It includes mortality or longevity prospects, the level of development and progressive changes that occur in life expectancy in relation to the socioeconomic development, and demographic evidence for comparable purposes, since we know that the pursuit of health and longevity are among the fundamental pillars of development.
After this part a wider theoretical and hypotheses review of literature was provided in this subsection. This subsection presents further details about these variables and their relationship with life expectancy at birth as a dependent variable. The assumptions based on both theoretical and empirical results suggest that the expected changes in the life expectancy at birth as an indicator for past, present, and future dynamics of mortality levels primarily were and will be under significant influence of the changes in the socioeconomic development in these countries and especially with improving of the living standard and health conditions of their people.
In this regard, Shkolnikov et al. Based on data and methodology that will be explained in section 4 the validity of our hypotheses framework will be tested. The hypotheses framework leads to a relevant research points and debates that will be discussed consequently in this section.
Several studies considered income as one of the main determinants of health ibid. The national living standards had a direct and positive impact on the demographic changes direct effect of income on mortality or to the life expectancy. A higher living standard raises consumption aspirations and fosters the growth and the development. Chamie pointed out that a further mortality declines also appear likely with increased concerns and changes with respect to life style, nutrition, and advances in medical technology.
Societies where the structural process is in a later phase generate less economic growth and development. In economics, the unified growth theory holds that the demographic transition plays a crucial role in initiating the shift from stagnation to growth Felice et al. However, the roots for the hypothetical framework bring us again back to the process of the first demographic transition. Typically, during the intermediate phase of the demographic transition when the fertility rate starts to fall, there are fewer dependent children who have to be supported.
In that period, the number of working age people grows relatively faster than the number of children and the share of old dependent people has not yet increased. As Mason and Lee have explained the concept of second demographic dividend and its connections with a low fertility as a demographic factor; however, they have underlined that steady and continuing improvement in adult mortality are also important, as is the rising proportion of the population at the older ages.
Thus, during this phase, more resources for investment in economic development and family welfare are available, and with all other things being equal, per capita income grows faster. Among a number of potential factors, the focus of the research is on the role of GDP per capita. In the long run, the trend in economic growth, as measured by GDP per capita, is very likely to be associated with the trend in mortality reduction, which is the main component captured by many of the stochastic mortality models.
The concave Preston curve has provided the rationale for much of the empirical work that has followed. However, according to Stengos et al. In that case, it would be the reverse impact from health to income that would be important. Worldwide data on life expectancy does appear to be strongly correlated with economic development and employment.
Improvements in economic conditions are an important force behind mortality decline. Sickles and Taubman showed evidence that life expectancy increases as a country improves its standard of living. Reviewing the theoretical focus and empirical work of Preston in on this topic, Sickles and Taubman showed that the data strongly suggest that longevity is an economic good, evidence that life expectancy increases as a country improves its standard of living long has been recognized since the higher income typically associated with development makes possible in part the consumption of goods and services that improve health.
A number of cross-country studies have found a positive effect of life expectancy, or a negative effect of mortality on income per capita, but the debate is still ongoing. A causal explanation of the dynamics by age and cohort effects and socioeconomic conditions might be a promising line of mortality research.
As a good example, Ediev pointed out the longevity in the eastern European countries. The sudden change of socioeconomic conditions in the former Eastern Block countries that joined the European Union slowed down health deterioration in those countries and extended exposure durations to lower mortality levels. According to Ediev , this was promptly reflected by the convergence of these countries to the western European trends.
In their study of the low-mortality population comprised of countries from Europe, North America, most of Oceania and Latin America, large parts of Asia excluding the high mortality area in Central and Southern Asia , as well as Northern Africa, Caselli et al.
Regardless of the diversity of these countries in various aspects, including medical standards, access to health care, and behavioral risk factors such as the prevalence of smoking, these differences were strongly related to economic development and contributed to wide variation in life expectancy levels. These authors emphasized that the economic stagnation or an economic crisis could have a stagnating effect on life expectancy, especially if there is an increase in the number of people without significant resources.
Another issue that Caselli et al. Furthermore, they pointed out that in Eastern Europe people would also have to decrease their alcohol consumption and countries in this region would have to improve their health care systems. However, it is interesting for our research that as countries with low mortality from Eastern Europe in their study besides Bulgaria, Czech Republic, Romania, Russian Federation, and Ukraine, they included Serbia and Macedonia as well. These authors claim that the societal, political, and disease environment in which an individual lives is also important and could explain why socioeconomic status has different effects in different populations at different times.
According to them, economic stagnation or economic crises could have a similar effect, especially if there is an increase in the number of people without significant resources.
The use of real GDP per capita as a measure of economic development is widely documented e. First, GDP per capita is relatively objective and easy to access, making the model more transparent. Second, the dynamics of the GDP per capita has been widely studied in the literature. Yet, there is a generally accepted measure for standard of living that economists refer to as the average real gross domestic product GDP per capita Mpofu, Moreover, the trend in GDP per capita may capture the trend in the overall economy.
It seems that the GDP per capita for our period of study may be a proxy of both purchasing powers during this period and of the level of economic development Wolpin, In some cases, as with income, it is easy to demonstrate the consequence of including a proxy because income is an explicit component of the optimizing framework. The importance of income per capita on life expectancy has awakened interest over the years to both policy makers and economists.
Avdeev et al. It seems that the better economic position and the higher expenditures on health contributed positively to maintaining lower mortality levels.
A large body of research has found strong links between GDP and actual mortality e. A well-established causal link goes from income to longevity. Many researchers argue that development should focus on income growth, since higher incomes indirectly lead to health improvements.
Most of the empirical studies, for example, assume that health improvements are the by-product of higher income as countries with higher income devote more resources for their health services, something that would translate into improved health status for their population Stengos et al. Accordingly, improvements in life expectancy in the USA have been matched by similar improvements in other rich countries.
Lutz and Kebede do not question the basic assumption that income growth and health are closely linked. Their multivariate results from a balanced panel of countries both developed and developing over the period — in 5-year intervals strongly confirmed what their analysis suggested: raising educational attainment was even stronger driver of increasing life expectancy and falling child mortality than income.
In the advanced stages of the first demographic transition, there are not much room for child mortality to further decline substantially, and as a consequence, more people survive to adult and old ages. According to Baker and Fugh-Berman , infant mortality is the single most important determinant of life expectancy.
They further point that because life expectancy is calculated as an average; hence, death rates in younger age groups have the greatest impact and that the disparities in IMRs could account for most differences in longevity. As Rabbi Fazle also discussed, high infant and child mortality rates result in lower values of life expectancy at birth than at older ages. This imbalance in life table according to him disappears only when the crossover occurs and happens when the inverse of the infant mortality becomes equal to the life expectancy at age 1.
Using Siler model, these authors have shown that gains in life expectancy through either bringing down infant mortality or decreasing the level of senescent mortality inevitably result in an increase in the proportion of life share.
The compelling evidence and work of Barthold Jones et al. Explaining a study with a Siler model with two different constant rates of mortality decline: one for infant and one for non-infant mortality, Missov and Lenart came to conclusion that Siler model converges with time to mortality schedule of population described on a period basis as levels of and improvements in infant mortality become negligibly small.
In addition, Shkolnikov et al. However, infant mortality and life expectancy trends are obviously unequally distributed globally. Hence, it seems that the life expectancy and infant survival are both often better in the developed countries, as compared to that of the developing countries or within the less developed countries. The link between infant mortality and life expectancy, and the tendency for less developed countries to have higher level of infant mortality and lower life expectancy at birth, is one of the key explanations for the socioeconomic inequalities that exist across these countries.
Child survival is highly correlated with the level of development United Nations, Thus, in our research, we use the infant mortality variable as an indicator for the overall development and health of the population, including its longevity or life expectancy. Cornia and Menchini clarify that the measurement of average well-being and of its distribution among the population, as well as cross-country comparisons, faces fewer methodological problems and does not require the adoption of arbitrary hypotheses and statistical conventions.
In the same way, the definition and meaning of the variables used—infant mortality rate and life expectancy at birth, according to them, are less ambiguous than that of monetary aggregates. The use of life expectancy at birth as an indicator of health well-being faces additional problems of interpretation because such an indicator is in fact computed on the basis of the age-specific mortality rates observed for different cohorts at a moment in time.
However, Cornia and Menchini noted that such rates do not reflect the real life chances of a person born in the reference year, as computation of such index would require to know the future risks of death at different ages for a person. In this regard, Glasen , p. Consequently, life expectancy at birth does not refer to any individual birth cohort but rather to a hypothetical cohort facing the age-specific death rates observed at the present time.
In analyzing changes in infant mortality rate, life expectancy at birth, and life expectancy at age 1, Cornia and Menchini emphasized that progress continued without interruptions for all these indicators for both developing and developed countries, but they did not assess whether these gains achieved with a similar, faster or slower pace than in the past. The mentioned EU accession candidate countries Macedonia, Montenegro, Serbia, Albania, and Bosnia and Herzegovina Footnote 2 are all within the Balkan region, and there are common facets among them with respect to key institutional features and economic patterns see, Eurostat, the statistical office of the European Union, ; European Commission, The five countries belong in the group of middle-income countries.
These countries previously experienced a strong decrease in infant mortality, rising living standard, and better education, as well as advance in healthcare and medicine. All these influenced their mode of life and indirectly their health and the length of life.
The countries of former Yugoslavia and Central and Eastern Europe as well had considerable success in industrialization, increasing education, reducing mortality, and producing equality ibid, pp. There are some differences in terms of the economic and social situation between the five countries, which appear to be somehow related to their different levels of socioeconomic development and its demographic patterns, but the differences are not so large.
As can be seen from Fig. Montenegro has also the highest level of GDP per capita in in comparison with the rest of four countries. Footnote 3 In all five countries, demographic behavior is thus relatively similar despite their socioeconomic differences. This is an important point, since Sebti, Courbage, Festy, and Kursac-Souali have proved also that the living standards and educational levels are classic determinants of demographic trends, with improvements in economic and cultural conditions generally being associated with progress in the first demographic transition.
The future mortality trends of the five countries will be driven mainly by mortality in adult ages, primarily the old and oldest-old. However, additional gains in life expectancy are possible owing to further reductions of mortality at these older ages. Therefore, it is expected with reference to past trends that a further extension of life expectancy at birth in all these five countries would be achieved through a decrease in mortality among the oldest old group of the population.
Source: UN databases. Such results are impressive, given that the levels were extremely high, around Footnote 4 In the European Union as a whole, the infant rate was 3.
Many of these countries have made a considerable headway in , but it was a long time before they reached the very low values observed in the European Union; only the Czech Republic has already reduced its infant mortality to the level of Belgium or England and Wales. Infant mortality rate in EU accession candidate countries: — Infant mortality rate per 1 live births in the EU accession candidate countries — Source: World Bank Group data. The fall of the Iron curtain and the wars in former Yugoslavia opened a decade of political, economic, and social turmoil Boulineau et al.
According to Frejka , the wars affected demographic trends significantly in Bosnia and Herzegovina, Croatia, Serbia, and Montenegro, less so in Slovenia and in Macedonia. In Bosnia and Herzegovina, the complex political events as well as the war impacted demographic trends and the availability of reliable statistics.
That is why some of those countries involved in the wars of the region of former Yugoslavia did not improve as much as other European countries their mortality levels and the age-structure of their country.
Hence, from the data about life expectancy at birth, which were used from the World Bank section 4 and Fig. Life expectancy at birth has stagnated in Serbia during s between Life expectancy at birth in EU accession candidate countries: — Life expectancy at birth in the EU accession candidate countries — Although having one of the lowest GDP per capita among our five countries—Albania boasts the highest life expectancy among many of the countries within our group Fig.
According to Eurostat, the statistical office of the European Union in , life expectancy for men in the enlargement countries ranged from a low of For women, life expectancy across the enlargement countries was slightly more homogeneous, ranging from a low of The life expectancy variable data and infant mortality rate data were obtained from World Bank development indicators databases World Bank, These data covers the period and include the five EU accession candidate countries Macedonia, Serbia, Bosnia and Herzegovina, Montenegro, and Albania.
In addition, due to data gap about life expectancy at birth within World Bank database for Serbia for , —, and —, additional data sources for Serbian life expectancy at birth for these years were used from the Statistical office of Republic of Serbia In order to examine the data at comparable level, the research was focused on regression model for the pooled cross-sectional time series with FIML method.
Cross-section-specific time series are those that have values that differ between cross-sections. A set of these series are required to hold the data for a given variable, with each series corresponding to data for a specific cross-section IHS Global Inc. Since cross-section-specific time series interact with cross-sections, they were defined in conjunction with the identifiers in pool object and there was applied estimation method that account for the pooled structure for the data.
Having in mind that the aim was to estimate a complex specification that cannot easily be estimated using the built-in features of the pool object and that it is not available in pooled estimation, in these circumstances, the pool was used to create a system using both common and cross-section specific coefficients. Footnote 5 After the parameters of a system of equations were estimated, the likelihood function under the assumption that the contemporaneous errors have a joint normal distribution was estimated as well.
The resulting system using FIML method was further customized and estimated using all of the techniques available for system estimation. The restricted diagonal estimation was chosen to be set up zero restrictions on the off-diagonals of the residual covariance matrix.
Only the diagonal elements of the residual covariance matrix that corresponded to the variances were estimated IHS Global Inc. The life expectancy at birth function has two factors with five equations. Our full system can be written as in Eq. In Eq. Footnote 6 The likelihood function can be written in the form as:. Taking account of the normalization rule and the zero restrictions, a typical structural equation, say the j th one, can be written as:.
The system was estimated by full information maximum likelihood FIML method. Over the years, a number of approaches for FIML estimation have been proposed. The standard model that was used has been shown in Eq. The log determinant of the derivatives of f t captures the simultaneity in the system of equations. For the unrestricted and diagonal restricted covariance variants of the model, the first-order conditions for the variance parameters was used and then the likelihood was rewritten in concentrated form:.
The diagonal restricted estimator replaces the off diagonal terms in the latter matrix with zeros. Table 1 presents the estimated common coefficients and regression statistics for FIML. In Table 2 the regression statistics for each of the five countries can be seen.
There are cross-equation coefficient restrictions that ensure symmetry of the cross partial derivatives. The log likelihood has to be maximized with respect to all of the parameters, subject to the symmetry conditions:. With the Wald Coefficient Tests, the symmetry restrictions were tested. Since maximum likelihood assumes the errors are multivariate normal, it also was tested whether the residuals are normally distributed. In our case, ordinary residuals were produced.
Residuals graph displays a separate graph of the residuals from each equation in the system. A group containing the residuals of each equation in the system is shown in Fig.
Footnote 7 The Jarque-Bera statistic rejects the hypothesis of normal distribution only for the third equation Bosnia and Herzegovina but not for the other equations. The value of the Jarque-Bera statistics for Bosnian equation 8. Residuals graph for the EU accession candidate countries: — Residuals graph for the EU accession candidate countries: Source: Author's design in Eviews 11 software. The results in Table 1 describe the system estimation specification using FIML method and provide coefficients and standard error estimates, z -statistics, p values, and summary statistics.
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We gratefully acknowledge intellectual contribution of the attendants of the network meeting in Cape Town, DS made a central contribution to the establishment of the network, and this paper before he died suddenly on 30th August This paper is dedicated to his memory.
The funding bodies had no role in the design of the study, data collection, analysis, or interpretation of data, or the writing of the manuscript. You can also search for this author in PubMed Google Scholar. All authors contributed to the design of the study, and participated in the expert opinion study.
TF conducted the quantitative analysis and led the drafting of the manuscript. HG led the scoping review process, collection of indicator data, and the write up for Ethiopia. EG led the review and write up for Brazil. CB led the review and write up for the United States. All authors provided feedback on the draft manuscript. DS passed away in August before the manuscript was finalised. All authors except DS read and approved the final manuscript. Correspondence to Toby Freeman.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Table 2: Punching above or below weight status and selected context and social determinants of health indicators of Ethiopia, Brazil and the United States of America, s with citations.
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Freeman, T. Why do some countries do better or worse in life expectancy relative to income? Int J Equity Health 19, Download citation. Received : 24 April Accepted : 29 October Published : 10 November Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. It tells us the average age of death in a population. Estimates suggest that in a pre-modern, poor world, life expectancy was around 30 years in all regions of the world.
Life expectancy has increased rapidly since the Age of Enlightenment. In the early 19th century, life expectancy started to increase in the early industrialized countries while it stayed low in the rest of the world.
This led to a very high inequality in how health was distributed across the world. Good health in the rich countries and persistently bad health in those countries that remained poor. Over the last decades this global inequality decreased. No country in the world has a lower life expectancy than the countries with the highest life expectancy in Many countries that not long ago were suffering from bad health are catching up rapidly.
Since the global average life expectancy has more than doubled and is now above 70 years. The inequality of life expectancy is still very large across and within countries. Why do women live longer than men? Life expectancy is a measure of premature death and it shows large differences in health across the world.
The population of many of the richest countries in the world have life expectancies of over 80 years. In the life expectancy in Spain, Switzerland, Italy, and Australia was over 83 years. In Japan it was the highest with close to 85 years. In the countries with the worst health life expectancy is between 50 and 60 years.
The population of the Central African Republic has the lowest life expectancy in with 53 years. Use the slider below the map to see the change over time or click on any country to see the changing of life expectancy around the world.
World Bank Data: Life expectancy. The three maps show the global history of life expectancy over the last two centuries. Demographic research suggests that at the beginning of the 19 th century no country in the world had a life expectancy longer than 40 years. Almost everyone in the world lived in extreme poverty , we had very little medical knowledge, and in all countries our ancestors had to prepare for an early death. Over the next years some parts of the world achieved substantial health improvements.
A global divide opened. In the life expectancy for newborns was already over 60 years in Europe, North America, Oceania, Japan and parts of South America. But elsewhere a newborn could only expect to live around 30 years. The global inequality in health was enormous in People in Norway had a life expectancy of 72 years, whilst in Mali this was 26 years.
Africa as a whole had an average life expectancy of only 36 years, while people in other world regions could expect to live more than twice as long. The decline of child mortality was important for the increase of life expectancy, but as we explain in our entry on life expectancy increasing life expectancy was certainly not only about falling child mortality — life expectancy increased at all ages.
Such improvements in life expectancy — despite being exclusive to particular countries — was a landmark sign of progress. It was the first time in human history that we achieved sustained improvements in health for entire populations. Many of us have not updated our world view. We still tend to think of the world as divided as it was in But in health — and many other aspects — the world has made rapid progress.
Today most people in the world can expect to live as long as those in the very richest countries in The United Nations estimate a global average life expectancy of According to the UN estimates the country with the best health in was Norway with a life expectancy of The three maps summarize the global history of life expectancy over the last two centuries: Back in a newborn baby could only expect a short life, no matter where in the world it was born.
In newborns had the chance of a longer life if they were lucky enough to be born in the right place. In recent decades all regions of the world made very substantial progress, and it were those regions that were worst-off in that achieved the biggest progress since then.
The divided world of has been narrowing. Globally the life expectancy increased from less than 30 years to over 72 years; after two centuries of progress we can expect to live much more than twice as long as our ancestors. And this progress was not achieved in a few places. In every world region people today can expect to live more than twice as long. The global inequalities in health that we see today also show that we can do much better.
The almost unbelievable progress the entire world has achieved over the last two centuries should be encouragement enough for us to realize what is possible. This visualization shows the dramatic increase in life expectancy over the last few centuries as a line chart.
For the UK — the country for which we have the longest time-series — we see that before the 19th century there was no trend for life expectancy: life expectancy fluctuated between 30 and 40 years. Over the last years people in all countries in the world achieved impressive progress in health that lead to increases in life expectancy. In the UK, life expectancy doubled and is now higher than 80 years. In Japan health started to improve later, but the country caught up quickly with the UK and surpassed it in the late s.
In South Korea health started to improve later still and the country achieved even faster progress than the UK and Japan; by now life expectancy in South Korea has surpassed life expectancy in the UK. The chart also shows how low life expectancy was in some countries in the past: A century ago life expectancy in India and South Korea was as low as 23 years. A century later, life expectancy in India has almost tripled and in South Korea it has almost quadrupled.
You can switch to the map view to compare life expectancy across countries. This view shows that there are still huge differences between countries: people in Sub-Saharan countries have a life expectancy of less than 50 years, while in Japan it exceeds In the pre-modern, poor world life expectancy was around 30 years in all regions of the world.
The estimates by historian James Riley shown here suggest that there was some variation, between different world regions, but in all world regions life expectancy was well below 40 years. The historical estimates are associated with a considerable uncertainty — it is worth reading the work by Riley to understand the limitations and strengths of the estimates.
Infectious diseases raged in all parts of the world and as we show in our entry on child mortality almost half of all children died before they reached adulthood.
And those that survived often died soon after. Without public health measures and without effective medicines diseases were killing most people at a very young age. This was the reality for humanity until very recently. Life expectancy in each region of the world stayed fairly stable for most of history until humanity started to make progress against poor health just a few generations ago. There is a lot of information in the following — rather unusual — chart.
On the x-axis you find the cumulative share of the world population. And all the countries of the world are ordered along the x-axis ascending by the life expectancy of the population. On the y-axis you see the life expectancy of each country. For red line you see that the countries on the left — India and also South Korea — have a life expectancy around On the very right you see that in no country had a life expectancy above 40 Belgium had the highest life expectancy with just 40 years.
In the life expectancy of all countries was higher than in and the richer countries in Europe and North America had life expectancies over 60 years — over the course of modernization and industrialization the health of the population improved dramatically. Therefore the world in was highly unequal in living standards — clearly devided between developed countries and developing countries.
This division is ending: Look at the change between and ! Now it is the former developing countries — the countries that were worst off in — that achieved the fastest progress. While some countries mostly in Africa are lacking behind. But many of the former developing countries have caught up and we achieved a dramatic reduction of global health inequality. The world developed from equally poor health in to great inequality in and back to more equality today — but equality on a much higher level.
How to read the following graph: On the x-axis you find the cumulative share of the world population. The countries are ordered along the x-axis ascending by the life expectancy of the population.
Once past childhood, people would be expected to enjoy the same length of life as they did centuries ago. This, as we will see in the data below, is untrue. Life expectancy has increased at all ages. The average person can expect to live a longer life than in the past, irrespective of what age they are.
The red line shows the life expectancy for a newborn. Until the midth century a newborn could expect to live around 40 years. At times, even less. The rainbow-colored lines show how long a person could expect to live once they had reached that given, older, age. The light green line, for example, represents the life expectancy for children who had reached age The most striking development we see is the dramatic increase in life expectancy since the midth century.
Life expectancy at birth doubled from around 40 years to more than 81 years. While England and Wales are not the only region that achieved this improvement, the last years are the only time that humanity achieved anything like this. The evidence that we have for population health before modern times suggest that around a quarter of all infants died in the first year of life and almost half died before they reached the end of puberty see here and there was no trend for life expectancy before the modern improvement in health: In the centuries preceding this chart, life expectancy fluctuated between 30 and 40 years with no marked increase ever.
Mortality rates declined, and consequently life expectancy increased, for all age groups. Child mortality is defined as the share of children who die before reaching their 5th birthday. We therefore have to look at the life expectancy of a five-year-old to see how mortality changed without taking child mortality into account.
This is shown by the yellow line. In a five-year-old could expect to live 55 years. Today a five-year-old can expect to live 82 years. An increase of 27 years. The same is true for any higher age cut-off.
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