Model-based approach for household clustering with mixed scale variables

The Ministry of Social Development in Mexico is in charge of creating and assigning social programmes targeting specific needs in the population for the improvement of the quality of life. To better target the social programmes, the Ministry is aimed to find clusters of households with the same needs based on demographic characteristics as well as poverty conditions of the household. Available data consists of continuous, ordinal, and nominal variables, all of which come from a non-i.i.d complex design survey sample. We propose a Bayesian nonparametric mixture model that jointly models a set of latent variables, as in an underlying variable response approach, associated to the observed mixed scale data and accommodates for the different sampling probabilities. The performance of the model is assessed via simulated data. A full analysis of socio-economic conditions in households in the Mexican State of Mexico is presented

Clustering South African households based on their asset status using latent variable models

The Agincourt Health and Demographic Surveillance System has since 2001 conducted a biannual household asset survey in order to quantify household socio-economic status (SES) in a rural population living in northeast South Africa. The survey contains binary, ordinal and nominal items. In the absence of income or expenditure data, the SES landscape in the study population is explored and described by clustering the households into homogeneous groups based on their asset status. A model-based approach to clustering the Agincourt households, based on latent variable models, is proposed. In the case of modeling binary or ordinal items, item response theory models are employed. For nominal survey items, a factor analysis model, similar in nature to a multinomial probit model, is used. Both model types have an underlying latent variable structure - this similarity is exploited and the models are combined to produce a hybrid model capable of handling mixed data types. Further, a mixture of the hybrid models is considered to provide clustering capabilities within the context of mixed binary, ordinal and nominal response data. The proposed model is termed a mixture of factor analyzers for mixed data (MFA-MD). The MFA-MD model is applied to the survey data to cluster the Agincourt households into homogeneous groups. The model is estimated within the Bayesian paradigm, using a Markov chain Monte Carlo algorithm. Intuitive groupings result, providing insight to the different socio-economic strata within the Agincourt region.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS726 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Combining Household Income and Expenditure Data in Policy Simulations

Analysis of the distributional impact of fiscal policy proposals often requires information on household expenditures and incomes. It is unusual to have one data source with high quality information on both, and this problem is generally overcome with statistical matching of independent data sources. In this paper Grade Correspondence Analysis (GCA) is investigated as a tool to improve the matching process. An evaluation of alternative methods is conducted using datasets from the UK Family Expenditure Survey (FES), which is unusual in containing both income and expenditure at a detailed level of disaggregation. Imputed expenditures are compared with actual expenditures through the use of indirect tax simulations using the UK microsimulation model, POLIMOD. The most successful methods are then employed to enhance income data from the Family Resources Survey (FRS) and the synthetic dataset is used as a microsimulation model dataset

Multidimensional Urban Segregation - Toward A Neural Network Measure

We introduce a multidimensional, neural-network approach to reveal and measure urban segregation phenomena, based on the Self-Organizing Map algorithm (SOM). The multidimensionality of SOM allows one to apprehend a large number of variables simultaneously, defined on census or other types of statistical blocks, and to perform clustering along them. Levels of segregation are then measured through correlations between distances on the neural network and distances on the actual geographical map. Further, the stochasticity of SOM enables one to quantify levels of heterogeneity across census blocks. We illustrate this new method on data available for the city of Paris.Comment: NCAA S.I. WSOM+ 201

Impact of different time series aggregation methods on optimal energy system design

Modelling renewable energy systems is a computationally-demanding task due to the high fluctuation of supply and demand time series. To reduce the scale of these, this paper discusses different methods for their aggregation into typical periods. Each aggregation method is applied to a different type of energy system model, making the methods fairly incomparable. To overcome this, the different aggregation methods are first extended so that they can be applied to all types of multidimensional time series and then compared by applying them to different energy system configurations and analyzing their impact on the cost optimal design. It was found that regardless of the method, time series aggregation allows for significantly reduced computational resources. Nevertheless, averaged values lead to underestimation of the real system cost in comparison to the use of representative periods from the original time series. The aggregation method itself, e.g. k means clustering, plays a minor role. More significant is the system considered: Energy systems utilizing centralized resources require fewer typical periods for a feasible system design in comparison to systems with a higher share of renewable feed-in. Furthermore, for energy systems based on seasonal storage, currently existing models integration of typical periods is not suitable

Childhood mortality in sub-Saharan Africa : cross-sectional insight into small-scale geographical inequalities from Census data

Objectives To estimate and quantify childhood mortality, its spatial correlates and the impact of potential correlates using recent census data from three sub-Saharan African countries (Rwanda, Senegal and Uganda), where evidence is lacking. Design Cross-sectional. Setting Nation-wide census samples from three African countries participating in the 2010 African Census round. All three countries have conducted recent censuses and have information on mortality of children under 5 years. Participants 111 288 children under the age of 5 years in three countries. Primary and secondary outcome measures Under-five mortality was assessed alongside potential correlates including geographical location (where children live), and environmental, bio-demographic and socioeconomic variables. Results Multivariate analysis indicates that in all three countries the overall risk of child death in the first 5 years of life has decreased in recent years (Rwanda: HR=0.04, 95% CI 0.02 to 0.09; Senegal: HR=0.02 (95% CI 0.02 to 0.05); Uganda: HR=0.011 (95% CI 0.006 to 0.018). In Rwanda, lower deaths were associated with living in urban areas (0.79, 0.73, 0.83), children with living mother (HR=0.16, 95% CI 0.15 to 0.17) or living father (HR=0.38, 95% CI 0.36 to 0.39). Higher death was associated with male children (HR=1.06, 95% CI 1.02 to 1.08) and Christian children (HR=1.14, 95% CI 1.05 to 1.27). Children less than 1 year were associated with higher risk of death compared to older children in the three countries. Also, there were significant spatial variations showing inequalities in children mortality by geographic location. In Uganda, for example, areas of high risk are in the south-west and north-west and Kampala district showed a significantly reduced risk. Conclusions We provide clear evidence of considerable geographical variation of under-five mortality which is unexplained by factors considered in the data. The resulting under-five mortality maps can be used as a practical tool for monitoring progress within countries for the Millennium Development Goal 4 to reduce under-five mortality in half by 2015

The Effect of Neighbourhood Housing Tenure Mix on Labour Market Outcomes: A Longitudinal Perspective

This paper investigates the effect of different levels of neighbourhood housing tenure mix on transitions from unemployment to employment and the probability of staying in employment for those with a job. We used individual level data from the Scottish Longitudinal Study (SLS), a 5.3% sample of the Scottish population, covering a 10 year period. We found a strong negative correlation between living in deprived neighbourhoods and labour market outcomes (getting or keeping a job). We found a small, but significant, positive correlation between living in mixed tenure (40-80% social housing) streets and transitions from unemployment to employment. In the conclusion we discuss the extent to which we think these results can be interpreted as 'neighbourhood effects' or selection effects.tenure mix, neighbourhood effects, labour market transitions, deprivation, longitudinal data, Scotland

Decomposing differences in labour force status between Indigenous and non-Indigenous Australians

Despite several policy efforts to promote economic participation by Indigenous Australians, they continue to have low participation rates compared to non-Indigenous Australians. This study decomposes the gap in labour market attachment between Indigenous and non- Indigenous Australians in non-remote areas, combining two separate data sources in a novel way to obtain access to richer information than was previously possible. It shows that among women at least two thirds of the gap can be attributed to differences in the observed characteristics between the two populations. For men, the differences in observed characteristics of the two populations can account for 36 to 47 percent of the gap. A detailed decomposition shows that lower education, worse health, and larger families (particularly for women) explain the lower labour market attachment of Indigenous Australians to a substantial extent. Compared with previous studies, this study is able to explain a larger proportion of the gap in employment between Indigenous and non-Indigenous people due to being able to include a larger set of explanatory variables. Authored by Guyonne Kalb, Trinh Le, Boyd Hunter and Felix Leung

Parental Educational Investment and Children's Academic Risk: Estimates of the Impact of Sibship Size and Birth Order from Exogenous Variations in Fertility

The stylized fact that individuals who come from families with more children are disadvantaged in the schooling process has been one of the most robust effects in human capital and stratification research over the last few decades. For example, Featherman and Hauser (1978: 242-243) estimate that each additional brother or sister costs respondents on the order of a fifth of a year of schooling. However, more recent analyses suggest that the detrimental effects of sibship size on children's educational achievement might be spurious. We extend these recent analyses of spuriousness versus causality using a different method and a different set of outcome measures. We suggest an instrumental variable approach to estimate the effect of sibship size on children's private school attendance and on their likelihood of being held back in school. Specifically, we deploy the sex-mix instrument used by Angrist and Evans (1998). Analyses of educational data from the 1990 PUMS five percent sample reveal that children from larger families are less likely to attend private school and are more likely to be held back in school. Our estimates are smaller than traditional OLS estimates, but are nevertheless greater than zero. Most interesting is the fact that the effect of sibship size is uniformly strongest for latter-born children and zero for first born children.

Inclusive Public Housing: Services for the Hard to House

Evaluates the Chicago Family Case Management Demonstration, a model for comprehensive services targeting families with multiple complex problems that are ineligible for mixed-income housing or unable to negotiate the private market. Outlines implications