A Second Taste of Demography: The United States

 

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A Few Prerequisites

Have you reviewed our previous material on Global Demographics? We will assume knowledge of, inter alia, basic definitions and concepts; historical and international contexts; Malthus and his critics; population pyramids; how the UN projects population (including second moments); drivers of births, deaths, migration; age distributions. Review that material here.

Quick and Dirty: dragged quickly from the Internet. Data will vary by source, year; consider these illustrative approximations rather than precise estimates

The U.S. is one of about 200 countries, albeit one of the largest in terms of population (ranked number three behind China and India), per capita GDP (ranked number six at market prices, behind Luxembourg, Ireland, Switzerland, Norway and Singapore; number nine in purchasing power parity, behind the previous five plus several oil exporters whose rank bounces with hydrocarbon prices), with the strongest military capability (but also with wide commitments) and strong, although recently declining, financial and soft power.

Aggregate Population I: Time Series

Looking at demographic and economic data often reminds me of a well-worn metaphor: blindfolded people examining a (hopefully quiescent) elephant. 

Each gets a piece of the picture, but fails to see the whole thing. When we have a very important collection of data, re-expression can help.

There are often a number of transformations we can use to get different “views of the In the next four charts we will examine the same basic U.S. population data four ways, using a simple linear chart; next, use a logarithmic chart; then examine changes (differences) in annual population; then annual growth rates.

Here’s annual data on the U.S. resident population for about 140 years. Because so many Americans went overseas during WWII – and also because births were low, as we’ll see – you can see the little “bump” in the chart in the 40s.

Now, here’s a question.  Look at the line carefully.  Compare the period 1880 to the 1940s; and then the post 40s population. Which period was growing faster?

Notice that in the early years, for (say) a year or a decade’s change, the rise in population is small, relative to “the rise over the run” in say the last few decades.

In other words, the slope of this line is steeper in recent decades than a century ago.

But if we want to know when the fastest growth appears, we don’t want the slope of this line – we want the percentage change.  That will be the slope, at some point, DIVIDED BY THE STARTING  LEVEL.  The slope is smaller a century ago – but we would also DIVIDE THAT SLOPE BY A SMALLER BASE! (Sorry for yelling.)

Our eye tends to deceive us when we look at linear trends in a series, especially over a long time span.

When you plot the log of a variable, the slope of the line is now (to a good approximation), the rate of growth.

Now we see that the slope of later years is a bit flatter, i.e. that the growth rate is actually slowing down.

Need a refresher on logarithms? Excellent! Download this PowerPoint deck on logarithms and exponential growth.

Here is another way to look at our basic population data, focusing on annual changes rather than levels. We’ve simply differenced the annual population figures in the previous population charts.

You see the smaller simple differences pre-WWII, though the base was lower, as already noted. It’s also interesting to see the effects of two of our three most deadly wars.

WWI was bad enough, but the 1918 flu epidemic killed roughly 10 times the number of Americans as combat.  Globally it might have killed 3 or 4 percent of the world’s population.  It was a terrible pandemic. (Have you had your flu shot this year?)

WWII had a higher number of casualties but was also a bigger war.  About 5 million Americans were in service in WWI, out of a total population of 103 million.  The U.S. lost about 120,000 servicemen and women in WWI, about half from combat, and half from influenza and other non-combat causes.

About 16 million were in the service during WWII, out of a total population of about 133 million. Most of the 400,000 service deaths in that war were combat-related.

As many of you will know, the deadliest war in U.S. history was the 1861-5 Civil War, with about 700,000 fatalities out of a total population base of a little over 30 million, over 2 percent of the population.

The Civil War was the most horrific death toll from a U.S. war, but globally other conflicts could be even worse. Notably, around the same time as the U.S. Civil War, the 1850-1864 Chinese civil war between the Qing dynasty and the Taiping Heavenly Kingdom (revolutionaries led by religious leader Hong Xiuquan) has been estimated to have cost 20 to 30 million lives, or roughly 5 to 10 percent of China’s population at the time.

Here’s one of my favorites, combining two transformations. We took the first chart, and superimposed the annual growth rate.

U.S. resident population has been growing at just about 1 percent per year for some time.  This is in contrast to a number of other developed countries in Europe, Japan, that are growing slowly if at all.   For example, Germany and Italy’s growth rate is near zero, Japan’s just a shade above (0.2 percent per year); France is about 0.6 percent per year (about the same as China!).  Recently, the U.S. has been growing at about the same rate as (wait for it!) Mexico.

Going forward, there is some uncertainty whether we'll stay anywhere near 1 percent, or even half that. I think it's more likely, than not, that U.S. population growth is going to fall a good deal further.  Why? Further reductions in fertility, and changes in immigration policy. More on those topics below.

Aggregate Population II: Displaying the Distribution by Age and Sex

Photo by SM

Now let’s look at another handy demographic tool – the pyramid!

If you read through my slides/blog post on global demographics, you know I love population pyramids.

If you need a refresher, click here. 

This pyramid uses recent single-year age population data for the United States, instead of the 5 year groupings above.   

The pyramid confirms that Millennials are a large cohort.  The other large cohort is the post-World War II “Baby Boom,” as already noted above.  The in-between “Gen X” cohort is smaller, sometimes referred to as the “Baby Bust” in contrast to the post World War II Baby Boom.    The Millennial’s dominant position will increase over the next several decades, as Boomers age and a noticeable fraction of us die off.

Millennials or “Gen Y’ers” are sometimes called the “Echo Boom,” although the large size of this cohort is not any explosion of fertility rates, but rather modest fertility among a large cohort as Boomer women reached childbearing age.    

More detailed demographic studies show that immigration made an important contribution to the Millennial cohort, both directly (many Millennials are immigrants) and indirectly (a significant fraction of Millennials were born to first- or second-generation immigrants).

As it happens, I’m not a huge fan of these population cohort groupings, but they are now ubiquitous. So I’ve given up and now use them myself. More than I should, probably.

Let's switch gears again, looking at pyramids from the UN World Population Prospects website, which we used extensively in the previous post on global demographics.

This figure represents U.S. population in 1950. It's somewhat pyramidical, bigger at the base (kids), smaller at the top (oldsters). But note the indentations representing the fertility decline during the Great Depression and the world wars; and the very large base just emerging as the postwar baby boom begins.

I haven’t shown up just yet – I was born in 1952.

Now we’ve skipped ahead 75 years from the UN’s first pyramid, the year in which we write. Today the chart is not really much of a pyramid anymore, it looks more like a distorted fireplug. The area has grown as UN estimates 2025 US population to be 347 million.

Also notice the shrinking base. Children still exist, but seniors are now becoming a much larger share of those dependent on the working age population; loosely, those between, say 20 and 65.

Barely noticeable: at the very bottom of the figure is just a little bit of green. Since this data was published in 2024, based on historical data up until that point, UN demographers had to estimate the number of new births in 2025. Given the relative stability of trends in fertility, infant mortality, and so on, the confidence interval for this projection one year forward is pretty tight.

Now skip ahead 30 years from the present. Total projected US population is 385 million, if we use the median UN projection.

Focusing on the median projections, the best estimates from simulation models described briefly here and in more detail here.

The pyramid has clearly inverted. Loosely, from age 45 and up we see something like a pyramid; but below 45, populations decline as age decreases.

Notice we have much more green and yellow.  The green represents the 80 percent confidence interval – projected US population between 353 million and 414 million – and the yellow represents the 95 percent confidence interval – between 336 million and 434 million.

Recall that these green and yellow areas represent confidence intervals from model simulations. Clearly these are much wider for the bottom half of the figure than the top. Why? Because we have a good estimate of today’s population; the future numbers of middle-aged and above will vary with future migration and death rates, which are not known precisely. But it turns out that forecasting fertility, which drives the bottom confidence intervals, is even less precise, so the bottom confidence intervals are usually much wider.

Also, notice that the lower 95 percent interval suggests there is a small probability that U.S. population will shrink between now and 2055.

If you have time, pull up our companion study of global demography (or go to the UN website, that will only take a moment) and compare this pyramid to the global pyramid for the entire world population in the same year, 2055. You’ll see that proportionately, the confidence intervals for global projections are smaller than that for the U.S.

Why? Any given country will tend to have proportionately larger confidence intervals than the entire world. Some country variations will cancel out, but more to the point, country estimates are affected by the necessity to estimate volatile international migration. At the global level, these cancel out.

We can treat interplanetary immigration, on the other hand, as negligible. And if Martians do show up, H.G. Wells predicts that their lack of immunity to Earth’s pathogens will do them in. Or possibly a deep aversion to yodeling?

If Martians can’t hack it here, perhaps there are distant aliens with sufficiently advanced civilizations to understand the germ theory of disease, vaccinations and antibiotics. (And no equivalent of RFK Jr in charge of their health care*).  Should we be concerned about immigration at the global level? I devoured Liu Cixin’s Remembrance of Earth’s Past trilogy (The Three Body Problem,** The Dark Forest, Death’s End), but the Trisolarians won’t be here for about 400 years so I’m going to neglect the effects of aliens on global population today.

Emigration at the global level? Yes, Elon, I will gladly chip in to buy your one-way ticket to Mars. Take some friends. Please.

Whatever Elon does, another dozen kids, or two; trip to Mars, or not; his personal effect will remain small relative to total world population. He may have a larger effect through DOGE’s gutting of public health programs, but that is for another discussion.

*Click here to learn about RFK Jr.'s rejection of the germ theory of disease, which has been understood since the 19th century work of Louis Pasteur, Joseph Lister and Robert Koch.

** Ive watched the excellent Chinese TV series Three Body; Netflix has made a popular film as well, albeit one that takes more liberties with Liu's original story.

By the end of the century, our confidence intervals are getting quite large.

The UN projects that in 2100 the US will contain 421 million residents (the median projection. The 80 percent interval for total population is now between 340 million and 537 million. The 95 percent interval is between 316 million and 625 million. Pretty wide!

Here we see data from the same UN projections, but a different perspective. While we lose detail on age and sex distribution, we get a much better look at the evolution of actual population and its projections over time.

Focusing first on the thick red line, the median projection, U.S. population grows until the end of the century, albeit at a slow and declining rate. As expected, the confidence intervals, at whatever levels, grow much wider with each passing year, as discussed above.

Compontents of Change I: Natural Increase (Fertility, Mortality)

Some obvious patterns from this chart include the following. 

With the exception of the 1918-1920 flu epidemic and the recent COVID pandemic, the US death rate was been on a long slow decline for the first 75 years or so of the data. The death rate almost flattened out until around 2010 when it began a slow rise, driven in part by the large baby boom entering its dotage. (And some increase due to "deaths of despair," and especially COVID-19 -- see below.)

The birth rate is much more volatile. A rapid decline preceded World War I and the Great Depression, continued through those trying times, and then  boomed after World War II.  Birth rates declined rapidly during much of the 60s and 70s, then continued to decline at a slower rate. 

The difference between the red and blue lines is, of course, the natural increase in U.S. population.

In the pandemic years 2020 and 2021, fertility rates fell a bit and death rates rose almost to the birth rate i.e. during the pandemic population growth practically ceased. It's now recovered slightly.

Let's look at the components separately. First, births.

(How to calm a crying grandson?  Read quietly from an economics tome, here, “Too Big to Fail.”  Turned him catatonic within minutes).

Nobody has precisely defined the terms baby boomer, Gen Xers, or Millennials.  Or even agreed on the terms, there are synonyms for the latter two.

I usually think of the boomers as those born from about 1947 until 1967; Gen Xers as 1968 to 1981; and Millennials as those born 1982 to 2004.

By the way, while there were certainly precursors, the current division into some version of these “generations” stems from the pop sociology of gurus William Strauss and Neil Howe, in their eponymous 1991 book: Generations: The History of America's Future, 1584 to 2069. Al Gore is a big fan, I’m less enamored of it. Perhaps I’m being a little harsh?

I would argue the division into these groups is mildly interesting and sometimes useful.  Certainly, I will claim Boomers Rule the Universe.  (Full disclosure, I’m a Boomer, so give that claim all the respect that deserves.  Which is not so much.)  Really, not a huge fan in terms of analytics, but I use the terms as well, they have passed into the lexicon.

You might have run across Strauss and Howe in another context.  Strauss (who died in 2007) and Howe (who continues to consult and give speeches based on their ideas) took their generations idea much further, arguing that history was a series of recurring 85-year cycles, in their 1997 book The Fourth Turning: What the Cycles of History Tell Us About America's Next Rendezvous with Destiny. While many quibble with Generations, reviewers, scholarly and popular, were often more critical of The Fourth Turning; personally, I think it is mostly gobbledygook, other reviewers have labeled it “pseudoscience” and “non-falsifiable” (a pretty damning insult if you’re a fan of Karl Popper, see our discussion of “How to Think Good.”) I mention all this because if you follow current political events you’ll note that Strauss and Howe have deeply influenced anarchist Steve Bannon.

Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with current age-specific fertility rates.

Data sources post 1960: FRED, from World Bank World Development Indicators.

A Total Fertility Rate of 2.1 is often cited as “replacement fertility,” i.e., the fertility rate sufficient to maintain a country’s population, neglecting immigration/emigration. A total fertility rate of 2.1 is often cited as "replacement fertility." But the actual equilibrium TFR varies with a country's life expectancy, deaths of infants, etc.

Goal: each woman is, on average, “replaced” by one daughter in the next generation. In low-mortality countries:

• Sex ratio at birth (SRB): ≈ 105 boys per 100 girls, so the share female at birth is ~100/(100+105)=0.488.
• If every woman survived through her childbearing years, the daughters per woman would be TFR × 0.488.Setting that to 1 daughter ⇒ TFR ≈ 1 / 0.488 ≈ 2.05.
• Because not all women survive the entire reproductive span, add a margin for female mortality, ~2.06–2.10 in most high-income settings. 

Thus, the origin of the rule-of-thumb “2.1.” “2.1” is a good guide in countries with low mortality of potential reproductive females and a sex ratio at birth of about 105; with a stable timing of births (little shift to later ages), no bias against females in nutrition, other necessities. Assumes a closed population (ignoring migration). It’s a long-run equilibrium, not capturing short term variation.

Life is not so dire today, absent a major war, but a number of poor countries have replacement fertility rates noticeably above 2.1, say around 2.3 to 2.6, such as Afghanistan, DR Congo, Somalia, and so on.

WDI’s 2023 TFR estimate is 1.6. U.S. TFR from this source has been below 2.1 since 2007, and is still falling.

Now let's examine the other side of natural increase (or decrease): deaths.

(Please excuse the shameless plug for my brother's business -- now run by his former employees).

While deaths -- unlike births, above -- have been more or less rising over time, as we saw above, the death rate fell relatively rapidly (omitting the 1918-19 influenza pandemic), fell more slowly until about 2010, when it began to increase again, slowly – until COVID-19 hit.  (The virus appeared in 2019, hence the name, but it began to turn up in death statistics in 2020, see details below).

Also, notice that year-to-year volatility in the number of deaths is noticably lower beginning circa 1960, even if we ignore the "Spanish" (Kansan?) flu epidemic spike in 1918-1919.

This reduction in annual volatility was largely due to the development and more widespread use of vaccines and antibiotics; the strengthening of public health systems, and (especially after 1965) wider access to health care.

Prior to 1960, there were many more deaths from infectious diseases: influenza, pneumonia, tuberculosis, measles, diphtheria, polio -- and mortality from these was especially concentrated among infants and children, amplifying year-to-year swings as many of these diseases themselves had large seasonal and annual variations. Circa 1960 (beginning beforehand, of course) the development and more widespread use of vaccines and antibiotics played a major role in reducing death rates but also reducing the observed volatility. Other contributions were made by the strengthening of public health systems, (especially after 1965 Medicaid and Medicare Acts) wider access to health care; and the benefits associated with growing incomes, including better nutrition, heating and cooling, and so on.

The recent increase in deaths is very noticiable in the data. The most obvious spike is contemporaneous with the worst of the COVID-19 epidemic, circa 2020 through 2022, though the virus is still circulating as of this writing. This chart compares U.S. COVID deaths to prior flu seasons, for scale:

There is a lot to say about COVID that would take us too far afield here, including discussion of measurement issues.  Order of magnitude, COVID cost the U.S. about a million lives.  Estimates of global lives lost from the pandemic range between 10 and 20 million. While somewhat dated, other blog posts during the first few years of the pandemic can be found here, and a paper discussing COVID's effects on global housing affordability is available here.

A Digression on "Deaths of Despair"

The COVID spike is not the only driver of increasing U.S. deaths. In 2015, Anne Case and Angus Deaton flagged distressingly large increases during the 2000s in death rates from drug overdoses, cirrhosis and other diseases linked to alcoholism, and suicides, especially among middle-aged Whites.

Case and Deaton argued that this increase was associated with declining social and economic conditions, especially among Whites living in rural areas, and/or with lower levels of education. They thus labeled these “Deaths of Despair.” This figure from their 2015 article was a startling revelation to economists and others who had not been previously following such data:

In the data analyzed by Case and Deaton, which ended in 2013, Black (non-Hispanic) mortality rates were higher than White (non-Hispanic) rates, but falling; Hispanics were in between Whites and Blacks, and falling.  

Later work by Case and Deaton (2022) and Friedman and Hansen (2024), among others, found Deaths of Despair continuing to rise, partly driven by fentanyl driving deaths even higher than previous criminal over-prescription of opiods, and heroin.

In the early 2000s, increased prescription of opioids, notably Purdue Pharma’s OxyContin, led to serious abuse. In 2011, the Centers for Disease Control and Prevention (CDC) declares prescription drug abuse an epidemic.  In 2013, the FDA requires new labeling for extended-release and long-acting opioids to emphasize risks of misuse, abuse, addiction, overdose, and death.  In 2014, Naloxone, an opioid overdose reversal drug, becomes more widely available to first responders and the public through various state laws.

In time, as prescriptions tightened and addicted users found those drugs expensive and difficult to obtain, use of other drugs, notably heroin, and then fentanyl (often combined with other drugs) took off.

Currently fentanyl is by far the leading cause of drug overdose deaths, but note that deaths from other drugs, including opioids, have increased; deaths from meth and cocaine are much higher than they’ve been earlier this century.

One category of alcohol-related deaths reported in Deaton and Muellbauer is direct alcohol poisoning, which is combined with drug overdoses in the "poisoning" category. They also report data on alcohol-related liver diseases (cirrhosis, alcohol-related hepatitis). Pan et al. (2025) present the aggregate rise in alcohol-related liver diseases for recent years, which show a dramatic increase over the past decade or so:

Much more detail about alcohol=related deaths can be found from the CDC here. In brief, and rounding, recent data reveal that direct alcohol poisoning results in about 3,000 deaths per year; alcohol contributes to about 22,000 deaths from drug poisonings. Chronic liver diseases kill over 30,000 in some recent years. Alcohol is listed as a contributing factor in perhaps 10,000 suicides every year. Unsurprisingly, Case and Deaton's three main categories of Deaths of Despair (poisonings, chronic alcohol related deaths, suicides) have significant interactions.

We should note that alcohol is involved in many deaths usually omitted from DoD: other disorders including psychosis, certain cancers, and of course alcohol-related traffic fatalities; together these might be responsible for another 45,000 deaths or so.

Suicide rates have been rising in the aggregate as well:

Suicide rates in the U.S. have reached a 70-year high, with an estimated age-adjusted rate of 14.7 per 100,000 in 2024—marking a 37% increase since 2000.

Firearms remain the leading method of suicide, responsible for over 54% of deaths in 2022

Middle-aged adults (35–54), elderly individuals (85+), and American Indian/Alaska Native populations show the highest suicide rates

Youth suicide attempts are rising sharply, especially among teen girls and marginalized groups, with 10% of high school students reporting attempts and 13% of females affected.

In recent years, Deaths of Despair are increasing for all races, and for Hispanics. The death rates among American Indians and Alaskan Natives, previously understudied, is particularly horrendous.

Data representing American Indian or Alaska Native, Asian or Pacific Islander, Black, and White groups refer to non-Hispanic individuals in each group. Panel A shows deaths of despair among individuals aged 45 to 54 years. 

*Increases for Asians are modest over a comparatively low base. This figure combines Asians (themselves a diverse population) with Pacific Islanders and Native Hawaiians; PI/NH have higher rates of Deaths of Despair than Asians.

There is much more to learn about Deaths of Despair, and changes in causes of death more broadly. But in this post Case and Deaton get the last slide of the section, which updates some of their 2015 findings. Spoiler alert: the update news is not good:

Mortality rates by race, sex, and education, age-adjusted 25–74. Abbreviations: BA, bachelor’s degree; BNH, Black non-Hispanic; WNH, white non-Hispanic.

“Deaths of despair, morbidity, and emotional distress continue to rise in the United States, largely borne by those without a college degree—the majority of American adults—for many of whom the economy and society are no longer delivering. Concurrently, all-cause mortality in the United States is diverging by education in a way not seen in other rich countries.”

End of Digression on DoD; A Few Comments on U.S. Life Expectancy

Here you see the rise in U.S. life expectancy over the past century.

Among other patterns – the fastest rise was in the first half of the data (though the variance was also much bigger).  Things smoothed out, but slowed down, after WWII.

Notice women live longer than men.  Men face earlier onset of heart disease, are more likely to die from violence, hold more (but not all!) of the dangerous jobs, and engage in more life-shortening behaviors like smoking (though sex differences in smoking rates are disappearing) and doing violence to each other.

Blacks have shorter life expectancies than Whites (though Black women have had about the same life expectancy as White men since around 1970).

Notice this is data on the estimated life expectancy at birth.  As your cohort ages, if you survive your life expectancy edges up (though of course you get closer to the terminal date).

For example, when I was born the life expectancy of a white male born that year was a bit under 67.  

Now that I’ve made it past 60, my life expectancy has shifted up to about 85.  (And I intend to be one of those above the average, with some help from my wife (married men live longer), my trainer, and good medical care.)

Now that we’ve had a brief look at births and deaths, let’s examine the third main component of population growth:

Immigration

First, take note that each immigration scenario is presented using the median projection for that scenario – no confidence intervals are presented; as discussed above, these would widen substantially over time. While simplified, this graphic does highlight how much difference changes in immigration policy and behavior might change the time path of U.S. population. 

The “Main Immigration” scenario is based on recent history (neglecting pandemic years), and assumes annual net immigration levels between 850,000 and 980,000 people.

The “High Immigration” scenario assumes a consistent annual net immigration of roughly 1.5 million people per year.

The “Low Immigration” scenario assumes a trajectory of between 350,000 and 600,000 net migrants per year (similar to latter years of the Trump I presidency).

The “Zero Immigration” scenario, self-explanatory, is about where we are as of this writing (the first year of the Trump II presidency). Although it would be difficult to imagine zero immigration for the next 75 years, it does provide a baseline for considering the role of immigration in U.S. population growth or decline.

Before we examine recent immigration to the U.S., let us begin with some historical background.

National Geographic

The first human migration to North America occurred during the late Pleistocene (circa 15,000 years ago?), when hunter-gatherers moved from northeast Asia into the Americas, most likely via Beringia, a land bridge exposed during periods of low sea levels. Migration likely occurred in multiple waves over several millennia, using both interior ice-free corridors and coastal routes. 

At some point (10 millennia ago?) North American population growth thereafter was driven almost entirely by natural increase, not continued large-scale immigration from other continents.

Immigration from other continents resumed with a vengeance after the European (re?)discovery of North America

Map source here.

First we summarize pre-Civil War European immigration, from 1600 to 1850. Early migrants to what later became the 13 American colonies came primarily from England, followed by substantial numbers from Scotland, Ireland, the Netherlands, Germany, and later Scandinavia. Other significant migration included French settlers in what later became Canada as well as the Great Lakes and Upper Midwest, the Mississippi Valley and lower Louisiana. Spanish immigrants settled in Forida, the Southwest, and California.

Migration motives included land acquisition, religious freedom, trade, and, in some cases, penal transportation.

By the late 18th and early 19th centuries, annual inflows increased, particularly from the British Isles and German-speaking regions. By 1850, the United States Census recorded roughly 2.2 million foreign-born persons, almost entirely European in origin.

Two other major events ran somewhat concurrently. Approximately 400,000 Africans were enslaved and transported to North America, out of roughly 12 million sent to the Americas overall. The United States banned the international slave trade in 1808, but slavery itself expanded internally through natural increase and forced internal migration (the domestic slave trade) until abolition in 1865. By the time of the Civil War the U.S. contained about 4 million slaves.

We have less reliable data on American Indian populations, but Thornton (1987) estimates peak pre-Columbian population at about 5 million, declining precipitously to perhaps a hundred thousand or two by the late 19th century, from wars, genocidal policies, and especially disease, before beginning a recovery.

For more detail, and references, on these early demographics, see the PowerPoint slides and references that you can download near the end of this post. Next we turn to the past couple of centuries, focusing on two waves of immigration, one roughly from post Civil War to the 1920s, and the second recent wave that began in the 1980s.

1850–1910 was the time of the “Great Wave” of immigration, primarily from Europe. The foreign-born share rose to the mid-teens as arrivals surged.

1920s–1960s: Quotas and restrictions, the Great Depression, WWII, and relatively low inflows steadily reduced the foreign-born share. The foreign-born share reached a modern low around 1970, when the Census Bureau reported 9.6 million foreign-born (4.7% of the U.S. population).

1970s–2024: The foreign-born stock rose for five decades, reaching modern record levels by 2024. Pew Research Center estimates the foreign-born population peaked at about 52 2024.  During this period, the origination of immigrants shifted markedly: previously immigrants were mostly European, recent decades have been dominated by Latin America and Asia; Pew summarizes these origin shifts since 1850 and highlights that by 2022 the largest origin group was Mexico, with India among the next largest. 

In January 2025 the second Trump administration began to institute new restrictions on immigration, and deportations, leading to an estimated decline of about 1.5 million foreign-born by mid-year. More on recent policies below. Let us examine some recent research comparing immigrants from the two great waves in the figure above.

Ran Abramitzky and Leah Boustan are two of the country's leading researchers on immigration. Among their projects, Streets of Gold reports on a major research project they undertook using "big data" from a century of data.

Abramitzky and Boustan make use of large micro census data sets from the Census and Ancestry.com. applying machine learning techniques to link records categorize the immigration and economic status of individuals; and then track those individuals and their progeny across decades.

It's long been understood from previous research that post 1980 "reform" immigrants are a "barbell" distribution, with highly educated/human capital individuals overrepresented in the immigrant population, as our less well educated/lower human capital individuals.

Abramitzky and Boustan find that generally, new immigrants themselves don't improve their economic lot substantially from their place on arrival in the United States. But their children systematically do better than similar cohorts of native born. Their grandchildren often do better as well, but by the next generation the performance of the progeny of immigrants and complete the native-born cohorts do about the same.

While they find that once landed, the first immigrants don't systematially move up economically, of course those first landed usually do improve their lot over their circumstances in their previous country.

Malpezzi family photos, a century ago

Case in point, my paternal grandfather left dire poverty as a tenant farmer in a remote part of Tuscany in 1900, to take up coal mining in Western Pennsylvania. His  family standard of living increased, once my grandmother, my eldest uncle and eldest aunt joined my grandfather a few years after his emigration. This was despite low wages and high retail costs in the local-monopoly-run coal town. Their incomes were supplemented by the family vegetable garden; after a day of mining coal with pick and shovel, my grandfather turned every square inch of their company house’s yard into a hand cultivated garden.  Five more children were born in America. Bottom panel: my grandmother with her seven surviving children, my father to her immediate left.

My grandfather’s male children, including my father, started out in the mines, pre-World War II.  This was a hard life made more difficult by the Depression, labor strife, and most notably by my grandfather's 1929 death in a mine roof collapse. His sons obtained more education than my grandfather ever had (with one exception, my oldest uncle, explained below). Before long they eagerly moved from mining into construction and wholesale distribution, interrupted by military service in two world wars. The daughters took up domestic work for a wealthy Pittsburgh family, then moved into factory and clerical work. 

The next generation, me, my brothers, and my cousins, moved further ahead, teaching at different levels, working in different areas of retail, construction, law, and a successful funeral home. One of us even stumbled into a PhD in economics.

That exception to the education of my father’s generation, noted above?  My oldest uncle (top right photo, during his WWI U.S. Army service) had started school in Italy, and was tested by the local elementary school on his arrival in their new hometown.  Since he only spoke a dialect of Italian at the time, they thought him stupid. When he nevertheless made a perfect score on his entry math test, they then were sure he cheated.  (How exactly he might have done so was a puzzle that did not seem to occur to them.)  Offended, and properly so, by the accusation, he walked out. Self taught, probably with some later help from siblings, he became a successful small contractor, nevertheless.

One major difference between the first and second waves of immigration can be seen in this chart.

Previously we examined the stock of immigrants, i.e., the percentage of U.S. population that was/is foreign born. Here we examine the relative contributions of natural increase (births less deaths) and immigration. 

These growth rate contributions are spliced together from several sources, each with its own errors. Consider these data approximate and provisional, especially the most recent years, as new data comes online and/or is revised after Censuses and other surveys.

In my pre-”retirement” teaching years (1990 to 2016) I told students that as a rough rule of thumb, U.S. population was growing at about 1 percent, though that rate was slowly declining. And that when we decomposed that 1 percent growth rate, a little over half of it is natural increase (births over deaths); a little under half is due to immigration, mostly legal or documented. I’ve recently extended these estimated contributions through 2024.

What the chart didn’t show in the 90s and 2000s is that immigrants and the children of immigrants had higher fertility rates than those who’ve lived here for generations, as we noted above.  So some of that natural increase, that was higher than most peer countries (e.g. Western Europe) was actually immigration once removed.

Another obvious feature is that in the first wave, a century or more ago, is that there was no separate accounting of “legal” and “illegal,” or “documented” and “undocumented,” immigration. Even passports were not generally required until WWI. Governments (including the U.S.) sometimes issued letters requesting safe conduct for travelers, but these were not universal. Of course, immigration officials at Ellis Island and other disembarkation points could and did decide if a foreigner could enter the U.S., and some were refused entry. But the system of passports and visas did not become widespread until circa 1920.

The more detailed PowerPoint deck downloadable below has much more detail about immigration legislation and policies, but for now we note several watershed events.

The Immigration Act of 1924 established national origin quotas. These were based on the 1890 Census, so  heavily favored immigrants from Northern and Western Europe while severely restricting immigration from Southern and Eastern Europe, Asia, and other regions.

The Immigration and Nationality Act of 1965, also known as the Hart-Celler Act, abolished the national origins quota system that had been in place since the 1920s and introduced a new framework for immigration that prioritized family reunification, skilled workers, and refugees. 

The Immigration Reform and Control Act of 1986 Act provided a pathway to legal status for many undocumented immigrants who had entered the U.S. before January 1, 1982, and had continuously resided in the country since then. Approximately 2.7 million immigrants gained legal status through this program. This legalization would later be derided by opponents as “amnesty.”

Much more detail regarding these three acts, other legislation, and their implementation, can again be found in the detailed teaching notes downloadable below. For now, note the decline in immigration after the 1924 Act (as well as the effect of World Wars and the Depression); and the increases after the 1965 and 1986 Acts.

And of course some very recent events are reflected in the data. Immigration fell, and natural increase fell substantially, during the COVID-19 Pandemic. Natural increase remains low (as we discussed in the section on fertility, above) but immigration, especially illegal/undocmented immigration, increased markedly circa 2022 and 2023. Later in 2024, and especially with the advent of the Trump administration in 2025 (not shown in this chart), immigration has also collapsed. It may be, when the numbers are finalized, that 2025 could be the first year on record when U.S. population actually declined. In any event, the overall U.S. population growth rate is much lower than any of us have experienced in our lifetimes.

 

Population Characteristics: Sex, Age, Households and Families

This pyramid uses recent single-year age population data for the United States. I noted my objections to these cohort labeling conventions above, but also noted my abject surrender in the face of their ubiquitous use.

The pyramid confirms that Millennials are a large cohort.  The other large cohort is the post-World War II “Baby Boom,” as already noted above.  The in-between “Gen X” cohort is smaller, sometimes referred to as the “Baby Bust” in contrast to the post World War II Baby Boom. The Millennial’s dominant position will increase over the next several decades, as Boomers age and a noticeable fraction of us die off.

Millennials or “Gen Y’ers” are sometimes called the “Echo Boom,” although the large size of this cohort is not any explosion of fertility rates, but rather modest fertility among a large cohort as Boomer women reached childbearing age.    

More detailed demographic studies show that immigration made an important contribution to the Millennial cohort, both directly (many Millennials are immigrants) and indirectly (a significant fraction of Millennials were born to first- or second-generation immigrants).

The “dependency ratio” is the ratio of working age people, to children and the elderly.

Another way we can examine broad demographic trends is to examine this dependency ratio, or the number of “dependents” (children, and those of retirement age) divided by the number for those of working age. This ratio fell through the 80s, as the baby boom entered the workforce, but is now rising again, as they reach retirement age. 

Obviously, some under 18 and over 65 work; some 18 – 64 do not. Nevertheless, the ratio is a reasonable rough indicators of at least potential “depending.”

See our previous blog post and associated PowerPoint deck for more discussion of the implications of dependency ratios.

Notice that this slide is a little dated. When I attempted to update the data, I received this notice:

Due to the lapse of federal funding, portions of this website will not be updated. Any inquiries submitted will not be answered until appropriations are enacted. You will be redirected in 10 seconds...

No file, and I was not redirected. Budget and personnel cuts to Census and other Federal data sources are a serious problem, worthy of a separate post. Cuts have been a problem for some time, but have accelerated under the Trump administration.  See this report from the American Statistical Association,

A family comprises 2 or more related people living together (one of whom is the householder).

A household comprises all families; plus unrelated individuals living together; plus single persons.

Thus, all families are households, but not all households are families. The U.S. currently cotnains about 82 million families, and 134 million households.

Census counts people living with an unmarried partner (of same or different sex); but unless there is some other relative present (e.g. a son or daughter of the householder), they are counted as households but not as families.

Note that the number of households equals the number of occupied housing units.

Average United States household size has been declining over the past century or so from over four as we turned into the 20th century to just over 2.5 today. 

The decline has slowed substantially from about 1990.

While the number of households is been rising, the rate of growth of households has slowed down over the past half-century. 

The largest spikes in volatility in household formation are caused by definitional changes in 1982, 1993, 2001 and 2003.

From the 1990s to the Great Financial Crisis (circa 2007), a little over one fourth of young adults lived with their parents. Since the Great Financial Crisis that ratio has climbed and recently rose above 30 percent.

This trend has the attention of housing economists and the housing industry, among others; and of course, America's Finest News Source:

Like most satire, the line between laughing and crying is sometimes hard to discern.

Note that these marriage and divorce rates are "flows" (annual changes in marriage status), not "stocks," e.g. number of married couples. Here is a snapshot of today's stock, counting males and females 15 years and older:

Table from Statista Research.

Another notable trend is the increase in children born out of wedlock, although this has stablized in recent years.

Let's finish up this post (well, almost), with one more slide on marriage:

Alois Stutzer and Bruno Frey studied Germans and found that married people were happy, it seems partly because happy people are more likely to marry but also a good marriage increases happiness in turn. They find the "division of labor seems to contribute to spouses' well-being, especially for women..." which is a finding I think I need to keep well in mind.

Thanks to Richard Green for mentioning this paper. I've added some photos of (mostly) well-known marriages. 

Left side: 

• Samuel L. Jackson and LaTanya Richardson (married 1980).
• Queen Victoria and Prince Albert (married 1840 until his death, 1861).
• Jan Styka, "Penelope Recognizes Odysseus" (1901) after he returns after 20 years of gadding about. Very loosely inspired the plot of O Brother, Where Art Thou?, but in 2026 we expect a film of The Odyssey by Christopher Nolan.
• George Bailey (Jimmy Stewart) and Mary Bailey (Donna Reed).  Mary is handing over the honeymoon cash to bail out George’s S&L after Uncle Billy (Thomas Mitchell, the actor not the law professor) misplaced the day’s deposits. Selfless and long-suffering woman bails out the feckless men. Again.

Right side: 

• Valerie (Carol Kane) and Miracle Max (Billy Crystal), The Princess Bride. Exact dates unknown, but is sure seems they were married a long time.
• George H.W. Bush and Barbara Bush, married 1945 (until her death, April 2018; his death, November 2018)
• SM and JK, married since 1986.
• Michael McConnell (L) and Jack Baker (R), married in 1971 (long before 2015’s Obergefell v. Hodges, and an earlier state legalization in 2013, thanks to a Minnesota court clerk’s oversight, and a subsequent successful legal battle). Considered to be the longest-married gay couple in the U.S.

Some Final Thoughts (For Now)

Despite its length, this blog post has only scratched the surface of a complex subject. Do review the companion blog on global demography as well, and most importantly of all, download this PowerPoint deck.

The deck is quite large, about 700 slides. Don't bother trying to read it on a phone, or open it in a browser. Download the file to a hard drive, then open it in PowerPoint.

In addition to much more detail on the topics above, you'll find some introductory material on race, ethnicity, and related social and policy issues including public finance, education, health and housing.

Near the end of the deck you'll find a slide that has an extensive reading list in that slide's notes. I'm still studying this subject, some of these readings I've been through carefully, some I'm still working on.

Captain Obvious observes that nobody would try to use all or even most of these slides in a class. Go through them, pick out any that might help in your own teaching, or that might give you an idea for a better presentation. I have been creating slide decks for teaching and other presentations for over 40 years. I've stolen lots of ideas and more than a few slides from friends and colleagues. Do feel free to use any of these, or pass them along.

Your reward for reading this far, and then downloading a ridiculouly large file? Cartoons.  Near the end. Also some reviews of movies that touch on U.S. demography.

Comments and corrections on any of this material are, as always, very welcome.

A Taste of Global Demography: Part 1

 

Demographics helps us understand many phenomena, including poverty.

In a previous blog post we provided an introduction to global poverty, focusing on some basic statistics and on some examples of people's lived experience at different income levels. We want to discuss more about poverty outcomes, analytics, and policies that affect poverty for better or for worse. That post is coming later this year.

But first we need to examine some preliminaries that are important for understanding poverty, as well as many other reasons. This post will begin our examinatio of the first preliminary, namely demographics.This particular post focuses on a few aspects of global demography, including some long run history, basic data sources, and presentation including "demogrtaphic pyramids." Later posts will, inter alia, examine the United States in more detail.

This version is dated September 1, 2025. I will tidy this up a bit in due course, so do feel free to email me with corrections and comments.

Here's a treat to whet your appetite. If you are not familiar with the late Hans Rosling, you'll find this link alone worth the price of admission to this blog.

Hans was a Swedish physician and scientist who, with support from his son Ola Rosling and daughter-in-law Anna Rosling Rönnlund, created presentations about demography and other aspects of development that are engaging, entertaining, and highly educational, for specialists and lay persons alike. We met the Roslings in our previous posting about global poverty, and it's unsurprising that we will use their work in this post as well.

Now that you've watched this short video, and are suitably entertained, and motivated, we can begin.

Graphic from Shutterstock

The aphorism is often attributed to the 19th century French philosopher and social scientist Auguste Comte, though it seems unlikely that he actually said it. Nevertheless, demography matters a lot. It underpins much of the economy, public health, and many social and political phenomena.

Demography is, of course, the statistical study of human populations — including their size, structure, distribution, and how they change over time through births, deaths, migration, and aging. Core elements include:

• Population size: How many people?
• Population structure: Age, sex, family composition.
• Population distribution: Where people live (urban/rural, regions).
• Population dynamics: Fertility, mortality, migration, aging.

This post examines demographics in the international context. Why are we so interested in international demographic comparisons? As already noted, understanding global demographics will provide one of the bases we need for our ongoing study of poverty. There are of course many other motivations:

Understanding diversity of challenges:

Africa: Youth bulge, rapid urban growth

Europe & East Asia: Aging and population decline

Latin America: Transitioning to slower growth, still urbanizing

North America: Immigration offsets aging

Lessons from others: 

Policy innovations (e.g., family support in France, housing for elders in Scandinavia)

Urban design: Managing urban density in Japan or Korea vs sprawl in the U.S.

Migration management: Canada’s points-based system vs ad hoc systems elsewhere

Global interdependence:

Labor markets, capital flows, housing demand in major cities (e.g., London, Dubai, Singapore) are shaped by global demographic shifts

Benchmarking and context:

Helps avoid "local myopia" in policy debates

Provides empirical testing ground for demographic-economic theories (e.g., demographic dividend, urban transition)

Geopolitics, migration pressures; interaction with climate, conflict...

Of particular interest to those of us who study housing markets, demography drives housing demand:

• Population size and growth; more people = more housing units needed. 
• Household formation (e.g., marriage rates, age at leaving home, fertility)
• More young adults often increases rental demand, the need for starter homes
• More children, families affect demand for larger units, suburban expansion
• More older adults can drive downsizing, assisted living
• Internal migration (rural → urban, city → city) shapes city growth and housing pressures. 
• International migration influences regional housing markets

Real estate developers and investors use demographic forecasts for:

• Site selection
• Market segmentation
• Long-term valuation

Long historical perspectives

Cartoon by Alex Gregory

It’s worth noting that Hobbes’ dire prediction of such a dystopian existence, even in 1651, was focused on those who lived outside well-functioning (by the standards of the time!) societies.

Even if life expectancy at birth was under 30, some beat the odds.  For much of human history, average life expectancy at birth was something like 25-30. But that does not mean everyone dropped dead at age 31. Some lived into what you think of as old age. Students (and some governments) will categorize anyone in their 60s as "old." I’m 72 as of this writing, so people in their 60s don't seem so old to me. Though that can vary with health and one's enviroment.

But back to "olden times." Even when life expecancy at birth was around 25 or 30, in contrast to Gregory's cartoon and some others' impressions, even hundreds and thousands of years ago individuals could live into 50s, 60s, occasionally beyond.

Why was average life expectancy so low? Because so many infants and children died, given poor nutrition, water and sanitation, infectious diseases. In earlier times, many adults, often younger adults, died from acts of violence.  See Steven Pinker's The Better Angels of Our Nature: Why Violence Has Declined.

Homo Sapiens has existed for perhaps 250,000 years; the beginnings of agriculture and small urban settlements could be dated somewhat over 10 millennia ago. Here's one annotated graphic of possible global population over the past 10,000 years. The shape is arresting:

In March 1999, Robert Fogel gave the paper “Catching Up with the Economy” as his Presidential Address to the American Economic Association.  It is a fascinating (and non-technical) look at the world’s economy, and our standard of living, over 10 millennia.

In this chart, Fogel maps world population to some watershed events in long run economic history, like the beginning of agriculture, the invention of writing and mathematics, the early industrial revolutions, and the invention of computers.

Below this chart, in the text Fogel notes: ‘There is usually a lag between the invention of a process or a machine and its general application to production. ‘‘Beginning’’ means the earliest stage of this diffusion process.’  Just as we have discussed in the “Tradition & Innovation” paper!  There’s a difference between invention, and innovation.

The first agricultural revolution, 10 or 11 thousand years ago can be characterized as:  the beginning of agriculture.  Before that, humans were hunter-gatherers, and moved around a lot.  This had huge implications, including the fact that for the first time people could stay in the same place for some time; they could invest in the area they lived in, by clearing fields, later plowing, later still irrigating.  And once agriculture hit a certain level of productivity, some of us could live in villages, towns and cities. There is currently a chicken-and-egg discussion about urbanization and agriculture among some anthropologists about how much one preceded or "caused" the other, which we will skip for now.

It is often noted that this past 11 millennia of a shift from hunter-gatherer societies to agriculture and urban life roughly corresponded to a broadly stable and favorable climate era, the Holocene. Much more to say on this in our lectures on climate.

We might label the first 9,000+ years in the chart, where population rises between imperceptibly and at all, as "Malthusian," after Thomas Malthus' famous 1798 An Essay on the Principle of Population argued that famine, war and other calamities would disastrously intervene whenever population began to grow, as food supplies and other resources would fail to keep pace. In Malthus' own words:

“Population, when unchecked, goes on doubling itself every 25 years or increases in a geometrical ratio. Subsistence, increases only in an arithmetical ratio.”

“The power of population is so superior to the power in the earth to produce subsistence for man, that premature death must in some shape or other visit the human race.” 

“With regard to the duration of human life, there does not appear to have existed from the earliest ages of the world to the present moment the smallest permanent symptom or indication of increasing prolongation.”

Bleak, indeed, and not a completely incorrect view of human history up to his era. But the environment was about to change, for the better.

The second agricultural revolution, which took place over several centuries circa 1600-1900, at first predated then coincided with the first industrial revolution.  First experienced in Britain and a few parts of continental Europe (the Netherlands, Denmark), new crops were grown based on species native to the Americas (potatoes, corn [known as maize in Europe]).  Crops were planted in rows, more efficiently managed (e.g. weeding was improved).  The enclosure movement facilitated larger scale operations, and broke the feudal system, though at great cost to many, including those who saw their traditional rights to land use usurped.

Timing is everything in life. Prior to the late 18th century (plus or minus), Malthus’ model was a defensible framework for thinking about population, resources (food), births and deaths. About the time Malthus wrote, the Second Agricultural Revolution, and the Industrial Revolution, were exploding the constraints on food production (and other resources) that Malthus assumed. Thus, ironically, the world was more or less Malthusian, until Malthus.

Malthus was influential in his day, but there were more-or-less contemporary critiques. For example:

“The liberal reward of labour, as it is to the effect of increasing wealth, so it is the cause of increasing population. To complain of it, is to lament over the necessary effect and cause of the greatest public prosperity.”  Adam Smith, The Wealth of Nations (1776; note Wealth predates Malthus' Essay, in fact Smith died almost a decade beforehand. Malthus was in fact a fan of Smith, although they diverged on this important topic.)

“Population regulates itself by the funds which are to employ it, and therefore always increases or diminishes with the increase or the diminution of capital. Every reduction of capital is therefore necessarily followed by a less effective demand for corn, by a fall in price, and by a diminished cultivation.”  David Ricardo, On the Principles of Political Economy and Taxation (1821)

“The most effectual encouragement to population is, the activity of industry, and the consequent multiplication of the national products.”  Jean-Baptiste Say, A Treatise On Political Economy (1803)

Digression: Is Malthus back?

While Malthus failed to forsee the then-nascent second agricultural revolution, and industrial revolution, that allowed human flourishing to turn a corner, in recent decades some people have been claiming that we are in a new Malthusian crisis. We call these folks (wait for it…) Neo-Malthusians.

While Malthus was criticized by a number of contemporaries, notably David Ricardo, and his model proved at odds with the time paths of population and human welfare after his publications, Malthus' world view was always influential. Adherents or "Malthus-adjacent" people were wide-ranging, including successive members of the Huxley family (Thomas, Aldous, Julian); Malthus fit well with the views of misguided eugenicists such as Richard T. Ely and Margaret Sanger, and influenced John Maynard Keynes, George Bernard Shaw, and H.G. Wells among others. 

Certainly the ultimate perversion of Malthusianism came when Adolf Hitler combined a Malthusian world view requiring German Lebensraum (living space) with his vision of the world as a struggle among races and his deranged belief in a Jewish conspiracy he claimed as the source of forces (including both capitalism and marxism, go figure) that impeded the rise of the "superior" Aryan race. 

[As we point out that Adolph Hitler was a Malthusian (and much worse), be clear that Malthusians, in general, are not Nazis and are not in any way equivalent to Hitler.]

Neo-Malthusianism was boosted half a century or so ago with the work of Paul and Anne Ehrlich (The Population Bomb) and "The Club of Rome" (The Limits to Growth) I read The Population Bomb just before college and it scared the crap out of me with claims such as the following:

“The battle to feed all of humanity is over ... In the 1970s and 1980s hundreds of millions of people will starve to death in spite of any crash programs embarked upon now.“

"India couldn't possibly feed two hundred million more people by 1980“

“I have yet to meet anyone familiar with the situation who thinks that India will be self-sufficient in food by 1971."

Fortunately, the Ehrlichs and others of their have been consistently wrong, at least in the last few centuries. Sticking with India for the moment, per capita food production, including basic grains, has been unsurprisingly volatile, but rising: see the thick line in this chart from India's authoritative Economic and Political Weekly:

Johnson, Baksi and Sethi (2024)

More to the point, per capita foodgrains available to consumers has risen as well; also with significant volatility but less than production.

Like Malthus, the Ehrlichs and other recent Malthusians such as Lester Brown, Jared Diamond, and Herman Daly, to mention just a few, remain influential.  Perhaps the best-known Malthusian to today's moviegoers is the arch-villain Thanos in today's Marvel Cinematic Universe. Asking students to critique Thanos' views and his prescription can be a useful classroom exercise.

Thanos is, of course, only a recent example of the plethora of fictional Malthusians. My favorite Charles Dickens novella, since childhood, A Christmas Carol, can be read as Dickens' critique of Malthus. Early on, Scrooge is engaged by fellow merchants collecting alms, who describe the bleak options open to the poor such as prisons and workhouses:

“Many can’t go there; and many would rather die.” 

“If they would rather die,” said Scrooge, “they had better do it, and decrease the surplus population. Besides—excuse me—I don’t know that.” 

“But you might know it,” observed the gentleman. 

“It’s not my business,” Scrooge returned. “It’s enough for a man to understand his own business, and not to interfere with other people’s. Mine occupies me constantly. Good afternoon, gentlemen!” 

Economists are often moved by numbers, and abstract arguments, but normal people often respond more to the personal. As many readers will recall, the turning point for Scrooge comes many pages later, when he faces the prospect of the death of his employee's child, Tiny Tim, given the family's destitute state:

“If these shadows remain unaltered by the Future, none other of my race,” returned the Ghost, “will find him here. What then? If he be like to die, he had better do it, and decrease the surplus population.” 

Scrooge hung his head to hear his own words quoted by the Spirit, and was overcome with penitence and grief. 

Dickens' critique of a harsh version of the Malthusian perspective was primarily ethical. These are fertile grounds for criticism, certainly, but as we've noted, circa two centuries ago, the second agricultural revolution and the industrial revolution undermined Malthus' central thesis and predictions. What happened to put paid to the later pessimism of the Ehrlichs, Donella and Dennis Meadows and Jay Forrester (lead authors of The Limits to Growth), and other neo-Malthusians? In particular, what allowed India, China, and other large and growing and poor countries to escape the Malthusian trap predicted by many 5 or 6 decades ago? Much of the credit goes to improvements in technology, particularly the so-called "Green Revolution."

Perhaps the name that comes to mind first with the Green Revolution is agronomist Norman Borlaug. (For UW Cheeseheads: his Norwegian family first emigrated to Dane County, but later moved to Iowa where Borlaug was born. We'll have more to say about the Norwegian migration to the upper Midwest when we discuss U.S. demography in a future post).

Post WWII, supported by the Rockefeller Foundation and the Government of Mexico, Borlaug led research on wheat breeding. He combined semi‑dwarf plant types with disease resistance and shuttle breeding to produce high‑yielding, rust‑resistant wheat. Successfully implementing these new variants starting in the mid sixties, these new varieties underpinned  yield gains in Mexico, then in India and Pakistan. Of course improvements in seed varieties by themselves worked only when coupled with improvements in fertilizer, irrigation, supportive price and input policies, and extension efforts. Together these lifted yields, reduced chronic food shortages including those that had been emerging in South Asia which so alarmed the Ehrlichs and others, and lowered global cereal prices. It should be noted that the initial Green Revolution was not without its own environmental and equity challenges, such as groundwater depletion, soil salinization, disparities across regions and small-scale farmers. Nevertheless, the net effect of the Green Revolution was extremely positive, and Borlaug received the 1970 Nobel Peace Prize for this work.

While Borlaug and colleagues' improvements in wheat are best known to American audiences, others made major contributions. China's Yuan Longping, “Father of Hybrid Rice,” was less famous in “the West” than Borlaug but arguably no less important. More people rely on rice as their main staple than on wheat: about half the world’s population lives on rice, about a third on wheat.

These efforts constantly need renewal, including an increased focus on Africa. Large parts of Sub‑Saharan Africa are rainfed, highly agro‑ecologically diverse, and dominated by crops (e.g., sorghum, millet, cassava, yam, plantain, cowpea) that respond differently to fertilizer and irrigation than semi‑dwarf rice and wheat. As a result, productivity growth has been slower and more heterogeneous than in Asia, though there are notable successes. Details of ongoing and future work can be found by consulting the Consultative Group on International Agricultural Research (CGIAR)—a global partnership of funders and independent research centers focused on food, land, and water systems.

Of course there is much more to the escape from the Malthusian trap than increased agricultural productivity, although that is probably the best place to start. Future posts will examine a much wider range of issues related to growth and development, including but not limited to, improvements in public health, education, institutional reforms and better governance, urbanization, technical change well beyond agriculture, and demographic shifts themselves, including family planning and the possibility of a "demographic transition" if/when a country's workforce increases in both size and quality.

What economists know, that Malthus and neo-Malthusians don’t

Some final thoughts, for the moment, on the still-influential Malthusian outlook.

The majority of modern economists, along with Dickens, Smith, Ricardo, and Say, find Malthus interesting but ultimately wanting, especially as a guide to broad demographic trends. One particularly notable response to the increasing popularity of the neo-Malthusian thesis was the work of economist Julian Simon; his best-known work in this area is The Ultimate Resource 2 (1996).

How can we summarize an economist's approach to these issues? First and foremost, people and firms respond to changes in relative prices. This takes two main forms:

• Consumer substitution in response to changes in relative prices.  On the demand side, when prices rise, people start to consume less of the goods that are relatively more expensive.
• Endogenous technical change.  On the supply side, when prices rise, people start to work harder to lower costs, use less of that particular good, find new supplies, and invent and market better substitutes.

What else do we know that Malthus didn’t?

Malthus did not foresee that rational individuals and families would reduce their population growth willingly as standards of living rose and risks like infant and child mortality fell, social security systems beyond productive children were put in place.

Ironically, Malthus was roughly correct for much of human history – until about 1800, when he wrote, which was also the beginning of the second agricultural revolution.Malthus was right for most of human history, then he’s been wrong for over 200 years.  Will he be right again, someday?  Hmmm. 

Just because Malthus et al. got some things wrong doesn’t mean we don’t have real problems. A far-from-complete list:

• Water and sanitation
• Nutrition
• Education (especially girls!)
• Traffic, congestion
• Energy
• Pollution, including but not limited to carbon and other greenhouse gases
• Safety and security, within and across countries

We’ve had progress in each of these areas, great progress in some of them. Much more to be done.

A tour of the globe; regional perspectives

Population by continent, from Our World in Data:

Global population in 2025 is estimated at about 8.2 billion. 

North America: 0.6 billion.

South America: 0.4 billion.

Asia: 4.8 billion, of which:

India: 1.5 billion

China: 1.4 billion

Europe: .75 billion

Africa: 1.6 billion

Oceania: 46 billion

Here’s a  chart of population of selected countries and regions, with UN projections to the year 2050. It’s the medium variant projection – remember, the farther out, the less confidence we have in the accuracy of our projections. But qualitatively, India and China are still two countries with about ¼ of the global population.

Africa will experience the fastest growth, though it’s also subject to the most variability. How fast will different parts of Africa go through demographic transitions that most other countries and regions started decades ago? That’s not entirely clear, although it’s started in many of them. 

U.S. population grows faster than most so-called developed countries; in fact faster than a number of emerging countries including China. The US differential performance is partly due to above average fertility (compared to other rich countries) but mostly due to immigration. This immigration has an important indirect effect – immigration immigrants are population – an indirect, first-generation immigrants have higher fertility and second generation and higher.

(Note the chart above is based on the UN's 2017 data and projections. Those have beeen updated, I haven't had time to update all my charts).

Here's some data from the UN's current 2024 demographic report:

India is now the largest national population; it was estimated that this eclipse took place in 2023.

If we had interval estimates, we might call it a tie for the moment, though it’s almost certain that India will pull away in the next decade.

Currently the U.S. is ranked #3. Despite continued growth rates (if immigration recovers post-Trump) the U.S. is projected to fall behind Pakistan sometime in the next three decades. And behind Nigeria and DR Congo as well, by the end of the century.

Other countries projected to move up into the top ranks include Ethiopia, possibly Tanzania.

Population pyramids

I pulled this one off the web – en francais! – because it showed why we call these pyramids. Ghana has  the classic shape.

A population pyramid is a graphical representation of a population’s age and sex distribution. It is typically shaped like a pyramid, like this illustration from Ghana; but can take other forms depending on demographic trends, as we will see below.

The x-axis shows the number (or percentage) of people, by sex; males are usually on the left, females on the right.

The y-axis shows age cohorts. These can vary; here, 0–4, 5–9, ..., 90+).

A pyramid with a wide base, like Ghana's, illustrates a population with high recent birth rates and large number of young people.

Ghana’s narrow top indicates high mortality rates at earlier ages, and few older adults.

Bulges or indentations often reflect historical events, such as baby booms, wars, or migration patterns.

Decades ago, more pyramids actually looked pyramid-like, i.e. similar to this one.  Quite a few lower income developing countries still do.

We can produce pyramids for countries, or states or cities, or for the entire world. The folks at Our World in Data have provided a graphic with a series of pyramids representing the global population back to 1950, and median U.N. projections out to 2100:

See Max Roser, The global population pyramid: How global demography has changed and what we can expect for the 21st century, April 18, 2019.  From his text, verbatim:

“We are at a turning point in global population history. Between 1950 and today, it was a widening of the entire pyramid – an increase in the number of children – that was responsible for the increase of the world population. From now on is not a widening of the base, but a ‘fill up’ of the population above the base: the number of children will barely increase and then start to decline, but the number of people of working age and old age will increase very substantially. As global health is improving and mortality is falling, the people alive today are expected to live longer than any generation before us.”

Next, let's examine a set of population pyramids by region; each region's population presented in raw numbers, in the top half of the graphic, and the percentage breakdown by age within each region, in the bottom half:

The top population pyramids present numbers. Clearly, most people live in Asia.

The bottom pyramids – upside down – present percentages within regions. Africa’s the youngest region, the pyramid is very – pyramidical. Europe and North America are kind of pig-in-the-python, middle age dominates.

What drives population growth (and decline), and the age distribution, within a country or region? The fundamentals are births, deaths, and migration. We discuss each briefly, in turn.

Fertility, Births

Chart from Max Roser, "The Global Decline of the Fertility Rate." Published online at OurWorldinData.org, 2014.

At first glance this chart is a bit complex, but not too difficult to understand once it is carefully examined.

Begin with the red line which presents fertility rates of countries, ranked from left to right by their estimated total fertility rates for women. These fertility rates are represented on the vertical axis, which is labeled on the right. The red line presents these fertility rates circa 1950 to 1955. The global average fertility rate then was approximately 5.

The highest recorded fertility rate in the early 50s was approximately 8, in Rwanda. Not all countries are labeled, for clarity; representative countries are labeled to give a sense of where what sort of countries exhibit such rates. 

Neglecting the countries which are not labeled, moving down the red line we see that Kenya has a high (if lower than Rwanda’s) fertility rate of about 7.5, the Philippines something over 7, Iran just below 7, and so on. As we move down the curve, horizontal axis presents the cumulative share of world population at that level of fertility and higher. So, once we get down to, say, Thailand, with a fertility rate of about 6, the horizontal axis tells us the countries with fertility rates above 6 (including but not limited to Rwanda, Kenya, Philippines, Iran, Egypt, Bangladesh and Brazil) comprise a bit over 20% of the world's population. 

When we examine the 1950s fertility rates for China and for India, slightly above and below 6, respectively, the horizontal lines for these countries are quite large, reflecting their large population. Examining the horizontal axis and the 1950s fertility for India, about 60% of the world's population had a fertility rate at or above India's level. 

Stepping through the rest of the data in the 1950s red line, the lowest fertility rate in the data is Luxembourg, which is below the replacement level along with several other countries, not labeled.

Notice that in the 1950s U.S. fertility was about 3.4; this was the height of the baby boom.

Moving down, orange line presents the fertility rate from the 1975 to 1980. The average global fertility rate has now fallen to a bit under 4. 

With each succeeding year we have data for more countries. Thus the very first point on the orange line, Yemen, is above 8; we did not have Yemeni data in the 1950s.  

There is not too much difference between the red and orange lines, i.e. between the highest 1950s fertility rates and the highest 1970s fertility rates (although the rates of individual countries do change).  After we get past the first 10% or so of countries, around Egypt, there was a marked decline in the fertility rates, particularly in the middle of the cumulative population distribution. In particular, India's fertility rate fell from a little below 6 to a little below 5, and China's plummeted from about 6 to about 3.

The blue line presents fertility rates for the latest data at the time this chart was created, i.e. 2005 to 2010. Now there's an even larger shift down among the lower income countries, and the global average fertility rate is fallen from a bit under 4 to about 2.5. 

The last two lines, the light and dark green lines, present expected fertility rates using UN population projections data for the middle of this century (2045 to 2050) with a global average fertility rate of 2.3, and projections for the end of the century, with an average fertility rate of 2.

The big picture is clear. For the past century, and projections into the end of this century fertility rates have been shifting down substantially especially for the highest fertility countries. 

Examining the blue line we see that about half the countries in the world are currently above replacement fertility and about half below; those below include the United States. 

Forecasts for the middle of the century suggest that about 70% of the world's population will be living in countries with below replacement fertility and by the end of the century the UN forecasts that 95% of the world's population will be in such countries. Of course, any such forecasts are subject to error and should be taken as indicative.

Mortality, Deaths

Child mortality rates have fallen everwhere.1950 child mortality rates in low-income countries was similar to the rates experienced in today’s high-income countries a century ago.

Poor countries’ rates are now converging at the lower rates of rich countries.

Among rich countries, U.S. child mortality rates are about the highest. More on this when we explore U.S. demographics in a future presentation.

https://ourworldindata.org/grapher/life-expectancy-vs-gdp-per-capita?time=2020

It's unsurprising that there is a correlation between life expectancy and GDP per capita. It's not perfectly tight; notice for example that the United States has a significantly lower life expectancy than other countries and its level of GDP per capita, in fact its life expectancy is about the same as China’s. We will discuss this in greater detail in the forthcoming United States demographic presentation. 

Three other obvious outliers are Nigeria, Eswatini (formerly Swaziland) and Equatorial Guinea. All three countries suffer from highly unequal income distributions and high rates of extreme poverty. Nigeria and Equatorial Guinea are countries whose GDP is boosted by oil revenues but who lag behind in governance and institutions and are above average in levels of corruption and capital flight. Eswatini is a small landlocked country inside South Africa with particularly poor health systems and some of the world's highest rates of HIV and tuberculosis infections.

Migration

We’re still learning about the history of our species overall, though archeological and biological evidence points towards human origins in East Africa, spreading through the rest of the world over many thousands of years.  This map is from National Geographic circa 2006; there have been new developments in the history of our species, but the broad outlines remain (“out of Africa,” maybe Kenya, maybe other locations in Africa).

Did the first Americans cross the Bering Straight when it was a land bridge, or make a long perilous sea journey, possibly landing in South America first?  

Not my area of expertise, and I’m not sure how settled the true experts consider the matter.  The last piece I read suggested both happen.  My Bayesian prior on this is a little “diffuse.”  

De Haas, Castles and Miller (2019), UN data for 2017

De Haas, Hein, Stephen Castles, and Mark J Miller. The Age of Migration: International Population Movements in the Modern World. Bloomsbury Publishing, 2019.

This table from De Haas et al. presents the number of immigrants, emigrants, and their respective shares of country populations, for the top 30 destination and origin countries in the world, as of 2017. Their table is based on UN data.

Immigration is represented on the left side of the table, and emigration on the right side of the table.

Here are a few key immigration results, rounded. Let’s begin with the three largest countries in the world:

China (not shown in the table): 2017 population 1.4 bn.; 1.4 mn. immigrants, 0.1% of the population 

India: 2017 population 1.3 bn.; 5.2 mn. immigrants, 0.4% of the population 

United States: 2017 population 325 mn.; 45 mn. immigrants, 13.7% of the population 

Most of the countries with the highest fractions of immigrants are the Gulf States, which have lots of guestworkers: Saudi Arabia, the United Arab Emirates, Kuwait and Oman. Other Gulf States with high fractions that don't make the top 30 list like Qatar and Bahrain are not shown. 

Singapore and Hong Kong, metropolitan polities, also have high rates of immigration. 

At the other extreme, besides the aforementioned China and India, countries with enough total immigrants to make the top 30, but low percentages, include Pakistan and Japan. South Korea is now a fairly low 3% but was close to 0% decades ago. North Korea, not presented, is unsurprisingly near zero. Other relatively low-fraction countries include the Philippines, Indonesia, Vietnam and Brazil.

Regarding emigration, none of our top three countries are big sources of immigrants. 

India provides many guestworkers as well as long-term residents in the Middle East and other regions. China's 10 million reflect their large global diaspora. This includes but is certainly not primarily students and academics currently residing in the U.S., Canada and Britain. The majority of overseas Chinese are in Southeast Asian countries, notably Indonesia.

Many emigrants come from countries experiencing conflict, such as Syria, Kazakhstan and Palestine. We'll have more to say about refugees and asylum-seekers later. Note that the numbers for Russia and Ukraine in this table are in 2017, after Russia's 2014 invasion of Crimea and the subsequent Russian small-scale war centered on the Donbas; but before the full 2022 invasion, which catalyzed large emigration from Ukraine as well as smaller but significant emigration from Russia. Further discussion of this elsewhere.

Many Americans have difficulty differentiating among Central American countries, which have significant differences in their economies, institutions, and governance.

Frankly I have a lot to learn about this region myself. Here are some basic data, mapped. 

While there is much discussion in the United States of migration from Central America to the U.S., notice that crossing the border from, say, Nicaragua to Costa Rica is itself moving from a country with a GDP per capita of roughly $5,000 to one of $15,000.

Rigorous analysis of migration is often traced back to Ernst Georg Ravenstein’s Laws of Migration (1885), although Everett S. Lee is often credited with formalizing and popularizing the push-pull framework in his influential 1966 article, “A Theory of Migration” (Demography, vol. 3, no. 1, pp. 47–57).  

Lee embedded his push-pull concept in a broader framework that also considered “intervening obstacles” (e.g., cost, distance, policies) and personal characteristics.

Age; Dependency Ratios

Dependency ratios measure the proportion of people typically not in the labor force (the “dependent” ages) relative to those in the working-age population. The working-age population is often defined as ages 15–64.

Youth Dependency Ratio (YDR) = (Population aged 0–14 ÷ Population aged 15–64) × 100

• Indicates the burden of supporting children and adolescents. 
• Higher in countries with high fertility rates, especially in low- and middle-income economies.

Old-age dependency ratio (OADR) = (Population aged 65+ ÷ Population aged 15–64) × 100

• Indicates the burden of supporting older adults, often linked to pension and healthcare costs. 
• Higher in countries with low fertility and longer life expectancy, especially in high-income economies.

Youth and old-age dependency ratios can be combined into a single Total Dependency Ratio.

Five decades ago, most of Latin America, nearly all of Asia and Africa had high youth dependency ratios, generally 60 percent or above.

Now youth dependency ratios have declined virtually everywhere, although they are still high in Africa, and parts of Asia and Latin America, For example Bolivia Venezuela Guatemala, Papua New Guinea, Cambodia Afghanistan Pakistan Yemen and Iraq.

Five decades ago, not many countries had substantial old age dependency ratios. 

Relatively high ratios could be found in western Europe often on the order of 20 percent; the U.S. was at about 16 percent.

Old age dependency ratios are now high in the United States and Canada and virtually all of Europe both Western and Eastern central Europe including Russia. Australia is also above the 40 percent threshold and China Brazil Argentina and Chile among others are approaching it. 

The highest ratios are in some of the European countries like Italy, as well as Japan and South Korea.

Check out population pyramids and population forecasts from the United Nations

My go-to source for basic demographic data is the United Nations World Population Prospects, updated every few years.  Getting to know the site and its data and publications is essential for anyone who wants to learn about demography.  (That would include you, if you've managed to read this far!) Click here to find the site.  Here is a screen shot a few clicks in, that takes us to our good friends, the population pyramids:

Notice the sliding bar at the top: you can readily view how a country's or region's population has evolved from 1950 to date, and UN demographer's forecasts of population out to 2100.  More on these forecast methods in a moment. Above we see the global population pyramid as of today.

At the same page, you will see a button for line charts. This yields one chart per country or region, summarizing the total population, and forecasts out to 2100. See below. Notice that there is a median prediction along with some intervals etc.:

The black line presents actual estimated global population from 1950 (2.5 billion) to 2024 (8.2 billion).

The UN’s best forecast of 2100 global population is 10.2 billion (the solid red line). But we know any point estimate is almost certainly “wrong.” How wrong?

According to the UN’s demographic models there is an 80 percent chance that 2100 global population will be some number between 9.4 billion and 11.0 billion.

They estimate that there is a 95 percent chance that 2100 global population will be somewhere between 9.0 billion and 11.4 billion.

Here are some details of the UN's projections. The thick red line that presents the median scenario (a.k.a medium variant) of UN population projections assumes:

• A gradual decline in global fertility rates.
• Convergence of country-level fertility toward replacement levels (~2.1 children per woman in countries with low death rates for infants and children).
• Probabilistic modeling based on historical trends and expert judgment.

The one thing we know with near certainty about any single point forecast: it will be wrong. But the medium variant is our best forecast, i.e., it is likely to be “less wrong” than other forecasts.

Other scenarios and measures depart more widely from the median scenario – our “best” forecast – over time, as we would expect. We have a pretty good idea of what population will be five years from now, much less confidence in our projections 50 years out. In addition, the effects of a given parameter change are compounded over time.

The +0.5 child and –0.5 child scenarios (blue dashed lines) modify the median scenario by adjusting the expected total fertility rate in every country by a constant +0.5 or –0.5 children per woman, relative to the medium variant’s assumption. 

Earlier UN projections used deterministic “low,” “medium,” and “high” scenarios (e.g., the ±0.5 child variants). But since the 2010s, UN demographers have used probabilistic methods to better reflect uncertainty and variability in demographic trends.

Currently, the UN constructs 60 stochastic (randomly simulated) trajectories of the future total fertility rate (TFR) — and consequently, future population — for each country, using a Bayesian hierarchical model. See references and links at the WPP website for technical details.

Each of the 60 simulations is represented by a faint gray line. Note that there is usually a lot of variation in scenario outcomes among the 60. These models incorporate historical fertility trends, country-specific dynamics, and global patterns, taking into account the uncertainty in fertility declines or increases for each country. Each trajectory represents one possible future path for fertility, mortality, and migration — and the resulting population outcome. These are drawn from the posterior distribution of the model, meaning they reflect uncertainty around the central (medium) forecast.

You can download some slides describing the Bayesian approach in general from my blog, here.

Let's look at just a few country case studies.

Case 1: China's large, declining population

Wow. Not very pyramidical.

The dearth of people in their early 60s is largely due to the famine and other disruptions of the ironically named “Great Leap Forward.”

You’ll then see “echoes” of this as the small 1960s cohort reaches childbearing age 20 or so years later.

Add in the one-child policy, the Cultural Revolution (1966 to 1976), urbanization, and now the high cost of raising a child, and it’s not surprising that China’s population is heading for a steep decline.

Even using the “outside” 95 percent CI’s, China is heading towards a serious old-age dependency problem, and it's coming fairly soon: 2055 is not so far away.

Again, notice the confidence intervals (the green and yellow areas) are much wider at the bottom than at the top: we think we have a decent idea of the net migration and eventual deaths of the population that exists today; these drive expected errors in 2055's older population. The younger population (those 30 and younger in 2055) are born in the years ahead; we are less confident that we understand the future fertility behavior driving the bottom.

By the end of the century, we expect more inversion, and wider confidence intervals throughout.

Also, compare the total area of this "pyramid" to the 2025 and 2055 pyramids. Note the shrinking size of the pyramid over time.

Here’s another look at China’s population, and some projections. We get a look at the time path of population, although we’ve lost disaggregation by age.

(Pro tip: if you go to the UN website, you can examine time series by age, and lots of other informative material).

Overall, China’s population has begun a steep decline.

This will be difficult to reverse, given China’s large population base, the fact that no modern case exists of reversing fertility declines, and resistance to non-Chinese immigration.

Case 2: Nigeria's large, growing population

The Nigerian pyramid is a real pyramid. That's what's going to drive their rapid population growth as this large base of young people moves up through the pyramid, even as fertility rates are expected to continue their decline in Nigeria (and many other African countries) as they develop, life expectancy increases, and social norms about child-bearing change.

Notice that the 2025 Nigerian pyramid is narrow, even at its base, compared to the scale of the horizontal axis. That’s because the UN sets the horizontal scale for each country at a constant level consistent with its largest population between 1950 and 2100.  See the next two slides.

And since I mentioned “scale,” let me complain one more time that with all the work and thought the UN demographers have put into this, I don’t understand WHY THEY MAKE THE FONTS ON THEIR CHARTS SO !#@!&* SMALL!  (See our class presentation on how to create better presentations.)

Nigeria’s fertility rate is well over replacement (about 4.5 births per woman) but is declining, and expected to decline further. If we concentrate on the mid estimate (the thick blue line), the UN’s demographic models suggest that by 2055 the pyramid might not yet invert, but it might flatten. 

And as we’ve noted elsewhere, there is always a lot of inertia in population pyramids. Today’s children are the parents of the next generation. 2025’s large-base, small-top carries through for quite some time.

But again, notice the very wide confidence intervals (the yellow and green) at the bottom, reflecting uncertainty about the exact time path of Nigeria’s future fertility. Our “best guess” is that the pyramid will straighten out at the bottom in 30 years, but there’s a non-negligible chance that it will invert, and another non-negligible chance that it continue to spread out as a (loosely speaking) pyramid.

Our best current projection of Nigeria’s 2100 “pyramid” is that it looks a bit like an old flint arrowhead (tapering out from a sharp point at old-age but then tapering in the other direction at about age 25). But the real story is that given Nigeria’s high current fertility rate, uncertainty about it’s future time path; and some uncertainty about future mortality and migration, and how all these interact over time, we don’t really know all that much about Nigeria’s population at the end of the century.

Flipping over to our time series format, our best estimate is that Nigeria’s 2100 population will come in at about 380 million, up from 230 million today.

But the aggregate confidence interval is huge, as our pyramids have already signaled. Current models project a 2100 population somewhere between less than 300 million to about a billion!

Case Study 3: The United States

Psyche! Nothing here.

The U.S. will get its own blog entry in a few weeks. (OK, maybe a few months, given my usual speed and numerous distractions. But you can download these and many other charts for the U.S., and close to 200 countries and regions, at the UN World Population website.

Back to our exploration of a few demographic tidbits.

One of many policy issues arising from demographic trends: how will we support an aging population?

https://www.nytimes.com/interactive/2023/07/16/world/world-demographics.html

How Much Do People Produce and Consume at Different Ages?

The National Transfer Accounts research program measures how different age groups produce, consume, save, and transfer economic resources, aligning with the UN System of National Accounts (SNAs).

The bellwether report edited by Lee and Mason (2011) analyzes data from 27 countries, although the project has been extended to some 60 countries today.

Data cover public transfers (like pensions and health care), private transfers (within families), and asset reallocations, offering nuanced insights into intergenerational flows.

From Lee and Mason, verbatim:

"The profiles for the bottom and top quartile of 23 NTA economies are unweighted averages of the profiles for the six poorest and six richest economies. The bottom quartile group consists of China, India, Indonesia, Kenya, Nigeria, and the Philippines. The top quartile group comprises Austria, Finland, Germany, Japan, Spain, and the US. Values are scaled by average labor income for ages 30–49. Data for hunter-gatherer profiles are from Kaplan (1994) and Howell (2010)"

“Low fertility has brought welcome relief from the dependency pressures of the young, with fewer needy children and more adults to provide for them. But as the global age transition proceeds, youth dependency is being replaced by old- age dependency. As we shall see, the elderly in both low- and high- income countries are consuming far more than they produce through their labor. Whereas in hunter- gatherer populations, only children depended on adults, in contemporary societies both children and the elderly are dependent. Compounding the problem in some countries, consumption by the elderly has risen dramatically in relation to that by younger adults. The elderly have become increasingly dependent and, at the same time, more numerous – a trend that is justifiably viewed with alarm.”

Generated with AI from Shutterstock

Surging elderly populations increase old-age dependency ratios, straining safety nets – pensions, health care systems, fiscal policies.

One safety valve often discussed is immigration, but first, immigration has become politically unpopular in many countries, and second, over the long run immigration’s efficacy will be limited as eventually almost all countries fall below replacement fertility and higher incomes, better care extend life expectancies.

What is the other safety valve? Increases in productivity, so that a (relatively) dwindling workforce can better support a growing senior cohort. Productivity increases can stem from several sources including better business practices and improved governance; but (especially in the long run), technical change. 

To summarize, what are some possible responses to population aging?

• Increases in fertility?
• Immigration?
• Accept slower improvements (or possible declines) in welfare?
• Among the elderly?
• In the aggregate?
• Delay retirement?
• Redistribution?
• In favor of the elderly?
• Or reductions in their safety nets?
• Offsetting productivity increases?
• Technical change (robotics, AI)
• Capital deepening, widening?
• Applying technology directly to elder care?
• “Logan’s Run” solutions?

Each is superficially self-explanatory, but worthy of a deeper dive some other time.

“Logan’s Run” solutions? One of many classic dystopian novels, films portraying a society that “solves” this problem via euthanasia. As an oldster myself, this is my least-favorite solution.

Younger readers might know Plan 75? Or Cloud Atlas? (Still trying to figure Cloud Atlas out, myself!)

Shutterstock

Countries With A Growing Youth Population Have Have Potential -- If They Prepare

https://www.investopedia.com/terms/d/demographic-dividend.asp

Ke Shen, Fudan University

Demographic Transition: Stages in Brief

• Stage 1: High birth and death rates → low, fluctuating population growth.
• Stage 2: Death rates fall (healthcare, sanitation, nutrition improve) but birth rates remain high → rapid population growth.
• Stage 3: Birth rates begin to fall → growth slows.
• Stage 4/5: Both birth and death rates are low; population stabilizes or declines.
• Within Stage 2–3: Age structure changes dramatically — initially more children, then, as fertility falls, a relatively larger share of working-age adults emerges.

Generated by AI using Shutterstock

A “demographic dividend” is possible when a rising share of the population is available as a workforce; and this share is productively employed. A successful dividend depends not just on mechanics of demography, but on education/human capital formation, other determinants of productivity in place.

A rising share can arise from an initial decline in fertility from a previous high level, leaving a smaller cohort of dependent children relative to the working-age population. This can be partially offset by increasing life expectancy, more dependent seniors.

A rising share can also arise from a boom in population following a war, famine, etc. See the “baby boom” in U.S. and elsewhere post Great Depression and WWII. Or both, for a time, in China.

Generally, demographic dividends are temporary.  As the large working-age cohort ages, the demographic dividend will diminish.

Potential problems impeding “demographic dividends?”

Much of the growth in global population, including the “sweet spot” for demographic dividends, over the next 30 years will be concentrated in countries with below-average labor productivity.  Will these countries be able to organize the necessary improvements in human and tangible capital, and improved institutions and governance, necessary to take advantage of their demographic potential?

Continued population-driven economic growth will thus depend on increasing productivity in sub-Saharan Africa, South Asia, Central Asia, and other lagging regions. Reductions in international and intra-national conflicts will also contribute to growth and development, should such reductions take place

Demography in Art: Some Related Novels, Film

In the following section, I quote directly from some humanities scholars, with attribution; for some of the rest I used ChatGPT to speed up my drafting. Also, I cite a few promising items I haven't dug into yet myself; writing a blog entry like this helps me build up my own reading list.

Overpopulation, by John Pitre

Pitre’s art is not my style, but he does get the idea of overpopulation across – it’s the kind of thing we posted in our dorm rooms back in the day, maybe if we had just read The Population Bomb.

Illustration for Nikkei Asia by Daniel Garcia

“China's aging and shrinking population presents a potential demographic crisis, putting pressure on an already strained pension system and threatening future economic growth.”

Did I mention I love me some population pyramids? Artist Mathieu Lehanneur has rendered pyramids for a slew of countries into sculptures entitled State of the World.

From the website, verbatim:

"Past, present and future evidence of the fate of world populations, those black spinned silhouettes owe their almost primitive and yet decorative shapes to demographic data provided by United Nations. Depending on the country, major events like wars or baby booms can be read and touched, but also globally, can be identified economic or sanitary development or, at different times, unstable balance between populations of retired and young people. State of the World is also a way to remind you that you are still alive and you are part of a story bigger than you!"

Detail of Romania, Saudi Arabia sculptures.

Gustav Klimt, Death and Life

From commentary by Marie Sirois:

“One of the key messages in Klimt’s works is the representation of the human life cycle. From procreation through to infancy and adolescence; from a man and a woman in the prime of their lives to the frail old man and the unsightly old woman, Klimt portrays the whole range of ages and stages of development. Klimt almost always presents his characters as passive, weak-willed subjects who are evidently giving themselves up willingly to fate.”

Reena Saini Kallat (India, b. 1973), Woven Chronicle, 2011–16. 

For a much better look and some details about this installation at Stanford's Cantor Arts Center, see this video. The video walks us through the exhibition "When Home Won't Let You Stay: Migration Through Contemporary Art." This particular work is featured about 12 minutes in.

Is Famine Ever Permissible? Eve Goldsmith Coxeter

From the website, verbatim:

"Part of the British Red Cross Museum collection, Eve Goldsmith Coxeter's Is Famine Ever Permissible? Compulsory Migration Ethiopia (1991) illustrates the journey endured by Iraqi Kurds as a result of expulsion from the country during the Gulf War. The tragic circumstances laid bare — the rising streaks of heat in the unforgiving climate, as they endlessly march across the desert in search of a safe haven – suggest that the answer to the titular question may be obvious."

I'm a fan of Banksy. Although I'm glad I dropped out of the bidding for Girl With A Balloon well before it got to $25 million.

So what does an anonymous artist with bundles of cash do with his or her money? As Banksy says, buy a yacht. But a very special yacht. Kudos, Banksy!

Collage from 1984 Christmas Carol

There are several excellent film versions of Charles Dickens classic, A Christmas Carol. As a child I watched the 1951 Alistair Sims version, which is free on YouTube as of this writing. My personal favorite is the 1984 George C. Scott version, which is available for rent or purchase on many streaming services.

1973’s Soylent Green is one of the Ehrlich-era dystopian films revolving around overpopulation that is still widely seen today (albeit as filler on cable networks).

Set three decades into the future (2022!), overpopulation has caused ecological collapse, including shortages of food, water and housing. The few rich do well (as is customary in dystopian fiction) but everyone else leads totally desperate lives. Most live in squalor, including water from communal standpipes (more on that in future lectures on water supply in low-income countries), and processed food wafers called (you guessed it) Soylent Green. Made from plankton, or so they claim. You’ll guess what they are really made from even if you haven’t seen the film.

But you can’t keep the truth from Charlton Heston!

A common trope in dystopian science fiction, which often has an extreme Malthusian bent: euthanize all the old people.

I do have some thoughts on how to address our aging populations, some of which are alluded to above. Details await another posting.

Well-known neo-Malthusian

The Infinity Stones are crystals, scattered throughout the universe, that have great power individually but when assembled, hoo-hah! 

The Wikipedia entry on Infinity Stones helpfully notes that these are “fictional items.” Thank goodness.

Anyway, Malthus-on-Steroids Thanos wants them so he can (get this) snap his fingers and destroy half the life in the universe. 

There are at least a dozen movies in what has become known as the Marvel Cinematic Universe that touch, more or less, on the Infinity Stones. The key episode is Avengers: Infinity War. Spoiler alert: Thanos gets collects them all, despite the efforts of The Avengers, snaps his fingers, half the beings in the universe dissolve. Movie ends.

Second spoiler alert: there is one more movie, Avengers: Endgame, where through time travel and a bunch of bogus physics, the surviving Avengers defeat Thanos, snap an alternate gauntlet holding the stones (left or right hand? I’m confused. Maybe something do to with chirality, i.e. whether a particle's spin (a quantum property) and its direction of motion are aligned or opposite? Well, science.  I should have studied harder in college.) Where was I? Yes, Tony Stark (Iron Man, the guy Elon Musk desperately wants to be) grabs the gauntlet, snaps it back, everyone (except Thanos, I presume) returns from obliteration, hale and hearty. Again, science.

So, what the heck was all this about (aside from the true motive, the estimated $30 billion box office of the collected films)? Thanos was bummed out by the ecological destruction of Titan, his home planet, after others ignored his plan to conserve resources by eliminating half the planet’s population. Apparently having read some catastrophic neo-Malthusians, he believed the universe was about to run out of resources, and so he decided to extend his proposal to the entire universe. 

"I will shred this universe down to its last atom and then, with the stones you've collected for me, create a new one teeming with life that knows not what it has lost but only what it has been given.”

Jeez, has this guy never heard of consumer substitution in response to changes in relative prices, and endogenous technical change? (See slide above).

Even if he didn’t know about substitution and technology, shouldn’t he know about exponential growth? Or the simple “rule of 72?” If you kill off half the life, and it grows back at 1 percent per year, we’d be back where we started in about – wait for it – about 72 years. Growing at 0.5 percent? About 144 years. (Actually 139 – the rule of 72 is an approximation).

https://en.wikipedia.org/wiki/Rule_of_72

So even if we accept Thanos’ world view, and are ignorant of basic growth theory, if we can count, his solution is – at best – extremely temporary.

So far, we've focused on paintings, a little sculpture, film.  How about some good old-fashioned books?

My grandchildren are all geniuses. Yours too?

    

Atwood, Margaret. The Handmade's Tale. McClelland and Stewart, 1985.

The Handmaid's Tale is set in the Republic of Gilead, a totalitarian, theocratic society where widespread infertility has destabilized society. The state controls women’s reproductive roles, making childbirth a political resource. The novel starkly illustrates how demographic decline—falling fertility—can reshape power structures and personal freedoms.

Dickens, Charles. A Christmas Carol. In Prose. Being a Ghost Story of Christmas. Chapman & Hall, 1843.

Dickens highlights poverty, family size, and mortality in Victorian London. The Cratchit family, large and struggling to survive on meager wages, embodies the challenges of urban working-class households. Demographic pressures of rapid industrialization underpin the novel’s social critique.

Emecheta, Buchi. The Joys of Motherhood. Heinemann, 1994.

Emecheta portrays the life of Nnu Ego in colonial and postcolonial Nigeria. The novel explores the tension between traditional expectations of large families and the economic realities of urban life; the role of wife and mother in such a large family isn't all it is supposed to be.

Harrison, Harry. Make Room! Make Room!  Doubleday, 1966.

Harrison’s novel dramatizes the perils of unchecked population growth. Scarcity of food, housing, and space create desperate conditions, highlighting neo-Malthusian anxieties of the 1960s. It inspired the film Soylent Green, discussed above.

James, P.D. The Children of Men. Alfred A. Knopf, 1992.

Humanity faces extinction due to global infertility. With no children being born, social and political structures collapse under the weight of hopelessness and aging populations. In the midst of this dystopia, a pregnant woman is found -- if she and her unborn child can survive. Later made into an excellent film.

Mo Yan. Frog. Penguin, 2015.

Mo Yan tells the story of Gugu, a midwife navigating China's draconian one-child policy (in place circa 1980 to relaxation beginning in 2013; although China retains state restrictions on fertility). The novel confronts the human costs of enforced demographic engineering, from forced abortions to gender imbalance.

Shelley, Mary. The Last Man. Henry Colburn, 1826.

A plague has ravaged the global population, apparently leaving a single survivor. Steeped in Romantic-era ideas about mortality, population vulnerability, and the fragility of civilization, this novel is the mother of all apocalyptic fiction rooted in demographic collapse. Shelley is, of course, also the author of Frankenstein, which is the cornerstone of thousands of later horror books and films. Pretty impressive legacy, don't you think?.

Swift, Jonathan. A Modest Proposal: For Preventing the Children of Poor People in Ireland, from Being a Burden on Their Parents or Country, and for Making Them Beneficial to the Publick.. Dublin: S. Harding, 1729.

Swift’s biting satire proposes that impoverished Irish families sell their children as food to the wealthy. Using (we like to think) hyperbole, Swift critiques both British exploitation and callous attitudes toward poverty. If you thought South Park or The Boondocks could get raw, wait until you read Swift. 

Voltaire. Candide: Or Optimism. Penguin, 1759.

Candide reflects demographic themes through its depiction of war, famine, disease, and natural disasters reducing populations. Families are broken apart, communities destroyed, and survival becomes precarious. Voltaire links demographic catastrophe with human folly and the dangers of blind optimism. Do we, as Dr. Pangloss repeatedly claims, "live in the best of all possible worlds?" Voltaire's satirization of professors is something every economist should recall daily. Models are not reality, although, used with proper appreciation of their limitations,  some models are useful for understanding reality.

Finally, we'll wrap up our brief discussion of fictions with a very short compare and contrast of two novels that might bookend the direction of many societies today: 1984, and Brave New World.

Huxley, Aldous. Brave New World. Chatto and Windus, 1932.

In Brave New World, families are abolished altogether: children are conceived and gestated in laboratories, then raised collectively by the state. Parenthood is considered obscene, and emotional bonds between parents and children are replaced by social conditioning and consumption. The erasure of the family underscores how demographic engineering underwrites the regime’s stability.

Orwell, George. 1984. New York: Signet Classic, 1949.

In 1984, families still exist, but the Party has hollowed them out as sites of intimacy and trust. Children are taught to spy on their parents for signs of disloyalty, and the Party seeks to channel all loyalty to Big Brother rather than kin. The family becomes not a refuge but a vector of surveillance and control, showing another way demographic life can be weaponized by the state.

Stepping back from famiies and demographics, Orwell's world is a totalitarian society ruled by fear, pain and sufering. Huxley's world, more subtly controlled, is pacified through drugs and elecctronic entertainment. Can we see elements of either or both of these dystopia accelerating today?  Hmmm.

Side note: the older Huxley was Orwell's (Eric Blair's) French teacher at Eton. While they were not in regular contact, in 1949 Huxley wrote to Orwell arguing that his vision of the future was the better one.

Download References and Further Reading Here

This Word file contains a select bibliography; I've included most if not all of the papers cited in this post but I have quite a few more.  (Including some I need to read more carefully myself.)

Click here to download the bibiliography.

Download Related Slides Here

If you've been to my blog before, you know that I am a PowerPoint freak. I use slides to outline and draft my work; and I create more slides than make the cut here (believe it or not).

The PowerPoint version is (1) longer and (2) less well organized (again, believe it or not). But if you want to dig further, and/or you want to find some slides for your own teaching or other non-profit presentations, feel free to use any of these.  You can download the full deck here. Note that some of the slides will benefit from a little updating.

If you download this deck -- over 500 slides -- your reward will be some cartoons at the end, that don't make it into this blog.

If you do use some of the slides, I wouldn't mind hearing about it, and/or any comments or corrections you might have.

Click here to download the slides. It's a large file, so I recommend you download it to a hard drive, then open it in PowerPoint. If you try to open it directly in a browser, the formatting may get messy.

A Note on How I use Artificial Intelligence (ChatGPT and Gemini)

Recently I have begun to use ChatGPT to speed up my work, just as from the 1980s on, word processing, spreadsheets, personal computers, the Internet, especially Google Scholar, downloadable pdfs of journal articles, EndNote, dictation software, Wikipedia, online Britannica, etc. have enhanced my productivity (or so I like to imagine!)

How old am I? I still own my slide rule which is well over half a century old; I used to draft using index cards and legal pads in cursive handwriting, and I used to spend hours in library stacks.  Good times.

Most of the time when I use ChatGPT, I know more or less what I want to say, but AI drafts much faster than I can. Academic work requires careful and detailed specification of queries, follow-up where necessary to avoid hallucinations, etc. I always edit ChatGPT’s drafts, for style as well as content; they tend to be a bit verbose. And I usually have something more to say on a given topic.

In other presentations I’ve provided examples of where AI can lead one astray. If you’re reading this circa 2024, you can probably provide a dozen examples yourself.

As I note elsewhere, there is a PowerPoint version of this presentation (and some other slides), which is available on request. In that deck, I’ve indicated which slides have been drafted using ChatGPT in the notes below the slides. 

When I use AI to make points that require some documentation, I provide links and references as well. 

If the points are common knowledge – e.g. what do we use demographics for? – I don’t necessarily provide references. 

If you are baffled by something I thought was common knowledge, try a quick Internet search; contact me if you need elaboration, or if you think I should have provided documentation.

MORE TO COME

In due course, I'll focus on U.S. demography, in a future post.