition, regions and other socio-demographic variables are found to play significant roles in determining household food expenditures.Bayesian MCMC, Censored equation system, Consumer Expenditure Survey, Food Consumption/Nutrition/Food Safety, C11, C34, D12, C11, C34, D12,

Evaluating integrated care for people with complex needs

Objectives: As part of the Vanguard programme, two integrated care models were introduced in South Somerset for people with complex care needs: the Complex Care Team and Enhanced Primary Care. We assessed their impact on a range of utilization measures and mortality. Methods: We used monthly individual-level linked primary and secondary care data from April 2014 to March 2018 to assess outcomes before and after the introduction of the care models. The analysis sample included 564 Complex Care Team and 841 Enhanced Primary Care cases that met specific criteria. We employed propensity score methods to identify out-of-area control patients and difference-in-differences analysis to isolate the care models’ impact. Results: We found no evidence of significantly reduced utilization in any of the Complex Care Team or Enhanced Primary Care cohorts. The death rate was significantly lower only for those in the first Enhanced Primary Care cohort. Conclusions: The integrated care models did not significantly reduce utilization nor consistently reduce mortality. Future research should test longer-term outcomes associated with the new models of care and quantify their contribution in the context of broader initiatives

A Binary-Ordered Probit Model of Cigarette Demand

This study analyzes the demand for cigarettes fitting observed zero outcomes with a trivariate model consisting of an equation for the starting smoking decision, an equation for the quitting decision, and an equation that models the level of cigarettes consumed. Five competing specifications are considered to explain level, with the ordered probit, which accommodates pile-ups of counts in the dependent variable, providing the best fit. Marginal effects of explanatory variables are calculated providing strong evidence of race and gender differences in consumption patterns. The estimated marginal effects are robust to alternative categorizations of the level of cigarettes.Demand and Price Analysis,

Household food expenditures in the United States: A Bayesian MCMC approach to censored equation systems

We apply a Bayesian Markov Chain Monte Carlo (MCMC) technique, along with data augmentation to accommodate censoring in the dependent variables, to the estimation of a large expenditure system of food expenditures. Our finding of significant error covariance estimates justifies estimation of the system in improving statistical efficiency. Income, household composition, regions and other socio-demographic variables are found to play significant roles in determining household food expenditures

Household Food Expenditures Away From Home by Type of Meal

This paper investigates expenditures of food away from home (FAFH) by husband-and-wife households with children, determines the differentiated impacts of economic and demographic variables on FAFH by type of meal and across household types. Using the 2008 and 2009 Consumer Expenditure Survey, the system of expenditures on breakfast, lunch, and dinner is estimated with a multivariate sample selection estimator and results are compared with the Tobit system estimates. Statistical significance of error correlations among equations justifies estimation of the sample selection systems. Differentiated effects of variables on probabilities and expenditure levels highlight advantages of the sample selection system over the Tobit estimator. The effects of demographic and socioeconomic factors are found to vary by type of meal. Income, work hours, race, education, geographical region and household composition are important determinants of FAFH

Food Expenditures away from Home by Elderly Households

This study investigates the differentiated effects of economic and socio-demographic variables on food away from home (FAFH) expenditures by type of facility among elderly households in the United States. Using data from the 2008–2010 Consumer Expenditure Surveys, the systems of expenditures on full-service, fast food, and other restaurants are estimated with a multivariate sample selection estimator which also accommodates heteroscedasticity in the error distribution. Statistical significance of error correlations among equations justifies estimation of the sample selection systems. Income, employment statuses, race, education, geographic region, and household composition are important determinants of FAFH expenditures. Income contributes to full-service and fast-food expenditures by the elderly implying that the future of FAFH industry is tied to macroeconomic conditions. Better education is associated with greater probabilities and larger levels of expenditures at all facilities. Effects of the Supplemental Nutrition Assistance Program (SNAP) are found to be strong and negative, invalidating policy concerns for the general population that participation in the program might enhance consumption of less healthy FAFH

A Binary-Ordered Probit Model of Cigarette Demand

This study analyzes the demand for cigarettes fitting observed zero outcomes with a trivariate model consisting of an equation for the starting smoking decision, an equation for the quitting decision, and an equation that models the level of cigarettes consumed. Five competing specifications are considered to explain level, with the ordered probit, which accommodates pile-ups of counts in the dependent variable, providing the best fit. Marginal effects of explanatory variables are calculated providing strong evidence of race and gender differences in consumption patterns. The estimated marginal effects are robust to alternative categorizations of the level of cigarettes