Gender differences in dropping out of university and switching majors

Some seven years ago, I had a couple of summer research scholarship students look at dropping out of university (specifically, the management degree at the University of Waikato), including the factors associated with dropping out, and what led to student persistence. One factor that strongly predicted dropping out was gender - male students had over one-third lower odds of completing their degree than female students.

So, I was interested to read this 2018 article by Carmen Astorne-Figari and Jamin Speer (both University of Memphis), published in the journal Economics Letters (sorry, I don't see an ungated version online). They specifically look at gender differences in dropping out, as well as gender differences in switching majors, at university. Using data from the 1997 cohort of the National Longitudinal Study of Youth (NLSY97), which included nearly 3000 students who attended college and reported at least one GPA, Astorne-Figari and Speer found that:

Males are 7.7 percentage points (or 22%) more likely to drop out. The male-female differential is about the same as the effect of one point of GPA... the gender gap reverses for major switching: women are 7.6 percentage points more likely than men to switch majors. These effects perfectly offset so that there is no gender gap in major persistence. This is in contrast to the racial/ethnic gaps, as both blacks and Hispanics are more likely to drop out and to switch majors.

So, the higher dropout rate for male students that my summer students found is not an aberration. Male students do appear to drop out in significantly greater numbers than female students do. However, the rate of completion of the major that students started their studies in does not differ between male and female students. One way of interpreting these results is that it appears that male and female students respond differently to the challenges of university study. If they find themselves in a field of study that they are not enjoying or doing well in, male students respond by dropping out, while female students respond by switching majors. Of course, it would be interesting to stratify the analysis by GPA, and see if there are really differences for students at the bottom end of the GPA distribution, but Astorne-Figari and Speer don't do that. However, they do look in more detail at STEM subjects and, interestingly, the results are slightly different:

Again, men are more likely to drop out of college, while women are more likely to switch out. Both gaps here are larger than in the overall sample, and the switching gap is particularly large... Women are 19.9 percentage points more likely to switch out of STEM, doubling the switching rate of men.

Unlike the overall results, there is a substantial gender gap in persistence for those who start in STEM fields. Women are 7.9 percentage points (18%) less likely to graduate in a STEM major conditional on starting one, driven by the huge gap in switching behavior.

So, completion rates of STEM majors are lower among female students than among male students. That may help to explain some of the gender gap in STEM graduates. However, it also poses a bit of a dilemma. Ideally, universities want to reduce the rate of students dropping out. But, if they implement some policy intervention to reduce dropout rates, more male than female students might be affected (if only because there are more male dropouts). That would tend to increase the gender gap in STEM even further, a point that Astorne-Figari and Speer also note:

...men’s higher dropout rates are actually keeping the STEM gender gap from being even larger, so better retention might also widen the gender gap in STEM.

However, one way of addressing disparities in dropout rates while not exacerbating the STEM gender gap might be to try policy interventions that help male students to change majors, rather than dropping out. Perhaps there is some other field that they are better suited to, but for some reason female students are better able (or more willing) to identify a new field than male students. This seems like something worth further investigation. 

Migration and working age population decline in Europe

It is more than simply a truism to say that populations are ageing over time. Structural ageing (changes in the age distribution of the population, whereby older people constitute a larger proportion of the total population) is a real phenomenon, observed across all countries and regions of the world. However, the areas worst affected by structural ageing tend to be remote regions, where young people are out-migrating to cities in large numbers.

One way that structural ageing can manifest is in the size of the working age population (this can be defined in various ways, but a common approach is the population aged 15-64 years). As the population ages, a larger proportion of the population is aged 65 years or over (and no longer in the working age population), and so the working age population shrinks. Similarly, as young people migrate out of a country or region, the working age population shrinks.

So, I was interested in this recent article by Daniela Ghio, Anne Goujon, and Fabrizio Natale (all European Commission Joint Research Centre), published in the journal Demographic Research (open access, with a shorter non-technical summary available on N-IUSSP). They look at to what extent cohort turnover and migration effects affect the size of the working age population for regions across the European Union countries (specifically, for NUTS3 regions - the smallest disaggregation of regions used by Eurostat) over the period from 2015 to 2019. Cohort turnover is specified as the difference between the size of the cohort of young people at labour market entry age (15-19 years) and the size of the cohort of older people at labour market exit age (60-64 years). Migration is the net migration of the working age population. Comparing those two values with change in the working age population over the period, Ghio et al. categorise four different types of regions. The first type of region was:

NUTS3 territorial units where both components are positive represented approximately 8% of territories (13% of EU working-age population in 2019), mainly distributed across the following countries: the Netherlands (20 territories), Belgium (15), Spain (12), and Germany (11).

In the vast majority of territories (94), the positive effects coincided with an increase in the size of the working-age population during the 2015–2019 period...

Next:

The cluster with positive cohort turnover effects and negative net migration was the smallest one: only 5% of EU territories accounting for 11% of the EU working-age population in 2019, mostly located in France (30). Among these, the majority (54 territories, corresponding to 8% of the EU working-age population) reported a decrease in the size of the working-age population.

Third up: 

The cluster with negative cohort turnover effects and positive net migration included the largest share (63%) and number (738) of EU territories, representing 54% of the EU working-age population in 2019.

Finally:

The cluster with both negative cohort turnover effects and net migration was the second largest and consisted of 266 territories, corresponding to 23% of EU territories and 22% of the EU working-age population in 2019, mostly distributed across eastern EU MS such as Bulgaria (18), Romania (31), and Hungary (9); central eastern EU MS such as Poland (40); south-eastern EU MS such as Croatia (18); and southern EU MS such as Greece (18) and Italy (41).

The four types of region are nicely illustrated in Figure 2 from the paper:

The overall decline in the size of the working age population is readily apparent in the first panel of the figure, on the left. Notice that much of that change is due to population ageing (the cohort turnover in the third panel, on the right) rather than net migration (the middle panel). I suspect this would be a general feature not just for Europe, but for all western countries, including New Zealand and Australia.

This is a nice paper, which offers an interesting characterisation of regions across two dimensions: (1) whether cohort change is increasing or decreasing the size of the working age population; and (2) whether net migration is increasing or decreasing the size of the working age population. I have done similar analyses in the past (unpublished as yet), but also looking dynamically as to how the changes in components (in my case, it was natural increase or decrease [births minus deaths] and net migration) move over time. This is the sort of analysis that local planners and policy makers are really interested in. Importantly, it doesn't require much in the way of data or heavy analytical skills. It would be really interesting to see a similar analysis for New Zealand - a good potential project for a future Honours or Masters student.

[HT: N-IUSSP]

Husbands vs. wives

On the Development Impact blog back in March, Markus Goldstein pointed to this fascinating NBER Working Paper by John Conlon (Harvard University) and co-authors, innocuously titled "Learning in the Household". Despite the title, the working paper reveals a study of how husbands and wives treat information revealed by each other differently (a finding that will no doubt come as no surprise to my wife).

Conlon et al. recruited 400 married couples and 500 unrelated strangers (with equal numbers of men and women) for their study, in Chennai (India) in 2019. In the experiment, research participants:

...play five rounds - with different treatments - of a balls-and-urns task... The goal in each round is to guess the number of red balls in an urn containing 20 red and white balls. Participants are informed that the number of red balls is drawn uniformly from 4 to 16 in each round...

In each round, participants receive independent signals about the composition of the urn. Concretely, they privately draw balls from the urn with replacement. Depending on the round, they either play the game entirely on their own or else can learn some of the signals from their teammate. Comparing learning across these rounds allows us to test for frictions in communication and information-processing which may interfere with social learning.

Seems straightforward so far. The experiment involves several rounds, which proceed somewhat differently from each other:

Individual round. The Individual round proceeds as follows. First, the participant draws a set of balls from the urn, followed by a guess of how many red balls are in the urn. Then, they draw a second set of balls from the urn and make a second (and final) guess. All drawing and guessing is done privately, without any opportunity to share information. This round serves as a control condition - a benchmark against which we compare the other conditions.

Discussion round. The Discussion round differs from the Individual round in that, for each participant, their teammate’s draws - accessible through a discussion - serve as their ‘second’ set of draws. Each person first makes one set of draws followed by a private guess, exactly as in the Individual round. Next, the couple are asked to hold a face-to-face discussion and decide on a joint guess. After their discussion and joint guess, each person makes one final, private guess.

By comparing the final private guesses in the individual round and the discussion round, Conlon et al. can test whether learning your teammate’s information through a discussion is just as good as receiving the information directly yourself. There are two further rounds as well:

Draw-sharing round. This round is identical to the Discussion round except that, after participants receive their first set of draws and enter their first guess, they are told their teammate’s draws (both number and composition) directly by the experimenter, e.g. “Your spouse had five draws, of which three were red and two were white.” They then make an additional private guess which can incorporate both sets of draws before moving on to the discussion, joint guess and final private guess...

Guess-sharing round. The Guess-sharing round is the same as the Draw-sharing round except that the experimenter informs each person of their spouse’s private guess (made based on their own draws only), rather than their spouse’s draws. The experimenter also shares the number of draws this guess was based on, e.g. “Your spouse had 5 draws and, after seeing these draws, they guessed that the urn contains 12 red balls.”

By comparing the final private guesses in the individual round and the draw-sharing round, Conlon et al. can test whether the identity of who learns the information matters (aside from who shares that information). By comparing the final private guesses in the discussion round and the draw-sharing round, Conlon et al. can test the extent to which communication frictions affect decision-making (since there are no frictions in the draw-sharing round, because the information is shared by the experimenter, and the teammates cannot discuss at all). Comparing the draw-sharing round and the guess-sharing round allows Conlon et al. to test whether beliefs about the competence of the teammate matters. Finally, having teams made up of spouses or made up of mixed-gender strangers or same-gender strangers allows Conlon et al. to see if spouses share information (or act on information) differently than strangers do.

Having run these experiments, Conlon et al.:

...first compare guesses in the Individual and Discussion rounds, played in randomized order. Husbands put 58 percent less weight (p<0.01) on information their wives gathered - available to them via discussion - than on information they gathered themselves. In contrast, wives barely discount their husband’s information (by 7 percent), and we cannot reject that wives treat their husband’s information like their own (p=0.61). The difference in husbands’ and wives’ discounting of each other’s information is statistically significant (p=0.02).

The lower weight husbands place on their wives’ information is not due of a lack of communication from wives to husbands. In another experimental treatment - the Draw-sharing round - husbands put less weight on their wife’s information even when it is directly conveyed to them by the experimenter (absent any discussion). In this case, husbands discount information collected by their wives by a striking 98 percent compared to information collected by themselves (p<0.01), while wives again treat their spouses’ information nearly identically to their own. Lack of communication between spouses or husbands’ mistrust of (say) wives’ memory or ability thus cannot explain husbands’ behavior. Rather, husbands treat information their wives gathered as innately less informative than information they gathered themselves. In contrast, wives treat their own and their husbands’ information equally.

The guess-sharing round doesn't appear to reveal too much of interest, with results that are similar to the draw-sharing round. Finally, comparing the results from teams of spouses with the results from teams of strangers, Conlon et al. find that:

In both mixed- and same-gender pairs, men and women both respond more strongly to their own information than to their teammate’s. Thus, the underweighting of others’ information appears to be a more general phenomenon. Husbands treat their wives (information) as they treat strangers; wives instead put more weight on their husband’s information than on strangers’ information.

There is a huge amount of additional information and supporting analysis in the working paper (far too much to summarise here), so I encourage you to read it if you are interested. Conlon et al. conclude that there is:

...a general tendency to underweight others’ information relative to one’s ‘own’ information, with a counteracting tendency for women to weight their husband’s information highly.

Now of course, this is just one study that begs replication in other contexts with other samples. However, I'm sure there is a large section of the population who would find that conclusion meets their expectations (and/or their lived experience).

[HT: Markus Goldstein at Development Impact]

The global inequality boomerang

Overall, global inequality has been decreasing over the last several decades. That may contrast with the rhetoric about inequality that you are familiar with from the media. However, the global decrease in inequality has mostly been driven by the substantial rise in incomes in China. However, China's contribution to decreasing global inequality may be about to change. In a new working paper, Ravi Kanbur (Cornell University), Eduardo Ortiz-Juarez and Andy Sumner (both King’s College London), discuss the possibility of a 'global inequality boomerang'.

Focusing on between-country inequality (essentially assuming that within-country inequality doesn't change), and using a cool dataset on the income distribution for every country scraped as percentiles from the World Bank's PovcalNet database, Kanbur et al. find that:

...there will be a reversal, or ‘boomerang’, in the recent declining (between-country) inequality trend by the early-2030s. Specifically, if each country’s income bins grow at the average annual rate observed over 1990–2019 (scenario 1), the declining trend recorded since 2000 would reach a minimum by the end-2020s, followed by the emergence of a global income inequality boomerang...

This outcome is illustrated in their Figure 4, which shows the historical decline in inequality since the 1980s, along with their projections forward to 2040:

Scenario 1 assumes an almost immediate return to pre-pandemic rates of growth. Scenario 2 assumes slower growth rates for countries with lower rates of vaccination. Neither scenario is particularly likely, but the future trend in global inequality is likely to be somewhere between them, and likely to be moving upwards. Why is that? Between-country inequality has been decreasing as China's average income has increased towards the global average. In other words, Chinese household incomes have increased, decreasing the gap between Chinese households and households in the developed world. However, once China crosses the global average, further increases in Chinese average income will tend to increase between-country inequality.

Of course, this might be a pessimistic take, because if other populous poor countries (including India, Indonesia, Pakistan, Nigeria, and Ethiopia) grow more quickly, then their growth might reduce inequality enough to offset the inequality-increasing effect of Chinese growth. However, that is a big 'if'. According to World Bank data, China's GDP per capita growth rate averaged 8.5 percent per year between 1991 and 2020. Compare that with 4.2 percent for India, 3.2 percent for Indonesia, 1.5 percent for Pakistan, 1.4 percent for Nigeria, and 3.9 percent for Ethiopia. These other countries would have to increase their growth rates massively to offset Chinese growth's effect on inequality.

This isn't the first time that Chinese growth has been a concern for the future of global inequality. Branko Milanovic estimated back in 2016 that China would be contributing to increasing global inequality by 2020 (see my post on that here). Things may not have moved quite that quickly (a pandemic intervened, after all). Kanbur et al. are estimating under their Scenario 1 that the turning point will be around 2029 (and around 2024 under their Scenario 2). Slower Chinese growth, and faster growth in the rest of the world, have no doubt played a part in this delay. However, it's likely that the turning point in global inequality cannot continue to be delayed for much longer.

Read more:

• Inequality was similar, or higher, in ancient times than it is today
• The last two centuries of global inequality
• Changing global inequality, migration and open borders
• More evidence of declining global inequality
• Chinese growth and global inequality
• Several research articles on inequality