Michael Klien

A few weeks ago, Daron Acemoglu (@DAcemogluMIT), David Autor (@davidautor), Keelan Beirne, and Andrew Scott circulated a provocative paper, “Baby Busts and Growth Booms: Demographic Change and the Macroeconomy,” which reports that lower birth rates are associated with higher growth in GDP per working-age adult across countries and with higher wage growth across US commuting zones, with no negative impact on aggregate GDP or earnings. Since I have been working on fertility and growth for some time, I read the paper carefully. I did not find the evidence compelling. My reason fits in two words: terra incognita. The fertility collapse now underway has no historical precedent. The seven decades of data the paper analyzes, however carefully, contain nothing remotely comparable to the ultra-low fertility rates we observe today. To their credit, the authors know this. The conclusion states: “Our findings describe the past; whether they provide a reliable guide to the economic consequences of coming demographic transitions is not yet established. … Changes this rapid are outside the support of the historical evidence, and could theoretically yield different adjustment dynamics from those we document.” I agree word by word. I simply would have given this caveat more prominence because it is the heart of the policy question. Let me explain why by discussing the evidence across countries and leaving the evidence from US commuting zones for another day. I want to keep this post short. In the introduction, the paper presents as its key finding a negative and quantitatively large relationship between 1950 birth rates and economic growth from 1970 to 2020. But the birth and fertility rates of 1950 provide little information about the consequences of the ultra-low birth and fertility rates we are experiencing today. Observations from a very different range of values are unlikely to capture the nonlinear cumulative effects of ultra-low birth and fertility rates. I include a table listing the bottom ten countries in 1950, 1980, and 2025, according to the UN World Population Prospects, consistent with the authors’ sample exclusions for microstates, territories, and tax havens. In 1950, Austria had the lowest birth rate, at 15.5 births per 1,000 people, with a total fertility rate (TFR) of 2.09, roughly the replacement rate. Most of the negative effects I have emphasized in my writings stem from rapid population decline, as will be the case for South Korea, with a TFR of 0.75. If you ask me whether I expect problems for a country with a TFR of 2.09, my answer is no: no shrinking cities, no empty schools, no social security dilemmas. In fact, I believe that TFRs between 2 and 2.4, such as those in Western Europe in the early 1950s, are probably the best-case scenario in terms of economic growth: you have either a stable population or a gentle increase, and you do not suffer the pressures of TFRs of 3.0 or higher. Thus, I do not find extrapolating from Austria’s growth experience from 1970 to 2020 (which is effectively what the regression does) very informative about South Korea’s growth experience from 2045 to 2095: the former was close to an ideal demographic situation, the latter not so much. Moreover, the range of TFRs (which say more than birth rates about the future behavior of the population) at the bottom of the cross-section is not wide enough to learn much about the nonlinearities that appear below 2.1. In summary, I cannot see the external validity of this regression. The authors, with outstanding craftsmanship, address this problem as best they can by using later-vintage birth rate and growth data. If I read Appendix Table A1 (Panel A, countries) correctly, they run the same specification with 30-year growth windows for each starting decade. The coefficients on the t−20 birth rate (measured, as in the paper, per 100 people) are: 1950 vintage (growth 1970–2000): −0.13 (s.e. 0.10), not significant; 1960 (1980–2010): −0.26 (0.09); 1970 (1990–2020): −0.32 (0.06); and 1980 (growth 2000–2020, only a 20-year horizon): −0.26 (0.05). They also report robustness to using log birth rates, but taking logs of the regressor does nothing about the nonlinearities that matter, namely those well below replacement. The problem is that even in 1980, birth and fertility rates were not as low as they are today. In the table, you can see that Germany, the country with the lowest birth rate at the time, had a TFR of 1.56 (and no country was below Denmark’s 1.54). But keep two facts in mind. First, less importantly, Germany, like the other countries in the 1980 panel, has experienced large waves of immigration over the past few decades. By comparison, countries with high fertility in 1980, such as Mexico, have had large emigration flows. Therefore, the reported coefficient averages effects that include each country’s endogenous migration response, and the low-birth-rate observations come precisely from countries with positive net migration. The paper argues that lagged birth rates are orthogonal to subsequent flows. But orthogonality is not the same as informativeness about countries that will have little migration to speak of. Second, more importantly, let me return to my central point about nonlinearities. A TFR of 1.56 means each generation is 76% the size of the last: the population declines about 0.9% per year, so after 100 years you retain roughly 40% of the initial population. A TFR of 0.75 means each generation is 37% of the last: about a 3.35% decline per year, leaving roughly 3.5% after a century. Hence, we are dealing with a factor of about 11. A TFR of 1.56 and a TFR of 0.75 both count as “low fertility” in a regression, but they are qualitatively different regimes. The 1980 variation the paper identifies lives in the gentle-decline world; nothing in their data resembles the second. My favorite part of the paper is the evidence linking lower birth rates to more labor-saving patents, larger shares of high-tech exports, and faster TFP growth, with a WWII casualty exercise presented as an out-of-sample test of the labor-scarcity channel. I take this evidence seriously. But the mechanism does not extrapolate any better than the regression. In any semi-endogenous growth model in the tradition of Chad Jones, fewer people eventually mean fewer ideas. With a TFR of 0.75, the pool of potential innovators shrinks by almost two-thirds in each generation, and that is likely to overwhelm any gain from developing labor-saving technology. In comparison, the war shock was bad for one cohort, but then it was over. I have some further minor quibbles about identification in the cross-section, but those are better left to a seminar discussion than to X. I want to close by acknowledging that the assessment of empirical exercises is ultimately subjective. I do not find the evidence from Austria in 1950 or from Germany in 1980 persuasive regarding what will happen to South Korea. Instead, I find the evidence I reported in Table 1 of “The Wealth of Working Nations,” with Gustavo Ventura (@King_ofSweden) and Wen Yao, relating GDP growth in G7 countries plus Spain to demographic forces, to speak much more directly to the issue at hand and to imply much more negative outcomes, yes, including for Germany: nitter.gq/JesusFerna7026/status/… Other readers might find the evidence in “Baby Busts and Growth Booms” more compelling, or at least intriguing. But we should not fool ourselves: we are sailing into demographic terra incognita, and in these waters, the pretense of knowledge is the most dangerous temptation. I am keenly aware of the limits of what I can determine.