Social capital and the spread of coronavirus in Europe

Like many researchers, I've been closely following the emerging research on the coronavirus pandemic, and looking to contribute to it. With some colleagues, I've been looking at how social capital might be related to the success (or otherwise) of lockdown policies, and the effect on coronavirus infections and deaths. The idea was intuitive - reducing infection rates essentially requires that people act in the best interests of society, so (holding other relevant factors constant) countries or regions with higher social capital should be suffering less, with fewer infections and coronavirus-related deaths.

Unfortunately, our data analysis to date hasn't been showing anything of interest, which we have put down to measurement error in the data. However, this new working paper by Alina Kristin Bartscher (University of Bonn) and co-authors offers a sensible explanation for our null results:

From a theoretical perspective, social capital, the spread of Covid-19 and containment policies interact in various ways. First, high-social-capital areas are known to be more vibrant and better connected, economically and socially... Hence, we expect the virus to spread more quickly in those areas in the beginning of the pandemic, when information about the virus and its severity were incomplete. Second, as soon as the importance of behavioral containment norms becomes more salient, we expect the relationship to change... we expect that informal rules of containment are more likely to be (voluntarily) adopted in areas with high social capital, leading to a relative decrease in infections.

In our analysis, we had adopted a cross-sectional approach to try to overcome some of the measurement issues with the high-frequency data. However, in adopting that approach we were conflating the early period, when the relationship between social capital and infections is expected to be positive, with the later period, when the relationship is expect to be negative. It should be no surprise then, that we were finding null results!

Anyway, the Bartscher et al. paper uses data for seven European countries (Austria, Germany, Italy, the Netherlands, Sweden, Switzerland and the UK), and uses electoral turnout in the European elections (or local elections for Switzerland) as a measure of social capital. They also use a number of different measures (both social capital, and outcome measures) for Italy. They found that:

First, the number of Covid-19 cases is initially higher in high-social-capital areas. Second, as information on the virus spreads, high-social- capital areas start to show a slower increase in Covid-19 cases in all seven countries. Third, high-social-capital areas also exhibit a slower growth in excess deaths in Italy. Fourth, individual mobility is reduced more strongly before the lockdown in Italian high-social-capital areas. Fifth, we provide suggestive evidence that the role of social capital is reduced when national lockdowns are enforced, as the differences in mobility between high- and low-social-capital areas vanish after the national lockdown is enacted.

More specifically, the found that high-social capital regions have between 12 percent and 32 percent fewer cumulative coronavirus cases. When they look at Italy in more detail, they found that one standard deviation higher social capital (electoral turnout) is associated with 7 percent lower excess mortality, and a 15 percent reduction is mobility. The latter result demonstrates the likely mechanism through which social capital leads to fewer deaths - people in areas with more social capital adhere more closely to the lockdown rules that people in areas with less social capital.

Overall, Bartscher et al. conclude that:

...the consistent pattern obtained from independent analyses of seven countries as strong evidence in favor of the hypothesis that social capital plays an important role in slowing down the spread of the virus.

This is probably not the last word on this line or research. Bartscher et al.'s approach of simply comparing regions above and below the median level of social capital within the country is rather crude, even though the results are as expected. It would be interesting to see whether the results hold when social capital is treated as a continuous measure rather than simply categorising high/low social capital areas. It would also be interesting to see if similar results are obtained for counties in the U.S. No doubt we will see analysis of U.S. counties sometime in the future.

The effect of studying STEM at high school on employment and wages

Much of the literature on studying STEM (Science, Technology, Engineering, and Maths) at high school focuses on how well it prepares or motivates students to study STEM at university. There is very little consideration of what happens to student studying STEM who don't then go on to university. This 2016 article by Robert Bozick (RAND), Sinduja Srinivasan (Economic Commission for Latin America and the Caribbean), and Michael Gottfried (University of California - Santa Barbara), published in the journal Education Economics (sorry, I don't see an ungated version online) is different. They look explicitly at the employment outcomes of high school STEM studies for non-university-attending students.

Specifically, Bozick et al. use data from the Education Longitudinal Study of 2002 (ELS:2002), which includes a sample of 3473 students who did not go on to attend university-level study. For those students, they have detailed information on their employment outcomes (whereas there is no such data for the university-bound students). What they find is essentially nothing in terms of employment in STEM:

...taking advanced academic STEM courses or applied STEM courses in high school does not improve the likelihood that non-college bound youth will secure jobs in the STEM economy. In fact, there is evidence that some applied STEM courses may serve as a barrier: non-college bound youth who took IT courses in high school were less likely to find employment in the STEM economy than their peers who did not take IT courses in high school...

There is also no statistically significant effect on wages:

In terms of academic STEM courses, all of the effects for above average and advanced math and science courses are positive, but none of them are significantly different from zero. The largest effect among these courses is for advanced math, which includes Calculus and similar classes of that caliber. Relative to students who had taken average math, students who had taken advanced math have a 0.095 wage advantage in their first job and a 0.077 wage advantage in their current job, which translates to approximately $2200 and $2160 per year, respectively. Again, though, these effects are not significantly different from zero.

Looking at wages specifically in STEM jobs, they also find no effect. In other words, studying STEM at high school does not set these students up for jobs in STEM, or as Bozick et al. put it:

Our study indicates that the courses currently offered in America’s high schools are not improving the labor market prospects of non-college bound youth entering the STEM economy after finishing high school.

However, I think the authors may have been a little too negative in their interpretation. Sure, the results are statistically insignificant. That means that the results are not statistically distinguishable from zero. However, if you look at the wage premium in the quote above, the wage premium of maths is also not statistically distinguishable from an advantage of $4000 per year. That is potentially a sizeable effect, given that the average wage in the sample is around $18,000 per year.

The problem here is a lack of statistical power. Studying STEM might have an impact on employment and wages, but this sample is too small to precisely estimate the size of the effect. You might think that a sample of over 3400 is large, but it depends on context. In this case, it limits what the authors can conclude from their results. All that they can say with any reliability is that the wage premium is less than $4000 per year, which isn't saying much.

The other problem with this study is self-selection. Students were not (and cannot be) randomised into whether they went to university or not. This sample also didn't randomise students into who studied STEM. So, it is somewhat limited in terms of what it can say about the causal effect of studying STEM on employment and wages.

Overall, we're going to need a lot more research, probably including studies with some form of randomisation or quasi-randomisation, and larger sample sizes, before we conclude that studying STEM at high school is a waste of time.

Loneliness, income, and unemployment in the time of COVID-19

When your public policy hammer of choice is a universal basic income, every social problem looks like a nail. At least, that's what I thought when I heard this story on Radio New Zealand this morning:

People on low incomes were more likely to suffer high levels of loneliness during lockdown.

The report 'Alone Together', published by the Helen Clark Foundation and consultancy firm WSP reveals the Covid-19 lockdown exacerbated the risks of loneliness, especially for those who had no work.

The report recommends everyone has access to a guaranteed minimum income, high speed internet and mental health support.

You could be forgiven for wondering, if loneliness is the problem, then a first order solution is not a guaranteed minimum income, but guaranteed minimum friends. Yes, the government should start an automatic match-making service to ensure that every person has many high-quality friends and therefore won't be lonely. There is no doubt a missed opportunity there.

On a more serious note, the report itself is available here, and indeed it does show that people on low incomes are lonelier. It finds this using data from the 2018 General Social Survey. However, it also says that:

It is striking how closely loneliness was linked to employment status and household income. The group most likely overall to report feeling lonely in 2018 were people who were unemployed.

If unemployment is a bigger issue than income (and it is: 7.2 percent of unemployed people report being lonely most or all of the time, compared with 6.1 percent of those in the lowest (under $30,000 per year) income bracket). So, based on that alone it would make more sense to advocate for a jobs guarantee, rather than a guaranteed minimum income. However, the report misses that obvious solution and doesn't mention it at all.

There are broader problems with the report though. Essentially the report assumes causal relationships, when all it is showing is correlation. People with low incomes may be lonelier, but that doesn't mean that raising their income will reduce loneliness. Maybe their income is low, and they are lonely, because they are unemployed. Even with a higher income, they would still be unemployed and lonely. Understanding the causal relationships is important in order to identify the appropriate policy (whether that be a guaranteed minimum income, a jobs guarantee, or something else).

Surprisingly, given that the report is subtitled "The risks of loneliness in Aotearoa New Zealand following Covid-19 and how public policy can help", the report mostly uses data from the 2018 General Social Survey. It does have a section where they report some survey data collected by Kate Prickett and others at Victoria University. In that section, they show that the survey data demonstrates higher levels of loneliness for the high-loneliness groups - for example:

...20 percent of those with household incomes under $30,000 reported feeling lonely most or all of the time, compared with 6.1 percent in 2018. Unemployment remained a risk factor, with 19.2 percent of those who lost their job as a result of Covid-19 reporting feeling lonely most or all of the time during the lockdown.

However, they don't report the equivalent changes in loneliness for other groups. So, we have no way of knowing whether the higher lockdown loneliness for the unemployed is greater than or less than that for the employed.

Finally, focusing additional resources on mental health was relegated to the sixth (and last) of the recommendations. I thought that was interesting. That would seem to me to be the most obvious solution, especially based on the data in this report.

Anyway, this report tells us which groups are lonely, but doesn't really help us to understand why. And without knowing why, it is difficult to identify the correct policies. At least with more resources devoted to mental health, you can feel like there will be improvements not just in loneliness, but in mental health and wellbeing more generally.

More on comparative advantage and the gender gap in STEM

Back in 2018, I wrote a post on comparative advantage and the gender gap in STEM, based on two research papers, where I noted:

So, even though female students may be better than male students in STEM subjects at school, we may see fewer of them studying those subjects at university (let alone taking them as a career), because female students are also better in non-STEM subjects at school, and they are better by more in non-STEM than in STEM, compared with male students. Economists refer to this as the students following their comparative advantage. Female students have a comparative advantage in non-STEM, and male students have a comparative advantage in STEM subjects.

In this post, I want to build on that by summarising two other research papers. The first is this article by Thomas Breda (Paris School of Economics) and Clotilde Napp (Paris-Jourdan Sciences-Economiques), published in the journal Proceedings of the National Academy of Sciences in 2018 (open access). Breda and Napp used data from the 2012 wave of PISA, covering some 300,000 15-year-old students across 64 countries. They showed that, in the PISA data:

...boys outperform girls in math by about 10% of a SD... In contrast, girls outperform boys by about a third of a SD in reading. Together, these observations suggest that girls have a comparative advantage in reading, something that appears more strikingly when we look at the gender gap in the difference between math and reading (MR) ability....

Breda and Napp then construct a measure of students' intentions to pursue maths-intensive studies and careers. They found that:

The gender gap in intentions cannot be explained by differences in math ability across genders...

That makes a lot of sense, because simply being good at maths isn't enough to encourage students to follow through on math. That depends on their comparative advantage - that is, is the student good at maths but better at other disciplines? When looking at the relationship between intensions and the difference between maths and reading (MR), Breda and Napp found that:

...the gender gap in intentions to pursue math-intensive studies and careers disappears almost entirely when one controls for individual-level differences in ability between math and reading.

In other words, the intention to study maths is more associated with the difference between maths and reading ability than it is by maths ability alone. On top of that, the difference between maths and reading ability does a better job of explain intentions than self-perceived maths ability.

The second paper (still a working paper), by Sofoklis Goulas (Stanford University), Silvia Griselda (University of Melbourne), and Rigissa Megalokonomou (University of Queensland), takes the concept of comparative advantage one step further. Their concept of comparative advantage is not just the difference between a high school student's average performance in STEM and non-STEM subjects, compared with the differences for other students in their class. What Breda and Napp refer to as comparative advantage, Goulas et al. refer to as absolute advantage. I think I prefer the Goulas et al. conception, because it more clearly conforms to what we think of as comparative advantage in a trade context - comparing opportunity costs of production between countries is analogous to comparing relative performance in STEM/non-STEM between students. A within-student comparison (like Breda and Napp) is more like a within-country comparison of production costs, i.e. absolute advantage.

Anyway, Goulas et al. have data from over 70,000 Grade 10 Greek students from 123 high schools over the period from 2001 to 2009. One of the interesting aspects of their data is that these students are assigned to classes automatically based on their surname (alphabetically). This means that they are essentially randomly allocated to classroom peers, which is important in overcoming selection bias (as I noted in Sunday's post on peer effects). Their measure of comparative advantage was the in-class ranking for each student, in terms of the difference in their average grades between STEM (algebra, physics, and chemistry) and non-STEM (modern Greek, Greek literature, and ancient Greek). Class ranking is a measure of relative performance in the class, for a group of students that it would be natural for students to compare themselves to (and for whom they probably have good information about).

Using this measure, Goulas et al. found that:

Females perform, on average, significantly higher than males in almost every subject... females' over-performance are even higher in non-STEM (=1.594) compared to STEM (=0.349)... Combining these, females have a lower comparative advantage in STEM subjects compared to males (0.409 for females and 0.487 for males).

Making use of their measures of absolute advantage (difference in average grades) and comparative advantage (within-class rank), they then look at the effects on future applications to STEM programmes in Grade 11. Focusing on the comparative advantage results, they found that:

The estimated coefficient of comparative STEM advantage is not significant for males but it is significant and equal to 0.19 for females (=0.030+0.161). This means that females who are ranked at the top of their classroom distribution in grade 10, are roughly 19% more likely to enroll in a STEM track in grade 11 than females who are ranked at the bottom of their classroom distribution, ceteris paribus...

Our findings suggest that between 4 and 6 percentage points of the 34-percentage-point gender gap (or 12-18%) in initial STEM specialization in high school are attributable to the influence of the comparative STEM advantage.

Looking at longer-term outcomes, Goulas et al. also find that comparative advantage in STEM in Grade 10 leads to a higher probability of applying to a degree-level STEM programme in university. The difference between being at the top and being at the bottom of the classroom distribution leads to a 10 percent higher likelihood of applying to a STEM degree programme. These results all appear to hold when comparing only with classmates of the same gender, when comparing at the school level rather than the class level, when changing the definition of what counts as STEM or non-STEM, and in a number of other robustness checks.

So, why does comparative advantage have such a large effect for female students, but not male students? Goulas et al. pose two mechanisms. First, they suggest lower monetary returns for women in STEM-related fields, which reduces the returns to STEM-related study. However, this is hard to reconcile with the Breda and Napp paper, which notes that the gender wage gap in STEM-related occupations is lower than for non-STEM-related occupations. The second mechanism is different preferences for STEM occupations. STEM occupations tend to be more competitive, and there is a gender gap in competitiveness (see this 2014 post, for example). Societal and environmental influences (including parents), and a lack of role models (which has been suggested as an important factor in female students not studying economics) could also contribute to this.

Coming back to the Breda and Napp article, they have an interesting suggestion on how to close the gender gap in enrolments, given the high contribution of comparative advantage:

As the gender gap in reading performance is much larger than that in math performance, policymakers may want to focus primarily on the reduction of the former. Systematic tutoring for low reading achievers, who are predominantly males, would be a way, for example, to improve boys’ performance in reading.

Redirecting education resources towards boys in order to reduce the gender gap in STEM would no doubt strike many people as counter-intuitive. It also comes with ethical issues. If STEM-related occupations are higher paying, then redirecting (male) students so that they instead study non-STEM-related subjects doesn't necessarily strike me as morally unambiguous solution. Breda and Napp make some noises in that direction but avoid being explicit about the ethical problems, while Goulas et al. more-or-less ignore the policy prescription and associated ethical issues. However, sooner or later, if we are serious about addressing the gender gap, we will have to engage with the ethical implications.

[HT: Marginal Revolution for the Breda and Napp paper; The Conversation for the Goulas et al. paper]

Read more:

• STEM-readiness and the gender gap in STEM at university
• Comparative advantage and the gender gap in STEM