Sex, Drugs and Economics

Wednesday, 24 November 2021

Coronavirus lockdowns and educational inequality in German high schools

The rapid shift to online learning affected schools (and teachers, and students) at all levels. Some schools (and teachers) were better prepared than others, having resources that were more easily adapted to online teaching modes. Some students were better prepared than others, having access to devices and stable internet connections, in order to more fully participate in online learning. The unfortunate thing is that the students who had the lowest access to online learning are likely to be those who were already under-achieving. At least, that is the headline result from this new article by Elisabeth Grewenig (Leibniz Center for European Economic Research) and co-authors, published in the journal European Economic Review (ungated earlier version here).

Grewenig et al. use data collected from 1099 parents of school-aged (i.e. not university) students in Germany, collected as part of the ifo Education Survey. The survey collected data on students' time use, both during June 2020 (when lockdowns were in effect and there was basically no in-person teaching), and retrospectively for the period before the coronavirus pandemic. Time use was separated into several categories: (1) school-related activities (school attendance; or learning for school); (2) activities 'deemed conducive to child development' (reading or being read to; playing music and creative work; or physical exercise); and (3) 'activities deemed generally detrimental' (watching television; gaming; social media; or online media); and (4) relaxing.

Comparing students' time use during the lockdowns with their time use before the pandemic, Grewenig et al. find that:

...the school closures had a large negative impact on learning time, particularly for low-achieving students. Overall, students’ learning time more than halved from 7.4 h per day before the closures to 3.6 h during the closures. While learning time did not differ between low- and high-achieving students before the closures, high-achievers spent a significant 0.5 h per day more on school-related activities during the school closures than low-achievers. Most of the gap cannot be accounted for by observables such as socioeconomic background or family situation, suggesting that it is genuinely linked to the achievement dimension. Time spent on conducive activities increased only mildly from 2.9 h before to 3.2 h during the school closures. Instead, detrimental activities increased from 4.0 to 5.2 h. This increase is more pronounced among low-achievers (+1.7 h) than high-achievers (+1.0 h). Taken together, our results imply that the COVID-19 pandemic fostered educational inequality along the achievement dimension.

So, low-achieving students (defined as those in the bottom half of the grade distribution for this sample for German and mathematics combined) reduced their study time by more than high-achieving students. To the extent that study time leads to greater academic achievement, this can only lead to an increase in the disparity in academic performance between students at the top and those at the bottom. The really disheartening finding though was that:

...only 29% of students on average had online lessons for the whole class (e.g., by video call) more than once a week. Only 17% of students had individual contact with their teacher more than once a week... The main teaching mode during the school closures was to provide students with exercise sheets for independent processing (87%)... although only 37% received feedback on the completed exercises more than once a week...

The distance-teaching measures over-proportionally reached high-achieving students. Low-achievers were 13 percentage points less likely than high-achievers to be taught in online lessons and 10 percentage points less likely to have individual contact with their teachers... Low-achievers were also less likely to be provided with educational videos or software and to receive feedback on their completed tasks.

If you thought that teachers, having scarce online teaching time available, would prioritise the low-achieving students, perhaps because the high-achieving students are more self-motivated and/or have better learning support through their parents, you would be sorely mistaken. That strongly suggests to me a failure in the way that German teachers were supported in their rapid shift to online teaching activities, since it was entirely foreseeable that low-achieving students would be more greatly affected by the changes. Alternatively, supporting the low-achieving students in low socioeconomic families to have better access to online resources would no doubt have helped as well (although New Zealand's experience suggests that something more proactive than simply having support or resources available for those who ask for it is required).

One issue with this research is the use of retrospective recall about students' time use from the period before coronavirus. Grewenig et al. argue that the degree of social desirability bias is low, and that the results are similar to those from the German Socioeconomic Panel (GSEP), where students report their own time use. However, comparing those two sources, it is clear that the reported number of hours of school-related activities before coronavirus is much higher in this sample than in the GSEP. That needn't be a problem, unless the disparity differs between parents of high-achieving students and parents of low-achieving students. Presumably, all parents are roughly equally able to observe their children's time use during lockdown. That probably is less likely of the period before coronavirus. If parents of low-achieving students are more likely to overestimate the number of hours of school-related activities than parents of high-achieving students, then that would bias the results towards showing a bigger decline in school-related activities for low-achieving students. Since those students are low-achieving, it is entirely plausible that they usually spend less time on school-related activities than their parents think they do. Unfortunately, there is no way to easily identify whether that is a problem in this sample.

With that caveat in mind, this study does point to an issue that we should be concerned about, which is how the pandemic has affected student learning, and in particular whether it has increased educational inequality. Hopefully, this is not a general result that extends beyond the German schooling system, but unfortunately it seems likely that it is.

Tuesday, 23 November 2021

The lockdown 'baby boom' in proper context is anything but a boom, and possibly not even related to the lockdowns

I was interested to read this New Zealand Herald article this week:

We've all heard the jokes about how lockdown leads to a "baby boom" - but it turns out being stuck at home does lead to a rise in birth rates.

New information from Stats NZ for the year ending in September 2021 confirms an increase in live births compared to the same time last year.

The data reveals there were 59,382 live births registered in Aotearoa, an increase from 57,753 last year.

And the fertility rate has risen slightly as well, sitting at 1.66 births per woman, up from 1.63 at the same time in 2020...

Significantly, the number of live births as at September 2021 is the highest since 2015 - long before the pandemic changed all of our lives and lockdown was the last thing on anyone's mind.

This was a little bit of a surprise, as the recent births data has shortly historically low birth rates in New Zealand. So, a 'baby boom' would come as a surprise. However, when we actually look at the data, we find that calling it a 'boom' is a mischaracterisation. Here's the data on the raw number of births by quarter in New Zealand, from 1991 to 2021 [*]:

The number of births per quarter fluctuates between about 13,500 and 16,500. There was a bit of a downward trend from 1991 to 2003, then an uptick, before the downward trend resumed from about 2009. You can see the recent rise in births at the end of the series. Indeed, the number of births is at its highest level since 2015. You might even convince yourself that this constitutes a 'baby boom'. However, then you'd also need to believe there was a boom from 2007 to 2011, where the number of births per quarter was mostly at or above the number in Q3 of 2021.

There is a problem with looking at the raw number of births though, and that is that it doesn't account for the size of the population. Population has grown a lot over the 30-year timespan shown in the graph above. To account for that, I calculated the number of births per 100 women aged 15-49 years (you can call this the period fertility rate; I use the rate per 100 women, because that makes the numbers a bit easier to interpret). [**] Here's the result for New Zealand as a whole, since 1996:

The trends are somewhat similar to the previous graph, although the overall downward trend is much more obvious. The recent increase in the birth rate is still apparent, but by itself there isn't much to suggest a 'baby boom', maybe just a slight reversal of the recent trend. As you can see, the rate is lower than it was in 2017, and for basically the entire period prior to 2013. It remains to be seen whether the increase in birth rate in Q3 of 2021 is a brief spasm in the data (similar to Q2 of 2015), or the start of a change in fertility trends. My intuition is that it is the former.

So, was this increase in births caused by lockdown? It is easy to speculate that it is, given the timing. However, we can do a little better than that. Auckland has suffered from longer periods of lockdown than the rest of the country. So, if there is a baby boom driven by lockdowns, it's likely that it would be more apparent for Auckland than for the rest of the country. That isn't what we see though. Here's the birth rates for Auckland over the period since 1996:

That doesn't look much different to New Zealand as a whole (which isn't a surprise - more than a third of the New Zealand total is contributed by Auckland). Also, if we compare the change in the birth rate across regions between Q3 of 2019 and Q3 of 2021 (I chose 2019 for the comparison, since it is the most recent year with no effect of coronavirus or lockdowns), we see this:

The biggest increase in the birth rate between 2019 and 2021 has been in the Tasman and Gisborne regions. Auckland barely features at all. The birth rate in Q3 of 2021 is actually lower in Wellington and the West Coast than it was in Q3 of 2019. It's hard to make a case that lockdowns are a cause for the increase in births, unless you can somehow make the case that the lockdowns had a bigger effect in Tasman and Gisborne, and smaller in Wellington and the West Coast (or, you can show that there is some other socio-demographic or economic effects that are able to explain the cross-region differences that seem to more than offset any impact of lockdowns). Now, you could argue that the biggest difference in the effects of lockdown between Auckland and the rest of the country is actually happening now, and so the regional differences in the effect of lockdowns should become apparent in the births data for Q1 of 2022. I guess we will wait and see for that.

So, while there has certainly been an increase in births, it is hardly a 'baby boom' (unless you have an extraordinarily liberal interpretation of what constitutes a boom). And, it is hard to make a case that it was caused by lockdown (unless lockdown and other socio-demographic or economic changes affected birth rates in different regions in some idiosyncratic way, such that Auckland ended up having a very low increase in the birth rate).

*****

[*] The data come from Statistics New Zealand, Infoshare.

[**] The calculations here are based on the births data from Infoshare, plus subnational population estimates for each region, from NZ.Stat. As the data are only provided for 30 June of each year (and only since 1996), I take the 30 June population as the denominator for the rates for Q3 of each year, and use a linear interpolation between each Q3 value to obtain population estimates for the other quarters.

Monday, 22 November 2021

Low-performing students, online teaching, and self-selection

A regular feature of this blog is highlighting some of the research on online teaching, and blended or flipped classroom teaching, and their effects on student learning (see the lengthy list of links at the end of this post for more). One common theme is that there are differences in the effects of online teaching between more-engaged or high-performing students and less-engaged or low-performing students. This 2013 article that I noticed recently, by Fletcher Lu and Manon Lemonde (both University of Ontario Institute of Technology) and published in the journal Advances in Health Sciences Education (may be open access, but just in case there is an ungated version here), also illustrates this effect.

Lu and Lemonde compare 20 students who chose to take an online statistics course and 72 students that chose to take the same course face-to-face. In addition to looking at performance for all students, they split the sample into high-performing students (those with assignment averages above the median) and low-performing students (those with assignment averages below the median).

Overall, Lu and Lemonde find no statistically significant difference in performance between students in the online and face-to-face teaching modes. However (emphasis is theirs):

For those students categorized as higher performing, their test results replicated the results of the many past studies showing no significant difference in their test performance between online versus face-to-face teaching delivery. But the students categorized as lower performing demonstrated test results that were significantly poorer for those enrolled in the online delivery version compared against their lower performing counter-parts in the face-to-face delivery version.

Now, we shouldn't overstate the significance of this particular study. The number of students was small, so it was probably underpowered to identify positive effects on the high-performing students (as have been observed in other studies). However, the bigger problem is self-selection of students into the mode of teaching. My intuition is that lower-performing (or less motivated) students disproportionately select themselves into online teaching modes. They may do this because they think that the online course will be easier than the face-to-face course (sometimes it will be, but not always), or because they mislead themselves into thinking that the added flexibility of online learning will be better for them (which it probably won't be, based on past research).

Self-selection is not just a problem for identifying the 'true' impacts of online teaching. It has real practical implications for teachers, academic departments, and universities. If we offer 'flexible' modes of teaching, where students can select into online or face-to-face teaching, we run the real risk of segregating the least capable students, and those that are least motivated, into a study mode that does real harm to their learning. It is unfortunate that Lu and Lemonde don't really test for selection effects in their sample (they say that they test for differences by comparing assignment averages, and don't find statistically significant differences, but their small sample size might account for that, and it would be much better to test for a difference in some measure of motivation, or some measure of prior achievement).

At this point, I think we really do need more research into which students actually select online teaching options rather than face-to-face. I hypothesise that, as I noted above, there is a core of less-motivated students who select online teaching. I suspect that there may also be some high-performing students who prefer the flexibility that online learning allows them (and the research highlighted in this post seems to suggest that might be the case). That will help in forming policies for flexible learning options that better suit all students.

Long-run inequality in the US, and the tale of two Ginis

Back in August, I posted about inequality over the long run in New Zealand back to the 1930s, based on research by John Creedy, Norman Gemmell and Loc Nguyen. The trends were interesting in their data:

Inequality was relatively high (perhaps similar to inequality today) in the 1930s and up to the early 1950s, then fell from the 1950s to the early 1980s. Inequality then rapidly increased back to its prior levels during the reforms of the late 1980s and early 1990s, and then has been relatively flat ever since.

I've previously noted the difference in New Zealand's experience of inequality from that in other countries (and this is something I draw attention to in my ECONS102 class), especially the rapid rise in inequality in early 1990s, followed by a long period where inequality has barely changed (while inequality has increased in other OECD countries). So, I'm interested to see how the longer-run data for other countries compares as well. With that in mind, I recently read this 2015 working paper by Markus Schneider (University of Denver) and Daniele Tavani (Colorado State University), which presents the long-run trend for the U.S. Here's their Figure 2:

The dark solid line in the Gini coefficient overall. Notice that the trend is quite different from the New Zealand long-run trend in two ways. First, there is a fairly continuous decrease in inequality from the 1920s to the 1940s. In contrast, Creedy et al. showed the decrease in inequality in New Zealand didn't stop until the 1950s (although that might be explained by the data that they were using). Second, there is a continuous increase in inequality from the 1940s to 2012. In contrast, Creedy et al. showed the trend in inequality in New Zealand was flat from the 1950s to the 1980s, followed by a sudden increase in the early 1990s, and then a flat trend again since.

However, aside from the long-run trend, Schneider and Tavani decompose their measure of inequality (the Gini coefficient) into two components, representing: (1) inequality at the top of the income distribution (G1 in the figure above); and (2) inequality at the bottom of the income distribution (G0 in the figure above). Looking at those measures, they find that:

...inequality at the top of the income distribution was relatively stable from the end of WWII until 1981, but has been increasing ever since...

From the end of WWII until the late 70s, increasing inequality as measured by the Gini was driven by inequality at the bottom as the distance between low-and mid-level incomes grew.

So, the long-run increase in inequality in the U.S. is a 'tale of two Ginis'. The increase was driven by inequality at the bottom from the 1940s to the early 1980s, and then driven by increases in inequality at the top from the 1980s to 2012. It would be interesting to see a similar decomposition for New Zealand, and that might explain the lack of increase in inequality in New Zealand in recent times, as there has been little change in the share of income of the top one percent (unlike for many other countries). [*]