Multilevel regression mixture analysis

Summary.  A two-level regression mixture model is discussed and contrasted with the conventional two-level regression model. Simulated and real data shed light on the modelling alternatives. The real data analyses investigate gender differences in mathematics achievement from the US National Education Longitudinal Survey. The two-level regression mixture analyses show that unobserved heterogeneity should not be presupposed to exist only at level 2 at the expense of level 1. Both the simulated and the real data analyses show that level 1 heterogeneity in the form of latent classes can be mistaken for level 2 heterogeneity in the form of the random effects that are used in conventional two-level regression analysis. Because of this, mixture models have an important role to play in multilevel regression analyses. Mixture models allow heterogeneity to be investigated more fully, more correctly attributing different portions of the heterogeneity to the different levels.     

Impacts of off‐farm employment on welfare,food security and poverty: Evidence from rural Vietnam

How off‐farm employment can enhance welfare in terms of food consumption and poverty alleviation is a critical question facing many developing countries. This study addressed that question by pursuing two objectives: (i) to quantify the impact of off‐farm employment on rural households’ welfare, food security and poverty; and (ii) to examine the factors that affect their decision to work off‐farm. Using panel data, we estimated a difference‐in‐difference combined with a propensity score matching model. The findings show that off‐farm employment improves income, ensures food security and contributes to poverty alleviation. The results also show that age, marital status, education, labour, financial capital, land, location, market access and losses from natural disasters are significant contributing factors to the decision to participate in off‐farm employment. The findings suggest that to improve the welfare of rural households, the Vietnamese government should proceed with policies that enhance their opportunities for participation in off‐farm employment.     

General Multi-Level Modeling with Sampling Weights

ABSTRACT

In this article we study the approximately unbiased multi-level pseudo maximum likelihood (MPML) estimation method for general multi-level modeling with sampling weights. We conduct a simulation study to determine the effect various factors have on the estimation method. The factors we included in this study are scaling method, size of clusters, invariance of selection, informativeness of selection, intraclass correlation, and variability of standardized weights. The scaling method is an indicator of how the weights are normalized on each level. The invariance of the selection is an indicator of whether or not the same selection mechanism is applied across clusters. The informativeness of the selection is an indicator of how biased the selection is. We summarize our findings and recommend a multi-stage procedure based on the MPML method that can be used in practical applications.