南亚国家卫生生产函数的估算

Istihak Rayhan, R. Hasan, M. Akter
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引用次数: 3

摘要

本研究的目的是估计南亚国家的卫生生产函数。健康生产函数表达了健康状况与卫生保健投入之间的函数关系,而出生时预期寿命已被广泛用作一个国家人口健康状况的指标,卫生保健投入可分为三大类:经济因素、社会因素和环境因素。估计南亚国家的卫生生产函数是一项前期工作。7个南亚国家1995-2015年的平衡面板数据取自《2017年世界发展指标》。采用Breusch-Pagan、Honda、King-Wu、标准化Honda和标准化King-Wu拉格朗日乘数检验对集合OLS模型的随机效应进行检验。采用Hausman检验在固定效应和随机效应模型之间选择合适的模型。采用bruch - pagan LM、Pesaran LM和Baltagi、Kao和Feng校正偏差的尺度检验检验残差的横截面相关性。面板校正标准误差(PCSE)模型用于处理残差的同期相关性。在本研究中,人均卫生支出和粮食生产指数被用作经济因素,教育和获得改善的水设施被用作社会因素,城市化被用作环境因素。实证结果表明,人均卫生支出、教育、获得改良水源和城市化对预期寿命有统计学显著的正影响,而粮食生产指数对南亚国家的影响在统计学上显著为负。本研究的结果有助于决策者采取适当的政策来延长南亚国家的预期寿命。
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Estimating Health Production Function for the South Asian Countries
The aim of this study is to estimate the health production function for the south Asian countries. The health production function expresses the functional relationship between health status and health care inputs, while life expectancy at birth has been widely used as an indicator of population health status of a country and health care inputs can be categorized into three broader categories named economic factors, social factors and environmental factors. It is a prior work to estimate health production function for the south Asian countries. A balanced panel data of seven South Asian countries are taken for the period of 1995-2015 from World Development Indicator 2017. Breusch-Pagan, Honda, King-Wu, Standardized Honda and Standardized King-Wu Lagrange Multiplier test are used to test the random effects on pooled OLS model. Hausman test is used to select the appropriate model between fixed effect and random effect model. Breusch-Pagan LM, Pesaran LM and Baltagi, Kao and Feng bias corrected scaled test are performed to check the cross-sectional dependence of the residuals. Panel Corrected Standard Error (PCSE) model has been used to deal with contemporaneous correlation in residuals. In this study health expenditure per capita and food production index are used as economic factors, education and access to improved water facilities are used as social factors and urbanization is used environmental factors. Empirical results reveal that health expenditure per capita, education, access to improved water sources and urbanization have statistical significant positive impact on life expectancy, but the impact of food production index is found statistically significantly negative in the south Asian countries. The findings of this study help the policy makers to take a suitable policy for extending life expectancy in the South Asian countries.
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