Regional Inequalities in Consumption Patterns: A Multilevel Approach to the Case of Italy

The main aim of this paper is to evaluate the disparities in the Italian regions on the demand side. In more detail, an attempt will be made to find if the consumption behaviour of Italian households is different in the regions. With this in mind, Istat's 2000 Italian Family Budget data set was analysed. The data in question, which were collected through a two‐stage sample over Italy's 20 regions, contains information regarding the expenses of approximately 23,000 households. In this analysis, both households and regions are considered as units: households are nested in the regions so that the basic data structure is hierarchical. In order to take this hierarchical structure into account, a multilevel model was used, making it possible for parameters to vary randomly from region to region. The model in question also made it possible to consider heterogeneity across different groups (regions), such as stochastic variation. First, regional inequalities were tested using a simple model in which households constituted the first level of analysis and were grouped according to their region (the second level). As a second step, and in order to investigate the interaction between geographical context and income distribution, another model was used. This was cross‐classified by income and regions. The most relevant results showed that there is wide fragmentation of consumption behaviour and, at the same time, various differentiated types of behaviour in the regions under analysis. These territorial differentials become clear from income class and items of consumption.     

Multilevel Analysis of Repeated Measures Data

Hierarchically structured data are common in many areas of scientific research. Such data are characterized by nested membership relations among the units of observation. Multilevel analysis is a class of methods that explicitly takes the hierarchical structure into account. Repeated measures data can be considered as having a hierarchical structure as well: measurements are nested within, for instance, individuals. In this paper, an overview is given of the multilevel analysis approach to repeated measures data. A simple application to growth curves is provided as an illustration. It is argued that multilevel analysis of repeated measures data is a powerful and attractive approach for several reasons, such as flexibility, and the emphasis on individual development.     

Tenure Choice with Sample Selection: Differences among Alternative Samples

Most tenure choice models using cross-sectional data have used either a sample of recent movers or a sample comprising all households. There are problems with estimating both types of models in cross-sectional data. A sample of recent movers oversamples renters, and a sample of all households will yield estimates based on household decisions made in the past. This research designs a method to correct for sample selection in a sample of recent movers. There are large differences in the importance of age, immigrant status, and immigrant length of stay as predictors of homeownership. At the same time, income effects are similar across models.     

Neighbors’ income distribution: economic segregation and mixing in US urban neighborhoods

The paper describes within-neighborhood economic segregation in US metropolitan areas in 1985 and 1993. It uses the neighborhood clusters of the American housing survey, standardized by metropolitan area income and household size, to explore income distribution within neighborhoods at a scale much smaller than the census tract (a representative sample of households or ‘kernels’ and their 10 closest neighbors). Joint and conditional distributions portray neighbors’ characteristics conditional on the kernel’s housing tenure, race, and income. The paper documents both significant income mixing in the majority of US urban micro neighborhoods and the extent of income mixing within neighborhoods of concentrated poverty.     

Modeling the day-of-the-week shopping activity and travel patterns

This research develops models that extend existing multi-day analyses of shopping activity patterns. The main hypothesis is that shopping activity choice behavior varies, depending on the day of the week. Using a nested logit model structure, this hypothesis is tested by developing models of daily activity choice behavior for each day of the week. In doing so, household activity choice behavior is represented by three stages, with a shopping activity participation decision as the highest stage, the scheduling decision as the next lower stage, and the number of shopping stops as the lowest stage. Empirical results confirm the nested logit model structure hypothesized in this study. Furthermore, it is found that major distinctions in the daily utility functions take place between the weekday and weekend models. However, differences among weekday models are also observed. This indicates that there appear to be behavioral differences even among weekdays although households are exposed to the same set of temporal constraints.     

The influence of gay and lesbian coupled households on house prices in conservative and liberal neighborhoods

Gays and lesbians perceive themselves to be targets of discrimination in the housing market. Previous research has found that the presence of gays and lesbians is associated with increased housing values. We reconcile the perceived discrimination and research results by classifying neighborhoods as more conservative or liberal according to voting outcomes of the “Defense Of Marriage Act”. Using a data set comprised of over 20,000 house sale observations, we show that an increase in the number of same-sex coupled households is associated with an increase in house prices in more liberal neighborhoods and a decrease in house prices in more conservative neighborhoods. This suggests that gay and lesbian coupled households do experience prejudice in conservative neighborhoods.     

A Tutorial on Multilevel Survival Analysis: Methods,Models and Applications

Data that have a multilevel structure occur frequently across a range of disciplines, including epidemiology, health services research, public health, education and sociology. We describe three families of regression models for the analysis of multilevel survival data. First, Cox proportional hazards models with mixed effects incorporate cluster‐specific random effects that modify the baseline hazard function. Second, piecewise exponential survival models partition the duration of follow‐up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. This is equivalent to a Poisson regression model that incorporates the duration of exposure within each interval. By incorporating cluster‐specific random effects, generalised linear mixed models can be used to analyse these data. Third, after partitioning the duration of follow‐up into mutually exclusive intervals, one can use discrete time survival models that use a complementary log–log generalised linear model to model the occurrence of the outcome of interest within each interval. Random effects can be incorporated to account for within‐cluster homogeneity in outcomes. We illustrate the application of these methods using data consisting of patients hospitalised with a heart attack. We illustrate the application of these methods using three statistical programming languages (R, SAS and Stata).     

School district quality and property values: Examining differences along school district boundaries

Examining differences across school district boundaries rather than school attendance zone boundaries has several advantages. These advantages include being applicable when attendance zones are not available or less relevant to educational outcomes as arises with within district school choice and for examining the effect of factors like school spending or property taxes that do not vary within districts. However, school district boundaries have often been in place for many years allowing households to sort based on school quality and potentially creating distinct neighborhoods on either side of boundaries. We estimate models of housing prices using repeated cross-sections of housing transactions near school district boundaries in Connecticut. These models exploit changes over time to control for across boundary differences in neighborhood quality. We find significant effects of test scores on property values, but those effects are notably smaller than both OLS and traditional boundary fixed effects estimates.     

Endogenous household interaction

Most econometric models of intrahousehold behavior assume that household decision making is efficient, i.e., utility realizations lie on the Pareto frontier. In this paper, we investigate this claim by adding a number of participation constraints to the household allocation problem. Short-run constraints ensure that each spouse obtains a utility level at least equal to what they would realize under (inefficient) Nash equilibrium. Long-run constraints ensure that each spouse obtains a utility level at least equal to what they would realize by cheating on the efficient allocation and receiving Nash equilibrium payoffs in all successive periods. Given household characteristics and the (common) discount factor of the spouses, not all households may be able to attain payoffs on the Pareto frontier. We estimate these models using a Method of Simulated Moments estimator and data from one wave of the Panel Study of Income Dynamics. We find that both short- and long-run constraints are binding for sizable proportions of households in the sample. We conclude that it is important to carefully model the constraint sets household members face when modeling household allocation decisions, and to allow for the possibility that efficient outcomes may not be implementable for some households.     

Longitudinal Meta-analysis

The goal of meta-analysis is to integrate the research results of a number of studies on a specific topic. Characteristic for meta-analysis is that in general only the summary statistics of the studies are used and not the original data. When the published research results to be integrated are longitudinal, multilevel analysis can be used for the meta-analysis. We will demonstrate this with an example of longitudinal data on the mental development of infants. We distinguish four levels in the data. The highest level (4) is the publication, in which the results of one or more studies are published. The third level consists of the separate studies. At this level we have knowledge about the degree of prematurity of the group of infants in the specific study. The second level are the repeated measures. We have data about the test age, the mental development, the corresponding standard deviations, and the sample sizes. The lowest level is needed for the specification of the meta-analysis model. Both the way in which the multilevel model has to be specified (the Mln-program is used) as the results will be presented and interpreted.     

Bayesian averaging,prediction and nonnested model selection

This paper studies the asymptotic relationship between Bayesian model averaging and post-selection frequentist predictors in both nested and nonnested models. We derive conditions under which their difference is of a smaller order of magnitude than the inverse of the square root of the sample size in large samples. This result depends crucially on the relation between posterior odds and frequentist model selection criteria. Weak conditions are given under which consistent model selection is feasible, regardless of whether models are nested or nonnested and regardless of whether models are correctly specified or not, in the sense that they select the best model with the least number of parameters with probability converging to 1. Under these conditions, Bayesian posterior odds and BICs are consistent for selecting among nested models, but are not consistent for selecting among nonnested models and possibly overlapping models. These findings have important bearing for applied researchers who are frequent users of model selection tools for empirical investigation of model predictions.     

The effect of estimation method and sample size in multilevel structural equation modeling

Multilevel structural equation modeling (multilevel SEM) has become an established method to analyze multilevel multivariate data. The first useful estimation method was the pseudobalanced method. This method is approximate because it assumes that all groups have the same size, and ignores unbalance when it exists. In addition, full information maximum likelihood (ML) estimation is now available, which is often combined with robust chi‐squares and standard errors to accommodate unmodeled heterogeneity (MLR). In addition, diagonally weighted least squares (DWLS) methods have become available as estimation methods. This article compares the pseudobalanced estimation method, ML(R), and two DWLS methods by simulating a multilevel factor model with unbalanced data. The simulations included different sample sizes at the individual and group levels and different intraclass correlation (ICC). The within‐group part of the model posed no problems. In the between part of the model, the different ICC sizes had no effect. There is a clear interaction effect between number of groups and estimation method. ML reaches unbiasedness fastest, then the two DWLS methods, then MLR, and then the pseudobalanced method (which needs more than 200 groups). We conclude that both ML(R) and DWLS are genuine improvements on the pseudobalanced approximation. With small sample sizes, the robust methods are not recommended.     

Sustainable development of small‐ and medium‐sized enterprises in the European Union: A taxonomy of circular economy practices

This paper focuses on the implementation of circular economy (CE) practices in small‐ and medium‐sized firms in all 28 European Union (EU) countries. The analyses take into account the hierarchical nature of the collected data as firms are nested within EU countries, that is, the heterogeneity between different types of firms and countries according to practices and attitudes towards CE. The multilevel latent class model identifies groups of firms and groups of EU countries that are homogeneous in terms of CE, that is, how the homogeneous groups of small‐ and medium‐sized enterprises (SMEs) are distributed across the groups of EU countries. These results, together with the fact that firms with similar CE attitudes and practices have different demographic and business profiles across groups of countries, shed further light on the topic of green behavior in the EU with implications for businesses' environmental policies. Moreover, indications emerge that European policies favoring the implementation of CE practices should be targeted at least for subgroups of European countries, considering the different composition by typology of SMEs operating in their territories and that, at the same time, policies should be defined within each group of countries to account for the specific features of each of the four classes of SMEs.     

A comparison of four methods for projecting households

"The purpose of this paper is to extend the headship rate method for projecting households to encompass both sexes. Four models are considered that explicitly incorporate the impact of changes in the number of men and women on the number and joint age distribution of husband-wife households. The models are applied to the Philippines using data from the 1988 National Demographic Survey to project households to 2010. The models are also evaluated by 'backcasting' and comparing the results with special tabulations from the 1970 and 1980 censuses and the 1975 National Demographic Survey."     

INTRAHOUSEHOLD INEQUALITY AT DIFFERENT WELFARE LEVELS: ENERGY INTAKE AND ENERGY EXPENDITURE DATA FROM THE PHILIPPINES*

ABSTRACTS This paper investigates the relationship between intrahousehold inequality and levels of household welfare. Under certain conditions it is demonstrated — with both the unitary model of the household and with some collective models — that the relationship between household welfare and inequality within the household can have an inverted u-shape. Using two sets of calorie adequacy data from a sample of 455 households in the Philippines, a spline analysis is used to test the hypothesis that inequality within the household first increases and then decreases as per capita household total expenditure increases. The two sets of calorie adequacy data are based on repeated 24- hour recalls of dietary intake, and on calorie requirements that are unadjusted and then adjusted for individual activity patterns. Results indicate that once activity patterns are accounted for, calorie intake shortfalls are borne fairly equally within the household at all per capita household total expenditure levels.     

Multilevel theory and the underspecification of multilevel models

This article emphasises the importance of the adequate specification of models of multilevel analysis in accordance with multilevel theories. Until recent times, multilevel theories tried only to explain the direct effect of group characteristics on an individual's characteristic. It seems to be more suited to adopt a more general theoretical approach, in which it is assumed that group characteristics affects individual processes. There a treshold effect and a process effect have to be distinguished. The propositions result in a model specification within the random coefficient model of multilevel analysis. The theory and model recommended are illustrated by means of data of Dar and Resh's (1986) study into social learning environment.     

Nested Unobserved Heterogeneity not Orthogonal to Regressors

Many social phenomena are of a nested nature and recordedin hierarchical data, e.g., repeated observations of siblings (individual level) within families(context level). In these phenomena, unobserved heterogeneity can occur at both levels and may becorrelated with the regressors. This article addresses nested unobserved heterogeneity notorthogonal to the regressors, which is rarely discussed in the methodology literature. The articleextends the econometric one-factor fixed-effects approach to handle nested fixed effects.F tests for model comparisons are used to test whether the total heterogeneity exists and whetherthe total heterogeneity consists solely of contextual heterogeneity. It then introduces methods todecompose the two levels of heterogeneity and provides formal tests for each level andtheir relative importance, which are developed from classical ANOVA. To provide a stronger testfor time-varying context-specific heterogeneity, the article develops an estimator using thedifference-in-differences method. An empirical example of a study on child behavior problems isused to illustrate the methods introduced in the article.     

Neighborhood income distributions

This paper studies purely empirically aspects of the distribution of income within small neighborhoods and contrasts it with the income distribution at higher level of aggregation, such as census tracts and metropolitan areas. It relies on a unique feature of the American Housing Survey, whose 1985, 1989 and 1993 waves provide data for small residential neighborhoods. These consist of a dwelling unit and up to ten of its nearest neighbors. The paper employs several parametric and non-parametric econometric tools to measure income sorting in US residential neighborhoods. It documents the patterns of dependence among neighbors' income and imperfect sorting, with moderate but very significant correlation among incomes of neighbors and of considerable income mixing in US neighborhoods. These results persist even if choice-based sampling and heterogeneity across the sample are accounted for. Neighborhoods associated with a randomly selected renter are more sorted than those associated with an owner even though such owners are more likely to define their neighborhoods.     

Intrametropolitan migration: A simultaneous model of employment and residential location of white and black households

This study explores the relationship between the home and job location of the household within a metropolitan area. The model is an extension of the Alonso-Muth framework and allows the household to simultaneously choose its residential and employment location, monthly rent, number of rooms and the type of structure of the dwelling unit, so as to maximize its utility subject to the budget constraint.The data base is the Home Interview Survey conducted in 1965 by the (San Francisco) Bay Area Transportation Study Commission. The coefficients in the simultaneous model are estimated by two-stage least squares. The cross-section sample is stratified by tenure into renters and homeowners; by race into black and white households; and further by position in the life-cycle.The results of the location equations reveal that both the home and job location are responsive to each other which implies that the decentralization of jobs will result in the decentralization of residences for black households. The results of the housing equations imply that black and white households have almost identical elasticity of demand for housing. There is very slight evidence of price discrimination against black households. However, there is evidence that black households do face a geographical segregated market for rental housing.The results of this study argue that the decentralization of population is not due solely to rising incomes and will continue as long as industry decentralizes. Furthermore, governmental policies of increasing the income of central city residents, through subsidies to employers to locate in the central city will have a strong effect: inducing those residents to remain in or move into the central city. Thus, a policy that is meant to alleviate the plight of the city center may only result in strengthening the racial and income split between the suburb and the central city inhabitants.     

Long-term effects of public low-income housing vouchers on neighborhood quality and household composition

In this paper we estimate the effect of housing voucher receipt on the composition of recipient households and the quality of the neighborhoods in which recipient households reside. Drawing on a dataset that contains extensive information on a large and diverse panel of low-income families for up to 5 years following voucher receipt, we isolate the effects of voucher receipt using propensity score matching techniques together with regression adjustment. Full-sample results show voucher receipt to have little effect on neighborhood quality in the short-term, but some positive long-term effects. We also find that voucher receipt is tied to a higher probability of change in household composition in the year of voucher receipt, but greater stability in subsequent years. Our large sample allows us to explore differential responses of geographic and socioeconomic subgroups. Our findings have several implications for both research and policy.