Marriage and gender inequality in 124 countries

Countries with higher levels of marriage have higher levels of gender inequality. This isn’t a major discovery, but I don’t remember seeing this illustrated before, so I decided to do it. Plus I’m trying to improve my Stata graphing.

I used data from this U.N. report on marriage rates from 2008, restricted to those countries that had data from 2000 or later. To show marriage rates I used the percentage of women ages 30-34 that are currently married. This is thus a combination of marriage prevalence and marriage timing, which is something like the amount of marriage in the country. I got gender inequality from the U.N. Development Programme’s Human Development Report for 2015. The gender inequality index combines the maternal mortality ratio, the adolescent birth rate, the representation of women in the national parliament, the gender gap in secondary education, and the gender gap in labor market participation.

Here is the result. I labeled countries with 49 million population or more in red; a few interesting outliers are also labeled. The line is quadratic, unweighted for population (click to enlarge).

You can see the USA sliding right down that curve toward gender nirvana (not that I’m making a simplistic causal argument).

Note that India and China together are about 36% of the world’s population. They both have nearly universal marriage by age 30-34, but women in China get married about four years later on average. That’s an important part of why China has lower gender inequality (it goes along with more educational access, higher employment levels, politics, history, etc.). China is a major outlier among universal-marriage countries, while India is right on the curve.

Any cross-national comparison has to handle this issue. China is 139-times bigger than Sweden. One way to address it is to weight the points by their relative population sizes. If you do that it actually doesn’t change the result much, except for China, which in this cases changes everything because in addition to being huge they broke the relationship between marriage and gender inequality. Here is the comparison. Now the dots are scaled for population, and the gray line is fit to all the countries except China, while the red line includes China (click to enlarge).

My conclusion is that the gray line is the basic story — more marriage, more gender inequality — with China as an important exception, but that’s up for interpretation.

I put the data and the code for making the charts in this directory. Feel free to copy and crib, etc.

11 thoughts on “Marriage and gender inequality in 124 countries

  1. Dear Philip, this is a great graph! Congratulations on idea and effort. However, I’m not sure if I see the same causal relationships between the variables, that marriage RESULTS in gender inequality. My interpretation would be actually quite the reverse: more gender equality RESULTS in lower inclination towards marriage (general, or up to 34 year of life). Where my way of thinking may have gone wrong?


  2. And even with China, the curve fit says “more marriage, more gender inequality”, just with a different shape for that relationship. But yeah, China does look like an outlier.


  3. If one also includes cohabitation I doubt the negative relationship will be that clear. 50+ percent children in Scandinavia and France are born to cohabiting couples and even in Spain it approaches 25%. And what’s more, recent Swedish data show a rise in marriage propensities, especially mong the higher educated.
    Greetings Gosta


    1. That would weaken the relationship some, although maybe not as much as if I were looking at parents of children.

      But it’s a theoretical question when cohabitors should be included with marrieds. For this comparison I prefer not to have them, though of course no reason not to compare.



  4. I’m looking at the way the data clusters, and it looks like there’s a story where fairly low gender inequality is necessary for low marriage rates to exist (or maybe that low marriage rates force society to deal with gender inequality), but that low gender inequality doesn’t require that marriage rates drop.

    The density of countries with low inequality seems pretty steady along the marriage rate axis, up to about 90% marriage rate.

    I can see a story where women must marry in societies with high inequality, since they’d otherwise be unable to support themselves. But liberated of this requirement, there are still other cultural (or in China’s case, maybe demographic) factors that can lead people to choose to get married at high rates, despite having realistic options not to.


  5. Gender inequality “measures gender inequalities in three important aspects of human development—reproductive health, measured by maternal mortality ratio and adolescent birth rates; empowerment, measured by proportion of parliamentary seats occupied by females and proportion of adult females and males aged 25 years and older with at least some secondary education; and economic status, expressed as labour market participation and measured by labour force participation rate of female and male populations aged 15 years and older”

    It appears that commenters and the author have interpreted gender inequality as economic inequality; it is not the same thing. In fact there is no income component in the gender inequality. So, I do not understand what is the point in plotting this measure against marriage rate. As an example, what is the maternal mortality ratio got to do with marriage rate? It is directly impacted by HDI and GNP, PPP. Hence, to me, this appears to be plotting tow random parameters and claiming a relationship, when none is expected in the first place. If your goal was to make a curve fit to show marriage makes the nation unequal, you have not demonstrated that.


  6. What if you have divided the countries into rough categories like western/non-western, developed/developing and so on? Eyeballing the diagram it seems that relation becomes much weaker if you would concentrate on “northern” cultures (european+north asian) or just european.


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