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The relationship between income and health has been and is still a major subject of interest to both policy makers and economic researchers since the direction of causality between these variables remains debatable. Given other factors that play into the link between income and health, it is difficult to measure the impact of income on health. Such factors include but are not limited to environmental conditions, nutrition, genetics, level of education.
However, in order to be more specific, I am particularly interested in examining the effect of income distribution on self-reported health. Hence my question is: "Does Income Distribution Influence Self-Reported Health Status?" Income distribution refers to the manner in which total income or output is divided among households in an economy. A perfectly equal income distribution would mean everyone in the country has exactly the same income. Self-reported health tells us how individuals describe or rate their own physical and mental health.
The tentative hypothesis to my question is that, other things being equal, societies with narrower income distribution report better health. A narrower income distribution means that the income gap between the top and bottom quintile is very slim. Hence, there is little or no unequal distribution of income. In other words, a country that has a more uniform income distribution will experience better health. When there is an unequal distribution of income, it will result to income inequality. Income inequality is the existence of disproportionate distribution of total national income among households whereby the share going to rich persons in a country is far greater than that going to poorer persons. Income inequality is a situation that is common to the developing or least developing countries and it is largely due to differences in the amount of income derived from ownership of property and to a lesser extent the result of differences in earned income. A typical measure of income inequality is the Gini Coefficient. The coefficient is usually a figure between 0 and 1 or 0 and 100 (when converted to percentages); a coefficient that is much closer to 0 implies that income inequality is low whereas a coefficient closer to 100 means that income inequality is high.
I begin my study by describing the economic model of decision-making as well as relevant researches associated with my question. I would also conduct an economic analysis to investigate how income distribution is related to self-reported health status by studying two least developing countries.
In order to answer my question, I would be comparing the gini coefficients of two least developing countries - Lesotho and Namibia. I would also examine the overall health in each country. Before proceeding a brief description of the political, geographical and economic make-up of these countries will be stated.
Lesotho is situated in the southern region of Africa. It is a small mountainous country covering a land area of approximately 30,000 sq. km. It is landlocked and completely encircled by South Africa and also one of the smallest countries in the world. The population is comprised almost totally of the Sotho people (an African ethnic group) and about 80% are Christians. The official language spoken in this country are English and Sesotho.
This country faces enormous challenges and the major factors contributing to the currently very difficult situation is high levels of both HIV, poverty and malnutrition in combination with seriously decreasing revenues to the Government budget due to the global economic downturn. Lesotho is one of the 49 Least Developed Countries (LDC) in the world and has a very low human development index. In the 2009 Human Development Index, Lesotho was ranked 156 out of 177 countries. According to the Gini coefficient , it is one of the most unequal countries in the world with a value of 63.2.
In terms of political make-up and governance, Lesotho is a parliamentary constitutional monarchy and is governed under the constitution of 1993. The king is head of state but has no executive or legislative powers. The government is headed by a prime minister, who is the leader of the majority party in the Assembly.
Healthcare delivery in Lesotho is challenging as a result of the fact that 81% of the population lives in remote rural villages, often several hours walk over rough mountain paths from the nearest clinic. Access to healthcare in Lesotho is also limited by poverty and by lack of personnel. The healthcare system in Lesotho is composed of health posts and health centers at a primary level, with 16 district hospitals comprising the secondary level of care. Health posts are run by volunteer community health workers and provide outreach type care, such as condom distribution and immunizations. Health centers are staffed by nurse clinicians, who provide outpatient primary care.
The large district hospitals provide a variety of outpatient services, including both primary care and specialized clinics such as HIV/AIDS and tuberculosis (TB) clinics as well as inpatient services, operating theatres, labour and delivery and emergency rooms. In addition to the government run facilities, there are eight Christian Health Association (CHAL) run hospitals and 79 CHAL run health centers throughout the country. CHAL facilities are sustainable mission
projects and serve approximately 40% of the population.
On the other hand, Namibia is one of the largest countries in the world with a vast, sparsely populated country situated along the south Atlantic coast of Africa. It is bordered by South Africa in the south, Angola and Zambia in the north and Botswana and Zimbabwe in the east. The majority of the Namibian population is black African, mostly of the Ovambo ethnicity, which forms about half of the population. Approximately 80% are Christians while about 50% are Lutheran. The official languages spoken in this country are English, German and Afrikaan.
The politics of Namibia takes place in a framework of a presidential representative democratic republic, whereby the president of Namibia is elected to a five-year term and is both the head of state and the head of government. Legislative power is vested in both the government and the bicameral Parliament, the National Assembly and the National Council. The judiciary is independent of the executive and the legislature.
Namibia, like Lesotho, also faces huge AIDS epidemic. Malaria is also a problem faced in Namibia and it is compounded by the AIDS epidemic. This means that an individual with malaria has a greater risk of dying if he/she is infected with HIV. In addition, Namibia also has a low human development index as well as a relatively higher gini coefficient. Currently, the human development index and gini coefficient is 0.686 and 70.7 respectively.
Table 1. Income distribution in Lesotho in 2002
Percent of Total Income
Source: EarthTrend Country Profiles 2003. http://earthtrends.wri.org/pdf_library/country_profiles/eco_cou_426.pdf
Based on table 1, it is evident that the distribution of income in Lesotho is very unequal. The richest 20% earns about 60.1% of the total income whilst the poorest 20% earns about 2.8% of the total income. This shows that there is high inequality in Namibia
Table 2: Income Distribution in Namibia in 2006
Per Capita Income
Percent of Total Income
Richest 5.4 %
Next 33.8 %
Next 27.5 %
Poorest 33.3 %
All Namibians 100%
Source: US AID/NAMIBIA. 2006. http://www.usaid.gov/na/overview2.htm#83
According to the table 2, we observe that the income distribution of Namibia is unequal. The richest 5.4% earns about 52% of the total income whilst the poorest earns 4.2% of the total income.
Table 3 : Gini Coefficients of Lesotho and Namibia
Source: CIA World Facts Book
Based on the information given in table 3, Lesotho and Namibia comprises of countries that have very high income inequality.
Article 1: Li, H., & Zhu, Y. 2006 December 2006. "Income, Income inequality, and Health: Evidence from China." Journal of Comparative Economics, Vol. 34, no 4: 668-693.
This article studies the relationship between income, income inequality, and health in China, using the individual data from the China Health and Nutrition Survey (CHNS). The authors also attempts to test the three income hypotheses namely: absolute income hypothesis income inequality hypothesis and relative income hypothesis. The absolute income hypothesis assumes that people with higher income have better health outcomes, but income inequality and relative income inequality has no direct effect on health. Income inequality hypothesis presumes that income inequality is a threat to the health of individuals within a society regardless of an individual's income level. Thus, according to this hypothesis, while the poor may suffer the most from inequality, the better off and even the rich suffer as well. This hypothesis has two versions - the strong and the weak version. The strong version states that inequality affects all members in a society equivalently, irrespective of their income levels. The weak version indicates that income inequality may harm the health of only the least well-off in a society (Li et al). Relative income hypothesis states that health depends on an individual's income relative to others in his or her reference group, rather than an individual's absolute income. According to this hypothesis, health declines when one is economically or financially disadvantaged relative to one's peers and improves when one is better-off or richer relative to others (Li et al).
The data source used was gotten from the China Health and Nutrition Survey which was a longitudinal survey with five waves in 1989, 1991, 1993, 1997 and 2000. The sample households were randomly drawn from eight provinces (Liaoning, Shandong, Jiangsu, Hunan, Guangxi and Guiszhou) and roughly 20 households were sampled per community. The CHNS data contained detailed information on household and individual characteristics as well as health-related information such as self-reported health status, health behaviours and activities of daily living (Li et al). The authors used the wave of 1993 for the cross section analysis since it contained most of the health variables. The sample was restricted to men and women who were at least 20 years old in 1993 and had a complete set of data on health and demographic variables. On the whole, there were 7286 observations in the 1993 sample. The authors used probit model as a method of estimation to test the various income hypotheses. Gini coefficients were utilized in order to measure the community-level income inequality.
The major result from this study shows that there is some evidence supporting the various income hypotheses. They found that there is a concave relationship between self-reported health status and per capita income; this supports the absolute income hypotheses. They also found that additional income brings about greater improvement in the health of the poor than of the rich. In addition, they found that, there is a significant association between self-reported health status and community-level income inequality; this supports the income inequality hypotheses (Li et al). Although, these findings do not support the relative income hypothesis.
The strength in this article lie, in the type of survey it utilizes since it is a longitudinal survey which compares individual differences over time. The survey also contains comprehensive information about individuals and is also a panel data. The use of a panel data limits the problem of omitted variable bias since It allows to control for the potential confounding effects of unobservable fixed effects in the relationship between health, income and income inequality. Another strength of this article lie in the external validity of this study sample because it samples a large number of people.
Weaknesses, on the other hand come from the self-reported health statuses which might create bias since it would be difficult to deny or confirm many claims. Another weakness of this paper is that it focuses only on one dimension of inequality - the community-level inequality rather than on the county level or provincial. This will weaken the external validity of this article because it will be difficult to apply its result to other countries.
Article 2: Lorgelly, P.K., & Lindley, J. 2008 February. "What is the relationship between income inequality and health? Evidence from British Household Panel Survey (BHPS)." Journal of Health Economics. Vol. 17, no 2: 249-265
This article attempts to add to the debate on the relationship between income inequality and health using individual panel data from the British Household Panel Survey (BHPS) in order to test and distinguish between the competing hypothesis - (absolute income hypothesis, relative income hypothesis and income inequality hypothesis) and explore the relationship at the regional, county as well as the national level. The data set used is a longitudinal survey and the first wave of data was collected in 1991 and repeated each year, such that to date there are 14 waves of data available from 1991 to 2004 (Lorgelly et al). The survey contained questions on income, employment, health and well-being, demographics and neighbourhood; information was collected at both the individual and household level. The authors used the first 12 waves because it contained complete information; a sample of 8645 individuals - 4100 males and 4545 females were collected. The self-completion questionnaire component of the BHPS includes a range of health question that cover various dimensions of health and the annual total household income, as given in the BHPS data set sums all income for all individuals in the household for the 12 months prior to the start of interview period (Lorgelly et al).
The authors used three ordered probits models (pooled, random effects, and fixed effect ordered probits) as a method of estimation to estimate the relationship between income inequality and health. In order to test for the absolute income hypotheses household income is included in the regression in logarithm form and the regional mean income is used as a measure to test the relative income hypothesis. Similar to the paper above, the gini coefficient is used to test the income inequality hypothesis.
The key findings from this study show that there is a positive and significant relationship between self-reported health and income but there is no significant relationship between the regional mean income and health, which means there is no support for the relative income hypothesis and the income inequality hypothesis. In other words, the major conclusion from this study is that there is limited evidence of an effect of income inequality on health within Britain (Lorgelly et al).
The strengths of this article is apparent in the source of data utilized since it is a longitudinal survey and it follows individuals across waves so that many individuals are included in all years. It also covers a long time period from 1991 to 2004 and also comprises of a large sample size - 8645. Another strength in this article is found in the method of analysis used since it made use of three types of ordered probits.
Clearly, weakness in this article comes from the self-reported health status which might create bias in the results because individuals might provide wrong information when answering questions
Article 3: Hilderbrand, V., & Van Kerm, P. 2009 November. "Income Inequality and Self-Rated
Health Status: Evidence from the European Community Household Panel.''
Demography. Vol. 46, no. 4: 805-825
This paper examines the effect of income inequality on individuals' self-rated health status in a pooled sample of 11 countries, using longitudinal data from the European Community Household Panel survey (ECHP). The data source gotten from the European Community Household survey is a standardized multipurpose annual longitudinal survey that provides comparable micro-data about living conditions in the European union over the period 1994-2001 (Hilderbrand et al). Over 60,000 households and 130,000 adults across the European Union were interviewed at each wave; the waves covered all the 15 members of the European union. The survey also contained information on income, employment, housing, health and education (Hilderbrand et al). The importance of this survey is that it provides longitudinal, individual-level data on income and demographics as well as individual health, that are comparable across countries and over time. In addition, the ECHP survey collects information on self-reported health status for all individuals older that 16.
The empirical strategy used by the author is the fixed-effects linear models which enabled to control for potential reporting bias. The results from this study shows that there is a statistically significant support in favour of the strong version of income inequality hypothesis for both men and women. The conclusion from this study is that there is consistent evidence that income inequality is negatively related to self-rated health status in the European Union for both men and women, particularly when measured at national level (Hilderbrand et al).
The strength in this article is evident in the data source it utilizes because it contains comparable micro-data about living conditions in the European union. This would strengthen the external validity of this article such that it can be applicable to other countries. Also, the large sample size and the time period (1994-2001) it covers constitutes a major strength of this article. The survey used, contained comprehensive information on the demographic of respondents surveyed.
Similar to other articles, weaknesses come from the self-reported health statuses which might create bias since it would be difficult to deny or confirm many claims made by the respondents.
Based on my research and analysis, I come to a conclusion that income distribution does influence self-reported health status. In order to alleviate the problem of income inequality, the government in these countries should make use of tax policies which would tax the poor people lower. Also, the government should provide affordable health insurance for it's citizen so that those that are financially advantaged can gain access to health care and medical services.