UNIVERSITY OF ECONOMICS ERASMUS UNVERSITY ROTTERDAM HO CHI MINH CITY INSTITUTE OF SOCIAL STUDIES VIETNAM THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS IMPACT OF INCOME INEQUALITY ON HEALTH IN MIDDLE AND HIGH INCOME COUNTRIES IN 1991 - 2010 BY PHAM DANG XUAN ANH MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, November 2016 UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS IMPACT OF INCOME INEQUALITY ON HEALTH IN MIDDLE AND HIGH INCOME COUNTRIES IN 1991 - 2010 A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By PHAM DANG XUAN ANH Academic Supervisor: DR. NGUYEN VAN NGAI HO CHI MINH CITY, November 2016 2 Declaration I hereby declare that this thesis has been exclusively the original work of myself and the result of my own research, except where due reference has been made in the content, and free from plagiarism of the work of others. I also certify that this master thesis has not been accepted in any degree or not under submission for any other degree or qualification, other than that of the degree of Master of Arts in Development Economics at Vietnam - Netherlands Programme. 3 Abstract: The income inequality and average heath of population level relation is tested in this paper with panel data of 48 high and middle income countries over 20 recent years.
Evidence of significantly negative impact of income distribution on life expectancy at birth and positive impact on infant mortality rate has been found. Moreover, GDP per capita also has similar impact on heath in opposite directions. Even though the marginal effects are quantitatively small, results are found to be quite robust when controlling for endogeneity concerns and other issues. JEL: I14, I15, O15, C33, C36 Key words: Income inequality, life expectancy, infant mortality rate, health, human development, GDP per capita, secondary schooling, health spending, panel data.
4 Table of Contents Page Declaration. ii Tables of contents. iv List of Figures .v List of Tables. Research methods and expected outcome.
Chapter 2: Literature review. Income and effects to health 2. Income inequality hypothesis 2. The conceptual framework.
Empirical Studies Findings. Chapter 3: Data and Model Specifications. Data sources and Description. Panel Data Model 3.
Tests and Control for robustness of results 4. Chapter 4: Results and Discussion. Limitations and further researches .60 5 Abbreviations OECD - Organisation for Economic Co-operation and Development WHO – World Health Organisation UNESCO - United Nations Educational, Scientific, and Cultural Organization AGOA - African Growth and Opportunity Act WIID – World Income Inequality Database UNU-WIDER – United Nations University-World Institute for Development Economics Research CME – Child Mortality Estimates LE – Life Expectancy IMR – Infant Mortality Rate IV – Instrumental Variable GDP – Gross Domestic Product GNI – Gross National Income PPP - Purchasing Power Parities FE – Fixed Effects RE - Random Effects GLS - Generalized Least Squared FGLS - Estimator or Feasible Generalized Least Squared OLS – Ordinary Least Squared 2SLS - Two-stage Least Squares LM – Lagrange Multiplier GMM - Generalized Method of Moments 6 List of Figures Page Figure 2.1: Life expectancy at birth and real GDP per capita in 48 countries, 1991- 2010.2: Possible channels income inequality might affect health .3: Gini ratio estimation by Lorenz curve .4: Life expectancy and infant mortality rate versus GDP per capita .5: Life expectancy and infant mortality rate versus Gini index .6: Life expectancy and infant mortality rate versus Health spending per capita .7: Life expectancy and infant mortality rate versus Secondary schooling enrolment ratio .36 7 List of Tables Page Table 3.1: Summary of data resources and denotation used in models .2: Summary of hypotheses testing of model effects selections .3: Descriptive statistics for the explanatory variables, 48 countries 1991 – 2010.4: Model selection and Tests for life expectancy .5: Model selection and Tests for IMR .6: Correlations of variables in models .7: Regression of Gini with impact of IVs .8: Comparison of OLS regressions and panel effect regressions - Life Expectancy .9: Comparison of OLS regressions and panel effect regressions – IMR .10: Effects of income inequality using fixed-effects and random-effects .11: Effects of income inequality using instrumental variables on life expectancy .12: Regressions on life expectancy with interactions of Gini and GDP per head - Trade Openness instrument .13: Regressions on life expectancy with interactions of Gini and GDP per head – Investment Ratio instrument .14: Regressions on infant mortality rate with interactions of Gini and GDP per head .15: Regressions with system GMM on life expectancy and infant mortality rate .16: List of countries in research according to World Bank .17: List of STATA output of empirical results .1 Problem Statement In the recent years, the researches on health and its surrounding relationships has been on the rise. Explanatory factors affect health as the whole population is point of interest of many authors.
The outcomes of studies are among most controversies, not only in the conclusions, but also in the discussions and criticism of limitations regarding the methodologies, data, underlying channels of mechanisms. Health, as definition, is “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity” as from the World Health Organization (WHO). The concerns of health are one of the most significant matters in modern societies. With the advances in technology and health care, all aspects of health have been considerably improved in almost every country, especially in life expectancy and infant mortality rate.
Life expectancy and mortality rate don’t necessarily reflect the quality of life in term of the income metrics, but in the most popular studies in this filed, the connection between the these two major metrics of life quality, and other income based measurements, has been investigated and hence, established (Lynch et al. Health, at individual or population level has exposed some degrees of relationships to inequality according to Rodger (1979), Preston (1975), and Deaton (2001). Besides that, there is recently increase in studies regarding health and population health and its nexus with income, and especially, inequality (Gravelle et al. Even though the measurement of inequality is itself hardly intuitive (Lynch et al., 2004), many economists tried to quantify it through numbers of metrics.
Therefore, the relationship between income inequality and the health are becoming important. In other aspect, the association between economic growth in terms of income distribution and quality of life metrics are ongoing topic in economic studies. The quality of life can only be raised if growth and standard of living go together. Among determinants of a highly developed society, health and education are key opponents.
Apart from education attainment, which is a proven factor interacting with wealth distribution, health at aggregate level such as life expectancy and infant mortality rate has 9 exposed some degrees of connections to income inequality according to Rodger (1979), Preston (1975), Deaton (2001). Alternatively, there are empirical works of researches on the connection between human capital and economic growth, in terms of income level. As results, there is recently increase in studies regarding health and population health and its nexus with income, and more extending, income inequality (Gravelle et al. Equally important, the mutual effect of health and income inequality is a source of debate in many papers.
In one hand, some papers have been indicated that the part of the income inequality hypothesis. On the other side, the effects of health outcomes on income conception and vice versa have been investigated for long time (Leigh et al. However, the connections between three concepts: economic inequality, health progress and their interactions with income driving mechanism are not easily established or observe with solid evidences. The consistent results of researches of this interest are still exceptionally unconvincing because of conflicting conclusions.2 Research objectives Due to the rising health concerns in welfare, especially when it comes to child mortality reduction and prolong human longevity, many studies has been accelerating the knowledge and connections of health policies in terms of income distribution instruments such as Gini or Robin Hood Indexes.
Exploring the pattern of Gini coefficient linking to life expectancy and IMR, with control of some insightful factors such as level of income, health spending, etc… are the main purposes of this income inequality on health indexes study, and are largely to contribute to literatures. The longevity and quality of life are essential to modern societies, but lacking of understanding of how income inequality could impact health, lacking convinced evidences, particularly combined with controversies in underlying patterns of pathways in evidences in groups of countries, making perspectives become distorted. Therefore, the proposed objectives of this research are to: 1. Estimate the effect of income inequality on life expectancy at birth and infant mortality rate in some of middle and high income countries in period of 1991-2010.
Estimate the effect of GDP per head in conjunction of income inequality on health with consideration of differentiating high, upper middle and lower middle income countries. Because the main objective of this research is to re-examine the effects of income inequality on health outcome, with the attempt to reveal underlying mechanisms with evidence from patterns of developed and lesser developed countries of divergent levels. Hence, the research will try to answer some questions: 1) Better income differences (lower inequality) lead to better life expectancy and reducing infant mortality rate at aggregation level? Furthermore, income per capita (GDP per head) has been long time considered the incentive for positive change in health perspectives; therefore, this study will also examine the second research question on: 2) Whether higher income per capita increases health indicators at aggregation level or in other hands, does income associate with differences in health in different income levels? 1.3 Research methods and expected outcome The main approach to this study is to use panel data of 48 countries of high and middle income over the period of 20 years (1991-2010) to draw the results with the attention to unobserved heterogeneity by using fixed and random effects as well as some econometric methods to overcome the confounding and other issues in models. All the results are to be examined in manner to ensure there are no biases affecting the interpretations and concluding statements.
Data is collected from various macro sources. Outcomes of research is projected to supplement the recent literatures and expected to fulfill the understandings of income inequality – health relation. Income inequality should be one of most important meditations for population health; in this case life expectancy at birth and infant mortality rate, along with income per head. In that hope, any actions by governments that adjust income inequality or distribution and income level have direct or indirect impacts on health of their own people.4 Thesis Structure This study is structured to feature the literature and framework of theory in following next section.
Subsequently, the econometric models will be presented with data descriptions as well as estimation strategies. Finally, result of estimation and discussions are to be shown on two last sections alongside conclusions. 12 Chapter 2: Literature Review In this review, I first introduce the theory grounds of income inequality hypothesis, as well as income level effects on health measurements. Subsequently, basis for the models will be analyzed and established through relation of theory and empirical results.1 Income and effects to health Preston (1975) was leading in investigate the impact of pattern of income to health across countries.
The striking result in his milestone paper revealed the relationship between per capita national income and life expectancy at birth for different period of times. This relationship is, however, was at diminishing return to income. Another conclusion was that if the income inequality was to be reduced, the life expectancy could be extended for specific country, ceteris paribus. Therefore, the negative relationship between income inequality and health has been suggested.