While salary benchmarking is widely used to help set compensation, there has been little econometric modeling of peer-group identification and the impacts of benchmarking on the compensation decision. We adapt some empirical tools from spatial econometrics to analyze compensation decisions...

We propose a new methodology to revise the international poverty line (IPL) after Ravallion et al. (2009) using the same database, but augmented with new variables to take into account social inclusion in the definition of poverty along the lines of Atkinson and Bourguignon (2001). We provide an...

The persistent correlations between income and mortality, and inequality and mortality in the US is well established. It has been demonstrated with various empirical models at different levels of analysis. However, there is no consensus on the relevant confounding factors or even the functional...

This tutorial is designed to introduce readers to Bayesian variants of the standard SAR and SEM models that are the most widely used and applied models in spatial econometrics. Particular attention is paid to the mathematical derivations required to obtain the full conditional distributions...

This article describes an R package bqror that estimates Bayesian quantile regression for ordinal models introduced in Rahman (2016). The paper classifies ordinal models into two types and offers computationally efficient, yet simple, MCMC algorithms for estimating ordinal quantile regression....

This paper develops new econometric methods to estimate hospital quality and other models with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are widely used to infer hospital quality. However, hospital admission is not random and some...

We develop a Markov Chain Monte Carlo algorithm for estimating nested logit models in a Bayesian framework. Appropriate "heating target" and reparametrization techniques are adopted for fast mixing. For illustrative purposes, we have implemented the algorithm on two real-life examples involving...

We develop a Markov Chain Monte Carlo algorithm for estimating nested logit models in a Bayesian framework. Appropriate "heating target" and reparametrization techniques are adopted for fast mixing. For illustrative purposes, we have implemented the algorithm on two real-life examples involving...

In this paper, we investigate the impact of peers on own outcomes where all agents embedded in a network choose more than one activity. We develop a simple network model that illustrates these issues. We differentiate between the ‘seemingly unrelated’ simultaneous equations model where...