convergence hypothesis economics

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• Research Highlights

The convergence hypothesis

April 6, 2020

Are poor countries catching up with rich countries?

Tyler Smith

The economies of today’s wealthiest nations raced ahead of the rest of the world about two centuries ago. That event initiated a new era in economic history, one defined by growth. 

More recently, countries like Japan seem to have successfully copied this playbook, and it appears others like China are following suit. But unfortunately those countries are the exception rather than the rule, according to a paper in the Journal of Economic Literature .

Authors Paul Johnson and Chris Papageorgiou found there’s little evidence that national economies are catching up to their richest peers. Most low-income countries haven’t been able to maintain what growth spurts they’ve had like traditional economic theory would predict.  

“The consensus that we find in the literature leads us to believe that poor countries, unless something changes, are destined to stay poor,” Johnson told the AEA in an interview.

In fact, slowdowns in the poorest countries have left millions in extreme poverty . Understanding which countries are catching up and how they’re doing so could help explain the elusive origins of economic growth.

The consensus that we find in the literature leads us to believe that poor countries, unless something changes, are destined to stay poor. Paul Johnson

The debate over catch-up growth—what economists have dubbed the convergence hypothesis —has a long history. The authors choose to focus on research published over the last 30 years. 

In this recent research, capital, technology, and productivity have been at the root of most understandings of economic growth and convergence. But that has traditionally led economists to conclude that however poor a country starts off, it will adopt the best practices of the rich countries and eventually be just as well-off as their forerunners.  

That’s the theory. But when the authors looked at the numbers over the last 60 years that’s not what they saw. 

Data from the Penn World Tables —which covers 182 countries—revealed an unprecedented level of global growth over the period, but it was spread unevenly across the globe and across income levels.

The researchers separated countries into three income levels: low, middle, and high. 

Each decade, high-income countries tended to grow faster than middle-income countries, which in turn tended to grow faster than low-income countries. 

Every group experienced periods of relatively slow growth. But low-income countries actually experienced negative average growth rates during the 80s and 90s. The contractions were mostly driven by periods of extreme violence, corruption, and other state dysfunctions. 

This unequal growth has led to steadily more and more dispersed national incomes around the world—the opposite of what a strong version of the convergence hypothesis predicts.

But while there wasn’t any absolute convergence, the researchers did find that the literature supported the idea of “convergence clubs.” In other words, countries that started with a similar income level in 1960 still had a similar income level in 2010, the end of the dataset.

The convergence clubs might be a clue for national leaders. According to Johnson, it suggests that policy interventions need to be bold enough to reach the next rung in the income ladder, or they risk slow, start-and-stop growth.

It also might help make sense of why some countries jump out of low-income clubs and eventually join the richest one, while other countries are stuck in poverty or middle-income traps.

Still other types of convergence are possible. Previous work has found that within some industries, such as manufacturing, convergence is happening. Countries may need to organize their workforces around these sectors to jumpstart growth. 

As the authors point out, even half a century is short compared to the long run. The limited data span may mean that pessimism isn’t ultimately warranted, but the authors’ work warns against being complacent.

“There have been signs of a little catch-up over the last few years . . . but we don't know if that will continue,” Johnson said.

“ What Remains of Cross-Country Convergence? ” appears in the March issue of the  Journal of Economic Literature.

The Great Divergence

This video explains how technology diffusion contributes to the growing productivity gap between rich and poor countries.

Can pathogen concentrations explain which countries developed sooner?

A new unified growth model of the demographic transition

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Chapter 20. Economic Growth

20.4 Economic Convergence

Learning objectives.

• Explain economic convergence
• Analyze various arguments for and against economic convergence
• Evaluate the speed of economic convergence between high-income countries and the rest of the world

Some low-income and middle-income economies around the world have shown a pattern of convergence , in which their economies grow faster than those of high-income countries. GDP increased by an average rate of 2.7% per year in the 1990s and 2.3% per year from 2000 to 2008 in the high-income countries of the world, which include the United States, Canada, the countries of the European Union, Japan, Australia, and New Zealand.

Table 5 lists 10 countries of the world that belong to an informal “fast growth club.” These countries averaged GDP growth (after adjusting for inflation) of at least 5% per year in both the time periods from 1990 to 2000 and from 2000 to 2008. Since economic growth in these countries has exceeded the average of the world’s high-income economies, these countries may converge with the high-income countries. The second part of Table 5 lists the “slow growth club,” which consists of countries that averaged GDP growth of 2% per year or less (after adjusting for inflation) during the same time periods. The final portion of Table 5 shows GDP growth rates for the countries of the world divided by income.

Each of the countries in Table 5 has its own unique story of investments in human and physical capital, technological gains, market forces, government policies, and even lucky events, but an overall pattern of convergence is clear. The low-income countries have GDP growth that is faster than that of the middle-income countries, which in turn have GDP growth that is faster than that of the high-income countries. Two prominent members of the fast-growth club are China and India, which between them have nearly 40% of the world’s population. Some prominent members of the slow-growth club are high-income countries like the United States, France, Germany, Italy, and Japan.

Will this pattern of economic convergence persist into the future? This is a controversial question among economists that we will consider by looking at some of the main arguments on both sides.

Arguments Favoring Convergence

Several arguments suggest that low-income countries might have an advantage in achieving greater worker productivity and economic growth in the future.

A first argument is based on diminishing marginal returns. Even though deepening human and physical capital will tend to increase GDP per capita, the law of diminishing returns suggests that as an economy continues to increase its human and physical capital, the marginal gains to economic growth will diminish. For example, raising the average education level of the population by two years from a tenth-grade level to a high school diploma (while holding all other inputs constant) would produce a certain increase in output. An additional two-year increase, so that the average person had a two-year college degree, would increase output further, but the marginal gain would be smaller. Yet another additional two-year increase in the level of education, so that the average person would have a four-year-college bachelor’s degree, would increase output still further, but the marginal increase would again be smaller. A similar lesson holds for physical capital. If the quantity of physical capital available to the average worker increases, by, say, $5,000 to $10,000 (again, while holding all other inputs constant), it will increase the level of output. An additional increase from $10,000 to $15,000 will increase output further, but the marginal increase will be smaller.

Low-income countries like China and India tend to have lower levels of human capital and physical capital, so an investment in capital deepening should have a larger marginal effect in these countries than in high-income countries, where levels of human and physical capital are already relatively high. Diminishing returns implies that low-income economies could converge to the levels achieved by the high-income countries.

A second argument is that low-income countries may find it easier to improve their technologies than high-income countries. High-income countries must continually invent new technologies, whereas low-income countries can often find ways of applying technology that has already been invented and is well understood. The economist Alexander Gerschenkron (1904–1978) gave this phenomenon a memorable name: “the advantages of backwardness.” Of course, he did not literally mean that it is an advantage to have a lower standard of living. He was pointing out that a country that is behind has some extra potential for catching up.

Finally, optimists argue that many countries have observed the experience of those that have grown more quickly and have learned from it. Moreover, once the people of a country begin to enjoy the benefits of a higher standard of living, they may be more likely to build and support the market-friendly institutions that will help provide this standard of living.

View this video to learn about economic growth across the world.

Arguments That Convergence Is neither Inevitable nor Likely

If the growth of an economy depended only on the deepening of human capital and physical capital, then the growth rate of that economy would be expected to slow down over the long run because of diminishing marginal returns. However, there is another crucial factor in the aggregate production function: technology.

The development of new technology can provide a way for an economy to sidestep the diminishing marginal returns of capital deepening. Figure 1 shows how. The horizontal axis of the figure measures the amount of capital deepening, which on this figure is an overall measure that includes deepening of both physical and human capital. The amount of human and physical capital per worker increases as you move from left to right, from C 1 to C 2 to C 3 . The vertical axis of the diagram measures per capita output. Start by considering the lowest line in this diagram, labeled Technology 1. Along this aggregate production function, the level of technology is being held constant, so the line shows only the relationship between capital deepening and output. As capital deepens from C 1 to C 2 to C 3 and the economy moves from R to U to W, per capita output does increase—but the way in which the line starts out steeper on the left but then flattens as it moves to the right shows the diminishing marginal returns, as additional marginal amounts of capital deepening increase output by ever-smaller amounts. The shape of the aggregate production line (Technology 1) shows that the ability of capital deepening, by itself, to generate sustained economic growth is limited, since diminishing returns will eventually set in.

Now, bring improvements in technology into the picture. Improved technology means that with a given set of inputs, more output is possible. The production function labeled Technology 1 in the figure is based on one level of technology, but Technology 2 is based on an improved level of technology, so for every level of capital deepening on the horizontal axis, it produces a higher level of output on the vertical axis. In turn, production function Technology 3 represents a still higher level of technology, so that for every level of inputs on the horizontal axis, it produces a higher level of output on the vertical axis than either of the other two aggregate production functions.

Most healthy, growing economies are deepening their human and physical capital and increasing technology at the same time. As a result, the economy can move from a choice like point R on the Technology 1 aggregate production line to a point like S on Technology 2 and a point like T on the still higher aggregate production line (Technology 3). With the combination of technology and capital deepening, the rise in GDP per capita in high-income countries does not need to fade away because of diminishing returns. The gains from technology can offset the diminishing returns involved with capital deepening.

Will technological improvements themselves run into diminishing returns over time? That is, will it become continually harder and more costly to discover new technological improvements? Perhaps someday, but, at least over the last two centuries since the Industrial Revolution, improvements in technology have not run into diminishing marginal returns. Modern inventions, like the Internet or discoveries in genetics or materials science, do not seem to provide smaller gains to output than earlier inventions like the steam engine or the railroad. One reason that technological ideas do not seem to run into diminishing returns is that the ideas of new technology can often be widely applied at a marginal cost that is very low or even zero. A specific additional machine, or an additional year of education, must be used by a specific worker or group of workers. A new technology or invention can be used by many workers across the economy at very low marginal cost.

The argument that it is easier for a low-income country to copy and adapt existing technology than it is for a high-income country to invent new technology is not necessarily true, either. When it comes to adapting and using new technology, a society’s performance is not necessarily guaranteed, but is the result of whether the economic, educational, and public policy institutions of the country are supportive. In theory, perhaps, low-income countries have many opportunities to copy and adapt technology, but if they lack the appropriate supportive economic infrastructure and institutions, the theoretical possibility that backwardness might have certain advantages is of little practical relevance.

Visit this website to read more about economic growth in India.

The Slowness of Convergence

Although economic convergence between the high-income countries and the rest of the world seems possible and even likely, it will proceed slowly. Consider, for example, a country that starts off with a GDP per capita of $40,000, which would roughly represent a typical high-income country today, and another country that starts out at $4,000, which is roughly the level in low-income but not impoverished countries like Indonesia, Guatemala, or Egypt. Say that the rich country chugs along at a 2% annual growth rate of GDP per capita, while the poorer country grows at the aggressive rate of 7% per year. After 30 years, GDP per capita in the rich country will be $72,450 (that is, $40,000 (1 + 0.02) 30 ) while in the poor country it will be $30,450 (that is, $4,000 (1 + 0.07) 30 ). Convergence has occurred; the rich country used to be 10 times as wealthy as the poor one, and now it is only about 2.4 times as wealthy. Even after 30 consecutive years of very rapid growth, however, people in the low-income country are still likely to feel quite poor compared to people in the rich country. Moreover, as the poor country catches up, its opportunities for catch-up growth are reduced, and its growth rate may slow down somewhat.

The slowness of convergence illustrates again that small differences in annual rates of economic growth become huge differences over time. The high-income countries have been building up their advantage in standard of living over decades—more than a century in some cases. Even in an optimistic scenario, it will take decades for the low-income countries of the world to catch up significantly.

Calories and Economic Growth

The story of modern economic growth can be told by looking at calorie consumption over time. The dramatic rise in incomes allowed the average person to eat better and consume more calories. How did these incomes increase? The neoclassical growth consensus uses the aggregate production function to suggest that the period of modern economic growth came about because of increases in inputs such as technology and physical and human capital. Also important was the way in which technological progress combined with physical and human capital deepening to create growth and convergence. The issue of distribution of income notwithstanding, it is clear that the average worker can afford more calories in 2014 than in 1875.

Aside from increases in income, there is another reason why the average person can afford more food. Modern agriculture has allowed many countries to produce more food than they need. Despite having more than enough food, however, many governments and multilateral agencies have not solved the food distribution problem. In fact, food shortages, famine, or general food insecurity are caused more often by the failure of government macroeconomic policy, according to the Nobel Prize-winning economist Amartya Sen. Sen has conducted extensive research into issues of inequality, poverty, and the role of government in improving standards of living. Macroeconomic policies that strive toward stable inflation, full employment, education of women, and preservation of property rights are more likely to eliminate starvation and provide for a more even distribution of food.

Because we have more food per capita, global food prices have decreased since 1875. The prices of some foods, however, have decreased more than the prices of others. For example, researchers from the University of Washington have shown that in the United States, calories from zucchini and lettuce are 100 times more expensive than calories from oil, butter, and sugar. Research from countries like India, China, and the United States suggests that as incomes rise, individuals want more calories from fats and protein and fewer from carbohydrates. This has very interesting implications for global food production, obesity, and environmental consequences. Affluent urban India has an obesity problem much like many parts of the United States. The forces of convergence are at work.

Key Concepts and Summary

When countries with lower levels of GDP per capita catch up to countries with higher levels of GDP per capita, the process is called convergence. Convergence can occur even when both high- and low-income countries increase investment in physical and human capital with the objective of growing GDP. This is because the impact of new investment in physical and human capital on a low-income country may result in huge gains as new skills or equipment are combined with the labor force. In higher-income countries, however, a level of investment equal to that of the low income country is not likely to have as big an impact, because the more developed country most likely has high levels of capital investment. Therefore, the marginal gain from this additional investment tends to be successively less and less. Higher income countries are more likely to have diminishing returns to their investments and must continually invent new technologies; this allows lower-income economies to have a chance for convergent growth. However, many high-income economies have developed economic and political institutions that provide a healthy economic climate for an ongoing stream of technological innovations. Continuous technological innovation can counterbalance diminishing returns to investments in human and physical capital.

Self-Check Questions

• Use an example to explain why, after periods of rapid growth, a low-income country that has not caught up to a high-income country may feel poor.
• A weak economy in which businesses become reluctant to make long-term investments in physical capital.
• A rise in international trade.
• A trend in which many more adults participate in continuing education courses through their employers and at colleges and universities.
• What are the “advantages of backwardness” for economic growth?
• Would you expect capital deepening to result in diminished returns? Why or why not? Would you expect improvements in technology to result in diminished returns? Why or why not?
• Why does productivity growth in high-income economies not slow down as it runs into diminishing returns from additional investments in physical capital and human capital? Does this show one area where the theory of diminishing returns fails to apply? Why or why not?

Review Questions

• For a high-income economy like the United States, what elements of the aggregate production function are most important in bringing about growth in GDP per capita? What about a middle-income country such as Brazil? A low-income country such as Niger?
• List some arguments for and against the likelihood of convergence.

Critical Thinking Questions

• What sorts of policies can governments implement to encourage convergence?
• As technological change makes us more sedentary and food costs increase, obesity is likely. What factors do you think may limit obesity?

Central Intelligence Agency. “The World Factbook: Country Comparison: GDP–Real Growth Rate.” https://www.cia.gov/library/publications/the-world-factbook/rankorder/2003rank.html.

Sen, Amartya. “Hunger in the Contemporary World (Discussion Paper DEDPS/8).” The Suntory Centre: London School of Economics and Political Science . Last modified November 1997. http://sticerd.lse.ac.uk/dps/de/dedps8.pdf.

Answers to Self-Check Questions

• A good way to think about this is how a runner who has fallen behind in a race feels psychologically and physically as he catches up. Playing catch-up can be more taxing than maintaining one’s position at the head of the pack.
• No. Capital deepening refers to an increase in the amount of capital per person in an economy. A decrease in investment by firms will actually cause the opposite of capital deepening (since the population will grow over time).
• There is no direct connection between and increase in international trade and capital deepening. One could imagine particular scenarios where trade could lead to capital deepening (for example, if international capital inflows which are the counterpart to increasing the trade deficit) lead to an increase in physical capital investment), but in general, no.
• Yes. Capital deepening refers to an increase in either physical capital or human capital per person. Continuing education or any time of lifelong learning adds to human capital and thus creates capital deepening.
• The advantages of backwardness include faster growth rates because of the process of convergence, as well as the ability to adopt new technologies that were developed first in the “leader” countries. While being “backward” is not inherently a good thing, Gerschenkron stressed that there are certain advantages which aid countries trying to “catch up.”
• Capital deepening, by definition, should lead to diminished returns because you’re investing more and more but using the same methods of production, leading to the marginal productivity declining. This is shown on a production function as a movement along the curve. Improvements in technology should not lead to diminished returns because you are finding new and more efficient ways of using the same amount of capital. This can be illustrated as a shift upward of the production function curve.
• Productivity growth from new advances in technology will not slow because the new methods of production will be adopted relatively quickly and easily, at very low marginal cost. Also, countries that are seeing technology growth usually have a vast and powerful set of institutions for training workers and building better machines, which allows the maximum amount of people to benefit from the new technology. These factors have the added effect of making additional technological advances even easier for these countries.

Principles of Economics Copyright © 2016 by Rice University is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

20.4 Economic Convergence

Learning objectives.

By the end of this section, you will be able to:

• Explain economic convergence
• Analyze various arguments for and against economic convergence
• Evaluate the speed of economic convergence between high-income countries and the rest of the world

Some low-income and middle-income economies around the world have shown a pattern of convergence , in which their economies grow faster than those of high-income countries. GDP increased by an average rate of 2.7% per year in the 1990s and 1.7% per year from 2010 to 2019 in the high-income countries of the world, which include the United States, Canada, the European Union countries, Japan, Australia, and New Zealand.

Table 20.5 lists eight countries that belong to an informal “fast growth club.” These countries averaged GDP growth (after adjusting for inflation) of at least 5% per year in both the time periods from 1990 to 2000 and from 2010 to 2019. Since economic growth in these countries has exceeded the average of the world’s high-income economies, these countries may converge with the high-income countries. The second part of Table 20.5 lists the “slow growth club,” which consists of countries that averaged GDP growth of 2% per year or less (after adjusting for inflation) during the same time periods. The final portion of Table 20.5 shows GDP growth rates for the countries of the world divided by income. (Note that the reason there is no data for 2001–2009 is because of the Great Recession, which lasted from 2007–2009. Many country’s GDP shrank during these years.)

Each of the countries in Table 20.5 has its own unique story of investments in human and physical capital, technological gains, market forces, government policies, and even lucky events, but an overall pattern of convergence is clear. The low-income countries have GDP growth that is faster than that of the middle-income countries, which in turn have GDP growth that is faster than that of the high-income countries. Two prominent members of the fast-growth club are China and India, which between them have nearly 40% of the world’s population. Some prominent members of the slow-growth club are high-income countries like France, Germany, Italy, and Japan.

Will this pattern of economic convergence persist into the future? This is a controversial question among economists that we will consider by looking at some of the main arguments on both sides.

Arguments Favoring Convergence

Several arguments suggest that low-income countries might have an advantage in achieving greater worker productivity and economic growth in the future.

A first argument is based on diminishing marginal returns. Even though deepening human and physical capital will tend to increase GDP per capita, the law of diminishing returns suggests that as an economy continues to increase its human and physical capital, the marginal gains to economic growth will diminish. For example, raising the average education level of the population by two years from a tenth-grade level to a high school diploma (while holding all other inputs constant) would produce a certain increase in output. An additional two-year increase, so that the average person had a two-year college degree, would increase output further, but the marginal gain would be smaller. Yet another additional two-year increase in the level of education, so that the average person would have a four-year-college bachelor’s degree, would increase output still further, but the marginal increase would again be smaller. A similar lesson holds for physical capital. If the quantity of physical capital available to the average worker increases, by, say, $5,000 to $10,000 (again, while holding all other inputs constant), it will increase the level of output. An additional increase from $10,000 to $15,000 will increase output further, but the marginal increase will be smaller.

Low-income countries like China and India tend to have lower levels of human capital and physical capital, so an investment in capital deepening should have a larger marginal effect in these countries than in high-income countries, where levels of human and physical capital are already relatively high. Diminishing returns implies that low-income economies could converge to the levels that the high-income countries achieve.

A second argument is that low-income countries may find it easier to improve their technologies than high-income countries. High-income countries must continually invent new technologies, whereas low-income countries can often find ways of applying technology that has already been invented and is well understood. The economist Alexander Gerschenkron (1904–1978) gave this phenomenon a memorable name: “the advantages of backwardness.” Of course, he did not literally mean that it is an advantage to have a lower standard of living. He was pointing out that a country that is behind has some extra potential for catching up.

Finally, optimists argue that many countries have observed the experience of those that have grown more quickly and have learned from it. Moreover, once the people of a country begin to enjoy the benefits of a higher standard of living, they may be more likely to build and support the market-friendly institutions that will help provide this standard of living.

View this video to learn about economic growth across the world.

Arguments That Convergence Is neither Inevitable nor Likely

If the economy's growth depended only on the deepening of human capital and physical capital, then we would expect that economy's growth rate to slow down over the long run because of diminishing marginal returns. However, there is another crucial factor in the aggregate production function: technology.

Developing new technology can provide a way for an economy to sidestep the diminishing marginal returns of capital deepening. Figure 20.7 shows how. The figure's horizontal axis measures the amount of capital deepening, which on this figure is an overall measure that includes deepening of both physical and human capital. The amount of human and physical capital per worker increases as you move from left to right, from C 1 to C 2 to C 3 . The diagram's vertical axis measures per capita output. Start by considering the lowest line in this diagram, labeled Technology 1. Along this aggregate production function, the level of technology is held constant, so the line shows only the relationship between capital deepening and output. As capital deepens from C 1 to C 2 to C 3 and the economy moves from R to U to W, per capita output does increase—but the way in which the line starts out steeper on the left but then flattens as it moves to the right shows the diminishing marginal returns, as additional marginal amounts of capital deepening increase output by ever-smaller amounts. The shape of the aggregate production line (Technology 1) shows that the ability of capital deepening, by itself, to generate sustained economic growth is limited, since diminishing returns will eventually set in.

Now, bring improvements in technology into the picture. Improved technology means that with a given set of inputs, more output is possible. The production function labeled Technology 1 in the figure is based on one level of technology, but Technology 2 is based on an improved level of technology, so for every level of capital deepening on the horizontal axis, it produces a higher level of output on the vertical axis. In turn, production function Technology 3 represents a still higher level of technology, so that for every level of inputs on the horizontal axis, it produces a higher level of output on the vertical axis than either of the other two aggregate production functions.

Most healthy, growing economies are deepening their human and physical capital and increasing technology at the same time. As a result, the economy can move from a choice like point R on the Technology 1 aggregate production line to a point like S on Technology 2 and a point like T on the still higher aggregate production line (Technology 3). With the combination of technology and capital deepening, the rise in GDP per capita in high-income countries does not need to fade away because of diminishing returns. The gains from technology can offset the diminishing returns involved with capital deepening.

Will technological improvements themselves run into diminishing returns over time? That is, will it become continually harder and more costly to discover new technological improvements? Perhaps someday, but, at least over the last two centuries since the beginning of the Industrial Revolution, improvements in technology have not run into diminishing marginal returns. Modern inventions, like the internet or discoveries in genetics or materials science, do not seem to provide smaller gains to output than earlier inventions like the steam engine or the railroad. One reason that technological ideas do not seem to run into diminishing returns is that we often can apply widely the ideas of new technology at a marginal cost that is very low or even zero. A specific worker or group of workers must use a specific additional machine, or an additional year of education. Many workers across the economy can use a new technology or invention at very low marginal cost.

The argument that it is easier for a low-income country to copy and adapt existing technology than it is for a high-income country to invent new technology is not necessarily true, either. When it comes to adapting and using new technology, a society’s performance is not necessarily guaranteed, but is the result of whether the country's economic, educational, and public policy institutions are supportive. In theory, perhaps, low-income countries have many opportunities to copy and adapt technology, but if they lack the appropriate supportive economic infrastructure and institutions, the theoretical possibility that backwardness might have certain advantages is of little practical relevance.

Visit this website to read more about economic growth in India.

The Slowness of Convergence

Although economic convergence between the high-income countries and the rest of the world seems possible and even likely, it will proceed slowly. Consider, for example, a country that starts off with a GDP per capita of $40,000, which would roughly represent a typical high-income country today, and another country that starts out at $4,000, which is roughly the level in low-income but not impoverished countries like Indonesia, Guatemala, or Egypt. Say that the rich country chugs along at a 2% annual growth rate of GDP per capita, while the poorer country grows at the aggressive rate of 7% per year. After 30 years, GDP per capita in the rich country will be $72,450 (that is, $40,000 (1 + 0.02) 30 ) while in the poor country it will be $30,450 (that is, $4,000 (1 + 0.07) 30 ). Convergence has occurred. The rich country used to be 10 times as wealthy as the poor one, and now it is only about 2.4 times as wealthy. Even after 30 consecutive years of very rapid growth, however, people in the low-income country are still likely to feel quite poor compared to people in the rich country. Moreover, as the poor country catches up, its opportunities for catch-up growth are reduced, and its growth rate may slow down somewhat.

The slowness of convergence illustrates again that small differences in annual rates of economic growth become huge differences over time. The high-income countries have been building up their advantage in standard of living over decades—more than a century in some cases. Even in an optimistic scenario, it will take decades for the low-income countries of the world to catch up significantly.

Bring It Home

Calories and economic growth.

We can tell the story of modern economic growth by looking at calorie consumption over time. The dramatic rise in incomes allowed the average person to eat better and consume more calories. How did these incomes increase? The neoclassical growth consensus uses the aggregate production function to suggest that the period of modern economic growth came about because of increases in inputs such as technology and physical and human capital. Also important was the way in which technological progress combined with physical and human capital deepening to create growth and convergence. The issue of distribution of income notwithstanding, it is clear that the average worker can afford more calories in 2020 than in 1875.

Aside from increases in income, there is another reason why the average person can afford more food. Modern agriculture has allowed many countries to produce more food than they need. Despite having more than enough food, however, many governments and multilateral agencies have not solved the food distribution problem. In fact, food shortages, famine, or general food insecurity are caused more often by the failure of government macroeconomic policy, according to the Nobel Prize-winning economist Amartya Sen. Sen has conducted extensive research into issues of inequality, poverty, and the role of government in improving standards of living. Macroeconomic policies that strive toward stable inflation, full employment, education of women, and preservation of property rights are more likely to eliminate starvation and provide for a more even distribution of food.

Because we have more food per capita, global food prices have decreased since 1875. The prices of some foods, however, have decreased more than the prices of others. For example, researchers from the University of Washington have shown that in the United States, calories from zucchini and lettuce are 100 times more expensive than calories from oil, butter, and sugar. Research from countries like India, China, and the United States suggests that as incomes rise, individuals want more calories from fats and protein and fewer from carbohydrates. This has very interesting implications for global food production, obesity, and environmental consequences. Affluent urban India has an obesity problem much like many parts of the United States. The forces of convergence are at work.

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• Authors: Steven A. Greenlaw, David Shapiro, Daniel MacDonald
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• Book title: Principles of Economics 3e
• Publication date: Dec 14, 2022
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• Book URL: https://openstax.org/books/principles-economics-3e/pages/1-introduction
• Section URL: https://openstax.org/books/principles-economics-3e/pages/20-4-economic-convergence

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The Convergence Hypothesis: History, Theory, and Evidence

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What Is Convergence Theory?

How Industrialization Affects Developing Nations

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Convergence theory presumes that as nations move from the early stages of industrialization toward becoming fully industrialized , they begin to resemble other industrialized societies in terms of societal norms and technology.

The characteristics of these nations effectively converge. Ultimately, this could lead to a unified global culture if nothing impeded the process.

Convergence theory has its roots in the functionalist perspective of economics which assumes that societies have certain requirements that must be met if they are to survive and operate effectively. 

Convergence theory became popular in the 1960s when it was formulated by the University of California, Berkeley Professor of Economics Clark Kerr.

Some theorists have since expounded upon Kerr's original premise. They say industrialized nations may become more alike in some ways than in others.

Convergence theory is not an across-the-board transformation. Although technologies may be shared , it's not as likely that more fundamental aspects of life such as religion and politics would necessarily converge—though they may. 

Convergence vs. Divergence

Convergence theory is also sometimes referred to as the "catch-up effect."

When technology is introduced to nations still in the early stages of industrialization, money from other nations may pour in to develop and take advantage of this opportunity. These nations may become more accessible and susceptible to international markets. This allows them to "catch up" with more advanced nations.

If capital is not invested in these countries, however, and if international markets do not take notice or find that opportunity is viable there, no catch-up can occur. The country is then said to have diverged rather than converged.

Unstable nations are more likely to diverge because they are unable to converge due to political or social-structural factors, such as lack of educational or job-training resources. Convergence theory, therefore, would not apply to them. 

Convergence theory also allows that the economies of developing nations will grow more rapidly than those of industrialized countries under these circumstances. Therefore, all should reach an equal footing eventually.

Some examples of convergence theory include Russia and Vietnam, formerly purely communist countries that have eased away from strict communist doctrines as the economies in other countries, such as the United States, have burgeoned.

State-controlled socialism is less the norm in these countries now than is market socialism, which allows for economic fluctuations and, in some cases, private businesses as well. Russia and Vietnam have both experienced economic growth as their socialistic rules and politics have changed and relaxed to some degree.

Former World War II Axis nations including Italy, Germany, and Japan rebuilt their economic bases into economies not dissimilar to those that existed among the Allied Powers of the United States, the Soviet Union, and Great Britain.

More recently, in the mid-20th century, some East Asian countries converged with other more developed nations. Singapore , South Korea, and Taiwan are now all considered to be developed, industrialized nations.

Sociological Critiques

Convergence theory is an economic theory that presupposes that the concept of development is

• a universally good thing
• defined by economic growth.

It frames convergence with supposedly "developed" nations as a goal of so-called "undeveloped" or "developing" nations, and in doing so, fails to account for the numerous negative outcomes that often follow this economically-focused model of development.

Many sociologists, postcolonial scholars, and environmental scientists have observed that this type of development often only further enriches the already wealthy, and/or creates or expands a middle class while exacerbating the poverty and poor quality of life experienced by the majority of the nation in question.

Additionally, it is a form of development that typically relies on the over-use of natural resources, displaces subsistence and small-scale agriculture, and causes widespread pollution and damage to the natural habitat.

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What Is the Catch-Up Effect?

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The Catch-Up Effect Definition and Theory of Convergence

The catch-up effect is a theory that the per capita incomes of all economies will eventually converge.

This theory is based on the observation that underdeveloped economies tend to grow more rapidly than wealthier economies. As a result, the less wealthy economies literally catch up to the more robust economies.

The catch-up effect is also referred to as the theory of convergence .

Key Takeaways

• The catch-up effect is a theory that the per capita incomes of developing economies catch up to those of more developed economies.
• It is based on the law of diminishing marginal returns , applied to investment at the national level.
• It also involves the empirical observation that growth rates tend to slow as an economy matures.
• Developing nations can enhance their catch-up effect by opening up their economies to free trade.
• They should pursue "social capabilities," or the ability to absorb new technology, attract capital, and participate in global markets.

Understanding the Catch-Up Effect

The catch-up effect, or theory of convergence, is predicated on several key ideas.

1. One is the law of diminishing marginal returns . This is the idea that, as a country invests and profits, the amount gained from the investment will eventually decline as the level of investment rises.

Each time a country invests, it benefits slightly less from that investment. So, returns on capital investments in capital-rich countries are not as large as they would be in developing countries.

2. This is backed up by the empirical observation that more developed economies tend to grow at a slower, though more stable, rate than less developed countries.

According to the World Bank, high-income countries averaged 2.8% gross domestic product (GDP) growth in 2022, versus 3.6% for middle-income countries and 3.4% for low-income countries.

3. Developing and underdeveloped countries may also be able to experience more rapid growth because they can replicate the production methods, technologies, policies, and institutions of developed countries.

This is also known as a second-mover advantage. When developing markets have access to the technological know-how of the advanced nations, they can experience rapid rates of growth.

Limitations to the Catch-Up Effect

Lack of capital.

Although developing countries can see faster economic growth than more economically advanced countries, the limitations posed by a lack of capital can greatly reduce a developing country's ability to catch up.

Historically, some developing countries have been very successful in managing resources and securing capital to efficiently increase economic productivity . However, this has not become the norm on a global scale.

Lack of Social Capabilities

Economist Moses Abramowitz wrote that in order for countries to benefit from the catch-up effect, they need to develop and leverage what he called "social capabilities."

These include the ability to absorb new technology, attract capital, and participate in global markets. This means that if technology is not freely traded, or is prohibitively expensive, then the catch-up effect won't occur.

Lack of Open Trade

The adoption of open trade policies, especially with respect to international trade, also plays a role. According to a longitudinal study by economists Jeffrey Sachs and Andrew Warner, national economic policies of free trade and openness are associated with more rapid growth.

Studying 111 countries from 1970 to 1989, the researchers found that industrialized nations had a growth rate of 2.3% per year per capita. Developing countries with open trade policies had a rate of 4.5%. And developing countries with more protectionist and closed economy policies had a growth rate of only 2%.

Population Growth

Another major obstacle for the catch-up effect is that per capita income is not just a function of GDP, but also of a country's population growth. Less developed countries tend to have higher population growth than developed economies. The greater the number of people, the less the per capita income.

According to the World Bank figures for 2022, more developed countries ( OECD members) experienced 0.3% average population growth, while the UN-classified least developed countries had an average 2.3% population growth rate.

Economic convergence appears to stem primarily from latecomer countries borrowing or imitating the established technologies available in industrialized countries.

Example of the Catch-Up Effect

During the period between 1911 to 1940, Japan was the fastest-growing economy in the world. It colonized and invested heavily in its neighbors South Korea and Taiwan, contributing to their economic growth as well. After the Second World War, however, Japan's economy lay in tatters.

The country rebuilt a sustainable environment for economic growth during the 1950s and began importing machinery and technology from the United States. It clocked incredible growth rates in the period from 1960 to the early 1980s.

As Japan's economy powered forward, the U.S. economy, which was a source for much of Japan's infrastructural and industrial underpinnings, hummed along. Then by the late 1970s, when the Japanese economy ranked among the world's top five, its growth rate had slowed down.

The economies of the Asian Tigers , a moniker used to describe the rapid growth of economies in Southeast Asia, have seen a similar trajectory, displaying rapid economic growth during the initial years of their development, followed by a more moderate (and declining) growth rate as they transitioned from a developing stage to that of developed.

How Can Underdeveloped Countries Benefit From the Catch-Up Effect?

Those without technological innovations and the financial resources to develop them can borrow from what has already been innovated and developed successfully to grow their GDP and per capita income.

Has Globalization Made the Catch-Up Effect More Prevalent?

Perhaps. For example, globalization has made advances in technologies and supply chain innovations more readily available to underdeveloped and developing nations, due to the loosening of trade restrictions and economic cooperation between countries.

Why Does the Catch-Up Effect Diminish for Industrialized Countries?

The effect is far less for such countries because, with their greater amounts of capital, freer trade, and beneficial economic policies, they have less room in which to achieve more.

The catch-up effect refers to a theory that holds that the per capita incomes of emerging and developing nations will eventually converge with the per capita incomes of more developed, wealthier countries.

The effect can be hampered by certain circumstances, such as the lack of capital and open trade policies, and the inability to attract investments.

The World Bank. " GDP Growth (annual %) ."

Abramowitz, Moses, via JSTOR. " Catching Up, Forging Ahead, Falling Behind ."  Journal of Economic History, vol 46, no. 2, June 1986, pp. 385-406.

Brookings. " Economic Reform and the Process of Global Integration ."

The World Bank. " Population Growth (annual %) ."

Oxford Academic. " Catching Up or Developing Differently? Techno-Institutional Learning with a Sustainable Planet in Mind ."

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The Convergence Hypothesis: Types and Paths | Economic Growth

Let us make an in-depth study of the Convergence Hypothesis. After reading this article you will learn about: 1. Types of Convergence 2. Possible Paths of Convergence.

Types of Convergence :

There are three types of convergence unconditional convergence, conditional conver­gence and no convergence.

(i) Unconditional Convergence:

By unconditional convergence we mean that LDCs will ultimately catch up with the industrially advanced countries so that, in the long run, the standards of living throughout the world become more or less the same. The Solow model predicts unconditional convergence under certain special conditions. For example, let us suppose that different countries of the world differed mainly in their capital-labour ratios.

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Normally, rich countries have high capital-labour ratio and high levels of output per worker. By contrast, low income countries have low capital-labour ratios and low levels of output per worker. We also assume that two groups of countries are the same in all other respects such as saving rates, population growth rates and the production function.

If this is true then the Solow model predicts that, in spite of any differences in initial capital-labour ratios, all these countries will ultimately attain the same steady state. Differently put, if countries have the same fundamental characteristics, capital-labour ratios and living standards will uncon­ditionally converge, even though some countries may start from way behind.

(ii) Conditional convergence:

Even if countries differ in their saving rates, population growth rates and production functions (due to unequal access to technology) they will converge to different steady state with different capital-labour ratios and different standards of living in the long run. If countries differ in the fundamental characteristics, the Solow model predicts conditional convergence.

This means that standards of living will converge only within groups of countries having similar characteristics. For example, if there is conditional convergence, a low income country with a low saving rate may catch up, one day or the other, a richer country that also has a low saving rate, but it will never catch up a rich country that has a high saving rate.

One reason for this is that poor countries have less capital per worker and thus higher marginal products of capital than do rich countries. So savers in all countries will be able to earn the highest return by investing in poor countries. Eventually, borrowing abroad will allow initially poor countries’ capital-labour ratios and output per worker to be the same as in initially rich countries.

(iii) No convergence:

The third possibility is no convergence. This means that the low income countries will never catch up over time. Therefore living standards may even diverge due to widening income gap — the rich getting richer and poor getting poorer.

Possible Paths of Convergence :

In Fig. 4.14(a) and 4.14(b) we show the possible paths of convergence and divergence of per capita output. In Fig. 4.14(a) T r represents the steady state growth path of the rich country. The slope of this line represents the rate of growth for the poor country. Three options are open to them.

Here T p represents a steady state growth path in which the rich and poor countries grow at the same rate. A favourable shock at time t 0 leads to convergence of output per capita in rich and poor countries as shown by the steady state growth path T p .

An adverse shock that slows down the growth rate of the poor country in the short run but leads to the same steady state growth as in T p is indicated by the growth path T’ p . The dotted line indicates movement outside of steady state.

Fig 4.14(b) shows divergence between the rich and poor countries. T r and T represent the steady state growth paths of the rich and poor country. We find divergence of per capita outputs across two countries over time. Here the scenario is different.

Irrespective of favourable shock (T h ) or an adverse shock (T’ p ) the steady state growth rate is the same as in T p , and long-run income per capita in the rich country will increasingly diverge from that in the poor country. The solid lines in both diagrams are steady state paths whereas the dotted lines represent transitions to equilibrium in response to a shock.

The path T h in Fig 4.14(a) shows how absolute convergence — in the sense of the same growth rates as also the same growth path — occurs. This is a strong form of convergence. A weaker form of convergence — called conditional convergence — is depicted by the paths T’ p and T p which show the same growth rates but different growth paths among countries.

The vertical distance of the growth path of a poor country from that of a rich country represents income differences due to differences in underlying parameters such as savings rates and population growth.

The developing countries can have free access to the technology developed by the pioneers. This implies that latecomers have a potential advantage over pioneers — an advantage of backwardness.

In the course of adopting and adapting new techniques, IRS appear in the guise of learning-by-doing. No doubt the firms which adopt new techniques find an improvement in efficiency following the adoption.

Hence, if firms in LDCs organise themselves so as to profit from the accumulated experienced as fully as do firms in developed countries and such improvements have a ceiling that is reached in finite time or cumulative output, as seems plausible, then catching up in that particular time of production with the technique in question will be complete.

Unfortunately from the point of view of the world’s low income countries there is hardly any empirical support for unconditional convergence. Most studies have found little tendency for low income countries to catch up with their rich counterparts.

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• Convergence and Poor Countries: 3 Mechanisms | Economics
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Interpreting Tests of the Convergence Hypothesis

This paper provides a framework for understanding the cross- section and time series approaches which have been used to test the convergence hypothesis. First, we present two definitions of convergence which capture the implications of the neoclassical growth model for the relationship between current and future cross-country output differences. Second, we identify how the cross-section and time series approaches relate to these definitions. Cross-section tests are shown to be associated with a weaker notion of convergence than time series tests. Third, we show how these alternative approaches make different assumptions on whether the data are well characterized by a limiting distribution. As a result, the choice of an appropriate testing framework is shown to depend on both the specific null and alternative hypotheses under consideration as well as on the initial conditions characterizing the data being studied.

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Bernard, Andrew B. and Steven N. Durlauf. "Interpreting Tests Of The Convergence Hypothesis," Journal of Econometrics, 1996, v71(1&2,Mar/Apr), 161-173.

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Case Study: The Convergence Hypothesis

Per capita growth rates tend to be inversely related to the starting level of development measured by real output per person. Therefore, poor countries grow faster than rich ones. According to the convergence hypothesis, over time poor countries will catch up with the rich ones because of their higher growth rates and the power of compound growth. Thus, we will observe a convergence in the standards of living across countries over time.

The goal of this case study is to examine whether we can find support for the convergence hypothesis in the data.

• Collect country-level data on
• Real GDP per capita in 1960; and
• Annual data on real GDP per capita growth rate over the period 1960 – 2020.

from the World Bank Databank , Penn World Table , or Our World in Data .

• Calculate the average real GDP per capita growth rate over the period 1960 – 2020.
• Plot your data with the average real growth on the vertical axis and the starting GDP per capita on the horizontal axis for a) all countries in your sample; and b) for OECD countries.

Do you find evidence of the convergence hypothesis using your entire sample? Using a sample of the OECD countries? Why or why not?

Module 10: Microinsurance and Economic Development Copyright © by Tsvetanka Karagyozova is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Convergence: Understanding its Significant Role in Finance and Economics

✅ All InspiredEconomist articles and guides have been fact-checked and reviewed for accuracy. Please refer to our editorial policy for additional information.

Convergence Definition

Convergence in finance refers to the notion that the price of a financial instrument, such as a futures contract, will gradually move toward its intrinsic value as the contract’s expiration date approaches. It’s a fundamental principle in pricing derivatives which ensures that markets are efficient and there are no arbitrage opportunities.

Types of Financial Convergence

Price convergence.

Price convergence occurs when the price of the same asset or commodity approaches the same price in different markets. This is largely due to globalization and the increased accessibility and movement of goods across borders. A critical component in the concept of arbitrage, price convergence mitigates the scenarios where merchants or traders are able to buy at a low price in one market to sell at a higher price in another. This principle is fundamental to market efficiency.

Interest Rate Convergence

The phenomenon of interest rate convergence is observed when interest rates across different regions or economies start to have a common rate of return for a similar risk classification. This convergence can be attributed to factors such as economic growth, inflation, and monetary policy, among others. The understanding of this financial convergence type plays a pivotal role in international lending and borrowing, currency exchange rates, investments, and finance management.

Economic Convergence

Economic convergence refers to the hypothesis that poorer economies will tend to grow at faster rates than richer economies. As a result, all economies should eventually converge in terms of per capita income and other economic indicators. The European Union is a prime example of economic convergence, where poorer nations have witnessed higher growth rates than their wealthier counterparts. This principle is fundamental in making macroeconomic decisions and policy-making, as it aids in analyzing the growth potential of economies.

Sector Convergence

Sector convergence refers to a phenomenon where businesses from different industry sectors start blending their offerings due to technological advancements and market demand. Business sectors such as technology, media and telecommunication–often bundled as TMT–are classic examples showcasing sector convergence. This leads to increased competitiveness, innovation, and consumer choice.

Understanding these various types of financial convergence is crucial for financial analysis and macroeconomic policy decision-making, providing a deeper understanding of market trends dynamics, and economic growth trajectories. It also determines how economies respond to various economic factors and contribute to the global economy. In capital markets, the principles of convergence play a huge role in arbitrage, trading, and pricing.

Role of Convergence in Financial Markets

Convergence plays a critical role and affects a myriad of operational factors within financial markets.

The Impact on Price Determination

When it comes to price determination, convergence comes into play by drawing the prices of similar commodities from disparate markets together, until they fulfill a similar price point. This is commonly seen in futures markets where the spot price and the futures price converge as the delivery date nears, thereby eliminating any arbitrage opportunity. In a perfectly efficient market, this convergence happens due to the interaction of supply and demand forces, and it ensures that prices reflect the intrinsic value of the security.

Facilitating Market Efficiency

In regard to market efficiency, the principle of convergence is a determinant factor. It enforces the Efficient Market Hypothesis (EMH), which asserts that at any given time, security prices fully reflect all available information. Essentially, this brings any discrepancies in the valuation of similar securities towards a common level. Hence, it prevents the possibility of garnering extraordinary profits without equivalent risk. If the convergence did not occur, the resultant price disparities across markets would provide opportunities for arbitrage, leading to increased market inefficiency.

Promoting Global Financial Integration

As for global integration of financial markets, convergence plays a pivotal role in levelling the playing field for all investors. In a globally connected financial world, information flows seamlessly across different markets. As such, convergence ensures that prices of identical or similar financial instruments in different markets move together. This global price convergence indicates a high degree of global financial integration, thereby fostering fairness and increasing efficiency. For instance, a bond issued by a firm in the U.S. should have a similar yield to a similar bond issued by the same firm in Europe, provided they share the same risk characteristics.

Simply put, convergence in financial markets is a critical and fundamental attribute. Without it, we would witness distorted price levels, dwindled market efficiency, and reduced global economic cooperation.

Convergence in Risk Management

The role of convergence in risk management.

Risk management in financial institutions involves the identification, analysis, and mitigation of uncertainties in investment decisions. The concept of convergence becomes a vital tool in this process.

Convergence between Market and Credit Risk

Convergence finds its application in the overlap of market and credit risk. Market risk refers to the risk that the value of investments will decrease due to changes in market factors. Credit risk, on the other hand, is the risk of a financial loss being incurred if a borrower doesn't meet their contractual obligations.

Convergence between these two types of risks occurs when market conditions affect the credit risk profile of a counterparty. For example, a slump in the oil market can significantly increase the credit risk of oil industry counterparts.

Implications on Financial Strategy

The convergence of market and credit risk has substantial implications for financial strategy. Risk managers, aware of convergence, may need to adjust their risk models and strategies. Risk mitigation tactics, such as diversification and hedging, may be redesigned to factor in the interrelationship of these two risks.

Another implication of convergence in risk management is the need for more robust stress testing. Given the intertwined nature of market and credit risks, a thorough analysis of potential scenarios is crucial in developing a comprehensive risk management strategy.

Lastly, convergence demands improved communication and collaboration between the market and credit risk departments. Shared understanding of risk profiles can lead to a unified risk management approach and help to better quantify the total risk exposure of a financial institution.

While the concept of convergence brings complexity, it also provides opportunities for more insightful and holistic risk management. The recognition of interrelated risks facilitates the crafting of more resilient and agile financial strategies.

Convergence vs. Divergence: A Comparative Analysis

To understand the role of convergence, it's imperative to discuss it alongside divergence and their respective impacts on financial markets.

The Effects of Convergence on Financial Markets

Convergence is a significant force in financial markets. When prices, returns, or key economic indicators trend towards each other or a common point over time, it's observed as convergence. An apt example is the interest rate parity in international finance. It predicts that interest rates between two countries should equalize over time due to currency exchange factors.

Market stability is often associated with convergence. As assets or economic conditions converge, markets can become more predictable. Investors generally appreciate this predictability, as it reduces uncertainty and risk. Convergence can also provide intriguing investment opportunities. For instance, if two asset prices are expected to converge, traders could profit by buying the lower-priced asset and selling the higher-priced one.

However, convergence isn't always a positive phenomenon. Rapid convergence can lead to asset price bubbles if investors blindly follow the trend without considering underlying fundamentals.

The Effects of Divergence on Financial Markets

Contrastingly, divergence refers to the scenario where prices, returns, or economic indicators stray further apart over time. Divergence can drive noticeable market volatility. It typically signifies that certain market sectors are overperforming or underperforming compared to others.

Investors perceive divergences differently: some see it as a warning sign, while others regard it as an investment opportunity. For instance, when company profits begin to diverge from stock prices (i.e., profits decrease while stock prices increase), it can signal an upcoming correction. On the other hand, if a stock's price is falling while its fundamentals are improving, it might represent a value buying opportunity.

However, interpreting divergence can be complex as it doesn't always mean prices will reverse. A prolonged divergence could lead to a new market trend rather than a reversal.

Convergence versus Divergence

The key comparison between convergence and divergence is essentially how they influence market stability. While convergence breeds more predictability, which can reduce market volatility and risk, divergence tends to increase volatility and uncertainty, thereby elevating risk.

Both force investors to adapt their strategies and can either create or remove investment opportunities in the market. That said, the role of convergence, especially, cannot be understated; it serves as a vital instrument for understanding international finance, predicting market shifts, spotting investment opportunities, and ensuring the long-term vibrancy and stability in financial markets.

Implications of Convergence on Corporate Social Responsibility

In the financial world, convergence has far-reaching implications. Of particular interest, is its influence on Corporate Social Responsibility (CSR).

CSR and Financial Convergence

The interplay between CSR and financial convergence is remarkable. Given that convergence attempts to standardize financial practices, it sets a basis for firms to align their conduct particularly in relation to social and environmental responsibility. In essence, the convergence of financial practices has gradually reshaped CSR, as companies are impelled to take into account their societal impacts, besides their financial performance.

Increasing Accountability through Convergence

Convergence brings a heightened sense of accountability, and within the CSR paradigm, companies that have assimilated these financial practices have begun to systematically assess their role in society. Decision-making processes have thus been adjusted to ensure that corporate outlets are minimizing their negative outcomes while enhancing their positive societal impacts.

Convergence fostering Transparency

Transparency is another key benefit that convergence offers in enhancing CSR. As the convergence of financial practices promotes standardization, firms are more likely to openly disclose their operations. Such transparency enables stakeholders to comprehend the social and environmental implications of a firm's activities, which ultimately improves their reputation and investor appeals.

Sustainability in the backdrop of Convergence

Though we haven't primarily focused on sustainability, it still requires a brief mention in relation to convergence. With the standardization of financial practices, companies are starting to implement strategies to sustain long-term growth without negative implications on society or the environment. By converging their practices to universally recognized standards, firms are tacitly instilling a sense of obligation to pursue sustainable ventures.

In conclusion, the convergence of financial practices not only influences the economic dynamics of businesses, but also the manner in which these businesses respond to social and environmental issues. It fosters transparency and accountability, hence enhancing CSR and by extension, the impetus towards sustainability.

Convergence in the International Context

Impact of convergence on global financial markets.

Convergence has considerable consequences for international markets. As economies and markets begin to align, there are manifold effects on security prices, market volatility, and liquidity conditions. With convergence, markets typically show less disparity in asset pricing and show greater risk sharing and overall stability. This smoother operation of financial markets is beneficial for investors, businesses, and consumers across the globe.

Such convergence also yields implications for the trend of globalization. It pushes towards economic synchronization between countries, allowing markets to become more correlated. As this happens, the impacts of regional macroeconomic conditions can no longer be restricted solely to their home borders. Instead, they generate wider ripples, affecting economic activity, policy decisions, and market outcomes at a global level.

Convergence and Economic Integration

Economic integration is, in essence, a form of convergence. By breaking down barriers to the flow of goods, services, capital, and labor, countries can unify their activities more closely, leading to an increase in inter-country convergence. While this integration can bring about a host of benefits such as increased trade, greater economic efficiency, and higher growth potential, they're not without challenges.

Economic convergence among integrated economies necessitates appropriate policy responses, dealing with issues like economic disparity, shock synchronization, and structural alignment. Failure to manage these efficiently can lead to economic instability, undermining the potential benefits.

Role of Convergence in International Finance

In the arena of international finance, convergence assumes a critical role. It shapes investment decisions and strategies, asset allocation, pricing, and risk management practices. As economies converge, investors tend to diversify their portfolio globally, seeking out potential rewards from markets that are aligning with or outperforming their home market.

Moreover, converging interest rates and inflation levels worldwide also affect the cost of capital, altering investment and financing decisions for international businesses. These influence the creation of new financial products and the evolution of global financial market structures.

Convergence in Investment Strategies

Convergence also significantly molds investment strategies. Typically, investors can capitalize on the effects of convergence by adopting strategies like convergence trade. In this strategy, investors bet on the price difference between related securities, expecting that the prices will converge eventually.

Investment in emerging markets is another strategy, one based on the expectation that these markets will gradually converge with developed economies. As convergence proceeds, lesser-known, undervalued markets may present lucrative investment opportunities, encouraging risk diversification and potential returns.

Overall, convergence cannot be undersold in the discourse on the world financial stage. As we continue to observe its effects on the global financial markets and economic integration, the importance of correctly comprehending and navigating it becomes ever clearer.

Technological Factors Influencing Financial Convergence

Fintech and its role in financial convergence.

The financial technology (fintech) sector has been instrumental in driving financial convergence. Fintech innovations can dismantle traditional financial structures and processes by introducing more streamlined and efficient alternatives. Peer-to-peer lending platforms, robo-advisors, and mobile banking are some key fintech developments facilitating convergence. For instance, robo-advisors have democratized financial advisory by making it accessible to a wider audience, thus converging the wealth management industry.

Fintech evolution can potentially accelerate the pace of financial convergence in the future. As technology continues to span across multiple financial sectors, boundaries between individual financial services are likely to diminish further, paving the way for a more integrated financial ecosystem.

Blockchain Technology and Financial Convergence

Another significant technological factor that contributes to financial convergence is blockchain technology. Blockchain promotes convergence by enhancing transparency, efficiency, and security in financial transactions. Its decentralized nature eliminates the need for an intermediary, thereby shortening the settlement process and reducing costs. This directly contributes to the convergence of payment and settlement processes in the financial industry.

Blockchain's potential impact on the future of financial convergence cannot be overstated. The technology can revolutionize multiple sectors within finance, from securities trading to cross-border payments, bringing them under the same umbrella. As a result, further convergence of financial services could be observed as the technology matures and achieves wider acceptance.

Implications for the Future of Financial Convergence

Looking forward to the future trajectory of financial convergence, it's clear that both fintech and blockchain will have substantial roles to play. Ultimately, these technological advancements seek to create a seamless and inclusive global financial system by integrating various financial services. However, it is also critical to recognize potential challenges such as regulatory, security, and privacy concerns that may emerge with these advancements. Careful management of these issues will be essential to ensure the successful realization of financial convergence in the years to come.

Regulatory Implications of Convergence

As convergence becomes a more prevalent practice within the financial sector, regulatory bodies face an array of new challenges and responsibilities. Firstly, due to the blending of distinct financial entities and services, regulations that were originally designed for individual sectors might no longer suffice. The evolving landscape could necessitate a reconfiguration of existing laws and create obstacles where regulatory gaps emerge.

Implications of Convergence

One chief concern is the potential threat to consumer protection. With the boundaries between sectors becoming blurred, consumers might easily get mixed up and fall victim to deceptive practices. Regulatory bodies thus need to ensure transparency and improve consumer awareness about the implications of convergence.

In addition, the integration of banking, insurance and investment services through convergence might consolidate power in fewer institutions, giving rise to systemic risk. This accumulation of operations could pose significant threats to financial stability.

Regulatory Responses

To respond to these challenges, regulatory bodies have to adapt their practices. One strategy could be the formulation of 'functional regulations', which focus on the nature of the financial activity, rather than the institution performing it. This ensures that similar activities performed by different types of institutions are regulated consistently.

Nevertheless, functional regulation alone could miss risks that are intrinsic to large, interconnected institutions. This points to the need for implementing 'systemic regulation' which focuses on the overall stability of the financial system, rather than individual entities. It is designed to manage risks that could potentially disrupt the entire financial system.

In summary, the increasing convergence in the financial sector has brought forth numerous regulatory implications and challenges. Regulatory bodies need to undertake significant structural changes to build a robust regulatory framework that can respond to these changes effectively, maintaining the balance between consumer protection and financial innovation.

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• Open access
• Published: 28 March 2024

Trends in household out-of-pocket health expenditures and their underlying determinants: explaining variations within African regional economic communities from countries panel data

• Nicholas Ngepah 1 &
• Ariane Ephemia Ndzignat Mouteyica   ORCID: orcid.org/0000-0001-5441-5306 1  

Globalization and Health volume  20 , Article number:  27 ( 2024 ) Cite this article

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The persistently high out-of-pocket health spending (OOPHE) in Africa raise significant concern about the prospect of reaching SDG health targets and UHC. The study examines the convergence hypothesis of OOPHE in 40 African countries from 2000 to 2019.

We exploit the \({\text{log }}t\) , club clustering, and merging methods on a panel of dataset obtained from the World Development Indicators, the World Governance Indicators, and the World Health Organization. Then, we employ the multilevel linear mixed effect model to examine whether countries' macro-level characteristics affect the disparities in OOPHE in the African regional economic communities (RECs).

The results show evidence of full panel divergence, indicating persistent disparities in OOPHE over time. However, we found three convergence clubs and a divergent group for the OOPHE per capita and as a share of the total health expenditure. The results also show that convergence does not only occur among countries affiliated with the same regional economic grouping, suggesting disparities within the regional groupings. The findings reveal that countries' improved access to sanitation and quality of governance, increased childhood DPT immunization coverage, increased share of the elderly population, life expectancy at birth, external health expenditure per capita, and ICT (information and communication technology) significantly affect within-regional groupings’ disparities in OOPHE per capita. The results also show that an increasing countries’ share of elderly and younger populations, access to basic sanitation, ICT, trade GDP per capita, life expectancy at birth, childhood DPT immunization coverage, and antiretroviral therapy coverage have significant impacts on the share of OOPHE to total health expenditure within the regional groupings.

Therefore, there is a need to develop policies that vary across the convergence clubs. These countries should increase their health services coverage, adopt planned urbanization, and coordinate trade and ICT access policies. Policymakers should consider hidden costs associated with access to childhood immunization services that may lead to catastrophic health spending.

Introduction

Insufficient investments in the health sector are hindering Africa's progress toward attaining Sustainable Development Goals (SDGs) and Universal Health Coverage (UHC) and improving the health outcomes of its populations. With a financial gap for healthcare of USD 66 billion per annum, the considerable health financing constraints that the continent faces arise primarily from the existing health financing mechanisms and strategies, including extensive out-of-pocket (OOP) payments [ 1 , 2 ]. In many countries, OOP spending leading to catastrophic health expenditures is worrisome, representing 40 percent or more of the total health expenditure. This source of financing is the most regressive and inequitable way of funding healthcare. The heavy dependence on this payment mechanism makes the financial costs a significant barrier to accessing healthcare services and increases the risk of impoverishment [ 3 ]. The Abuja call and WHO recommendation to reduce out-of-pocket payments to the upper limit of 20 percent of the total health expenditure are seen as a solution to address the health financing and equity issues and ensure financial protection in the continent [ 3 , 4 ].

Empirical studies showed considerable cross-country variations in out-of-pocket expenditures within and between countries. For instance, a previous study found that out-of-pocket payments do not converge between countries. Burkina Faso, Paraguay, and Thailand exhibited regressive trends, whereas Guatemala and South Africa displayed progressive trends [ 5 ]. Additionally, a separate study identified regressive patterns in out-of-pocket healthcare spending in high-income Asian countries [ 6 ]. Variations in catastrophic health expenditure among 12 Latin American countries and the Caribbean were observed, ranging from 1 to 25 percent [ 7 ]. Evidence of disparities in aggregate out-of-pocket expenditure per capita within ten high-income countries was also highlighted in another study [ 8 ]. Notably, the study demonstrated a decline in these disparities over time..At the micro-level, variations in OOPHE are mainly caused by variations in socioeconomic status, income, gender, age, geographical location, elderly population, health insurance, and education of the households [ 9 , 10 ]. For instance, OOPHE was found to be significantly higher for females, individuals with high socioeconomic status, and those with large household sizes; however, the presence of insurance was associated with a reduction in these expenditures [ 9 ]. Analyzing 34 studies in Sub-Saharan Africa (SSA), it was revealed that various factors—such as household economic status, type of health provider, socio-demographic characteristics of household members, type of illness, social insurance schemes, geographical location, and household size—are significant risk factors associated with catastrophic health expenditure [ 10 ].

.However, at the macro-level, indicators such as gross domestic product (GDP) per capita, foreign debt, government fiscal capacity, inflation rate, and unemployment rate are identified as significant determinants of OOPHE inequality [ 11 , 12 , 13 , 14 ]. For example, the impact of GDP per capita on the ratio of OOPHE to total health expenditure (THE) was found to be negative across 191 countries [ 11 ]. However, a decade later, the significance of GDP per capita on the share of OOPHE to THE was deemed insignificant in a study spanning 126 countries from 1995 to 2009 [ 12 ]. Similarly, no significant impact of GDP growth and national debt on OOPHE was found in OECD and European countries [ 13 ]. Additionally, a positive association between external aid for health and OOPHE was identified in some low-income countries [ 14 ].

While previous studies have examined the variations in OOPHE in developing and developed countries, only a few empirically focus on cross-country disparities in OOPHE in SSA region or a few countries from that region [5, 9, 10, and 15]. These studies predominantly depend on survey-based data. No specific study uses aggregated macro-level data to examine the convergence of OOPHE in Africa, including North Africa. At an aggregate level, the share of OOPHE in total health expenditure reveals the degree of financial protection in countries in three dimensions of coverage: the percentage of the population covered, the range of public health services provided, and the proportion of costs covered by collective third-party payer schemes for the necessary health services. It also show the progress that countries have made toward achieving UHC [ 15 ]. The share of OOP payments is higher in countries with low coverage dimensions, among which African countries are predominant, with wide variations across countries [ 3 , 16 ]. Therefore, this study will fill the gap by providing accurate, up-to-date evidence on the trajectories of OOP spending between African countries, which is is essential for examining the health systems' performance toward financial protection for populations, a significant aspect of UHC.

Since its inclusion as one of the health-related SDGs, UHC has become a prominent feature in Africa's health policy agenda. The African region set ambitious health targets for 2030: ending epidemics and achieving UHC for all. However, reaching these objectives require substantial increases in domestic investments in health and a radical change in the way health is harmonised to national, regional, and continental priorities. The African Union (AU) and Regional Economic Communities have developed several health financing initiatives that aim at increasing domestic investments in health, closing health funding gap, aligning health spending with national, regional, continental, and global priorities, improving health outcomes [ 17 ]. Furthermore, several calls for joint priorities have been made at regional and continental levels over the years [ 18 ].

Several studies showed that integration of healthcare markets, regionalism process, common health policies, and the diffusion of healthcare technologies are significant drivers of convergence in health expenditure [ 19 , 20 , 21 ]. Additionally, it was noted that achieving convergence in health expenditure during the Millennium Development Goals (MDGs) era could have strengthened the implementation of UHC and enabled a regional dimension of UHC in Africa [ 22 ]. This is also true regarding OOP health expenditure because convergence in this indicator is vital for coordinating health coverage systems within integrated African economies. Regional integration is increasingly also seen as a significant factor for improving health and welfare systems. Consequently, African countries have embraced regional integration as a crucial component of their development strategies. Given these considerations, this study uses a non-linear time-varying factor approach to investigate the convergence hypothesis of OOP health expenditures for 40 countries, including North African countries, from 2000 to 2019. We also use the data-driven algorithm to detect potential convergence clubs among the selected countries. The methodology was developed by [ 23 , 24 ].

Additionally, it has been demonstrated that within countries, several forces determine convergence or divergence [ 25 ], while intra-regional disparities can be explained by cross-country differences in certain factors [ 26 ]. Furthermore, it is emphasized that member states of the African Union (AU) and RECs bear the responsibility of mobilizing sustainable domestic health resources in alignment with continental and regional initiatives, as well as WHO recommendations, to reduce out-of-pocket health spending [ 17 ]. However, it has been revealed that many countries in regional groupings face significant challenges, including corruption, poor governance, inadequate investments in health, debt distress, inflation, deteriorating macroeconomic conditions, climate change, and a lack of political will to implement health financing policies and meet their health commitments [ 1 , 27 ]. These challenges exacerbate existing variations in out-of-pocket health expenditure and impede progress toward deeper and successful regional integration within the RECs and the African Union. Yet, no study has empirically assessed how countries' macro-level characteristics affect the distribution of OOP spending in the RECs. Therefore, this study will also fill this research gap by investigating the determinants of OOP health expenditure disparity in the eight RECs recognized by the African Union.

Given the above-mentioned considerations, this study exploits a non-linear time-varying factor approach to investigate the convergence hypothesis of OOP health expenditures for 40 countries, including North African countries, from 2000 to 2019. The method allows for the detection of convergence club and club merging between heterogeneous countries. It is particularly apt for our study as it accounts for potential individual and transitional variations, including the prospect of transitional divergence. This consideration is pivotal, as the traditional methods are subject to issues associated with assumptions and stationary tests, only testing the hypothesis of full sample convergence [ 23 , 24 ]. Moreover, the non-linear time-varying factor approach dispenses with assumptions related to trend stationarity or stochastic non-stationarity, enhancing the robustness of our findings.

Additionally, the study also uses the multilevel linear mixed-effect model to investigate whether countries’ macro level characteristics affect the disparities in OOPHE in RECs. The multilevel linear mixed-effect approach suits our study because it incorporates fixed and random effects. It is also appropriate for dealing with hierarchical data structure and repeated measurement of countries. In contrast to standard regression approaches, it is based on a non-independence assumption and allows data analysis organized at multiple levels [ 28 ]. In this case, the 40 selected African countries are nested into the eight regional economic communities recognised by the African Union.

The remainder of the paper follows: Section 2 describes the methods and data. Section 3 provides the results and discussion. Section 4 concludes and provides the policy implication.

\(Log\,t\) -test for convergence

The concept of convergence originated from the neoclassical growth model. It refers to the process of equalization or uniformity of levels of development among countries or entities. It plays a significant role in fostering efficient and successful integration, whether at regional, continental or global scales. Studies on convergence have significant implications for economic, social, and political levels [ 22 ]. Borrowing this concept from the economic growth literature, a few health economists have investigated the convergence hypothesis in health expenditure and health outcomes using various methods, particularly in OECD and EU countries [ 25 , 29 ]. This study employs the non-linear time-varying factor method to investigate the convergence in OOPHE performance across 40 African countries. The method was developed by [ 23 , 24 ]

Decomposition of panel data

The panel data for a variable \({X}_{it}\) , representing the natural logarithm of per capita OOPHE and OOPHE as a percentage of total health expenditure for a given panel unit \(i\) at time \(t\) , is decomposed into systematic \({\alpha }_{it}\) and transitory \({\omega }_{it}\) components:

This decomposition distinguishes between common and idiosyncratic components of the panel by transforming Equation ( 1 ) into the following time-varying factor:

Here, \({\mu }_{t}\) represents the time-varying common part, which captures the factors affecting OOPHE in the 40 selected countries, such international trade. On the other hand, \({\delta }_{it}\) is a country-specific time varying loading factor capturing the distance between \({X}_{it}\) and the common component \({(\mu }_{t})\) . It represents the time-varying idiosyncratic part related to, for instance, governance quality, macroeconomic policy, population structure, and service coverage. Equation ( 2 ) allows us to test for the full sample convergence by testing whether the idiosyncratic element (also called factor loadings)  \(({\delta }_{it})\)  converges to a constant \(\delta\) by using ratios instead of differences to eliminate the common element. To empirically testing for club convergence, [ 23 ] define a relative transition parameter  \(({h}_{it})\)  which measures OOPHE relative to the panel average, as follows:

The relative transition parameter  \(({h}_{it})\)  traces out the transition path for OOPHE of country \(i\) relative to the panel average. Whenever the factor loadings \({\delta }_{it}\) converges to a constant \(\delta\) , the relative transition parameter \({(h}_{it})\) converges to unity and the cross-sectional variation \({H}_{it}\) of the relative transition path converges to zero as \(t\to \infty\) , as follows:

Semi-parametric model

Phillips and Sul (2007) develop a semi-parametric model for \({\delta }_{it}\) as follows:

The component \({\delta }_{i}\) is fixed; \({\partial }_{it}\) represents an \(iid\) standard normal random variable; \({\theta }_{i}\) are idiosyncratic scale parameters; \(L\left(t\right)\) is a slowing-varying function of time which can take the forms \(log\,t\) or \({log}^{2}\,t\) . Using the Monte Carlo simulation, [ 23 , 24 ] show that the form \(logt\) provides the least amount of size distortion and the best test power. The coefficient \(a\) shows the speed of convergence (the rate at which the cross-sectional variation decays to zero). Equation ( 5 ) ensures that \({\delta }_{it}\) converge to \({\delta }_{i}\) whenever \(a \ge 0.\) The null hypothesis is \({H}_{0}: {\delta }_{i}\) = \(\delta\) and \(a\ge 0\) , which indicates convergence for all countries. The alternative hypothesis is \({H}_{1}: {\delta }_{i}\) ≠ \(\delta\) for all \(i\) or \(a<0\) , which means convergence for some countries. The alternative hypothesis can indicate overall divergence and club convergence. The latter implies that some countries form convergence groups at different equilibria.

Empirical algorithm to test for convergence

[ 23 , 24 ] propose the following \(logt\) regression model to test the convergence hypothesis:

Where \(L(t)\) = \({\text{log}}(t)\) . \(\frac{{H}_{1}}{{H}_{t}}\) is the ratio of the cross-sectional variation at the beginning of the sample \({H}_{1}\) divided by the respective variation for every point in time t. - \(2log\left(log\,t\right)\) is the penalization function that improves the performance of the test under the alternative hypothesis. \(r>0\) , whereby \(r\) equal to 0.3. The extensive Monte Carlo simulation indicates that this choice of \(r\) is satisfactory for the size and power properties of the test. The null hypothesis is tested using a one-sided \(t-test\) , robust to heteroscedasticity and autocorrelation. The null hypothesis is rejected if \({t}_{\gamma }\) is less than \(-1.65\)   \(({t}_{\gamma }< -1.65)\) .

However, rejecting the null hypothesis of convergence does not entail that there is no evidence of convergence in the panel subgroups. Convergence clubs may exist around separate points of equilibria. [ 23 ] proposed an empirical algorithm that identifies subgroups of countries that converge to different equilibria. The steps of the clustering algorithm are as follows:

Step 1 (Ordering)

The countries in the panel are ordered in decreasing order according to the last observation of the variable of interest.

Step 2 (Core group formation)

This step consists of identifying a core group of \(R\) countries with strong evidence of convergence and the highest values of the variable of interest to form a subgroup \({M}_{R}\) for some \(>R\ge 2\) . We perform a \({\text{log }}t\) test. We select the core by maximizing \({t}_{\gamma }\) over \(R\) based on the minimum criteria \(\left\{{t}_{\gamma }(R)\right\}>-1.65\) .

Step 3 (Sieve countries for club membership)

We add one country from the remaining countries at a time to the core group. We perform the t-statistic test from the \({\text{log }}t\) regression for each addition. The new country meets the membership condition if \({t}_{\gamma }> -1.65\) . Thus, all countries that meet the membership condition are added to the core group to form an extended core group. If such a condition is not met, we repeat the procedure to create the next group.

Step 4 (recursion and stopping)

We perform the \({\text{log }}t\) test for all the remaining countries not included in the convergence club formed in Step 3. If the conditions for membership are met, the subgroup becomes a second convergence club. Otherwise, we repeat steps 1 to 3 to find additional sub-convergence clusters.

The merging algorithms

The critical value plays a significant role since the number of the identified convergence clubs depends on the core group formation. The higher the critical value, the less likely we add the wrong members to the convergence clubs. However, it has been pointed out that a high critical value can lead to overestimating the initial convergence clubs [ 24 ]. For this reason, the authors proposed a merging algorithm test for adjacent clubs after the clustering algorithm to avoid this overestimation of the initial clubs.

The determinants of regional grouping disparities in out-of-pocket health expenditures

Following the literature on the determinants of OOP spending inequality, we examine countries' macro-level factors affecting regional grouping disparities in OOP health expenditure. To do so, we use the multilevel linear mixed-effect approach because it is suitable for the study. It incorporates fixed and random effects and can be used to examine hierarchical and clustered data structure and repeated measurements of countries. In contrast to standard approaches, including the fixed effect and pooled regression approaches, the multilevel linear mixed-effect method deals with non-independence between data points. It organizes data analysis at multiple levels [ 28 ]. In this study, the 40 selected African countries in this study are nested into eight RECs.

In addition, other methods, including ANOVA are challenging to apply when analyzing unbalanced data or more complex variance structures [ 30 ]. In this regard, earlier studies suggested using the minimum norm quadratic unbiased estimation (MINQUE) and the minimum variance quadratic unbiased estimation (MIVQUE) to examine unbalanced data [ 31 , 32 ]. However, recently, the multilevel linear mixed-effect method using maximum and residual maximum likelihood has been widely used in various fields for estimating variance parameters when dealing with balanced and unbalanced data [ 28 ]. The approach suits a broader class of variance models than the simple variance elements. This section presents the specification of the multilevel linear mixed-effect models. However, fixed effect and Ordinary Least Square (OLS) models were also estimated for robustness check. The matrix formulation of the multilevel linear mixed-effect model is as follows:

Where \(y\) is the \(N\times 1\) vector of response, also known as the outcome variable; \(X\) is the \(N\times p\) design matrix for fixed effects; \(\beta\) is a \(p\times 1\) vector of fixed-effects; \(\delta\) is the \(N\times q\) covariate matrix for random effects; \(\mu\) is a \(q\times 1\) vector of random effects (the random complement to the fixed \(\beta\) ); \(\varepsilon\) is the \(N \times 1\) the vector of errors, assumed to be multivariate normal with mean zero and variance matrix \({\varphi }_{\varepsilon }^{2}D\) . In Equation ( 8 ), the fixed effect component \((X\beta)\)  is analogous to the linear predictor in the standard OLS regression model; the random effects ( \(\mu\) ) are orthogonal to \(\varepsilon\) with a variance-covariance matrix \(M\) so that: \(Var \left[\begin{array}{c}\mu \\ \varepsilon \end{array}\right]= \left[\begin{array}{lr}M & 0\\ 0 & {\varphi }_{\varepsilon }^{2}D\end{array}\right]\) . Although they can be predicted, the random effects ( \(\mu )\) are not estimated directly. They are rather characterized by the variance components of \(M\) , estimated with the overall residual variance \({\varphi }_{\varepsilon }^{2}\) and the residual-variance parameters contained in \(D\) . The design matrix formulations of \(X\) and \(\delta\) allow us to estimate multilevel or hierarchical designs and provide a flexible approach to modelling within-cluster correlation. The general notation of \(D\) allows residual errors to be heteroskedastic and correlated. In clustered data cases, all \(N\) observations are not considered at once but instead the multilevel linear mixed-effect model is organized as a series of \(G\) -independent groups, as follows:

The cluster \(j\) consists of \({n}_{j}\) observations and \(j=1, \dots , G\) . The response \({y}_{ij}\) is the dependent variable for the \(ith\) observation within \(jth\) group, with \({X}_{ij}\) and \({\varepsilon }_{ij}\) defined analogously. The matrix \({\delta }_{ij}\) is a \({n}_{j} \times q\) design matrix for the \(ith\) observations within the \(jth\) cluster random effects. The random effects \({\mu }_{j}\) has a mean equal to zero and a \(q \times q\) variance matrix Σ. It also represents the \(G\) realization of a \(q \times 1\) the vector and is normally distributed. Following Equation ( 1 ), we can write the following:

The model presented in Equation ( 10 ) makes the specification of the random effect component easy and provides more than one level of the random variable [ 33 ]. This Equation is called a one-level model and can be expanded to more levels. This study broadens the Equation to two levels as countries are nested within regional economic communities. Regional groupings represent the first level, while countries are the second. The model is based on the assumption of constant variance and independent residuals. For the purpose of this study, Equation ( 9 ) can be expressed as follows:

With \({\gamma }_{ij}\) representing OOPHE for country \(i\) in REC \(j\) ; \({\beta }_{0}\) is the fixed effect intercept showing the overall mean of OOPHE when all predictor variables are zero; \({\beta }_{1}\) to \({\beta }_{14}\) are the fixed effect slopes, representing the effect of each macro-level characteristic on the dependent variable; GDPc, young, old, urb, san, ext, le, dpt, ant, gov, ghe, ict, ghfs, and trd are the independent variables for country \(i\) in REC \(j\) ; \({\mu }_{j}\) are the random effects at the REC level, capturing unobserved heterogeneity in OOPHE across countries within each REC; \({\varepsilon }_{ij}\) are the residual errors which represent the deviations of individual observations from REC mean. GDPc (GDP per capita), young (the percentage of population below 15 years old), old (percentage of population above 65), urb (percentage of urban population), san (percentage of people using basic sanitation services), ext (external health expenditure per capita), le (life expectancy at birth), dpt (percentage of children ages 12-23 months with DPT immunization), ant (antiretroviral therapy coverage), gov (governance index), ghe (government health expenditure as a percentage of GDP), ict (information and communication technology index), ghfs (government schemes and compulsory contributory health care financing schemes), trd (trade as a percentage of GDP).

This study uses annual data retrieved from the World Development Indicators (WDI), World Governance Indicators (WGI), and the World Health Organization (WHO). The study spans from 2000 to 2019 and covers 40 African countries and eight Regional Economic Communities. We selected the countries based on data availability. We dropped the countries with too many missing data to avoid missing data. The list of chosen variables, their description, measurement, and sources can be found in Panel A of the Appendix, whereas the list of countries is found in Panel B of the same Appendix.

To investigate whether countries' macro-level factors explain OOP health expenditure inequality in regional groupings, we computed the Gini coefficient of the two OOP health expenditure indicators (OOP health expenditure per capita and OOP health expenditure as a percent of THE) at the regional grouping level. At the same time, the explanatory variables remain at the country level. We consider the logarithm form of all the variables in the analysis to reduce data variability. We also computed the ICT and quality of governance indices using principal component analysis (PCA) to investigate the impacts of governance quality and ICT on OOP health expenditure disparity within the regional groupings. We used Stata 16 software to do all the analysis.

Results and discussion of findings

Correlation matrix and principal component analysis results.

We use the PCA approach to construct the quality of governance and ICT indices. The approach is appropriate when creating indices using datasets that contain multicollinearity and missing values. It helps reduce noise in the data [ 34 ]. Firstly, the study applies the correlation matrix test to assess the relationship between the six governance indicators: government effectiveness, political stability and absence of violence/terrorism, control of corruption, the rule of law, voice and accountability, and regulatory quality. The results in Panel A1 of Table 1 show evidence of high and moderate collinearity between the indicators. Similarly, a correlation matrix test is conducted for the ICT indicators (the percentage of people using the internet and mobile cellular subscriptions), and the results in Panel B show high collinearity between these indicators.

Given the correlation matrix results, we then perform the PCA test to construct the governance quality and ICT indices. The results in Panels A2 and B2 of Table 1 show that component 1 is a preferable choice for both indices because this component's eigenvalue is higher than the other components. Additionally, the variables with loading exceeding 0.4 in absolute value are important contributors to component 1 [ 34 ].

Descriptive statistics

Table 2 shows the descriptive statistics of the variables. Between 2000 and 2019, the average OOPHE per capita was about 90.50 USD, with a high standard deviation of 96.93 USD, indicating significant variations between countries. On average, OOPHE represented 34.90 percent of THE, with a minimum of 3.46 percent and a maximum of 58.10 percent. Notably, this level of OOPHE in Africa is above the upper limit of 20 percent recommended by the World Health Organization to avoid catastrophic health expenditure and reduce impoverishment to a negligible level [ 35 ].

On average, government health expenditure accounted for only 1.60 percent of GDP between 2000 and 2019. This level of government health spending illustrates the lack of government prioritization of the health sector, and it is far less than the 5 percent of GDP suggested by the World Health Organization to ensure the financial protection of populations [ 35 ]. The average external health expenditure per capita was 27.09 USD, with a standard deviation of 34.05 USD, indicating significant differences between countries. The government's average compulsory healthcare financing scheme was 118.44 USD, ranging from 0.55 to 841.43 USD. The average GDP per capita was approximately 5316.54 USD, with the lowest being 715.45 USD and the highest 22869.76 USD. The high standard deviation of 5767.77 USD reveals significant cross-country variations. This level of variation in GDP per capita is to be expected, given the different levels of economic development between African countries.

Regarding health service coverage, approximately 34.82 percent of the population used basic sanitation services, whereas only 22.19 percent of people living with HIV had accessed antiretroviral therapy during the study period. Additionally, an average of 76.55 percent of children ages 12-23 months were immunized against DPT. However, this percentage remains the 90 percent minimum DPT immunization national coverage target recommended by the World Health Organization [ 36 ].

Furthermore, roughly 3.38 percent of the population was 65 years old and above, whereas approximately 41.27 percent was below 15. In addition, the urban population accounted for about 42.22 percent of the people, ranging from 8.25 to 89.74 percent. The average life expectancy at birth was 58.85 years, ranging from 39.44 to 76.88 years. On average, governance quality was 4.49, with a maximum of 10.23. Only 1.34 percent of people accessed ICT during the study period. Trade represented approximately 66.36 percent of GDP, with a minimum of 1.22 percent and a maximum of 175.80 percent.

Log-t regression, club clustering, and merging test results

Panels A and B of Table 3 illustrate the results of the log-t regression test for each OOPHE indicator. The null hypothesis of the whole panel convergence for both OOPHE indicators is rejected at the 5 percent significance, indicating that the t-statistics of the estimated regression coefficients \(\widehat{\gamma }\) are less than the -1.65 critical value ( \({t}_{\gamma }\) = -119.43 and -186.58 < -1.65). These findings suggest that African countries exhibit a divergence behavior, implying that governments did not jointly reduce OOP health expenditures to an acceptable level and failed to jointly provide financial protection to their populations, especially the less well-off.

Then, we perform the club clustering algorithm to detect the possible presence of convergence clubs. The results are also presented in Panels A and B of Table 3 . The first column of the table reveals the initial clusters, with the number of countries indicated in brackets for each set. The results for OOPHE per capita suggest five initial clubs, all of which are statistically significant in that the estimated t-statistics are greater than the 5 percent critical value. Hence, 6, 23, 3, 3, and 4 African countries defined the first, second, third, fourth, and fifth initial clusters, respectively (Panel A of Table 3 ). The results also show evidence of one diverging member: Mauritius.

We also conducted the club merging algorithm. The results for the initials clubs 1 and 2 and clubs 2 and 3 do not support the null convergence hypothesis. Hence, clusters 1 and 2 do not merge into a larger group. We, therefore, have the first and second final clubs 1 and 2, originating from the initial clubs 1 and 2. In contrast, results for clubs 3 and 4 and clubs 4 and 5 suggest that the null hypothesis of convergence cannot be rejected. Therefore, the initial clubs 3, 4, and 5 merge into a larger convergence club of 10 countries and form the third final club (see Panels A1 and A2 of Table 3 ).

Panel B of Table 3 illustrates the results of the share of OOPHE to THE. The club clustering algorithm also reveals the existence of five initial clubs and one divergence club. The five initial clubs are statistically significant. Therefore 15, 4, 11, 3, and 3 African countries form the first, second, third, fourth, and fifth initial clubs for the variable of interest. However, Botswana, Burundi, Kenya, and South Africa form the divergence club. The finding of the divergence club indicates that idiosyncratic factors, including institutional, demographic, economic, and social aspects, that lead to different OOPHE levels are prominent and specific to these four countries.

Additionally, the club merging algorithm results reveal that the results for initial clubs 1 and 2 lead to rejecting the null hypothesis of convergence. Hence, we obtain the final club 1, corresponding to the initial club 1. In contrast, initial clubs 2 and 3 results cannot reject the null hypothesis of convergence, implying that initial clubs 2 and 3 merge into a more prominent convergence club. Consequently, we obtained the second final club comprised of 15 countries. However, initial clubs 3 and 4 results reject the null convergence hypothesis. Thus, these two clubs cannot merge. The initial clubs 4 and 5 results support the null convergence hypothesis. Therefore, the initial clubs 4 and 5 merge into another larger group and define the final club 3, with six countries.

Descriptive statistics by final convergence club

We present the descriptive statistics of the two OOPHE indicators between the final convergence clubs in Table 4 . On average, the countries in the final club 1 are the worst-performing among African countries regarding OOPHE per capita. Countries such as Algeria, Equatorial Guinea, Morocco, Sudan, and Tunisia had higher OOPHE per capita during the study period than the average of the full panel sample [ 37 ]. However, the countries in final club 3 exhibit relatively lower OOPHE per capita, with an average OOPHE per capita of less than 28.66 USD. Countries such as Gambia, Rwanda, Madagascar, and Tanzania had an average OOPHE per capita below 30 USD [ 37 ]. Convergence among the countries in final club 2 is slow, as indicated by the negative value of the estimated \(\widehat{\gamma }\) . The diverging country: Mauritius, had the highest level of per capita OOPHE during the study period [ 37 ].

On average, OOPHE accounted for 44.99 and 34.19 percent of THE for the final clubs 1 and 2, exceeding the limit suggested by the WHO [ 33 ]. The countries in the final club 1, excluding Senegal and Tunisia, had an average OOPHE share above 40 percent of THE. Similarly, all countries in final club 2 failed to bring their shares of OOPHE in THE below the recommended 20 percent. However, most countries had an average OOPHE as a share of THE below 40 percent [ 37 ]. In contrast, countries in final club 3 seem to perform relatively well, with an average of 20.93 percent. Some of these countries, including Namibia and Eswatini, had average OOPHE as a percentage of THE below 15 percent [ 37 ]. The divergence club includes two countries whose average is below 10 percent of THE (South Africa and Botswana) and two countries with averages above the 20 percent limit.

Our findings also provide meaningful insight into the level of health integration between the regional groupings' members. We considered the following number of countries for each REC: COMESA (12 members), SEN-SAD (22 members), EAC (5 members), ECCAS (9 members), ECOWAS (14 members), IGAD (3 members), SADC (10 members), and UMA (4 members). We excluded some countries due to data availability.

Regarding OOPHE per capita, just three out of the 22 members of SEN-SAD belong to the final club 1, three in that club are members of UMA. The remaining countries belong to different regional groupings. The final club 2 consists of four members from SADC, nine from ECOWAS, six from ECCAS, eleven from SEN-SAD, four from COMESA, two from IGAD, three from EAC, and only one country (Mauritania) is a member of UMA. However, in the final club 3, four countries are members of ECOWAS, four belong to COMESA, six are members of SADC, three are members of ECCAS, three have membership with EAC, and only two (Benin and Gambia) are affiliated with SEN-SAD. The governments in the final club 3 have effectively implemented regional and continental health policies to reduce OOP health expenditure per capita, outperforming other countries of their respective regional groupings.

The results also show convergence among countries within the same regional grouping in each of the three final clubs for the share of OOPHE in THE indicator. SEN-SAD members seem to converge more to the final club with a higher percentage of OOPHE in total health expenditure (with ten members converging), followed by ECOWAS, ECCAS, and COMESA, with six, four, and four members converging, respectively. The final club 1 comprises two members from IGAD, one from UMA, and one from EAC. In the second final club, which comprises 15 countries, eight are affiliated with ECOWAS, eight have membership with SEN-SAD, three belong to UMA, three are members of SADC, two are of ECCAS, and two are affiliated with COMESA. Lastly, the third final club comprises four members from SADC, three from COMESA, two from ECCAS, and two from EAC. The findings also reveal that more efforts have been made by the converging countries affiliated with SADC, COMESA, ECCAS, and EAC to reduce their shares of OOP in total health expenditure to just a little above the 20 percent limit suggested by the WHO and improve the financial protection of their populations [ 38 ].

However, we also found that many countries within most regional groupings appear to diverge, indicating increasing disparities in OOPHEs among some countries affiliated with the same regional groupings [ 38 ]. These findings align with a previous study that also found significant variations in OOP health expenditure among SADC countries [ 39 ]. Notably, the study revealed that Mauritius had the highest OOP spending per capita, followed by DR Congo, while Namibia, Botswana, and South Africa had low OOPHE. A separate study showed that the differences in health financing options across countries in the same regional grouping lead to disparities in OOP health expenditures [ 40 ]. Additionally, it has been shown that the level of prioritization of health by governments through their budgetary allocation remains low among SADC countries [ 3 ]. ECOWAS countries spend less than the annual minimum of 34 USD per person on health recommended by the World Health Organization [ 3 ].

Given the above, we perform the multilevel linear mixed effect test to examine whether country-specific macro-level characteristics explain the disparities in OOP health expenditures within the RECs.

The determinants of OOP health spending disparities within the regional economic communities

We first apply the intra-class Correlation (ICC) test to verify the suitability of the multilevel linear mixed-effect model for our study. The results are presented in Table 5 . The ICC values of 0.94 and 0.93 are greater than zero, indicating that the multilevel linear mixed-effect model is appropriate for this study, as reported by [ 41 ].

Tables 6 shows the results of the OOPHE per capita and the share of OOPHE in THE models. The first column shows the list of variables used. The multilevel linear mixed-effect models were well-fitted to empirical data with probability Chi2 and Chibar2 equal to zero (Prob> Chi2=0.0000 and Prob>Chibar2=0.0000).

OOP health expenditure per capita results

The results in Table 6 reveal that countries' GDP per capita, antiretroviral therapy coverage, the share of trade in GDP, the share of government health expenditure in GDP, and government compulsory healthcare financing schemes per capita are statistically insignificant in explaining OOPHE per capita inequality in RECs. However, nine variables used are statically significant at 1 percent. A one percent rise in countries' share of the elderly population is associated with a 0.34 log points increase in OOPHE per capita inequality within the RECs. Considering the younger people, the negative sign of the estimated coefficient is unexpected because a higher share of countries’ population below 15 years old is associated with lower OOPHE per capita inequality within regional groupings.

A unit increase in countries’ urban population leads to a 0.27 log point reduction in OOPHE per capita disparity within the RECs. The results also show that a unit increase in countries’ life expectancy at birth rises within-regional grouping disparity by 1.101 log points. This finding suggests that in countries where people enjoy a longer life, OOPHE per capita is substantially higher. However, regarding access to basic sanitation, the negative sign of the estimated coefficients indicates that a unit increase in these variables is associated with a lower disparity in OOPHE per capita within the RECs. Similarly, increased governance quality reduces within-regional groupings' OOPHE per capita inequality by 0.12 log points. The effects of the other variables are modest (see Table 6 ).

OOP health expenditure as a share of total health expenditure results

The results in Table 6 show that countries’ urbanization, external health expenditure per capita, government expenditure as a percentage of GDP, governance quality, and government compulsory healthcare financing schemes per capita are statistically insignificant in explaining within-regional grouping inequality in the share of OOPHE in THE. However, a unit rise in countries' life expectancy at birth increases inequality in OOPHE as a share of THE in RECs by 1.48 log points. Considering countries' demography, the negative estimated coefficients are unexpected because countries' higher shares of elderly and younger populations are associated with a lower disparity in OOPHE as a percentage of THE within regional groupings.

Concerning the health service coverage variables, the results are also ambiguous. Countries' higher DPT immunization coverages are associated with higher inequality in the share of OOPHE in THE in the regional groupings. However, countries' increased access to basic sanitation reduces such inequality by 0.29 log points. The remaining variables moderatelly affect the disparities in OOPHE in THE in the regional groupings (see Table 6 ).

Robustness of estimates

The results in Table 6 show that the two alternative models substantially affected the estimated coefficients of all the variables used. However, the results from the fixed-effect models were relatively similar to the multilevel linear mixed-effect in terms of the significance, signs, and estimated coefficients of most variables. Considering the pooled regression models, the results obtained differ from the multilevel linear mixed effect in most cases in terms of the signs, significance, and estimated coefficients.

Our findings do not support the existence of an overall convergence for the two OOP spending indicators considered. These results suggest an increased disparity in out-of-pocket (OOP) health spending across African countries over time. Differences across the 47 member states within the World Health Organization (WHO) African Region were also found, with 28 member states funding over a quarter of their current health expenditure through OOP payments [ 4 ]. However, the club clustering and merging results reveal that OOP health expenditures converge into three final convergence clubs and one divergent group. The evidence of final convergence clubs suggests that sub-groups of countries with similar characteristics and higher levels of OOP health expenditure inequality will likely experience decreased OOP payments in the long run. In contrast, the divergence groups found imply that a country or sub-group of countries follow different paths in terms of OOP spending.

The results also show evidence of convergence among countries within the same regional groupings in each final convergence club. Converging countries affiliated with UMA, SEN-SAD, ECOWAS, COMESA, and IGAD tend to belong to final convergence clubs with higher levels of OOP health spending. In contrast, sub-groups from the SADC, ECCAS, EAC, and COMESA regions are members of final convergence clubs with relatively lower levels of OOP health expenditures. This can be explained by the considerable variation in how health is prioritized and institutionally located. For instance, there is no indication of regional health infrastructure or activity, including services, policies, and programs in CEN-SAD, AMU, and ECCAS, mainly due to the lack of resources, non-payment of subscriptions by member states, international political conflict, or political conflict between individual member states [ 38 ]. These challenges hamper efforts toward deeper health integration in these regional groupings. Additionally, ECOWAS countries experience challenges in implementing and sustaining effective health policies [ 3 ]. The health priority in these countries remains inadequate, with most countries having recorded a reduction in government health spending over the years. Consequently, out-of-pocket (OOP) payments remain the prominent healthcare financing source in most countries.

Our results also reveal that some countries within the same regional groupings seem to follow dissimilar paths regarding OOP health expenditures, indicating increasing disparities across these countries. The results also suggest that convergence club also occurs among sub-groups of different regional economic communities. In this line, countries with similar periods of national health policy implementation tend to converge to the same sub-group, despite their regional grouping memberships [ 22 ]. Additionally, countries that have successfully implemented the UHC program appear to converge to relatively better-performing final clubs.

The study also investigated whether the country's macro-level factors explain the disparities in OOP health expenditure within the RECs, using the multilevel linear mixed-effect model. Our findings show that increasing countries' share of the population below 15 years old and urbanization significantly reduces within-regional disparities in OOPHE per capita. The negative sign of the urbanization variable is unsurprising because planned urbanization presents many advantages for more effective health policies and practices. It also offers many opportunities for urban dwellers, including access to clean water and decent sanitation [ 42 ].

The results also suggest that countries’ improved access to basic sanitation reduces within-regional OOPHE per capita disparities. Previous studies showed that a large share of health expenditures from vulnerable people is induced by preventable water- and sanitation-related diseases [ 43 , 44 ]. The gain in improved access to basic sanitation is an imperative prerequisite to reducing OOP health spending through preventing some illnesses and hence reduces inequality in OOP spending. Additionally, we found that countries’ increased governance quality significantly reduces inequality in the variable of interest within the RECs. In this line, a separate study revealed that Indonesia successfully implemented a new UHC policy within a decentralized system that promotes a move toward equitable financial protection and access [ 45 ].

Furthermore, our findings indicate that increasing countries’ population aging, life expectancy at birth, external health expenditure per capita, and ICT contribute to rising disparities in OOPHE per capita within RECs. Regarding population structure, the elderly population and life expectancy at birth are regarded as predisposing risk factors for financial hardship among the vulnerable segment of the population. As countries' population ages, chronic diseases take a considerable toll on individuals as they usually require long-term care, leading to a high prevalence of catastrophic health spending and impoverishment [ 18 ]. A probable reason for the positive effect of ICT might be the variations in the diffusion and access of this variable in the RECs [ 46 ].

Regarding OOPHE as a percentage of THE model, our results reveal that an increasing countries' share of aging and younger populations, access to basic sanitation, trade, and ICT have negative and significant impacts on disparities in the percentage of OOP to total health expenditure within the regional groupings. The results for the demographic variables are ambiguous, as it implies that an increase in these variables reduces OOP spending inequality in the RECs. However, a possible explanation for such findings might be associated with traditional herbal medicines and nutrition. For instance, a previous study demonstrated that a significant proportion of the South African population utilized traditional herbal medicines, particularly in townships and rural areas. The study also indicated a high prevalence of traditional herbal medicines for treating chronic diseases among older people in South Africa. [ 47 ]. In contrast, in Nigeria, such prevalence was observed among younger people [ 48 ].

The empirical findings also suggest that increasing countries' GDP per capita, life expectancy at birth, childhood DPT immunization coverage, and antiretroviral therapy coverage among people living with HIV tend to increase within-regional grouping disparities in the share of OOPHE to THE. A separate study found significant hidden costs related to childhood immunization leading to considerable household spending in out-of-pocket payments. These OOP expenditures, which include travel costs, traveling distance to health facilities, cost of registration, consultation, admission, prescribed medication to adverse effects following immunization, and food may lead to distressed financing of household and catastrophic health spending [ 49 ]. As suggested by our results, the positive effects of GDP per capita are indisputable because increased GDP per capita raises people's ability to spend on health [ 50 ].

This study investigated convergence in OOP health expenditures in 40 African countries covering 2000-2019 and using the \(log t-test\) , club clustering, and merging tests. The findings do not support the hypothesis of overall panel convergence. Instead, we found evidence of three final convergence clubs and a divergent group for the two OOP health expenditure indicators. The results also reveal that convergence does not only occur among countries affiliated with the same RECs. We also found that UMA, SEN-SAD, COMESA, and IGAD countries generally converge to final clubs with higher OOP expenditures. In contrast, SADC, ECCAS, EAC, and some COMESA countries mostly converge to final clubs with relatively lower OOP health spending.

Then, we used the multilevel linear mixed-effect model to examine whether countries' macro-level characteristics explain the disparities in OOP expenditures within the eight RECs recognized by the African Union. We found that countries' improved access to sanitation, governance quality, and increased childhood PDT immunization coverage can reduce inequalities in OOP health expenditure per capita within the RECs; however, when their elderly population, life expectancy at birth, external health expenditure, and ICT access increase, disparities in OOP spending per capita tend to rise within the regional groupings. We also found that as countries' elderly and younger populations grow, the differences in the share of OOP expenditure to total health expenditure decrease. The results also show that increased access to basic sanitation, ICT, and trade within countries significantly reduces within-regional grouping disparities in terms of the share of OOP to total health expenditure. Countries' increased GDP per capita, life expectancy at birth, childhood DPT immunization coverage, and antiretroviral therapy coverage significantly increase inequality in the variable of interest within the RECs.

Policy implications

Based on this study's findings, several policy implications can be drawn to reduce disparities in OOP health expenditures and encourage deeper regional integration. The study suggests the need for homogenous health policies for each convergence club, and the focus should be on converging countries that belong to the same regional grouping within the clusters. However, there is a need for country-specific policies for the diverging countries. Additionally, countries should endeavour to increase health services coverage by improving access to basic sanitation. Policymakers should also consider the significant OOP expenditures associated with access to antiretroviral therapy and childhood DPT immunization, when developing policies. In addition, educating the people regarding the benefits of such services might also increase the range among countries.

Planned urbanization in African countries should be pursued because of the many advantages associated to it. Policymakers should consider the proportion of older people and children when designing and implementing health policies. Policymakers should also promote coordinated policies toward enhancing trade and access to ICT. Additionally, improving governance quality will help ensure equitable distribution of and access to healthcare and reduce the existing OOP health spending disparities. The health priority of many African countries should increase. This can be done through increasing government budgetary allocations. African governments should increase their share of health to at least 5 percent of GDP as the WHO recommended. Alternatively, governments should allocate at least 15 percent of their national budgets to health, as the 2001 Abuja Declaration suggested.

Although the study provides valuable insight into the trends in household OOPHE and their underlying determinants, a few limitations and further research possibilities emerge. The empirical study uses data obtained from WDI and WGI datasets. However, several African countries in these datasets have too many missing data, thus the number of countries were reduced to 40 countries. Further studies could account for All African countries, which may provide a more comprehensive understanding of the continent’s OOPHE trends and its economics and social dynamics. Furthermore, The study does not take into account the impact of other important factors such as Tuberculosis prevalence, HIV prevalence and non-communicable diseases to assess the effect of prevalence of disease burdens on OOPHE disparities in the African RECs. Moreover, the impact of factors such as inflation rate, foreign debt, unemployment rate, the percentage of population with access to basic drinking water, and CO2 emission have not been accounted for in our study. The exclusion of these variables could lead to biased or incomplete findings. Future studies may look into the effects of such factors on OOPHE disparities in the RECs, as an efforts to understand the complex interplay between health, economics, environment, and social factors in shaping OOPHE disparities.

Availability of data and materials

The datasets used in the study are publicly available and can be extracted from the World Bank and World Health Organization database.

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This research is an original contribution by two authors. The tasks and contribution to the work by each author are as follows: Ariane Ephemia Ndzignat Mouteyica: Conceptualization, data curation, formal analysis, methodology, software, validation, writing-Original draft. Nicholas Ngepah: supervision, writing-Reviewing and Editing, validation. All the authors have approved the work.

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Ngepah, N., Ndzignat Mouteyica, A.E. Trends in household out-of-pocket health expenditures and their underlying determinants: explaining variations within African regional economic communities from countries panel data. Global Health 20 , 27 (2024). https://doi.org/10.1186/s12992-024-01032-0

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In ancient Greek, the word theorein and its connected noun theoría link to observation, consideration and looking more closely at the subject matter; they simply lead to scientific contemplation and seeking truth. Today, in the context of highly specialized fields of science, seeking the eternal truth is not at stake. But still, each field of scientific exploration aims to increase understanding of its subject matter through formulating theories. Above that, theoretical insights do not contribute to interpretation and understanding of a system for its own sake. It is hard to imagine that a system can be modified without understanding its fundamental laws, when only disordered empirical facts are on the table. The everlasting struggle to master conditions that determine human lives therefore lies in theoretical comprehension, which gives possibilities to actively shape researched systems including socioeconomic order. Empirical findings thus refer to the surface, how a phenomenon demonstrate itself to human senses, while theoretical insights, based on logical explanation of the empirical dimension, reveals the underlying ‘nature’ of a phenomenon. In terms of time, purely empiricist approach may refer only to what has already happened, while theoretical insights, which forms the underlying model, furnish our understanding with a certain extrapolating power. In response to empirical findings on inequality, the present chapter focuses on distributional dynamics of mainstream economic theories.

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A bit less known is that these conditions were firstly introduced by Uzawa ( 1963 ).

Continuous time is considered in order to be in line with upcoming Ramsey-Cass-Koopmans model.

Variables in the intensive form use small letters.

For the sake of simplicity, it is abstracted from depreciation.

The reason is that along the balanced growth path, where the consumption per capita grows at rate g , the integrand term in U grows at rate − ρ  +  n  + (1 −  θ ) g . As we want the integral to converge, the term must be negative.

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Department of Economics, Faculty of Economics, University of Economics, Prague, Czech Republic

Robin Maialeh

Unicorn Research Centre, Prague, Czech Republic

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Maialeh, R. (2020). Growth Theories and Convergence Hypothesis. In: Dynamic Models and Inequality. Contributions to Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-46313-7_3

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DOI : https://doi.org/10.1007/978-3-030-46313-7_3

Published : 30 June 2020

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-46312-0

Online ISBN : 978-3-030-46313-7

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