My take on that iPhone-fertility paper

If you've been reading the news over the last week, you may have seen talk about new research linking fertility decline in the US to the release of the iPhone. For example, the New Zealand Herald reported that:

Middlebury College economist Caitlin Myers and her student Ezekiel Hooper tested a hypothesis that smartphones - which emerged with the arrival of the first iPhone in 2007 - might have something to do with it.

Until 2011, iPhones were available from a single US cellular network, AT&T, so they compared US counties that had near-universal AT&T coverage with those that had little or none during those years.

And they found that access to the iPhone correlated with reductions in births by 4.5% to 8% at ages between 15 and 19, and by 3.2% to 6.6% at ages between 20 and 24.

There were also statistically significant but smaller declines among older women.

Other news sources picked up that the research attributed 33 to 52 percent of the decline in fertility to the iPhone's release (see here and here, for example). That result made me sceptical, and my concerns really echo those of Tyler Cowen here:

In 2008, 1.9% is the share of the mobile-subscribing population with an iPhone wireless subscription.  As a percent of all adults that is 1.6%.

In 2009, it is 4.3%.  3.6% of all adults.

In 2010, 6.8%.  5.5% of all adults...

So when the authors talk about diffusion explaining 33–52% of the decline in the general fertility rate among American women 15–44, I still do not get how that is supposed to operate.

If less than six percent of all adults have an iPhone by 2010, how could iPhones reduce fertility by between one-third and half? This requires very large spillovers from a small group of early adopters, and I am not convinced the paper has made those spillovers quantitatively plausible (we'll get to the authors' views on that later).

The research is reported in this NBER Working Paper by Caitlin Myers and Ezekiel Hooper (both Middlebury College). They use data on national wireless broadband coverage at the census block level to categorise US counties into those where less than 10 percent of the population have coverage by AT&T ('control' counties) and those where more than 90 percent of the population have coverage by AT&T ('treated' counties). Their sample includes 1399 'control' counties, and 914 'treated' counties (with 794 counties excluded from the sample). The reason that Myers and Hooper chose AT&T is because AT&T had an exclusive arrangement with Apple for almost the first four years after it was first launched in June 2007. The first Android phones didn't become available until October 2008, and didn't become widespread in the 'control' counties until a year later. So, there was a period where AT&T coverage is a reasonable proxy for the prevalence of iPhones.

Myers and Hooper then compare control counties with treated counties in terms of annual age-specific fertility rates (in five-year age groups). However, they recognise a key problem, which is that the treated and control counties differ in meaningful ways, the most obvious of which is that the treated counties are more urban than the control counties. This is a problem for their analysis because fertility rates have been declining more rapidly in urban areas than in rural areas, and therefore this would lead to overstatement of the measured effect of iPhone coverage on fertility. Specifically, the CDC reports that from 2007 to 2017, the total fertility rate fell by 12 percent in rural counties (many of which will be in the control sample), but by 18 percent in large metro counties (which are almost certainly in the treated sample).

Myers and Hooper try to deal with this problem by re-weighting their data in two ways. The first is by using an "entropy balanced Poisson event study", which effectively re-weights the control counties by giving more weight to those that are most similar to the treated counties in terms of their cross-sectional characteristics at the time of the iPhone launch. The second is by using a "synthetic difference-in-differences estimator", which creates a set of synthetic control counties by re-weighting the control counties so that the time series of fertility most closely matches each of the treated counties.

Using those methods, Myers and Hooper find the results that the news media has picked up. Specifically:

Both estimators imply large, statistically significant declines in births to young women. The post-gestation ATT ranges from −4.5 to −8.0% at ages 15–19 and −3.2 to −6.6% at ages 20–24 (the entropy-balanced Poisson at the lower-magnitude end, SDID at the higher), with smaller effects at older ages. Scaled to the U.S. county universe, these estimates imply the iPhone accounts for between 33 and 52% of the 2007–2011 decline in the general fertility rate. The pattern is similar across race, parity, marital status, and education, with the exception of Black women, for whom we estimate no effect.

The key results are summarised in Figure 3 from the paper (for the entropy balanced Poisson event study):

And in Figure 4 from the paper (for the synthetic difference-in-differences (SDID) estimator):

In both cases, the point estimates from the time before 2008 show no statistically significant difference between treated and control counties, while there is a negative (and increasing) difference between treated and control counties from 2008 onwards. However, notice that in Figure 3 (the first figure above), it seems clear visually that the downward trend starts before 2008, even if it is statistically insignificant. In Figure 4, there is no pre-trend, but remember that in the SDID analysis, the controls are reweighted to replicate the pre-treatment time series of fertility for the treated counties, so there should be no difference in the pre-treatment values by construction.

Myers and Hooper run various robustness checks that address some of the more obvious criticisms of their approach, including sensitivity to the choice of treatment and control cutoffs, using a continuous treatment variable, estimating the model in levels rather than logs, various placebo treatments, and truncating the sample to exclude any contamination from the release of Android phones. Among the placebo tests, they run analyses using Verizon's and Sprint’s pre-2011 coverage, and find no effects. So, their findings are not general to the difference between counties that attract mobile operators and those that don't. They also address the plausibility of the results, noting that:

The iPhone is not a treatment that operates at the individual level. Whether one’s own phone matters likely depends on whether one’s peers have phones; a phone in a friend group full of non-owners is a different intervention than a phone in a group where everyone has one. Spillovers run between phone-owning peers and their non-owning friends, and operate at the level of the group, not just the match: if smartphones reduce friend-group meetups and parties, then matches that would have formed under no-iPhone simply never do—the unformed match is itself the outcome.

That may be so, but the implied size of the spillovers is far larger than is plausible. If, as Cowen suggests, less than 15 percent of the population have iPhones, unless iPhone ownership and the spillovers from iPhone ownership were heavily concentrated among women of childbearing age, the overall effect simply can't be that large.

So, what has gone wrong. The overall approach that Myers and Hooper apply seems valid on the face of it, and re-weighting of controls to better match the treated sample is a common method of causal inference. The problem here is that the weighting is extreme. Myers and Hooper note that, in relation to the entropy balanced Poisson event study approach:

Balance comes at a cost: equalizing the marginal means requires putting high weight on a small number of treated-like controls. The Kish (1965) effective sample size of the balanced control pool is 77 out of 1,399 raw controls...

So, basically the analysis is heavily skewed towards a comparison between the treated counties and a small number of control counties, which are the control counties that are most like the treated counties (which also makes them the most unlike the other control counties). Those control counties are doing a lot of the work in this analysis.

There are also other possible differences between urban and rural counties that are approximately contemporaneous with the release of the iPhone. First among these is the 'Great Recession' and the housing slump around that time. Myers and Hooper do control for county-level changes in house prices, so that reduces concerns about contamination from that source. They also control for unemployment and poverty rates, which might pick up differential changes in labour markets. However, there was a change in contraceptive availability that directly affects young women's fertility, which is expanded access to the 'morning after pill' for 17-year-olds, although that occurred in 2009. Finally, after the 'Great Recession' there was a slowdown in Hispanic immigration, which might have affected urban and rural counties differently. Given that Hispanic immigrants tend to have relatively higher fertility than the US-born, so if the decline in Hispanic immigration was greater in control counties (and especially for the small number of heavily weighted control counties), then that might explain the effect. Myers and Hooper control for county Hispanic population share. However, it would be better to control for Hispanic population share among the age group that is being analysed, or to control for changes in Hispanic immigration.

This paper has certainly gotten people talking. Smartphones might be part of the story of why fertility has declined, but I don't think that we should uncritically take away from this study that the iPhone caused half of the decrease in US fertility between 2007 and 2011. More likely, it had a modest effect (if at all), and is confounded by a number of other changes that differentially impacted rural and urban US counties at around the same time.

[HT: Marginal Revolution]

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
• Can fertility return to replacement levels?
• You can make future population decline disappear just by changing the way you categorise people and fertility
• The modest impact of Australia's baby bonus on fertility timing

The modest impact of Australia's baby bonus on fertility timing

The challenge of returning low-fertility countries to a higher-fertility state has become especially clear in recent years. As noted in this post, aside from the post-WWII Baby Boom, there have been no significant episodes of increasing fertility. And that's not for want of trying. Some governments have become increasingly generous over time in their attempts to encourage higher fertility. Others have flailed around looking for a solution. One notable example is the Australian 'baby bonus', which initially paid each mother a lump sum of $3000 for each child born after 30 June 2004. The amount was increased to $4000 in July 2006, then to $5000 in July 2008, before being reduced to $3000, and eventually removed (and replaced with changes to the Family Tax Benefit) in March 2014.

How (un)successful have policies like Australia's baby bonus been? This new article by Sarah Sinclair (RMIT University) and co-authors, published in the journal Economic Modelling (open access), takes an unusual approach to answering that question. Rather than identifying policies and then testing directly for whether fertility changes happened at those points in time, Sinclair et al. first use time series models to identify structural breaks in the time series of fertility for 31 maternal-age-by-birth-order series. A structural break occurs when the time trend for the series changes meaningfully at a particular point in time. Using their approach, Sinclair et al. look for points in time where many of the time series have meaningful changes. What they find is not much of anything, with:

...the clearest and most consistently identified turning points for second births, with breaks in 2005 and 2015 detected across dates that plausibly align with major changes in family transfer settings. Other shifts, such as in selected age groups and some higher-order births, are less robust, and we detect no structural break in the aggregate fertility rate.

The timing of the 2005 and 2015 changes is consistent with the timing of the major changes to the baby bonus (or, at least, consistent with nine months after the major changes to the baby bonus). However, notice that they found effects only for second births, and not for births overall (or the aggregate fertility rate). That suggests, as they conclude, that the baby bonus affected the tempo of fertility, but not fertility overall. In other words, women brought forward the birth of a second child as a result of the baby bonus, but did not have more children overall.

Of course, identifying structural breaks is not the same as estimating a causal policy effect, but the timing of the breaks provides suggestive evidence about whether policy changes may have mattered. However, one aspect of this paper in particular is kind of unusual. When Sinclair et al. outline the 2005 and 2015 structural breaks in second births, their results are shown in Figure 5 in the paper:

The grey line tracks the second birth rate each month, while the red line shows the overall trend. Notice that in the top panel of the figure, there is a clear change in the trend in 2005. The pre-2005 trend is downwards, and then there is a big jump upwards in the second birth rate in 2005, before it returns to its previous downward trend. The oddity occurs in the lower panel of the figure, where Sinclair et al. show a somewhat less downward sloping trend in the second birth rate up to 2015, before there is a big jump up, and then a much steeper decline. The second figure ignores that there was already a structural break in 2005, where the trend jumped upwards. It seems to suggest that the end of the baby bonus induced a big increase in second birth rate. Now, that could be true, if couples anticipated the removal of the baby bonus, and tried to have a baby before the bonus was removed. However, Sinclair et al. don't really discuss this.

A more interesting interpretation occurs if you squint at the top panel of Figure 5, and imagine one structural break in 2005, and then a second at 2015. In between those two years, the trend in the second birth rate might be mildly upwards. Of course, we don't know this for sure, as Sinclair et al. didn't test for multiple breaks in their time series. But perhaps their results overstate the case against the fertility impacts of the baby bonus. To be clear, these would still be impacts on the tempo of fertility, not on total fertility, but perhaps the baby bonus did have an enduring effect on bringing forward second births. This would be something for future researchers to follow up on.

Nevertheless, this research adds to the evidence that relatively generous cash payments like Australia's baby bonus are unlikely, on their own, to reverse declining total fertility rates. It is becoming abundantly clear that low fertility will be an enduring feature of future population change.

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
• Can fertility return to replacement levels?
• You can make future population decline disappear just by changing the way you categorise people and fertility

Koi Tū's case for a New Zealand population strategy

At the end of last month, Koi Tū Centre for Informed Futures released a report arguing that New Zealand needs a population strategy. The report, by Georgia Lala, Paul Spoonley and Sir Peter Gluckman, received a lot of media attention (see here and here and here), and attracted a response on The Conversation from my colleagues at Te Ngira Institute for Population Research. My view is that the case for a population strategy is strong, not because it would allow New Zealand to control demographic change, but because it would force governments to plan more coherently for changes that are already underway.

Now, Lala et al.'s argument is that New Zealand is facing a demographic inflection point. I’m not convinced that 'inflection point' is quite the right term, at least in the mathematical sense, but the underlying argument is sound. It is clear that New Zealand is facing a number of intersecting challenges, including:

1. Slowing population growth - Lala et al. note that "annual growth dropped to 0.7% between June 2024 and June 2025 from 1.7% between June 2023 and June 2024", and that slowing growth means that New Zealand faces a 'double-edged sword' of a constrained tax base and increasing costs;
2. Declining fertility - This is a long-run trend that all countries are facing, and no country has developed a sustainable policy solution (see here for more on that point);
3. Growing reliance on immigration - Immigration has become increasingly important for growing the labour force and population (but isn't a solution for population ageing), but the problem is that immigration (and net international migration) is very volatile, and New Zealand doesn't stack up well against other countries in the competition for global talent;
4. An ageing population - Population ageing has implications in terms of a smaller tax base, and higher healthcare and superannuation costs; and
5. Growing ethnic and cultural diversity - Lala et al. especially draw attention to diversity in Auckland, but it is a reality that is playing out across the country (it is just that Auckland is ahead of other places in terms of diversity).

The issues are just as important, if not more important, at the regional and local levels, and Lala et al. draw attention to that as well. [*] In the section on immigration, I felt like there was too much focus on citizenship (of emigrants), which misses the point that people leaving New Zealand are also increasingly diverse, reflecting the diversity of the New Zealand population. And the large net outflow of Māori in recent years, which is obvious from Figure 10 in the report, probably needed further comment. That matters because Māori migration patterns are not just another component of aggregate population change. They have implications for whānau, iwi, regional labour markets, and the government's obligations under Te Tiriti o Waitangi.

Lala et al. argue for a population strategy, which they define as:

...both actions by a government to identify demographic trends and, subsequently, actions to address the effects of such change.

This seems like a sensible recommendation, and you might be tempted to wonder why we don't have this already, given that national and local government should be keenly concerned about, and adequately planning for, population change. However, once Lala et al. spell out what would be required for a coherent population strategy, it becomes clear that New Zealand falls well short (and, indeed, they can't point to a single country that does all of the things they want from a population strategy for New Zealand). According to Lala et al., the enabling environment for a population strategy requires three things (emphasis is theirs):

First, a population strategy would help elevate key demographic topics above day-to-day political contestation...

Second, a population strategy would help elevate policy planning beyond an election cycle...

Finally, a coherent population strategy could enable strategic decisions across multiple sectors of government including between central and local government.

The problem is clear. We don't really have any of those elements. Decision-making related to population is highly politicised (think about immigration policy, or support for families, for example), policies are subject to reversal with every change of government, and there is little coherence of planning between central and local government (consider the example of Auckland housing, where central and local government are in constant disagreement).

Finally, Lala et al. argue for an independent population commission to:

...provide a robust governance and implementation model to ensure the effective execution of a population strategy.

There is a lot to like in this proposal for a population strategy. New Zealand definitely needs to be more intentional in population planning. However, there are also some serious challenges, as my colleagues Tahu Kukutai, John Bryant, and Polly Atatoa-Carr note in their article in The Conversation. They draw attention to fertility trends, which have proven stubbornly resistant to policy-induced change. They also point to migration, which is not as easy to control as many politicians believe. This is because New Zealanders have the right to live and work in Australia and vice versa, with large flows between the two countries. There is also a huge diaspora of New Zealanders living overseas, who have the right to return at any time (as we saw during the COVID pandemic). Kukutai et al. also argue that a population strategy should have diversity as a foundational design principle, rather than an afterthought. Finally, they note the challenges of adopting a data-informed policy-making, when the quality of population data is in question with the changes to the census.

Kukutai et al. raise some valid points. However, those are challenges to a strategy that should be confronted during its design and development, not a reason to avoid having a strategy at all. I've argued in public forums in the past that a population strategy may fit into the 'too hard basket', in particular because New Zealand can't manage the international migration flows of New Zealand citizens, and so much population change in New Zealand is driven by the economic cycle in Australia. I now believe that also isn't a good reason for not having a population strategy for New Zealand, it is a good reason for us to have a strategy so that these challenges can be met head-on.

The future may be uncertain, but the future demographic challenges that New Zealand will face are already visible. A population strategy would help New Zealand to grapple with those challenges in a more coherent way. At a minimum, such a strategy would need to connect migration settings, regional planning, infrastructure, housing, health workforce planning, Māori and Pacific population futures, and the future of population data. Good on Koi Tū for making this case, and hopefully the government is listening.

*****

[*] And Paul Spoonley (one of the report's authors) and I will be working together on some further research looking in greater detail at regional population change, in the near future.

You can make future population decline disappear just by changing the way you categorise people and fertility

Fertility has been on a long-term declining trajectory worldwide and, apart from the occasional blip, in every country. There seems to be no prospect of a reversal of this trend, and no prospect of fertility returning to the replacement level of approximately 2.1 births per woman. So, when you see a research paper claiming that "high-fertility, high-retention groups persist, gain share, and lead the total population to grow", you should sit up and take notice. That is, at least, until you've carefully thought about the paper in question.

That's what happened to me with this 2025 NBER Working Paper by Sebastian Galiani (University of Maryland, College Park) and Raul Sosa (Universidad de San Andres). They create and calibrate models of fertility based on two different subgroupings (by race, and by religion), and taking account of cultural transmission of fertility rates from mothers to daughters. They then use their calibrated models to simulate population change going forward for ten generations. What they find when the population is categorised by race is a decreasing population, as shown in Figure 1 Panel A from the paper:

And when Galiani and Sosa categorise the population by religion, they instead find an increasing population, as shown in Figure 2 Panel A from the paper:

Now, this struck me as really odd. We’re talking about the same country and the same underlying population. If you split that population into subgroups and take a weighted average of what happens in each subgroup, you should get back the outcome for the population as a whole. If you are measuring the same underlying thing consistently, changing the subgroups (race in one analysis, and religion in another) shouldn’t magically create or destroy population growth in the model. At most, it should change which groups are growing faster and therefore how the composition by group changes over time, with high-fertility groups making up a larger share of the population and lower-fertility groups making up a smaller share. But the headline result here is much stronger than that, with the direction of population growth in aggregate changing direction entirely depending on the groupings that are employed. Galiani and Sosa use those results to conclude that:

...whenever at least one group remains above replacement on the female line and transmits identity effectively, its share rises and turns the aggregate path upward.

The first part of that conclusion makes sense, but the second part stretches credibility. It made me wonder whether the results were being driven by unusual features of the model, or by different modelling choices in the two analyses. 

So, I dug into the paper, which is not an easy task as it is quite theoretical. And there are consequential differences between the two analyses (by race and by religion) that drive the difference in results. First, they use different measures of fertility, with the analysis by race based on the total fertility rate (TFR), while the analysis by religion is based on completed fertility (see this post for a brief discussion on the difference between those two measures). There is a consequential difference between the two measures. By definition, completed fertility can only be observed for women who have finished their childbearing years, so it covers a period over the last twenty or more years. In contrast, the total fertility rate that Galiani and Sosa use was measured in 2023, after a long period of fertility decline. By construction then, the analysis using completed fertility (the analysis by religion) will be assuming higher fertility than the analysis using the total fertility rate (the analysis by race). This is highlighted by Table 1 in the paper, which shows that nearly every racial group has a total fertility rate that is below replacement (Hispanic is highest among the large groups at a TFR of 1.946, while Native Hawaiian and Pacific Islanders have a TFR of 2.218), whereas there are several religious groups with completed fertility rates above replacement (including Mormons at 3.4, and Muslims at 2.4). 

Second, their calibration implies much bigger gaps across religious groups than across racial groups. Specifically, they assume greater dispersion in fertility and retention by religion than by race. That means that the forces driving fertility change within population groups are much stronger in the analysis by religion than the analysis by race. So, essentially this doubles down on the effect of higher fertility that arises from the different data sources.

Overall, I don't find the comparison across the two models to be credible. They are employing different measures, taken from different points in time, and applying different modelling assumptions. In contrast, the results within each model showing that the relative group proportions change over time to favour groups that have higher fertility are plausible and are worth taking account of. For instance, Galiani and Sosa conclude that:

Although the objective is not to forecast outcomes for particular groups, our world simulations imply not only a more religious composition but also that, within the horizon we study, Muslims become the largest tradition by share.

That seems like a sensible conclusion to draw based on the evidence, especially as they explicitly note that they aren't trying to forecast the population. Nevertheless, they do forecast the population, and their results are not entirely consistent with what is expected to happen. World population is set to start declining later this century in large part because of declining overall fertility, and their results based on religion suggest that this is suddenly going to reverse course, and remain upward over a time horizon of ten generations. In reality, the long-run trend in fertility is difficult to change in the real world, and applying some complicated economic modelling in a way that appears to overturn the on-the-ground reality is not going to contribute to a change.

[HT: Marginal Revolution]

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
• Can fertility return to replacement levels?

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

The economics of fertility in high-income countries

Earlier this year, Melissa Kearney and Phillip Levine released an NBER Working Paper on the economics of fertility in high-income countries. In part, this paper is a follow-up on their 2022 article on cohort effects and fertility (which I discussed here), as well as building on this theoretical and empirical review (ungated here) by Doepke et al. (which I discussed here).

Kearney and Levine first review the trends and patterns in fertility in high-income countries, focused in particular on cohort-based measures. This exercise re-establishes the by now well-known trend of declining fertility, across the six example countries that they selected (Canada, Japan, Netherlands, Norway, Portugal, and the US).

Kearney and Levine then turn their attention to why fertility has declined, as well as why various policies and incentives have mostly failed to arrest the declining fertility trends. Taking an economic perspective that builds from Gary Becker's work on the economics of the family, but broadens its consideration (as shown by Doepke et al.), Kearney and Levine state that:

...the evidence points us to the view that the recent decline in fertility is likely less about changes in current constraints and more about cumulative cultural and economic forces that influence fertility decisions over time. Generally, economists are loathe to rely on changes in preferences to explain behavior because that can explain virtually anything. But there are reasons to believe that the lifestyle, broadly defined, that is consistent with having a child or multiple children is becoming less desirable for many adults.

Kearney and Levine point out several times (as in the quote above) how much economists dislike resorting to changes in preferences as an explanation, because changes in preferences can be used to explain essentially anything (which renders models basically worthless). However, they acknowledge that in this context, and based on the evidence from many studies, that it is likely that "shifting priorities" (a convenient alternative name for changing preferences) are at play. These "shifting priorities":

...refer broadly to changes in individual values, which potentially reflect evolving opportunities and constraints, changing norms and expectations about work, parenting, and gender roles, and social and cultural factors.

However, Kearney and Levine still want to avoid letting changes in preferences take over. That leads them to note that:

...changes in preferences may not be generated randomly and it is important to consider the forces that might have led to such changes. In our review of empirical evidence below, we highlight a number of potential social and cultural factors that might have altered preferences for and attitudes toward childbearing in recent decades, including peer effects, media and social media influences, the role of religion and religious messaging, and changing norms around parenting and gender roles in the home and society.

For me, the key contributions of the paper are not the review sections, but the theoretical and empirical implications. For example, in terms of theory, Kearney and Levine suggest that economic modelling of family decisions needs to change. Specifically:

We propose that it is now more appropriate to consider and model labor force participation as the default option, and fertility as the discretionary activity. This reflects a major shift in societal norms and practices over the past several decades. Women in earlier cohorts were more likely to have children and less likely to work. Back then, it is reasonable to consider having children as a widespread priority for women, perhaps reflecting societal norms and expectations, and sustained participation in the paid labor force as the more “optional” choice.

That presumptive ranking quite possibly has reversed. If market work is now the norm, the labor market norms and practices, including the expectations of “greedy jobs” as described by Goldin (2014), may alter fertility behavior. The tradeoff between market work and childbearing is now about the tension between a lifetime career and the way motherhood interrupts or alters that lifetime career progression, rather than about whether women work at all after they are married or have had their first child.

In terms of empirical implications, Kearney and Levine note that economists could learn a lot from demographers, in particular in relation to recognising cohort effects. They also note that:

...a challenge for economic research going forward is that the empirical methods we often rely on for causal identification are not particularly well-suited for studying changes across cohorts, nor the impact of widespread social and cultural changes... The statistical demands on the data for causal identification often lead to a focus on the immediate impact of period-specific factors. But as noted throughout this paper, the key questions that remain to be answered in this area are about cohort-level changes and the role of less immediate and discrete changes.

In addition, a typical approach to identifying period-specific effects might generate misleading or limited policy lessons. Consider an intervention that relaxes some constraints on having a child at a point-in-time. Younger women—say, 18-year-olds—may incorporate that change into their long-term decision making, but they may not respond immediately. Meanwhile, women in their early 30s may be less responsive, having already made many related life choices (regarding careers, relationships, lifestyle, etc.). In such cases, we might observe little to no immediate effect, even if the policy ultimately influences lifetime fertility...

A policy change may lead women to move up the timing of a birth to respond to some incentive, but to have the same number of children over their childbearing years. Our methods may conclude that this policy “worked,” even though completed fertility was unaffected. 

It is important for economists to recognise where the current widely used empirical methods are likely to lead to incorrect conclusions being drawn, and Kearney and Levine have provided some important cautions here. Fertility decline is topical, and many economists will be working on research questions related to this, especially as policy initiatives are rolled out by governments trying to return to above-replacement fertility. This review by Kearney and Levine is both timely and very helpful.

[HT: Marginal Revolution]

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 the falling total fertility rate in New Zealand

Earlier this week, I was interviewed by Paul Brennan on Reality Check Radio, on New Zealand's declining birth rate. You can listen to the interview here. We didn't have time to go through all of the questions I was given beforehand, so I thought I would add some points here, along with some links to some of the underlying data and research.

First, we need to understand what the numbers mean. The age-specific fertility rate is the number of births per women of each year of age (often it's reported in five-year age groups). The completed fertility rate is the number of births per woman over their entire childbearing years (typically assumed to finish at age 50, since so few women older than 50 give birth). Ideally, we want to know the completed fertility rate for each cohort, but we have to wait decades to find that out. So instead, national statistical agencies like StatsNZ measure the total fertility rate, which is the number of babies a woman would be expected to have, on average, if they experienced each of the age-specific fertility rates in that year.

To maintain a stable population (ignoring the impact of migration), a population needs to maintain a completed fertility rate of 2.1. This is more than two (which is theoretically all you would need to replace each couple), because not all women live to childbearing ages. So, each woman that lives to childbearing age needs to have more than two children to maintain a stable population. Now, it is worth noting that the total fertility rate is not the same as the completed fertility rate, because age-specific fertility rates change over time. In fact, when birth rates are falling, the total fertility rate probably over-estimates the completed fertility rate, but that's a story for another post.

Here's the total fertility rate for New Zealand since 1921 (source here):

The baby boom is easy to see, with the total fertility rate peaking at 4.31 in 1961. It then declined to approximately replacement level by the late 1970s, and until about 2012 the total fertility rate remained at or around replacement level. In fact, since 1978 the total fertility rate has only been above the replacement level in 1989-90 and 2007-2010. After 2012, the total fertility rate has been falling, down to 1.56 for the year ended December 2023. It is this recent decline that has many people freaking out.

What has contributed to the decline? In my view, there are two factors. First, children are really expensive. As noted here, JUNO estimated in 2021 that the cost to raise a child to age 18 in New Zealand was $265,680. That's pretty expensive, especially when you consider that the cost of housing has grown a lot in the last decade or so. According to Infometrics, the ratio of house price to income has grown from 4.9 in 2010 to 7.0 in 2024 (the period when the total fertility rate has been declining). Before 2010, the ratio was relatively stable (back to 2005, based on that dataset). As housing costs go up, that squeezes family's ability to afford to raise children.

Second, there are two long term social changes at play (and I only mentioned one of them in the interview). Women have been delaying fertility. As shown here, the median age of mothers giving birth to their first child was 27.4 years in 1998, but had increased to 29 years by 2018. Women delay fertility for a number of reasons, but two contributors are a longer period spent in education (more women going on to and completing tertiary study), and a greater focus on career development before having a family. One consequence of women delaying the start of fertility is that it leaves fewer years of childbearing age to have children (and so women have fewer children), and greater likelihood of fertility problems (and so more women remain childless).

The second social change is higher labour force participation of women. Even though the gender wage gap remains persistent, women do earn more than in earlier decades, which means that the opportunity cost of time spent out of the workforce has increased. This increases the 'implicit cost' of having children.

Of course, New Zealand is not alone in facing declining total fertility rate. It's been a trend across many (but not all) OECD countries (and most other countries as well). Here's the trends since 2012 (New Zealand is the red line, and the OECD average is the bold black line):

Can countries turn the trend in declining fertility around? The extreme example here is Hungary, which has offered some very large incentives to increase fertility, including a lifetime exemption from paying taxes for women with four or more children. Estimates vary, but Hungary may spend as much as 6 percent of GDP on families. How much extra fertility has that spending 'bought'? Hungary's total fertility rate increased from 1.25 in 2010 to 1.59 in 2021, a 27 percent increase (although some have claimed that the increase in total fertility rate is just an artefact of the data).

Closer to home, Australia introduced a baby bonus in 2004, worth AU$2500 per baby (now AU$5000 per baby). The baby bonus has been estimated to have increased the Australian total fertility rate by about 7 percent, which is hardly a huge change. In fact, Australia's total fertility rate was indistinguishable from New Zealand's in recent years, and was just 1.63 in 2022.

Taken together, it seems unlikely that countries can have a large enough impact on the total fertility rate to fight the tide that is driven by costs and long-term social changes. At least, it can't be done at the current levels of spending (and now South Korea is talking about introducing an incredibly generous baby bonus worth US$70,000).

The main demographic consequence of a falling total fertility rate is an ageing population. The median age will increase over time, and the proportion of the population in older age groups will grow. The main economic consequences relate to a need to recalibrate the infrastructure and social services that the population will need in the future. We may need fewer early childcare centres and schools, more elder daycare and rest homes, and greater healthcare capacity for people who are living longer (albeit also possibly healthier for longer). We will also need more age-friendly policies. Whether there will be a negative fiscal impact is less certain.

A declining total fertility rate is not something for us to fear. However, it is something that we need to take account of.

Legalised marijuana sales reduce the birth rate

The legalisation of marijuana in the US, where states have made medical marijuana sales legal, and then retail marijuana sales legal, and all at different times, has provided a wealth of possibilities for studying the effects of marijuana legalisation. From these studies, we have learned that medical marijuana sales may decrease harm from opiates, decrease violent crime, while legalised retail marijuana may increase house values (see also here), and lower crime rates, but may displace drug dealers into selling harder drugs. There is also evidence (from Europe) that legal access to marijuana reduces academic performance by students.

So, I was interested to read this recent working paper by Sarah Papich (University of California, Santa Barbara), which focuses on the effect of legalisation of marijuana sales on a completely different outcome - birth rates. The effect of marijuana use on the birth rate is theoretically ambiguous, because:

The medical literature suggests that marijuana use has two competing effects on fertility... First, marijuana use could lower the likelihood of pregnancy through effects on both men’s and women’s reproductive systems. Marijuana use is associated with lower sperm counts (Gundersen et al. 2015) and delayed ovulation (Bari et al. 2011), both of which make conception less likely. Second, marijuana use could lead to sexual behaviors that raise the likelihood of pregnancy. Using marijuana heightens the hedonic effect of sexual activity and diminishes the ability to think about long-term consequences of failing to use contraception. Marijuana use is associated with an increase in the amount of sexual activity (Sun and Eisenberg 2017) and a decrease in the likelihood of using contraception (Guo et al. 2002).

So, an increase in marijuana use could increase the birth rate, because people have more sex, and riskier sex, or it could decrease the birth rate, because marijuana users are less fertile. Papich uses a different-in-differences research design, which compares the difference in birth rates between states before marijuana is legalised, with the difference in birth rates between states after marijuana is legalised. Her key data come from the US National Vital Statistics System. Papich also distinguishes between the effects of legalising medical marijuana sales and legalising retail marijuana sales, while controlling for:

...shares of the total population by race, ethnicity, age, and education; unemployment rate; median household income; state cigarette tax; state beer tax; an indicator for whether the state has expanded Medicaid; indicators for abortion restrictions in the form of ambulatory surgical center laws, admitting privilege laws, and transfer agreement laws; an indicator for whether same-sex marriage is legal; the Medicaid eligibility threshold for pregnant women as a percentage of the federal poverty level; a WIC EBT indicator; and an indicator for whether marijuana has been decriminalised.

Papich finds that:

...days of marijuana use per month increase by 41% in response to RMLs and 23% in response to MMLs.

Ok, so people use more marijuana in response to medical marijuana laws (MMLs), and even more in response to recreational marijuana laws (RMLs). But what about birth rates? Papich then finds that:

...RMLs lead to a 2.78% decline in the average birth rate. This result provides evidence that marijuana’s physical effects, which suppress the likelihood of pregnancy conditional on sexual activity, have the dominant effect on fertility. Age heterogeneity analysis shows the largest decrease in the birth rate occurs among women 30-34, closely followed by women 35-39 and then by women 40-44. The birth rate in all three of these age groups declines by over 6%. This heterogeneity analysis suggests that women are having fewer total children in response to RMLs rather than delaying births...

I find that MMLs lead to a statistically insignificant decrease in birth rates.

Papich also looks at the effects on sexual activity, but those results are not as consistent, and not as convincing. However, she does provide some alternative evidence that sexual activity increases, or at least risky sexual activity increases:

RMLs are estimated to increase a state’s male gonorrhea cases by 6.1 cases per 100,000 population, a 5% increase from the mean. The effect of MMLs is statistically insignificant, with a positive point estimate.

The combination of those results suggests that legalising marijuana sales reduces birth rates, and the mechanism is likely to be through reduced fertility. In her conclusion, Papich notes some open questions that remain:

Data on contraceptive use before and after RMLs would provide insight into another mechanism through which marijuana legalization could affect fertility. Additional mechanisms, such as changes in the seriousness of romantic relationships when marijuana use increases and the effect of fewer people being imprisoned for marijuana possession, are promising areas for future research.

To those suggestions, I would add assessing whether the decrease in birth rates is primarily a result of reduced male fertility (since it appears that male marijuana use increased more than female marijuana use when recreational marijuana was legalised) or reduced female fertility. However, the headline result still holds - legalising marijuana sales reduces the birth rate.

[HT: Marginal Revolution, last year]

The new economics of fertility

When I was doing my Honours degree in economics, I encountered the economics of fertility for the first time. The standard model for fertility came from Nobel Prize winner Gary Becker, and suggested that fertility decisions were based on a trade-off between the number of children (quantity) and investment in those children's education (quality). The literature has since moved on, and this recent NBER Working Paper (forthcoming in the Handbook of Family Economics; ungated version here), by Matthias Doepke (Northwestern University), Anne Hannusch (University of Mannheim), Fabian Kindermann (University of Regensburg), and Michèle Tertilt (University of Mannheim)reviews the current state of the field. A good summary of the (long and detailed) paper is in the introduction:

We start by reviewing the regularities that inspired the first generation of economic models of fertility. These include a negative relationship between income and fertility; a link between the demographic transition and economic development; and, at a later stage of development, a negative relationship between women’s labor force participation and fertility. We argue that economic models based on two main ideas, relating to the quantity-quality tradeoff and the opportunity cost of mother’s time, were able to account for these regularities.

Based on empirical research of the past two decades, we then show that these regularities no longer characterize today’s data. The income-fertility relationship is now largely flat within many countries and increasing in the cross-section of high-income countries. Recent work on the quantity-quality tradeoff argues that it is no longer detectable in high-income countries. Meanwhile, the relationship between women’s labor force participation and fertility across countries has reversed. Even within countries, the relationship between women’s education and their fertility is no longer always decreasing...

The new facts about fertility behavior in high-income countries do not mean that the ideas of a quantity-quality tradeoff or of a central role of the opportunity cost of mothers’ time were wrong. The tradeoffs emphasized by these models still exist and continue to be important in explaining fertility behavior in many places, including lower-income countries. What has changed, however, is that these tradeoffs no longer drive the major variation in the data for high-income countries...

...in high-income countries, child labor has disappeared and education for most children continues past childhood into the adult years. These changes imply that the tradeoff inherent in quantity-quality models between sending children to school versus having more resources to raise a larger family has lost salience. Similarly, models based on women’s opportunity cost of time posit that raising more children requires mothers to spend less time working in the market. While this tradeoff still exists today, it has weakened as alternative forms of childcare have become more prominent. When childcare is provided by someone other than the mother—whether a hired nanny, a government-run kindergarten, or the child’s father—the cost of children is no longer linked as directly to the mother’s opportunity cost of time...

...the compatibility of family and career has become a key determinant of fertility in high-income economies. Where the two are easy to combine, many women have both a career and multiple children, resulting in high fertility and high female labor force participation. When career and family goals are in conflict, fewer women work and fewer babies are born. We point out four factors that help mothers combine a career with a larger family: the availability of public child care and other supportive family policies; greater contributions from fathers in providing childcare; social norms in favor of working mothers; and flexible labor markets. 

Doepke et al. also suggest some promising areas for future research on the economics of fertility, including research on parental time use and the intensity of parenting (probably using time use diary data or similar), extending existing models to account for the extended family and heterogeneity in family types (including same-sex couples), and exploring the macroeconomic consequences of sustained below-replacement fertility.

As well as being a thorough review of the literature, the paper also has some insights that are interesting in their own right, such as the unintended consequences of fertility treatments (or offsetting behaviour), where Doepke et al. note that:

...the very availability of IVF treatments causes women to delay their entire fertility planning further into later periods characterized by lower IVF success, thereby rendering the technology less effective.

Sadly, the economics of fertility (and population economics more generally) continues not to get the attention it deserves. At least, the state of the art is now summarised in one place.

[HT: Marginal Revolution, last year]

Cohort effects and the downturn in US fertility since the Great Recession

There is an excellent new article in the Journal of Economic Perspectives (open access, with a less technical summary here) by Melissa Kearney (University of Maryland), Phillip Levine (Wellesley College), and Luke Pardue (University of Maryland) on fertility rates in the U.S. In particular, the article looks to explain this puzzle (from their Figure 1):

The figure tracks the period fertility rate - the number of births per 1000 women of childbearing age (15-44 years) in each year. Notice that the trend is reasonably flat from 1980 to 2007, and then the trend turns sharply downwards after that. Recessions are known to generate short-run decreases in fertility, and you can see the recessions in the early 1980s and 1990-91 quite readily. But after those recessions, the birth rate climbs back up to the trend. Not so with the Great Recession, with birth rates continuing downwards for more than a decade, despite the end of the recessionary period.

Kearney et al. do an excellent job of unpacking the available evidence on how U.S. birth rates have changed. They start by looking at different demographic groups, and find a dramatic decline in births to teenage mothers. However, that change predated the Great Recession, with teen births trending downwards since the early 1990s. At other ages, since 2007 there has been a decline in births to mothers in their 20s, and a slowing of the previous increase in the number of births to older mothers. Decomposing the change in births over time by demographic group (age, race, and education level), Kearney et al. find that:

...changing birth rates within demographic groups is responsible for the declining birth rate since 2007, not changing population shares. From 2007 to 2019, the birth rate declined by 10.8 births per 1,000 women 15 to 44 (from 69.1 to 58.3)... Across all groups, had birth rates been constant and only population shares shifted between 2007 and 2019, the birth rate would, in fact, have risen by 2.6 births per thousand. On the other hand, if population shares were held constant and only within-group birth rates moved over that period (the change captured by the first term), the overall birth rate would have fallen by 12.8 births per 1,000 women...

The three teen categories by race/ethnicity explain 37 percent of the overall decline. Hispanic teens contributed the largest share, explaining 14 percent of the overall decline; their birth rate fell dramatically, from 82.2 to 24.7 over the period.

Other demographic groups with smaller declines in their birth rate also contributed extensively to the overall decline because of their relatively large population shares. For instance, the third-largest contributing group is White women between the ages of 25 and 29 with college degrees; their birth rate fell from 101.1 to 65.1, accounting for 11.9 percent of the overall decline.

So, again, the biggest contributors to the decline in births has been a decline in births to younger women, in their teens and 20s. So, what has caused that change? Kearney et al. look at a variety of policy and economic variables, and find that:

...when we sum the estimated coefficients on our ten economic/policy variables with their average change between 2007 and 2018, their combined effect is 6.2 percent of the total decline in the birth rate from 69.1 to 58.3 births per 1,000 women age 15 to 34 between 2007 and 2018.

What does that leave? Well, up to this point in the paper, I'd been silently yelling "It's a cohort effect!" And I wasn't to be disappointed, because that's where Kearney et al. turned next. The results are neatly summarised in their Figure 5:

The figure tracks the average number of births for women at different ages, grouped by five-year birth cohort. So, each line tracks all women born in a cohort. The first three cohorts (women born in 1968-72, 1973-77, and 1977-82) are pretty similar. But then things change dramatically. The cohort of women born in 1983-87 had less births at each age than the earlier cohorts. The cohort of women born in 1988-92 had less still, and the cohort of women born in 1993-97 have had even less. Now, given that these more recent cohorts of women have not completed their fertility (they could yet have more babies), it is possible that there will be some catch-up. But, as Kearney et al. note:

...the number of births they would have to have at older ages to catch up to the lifetime childbearing rates of earlier cohorts is so large that it seems unlikely they will do so.

So, what has been causing this generational change in fertility behaviour? Here, Kearney et al. become more speculative (which is the best we can do at this stage), and refer to the 'second demographic transition':

The theory of the second demographic transition highlights instead an overall shift to a greater emphasis on individual autonomy, with a corresponding de-emphasis on marriage and parenthood. The specific manifestations of this shift are taken to include a decoupling of marriage and childbearing, a change in the relationship between education and childbearing, a rise in childlessness, and the establishment of a two-child norm for those having children.

Specifically, Kearney et al. note that, although there was no abrupt change exactly in 2007:

...women who grew up in the 1990s were the daughters of the 1970s generation and women who grew up in the 1970s and 1980s were daughters of the 1950s and 1960s generation. It seems plausible that these more recent cohorts of women were likely to be raised with stronger expectations of having life pursuits outside their roles as wives and mothers. It also seems likely that the cohorts of young adults who grew up primarily in the 1990s or later - and reached prime childbearing years around and post 2007 - experienced more intensive parenting from their own parents than those who grew up primarily in the 1970s and 1980s. They would have a different idea about what parenting involves. We speculate that these differences in formed aspirations and childhood experiences could potentially explain why more recent cohorts of young women are having fewer children than previous cohorts.

It doesn't quite answer the question of why this sudden change occurred in 2007, so it's a little unsatisfying as an ending to the paper. However, this is a very thorough piece of work, and had me wondering about what the cohort effects look like for New Zealand. That might make an interesting research project for a suitably motivated Honours student.

[HT: N-IUSSP]

The pandemic 'baby bust' in other countries

Last November, I wrote a post about the supposed lockdown 'baby boom' in New Zealand, noting that:

...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...

The fertility experience of other countries through the pandemic has been quite different from New Zealand's as outlined in this paper by Tomas Sobotka (Vienna Institute of Demography) and co-authors. They use Short-Term Fertility Fluctuations data from the Human Fertility Database, which includes monthly data on the number of births by country. Their paper was written in March last year, so it only includes data up to January 2021, and they exclude countries that had not yet reported December 2020 data, leaving them with 22 countries, mostly in Europe (plus the US, South Korea, and Taiwan). To overcome seasonality in the data, they compare the number of births in each month with the same month one year earlier (this short-term approach also mostly overcomes structural changes in the size of the female population of reproductive age, which won't change drastically from one year to the next). What they find is a substantial decline in the number of births across these countries as a whole, neatly summarised in Figure 14 from the paper:

A value of zero in the figure would mean no change in births from one year earlier. Clearly, there is a bigger decrease in births on average, particularly from October 2020 onwards (which would be the number of babies conceived from January 2020 onwards, when the pandemic was getting underway). However, not all countries had decreases in births (notice that Finland actually had more births over this period than twelve months earlier). And, there isn't a sudden drop-off in births on average either, so rather than the pandemic creating a sudden and large fertility shock across these countries, it appears to have mostly accelerated existing downward trends in fertility.

It would be really interesting to follow up on this work with more recent data, and data across more countries (which will have now reported births through this period and beyond). As far as I can see, Sobotka et al. have not yet done so.

National football team performance and fertility

Like the media belief in a lockdown baby boom (see here), there is a belief in the media that sports team performances affect fertility and birth rates (e.g. see here, or here). Most stories like 'Super Bowl babies' have been proven to be a myth. However, throwing more data at a question like this is often good. That's what Luca Fumarco (Masaryk University), and Francesco Principe (University of Padova) did in this new article published in the journal Economics Letters (ungated earlier version here). Specifically, Fumarco and Principe looked at how national football (soccer) team performances at international competitions (FIFA World Cup and UEFA European Football Championship) affected the number of births nine months later, for 50 European countries.

National team performance was measured using the weighting of each match used in FIFA's Elo rating system (more on that later). Births were monthly counts. Fumarco and Principe find that:

Across all of the specifications, we see that, on average, an increase in performance by one standard deviation is associated with a reduction in monthly births by 0.3% nine months after the event.

They also perform a robustness check looking at the effect on other numbers of months after the event, and find that:

The effect of performance on monthly births is statistically significant nine months after the tournament... while the effect after ten and eleven months is not significant....

And the results were also statistically insignificant for 1-8 months after the event. So, on the surface, this seems to support the idea of a 'baby slump' rather than a baby bump from better national team performance. Fumarco and Principe conclude that:

...an increase in national team performance in international football competitions is associated with a drop in births nine months after the event...

We hypothesize that these results might be explained by individuals’ time allocations choices... the attendance of live events (e.g., from late afternoon to late night, on TV, at the stadium, on big screens in public places...) may reduce the time spent on physical intimacy...

The mechanism they propose is speculative. However, there is good reason to doubt the headline results in this study. First, I'm not convinced that their measure of national team performance is valid. They claim to use national teams' performance "as measured by the ELO rating system", but clearly they do not. The Elo rating system that FIFA uses takes into account the strength of the opposition and goal difference (see here), neither of which make an appearance in Fumarco and Principe's measure. [*] Fumarco and Principe take into account only the weighting of the match, which increases as the tournament progresses. That is a fairly crude measure of team performance, and not a whole lot better than the number of matches played, or the number of matches won. It would be interesting to see how the results panned out simply using the number of games.

Second, on a related note, Fumarco and Principe appear to use the full time series of monthly births for each country in their analysis (~17,000 observations). However, the tournaments only happen every two years, and most teams don't play in every tournament (or even any tournament). In those cases, Fumarco and Principe set the team performance variable equal to zero, which is not so different from a team that lost all of its games (which would be assignment 3 points, as they assign a minimum of 1 point per game). Including a bunch of months where there is no tournament and every country has a zero for team performance will seriously skew the results. Now, Fumarco and Principe use a variety of fixed effects, including month fixed effects, and month x year fixed effects. That will reduce some of this problem, but won't eliminate it entirely. It would be interesting to instead see how robust the results were to including only the month that is nine months after each tournament (i.e. April of each year), and applying a difference-in-differences format using countries that did not participate in each tournament as controls.

Third, there is no control for population in their model. It should be obvious enough that the number of births depends on the number of women of childbearing age. So, by excluding population size from the model there is a serious omitted variable bias. They do include country fixed effects, but that will simply reduce the size of this bias, not eliminate it.

This is a study that started with an interesting research question, but I don't think we can really take their results as given (even notwithstanding that they are correlations rather than causal). This is the sort of research that a good student could easily follow up on and improve upon.

****

[*] A side note: For a number of years, I generated Elo-type ratings for a number of international sports, along with Super Rugby and the NFL (see here). So, I have a bit of experience with these systems.