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?
Jump in the discussion.
No email address required.
Notes -
I think you are underestimating how low life expectancy was in the Caribbean at the time
That is an entirely different point than the one I was responding to, which was that life expectancy is obviously more important than the other metrics in the index.
Why, if the issue is the degree to which women have income independent of the man in their life?
That is precisely my point: Life expectancy is NOT obviously more important, among other things because it depends on the relative levels of the various factors.
I notice that you seem to assume that we are talking about first-world countries. We are not. Do you think that "power over household finances" and "likelihood of getting abused" are measured in Burundi? In Myanmar? In Sri Lanka? I sincerely doubt that there is data on either of those in more than 20 countries in the world. Especially since "power" of any kind is difficult to measure objectively.
Again, how many countries measure that? How many have an incentive to measure it accurately? Not to mention that is ignores post-separation/abandonment/death income. Compare that with income per se, which all countries with an income tax have an incentive to measure. Which measure is more likely to be complete and accurate? Note that Wikipedia has data on divorce rates for only 105 countries and this UN document on divorce includes almost no African countries. And please don't argue that the UN data is from 10 years ago; a metric that only has recent accurate data is of limited utility, because knowing about change over time is important.
But, the GDI does not include a measure of workplace participation. It includes a measure of earned income.
And as for "and women entering the workforce at greater rates is just as important as women living longer," so what? I understand that you, personally, value longer life differently than they do (or, more accurately, than how you understand them to value them), but that does not, in itself, make their measure illegitimate or fraudulent. They are just measuring different things.
Put up some numbers then. When I looked it up slaves had a life expectancy of 22 years. I couldn't find statistics for the Caribbean but I doubt for free white people it was lower than that.
If you read what I said, my point was much more about how unimportant workforce participation is. The only time life expectancy was brought up was in comparison to workforce participation.
This is literally how prioritization in general works. I believe life expectancy is more important than money, in general, given reasonable amounts of each. Saying "health is more important than money" is a perfectly reasonable statement. At the same time, I would gladly take a billion dollars over a year of life. Does this prove I was lying or mistaken? No, it's just that the statement "health is more important than money" does not necessarily imply "health is infinitely more important than money." What I have consistently argued is that wage should not be weighted the same as the other measurements. It perhaps deserves a place in the index, but should be deweighted so that differences in education and life expectancy matter more. This means it takes a very large difference in average income to overcome a difference in life expectancy, and is the common-sense interpretation of what I have been saying.
"Gender development" implies all sorts of things from gender equality to independence. If the claim was actually that the GDI measured gender independence alone, I would be fine with workforce participation being weighted as heavily as it is.
Yes, absolutely, I know they're measured in those countries. Here's abuse rates in Myanmar. Here's Myanmar power over household finances by gender, plus this includes another study on abuse rates. Here's abuse rates in Sri Lanka. Here's household purchasing power in Sri Lanka. A study on abuse rates in Burundi is referenced on this site though I unfortunately couldn't find the study itself online. This page includes a study on household purchasing power by gender in Burundi.
I agree "power" is hard to measure objectively, but in the end all survey results are heuristics anyways, and existing measurements do come pretty close. They are more useful and more relevant to gender development than workforce participation is, imo.
These statistics already exist because people are very interested in them, and people are very interested in them because they are good heuristics for actual gender development.
Alright, so post-divorce income is a bad measure, but there are still ways to improve the existing measurement. The best way IMO would be to simply discount workforce participation, and measure male vs female incomes based on the income of those who are actually working. Another way would be to weigh income as less important relative to the other two factors. Both easy ways that don't rely on some hard-to-find fourth measure, though I think I've established there are other legitimate fourth measures (with statistics available in just about every country) which could be used.
That's the problem. Since it includes everyone in its average, including those who are not working, it is essentially a workplace participation measure pretending to be a wage gap measure. The semi-official site Human Development Reports describes this measure as "command over economic resources" which I find highly inaccurate, since many of those not working do have lots of command over economic resources.
So it is a lie. It portrays itself as an objective measurement of gender equality, but relies on assumptions (such as the assumption that men and women should be identical) which are incorrect. Women will never enter the workforce at the same rate as men, but this doesn't mean there is gender inequality, as this measure implies. Men will never live as long as women (well, in both cases I assume no vast technological advances or societal changes that upend all of our assumptions) and that doesn't mean there's gender inequality either. They have correctly adjusted for that latter fact, but incorrectly (imo deliberately) not adjusted for the other.
Also, just to be clear once again, it's not that I care about longer life specifically, it's that I don't care about workforce participation. I care about the actual wage gap, I care about longevity, and I care about education rates by gender, just workforce participation specifically should not be put on the same pedestal as those three.
Dude, no one said you were lying or mistaken. The point is that you claimed that your position is obviously the correct one. It isn't, because it depends on how you value the two elements. As I said, "it does not seem to me to be obvious that everyone must agree." And as you say, "this is literally how prioritization works" -- people prioritize different things.
But, how many countries measure all of those things, and for how long? And how accurately? The Sri Lanka study says data was first collected in 2019. And it is based on a survey, which have all sorts of inherent challenges, and are of dubious value as a cross-country metric if the same survey is not used in every country. Moreover, this World Bank report says this about its data on gender-based violence: "When a country did not have any eligible data between 2000 and 2018, their rates were estimated based on countries with similar characteristics, and these estimates were fed into the regional averages." It does not sound to me as if that data is particularly easy to obtain.
Yes, I am sure there are. But that is not really the issue; the issue is the OP claim that the existing metric is invalid.
I guess I don't understand; isn't that what the current practice is? It uses data on earned income.
I don't see how it is "essentially a workplace participation measure." For example, it captures differences between countries where women work, but are excluded from certain occupations, and those where they are not. A pure workplace participation measure does not include that. A measure of earned income is in essence a composite of labor force participation and wages. Would it be nice to measure them separately? Yes. But, again, 1) accurate measures of wages, rather than income; might be difficult to obtain; 2) once again, the fact that the GDI is imperfect does not mean that it is useless; 3) it is perfectly possible that your suggestion was tested, and it was found that it did not materially improve the performance of the index. 4) most importantly, this indicates that the GDI might actually be doing pretty much what you suggest. It says:
The GDI employs objective data, but it is an index; of course it relies on certain assumptions, and makes certain decisions about what to include and how to weigh each element of the index. No one claims otherwise. That is the nature of indexes. The GDI is not even the UN's only measure of gender inequality; there is at least one other. It is AN index of gender inequality, not THE index.
Of course it means that there is gender inequality; there is inequality in the labor participation rate. Just as there is inequality in rates of breast cancer. What it does not necessarily mean is that there is unjust gender inequality. But, how are you going to know if a given inequality is unjust, if you don't measure it, and see if it has changed, or whether it is different in different countries. I am sure that back in the early 1960s, when the female labor participation rate was under 40%, people assumed that that was just natural and normal. But then it changed. So now we have reason to think that the rate in the early 1960s was possibly the result of unjust restrictions on women.
And obviously it's correct to value longevity above voluntary workforce participation. I did specify voluntary; if women are banned from working or face serious challenges entering the workforce when necessary that's another matter, but whenever I've brought this up I've specifically stated that how long a woman lives obviously matters more than whether she chooses to enter the workforce.
In the future, if you're going to keep arguing this point, please do so in reference to what I'm actually saying, rather than subtly rewording my points and then arguing against the reworded versions. I never said my position was obviously the correct one.
Sure, those are reasonable objections to a few alternatives I came up with off the cuff, as I've already acknowledged. I'd still prefer either some alternative be used, or the existing measure (which heavily weighs workforce participation) not be used at all due to its relative unimportance compared to the other two metrics.
It's very much worth mentioning that the actual metric we are discussing here, sex-disaggregated data, also is not available in all countries:
Somehow I doubt you care, though you seem to care very much about how the data I've suggested as an alternative is not available everywhere.
No it's not. There literally is no OP claim that the existing metric is invalid. You made that up yourself. I argue that it is more flawed than it would be without that measurement, not that it is invalid. No metric, no matter how bad, is ever totally invalid, though some are invalid enough to be functionally useless.
It measures male and female incomes based on an average of all people, including those who do not work.
Firstly, a pure workplace participation measure absolutely does include that. If women are excluded from certain occupations, many marginal women will not work at all, so a workplace participation measure would pick that up. Secondly, you imply that the GDI objectively picks up that sort of situation better. It does not. The GDI calculates "command over economic resources" by basically multiplying average wage by workplace participation. It would pick up an "exclusion from certain occupations" better than a workplace participation measure in some cases, and worse in others, depending on whether the occupation in question is highly paid.
Thirdly, I don't argue that workplace participation would necessarily be much better, but rather that at least it would be more honest.
I hope you don't really think this is a good counterargument. You've basically gutted my position to "Indices rely on assumptions" and then laughed down haughtily at the steaming entrails of what used to be my point. Obviously indices rely on assumptions. As you say, no one claims otherwise. I never claimed anyone claims that! Some assumptions are less accurate than others, and the one I mentioned is far less accurate than most, leading to this index being less useful and more biased than most.
Nah, "gender inequality" refers to something more fundamental than "exactly alike on all axes". No clarification was needed here. The first definition I found online was, "Legal, social and cultural situation in which sex and/or gender determine different rights and dignity for women and men, which are reflected in their unequal access to or enjoyment of rights, as well as the assumption of stereotyped social and cultural roles." This is obviously closer to the common meaning of "gender inequality" than your hyperspecific definition where "gender inequality" supposedly means inequality along any axis. If you disagree, please find me one example anywhere of anyone using that phrase the way you think it should be used.
Or, as I mentioned, the rate is now possibly the result of pressure placed on women to enter the workplace. But I agree it should be measured, it just shouldn't be implied to be as important as years of education or lifespan.
No, your personal values are not obviously correct
See above.
Please refrain from making accusations of bad faith. I have no interest in defending the GDI, because I have no idea how accurate the GDI is at measuring what it seeks to measure. All I know is that that the criticisms and suggested "improvements" are glib. I see no evidence that, before I mentioned it, you or any of the other critics even considered for a moment that availability of data is even a relevant factor in constructing an index. Nor do I see any evidence that the lacunae in the data you suggest being added are less than or equal to those in the data being used currently.
Well, OP said the data was manipulated, so I think it is fair to infer that he thinks it is invalid. And, as it happens, in subsequent comments he has certainly opined that it is invalid.
I don' t believe that it the case, because "[t]he female (or male) income share is computed by multiplying the ratio of the female (male) wage to the average wage by the female (or male) share of the economically active population."
Except that it is not meant to pick up exclusion per se, but rather the effect of that exclusion on income. Surely it does that better than merely measuring labor force participation, which does not directly measure income at all. Note that [differences in labor force participation among races/ethnicities in the US](https://www.bls.gov/opub/reports/race-and-ethnicity/2021/home.htm} are far narrower than differences in income
Google tells me that is from an NIH publication, which cites a 2018 publication by the European Institute for Gender Equality. Do you have any evidence that that is the definition used by the GDI?
?? Such claims are pretty much central to the woke perspective on the issue.
Good thing I never said they were, then.
Bad faith is a spectrum, and to my eyes you seem to be nitpicking and looking for areas where I'm wrong rather than looking for common ground or mutual understanding. This isn't pure bad faith but it is very unpleasant and counterproductive. For example, you keep trying to litigate past points and how wrong they were rather than moving the discussion forward. More annoyingly, you keep rewording my points. I don't know why you are behaving this way, but it is evident that you are.
You say this, but you haven't put in the work to verify the currently used data either. Don't accuse me of not caring about the accuracy of the existing index if you've done significantly less work to confirm that veracity yourself. As I already mentioned, and as you keep ignoring, the suggested alternatives were not meant to have much weight to them anyways and were very off-the-cuff.
You seem a lot more concerned with going back and relitigating this point over and over again, attacking my suggestions which I've admitted three times now are not meant to be taken very seriously, rather than allowing the conversation to continue forward. Like a dog playing tug of war you just won't let go even though the game is obviously meaningless. What more do you want from me? I have said they were poorly thought-out, lack important information that the currently-used measures have, and are probably not suitable alternatives. Must I grovel and beg for you to feel satisfied with dropping this point, or will you continue to bring it up until I'm forced to ignore you entirely? Conversations move on.
This is precisely what it means to include nonworking population in the average.
Let's say women earn $90 for every $100 men make, and work 50% as much. The measure thus multiplies the wage ratio (0.9) by the share of active population (0.5) and arrives at 0.45. This is mathematically equivalent to what I am describing.
No I'm not, what I said makes zero assumptions about that ability. If you think it does, name any one single assumption I made.
OK? Never said or implied it wasn't.
Yes, of course the measure that measures income measures income better than the one that measures workforce participation, lol. That's obviously not what my point was.
Must we relitigate this again? My original claim was that this measure had flaws. It was not any stronger than the one mentioned above. If by "original claim" you mean the one where I said the measure was a lie, that was objectively not my original claim, and also was not my central claim, but rather my motivation for making the central claim (which is that the measure is flaws).
Isolated demand for rigor, have you done so? Must I somehow create a meta analysis of all indices just to make the claim that a single one is more biased than average? This is just hilarious, and objections like this are a big part of why I think your responses have been at least partially in bad faith. Generally, when normal people talk, they make claims like "that job is harder than average" without necessarily needing access to comprehensive data pools to back up their claims. See, I did it just now, without a comprehensive data pool to back up my claim that this is normally how people talk.
Why would it need to be used by the GDI? I am talking about common usage, as I clearly indicated by stating it was the first googled result.
Look, [gender inequality] is a specific thing which everyone generally agrees is bad. If someone says "women are not equal to men", without qualification, that's generally seen as wrong. There's no need for clarification, nobody assumes "ah of course he meant in less important ways such as average height." Without clarification it is assumed that such claims are made more generally, which is why when people make them to prove some other point, such as yours about how men and women differ in height etc., they always follow up with lots of clarification. Similarly, you obviously understood what I meant, but decided to correct it anyways, unnecessarily.
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link