A collection of marginalized two-part random-effects models for analyzing medical expenditure panel data: Impact of the New Cooperative Medical Scheme on healthcare expenditures in China

2018 ◽  
Vol 28 (8) ◽  
pp. 2494-2523 ◽  
Author(s):  
Bo Zhang ◽  
Wei Liu ◽  
Ning Zhang ◽  
Arlene S Ash ◽  
Jeroan J Allison
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Medical Expenditure ◽  
Marginal Effect ◽  
Healthcare Expenditures ◽  
New Cooperative Medical Scheme ◽  
Random Effects Models ◽  
Generalized Gamma ◽  
Incremental Effect ◽  
Cooperative Medical Scheme

Marginalized two-part random-effects generalized Gamma models have been proposed for analyzing medical expenditure panel data with excessive zeros. While these models provide marginal inference on expected healthcare expenditures, the usual unilateral specification of heteroscedastic variance on one of the two shape parameters for the generalized Gamma distribution in these models fails to encompass important special cases within the generalized gamma modeling framework. In this article, we construct marginalized two-part random-effects models that employ the log-normal, log-skew-normal, generalized Gamma, Weibull, Gamma, and inverse Gamma distributions to delineate the spectrum of nonzero healthcare expenditures in the second part of the models. These marginalized models supply additional choices for analyzing healthcare expenditure panel data with excessive zeros. We review the concepts of marginal effect and incremental effect, and summarize how these effects are estimated. For studies whose primary goal is to make inference on marginal effect or incremental effect of an independent variable with respect to healthcare expenditures, we advocate empirical mean square error criterion and information criteria to choose among candidate models. Then, we use the proposed models in an empirical analysis to examine the impact of the New Cooperative Medical Scheme on healthcare expenditures among older adults in rural China.

Estimating marginal and incremental effects in the analysis of medical expenditure panel data using marginalized two-part random-effects generalized Gamma models: Evidence from China healthcare cost data

2017 ◽  
Vol 27 (10) ◽  
pp. 3039-3061
Author(s):  
Bo Zhang ◽  
Wei Liu ◽  
Yingyao Hu
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Healthcare Cost ◽  
Healthcare Costs ◽  
Medical Expenditure ◽  
Parameter Estimates ◽  
Type I ◽  
Type Ii ◽  
Generalized Gamma ◽  
Marginal Inference

Conditional two-part random-effects models have been proposed for the analysis of healthcare cost panel data that contain both zero costs from the non-users of healthcare facilities and positive costs from the users. These models have been extended to accommodate more flexible data structures when using the generalized Gamma distribution to model the positive healthcare expenditures. However, a major drawback with the extended model, which is inherited from the conditional models, is that it is fairly difficult to make direct marginal inference with respect to overall healthcare costs that includes both zeros and non-zeros, or even on positive healthcare costs. In this article, we first propose two types of marginalized two-part random-effects generalized Gamma models (m2RGGMs): Type I m2RGGMs for the inference on positive healthcare costs and Type II m2RGGMs for the inference on overall healthcare costs. Then, the concepts of marginal effect and incremental effect of a covariate on overall and positive healthcare costs are introduced, and estimation of these effects is carefully discussed. Especially, we derive the variance estimates of these effects by following the delta methods and Taylor series approximations for the purpose of making marginal inference. Parameter estimates of Type I and Type II m2RGGMs are obtained through maximum likelihood estimation. An empirical analysis of longitudinal healthcare costs collected in the China Health and Nutrition Survey is conducted using the proposed methodologies.

scholarly journals Heterogeneous Impacts of Basic Social Health Insurance on Medical Expenditure: Evidence from China’s New Cooperative Medical Scheme

Healthcare ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 131 ◽  
Author(s):  
Fang ◽  
He ◽  
Rozelle ◽  
Shi ◽  
Sun ◽  
...  
Keyword(s):  
Low Income ◽  
Social Health Insurance ◽  
Good Health ◽  
Income Group ◽  
Medical Expenditure ◽  
High Income Group ◽  
New Cooperative Medical Scheme ◽  
Health Group ◽  
Cooperative Medical Scheme ◽  
Low Income Groups

This paper examines the effects of China’s New Cooperative Medical Scheme (NCMS) on medical expenditure. Utilizing the quasi-random rollout of the NCMS for a difference-in-difference analysis, we find that the NCMS increased medical expenditure by 12.3%. Most significantly, the good-health group witnessed a 22.1% rise in medical expenditure, and the high-income group saw a rise of 20.6%. The effects, however, were not significant among the poor-health or low-income groups. The findings are suggestive of the need for more help for the very poor and less healthy.

scholarly journals Non-Negativity of a Quadratic form with Applications to Panel Data Estimation, Forecasting and Optimization

Stats ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 185-202
Author(s):  
Bhimasankaram Pochiraju ◽  
Sridhar Seshadri ◽  
Dimitrios Thomakos ◽  
Konstantinos Nikolopoulos
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Symmetric Matrix ◽  
Panel Data Models ◽  
Random Effects Models ◽  
Panel Data Estimation ◽  
Quadratic Minimization ◽  
Fixed And Random Effects ◽  
Estimated Error ◽  
Data Estimation

For a symmetric matrix B, we determine the class of Q such that Q t BQ is non-negative definite and apply it to panel data estimation and forecasting: the Hausman test for testing the endogeneity of the random effects in panel data models. We show that the test can be performed if the estimated error variances in the fixed and random effects models satisfy a specific inequality. If it fails, we discuss the restrictions under which the test can be performed. We show that estimators satisfying the inequality exist. Furthermore, we discuss an application to a constrained quadratic minimization problem with an indefinite objective function.

scholarly journals Determinants of Firm Leverage in Chile: Evidence from Panel Data

2020 ◽  
Vol 12 (1) ◽  
pp. 41 ◽  
Author(s):  
Viviana Fernández
Keyword(s):  
Panel Data ◽  
Capital Structure ◽  
Random Effects ◽  
Driving Forces ◽  
Developed Countries ◽  
Extensive Literature ◽  
Economic Sectors ◽  
Specification Tests ◽  
Pecking Order ◽  
Random Effects Models

There is an extensive literature on the determinants of capital structure for developed countries, but little has been said about emerging economies. This article analyzes the driving forces of capital structure in Chile for the period 1990-2002. We study interest-bearing liabilities for firms classified by economic sectors. Our results give more support to the trade-off theory than to the pecking-order hypothesis. The contribution of our work is also methodological. Our econometric specification is based on a random-effects panel data model for censored data developed by Anderson (1986) and extended by Kim and Maddala (1992). We extend Anderson-Kim-Maddala’s work to panel data models for uncensored data, and devise specification tests for non-nested random-effects models. Most literature on capital structure focuses on the cross-section variation of the data by averaging observations over time.

Economic Development Effects of Major and Minor League Teams and Stadiums

2020 ◽  
pp. 152700252097584
Author(s):  
Nola Agha ◽  
Daniel Rascher
Keyword(s):  
Time Series ◽  
Economic Development ◽  
Panel Data ◽  
Random Effects ◽  
Census Bureau ◽  
Difference In Differences ◽  
Cross Sectional ◽  
Random Effects Models ◽  
Minor League ◽  
Time Series Comparison

Professional teams and leagues claim new stadiums lead to economic development. To test this, we utilize data from the Census Bureau on net establishment and employment changes across 871 Metropolitan and Micropolitan Statistical Areas from 2004 to 2012. Difference-in-differences and panel data techniques allow for a cross-sectional and time series comparison for both teams and new stadia in both professional and development leagues. Nearly all results from hundreds of models are insignificantly different from zero. Results from between- and random-effects models suggest that teams move into markets that already have higher employment and establishment growth. (JEL R58, H71, L83, Z28)

scholarly journals Banking system, Institutional quality, and Economic growth: Panel data analysis on a sample of countries in the MENA region

2021 ◽  
Vol 2 (2) ◽  
pp. 122-128
Author(s):  
Lamia Jamel
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Financial Development ◽  
Random Effects ◽  
Fixed Effects ◽  
Panel Data Analysis ◽  
Banking System ◽  
Institutional Quality ◽  
Marginal Effect ◽  
Mena Region

The nexus among financial development and economic growth has long remained a subject matter of considerable debate in the financial and economic literature. This article examines the relationships between financial development, institutions, and economic growth. This indicates that the marginal effect of financial development on economic growth depends on the quality of institutions. To do so, we employ a panel data of 14 MENA countries during the period of study from 2008 to 2019. For the econometric methodology, we use fixed and random effects models. To choose between fixed effects and random effects, we employ the Hausman test. Based on the empirical findings, we demonstrate that financial development has a positive effect on economic growth. Furthermore, we have observed that institutional quality seems to be a necessary complement to financial development. Consequently, it is important to implement policies leading to the deepening of financial systems, through a including a solid institutional framework. Thus, by promoting such development and better institutional quality, economic growth will thus be accelerated.

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