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The Gini coefficient of Egypt is about .3. This is about .02 or so away from the coefficient of countries like Finland, Sweden, etc. And for comparison, the USA is above Egypt by .04 points or so.

So this leads me to the question, why is Egypt's Gini coefficient / relative poverty low?

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    Why not? What is so surprising about it? How many other countries have a similar Gini coefficient? Dec 4, 2022 at 17:11
  • @Trilarion Not sure, but the surprise might be related to a relatively small middle class - this source suggests less than 40%, expected to grow in the future to almost 60%.
    – Alexei
    Dec 4, 2022 at 21:10
  • Gini index of income or gini index of wealth? I think they were different.
    – EarlGrey
    Dec 6, 2022 at 8:06

3 Answers 3

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This Worldbank article might shed some light on this.

Indeed Egypt has a rather low Gini coefficient:

Egypt ranks as one of the world’s most equal countries judging by official estimates of income and consumption inequality. Estimates of inequality, like estimates of poverty, are derived from national household surveys that collect detailed income and/or consumption data for a sample of households, assumed to be representative of the country’s population.

However, this is surprising for many people, as this does not seem to match the perceived reality:

(..) There is a sense that economic success in Egypt is reserved for the privileged, and that opportunities for the majority are largely missing.

The same article indicates possible reasons for inequality being higher in reality than pictured by the statistics:

Household surveys limitations

(..) conjecture that inequality is indeed being underestimated. This should come as no surprise. Household surveys often fail to capture top incomes, either due to non-response of the rich or under-reporting of their incomes or both, which then leads to an underestimation of income inequality;

Income tax records unavailability

Income tax records arguably denote the ideal source of data as far as top incomes are concerned. Unfortunately, tax record data are hard to come by, and Egypt is no exception to that rule.

Workaround

A possible workaround for these limitations is using data on house prices as a factor correlated with income, thus leading to a more realistic Gini coefficient:

(..) explore the feasibility of using data on house prices to estimate the top tail of the income distribution. The house price database is compiled from real estate listings that are available in the public domain.

The study finds evidence that inequality is indeed being underestimated in Egypt by a considerable margin. The Gini coefficient for urban Egypt is found to increase from 0.36 to 0.47 after correcting for the missing top tail. Note that this potentially moves Egypt to the mid-range.

As a side note, the same article mentions that it is very likely that the Gini coefficient is lower than reality in many other countries. Examples:

When household surveys are combined with income tax data, the Gini coefficient for (i) the United States in 2006 increases from 0.59 to 0.62 (Alvaredo, 2011), (ii) Colombia in 2010 from 0.55 to 0.59 (Alvaredo and Londoño Vélez, 2013), and (iii) Korea in 2010 from 0.31 to 0.37 (Kim and Kim, 2013). Ideally, one would apply these corrections to all countries. Where Egypt would rank in this hypothetical version of Figure 1, nobody knows.

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    Should there not be an underreporting of the lowest income tier as well if one looks at tax records and house prices? Because the poorest do not participate much in the "official" economy. In particular, they often only have informal income, so that they don't appear at all in tax data, and do not buy any real estate, do not rent apartments, do not have bank accounts etc. Nothing they do leaves countable data. And still there may be huge relative differences in income even among this group. How does one account for them? Dec 6, 2022 at 12:27
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    By the way, even in well-documented economies like Germany, the official statistics to not reflect the actual inequality in wealth. The Spiegel magazine reported that the super-rich 45 persons are under-represented in the data because they may be simply missed by samples, and because they tend to not answer surveys. But they increase the measured inequality significantly. Dec 6, 2022 at 12:50
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    The paper the Spiegel article is based on is here: diw.de/documents/publikationen/73/diw_01.c.575768.de/dp1717.pdf, but the server is currently down. Dec 6, 2022 at 12:50
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The Gini co-efficient measures relative, not absolute wealth and poverty.

Egypt has a fairly poor but relatively equal society. There are few people living in deep poverty, and few people with very large amounts of wealth. In Egypt everybody is slightly poor.

If everybody in a country becomes richer by the same proportion, the Gini coefficient won't change. So the Nordic countries are wealthier than Egypt, but the distribution of wealth is similar. In Sweden, everybody is slightly rich.

The Gini index can't say anything about the quality of life for the median person. Eg, Bangladesh and the Netherlands have a similar spread of wealth, but very different median wealth.

The US is much wealthier than Egypt, but it is also less equal, there are greater differences between rich and poor, but not as much as in (for example) Southern Africa, in which there are some who are very rich and many who are very, very poor.

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    Some evidence for this would be good, e.g. income and wealth distribution data.
    – Stuart F
    Dec 4, 2022 at 15:54
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    I agree, but the issue is that the data is the gini index. If I wanted evidence of wealth distribution, I'd use the gini index, since that is the purpose of the index. To look further you need to look at the data underlying the index, which is analysed in another answer.
    – James K
    Dec 4, 2022 at 16:58
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If you examine the data at https://www.gapminder.org/tools/ you will find that the Gini coefficient does not really correlate well with much. Various indicators of political freedom or democratic process, spending on this-and-that, pensions, taxation, education, literacy, work force participation, etc. Examine it and you find that there are some little trends in regions, but that these do not survive world wide. For example, you might find that among sub-Saharan African countries there was a weak trend on some feature, but that this entirely disappears when other countries are brought in.

As well, the spread in the Gini coefficient within single culture regions is about the same size as it is world-wide. For example, the spread across Africa is similar to the spread across Europe is similar to the spread in Asia is similar to the spread in the Americas.

For example, there is an extremely weak and noisy correlation between hours worked per week and the Gini coef, higher working hours giving a slight trend to increased coef. But the noise is the same general size as the signal. And if one or two of the outliers were dropped (Ethiopia for example) the trend is non-existant.

Most especially, we don't see any region or culture hitting especially low or especially high. All have substantial range, and all vary over time.

So there does not seem to be any simple feature of a society that predicts the Gini coefficient. It seems to be the result of a large number of influences that are not readily captured in statistical analysis. And possibly in the interactions of those features.

Consider just one hypothetical example influence. Suppose that a particular country were attractive, for whatever reasons, for wealthy people to migrate to. One might suspect this would drive up the Gini coefficient, as "the rich" would be showing up in that country. If so, it's quite difficult to spot that country from the data. One might suspect that should be the USA. However, Brazil has a significantly higher coef.

So the conclusion is, the Gini coef does not correlate well with any easily identifiable single characteristic of a country. Particularly if you examine it over time. Possibly you could work out some complicated model of a culture that would consistantly hit low Gini coef values. It would be far more complicated than picking out a single parameter and trying to predict the coef from that. Probably it would be more than even two or three parameters.

By the way, there is a further conclusion. If a politician claims inequality is a severe problem, and that his pet program will allieviate it, he should be examined with some considerable suspicion. The data at hand indicates that inequality does not correlate well with any other societal indicator. Happiness, household income, life expectancy, level of education, nothing correlates. So it is a good question whether we should be particularly concerned about the Gini coef. And we should be suspicious whether his program will change it.

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    "Suppose that a particular country were attractive, for whatever reasons, for wealthy people to migrate to." Monaco, Switzerland, Malta or Cyprus come to mind. Unfortunately, Monaco does not report data there and the others have GINI coefficients ~0.3. Dec 4, 2022 at 22:31
  • Suppose that a particular country were attractive, for whatever reasons, for wealthy people to migrate to. One might suspect this would drive up the Gini coefficient, as "the rich" would be showing up in that country. If so, it's quite difficult to spot that country from the data. It is difficult to spot especially if you check the Gini coefficient for wealth, impossible with the Gini index for income. Rich people moving their assets very rarely have assets, job or income or salary. It is always everything put in some trust, foundations or other wealth holders...
    – EarlGrey
    Dec 6, 2022 at 8:10

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