People's offsetting behaviour thwarts well-intentioned interventions in social media and smartphone use

People lead complicated lives. They have many competing goals, and have to trade off between those goals. Economists assume that they choose their actions with the overall aim of maximising their utility (satisfaction, or happiness). However, the many competing goals can sometimes thwart well-intentioned interventions. For example, when seatbelts were made compulsory, that made driving faster safer to do, and people responded by driving faster, and therefore less safely (for related examples, see here and here). Economists refer to that as offsetting behaviour.

Two recent examples of this arose in research papers I read this week. The first is this NBER Working Paper by Hunt Allcott (Stanford University) and a long list of co-authors, who investigated the impact of people temporarily deactivating Facebook or Instagram on their emotional state. Working with Meta (where some of the co-authors work), they:

...recruited 19,857 Facebook users and 15,585 Instagram users who spent at least 15 minutes per day on the respective platform. We randomly assigned 27 percent of participants to a treatment group that was offered payment for deactivating their accounts for the six weeks before the election. The remaining participants formed a control group that was paid to deactivate for just the first of those six weeks.

They then compare the difference in emotional state between before and after the deactivation for the treatment group (who deactivated for six weeks) and the control group (who deactivated for one week), and find that:

...users in the Facebook deactivation group reported a 0.060 standard deviation improvement in an index of happiness, anxiety, and depression, relative to control users...

...users in the Instagram deactivation group reported a 0.041 standard deviation improvement in the emotional state index relative to control.

Those effects are quite small in comparison to other interventions, and in comparison to changes in emotional state over time, and:

Under the approximation that emotional state index is normally distributed, the estimated effects of Facebook or Instagram deactivation would move the median user from the 50th percentile to the 52.4th or 51.6th percentile, respectively.

Why was the effect so small? Users who deactivated Facebook or Instagram spent more of their newly-freed-up time on other apps. Those who deactivate Facebook increased their use of Instagram, but also:

Facebook and Instagram deactivation both increased use of Twitter, Snapchat, TikTok, YouTube, web browsers, other social media apps, and other non-categorized apps by a few minutes per day.

It's little wonder that deactivating Facebook or Instagram had such small effects, given the offsetting behaviour of the users pivoting to using other apps, including other social media apps, instead. None of this is to say that the intervention made the users worse off, but it probably didn't make them better off overall either.

The second example is this NBER Working Paper by Billur Aksoy (Rensselaer Polytechnic Institute), Lester Lusher (University of Pittsburgh), and Scott Carrell (University of Texas at Austin), which looked at the effects of the app 'Pocket Points' at Texas A&M University. Specifically:

Pocket Points is marketed as a soft commitment device and provides incentives for students to stay off of their phones. In particular, Pocket Points rewards students with “points” for staying off their phones during class: Students open the app, lock their phone, and start accumulating points, all while the app verifies through GPS coordinates that the student is indeed in class. These points can then be used to get discounts at participating local and online businesses.

One thousand Texas A&M students were invited to participate in the experiment in 2017, and half were randomised to treatment, where they were instructed to download the Pocket Points App and create an account. Aksoy et al. then compare the treatment and control students. They also distinguish effects between those who used the app at least once, and those who used the app more than once a week (based on survey results). Importantly, first Aksoy et al. report that:

...treatment students were about 25 percentage points more likely to download the app... and over 31 percentage points more likely to use the app... than control students. Additionally, treatment students were 13 percentage points more likely to use the app more than once a week...

So, the treatment worked in encouraging students to use Pocket Points. But did it work? Aksoy et al. find some positive effects in the classroom, such as:

...Pocket Points usage is associated with a 0.42 standard deviation reduction in phone distraction rate in the classroom... we observe increases in student satisfaction with their academic performance for the semester: Students who used the app more than once a week experienced more than a one standard deviation increase in satisfaction...

That seems promising. However, when they look at student grades (from their official TAMU transcripts), Aksoy et al. find that:

...students who used the app more than once a week experienced a 0.50 unit increase in GPA. These estimates, however, are statistically insignificant...

So, even though the Pocket Points app reduced in-class distractions, it had no statistically significant effect on students' grades. That may be because there were also:

...significant decreases in time spent studying on campus... treated students spent approximately 18.2 hours/week studying, 12.0 of which were on campus, whereas control students spent 20.3 hours/week studying, 14.1 of which were on campus. Thus, it appears that the increased learning and attendance in the classroom came with a reduction in time spent studying.

It's little wonder that there was no effect on students' grades, given the offsetting behaviour of students spending less time studying, perhaps because they believed (perhaps rightly) that their in-class study time was more effective without phone distractions. None of this is to say that the app made the students worse off, but it probably didn't make them better off overall either.

When we implement an intervention that we hope will lead to better outcomes, such as improved emotional state due to less time spent on social media, or improved student performance due to more focused studying in class, we need to be prepared for the offsetting behaviour of the people affected by the intervention. Their lives are complicated, and they are trading off between competing goals. Just because we want to make one of their goals easier to achieve, that doesn't mean that they will focus extra energy on that goal. As we have seen from the two examples above, they may simply re-focus their energies elsewhere, leaving the outcome that we want to improve unchanged.

[HT: Marginal Revolution, last year]

Can fertility return to replacement levels?

Many countries, including almost all developed countries and many developing countries, are now experiencing below-replacement fertility, with fertility rates having declined substantially over the past decade or more. That means that each generation will be progressively smaller than the last, and almost inevitably that leads to a declining population (in the absence of offsetting migration flows). Can countries reverse the trend of declining fertility, and return to replacement levels? Two new articles suggest that might be difficult.

The first is this article by Michael Geruso and Dean Spears (both University of Texas at Austin), published in the Journal of Economic Perspectives (open access). They look explicitly at the question of whether persistently low fertility can be reversed, but first they do a great job of setting the scene:

Fertility is low or falling across the world: among high-, middle-, and low-income countries; among secular and religious populations; and in economies where the state is large and where it is small. Birth rates have been falling not only for decades, but for centuries. They have been falling for as long as there are good historical records to document them...

The TFR [total fertility rate] has fallen from a global average that was a little under five in 1950 to a global average that is a little over two in 2025...

The 115 richest countries in the world together have an average total fertility rate of 1.5... A birth rate of 1.5 would lead to a decline of 44 percent in generation size over two generations...

Geruso and Spears then look at the trends in some detail, focusing attention on completed cohort fertility (CCF), which captures the average number of lifetime births for women born in a particular place and year. That is a better measure than the TFR, because it is not affected by the timing of births - a woman having two children at ages 25 and 34 instead of aged 25 and 27 would not change the CCF, but would affect the TFR in the years in which they gave birth (increasing TFR when they were aged 34, but decreasing it when they were aged 27). In any case though, the trends in the two measures (CCF and TFR) are broadly similar, with both showing declining fertility over time across all of the countries that Geruso and Spears consider (with the exception of the US in the 1980s to 2000s, where there was a modest increase in fertility).

Geruso and Spears then use global data from the Human Fertility Database (HFD), and Indian data from the National Family Health Survey, and explore the contribution of childlessness to the overall decline in fertility. In both datasets, they find that the majority of the decline in fertility is due to a decline in the number of children among women who have at least one child, rather than an increase in childlessness. For countries in the HFD, childlessness accounts for 37 percent of the decline in fertility between the cohort of mothers born in 1956 and the cohort born in 1976, while in India, childlessness accounts for just 9 percent of the differences in fertility across districts.

Finally, Geruso and Spears turn to the prospects for a reversal of the fertility trend. On this, they start by noting that in the HFD:

...there have been 24 countries in which cohort fertility ever fell below 1.9. In none of these cases have subsequent cohorts from the same country ever had fertility as high as 2.1...

And aside from the post-WWII Baby Boom, there are no significant episodes of increasing fertility. And the Baby Boom was the result of a fairly unique set of circumstances that are (hopefully) unlikely to be repeated. Geruso and Spears then look at the microeconomic and programme evaluation literature, and note that:

...the clear-cut bottom line is that whatever impacts pro-natal policies and broader changes might have caused, none has caused low birth rates to reverse enduringly back to replacement levels.

Even a particularly strict programme in Romania that "banned abortion and made modern contraception effectively inaccessible" had only a short-term effect on the total fertility rate, and no effect on completed cohort fertility. So, this paper gives no reason to believe that declining fertility can be reversed. Geruso and Spears conclude that:

To put it bluntly, history offers no examples of societies recognizing very low birth rates as a social priority and then responding with effective changes that restore, and sustain, replacement-level fertility.

The second article is this one by Kimberly Babiarz (Stanford University), Paul Ma (University of Minnesota), Grant Miller (Stanford University), and Shige Song (City University of New York), forthcoming in the journal Review of Economics and Statistics (ungated earlier version here). They don't look explicitly at fertility decline, but they do look in detail at fertility in China, and in particular at the impact of the Wan Xi Shao (Later, Longer, Fewer) campaign, which predated the One Child Policy. That policy:

...aimed to limit fertility by promoting older age at marriage (“Later”), longer intervals between births (“Longer”), and fewer births per couple (“Fewer”).

The campaign was very successful, with the total fertility rate falling from 6 to about 2.75 over the course of the 1970s. The One Child Policy began in 1980, so by the time it was instituted, China's fertility had already fallen almost to replacement level. Babiarz et al. aren't the first to note this, but they extend the analysis further, exploiting differences in the timing of implementation of the policy across Chinese provinces to investigate how much of the decline in fertility was due to the policy, how it affected fertility decisions within Chinese families, and how many 'missing girls' are attributable to the policy implementation in combination with a societal preference for sons.

Now, as a policy the LLF aimed to:

...reduce crude annual birth rates in rural areas to 15 per 1,000 population via three primary mechanisms: (1) later marriage—delaying marriage to ages 23 and 25 (for rural women and men respectively); (2) longer birth intervals—increasing birth intervals to a minimum of four years; and (3) fewer lifetime births—limiting couples to 2–3 children in total...

The policy was implemented differently in urban areas, and about 87 percent of births in the sample occurred in rural areas, so Babiarz et al. focus attention on births in rural areas. Their main data source is the 1988 Two-per-Thousand National Survey of Fertility, which was a nationally representative survey that included around 400,000 women living in rural areas. I'm not going to go into detail on their methods (you should read the paper), but using an event study design, they find that the policy:

...reduced China’s total fertility rate by almost one birth per woman, accounting for about 30.6% of China’s overall fertility decline prior to 1980, or approximately 18.2 million averted births... Decomposing this TFR change into “quantum” and “tempo” effects, we show that, although the policy raised mothers’ median age at first birth by 5.2 months, the decline in TFR was largely the result of fewer lifetime births rather than changes in the timing of births.

They also find that:

...the LLF policy led directly to an increase in the use of both male-biased fertility-stopping rules and postnatal selection (via neglect or possible infanticide). Although postnatal selection was relatively rare, our results imply that the LLF policy resulted in about 180,000 additional missing girls, or approximately 19% of all missing girls during the 1970s.

So, the policy was quite successful in reducing Chinese fertility faster than it otherwise would have. However, this came with the unintended consequence of fewer female births relative to male births, and the phenomenon of 180,000 'missing girls' (who would have been born if the policy had not been in place).

How does this relate to the Geruso and Spears article, and what does it tell us about changing fertility? The Babiarz et al. article shows what it takes to move fertility quickly, but only in one direction (downwards). The LLF policy was dramatic, and successful, but it took a concerted government effort, supported by severe penalties, to achieve its aim. And this was in an environment where fertility was already declining. That's a very different challenge from trying to engineer a sustained increase in fertility back to replacement levels.

So, where does that leave us? If we have essentially no historical examples of societies successfully and sustainably reversing very low fertility, then the practical policy question shifts to planning for a future with progressively smaller age cohorts and older populations. That may mean reconsidering institutions that rely on a foundation of population growth (retirement and superannuation, and health and long-term care), as well as family-friendly policies and immigration settings. Policy proposals that treat women as a demographic instrument (like this one) aren’t a solution - they’re a warning sign that we’re asking policy to do something it may not be able to do.

[HT: Marginal Revolution, for the Babiarz et al. article]

Read more:

• The pandemic 'baby bust' in other countries
• Cohort effects and the downturn in US fertility since the Great Recession
• The new economics of fertility
• The economics of the falling total fertility rate in New Zealand
• The economics of fertility in high-income countries

Book review: Economists in the Cold War

In 2024, I reviewed Alan Bollard's book Economists at War, noting that it sat awkwardly in-between being a biography and an economic history. I just finished reading Bollard's 2023 book Economists in the Cold War, which follows a similar approach.

This book is basically a sequel to the earlier book, and adopts a similar format, focusing on seven economists: Harry Dexter White, Oskar Lange, John von Neumann, Ludwig Erhard, Joan Robinson, Saburo Okita, and Raul Prebisch. Each chapter is devoted to the life and works (and times) of one of these eminent economists. This book differs from the earlier volume by setting each of the seven economists against one of their contemporaries, respectively: John Maynard Keynes, Friedrich Hayek, Leonid Kantorovich, Jean Monnet, Paul Samuelson, Zhou En-lai, and Walt Rostow.

There is a bit of overlap with the earlier book, which features Keynes, Kantorovich, and von Neumann. However, there is plenty of new material in this book, and I especially appreciated the chapters on Lange, Erhard, Okita, and Prebisch, who I knew little about. I also really enjoyed the chapter on Joan Robinson, which helped me to solve the mystery (to me, at least) of why she never won the Nobel Prize in Economics. On that point, Bollard writes that:

Once more, Robinson had no compunction about forming strong public views from limited evidence on contentious issues... It has been suggested that the polemical content of these writings may have cost Joan Robinson the Nobel Prize in economics which her mainstream contributions might otherwise have earned... She never saw the need to separate her economic findings and her political opinions.

Bollard has a good way of bringing in anecdotes, even though he is adamant that he is not writing a biography of each economist. On Oskar Lange, Bollard tells us that:

...he was once invited to lunch by Al Capone the famous gangster, who he found to be self-educated and well-read with a good knowledge of politics and economics. They had a most interesting conversation, and at the end Capone offered: 'Professor, if you ever have a problem, anything at all, please do not hesitate to call me!'...

It is not just any economist who can call on such support! On the negative side, there is a fair amount of repetition, both between this book and the earlier volume, and even within the book itself. For instance, Bollard twice tells us that British government economist Alex Cairncross's brother John was a spy for the Soviets, within the span of 17 pages. This, and the several other similar instances, is a minor point in an otherwise excellent book, but was quite distracting for me.

Overall, I rate this book as highly as the earlier volume, but as I noted at the beginning it suffers from a similar flaw. In trying to avoid being biography and economic history, it ends up awkwardly caught in-between. Perhaps my views have softened somewhat on this in the last couple of years, or perhaps it was that this book covered a lot of new ground for me, but I thought that overall this was the better of the two books. Like Bollard's earlier book, I recommend this one for anyone interested in the key players and in the development of 20th Century economics.

Déjà vu: It's not a tax, it's a levy

In 2018, I mocked the government for their insistence that an increase in fuel tax was an excise, not a tax. Since I'm a firm believer in equal treatment of the government of the day when they display their economic illiteracy, I thought I needed to pick up on this story from earlier in the week:

Is it a tax? Is it a levy? An additional charge for a liquefied natural gas import terminal has turned into a communications nightmare for the Government...

Asked if this was a new tax on households, the prime minister was quick to intervene.

“This isn’t a tax, it’s a levy to fund a key piece of infrastructure,” he said.

So, it's a levy, and that is different from a tax? Not according to the OED, which defines a levy as:

Levy, n.

A duty, impost, tax.

Or, if you prefer the Merriam Webster Dictionary:

1 a : the imposition or collection of an assessment

Merriam Webster then defines an assessment as (emphasis is mine):

2 : the amount assessed : an amount that a person is officially required to pay especially as a tax

A levy is a tax. It has the same effects as a tax (for example, see this post for the details) - it raises the price that consumers pay, it lowers the effective price that sellers receive (after paying the levy to the government), it delivers revenue to the government, and it creates a deadweight loss (even if there may be offsetting benefits from how the revenue is spent). Whether the government uses that revenue for a liquefied natural gas import terminal, or for any other purpose, that doesn't change the fact that the levy is a tax.

I wrote back in that 2018 post that:

...this isn't the first (and it won't be the last) government to try their hardest not to refer to taxes as taxes.

It seems I was correct in that assessment.

Read more:

• It's not a tax, it's an excise