Two-part quantile regression models for semi-continuous longitudinal data: A finite mixture approach

This article develops a two-part finite mixture quantile regression model for semi-continuous longitudinal data. The proposed methodology allows heterogeneity sources that influence the model for the binary response variable to also influence the distribution of the positive outcomes. As is common in the quantile regression literature, estimation and inference on the model parameters are based on the asymmetric Laplace distribution. Maximum likelihood estimates are obtained through the EM algorithm without parametric assumptions on the random effects distribution. In addition, a penalized version of the EM algorithm is presented to tackle the problem of variable selection. The proposed statistical method is applied to the well-known RAND Health Insurance Experiment dataset which gives further insights on its empirical behaviour.

The Design of the RAND Health Insurance Experiment: A Retrospective

This article, prepared as part of a special issue on multiarmed experiments, describes the design of the RAND Health Insurance Experiment, paying particular attention to the choice of arms. It also describes how the results of the Experiment were used in a simulation model and, looking back, how the design might have differed, and how the results apply today, 4 decades after the Experiment was conducted.

APAKAH PEKERJA MIGRAN INDONESIA SEHAT MENTAL?

This study aims to describe the mental health of Indonesian migrant workers in Hong Kong. The respondents of this study were Indonesian female migrant workers who worked in domestic sectors in Hong Kong, the number of respondents in this study were 100 respondent, female Indonesia migrant workers. This study uses survey method and quantitative approach with a sampling technique aimed at criteria of minimum 1 year working period, women and working in the household or domestic sector. This research was carried out using a mental health questionnaire. Mental Health Inventory (MHI) constructed by RAND Health Insurance Experiment (Veil & Ware, 1983) 38 items that measure aspects of anxiety, depression, emotional control, affect. Data analysis using SPSS is by using description analysis and different test (T-test). Result shown that in general respondents showing a good mental health condition (81%), very good mental health condition (1%), and poor mental health condition (18%).Keywords: mental health, Indonesian migrant workers, women domestic workersAbstrakPenelitian ini bertujuan untuk mengetahui gambaran kesehatan mental pekerja rumah tangga perempuan migran Indonesia di Hong Kong. Responden penelitian ini adalah pekerja migran perempuan Indonesia yang bekerja sebagai pekerja rumah tangga di Hong Kong, jumlah responden penelitian ini adalah sebanyak 100 orang pekerja rumah tangga perempuan asal Indonesia. Penelitian ini menggunakan metode survey dengan pendekatan kuantitatif dengan teknik sampel bertujuan kriteria minimal masa kerja 1 tahun, perempuan dan bekerja pada sektor rumah tangga. Penelitan ini dilakukan dengan menggunakan alat ukur kuesioner kesehatan mental Mental Health Inventory (MHI) dikontruksi oleh RAND Health Insurance Experiment (Veil & Ware, 1983) yang terdiri dari 38 item yang mengukur aspek kecemasan, depresi, kontrol emosi, afek. Analisis data menggunakan SPSS yaitu dengan menggunakan analisis deskripsi dan uji beda (T-test) dan berdasarkan hasil analisa menggunakan SPSS menunjukkan bahwa secara umum responden mengindikasikan kondisi kesehatan mental yang kurang baik (18%), baik (81%), dan sangat baik (1%). Kata kunci: Kesehatan mental, pekerja migran Indonesia, pekerja rumah tangga perempuan

Estimating Poisson pseudo-maximum-likelihood rather than log-linear model of a log-transformed dependent variable

Purpose The purpose of this study is to account for a recent non-mainstream econometric approach using microdata and how it can inform research in business administration. More specifically, the paper draws from the applied microeconometric literature stances in favor of fitting Poisson regression with robust standard errors rather than the OLS linear regression of a log-transformed dependent variable. In addition, the authors point to the appropriate Stata coding and take into account the possibility of failing to check for the existence of the estimates – convergency issues – as well as being sensitive to numerical problems. Design/methodology/approach The author details the main issues with the log-linear model, drawing from the applied econometric literature in favor of estimating multiplicative models for non-count data. Then, he provides the Stata commands and illustrates the differences in the coefficient and standard errors between both OLS and Poisson models using the health expenditure dataset from the RAND Health Insurance Experiment (RHIE). Findings The results indicate that the use of Poisson pseudo maximum likelihood estimators yield better results that the log-linear model, as well as other alternative models, such as Tobit and two-part models. Originality/value The originality of this study lies in demonstrating an alternative microeconometric technique to deal with positive skewness of dependent variables.

Health Insurance and the Demand for Healthcare

Health insurance increases the demand for healthcare. Since the RAND Health Insurance Experiment in the 1970s this has been demonstrated in many contexts and many countries. From an economic point of view this fact raises the concern that individuals demand too much healthcare if insured, which generates a welfare loss to society. This so-called moral hazard effect arises because individuals demand healthcare that has less value to them than it costs to provide it. For that reason, modern health insurance plans include demand side cost-sharing instruments like deductibles and copayments. There is a large and growing literature analyzing the effects of these cost-sharing instruments on healthcare demand. Three issues have recently received increasing attention. First, cost-sharing instruments such as yearly deductibles combined with stop losses create nonlinear price schedules and dynamic incentives. This generates the question of whether patients understand the incentives and what price individuals use to determine their healthcare demand. Second, it appears implausible that patients know the benefits of healthcare (which is crucial for the moral hazard argument). If patients systematically underestimated these benefits they would demand too little healthcare without health insurance. Providing health insurance and increasing healthcare demand in this case may increase social welfare. Finally, what is the role of healthcare providers? They have been completely absent in the majority of the literature analyzing the demand for healthcare, but there is striking evidence that the physicians often determine large parts of healthcare spending.