Tag Archives: demography

Why we need open science in demography, and how we can make it happen

“Why we need open science in demography, and how we can make it happen” is the title of a talk I gave at the Max Planck Institute for Demographic Research yesterday, as part of an open science workshop they hosted in Rostock, Germany. (The talk was not nearly as definitive as the title.)

The other (excellent) keynote was by Monica Alexander. I posted the slides from my talk here. There should be a video available later. The organizing committee for the event is working to raise the prominence of open science discussions at the Institute, and consider practices and policies they might adopt. We had a great meeting.

As an aside, I also got to hear an excellent tutorial by E. F. Haghish, who has written Markdoc, a “literate programming” (markdown) package for Stata, which is very cool. These are his slides.

rostock talk 2rostock group shot

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AEI panel on ‘demographic decline’

I was on a panel at the American Enterprise Institute, titled, “Demographic decline: National crisis or moral panic?” The event featured Lyman Stone, who argued that “demographic decline” in the U.S. is a national crisis, and my reply. Nick Eberstadt from AEI also offered comments. The moderator was AEI’s Karlyn Bowman.

The video of the event (which was on CSPAN) is below.

In my presentation I used the projections and other material I described earlier, here (where you can also link to the data and package I used). The gist of my talk is that with immigration we don’t have an issue of declining population.

I also emphasized the political implications of catastrophic “demographic decline” talk, which are based on a combination of doomsday demographics and increasing race/ethnic diversity. For that part I included these two figures, which I worked up for the next edition of my textbook. The first shows Census Bureau projections of the U.S. population by race/ethnicity, which is the basis for the White supremacist panic. (Important caveat about this figure is the assumptions about the ethnic identity of the descendants of today’s Latinos, see Richard Alba.)

re-forecast

For the politics of immigration, which is a giant topic, I presented this very simple figure showing the rise of Latin American and Asian immigration since 1965.

imre-history

Here is the video on YouTube. If you prefer the CSPAN production style (or don’t want to give AEI click), theirs is here. My talk is 15 minutes, starting at 13:40.

Happy to hear your responses, including on the dicey issue of whether to participate in an AEI event.

(In the YouTube comments, the first person calls me a “Jewish supremacist” and demands to know my view on Israeli immigration policy, and another says, “This guy is through and through an open borders globalist.” So that’s the dialogue, too.)

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The Coming Divorce Decline, Socius edition

“The Coming Divorce Decline, ” which I first posted a year ago, has now been published by the journal Socius.  Three thousand people have downloaded it from SocArXiv, I presented it at the Population Association, and it’s been widely reported (media reports), but now it’s also “peer reviewed.” Since Socius is open access, I posted their PDF on SocArXiv, and now that version appears first at the same DOI or web address (paper), while the former editions are also available.

Improvement: Last time I posted about it here I had a crude measure of divorce risk with one point each for various risk factors. For the new version I fixed it up, using a divorce prediction model for people married less than 10 years in 2017 to generate a set of divorce probabilities that I apply to the newly-wed women from 2008 to 2017:

…the coefficients from this model are applied to newly married women from 2008 to 2017 to generate a predicted divorce probability based on 2017 effects. The analysis asks what proportion of the newly married women would divorce in each of their first 10 years of marriage if 2017 divorce propensities prevailed and their characteristics did not change.

The result looks like this, showing the annual probability falling from almost 2.7% to less than 2.4%:

divprobnewlyweds

The fact that this predicted probability is falling is the (now improved) basis for my prediction that divorce rates will continue to decline in the coming years: the people marrying now have fewer risk factors. (The data and code for all this is up, too).


Prediction aside: The short description of study preregistration is “specifying your plan in advance, before you gather data.” You do this with a time-stamped report so readers know you’re not rejiggering the results after you collect data to make it look like you were right all along. This doesn’t always make sense with secondary data because the data is already collected before we get there. However, in this case I was making predictions about future data not yet released. So the first version of this paper, posted last September and preserved with a time stamp on SocArXiv, is like a preregistration of the later versions, effectively predicting I would find a decline in subsequent years if I used the same models — which I did. People who use data that is released on a regular schedule, like ACS, CPS, or GSS, might consider doing this in the future.


Rejection addendumSociological Science rejected this — as they do, in about 30 days, with very brief reviews — and based on their misunderstandings I made some clarifications and explained the limitations before sending it to Socius. Since the paper was publicly available the whole time this didn’t slow down the progress of science, and then I improved it, so I’m happy about it.

Just in case you’re worried that this rejections means the paper might be wrong, I’m sharing their reviews here, as summarized by the editor. If you read the current version you’ll see how I clarified these points.

* While the analyses are generally sensible, both Consulting Editors point out the paper’s modest contribution to the literature relative to Kennedy and Ruggles (2014) and Hemez (2017). The paper cites both of these papers but does not make clear how the paper adds to our understanding derived from those papers. If the relatively modest extension in the time frame in this paper is sociologically consequential, the paper does not make the case clearly.

* There is more novelty in the paper’s estimates of the annual divorce probability for newly-married women (shown in Table 3 and Figure 3), based on estimating a divorce model for the most recent survey year, and then applying the coefficients from that model to prior years. This procedure was somewhat difficult for the readers to follow, but issues were raised, most notably the question of the sensitivity of the results to the adjustments made. As one CE noted, “Excluding those in the first year of marriage is problematic as newlyweds have a high rate of divorce. Also, why just married in the last 10 years? Consider whether married for the first time vs remarried matters. Also, investigate the merits of an age restriction given the aging of the population Kennedy and Ruggles point to.”

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Against the generations, with video

I had the opportunity to make a presentation at the National Academies to the “Committee on the Consideration of Generational Issues in Workforce Management and Employment Practices.” If you’ve followed my posts about the “generation” terms and their use in the public sphere you understand how happy this made me.

The committee is considering a wide array of issues related to the changing workforce — under a contract from the Army — and I used the time to address the uses and misuses of cohort concepts and analysis in analyzing social change.

In the introduction, I said generational labels, e.g., “Millennials”:

encourage what’s bad about social science. It drives people toward broad generalizations, stereotyping, click bait, character judgment, and echo chamber thinking. … When we give them names and characters we start imposing qualities onto populations with absolutely no basis, or worse, on the basis of stereotyping, and then it becomes just a snowball of clickbait confirmation bias. … And no one’s really assessing whether these categories are doing us any good, but everyone’s getting a lot of clicks.

The slides I used are here in PDF. The whole presentation was captured on video, including the Q&A.

From my answer to the last question:

Cohort analysis is really important. And the life course perspective, especially on demographic things, has been very important. And as we look at changes over time in the society and the culture, things like how many times you change jobs, did you have health insurance at a certain point in your life, how crowded were your schools, what was the racial composition of your neighborhood or school when you were younger — we want to think about the shadow of these events across people’s lives and at a cultural level, not just an individual level. So it absolutely is important. … That’s a powerful way of thinking and a good opportunity to apply social science and learn from it. So I don’t want to discourage cohort thinking at all. I just want to improve it… Nothing I said should be taken to be critical of the idea of using cohorts and life course analysis in general at all.

You know, this is not my most important work. We have bigger problems in society. But understanding demographic change, how it relates to inequality, and communicating that in ways that allow us to make smarter decisions about it is my most important work. That’s why I consider this to be part of it.

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Fertility rate implications explained

(Sorry for the over-promising title; thanks for the clicks.)

First where we are, then projections, with figures.

For background: Caroline Hartnett has an essay putting the numbers in context. Leslie Root has a recent piece explaining how these numbers are deployed by white supremacists (key point: over-hyping the downside of lower fertility rates has terrible real-world implications).

Description

The National Center for Health Statistics released the 2018 fertility numbers yesterday, showing another drop in birth rates, and the lowest fertility since the Baby Boom. We are continuing a historical process of moving births from younger to older ages, which shows up as fewer births in the transition years. I illustrate this each year by updating this figure, showing the relative change in birth rates by age since 1989:

change in birthrates by age 1989-2016.xlsx

Historically, postponement was associated with reduction in lifetime births — which is what really matters for population trends. When people were having lots of children, any delay reduced the total number. With birth rates around two per woman, however, there is a lot more room for postponement — a lot of time to get to two. (At the societal level, both reduction and postponement are generally good for gender equality, if women have good health and healthcare.)

This means that drops in what we demographers call “period” fertility (births right now) are not the same as drops in “completed” fertility (births in a lifetime), or falling population in the long run. The period fertility measure most often used, the unfortunately named total fertility rate (TFR), is often misunderstood as an indicator of how many children women will have. It is actually how many births they are having right now, expressed in lifetime terms (I describe it in this video, with instructions).

Lawrence Wu and Nicholas Mark recently showed that despite several periods of below “replacement” fertility (in terms of TFR), no U.S. cohort of women has yet finished their childbearing years with fewer than two births per woman. Here is the completed fertility of U.S. women, by year of birth, as recorded by the General Social Survey. By this account, women born in the early 1970s (now in their late-forties by 2018) have had an average of 2.3 children.

Stata graph

Whether our streak of over-two completed fertility persists depends on what happens in in the next few years (and of course on immigration, which I’ll get to).

Last year at this time I summed up the fertility situation and concluded, “sell stock now,” because birth rates fell for women at all ages except over 40. That kind of postponement, I figured, based on history, reflected economic uncertainty and thus was an ill omen for the economy. The S&P 500 is up 5% since then, which isn’t bad as far as my advice goes. And I’m still bearish based on these birth trends (I bet I’ll be right before fertility increases).

Projection

It is very hard to have an intuitive sense of what demographic indicators mean, especially for the future. So I’ve made some projections to show the math of the situation, to get the various factors into scale. My point is to show what the current (or future) birth rates imply about future growth, and the relative role of immigration.

These projections run from 2016 to 2100. I made them using the Census Bureau’s Demographic Analysis and Population Projection System software, which lets me set the birth, death, and migration rates.* I started with the 2016 population because that’s the most recent set of life tables NCHS has released for mortality. Starting in 2018 I apply the current age-specific birth rates.

First, the most basic projection. This is what would happen if birth rates stayed the same as those in 2018 and we completely cut off all immigration (Projection A), or if we had net migration running at the current level of just under +1 million each year, using Census estimates for age and sex of the migrants (Projection B).

projections.xlsx

From the 2016 population of 323 million, if the birth rates by age in 2018 were locked in, the population would peak at 329 million in 2029 and then start to decline, reaching 235 million by 2100. However, if we maintain current immigration levels (by age and sex), the population would keep growing till 2066 before tapering only slightly. (Note this assumes, unrealistically, that the immigrants and their children have the same birth rates as the current population; they have generally been higher.) This the most important bottom line: there is no reason for the U.S. to experience population decline, with even moderate levels of immigration, and assuming no rebound in fertility rates. Immigration rates do not have to increase to maintain the current population indefinitely.

Note I also added the percentage of the population over age 65 on the figure. That number is about 16% now. If we cut off immigration and maintain current birth rates, it would rise to 25% by the end of the century, increasing the need for investment in old age stuff. If we allow current migration to continue, that growth is less and it only reaches 23%. This is going up no matter what.

To show the scale of other changes that we might expect — again, not predictions — I added a few other factors. Here are the same projections, but adding a transition to higher life expectancies by 2080 (using Japan’s current life tables; we can dream). In these scenarios, population decline is later and slower (and not just at older ages, since Japan also has lower child mortality).

projections.xlsx

Under these scenarios, with rising life expectancies, the old population rises more, to between 27% and 29%. Generally experts assume life expectancies will rise more than this, but that’s the assumed direction (now, unbelievably, in doubt).

Finally, I’ve been assuming birth rates will not fall further. If what we’re seeing now is fertility postponement, we wouldn’t expect much more decline. But what if fertility keeps falling? Here is what you get with the assumptions in Projection D, plus total fertility rates falling to 1.6, either by 2030 or 2050. As you can see, in the 1.6 to 1.8 range, the effects on population size aren’t great in this time scale.

projections.xlsx

Conclusion: We are on track for slowing population growth, followed by a plateau or modest decline, with population aging, by the end of the century, and immigration is a bigger question than fertility rates, for both population growth and aging.

Perspective

In a global context where more people want to come here than want to leave (to date), worrying about low birth rates tends to lend itself to myopic, religious, or racist perspectives which I don’t share. I don’t think American culture is superior, whites are in danger of extinction, or God wants us to have more children.

I do not agree with Dowell Myers, who was quoted yesterday as saying, “The birthrate is a barometer of despair.” That even as some people are having fewer children than they want, or delaying childbearing when they would rather not. In the most recent cohort to finish childbearing, 23% gave an “ideal number of children for a family to have” that was greater than the number they had, and that number has trended up, as you can see here:

Stata graph

Is this rising despair? As individuals, people don’t need to have children any more. Ideally, they have as many as they want, when they want, but they are expensive and time consuming and it’s not surprising people end up with fewer than they think “ideal.” Not to be crass about it, but I assume the average person also has fewer boats than they consider ideal.

And how do we know what is the right level of fertility for the population? As Marina Adshade said on Twitter, “Did women actually have a desire for more children in the past? Or did they simply lack the bargaining power and means to avoid births?”

However, to the extent that low birth rates reflect frustrated dreams, or fear and uncertainty, or insufficient support for families with children, of course those are real problems. But then let’s name those problems and address them, rather than trying to change fertility rates or grow the population, which is a policy agenda with a very bad track record.


* I put the DAPPS file package I created on the Open Science Framework, here. If you install DAPPS you can open this and look at the projections output, with graphs and tables and population pyramids.

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The changing household age range, U.S. 1900-2017

One way to understand daily interaction, and intergenerational resource exchange, is just to look at the structure of households. This doesn’t tell you everything that goes on in households, but it gives some strong clues. And we can measure it going back more than a century, thanks to IPUMS.org’s collection of Census microdata.

In 1900, the most common situation for an American was to live in a household where the age difference between the oldest and youngest person was about 38 years. Now the most common situation is an age range of 0 — either living alone, or with someone of the exact same age. And there are a lot more people living in households with only similar-aged adults, with age ranges of less than 10.

In between 1900 and 2017, life expectancy increased, the age at first birth increased, and the tendency to live in multigenerational households fell and then rose again. So the household structure story is complicated, and this is just one perspective.

But it’s one indicator of how life has changed. Line up your household from youngest to oldest, look to your left and look to your right — how far can you see?

household age range

 

Data and Stata code (for all decades 1900-2000, then individual years to 2017) are available on the Open Science Framework, here.

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Family Demography seminar syllabus

Sabbatical over

Syllabuses done

Welcome all students

Come many come one


34864841863_175967af26_k

Shanghai Museum, Summer 2017 (photo PNC, Flickr CC)

Here is my revised syllabus for a graduate seminar in family demography. Comments and suggestions always welcome. This is just the reading list, but the bureaucratic parts are available in the PDF version. A lot of the papers are paywalled, but you can get most by pasting the DOIs into the sci-hub pirate site search box (if it’s not blocked where you are.)

Week 1

Theoretical perspectives in demography

Week 2

Demographic transition

Week 3

Fertility in poor countries

Week 4

Second demographic transition

Week 5

U.S. History

Week 6

Marriage and social class

  • Cherlin, Andrew J. 2014. Labor’s Love Lost: The Rise and Fall of the Working-Class Family in America. New York: Russell Sage Foundation.
  • Cohen, Philip N. 2014. The Family: Diversity, Inequality, and Social Change. New York: W. W. Norton & Company. Chapter 8, “Marriage and cohabitation.”

Week 7

Divorce

Week 8

Transition to adulthood

Week 9

Women and families in Asia and Africa

  • Yeung, Wei-Jun Jean, Sonalde Desai, and Gavin W. Jones. 2018. “Families in Southeast and South Asia.” Annual Review of Sociology 44 (1): 469–95. https://doi.org/10.1146/annurev-soc-073117-041124.
  • Desai, Sonalde, and Lester Andrist. 2010. “Gender Scripts and Age at Marriage in India.” Demography 47 (3): 667–87.
  • Clark, Shelley, Sangeetha Madhavan, Cassandra Cotton, Donatien Beguy, and Caroline Kabiru. 2017. “Who Helps Single Mothers in Nairobi? The Role of Kin Support.” Journal of Marriage and Family 79 (4): 1186–1204. https://doi.org/10.1111/jomf.12404.

Week 10

U.S. economic conditions and family outcomes

Week 11

Policy, race, and nonmarital births

Week 12

More U.S. inequality issues

  • Brady, David, Ryan M. Finnigan, and Sabine Hübgen. 2017. “Rethinking the Risks of Poverty: A Framework for Analyzing Prevalences and Penalties.” American Journal of Sociology 123 (3): 740–86. https://doi.org/10.1086/693678.
  • Western, Bruce, and Christopher Wildeman. 2009. “The Black Family and Mass Incarceration.” Annals of the American Academy of Political and Social Science 621 (1): 221–242.
  • Two selections from Families in an Era of Increasing Inequality (2015) edited by Paul R. Amato, Alan Booth, Susan M. McHale, and Jennifer Van Hook, 3–23. National Symposium on Family Issues 5. Springer International Publishing.
    • McLanahan, Sara, and Wade Jacobsen. “Diverging Destinies Revisited.”
    • Cohen, Philip N. 2015. “Divergent Responses to Family Inequality.”

Week 13

Family structure and child wellbeing

Week 14

Maternal mortality

 Week 15

Immigrant families

  • Menjívar, Cecilia, Leisy J. Abrego, and Leah C. Schmalzbauer. 2016. Immigrant Families. John Wiley & Sons.

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