I think that UN manipulating it's own index is not culture wars even if the index is related to gender. Let me know if I am wrong.
Human development
The Gender Development Index (GDI), along with its more famous sibling Human Development Index (HDI) is a an index published annually by UN's agency, the United Nations Development Programme (UNDP). Whether an index is manipulated or not can be judged only against a precise definition of what the index claims to be measuring. So how do you measure human development? Whatever you do, you will never capture all nuances of the real world - you will have to simplify. The UNDP puts it this way:
The Human Development Index (HDI) was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone.
So the UNDP defines the Human Development Index as a geometric mean of three dimensions represented by four indices:
| Dimension | Index |
|---|---|
| Long and healthy life | Life expectancy at birth (years) |
| Knowledge | Expected years of schooling (years) |
| Mean years of schooling (years) | |
| Decent standard of living | Gross National Income (GNI) per capita (2017 PPP$) |
Source: https://hdr.undp.org/data-center/human-development-index#/indicies/HDI
Gender Development
So far so good. Next, on it's website the Gender Development Index (GDI) is defined like this:
GDI measures gender inequalities in achievement in three basic dimensions of human development: health, measured by female and male life expectancy at birth; education, measured by female and male expected years of schooling for children and female and male mean years of schooling for adults ages 25 years and older; and command over economic resources, measured by female and male estimated earned income.
Source: https://hdr.undp.org/gender-development-index#/indicies/GDI
While in the actual report HDI it is simply defined as a ratio of female to male HDI values:
Definitions - Gender Development Index: Ratio of female to male HDI values.
Source: https://hdr.undp.org/system/files/documents/global-report-document/hdr2021-22pdf_1.pdf
Let's look, for instance, at the Gender Development Index of United Kingdom. The value 0.987 means that despite longer life and more education, in UK, females are less developed than males.
| Dimension | Index | Female value | Male value |
|---|---|---|---|
| Long and healthy life | Life expectancy at birth (years) | 82.2 | 78.7 |
| Knowledge | Expected years of schooling (years) | 17.8 | 16.8 |
| Mean years of schooling (years) | 13.4 | 13.4 | |
| Decent standard of living | Gross National Income (GNI) per capita (2017 PPP$) | 37,374 | 53,265 |
Source: https://hdr.undp.org/system/files/documents/global-report-document/hdr2021-22pdf_1.pdf
Wait, what?? What does it mean that females in UK have command over economic resources of post Soviet Estonia (GNI Estonia=38,048) while males in UK have command over economic resources of EU leader Germany (GNI Germany=54,534)?
The manipulation
The UNDP calculates separate command over economic resources for females and males, as a product of the actual Gross National Income (GNI) and two indices: female and male shares of the economically active population (the non-adjusted employment gap) and the ratio of the female to male wage in all sectors (the non-adjusted wage gap).
The UNDP provides this simple example about Mauritania:
Gross National Income per capita of Mauritania (2017 PPP $) = 5,075
| Indicator | Female value | Male value |
|---|---|---|
| Wage ratio (female/male) | 0.8 | 0.8 |
| Share of economically active population | 0.307 | 0.693 |
| Share of population | 0.51016 | 0.48984 |
| Gross national income per capita (2017 PPP $) | 2,604 | 7,650 |
According to this index, males in Mauritania enjoy the command over economic resources of Viet Nam (GNI Viet Nam=7,867) while females in Mauritania suffer the command over economic resources of Haiti (GNI Haiti=2,847).
Let's be honest here: this is total bullshit. There are two reasons why you cannot use raw employment gap and raw wage gap for calculating the command over economic resources:
Argument 1
Bread winners share income with their families. This is a no brainer. All over the world, men are expected to fulfil their gender role as a bread winer. This does not mean that they keep the pay check for themselves while their wives and children starve to death. Imagine this scenario: a poor father from India travels to Qatar where he labours in deadly conditions, so that his family can live a slightly better life. According to UNDP, he just became more developed, while the standard of living his wife is exactly zero.
Argument 2
Governments redistribute wealth. This is a no brainer too. One's command over economic resources and standard of living is not equal to ones pay check. There are social programs, pensions, public infrastructure. Even if you have never earned a pay check yourself, you can take a public transport on a public road to the next public hospital. Judging by the Tax Freedom Day, states around the world redistribute 30% to 50% of all income. And while men pay most of the taxis (obviously, they have higher wages) women receive most of the subsidies (obviously, they have lover wages). But according the UNDP, women in India (female GNI 2,277) suffer in schools and hospitals of the war-torn Rwanda, while men in India (male GNI 10,633) enjoy the infrastructure and social security of the 5-times more prosperous Turkey.
Don't get me wrong, the employment gap and pay gap are not irrelevant for the standard of living and command over economic resources. Pensions and social security schemes mostly do not respect the shared family income and as a result the partner doing less paid work - usually a women - gets lower pension, unemployment benefit etc. What's worse, the non-working partner is severely disadvantaged in case of divorce or break up. But while this has an impact on each gender's standard of living it certainly does not define 100% of that value.
Argument 3
You may argue that the command over economic resources measured by estimated earned income is some kind of proxy for all other disadvantages women face in society. But do you remember what I said in the beginning?
Whether an index is manipulated or not can be judged only against a precise definition of what the index claims to be measuring.
The HDI measures "people and their capabilities" and the GDI is a ratio of these capabilities measured separately for men and women. The economic dimension of the GDI is supposed to be standard of living or command over economic resources - neither of which can be represented by earned income alone.
The taboo
Wikipedia says: "For most countries, the earned-income gap accounts for more than 90% of the gender penalty." (I have not verified this.) This is important, because when we look at the other two dimensions it becomes clear that while men have shorter and less health lives they also increasingly fall behind in mean and expected years of schooling. Without the misrepresentation of the command over economic resources value, the index would show something very uncomfortable: that according to UN's own definition of Human Development men are the less developed gender.
PS: Is there a way to give those tables some borders and padding?
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Notes -
I don't understand how you would do that. The lifespan adjustment is not speculative. We know that there is a biological basis for some of the differences in lifespan between genders, because we have an understanding of some of the biology that renders men more susceptible to certain patologies. And, we have some idea of how big the difference is; for example, it seems to have been about 5 years in the US for decades, ever since the risk of death from childbirth was substantially decreased (and, of course, the ability of a society to reduce the risk of death from childbirth is precisely the type of thing that is meant by "development").
In contrast, income differences afaik have not reached a steady state for decades. So, again, how do we know how much to adjust for? There might be a way, but it is not obvious to me what it is.
You are flying EXTREMELY close to the sun here. If we're going to be correcting for the difference in lifespan due to biological factors, shouldn't we also be correcting for the difference in income and command over economic resources? We know the biological basis for the difference (women are physically weaker and generally less competitive in terms of personality) so why not correct for it?
Yes, we should correct for it. Though I doubt that the physical weakness factor plays much of a part in differences in earnings in all but the most underdeveloped countries. And note that the GDI is a measure of development (because the GDI is an adjustment to the HDI), and development implies that fewer and fewer jobs in a country require physical strength, so the contribution of differences in physical strength to differences in income should decline at higher and higher levels of development, which means that total difference in earnings should decline as countries develop. So, it sounds like difference in earnings between men and women is a pretty good metric of development.
As for differences in personality, surely there are also personality traits more common to men which tend to reduce their earnings. We would have to adjust for them, as well.
There's still lots of well-paying jobs that require physical fitness.
Why "surely"?
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And what of the ability of society to reduce the risk from male-specific pathologies? Pregnancy is, of course, even more absolute and immutable than any other biological differences men have, and yet we can (and should!) penalize countries that have a high mother mortality during childbirth, not correct it out of the statistics by omitting women who die during childbirth from calculations of female lifespan.
As I mention elsewhere, people in the top 1% in the US have a gap of 1.5 years. Entire countries have a gap of 3 years. And gender-based discrimination hasn't been eliminated in any of those groups.
In fact, the income gap in the US has been stable for at least two decades. See https://www.pewresearch.org/social-trends/2023/03/01/the-enduring-grip-of-the-gender-pay-gap/. So, if we really want to go with stable differences as the measure for natural state of the world, we should assume a ~20% pay gap as parity.
Like what? Murder and accidents? As noted previously, the propensity to get murdered or die in accidents is part of the biology of being a young male.
In addition to what I mentioned re your other response, that is exactly what you would expect, given that the biological propensity of males for risk-taking fades with age.
Those almost entirely countries with quite low life expectancy overall, or very small and/or racially homogeneous countries? Yeah, there are probably subgroups where the biological differences are smaller. And some where they are larger. HBD is supposedly a thing, is it not? But the index can't have a different adjustment for every country, so it tries to come up with a global average.
2002 Mean Income in 2022 dollars: Male $63,720, Female $36,660 (57.5 pct of male)
2012 Mean Income in 2022 dollars: Male $61,980, Female $38,950 (63 pct of male)
2022 Mean Income in 2022 dollars: Male $70,340, Female $48,550 (69 pct of male)
So, it does not seem that the income gap has been stable for 20 years.
The propensity to die in childbirth is part of the biology of being a female. If we make social choices that result in high murder and accident rates that disproportionately affect men, then we as a society have a gender inequitable social choices, every bit as much as if we made social choices that resulted in more deaths during pregnancy among women.
If every country has a different "natural" gap in lifespans between men and women, why have it as part of the index at all? The only meaningful metric would be the difference between actual gap per country and natural gap per country, so you'd need an adjustment term for every country regardless. Assuming a universal, constant 5 year natural gap adds zero information over a universal, constant 0 year gap (or, for that matter, a 10 year gap, or 20 year gap, etc.); it just benefits those countries whose actual gap is close to the constant at the expense of those that happen to be far from the constant.
A fair point.
As I understand it, reducing maternal mortality is less a function of social choice than it is of economic development; at higher levels of income, societies can afford to provide goods (clean water, medicine, fully staffed and equipped hospitals) which reduce mortality. That is not so much the case for murders and especially not for accidents; in fact, it seems to me that in some ways more affluent societies often = more opportunities for reckless guys to kill themselves (automobiles, etc). Accidents were the #4 cause of death in the US in 2021. And thus, although rates of accidental death have declined a great deal in the last 100 years, they certainly have not declined as much as maternal mortality has (from about 100 per 1000 live births in the nineteen-teens (see page 46 here) to about 17 or 18 per 100,000 in 2007-2016.
Because lifespan is a standard metric re economic development, and because the GDI is meant to be a supplement to the Human Development Index, which includes lifespan.
Policy affects murder rates and accidents. Change policing/education/punishment/lead exposure/choose your own adventure, and you get different crime outcomes. Choosing certain sets of policies that disproportionately disadvantage men damages gender equity and should be included in any metric attempting to represent it.
You're assuming your conclusion: Iceland has achieved perfect gender equity, therefore it has achieved perfect gender equity.
What error, exactly, do you think is being minimized? You propose that we have two unknown distributions (biological contributions to lifespan gap and and gender inequity contributions, by country); we only get observations of their sum. You've provided no justification for assuming that the mean of one distribution (biological factors) is the mean of their sum. In fact, the only way that's mathematically possible is if the mean of the gender inequity distribution is zero (i.e. you know a priori that all the instances of anti-female discrimination are perfectly balanced by instances of anti-male discrimination). That's certainly a position you can take, but I can say with a high degree of certainty that that's not the position that the people at the UN hold.
But let's roll with your theory: you somehow know the social inequity distribution has a mean of 0 years, and the biological distribution has a mean equal to 5 years. What, then, can we say about the social inequity variable for one country given the observed variable? Still very little; we still don't know what the unknown distributions are, or even if they're normal, let alone what their standard deviations are. You can't even say that large sum deviations from 5 years hint at large social inequity values, because it could be driven entirely by biological deviations.
The only world where this model offers a good measurement of the gender inequity variable is one where the mean of the biological distribution is 5 years with small deviations compared to the social inequity distribution, which would have a mean of zero (no net discrimination) and account for the large majority of the variation in the sum distribution. Iceland does, it turns out, have to be a relative hellhole for women compared to Pakistan and Sudan.
I don't think that's the world that exists, and I strongly suspect it's not the world the folks making the index at the UN think exists either. Do you?
Iceland was your example, was it not? And, no, I am not. Were I making that argument, I would have said that the estimate should be 3. The point is that we don’t know, so the issue is, given what we know today, what is our best estimate of the average biological component across countries, races, etc? Our best estimate isn't zero, nor particularly close to zero.
The usual: the extent to which changes in the metric reflect actual changes in what the metric is attempting to assess. Both false highs and false lows, but of course there is usually a tradeoff between them. Were the data binary, using something like the area under the ROC curve. The data here is continuous, and supposedly there is an analogous method for use with continuous variables, but I don't know enough about the topic to say if area under the ROC curve is appropriate here. I know that many other methods exists, but that is all I know about them.
Of course you can say it hints at large social inequity values, and of course it could be driven entirely by biological deviations. Those are not mutually exclusive statements.
As for the rest, I really don't understand your point. Perhaps I misunderstand you, but wouldn't your logic be the same using 4 years, rather than five? And 3 years? And 3 months? Indeed, everything but zero, which we know is incorrect? If your argument leads to the conclusion that the only legitimate estimate is one that we know is wrong, it seems to me that something has gone awry. I also think you might be using a number that is meant to be an average across countries, and assuming that it claims that that is the average in every country.
Also, suppose you saw a country in which women lived 10 years less than men. Would you not stroke your chin and say, "hm, I suspect that something is amiss in that country, because women usually live longer than men"? Isn’t 5 years a rule of thumb for when you should start scratching?
Finally, I think we are losing sight of the point of the GDI, which is to ensure that progress on the HDI does not cause people to overlook the fact that sometimes such progress is not shared as broadly as it could be.
This criticism also applies to 0 years. It's simply impossible to separate the biological component from the gender inequity component with only observations of the sum. It's not impossible if you bring in other data, but the GDI doesn't cite any data sources in its methodology to justify the 5 years. You can assign whatever probability you want to the UN Development Program having a crack team behind the scenes doing secret HBD research.
There's a certain vacillation here. On one hand, the GFI is a highly technical, synthetic metric only to be used by experts in conjunction with the HDI in measuring temporal trends and doing international comparisons. If that's the case, though, why apply the unevidenced fudge factor? If it makes some countries look like they favor women, why try to correct for it at all? Experts would know that it's driven by the difference in male and female life expectancy.
So suppose I am a teacher, and I am biased against men. So here is how I grade essays: First, I grade them blindly. Then, after removing the blinds on the names, I enter the grades in a spreadsheet. Next, I reduce the scores of male students by an average of five points, with a distribution identical to the distribution of differences in earnings by gender. I enter the new scores in my grade book. After the scandal is revealed, the school adds five points to every male's score. Are you saying that the result would not likely be a more accurate representation of the actual scores than was my original list?
??? How do you know this? Are you privy to the no doubt voluminous documentation that has doubtless been generated over the years? Have you perused the numerous papers available on Google Scholar which discuss the GDI to see if any assess the methodology in question?
Or, you could do what they do, and those same experts, and outsiders who use the GDI, are free to delete the adjustment if they want to. As I have said several times, if including the adjustment results in an index which more accurately reflects actual conditions than an index using unadjusted numbers, then that is a good reason to use it.
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If you're asking "could the index still be useless for forming policy if it used 4 years or 3 months?" Sure. If you have no way to know that you got it right no matter what value you use, then you have no way to know you got it right, period.
I'd say that 10 years less is a clear statistical outlier. Just "less than 5" doesn't make something a statistical outlier.
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