The Preston curve is an empirical cross-sectional relationship between life expectancy and real per capita income. It is named after Samuel H. Preston who first described it in 1975. [1] [2] Preston studied the relationship for the 1900s, 1930s and the 1960s and found it held for each of the three decades. More recent work has updated this research. [3]
The Preston curve indicates that individuals born in richer countries, on average, can expect to live longer than those born in poor countries. However, the link between income and life expectancy flattens out. This means that at low levels of per capita income, further increases in income are associated with large gains in life expectancy, but at high levels of income, increased income has little associated change in life expectancy. In other words, if the relationship is interpreted as being causal, then there are diminishing returns to income in terms of life expectancy. [4]
A further significant finding of Preston's study was that the curve has shifted upwards during the 20th century. This means that life expectancy has increased in most countries, independently of changes in income. Preston credited education, better technology, vaccinations, improved provision of public health services, oral rehydration therapy and better nutrition with these exogenous improvements in health. [4] According to Preston, the independent increases in life expectancy have been greatest in the poor countries, although he also believed that a good portion of the potential gains from better medical technology have not been realized. [4] Several poor countries in Sub-Saharan Africa have actually seen declines in life expectancy in the 1990s and 2000s as a result of the HIV/AIDS epidemic, even if their per capita incomes have increased during this time. [4]
Overall Preston found that improvements in health technology (the upwards shifts in the curve) accounted for 75% to 90% of the increase in life expectancy, while income growth (movement along the curve) was responsible for the rest. [5]
Analysis of more recent data, for example by Michael Spence and Maureen Lewis, suggests that the "fit" of the relationship has become stronger in the decades since Preston's study. [6] Though the source of income growth, rather than growth itself has been shown to be significant, with Ryan Edwards finding divergences from the Preston Curve partially explained by the size of the mining sector (a mining dominated economy). [7]
While the relationship between income and life expectancy is log linear on average, any one individual country can lie above or below curve. Those below the curve, such as South Africa or Zimbabwe, have life expectancy levels that are lower than would be predicted based on per capita income alone. Countries above the curve, such as Tajikistan, have life expectancies that are exceptionally high given their level of economic development. [5] In 2000, the USA lay just below the curve, indicating that it had a slightly lower life expectancy than other rich countries. [8]
If the relationship is estimated with nonparametric regression then it produces a version of the curve which has a "hinge" – i.e. a kink in the relationship where the slope of the regression equation falls off significantly. This point occurs around the per capita income level of $2,045 (data for the year 2000) which is about the per capita income level of India. This level of income is generally associated with a crossing of a "epidemiological transition", where countries change from having most of their mortality occur due to infant mortality to that due to old age mortality, and from prevalence of infectious diseases to that of chronic diseases. [8]
The fact that the relationship between income and health is concave indicate that a transfer of income from the rich to the poor might increase the average health of a society. [3] This policy prescription will have this effect only if the relationship between income and health is causal – i.e. if higher income causes longer life expectancy (see below). If the relationship is driven by other factors, if it is spurious, or if it is in fact health that leads to higher income, then this policy outcome will no longer be true. [3]
The existence of the Preston curve has been used by Lant Pritchett and Larry Summers to argue that poor countries should focus on economic growth, and that health improvements will come about spontaneously as a result of increases in income. [9] According to these authors, in 1990 better economic performance could have prevented more than half a million child deaths worldwide. [9] However, the upward shifts of the Preston curve still imply that the main portion of gains in life expectancy has come about as a result of improved health technology rather than just increases in per capita income. [3] [5] Preston did, however, acknowledge that in the poorest countries economic growth may be necessary for improvements in health, as even the most inexpensive technologies have a cost of adoption that poor countries may not be able to afford. [10]
Preston's work has also contributed to the broadening of the definition of economic development. [3] Gary Becker et al. have included longevity in a more general welfare measure and have illustrated that increases in life expectancy have made up a large portion of increases in overall global welfare since the 1960s. [11] In the same work, Becker et al. also found that while cross-country incomes have diverged, the distribution of health has converged. [11]
The Preston curve is a relationship found in cross-country data - that is, it holds for a sample of countries taken at a particular point in time. Some research however suggests that a similar relationship does not hold in time series and longitudinal data within individual countries. [6] In particular, per capita incomes between countries have generally diverged over time, while life expectancies, and other health indicators such as the infant mortality rates, have converged (this trend was interrupted in the 1990s with the outbreak of the AIDS epidemic in Sub-Saharan Africa). This suggests that over time changes in income may have no impact on health or even be negatively related. [6]
A further limitation of the correlation is that it does not necessarily imply that the causality runs from income to health. It could actually be that better health, as proxied by life expectancy, contributes to higher incomes, rather than vice versa. [3] Better health can increase incomes because healthier individuals tend to be more productive than sick ones; on average they work harder, longer and are more capable of focusing efficiently on production tasks. [6] Furthermore, better health may affect not just the level of income but also its growth rate through its effect on education. [6] Healthier children spend more time at school and learn faster, thus acquiring more human capital which translates into higher growth rates of incomes later in life. Diseases such as malaria can short circuit these processes. [12] Likewise there is evidence that more healthy individuals save more and thus contribute to the faster accumulation of physical capital of an economy. [6] Jeffrey Sachs in particular has emphasized the role that the disease burden has played in the impoverishment of countries located in the tropical zones. [13]
The problem of reverse causality between health and income means that any estimates of the impact of income on life expectancy could mistakenly reflect the influence of life expectancy (more generically, health) on income instead. As such, studies which do not account for this potential two-way causation may overestimate the importance of income for life expectancy. In economic research, this kind of problem has traditionally been dealt with through the use of instrumental variables which allow the researcher to separate out one effect from another. [9] This strategy requires identification of an "instrument" – i.e. a variable which correlates with per capita income but not with the error term in the linear regression. However, since any variable which is likely to correlate with income is also likely to correlate strongly with health and life expectancy this is a difficult task. Some research suggests that in low and middle-income countries, the causality does indeed go from income to health, while the opposite is true for rich countries. [14]
Human life expectancy is a statistical measure of the estimate of the average remaining years of life at a given age. The most commonly used measure is life expectancy at birth. This can be defined in two ways. Cohort LEB is the mean length of life of a birth cohort and can be computed only for cohorts born so long ago that all their members have died. Period LEB is the mean length of life of a hypothetical cohort assumed to be exposed, from birth through death, to the mortality rates observed at a given year. National LEB figures reported by national agencies and international organizations for human populations are estimates of period LEB.
The standard of living in the United States is high by the standards that most economists use, and for many decades throughout the 20th century, the United States was recognized as having the highest standard of living in the world. Per capita income is high but also less evenly distributed than in most other developed countries; as a result, the United States fares particularly well in measures of average material well being that do not place weight on equality aspects.
The Physical Quality of Life Index (PQLI) is an attempt to measure the quality of life or well-being of a country. The value is the average of three statistics: basic literacy rate, infant mortality, and life expectancy at age one, all equally weighted on a 1 to 100 scale.
Economic growth can be defined as the increase or improvement in the inflation-adjusted market value of the goods and services produced by an economy in a financial year. Statisticians conventionally measure such growth as the percent rate of increase in the real and nominal gross domestic product (GDP).
Infant mortality is the death of an infant before the infant's first birthday. The occurrence of infant mortality in a population can be described by the infant mortality rate (IMR), which is the number of deaths of infants under one year of age per 1,000 live births. Similarly, the child mortality rate, also known as the under-five mortality rate, compares the death rate of children up to the age of five.
A developing country is a sovereign state with a less developed industrial base and a lower Human Development Index (HDI) relative to other countries. However, this definition is not universally agreed upon. There is also no clear agreement on which countries fit this category. The terms low and middle-income country (LMIC) and newly emerging economy (NEE) are often used interchangeably but refers only to the economy of the countries. The World Bank classifies the world's economies into four groups, based on gross national income per capita: high, upper-middle, lower-middle, and low income countries. Least developed countries, landlocked developing countries and small island developing states are all sub-groupings of developing countries. Countries on the other end of the spectrum are usually referred to as high-income countries or developed countries.
The Kerala model refers to the practices adopted by the Indian state of Kerala to further human development. It is characterised by results showing strong social indicators when compared to the rest of the country such as high literacy and life expectancy rates, highly improved access to healthcare, and low infant mortality and birth rates. Despite having a lower per capita income, the state is sometimes compared to developed countries. These achievements along with the factors responsible for such achievements have been considered characteristic results of the Kerala model. Academic literature discusses the primary factors underlying the success of the Kerala model as its decentralization efforts, the political mobilization of the poor, and the active involvement of civil society organizations in the planning and implementation of development policies.
The Kuznets curve expresses a hypothesis advanced by economist Simon Kuznets in the 1950s and 1960s. According to this hypothesis, as an economy develops, market forces first increase and then decrease economic inequality. The Kuznets curve appeared to be consistent with experience at the time it was proposed. However, since the 1970s, inequality has risen in the US and other developed countries, which invalidates the hypothesis.
In demography and medical geography, epidemiological transition is a theory which "describes changing population patterns in terms of fertility, life expectancy, mortality, and leading causes of death." For example, a phase of development marked by a sudden increase in population growth rates brought by improved food security and innovations in public health and medicine, can be followed by a re-leveling of population growth due to subsequent declines in fertility rates. Such a transition can account for the replacement of infectious diseases by chronic diseases over time due to increased life span as a result of improved health care and disease prevention. This theory was originally posited by Abdel Omran in 1971.
Income and fertility is the association between monetary gain on one hand, and the tendency to produce offspring on the other. There is generally an inverse correlation between income and the total fertility rate within and between nations. The higher the degree of education and GDP per capita of a human population, subpopulation or social stratum, the fewer children are born in any developed country. In a 1974 United Nations population conference in Bucharest, Karan Singh, a former minister of population in India, illustrated this trend by stating "Development is the best contraceptive." In 2015, this thesis was supported by Vogl, T.S., who concluded that increasing the cumulative educational attainment of a generation of parents was by far the most important predictor of the inverse correlation between income and fertility based on a sample of 48 developing countries.
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Healthcare in Europe is provided through a wide range of different systems run at individual national levels. Most European countries have a system of tightly regulated, competing private health insurance companies, with government subsidies available for citizens who cannot afford coverage. Many European countries offer their citizens a European Health Insurance Card which, on a reciprocal basis, provides insurance for emergency medical treatment insurance when visiting other participating European countries.
Health in Iraq refers to the country's public healthcare system and the overall health of the country's population. Iraq belongs to WHO health region Eastern Mediterranean and classified as upper middle according to World Bank income classification 2013. The state of health in Iraq has fluctuated during its turbulent recent history and specially during the last 4 decade. The country had one of the highest medical standards in the region during the period of 1980s and up until 1991, the annual total health budget was about $450 million in average. The 1991 Gulf War incurred Iraq's major infrastructures a huge damage. This includes health care system, sanitation, transport, water and electricity supplies. UN economic sanctions aggravated the process of deterioration. The annual total health budget for the country, a decade after the sanctions had fallen to $22 million which is barely 5% of what it was in 1980s. During its last decade, the regime of Saddam Hussein cut public health funding by 90 percent, contributing to a substantial deterioration in health care. During that period, maternal mortality increased nearly threefold, and the salaries of medical personnel decreased drastically. Medical facilities, which in 1980 were among the best in the Middle East, deteriorated. Conditions were especially serious in the south, where malnutrition and water-borne diseases became common in the 1990s. Health indicators deteriorated during the 1990s. In the late 1990s, Iraq's infant mortality rates more than doubled. Because treatment and diagnosis of cancer and diabetes decreased in the 1990s, complications and deaths resulting from those diseases increased drastically in the late 1990s and early 2000s.
The Tajikistan health system is influenced by the former Soviet legacy. It is ranked as the poorest country within the WHO European region, including the lowest total health expenditure per capita. Tajikistan is ranked 129th as Human Development Index of 188 countries, with an Index of 0.627 in 2016. In 2016, the SDG Index value was 56. In Tajikistan health indicators such as infant and maternal mortality rates are among the highest of the former Soviet republics. In the post-Soviet era, life expectancy has decreased because of poor nutrition, polluted water supplies, and increased incidence of cholera, malaria, tuberculosis, and typhoid. Because the health care system has deteriorated badly and receives insufficient funding and because sanitation and water supply systems are in declining condition, Tajikistan has a high risk of epidemic disease.
HIV/AIDS affects economic growth by reducing the availability of human capital. Without proper prevention, nutrition, health care and medicine that is available in developing countries, large numbers of people are developing AIDS.
The Healthy Life Years (HLY) indicator, also known as disability-free life expectancy (DFLE) or Sullivan's Index, is a European structural indicator computed by Eurostat. It is one of the summary measures of population health, known as health expectancies, composite measures of health that combine mortality and morbidity data to represent overall population health on a single indicator. HLY measures the number of remaining years that a person is expected to live at a certain age without the disability.
Bangladesh is an under-devoloped nation. Despite rapid economic growth, poverty remains a major issue. However, poverty has declined sharply in recent history. Shortly after its independence, approximately 90% of the population lived under the poverty line. However, since economic reforms and trade liberalization of early 1990s, along with accelerated economic growth since early-2000s, Bangladesh have experienced a dramatic progress in reducing poverty. The remarkable progress in poverty alleviation has been recognized by international institutions. According to World Bank, more than 33 million Bangladeshi people have been lifted out of poverty since 2000; as measured by the percentage of people living on the equivalent of US$1.90 or less per day in 2011 purchasing price parity terms.
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Democracy and economic growth and development have had a strong correlative and interactive relationship throughout history. While evidence of this relationship's existence is irrefutable, economists' and historians' opinions of its exact nature have been sharply split, hence the latter has been the subject of many debates and studies.
Environmental risk transition is the process by which traditional communities with associated environmental health issues become more economically developed and experience new health issues. In traditional or economically undeveloped regions, humans often suffer and die from infectious diseases or of malnutrition due to poor food, water, and air quality. As economic development occurs, these environmental issues are reduced or solved, and others begin to arise. There is a shift in the character of these environmental changes, and as a result, a shift in causes of death and disease.
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