I’m a data nerd. I accept that. I love taking a look at numbers and seeing what they tell me. But like any good data analysis, it’s best to have a purpose for doing it. So today I’d like to discuss a bit of analysis that I did regarding income and inequality, a topic which I think has some clear importance for people around the world.
Let me say right from the outset that I am not an expert in economics. My analysis is simplistic, and there are likely economists who could do a much better job with this data set and would have better idea of what else to look for.1 I’m sure that there probably are economists who have looked at this same data and written up about it. But like I said, I’m a data nerd. I wanted to get my hands in there (metaphorically speaking) and play with the data myself. So take it with a grain of salt, and if something strikes your interest, be sure to look it up for more detail from another source. With that said, however, I think that there are some interesting bits of information that one can take even from this relatively simple analysis.
Gross National Income
The first set of data I examined was a list of 201 countries and territories by Gross National Income (GNI) at Purchasing Power Parity (PPP), per capita. Most of the data was taken from the International Monetary Fund in 2005, but several points were taken from the CIA World Factbook and may be from earlier time points. If you want the raw data, here is the source from which I took it.
The first thing is to define just what I’m talking about here. Gross National Income is a measure of the total value of the goods and services produced within a country, plus income it has received from other countries and less payments made to other countries. This may be a little difficult to wrap one’s mind around, but in rough terms, it is a measure of the total income from business activity that a country has made over a certain time period. When one looks at GNI per capita, this means it has been divided by the population of the country, which gives us (again, roughly speaking) the average amount of income per person in the country. I chose this measure because it gives us a close estimate of what most people think of when they think of earning money. For your average Joe (or Joan), when you earn more money, you tend to be better off. Of course, there are other variables at play here, but I wanted a number that would show me roughly what the average citizen earned.
The one extra step to this is to include Purchasing Power Parity. This is a way of equalizing the different values of various currencies so that they can properly be compared. PPP is given in terms of “international dollars”, but this hypothetical currency is essentially standardized to the US dollar. So really, all values I’m going to talk about are going to be in US dollars.
Now that this boring description is over, let’s get to the interesting stuff!
The first thing we can take a look at is what the distribution of countries looks like. As you can see in the graph, 43% of the countries had an average income of $5000 or less. That, right there, I find astounding. Remember that this is equalized so that this $5000 is worth the roughly same amount in each country: It will buy the same amount of goods. So over 60% of countries have an average income less than $10,000 a year. Now, it’s important to interpret this correctly. Because each country has its own level of variability, this is not saying that 43% of people in the world have incomes less than $5000 per year. This is only saying that the average income in 43% of countries is less than $5000 per year. However, since monetary indicators like income tend to be positively skewed (since there is no maximum amount one can earn), this likely means that within any given country, the majority of people are likely below that average value. Because of this, it is likely that within those 43% of countries, more than half the population will be below the $5000 amount. So this statistic likely is hiding the true poverty level within them. I’ll try to get into more detail on that later.
The one other thing to point out about this distribution is which country is at the top end of that distribution. One country is over the $65,000 mark, and that is Luxembourg. Part of this is likely due to the relatively loose tax laws in that country, making it a very favourable place for foreign investment. It also has a fairly small population of just over half a million, so the large income is divided by relatively few people.
GNI by Region
Next, what I did was to organize countries into their respective continents. This was in order to get a better sense of the regional differences present in income. Of course, this is not the necessarily a perfect way to do things. There is much variability across, for example, the 50 countries in Asia, or the 54 countries in Africa. But this still gives a pretty good sense of where each continent fares in terms of world income. You will, of course, immediately notice that Europe is booming with an average income of $22,602. This is no doubt helped along by “banking countries” like Luxembourg, San Marino, Monaco, and Switzerland (there’s a reason that Swiss bank accounts have a reputation). But overall, Europe enjoys very robust economies and a high quality of life—though like I said, there is certainly variation here as well. North America also fares very well, and would likely be higher, except that countries in the Caribbean and Central America bring down the average. For instance, while the United States tops the list here at $41,557, Haiti fares much poorer at $1,614.
Down at the bottom of the scale lies Africa, as one might expect. As you can see, North America enjoys an average income that is four times the average income of African countries. Europe has about six times the average income as Africa. As you can tell, it is a wide gap.
Another way to examine GNI by region is to divide the distribution into five equal portions (quintiles). Each quintile represents 20% of the total number of countries. By the graph on the right, then, you can see the number of countries in each region that fall within each quintile. Essentially, darker orange is better than lighter orange. As you can see, Europe has by far the largest percentage of countries within the highest income quintile. South America, on the other hand, has none, and Africa has just one (Equatorial Guinea). On the other end of the distribution, Africa has an enormous percentage that fall in the lowest quintile, whereas South America lies mostly in the middle of the distribution. Asia does fairly well from a few wealthy countries (Hong Kong, Japan, Qatar, and United Arab Emirates among others), but otherwise has fairly low average incomes.
From an examination of GNI, I’d now like to move to data about the Gini coefficient. This coefficient is a little difficult to explain, but it is a measure of the inequality in wealth between the rich and the poor in a given population. Essentially, if each percent of a population earned exactly one percent of the income, the coefficient would be 0. If, on the other hand, the top 1% of the population earned 100% of the income, the coefficient would be 100. Put simply, a lower coefficient generally reveals a more egalitarian society. Of course, it is not a perfect measure, and has its disadvantages, but here I will be using it as a fairly decent measure of equality in wealth in each country. For this section, I used two sources. My primary source was the CIA World Factbook, which unfortunately has data from different years, and lists only 134 countries (as opposed to the 201 countries I had in the GNI analysis). To supplement this, then, I used a list from Wikipedia, which itself uses several sources. I used the columns for the UN Human Development Report from 2007/08, and the Global Peace Index from 2008. This helped to get the final list up to 161 countries.
As we did with the GNI data, the first thing to do is look at the general distribution of the Gini scores. As you can see from the graph to the right, most countries fall between about 30 and 40, which is not too bad. Only one country is below 25.0: Sweden. Of course, Sweden is a country known for is extensive social spending and high quality of life. On the opposite end of the distribution, at 70.7, is Namibia. Some other countries that may be important to my readers include Canada, which is ranked 42nd with a Gini coefficient of 32.1, and the US, ranked 108th with a Gini coefficient of 45.0.2
Gini by Region
Next, let’s take a look at the Gini index by region. Just as a word of caution, the value for Oceania is a bit tenuous. I couldn’t get the Gini value for most of the small island countries in the South Pacific, so the value is based on just three countries (Australia, New Zealand, and Papua New Guinea). With that said, however, let’s take a look.
As you can see in the graph, Europe has the lowest Gini index out of any of the continents. This is evident from the fact that 19 out of the top 20 countries (meaning a low Gini score) are located in Europe. South America, on the other hand, has the highest average Gini score. North America and Africa are virtually equal, due partly to Cuba’s low score of 30.0.3
Let’s take a look at the scores broken down by quintile. Like before, a darker orange indicates a “better” value and a lighter orange a “poorer” value.4 Europe, as one might now expect, does extremely well, with 92% of its countries in the first or second quintile. South America, on the other hand, doesn’t have any countries within these first two quintiles. A full 55% of the countries in South America are in the fifth quintile, indicating a score in the lowest 20% of all countries. As you can see, however, Africa actually does better than North America when one looks at the distribution. It has 37% of its countries in the first three quintiles, whereas North America has just 21%.
My final bit of analysis has to do with a combination of both the GNI and Gini index. I wanted to get a little bit of insight into countries that do well on both measures, those that do poorly on both, and those who do well in one and poorly in the other.5
First, let’s look at those countries that do well overall. These include the following: Luxembourg ($66,821, 26.0), Norway ($41,941, 25.0), Sweden ($29,537, 23.0), Austria ($32,962, 26.0), and Iceland ($35,686, 28.0). All of these countries have excellent incomes, plus they enjoy a fairly equal distribution of that income among the population. Canada ($34,444, 32.1) comes in about 14th place, and the US ($41,557, 45.0) comes in right below at 15th place. While Americans certainly have a higher average income than Canadians, the wealth is not distributed as equally. In other words, there is a greater gap between the rich and the poor. Although the US brings in a great deal of wealth, a fair amount of it goes into the pockets of people who are already wealthy. These two values quite clearly display the general antagonism against “socialism” and social spending that is present in the American populace.
At the other end of the scale are the countries that do poorly in both areas. These include the following: Afghanistan ($800, 60.0), Sierra Leone ($901, 62.9), Democratic Republic of the Congo ($675, 55.0), Central African Republic ($1,163, 61.3), and Liberia ($900, 52.6). These countries have very low average incomes, and even then, the income that they have is unequally distributed to an extreme degree. This suggests something that makes the situation even worse: The Gini scores mean that the averages listed are likely pulled up quite a bit from the few extremely rich people in the country. In other words, the majority of the population likely lives on much less than these average incomes. Were we to remove the outliers of the extremely wealthy, the average would likely drop greatly.
Now we have the strange case of countries with low average income, but good Gini scores. These include the following: Somalia ($600, 30.0), Ethiopia ($859, 30.0), North Korea ($1,400, 31.0), Tanzania ($720, 34.6), and Tajikistan ($1,373, 32.6). At first, one might wonder why these countries developed these conditions. However, one of the reasons may have to do with communism. Many of the countries that have low GNI values and low Gini scores seem to be currently or formerly communist/socialist. For example of the countries in the top 20, Somalia, Ethiopia, Tanzania, Yemen, Mongolia, Bangladesh, Benin, Albania, and Guinea were all formerly communist or socialist,6 and North Korea, Cuba, and Laos are currently communist/socialist. I find it difficult to believe that this is a coincidence. So while it seems that the communist tendencies in these countries has generally left them poor, it has at least spread what little income is present fairly equally throughout the population. The consequences of the weak economies in these countries seems to be at least cushioned a little by the relative equality between the rich and poor.
Finally, let’s look at the countries which are high in average income, but have poor (high) Gini scores. These include the following: Equatorial Guinea ($23,154, 65.0), Hong Kong ($32,292, 53.3), Singapore ($28,228, 48.1), United States ($41,557, 45.0), and South Africa ($11,035, 65.0). This indicates that the high incomes that are listed are likely pulled up to a great degree by wealthy people in the country. As I’ve mentioned, the US does quite poorly in this regard. Of course, this is not a perfect comparison—the situation in Equatorial Guinea is clearly much worse than in the United States. While the ratio between the rich and the poor is somewhat similar between the US and Equatorial Guinea (although, of course, the US is 20 points lower in the Gini index), the entire population enjoys a greater degree of wealth overall. But although these differences are obviously important, the poor Gini scores reveal that work needs to be done to close the gap between the rich and the poor in these countries. Though the poor in the US may be better off in absolute terms than the poor in Equatorial Guinea, both could benefit from a greater degree of equalization between the haves and the have-nots.
I hope that this analysis has been in some way enlightening and helpful. Let me stress, once again, that this could benefit from more detailed analysis from someone more experienced in economics. However, I think this gives a general idea of how the income earned throughout the world is distributed, both within countries and among them. I know that as a result of this analysis I have gained a greater appreciation of the privileges I benefit from in the country in which I live, and I hope that you, my readers, have as well.
As a final note, if anyone wishes to see the raw data, plus all the tables and graphs I used to do this analysis, it is available in .xlsx format. I have an .xls version as well, but I can’t guarantee that all the formulas and formatting will work correctly.
- If anyone can offer some advice on how I could have done this analysis better, I would love to hear from you. Feel free to leave a comment. [↩]
- The US is quite far down on the list, ranking below countries like Kenya, Nigeria, and Iran. [↩]
- Without Cuba’s score, North America’s score would be pushed up to 48.3. [↩]
- It’s important to remember that the Gini index needs to be used in conjunction with other measures to get a more accurate picture of the well-being of a country. However, here I am assuming that, all else being equal, a lower Gini score indicates a “better” distribution of wealth than one with a high score. [↩]
- What I did was took the rankings of both indicators for each country and multiply them. This weights both values equally and gives me a rough idea of which countries are high or low in both. Then I inverted one ranking and multiplied it by the other to give me a list of countries that are high in one and low in the other. [↩]
- The criterion used here to classify a country as “communist” or “socialist” is a declaration of such in their constitution. [↩]