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.

Assessing Household Food Waste in Italy: A Methodology for Detecting Drivers and Quantities

It has been estimated that one third of edible food destined for human consumption is lost or wasted along the food supply chain globally. Much of the waste comes from Global North, where consumers are considered as the bigger contributors. Different studies tried to analyze and estimate the Household Food Waste (HFW), especially in UK and Northern Europe. The result is that accurate studies at national level exist only in UK, Finland and Norway while no such studies are available in Italy, except for survey- based researches. Though, there is a widespread awareness that such methods might be not able to estimate Food Waste. Results emerging from literature clearly suggest that survey estimate inferior amounts of Food Waste as a result, if compared to waste sorting and weighting analysis or to diary studies. The hypothesis that household food waste is under-estimated when gathered through questionnaires has been enquired into. First, a literature review of behavioral economics and heuristics has been proposed; then, a literature review of the sector listing the existing methodologies to gather national data on Household Food Waste has been illustrated. Finally, a pilot experiment to test a mixed methodology is proposed. While literature suggests that four specific cognitive biases might be able to affect the reliability of answers in questionnaires, results of the present experiment clearly indicate that there is a relevant difference between how much the individual thinks to waste and he/she actually does. The result is a mixed methodology based on questionnaire, diary and waste sorting, able to overcome the cons of each single method.