Sociologist, scientist? Toward transparency, accountability, and a sharing culture

With the help of the designer Brigid Barrett, I have a new website at philipncohen.com, and a redesigned blog to match (which you’re looking at now). We decided on the tagline, “Sociologist / Demographer” for the homepage photo. It’s true I am those two things, but I also like how they modify each other, a type of sociologist and a type of demographer. First some reflections, then a little data.

I shared the website on Twitter, and wrote this in a thread:

Having “sociologist” attached to your name is not going to signal scientific rigor to the public in the way that other discipline labels might (like, I think, “demographer”). A lot of sociologists, as shown by their behavior, are fine with that. Your individual behavior as a researcher can shape the impression you make, but it will not change the way the discipline is seen. Until the discipline — especially our associations but also our departments — adopts (and communicates) scientific practices, that’s how it will be. As an association, ASA has shown little interest in this, and seems unlikely to soon.

A substantial portion of sociologists rejects the norms of science. Others are afraid that adopting them will make their work “less than” within the discipline’s hierarchy. For those of us concerned about this, the practices of science are crucial: openness, transparency, reproducibility. We need to find ways at the sub-discipline level to adopt and communicate these values and build trust in our work. Building that trust may require getting certain publics to see beyond the word “sociologist,” rather than just see value in it. They will see our open practices, our shared data and code, our ability to admit mistakes, embrace uncertainty, and entertain alternative explanations.

There are other sources of trust. For example, taking positions on social issues or politics is also a way of building trust with like-minded audiences. These are important for some sociologists, and truly valuable, but they’re different from science. Maybe unreasonably, I want both. I want some people to give my work a hearing because I take antiracist or feminist positions in my public work, for example. And also because I practice science in my research, with the vulnerability and accountability that implies. Some people would say my public political pronouncements undermine not just my science, but the reputation of the discipline as a whole. I can’t prove they’re wrong. But I think the roles of citizen and scholar are ultimately compatible. Having a home in a discipline that embraced science and better communicated its value would help. A scientific brand, seal of approval, badges, etc., would help prevent my outspokenness from undermining my scientific reputation.

One reply I got, confirming my perception, was, “this pretence of natural science needs to be resisted not indulged.” Another wrote: “As a sociologist and an ethnographer ‘reproducibility’ will always be a very weak and mostly inapplicable criterion for my research. I’m not here to perform ‘science’ so the public will accept my work, I’m here to seek truth.” Lots of interesting responses. Several people shared this old review essay arguing sociology should be more like biology than like physics, in terms of epistemology. The phrase “runaway solipsism” was used.

I intended my tweets to focus on the open “science practices” which which I have been centrally concerned, centered on scholarly communication: openness, transparency, replicability. That is, I am less interested in the epistemological questions of what is meaning and truth, and solipsism, and more concerned with basic questions like, “How do we know researchers are doing good research, or even telling the truth?” And, “How can we improve our work so that it’s more conducive to advancing research overall?”

Whether or not sociology is science, we should have transparency, accountability, and a sharing culture in our work. This makes our work better, and also maybe increases our legitimacy in public.

Where is ASA?

To that end, as an elected member of the American Sociological Association Committee on Publications, two years ago I proposed that the association adopt the Transparency and Openness Promotion Guidelines from the Center for Open Science, and to start using their Open Science Badges, which recognize authors who provide open data, open materials, or use preregistration for their studies. It didn’t go over well. Some people are very concerned that rewarding openness with little badges in the table of contents, which presumably would go mostly to quantitative researchers, would be seen as penalizing qualitative researchers who can’t share their data, thus creating a hierarchy in the discipline.

So at the January 2019 meeting the committee killed that proposal so an “ad hoc committee could be established to evaluate the broader issues related to open data for ASA journals.” Eight months later, after an ad hoc committee report, the publications committee voted to “form an ad hoc committee [a different one this time] to create a statement regarding conditions for sharing data and research materials in a context of ethical and inclusive production of knowledge,” and to, “review the question about sharing data currently asked of all authors submitting manuscripts to incorporate some of the key points of the Committee on Publications discussion.” The following January (2020), the main committee was informed that the ad hoc committee had been formed, but hadn’t had time to do its work. Eight months later, the new ad hoc committee proposed a policy: ask authors who publish in ASA journals to declare whether their data and research materials are publicly available, and if not why not, with the answers to be appended in a footnote to each article. The minutes aren’t published yet, but I seem to remember us approving the proposal (minutes should appear in the spring, 2021). So, after two years, all articles are going to report whether or not materials are available. Someday. Not bad, for ASA!

To see how we’re doing in the meantime, and inspired by the Twitter exchange, I flipped through the last four issues of American Sociological Review, the flagship journal of the association, to assess the status of data and materials sharing. That is, 24 articles published in 2020. The papers and what I found are listed in the table below.

There were six qualitative papers and three mixed qualitative/quantitative papers. None of these provided access to research materials such as analysis code, interview guides, survey instruments, or transcripts — or provided an explanation for why these materials were not available. Among the 15 quantitative papers, four provided links to replication packages, with the code required to replicate the analyses in the papers. Some of these used publicly available data, or included the data in the package, while the others would require additional steps to gain access to the data. The other 11 provided neither data nor code or other materials.

That’s just from flipping through the papers, searching for “data,” “code,” “available,” reading the acknowledgments and footnotes, and so on. So I may have missed something. (One issue, which maybe the new policy will improve, is that there is no standard place on the website or in the paper for such information to be conveyed.) Many of the papers include a link on the ASR website to “Supplemental Material,” but in all cases this was just a PDF with extra results or description of methods, and did not include computer code or data. The four papers that had replication packages all linked to external sites, such as Github or Dataverse, which are great but are not within the journal’s control, so the journal can’t ensure they are correct, or that they are maintained over time. Still, those are great.

I’m not singling out papers (which, by the way, seem excellent and very interesting — good journal!), just pointing out the pattern. Let’s just say that any of these authors could have provided at least some research materials in support of the paper, if they had been personally, normatively, or formally compelled to do so.

Why does that matter?

First, providing things like interview guides, coding schemes, or statistical code, is helpful to the next researcher who comes along. It makes the article more useful in the cumulative research enterprise. Second, it helps readers identify possible errors or alternative ways of doing the analysis, which would be useful both to the original authors and to subsequent researchers who want to take up the baton or do similar work. Third, research materials can help people determine if maybe, just maybe, and very rarely, the author is actually just bullshitting. I mean literally, what do we have besides your word as a researcher that anything you’re saying is true? Fourth, the existence of such materials, and the authors’ willingness to provide them, signals to all readers a higher level of accountability, a willingness to be questioned — as well as a commitment to the collective effort of the research community as a whole. And, because it’s such an important journal, that signal might boost the reputation for reliability and trustworthiness of the field overall.

There are vast resources, and voluminous debates, about what should be shared in the research process, by whom, for whom, and when — and I’m not going to litigate it all here. But there is a growing recognition in (almost) all quarters that simply providing the “final” text of a “publication” is no longer the state of the art in scholarly communication, outside of some very literary genres of scholarship. Sociology is really very far behind other social science disciplines on this. And, partly because of our disciplinary proximity to the scholars who raise objections like those I mentioned above, even those of us who do the kind of work where openness is most normative (like the papers below that included replication packages), can’t move forward with disciplinary policies to improve the situation. ASR is paradigmatic: several communities share this flagship journal, the policies of which are serving some more than others.

What policies should ASA and its journals adopt to be less behind? Here are a few: Adopt TOP badges, like the American Psychological Association has; have their journals actually check the replication code to see that it produces the claimed results, like the American Economic Association does; publish registered reports (peer review before results known), like all experimental sciences are doing; post peer review reports, like Nature journals, PLOS, and many others do. Just a few ideas.

Change is hard. Even if we could agree on the direction of change. Brian Nosek, director of the Center for Open Science (COS), likes to share this pyramid, which illustrates their “strategy for culture and behavior change” toward transparency and reproducibility. The technology has improved so that the lowest two levels of the pyramid are pretty well taken care of. For example, you can easily put research materials on COS’s Open Science Framework (with versioning, linking to various cloud services, and collaboration tools), post your preprint on SocArXiv (which I direct), and share them with the world in a few moments, for free. Other services are similar. The next levels are harder, and that’s where we in sociology are currently stuck.

For some how-to reading, consider, Transparent and Reproducible Social Science Research: How to Do Open Science, by Garret Christensen, Jeremy Freese, and Edward Miguel (or this Annual Review piece on replication specifically). For an introduction to Scholarly Communication in Sociology, try my report with that title. Please feel free to post other suggestions in the comments.

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Four 2020 issues of American Sociological Review

Reference	Quant/Qual	Data type	Data available?	Code available?	Note
Faber, Jacob W. 2020. “We Built This: Consequences of New Deal Era Intervention in America’s Racial Geography.” American Sociological Review 85 (5): 739–75.	Quant	Census+	No	No
Brown, Hana E. 2020. “Who Is an Indian Child? Institutional Context, Tribal Sovereignty, and Race-Making in Fragmented States.” American Sociological Review 85 (5): 776–805.	Qual	Archival	No	No
Daminger, Allison. 2020. “De-Gendered Processes, Gendered Outcomes: How Egalitarian Couples Make Sense of Non-Egalitarian Household Practices.” American Sociological Review 85 (5): 806–29.	Qual	Interviews	No	No
Mazrekaj, Deni, Kristof De Witte, and Sofie Cabus. 2020. “School Outcomes of Children Raised by Same-Sex Parents: Evidence from Administrative Panel Data.” American Sociological Review 85 (5): 830–56.	Quant	Administrative	No	Upon request	Info on how to obtain data provided.
Becker, Sascha O., Yuan Hsiao, Steven Pfaff, and Jared Rubin. 2020. “Multiplex Network Ties and the Spatial Diffusion of Radical Innovations: Martin Luther’s Leadership in the Early Reformation.” American Sociological Review 85 (5): 857–94.	Quant	Network	No	No	Says data is in the ASR online supplement but it’s not.
Smith, Chris M. 2020. “Exogenous Shocks, the Criminal Elite, and Increasing Gender Inequality in Chicago Organized Crime.” American Sociological Review 85 (5): 895–923.	Quant	Network	No	No	Code described.
Storer, Adam, Daniel Schneider, and Kristen Harknett. 2020. “What Explains Racial/Ethnic Inequality in Job Quality in the Service Sector?” American Sociological Review 85 (4): 537–72.	Quant	Survey	No	No
Ranganathan, Aruna, and Alan Benson. 2020. “A Numbers Game: Quantification of Work, Auto-Gamification, and Worker Productivity.” American Sociological Review 85 (4): 573–609.	Mixed	Mixed	No	No
Fong, Kelley. 2020. “Getting Eyes in the Home: Child Protective Services Investigations and State Surveillance of Family Life.” American Sociological Review 85 (4): 610–38.	Qual	Mixed	No	No
Musick, Kelly, Megan Doherty Bea, and Pilar Gonalons-Pons. 2020. “His and Her Earnings Following Parenthood in the United States, Germany, and the United Kingdom.” American Sociological Review 85 (4): 639–74.	Quant	Survey	Yes	Yes	Offsite replication package.
Burdick-Will, Julia, Jeffrey A. Grigg, Kiara Millay Nerenberg, and Faith Connolly. 2020. “Socially-Structured Mobility Networks and School Segregation Dynamics: The Role of Emergent Consideration Sets.” American Sociological Review 85 (4): 675–708.	Quant	Administrative	No	No
Schaefer, David R., and Derek A. Kreager. 2020. “New on the Block: Analyzing Network Selection Trajectories in a Prison Treatment Program.” American Sociological Review 85 (4): 709–37.	Quant	Network	No	No
Choi, Seongsoo, Inkwan Chung, and Richard Breen. 2020. “How Marriage Matters for the Intergenerational Mobility of Family Income: Heterogeneity by Gender, Life Course, and Birth Cohort.” American Sociological Review 85 (3): 353–80.	Quant	Survey	No	No
Hook, Jennifer L., and Eunjeong Paek. 2020. “National Family Policies and Mothers’ Employment: How Earnings Inequality Shapes Policy Effects across and within Countries ,  National Family Policies and Mothers’ Employment: How Earnings Inequality Shapes Policy Effects across and within Countries.” American Sociological Review 85 (3): 381–416.	Quant	Survey+	Yes	Yes	Offsite replication package.
Doering, Laura B., and Kristen McNeill. 2020. “Elaborating on the Abstract: Group Meaning-Making in a Colombian Microsavings Program.” American Sociological Review 85 (3): 417–50.	Mixed	Survey+	No	No
Decoteau, Claire Laurier, and Meghan Daniel. 2020. “Scientific Hegemony and the Field of Autism.” American Sociological Review 85 (3): 451–76.	Qual	Archival	No	No	“Information on the coding schema is available upon request.”
Kiley, Kevin, and Stephen Vaisey. 2020. “Measuring Stability and Change in Personal Culture Using Panel Data.” American Sociological Review 85 (3): 477–506.	Quant	Survey	Yes	Yes	Offsite replication package.
DellaPosta, Daniel. 2020. “Pluralistic Collapse: The ‘Oil Spill’ Model of Mass Opinion Polarization.” American Sociological Review 85 (3): 507–36.	Quant	Survey	Yes	Yes	Offsite replication package.
Simmons, Michaela Christy. 2020. “Becoming Wards of the State: Race, Crime, and Childhood in the Struggle for Foster Care Integration, 1920s to 1960s.” American Sociological Review 85 (2): 199–222.	Qual	Archival	No	No
Calarco, Jessica McCrory. 2020. “Avoiding Us versus Them: How Schools’ Dependence on Privileged ‘Helicopter’ Parents Influences Enforcement of Rules.” American Sociological Review 85 (2): 223–46.	Qual	Ethnography w/ survey	No	No
Brewer, Alexandra, Melissa Osborne, Anna S. Mueller, Daniel M. O’Connor, Arjun Dayal, and Vineet M. Arora. 2020. “Who Gets the Benefit of the Doubt? Performance Evaluations, Medical Errors, and the Production of Gender Inequality in Emergency Medical Education.” American Sociological Review 85 (2): 247–70.	Mixed	Administrative	No	No
Kristal, Tali, Yinon Cohen, and Edo Navot. 2020. “Workplace Compensation Practices and the Rise in Benefit Inequality ,  Workplace Compensation Practices and the Rise in Benefit Inequality.” American Sociological Review 85 (2): 271–97.	Quant	Administrative	No	No
Abascal, Maria. 2020. “Contraction as a Response to Group Threat: Demographic Decline and Whites’ Classification of People Who Are Ambiguously White.” American Sociological Review 85 (2): 298–322.	Quant	Survey experiment	No	No	Preanalysis plan registered. Data embargoed.
Friedman, Sam, and Aaron Reeves. 2020. “From Aristocratic to Ordinary: Shifting Modes of Elite Distinction.” American Sociological Review 85 (2): 323–50.	Quant	Archival	No	No

The arriving divorce decline

In “The Coming Divorce Decline” I showed the U.S. divorce rate falling from 2008 to 2017, and predicted that, because the married population was being stocked with increasingly non-divorce-prone marriages, the rate would continue to fall. After the first draft (based on 2016 data), divorce fell in 2017, providing the first support for my prediction before the paper was even “published” (accepted for Socius). Now the 2018 data is out, and divorce has become less common still.

Here’s a quick update.

Based on the number of divorces reported in the survey each year, by sex, and the number of married people, I calculate the refined divorce rate, or the number of divorces per 1,000 married people. That fell another 3% for both women and men in 2018, to 15.9 and 14.3 respectively (the rates differ because these are self reports and women report more).

When I run the model from the paper again on the new data (on women only), I can show the drop in the adjusted odds of divorce, updating Figure 1 of the paper (the 2018 change in an unadjusted model is significant at p=.06; adjusted is p=.14, the adjusted change from 2016 is significant at p=.002).

For other takes on the latest data, see this report on the marriage-divorce ratio from Valerie Schweizer, and this on geographic variation from Colette Allred, both at the National Center for Family and Marriage Research.

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• The data and code for the paper are available here. This update uses the same code with one new year of data.
• If you like my new Stata figure scheme (modified from Gray Kimbrough’s Uncluttered) you’re welcome to it: here.
• Slides from my presentation this fall at the European Divorce Conference are here.
• Divorce posts are gathered under this tag.

New working paper: The rising marriage mortality gap among Whites

I wrote a short working paper on U.S. mortality trends for the last decade. You can go straight to the paper on SocArXiv, or the code and output, if you want the full version.

The issue is that premature mortality has been rising for Whites, partly because of the opioid epidemic and also from suicide and alcohol, and also from other causes related to stress and hardship. (See, e.g., Case and Deaton, and Geronimus.) And a recent NCHS report showed that mortality nationally declined much more for married people since 2010.

So I got the Mortality Multiple Cause Files from the National Center for Health Statistics, for two years: 2007 and 2017. These are a complete set of death certificates, which include race/ethnicity, marital status, and education. I linked these to the American Community Survey, to create age-specific mortality rates by age, sex, marital status, and education, for non-Hispanic Whites, Hispanics, and Blacks, in the ages 25-74 (old enough to finished with college, but too young to die).

The basic result is that virtually all of the growth in premature death is among Whites, and further among non-married Whites. (Whites still dies less than Blacks, and more than Hispanics, at each age and marital status.)

Here is the figure of age-specific mortality rates, by race/ethnicity, sex, and marital status for 2007 and 2017. At the bottom of each column I calculated “marriage mortality ratios,” which are how much more likely single people are to die than married people. Note these death rates are deaths per 10,000, but they’re on a log scale so you can see changes where rates are very low.

In the figure you can see how much the marriage mortality ratio jumped up, for Whites only. Now, at the most extreme, single White men age 35-39 are more than 4-times more likely to die than married White men (that’s in the bottom left).

Then I zoom into Whites specifically, and do the same thing for four levels of education:

In the lowest education group of Whites (the far left), mortality rates for married and single people increased similarly, so the marriage mortality ratio didn’t increase. However, for the other education levels, death rates increased for single people more than married people, so the ratio increased (across the bottom). Even among White college graduates, there were increases in mortality for single people. I did not expect that.

My bottom line is that marriage is taking an ever-more prominent place in the social status hierarchy, and now we can add growing mortality inequality, at least among Whites, to that pattern.

Early version, comments welcome!

Divorce fell in one Florida county (and 31 others), and you will totally believe what happened next

You can really do a lot with the common public misperception that divorce is always going up. Brad Wilcox has been taking advantage of that since at least 2009, when he selectively trumpeted a decline in divorce (a Christmas gift to marriage) as if it was not part of an ongoing trend.

I have reported that the divorce rate in the U.S. (divorces per married woman) fell 21 percent from 2008 to 2017.  And yet yesterday, Faithwire’s Will Maule wrote, “With divorce rates rocketing across the country, it can be easy to lose a bit of hope in the God-ordained bond of marriage.”

Anyway, now there is hope, because, as right-wing podcaster Lee Habeeb wrote in Newsweek, THE INCREDIBLE SUCCESS STORY BEHIND ONE COUNTY’S PLUMMETING DIVORCE RATE SHOULD INSPIRE US ALL. In fact, we may be on the bring of Reversing Social Disintegration, according to Seth Kaplan, writing in National Affairs. That’s because of the Culture of Freedom Initiative of the Philanthropy Roundtable (a right-wing funding aggregator run by people like Art Pope, Betsy Devos, the Bradley Foundation, the Hoover Institution, etc.), which has now been spun off as Cummunio, a marriage ministry that uses marriage programs to support Christian churches. Writes Kaplan:

The program, which has recently become an independent nonprofit organization called Communio, used the latest marketing techniques to “microtarget” outreach, engaged local churches to maximize its reach and influence, and deployed skills training to better prepare individuals and couples for the challenges they might face. COFI highlights how employing systems thinking and leveraging the latest in technology and data sciences can lead to significant progress in addressing our urgent marriage crisis.

The program claims 50,000 people attended four-hour “marriage and faith strengthening programs,” and further made 20 million Internet impressions “targeting those who fit a predictive model for divorce.” So, have they increased marriage and reduced divorce? I don’t know, and neither do they, but they say they do.

Funny aside, the results website today says “Communio at work: Divorce drops 24% in Jacksonville,” but a few days ago the same web page said 28%. That’s probably because Duval County (which is what they’re referring to) just saw a SHOCKING 6% INCREASE IN DIVORCE (my phrase) in 2018 — the 10th largest divorce rate increase in all 40 counties in Florida for which data are available (see below). But anyway, that’s getting ahead of the story.

Gimme the report

The 28% result came from this report by Brad Wilcox and Spencer James, although they don’t link to it. That’s what I’ll focus on here. The report describes the many hours of ministrations, and the 20 million Internet impressions, and then gets to the heart of the matter:

We answer this question by looking at divorce and marriage trends in Duval County and three comparable counties in Florida: Hillsborough, Orange, and Escambia. Our initial data analysis suggests that the COFI effort with Live the Life and a range of religious and civic partners has had an exceptional impact on marital stability in Duval County. Since 2016, the county has witnessed a remarkable decline in divorce: from 2015 to 2017, the divorce rate fell 28 percent. As family scholars, we have rarely seen changes of this size in family trends over such a short period of time. Although it is possible that some other factor besides COFI’s intervention also helped, we think this is unlikely. In our professional opinion, given the available evidence, the efforts undertaken by COFI in Jacksonville appear to have had a marked effect on the divorce rate in Duval County.

A couple things about these very strong causal claims. First, they say nothing about how the “comparable counties” were selected. Florida seems to have 68 counties, 40 of which the Census gave me population counts for. Why not use them all? (You’ll understand why I ask when they get to the N=4 regression.) Second, how about that “exceptional impact,” the “remarkable decline” “rarely seen” in their experience as family scholars? Note there is no evidence in the report of the program doing anything, just the three year trend. And while it is a big decline, it’s one I would call “occasionally seen.” (It helps to know that divorce is generally going down — something the report never mentions.)

To put the decline in perspective, first a quick national look. In 2009 there was a big drop in divorce, accelerating the ongoing decline, presumably related to the recession (analyzed here). It was so big that nine states had crude divorce rate declines of 20% or more in that one year alone. Here is what 2008-2009 looked like:

So, a drop in divorce on this scale is not that rare in recent times. This is important background Wilcox is (comfortably) counting on his audience not knowing. So what about Florida?

Wilcox and James start with this figure, which shows the number of divorces per 1000 population in Duval County (Jacksonville), and the three other counties:

Again, there is no reason given for selecting these three counties. To test the comparison, which evidently shows a faster decline in Duval, they perform two regression models. (To their credit, James shared their data with me when I requested it — although it’s all publicly available this was helpful to make sure I was doing it the same way they did.) First, I believe they ran a regression with an N of 4, the dependent variable being the 2014-2017 decline in divorce rate, and the independent variable being a dummy for Duval. I share the complete dataset for this model here:

	div_chg	duval
1.	-1.116101	1
2.	-0.2544951	0
3.	-0.3307687	0
4.	-0.5048307	0

I don’t know exactly what they did with the second model, which must somehow how have a larger sample than 4 because it has 8 variables. Maybe 16 county-years? Anyway, doesn’t much matter. Here is their table:

How to evaluate a faster decline among a general trend toward lower divorce rates? If you really wanted to know if the program worked, you would have to study the program, people who were in the program and people who weren’t and so on. (See this writeup of previous marriage promotion disasters, studied correctly, for a good example.) But I’m quite confident that this conclusion is ridiculous and irresponsible: “In our professional opinion, given the available evidence, the efforts undertaken by COFI in Jacksonville appear to have had a marked effect on the divorce rate in Duval County.” No one should take such a claim seriously except as a reflection on the judgment or motivations of its author.

Because the “comparison counties” was bugging me, I got the divorce counts from Florida’s Vital Statistics office (available here), and combined them with Census data on county populations (table S0101 on census.data.gov). Since 2018 has now come out, I’m showing the change in each county’s crude divorce rate from 2015, before Communio, through 2018.

You can see that Duval has had a bigger drop in divorce than most Florida counties — 32 of which saw divorce rates fall in this period. Of the counties that had bigger declines, Monroe and Santa Rosa are quite small, but Lake County is mid-sized (population 350,000), and bigger than Escambia, which is one of the comparison counties. How different their report could have been with different comparison cases! This is why it’s a good idea to publicly specify your research design before you collect your data, so people don’t suspect you of data shenanigans like goosing your comparison cases.

What about that 2018 rebound? Wilcox and James stopped in 2017. With the 2018 data we can look further. Eighteen counties had increased divorce rates in 2018, and Duval’s was large at 6%. Two of the comparison cases (Hillsborough and Escambria) had decreases in divorce, as did the state’s largest county, Miami-Dade (down 5%).

To summarize, Duval County had a larger than average decline in divorce rates in 2014-2017, compared with the rest of Florida, but then had a larger-than-average increase in 2018. That’s it.

Marriage

Obviously, Communio wants to see more marriage, too, but here not even Wilcox can turn the marriage frown upside down.

Why no boom in marriage, with all those Internet hits and church sessions? They reason:

This may be because the COFI effort did not do much to directly promote marriage per se (it focused on strengthening existing marriages and relationships), or it may be because the effort ended up encouraging Jacksonville residents considering marriage to proceed more carefully. One other possibility may also help explain the distinctive pattern for Duval County. Hurricane Irma struck Jacksonville in September of 2017; this weather event may have encouraged couples to postpone or relocate their weddings.

OK, got it — so they totally could have increased marriage if they had wanted to. Except for the hurricane. I can’t believe I did this, but I did wonder about the hurricane hypothesis. Here are the number of marriages per month in Duval County, from 13 months before Hurrican Irma (September 2017), to 13 months after, with Septembers highlighted.

There were fewer marriages in September 2017 than 2016, 51 fewer, but September is a slow month anyway. And they almost made up for it with a jump in December, which could be hurricane-related postponements. But then the following September was no better, so this hypothesis doesn’t look good. (Sheesh, how much did they get paid to do this report? I’m not holding back any of the analysis here.)

Aside: Kristen & Jessica had a beautiful wedding in Jacksonville just a few days after Hurricane Irma. Jessica recalled, “Hurricane Irma hit the week before our wedding, which damaged our venue pretty badly. As it was outdoors on the water, there were trees down all over the place and flooding… We were very lucky that everything was cleaned up so fast. The weather the day of the wedding turned out to be perfect!” I just had to share this picture, for the Communio scrapbook:

Photo by Jazi Davis in JaxMagBride.

So, to recap: Christian philanthropists and intrepid social scientists have pretty much reversed social disintegration and the media is just desperate to keep you from finding out about it.

Also, Brad Wilcox lies, cheats, and steals. And the people who believe in him, and hire him to carry their social science water, don’t care.

Less than half of women with PhDs in survey keep ‘maiden’ names

Marital Name Change Survey first results and open data release.

Over the last three days 3,400 ever-married U.S. residents took my Marital Name Change Survey. I distributed the survey link on this blog, Facebook and Twitter. I don’t know who took it, but based on the education and occupation data a very large share of the respondents were women (88%) with professional degrees (30%) or Phds (27%). It’s not a representative sample, but the results may still be interesting.

Here I’ll give a few topline numbers as of 8:00 this morning, and then link to a public version of the data and materials. These results reflect a little data checking and cleaning and of course are subject to change.

Respondents were asked about their most recent marriage. Half were married in the 2010s, but the sample includes more than 400 married in the 1990s and 200 earlier.

The vast majority (84%) were women married to men; 11% were men married to women and 4% (~140) were in same-gender marriages. Here are some observations about the women married to men. The name-change choices are shown below, with “R change” indicating the respondent changed their name, and “Sp change” indicating their spouse changed. The “Other” field included a write-in, and the vast majority of those were variations on hyphenations or changes to middle names.

Because of the convenience nature of the sample, I don’t put much stock in the overall trend (I’ll try to develop a weighting scheme for this, but even then). However, I think the PhD sample is worth looking at. Here is the trend of women with PhDs (now or at the time of marriage) married to men.

By this reckoning, the feminist-name heyday was in the 1980s, followed by a backslide, and now a rebound of women with PhDs keeping their names. The 2010s trend is like that found in the Google Consumer survey reported by Claire Cain Miller and Derek Willis in NYT Upshot.

Note, these no-change rates are higher than those reported by Gretchen Gooding and Rose Kreider from the 2004 American Community Survey, which showed 33% of married women with PhDs had different surnames than their husbands (regardless of when they got married). I show 53% in the 2000s had different names than their husbands, and 57% in the 2010s. Maybe that’s because I have more social science and humanities PhDs, or just a more woke sample.

These results also show a strong age-at-marriage pattern, with PhD women much more likely to keep their names if they married at older ages. Over age 40, 74% of women with PhDs kept their names, compared with 20% who married under age 25. (Note this is based on education at the time of the survey; I also collected education at the time of marriage, which I discuss below.)

I asked people how important various factors were if people considered changing their names. Among PhD women marrying men who did not change their names, the most important reasons were feminism (52% “very important”), professional considerations (34%), convenience (33%), and maintaining independence within the marriage (24%). Among those who took their husbands’ names, the most important factors were the interests of their children (48%) and showing commitment to the marriage (25%).

A few other observations: PhD women were most likely to keep their names if they had no religion (53%), were Jewish (46%), or other non-Christian religion (43%); protestants (27%), Catholics (29%), and other Christians (21%) were less likely to keep their names. Finally, those who had lived together before marriage were most likely to keep their names (51% for those who lived together for three years or more, compared with 27% for those who did not live together at all).

Data availability

I don’t have time now to analyze this more, but that shouldn’t stop you. Feel free to download the data and documentation here under a CC-BY license (the only requirement is attribution). This includes a Stata data file, and PDFs of the questionnaire and codebook. This will all be revised when I have time.

Open-ended responses

I am not including in the shared files (yet) the open-ended question responses, which include descriptions of “other” name change patterns, as well as a general notes field, which is full of fascinating comments; given the non-random nature of the survey, this may turn out to be its most valuable contribution.

Here are a few.

Reasons:

I changed my name to my spouses because I HATED my father and it was the easiest way to ditch his name. I kept my married name after divorce. I’m currently pregnant (on my own) and plan to change my name again and now I will take the surname of my step-father, who has been my “dad” since I was 5.

“True partnership”

My wife and I had been together 10 years and through several iterations of domestic partnerships prior to marrying. Including before she completed her PhD. I didn’t want to change my name because my name flows really poetically and a change would ruin it (silly but true). She didn’t want to change her name in part because it’s what everyone in her profession know her as. I think we both also feel like our names represent our life histories and although we are a true partnership, that doesn’t negate our family histories or experiences. Which I guess is feminist of us. But we never explicitly discussed feminism as an issue.

This is complicated.

My partner and I both had our own hyphenated names already! We kept our own hyphenated names initially (and our marriage was not legally recognized at the time so there wasn’t a built-in or convenient option to change at that point anyway). When we had kids, we have them a hyphenated name, one of my last names and one of hers. Eventually we both changed to match the kids, so we all share the same hyphenated name now.

And so on. Fascinating reading!

Sibship size and educational attainment update

I just read this Demography paper by Benjamin G. Gibbs, Joseph Workman, and Douglas B. Downey for my work on the new edition of The Family (don’t hold your breath, but I’m working on it). It seeks to modify the traditional “resource dilution” model of explaining why children with more siblings end up with lower educational attainment, arguing that the effect depends on who else is sharing in the child rearing. Worth a read.

Anyway, since their 2016 paper only used the General Social Survey through 2010, I figured I could make a quick update through 2018. So this is based on their model, but a lot simpler — I didn’t impute missing values, and I didn’t include all the background variables (most notably parents’ education), or get into the religious context, which is the whole point of their analysis.

So, this is my result, showing proportion of people age 25 and older who have a BA or higher, by the number of siblings they have and the decade of their birth. These are average marginal effects from a model that includes race and age, as well as region, family structure (married parents), and religion at age 16.

I find no difference between 0 and 1 sibling, a little lower odds for those with 2 siblings, and then the proportion with a BA or higher drops off. The sibling effect has decreased, at least proportionally — for example, the 3-sibs to 1-sib ratio fell from 1.9-to-1 for those born in the 1910s to 1.4-to-1 for the 1940s to 1960s cohorts and even lower for the 1970s. However, the gaps crept back up in the years after Gibbs’ et al’s analysis ended, back to 1.5-to-1 for the 1990s cohort.

No doubt this is relevant to the decisions people make about how many children to have, discussed in yesterday’s post on fertility trends, and in another Atlantic piece today.

Some people who say getting married is good for kids so you should do it are also more-kids proponents. They should make their priorities clear.

I put the Stata code for this on the Open Science Framework, here.

Review of Relational Inequalities: An Organizational Approach, with audio

I had the privilege of sitting on an author-meets-critics panel for the the book Relational Inequalities: An Organizational Approach, by Donald Tomaskovic-Devey and Dustin Avent-Holt, at the Eastern Sociological Society meetings this weekend. The panel was organized by Steven Vallas, and included Adia Harvey Wingfield. Because two other panelists canceled, I had a lot of time and ended up speaking for 25 minutes. We had a great discussion after the formal remarks, which only deepened my appreciation for the book. I recorded my remarks. Here is audio, with 4 minutes of ums and dead ends edited out:

 

And here is a lightly edited transcript:

I want to thank Steve, as well as Don and Dustin, for organizing and writing, respectively. It’s really been a pleasure. In the same way that once upon a time I used to run faster when I played competitive sports, because someone was yelling at me to run faster, reading a book knowing that I’m going to offer commentary on it to an audience of people whose opinions I respect makes me try harder and pay more attention, and focus more on it. So it’s a privilege to have this be one part of my job. I don’t normally read books all the way through and think about them carefully and sketch out my thoughts, so I really learned a lot doing that.

In the process, you know, it’s 10 months ago whenever we got this invitation, and then finally the book comes, and then I skim through it, then I put it down, and then you know it comes down to the last couple of days in my room reading the book carefully, and it’s been great. And fresh. Very fresh, right through breakfast.

I want to start by talking about my own work. Just kidding.

I have an outline. I start with praise. And then questions about what’s the relationship between organizations and inequality, as far as creating, reflecting, reproducing inequality; discussion of the role of education, as one of the things that it is external to organizations; and then a discussion of inequality within and between organizations, and where this fits in with the path of social change.

Praise

It’s a really really good book. And I look forward to putting it on our comprehensive exam reading list for the inequality reading group, I think it teaches this stuff really well – the literature on organizations and inequality. A great audience for it is people who are designing research projects having to do with inequality, and what is the role of organizations going to be in the work.

One of the things that’s really important, and you have to get to it right away, is the disconnect between the method of most research which is individual observation, and mostly surveys, and the theorized mechanisms about how inequality works, which are largely relational. And so we look at individuals and we say, oh look people with more education have more income, or we say we have racial inequality and we have immigration, and we have all these measures which are usually at the individual level, and then the mechanisms which we think are producing these are schools and segregation and discrimination, and things that are all interactional, or relational, between people within and around organizations. And so that’s just a sociological take that is very important here.

I love the mezo/contextual way of thinking in the analysis, between the individual and the country or the state or something like that, and at the organizational level that complexity and variation – how there is so much difference in the patterns of inequality within organizations. Yes, men make more money than women, but how that works is very different across different organizations and places and times, and the dispersion is different, and the patterns of dispersion change, and all that variation gives us leverage to understand how inequality works, but also where policy and law can intervene. Because if you have a range of practices, and you can see the consequences of the range of practices, that’s where you get something like the idea for a policy – we should do more of this and less of this, and so on. So that variation is key, and having it at the organizational level is important.

They set out a really useful research agenda. They talk a lot about workplace ethnographies and surveys, and various ways that organizational dynamics of inequality have been studied, and the research agenda that emerges has to do with comparative organizational studies, with attention to the role of external influences on organizations. So the gold standard is sort of multi-organizational research where the context is carefully considered between the different organizations and the workings of the relations within the organizations, and hopefully between them.

The relational framework they have here is sort of Charles Tilly’s Durable Inequality plus Cecilia Ridgeway – that’s my background reading on this, which is kind of thin, admittedly. And so it’s categories and the durableness of them within institutions and organizations, and putting people into cognitive categories and how that represents the integration of social structure into personality and interaction and so on. So that’s sort of the frame, which I think is really useful.

And then the moral framework they have is very clear, at the end; and the policies they give us to talk about, both “what about worker cooperatives,” and, “what about a universal basic income” – sort of state level and organizational level policies that address the variety of problems and inequalities that we have.

Organizations and inequality

A key question, and a motivating question for them, is what is the role of organizations in the wider system of inequality – that is, are they creating inequality, are they reflecting inequality that comes to them from the outside of the organization, what’s their role in the reproduction of inequality. And so you have the organization – it’s a workplace, which is mostly what they talk about – and there are things coming at it from the outside: cognitive categories and hierarchies, status between groups, privilege groups, esteem groups, minority groups that are less privileged and so on. And then there’s a law and regulatory policy environment that they’re working within, there are market conditions that they’re working within, and then there are the workers that are coming to them with their range of unequal skills and education, their health, their social capital, their histories of incarceration – everything that workers bring to the organization. So you could ignore organizations and say, look we have all this inequality out there, outside the organization, and the organization is basically just sort of applying formulas to this: “Well, men are privileged over women, so we pay them a little bit more, we discriminate against people with criminal records, if you don’t have the skills to do the job you’re out, if you’re health is not good, if you have children, if you can’t show up…” You could think of organizations as just sort of administering the system of inequality, the structures of inequality that they’re in, or you can think of them as implementing or enacting the inequality. So until the organization gets its hands on it, all that inequality is sort of not really operationalized, it’s not really functioning – the status inequality between men and women doesn’t really happen until somebody decides to pay the man more than the woman. That’s sort of their view, not necessarily – [Don: “I agree”] – not necessarily true, but that’s the question, are organizations doing that, or they just sort of receiving that.

And the authors point out – I’ll give you a little taste of this (p. 14): “Most inequalities are generated through the relationships in and around workplaces.” That’s a very strong statement, although “most” is a little bit vague, it’s 51% to 99%. That clearly gives you a strong reason to focus on workplaces, and it’s somewhat debatable.

And they point out in a footnote (p. 58): “Obviously, power can be exercised as violence in addition to discursive claims-making [so it’s not just people debating over rewards within organizations]. Strong-armed robbery and colonial conquest are examples of violent exploitation, genocide, ethnic cleansing, political suppression via arrest of social movements’ claims of dignity and access are the violent faces of closure.” Well, none of that stuff is happening within workplaces. So if you think colonial conquest, genocide, ethnic cleansing, and political suppression are important parts of inequality, and we know that those aren’t happening within workplaces, you know the field is generating a lot of inequality outside workplaces. You have to weigh that up against their, “most of inequality comes from within workplaces,” And to their credit, it’s an empirical question, which they note. It’s hard to quantify and it’s kind of pointless to quantify but the question is where should our focus be?

By the time they’re to their conclusion, they write, “We are not arguing that only organizations matter for inequality,” ok, they are definitely not arguing that – but if you have to say that, it’s obviously relevant, so that’s a question. It really is an organizations manifesto, the book, the importance of organizations, and it makes the case very strongly. It’s extremely useful and valuable and informative. And the fact that they make the claims really strongly helps motivate it and make it clear. And whether I want to argue about whether it’s 51% or 80% of inequality that comes from workplaces, for most uses of it that’s not the point.

Related to the question of what organizations do – whether they’re creating or reflecting – is inequality, unequal what? What are we talking about? Most obviously money, some people have more money than others. But especially when you’re talking about intersectional questions, are race and class and gender just three different ways of deciding who’s going to have how much money? No, it’s much more than money, it’s cultural in terms of who’s valued and esteemed, and who gets to set the discourse, and it’s status in terms of whose opinions get respected, and voice within organizations, and it’s also geographic with segregation, and so on. And so they talk a lot about “organizational resources” being what’s at issue. Whenever I teach inequality I push sociology grad students to get beyond thinking of all these status inequalities as being different ways of deciding how much money we get. And especially, what is the content of the inequality. Unequal amounts of what are we actually talking about? And that’s why I think the feminist discourse over sexuality is so important. Because control over sexuality is sort of orthogonal to the amount of money that you have – it’s obviously related, but it’s a different quality. So that stuff is really important and there’s a lot of food for thought on that here.

I mentioned genocide and ethnic cleansing, and there are other things which are happening outside organizations that are relevant. Things that happen outside workplaces, that may be in other organizations: welfare, taxation, the education system, residential segregation, incarceration – these are all things that are packaging inequality that arrive at the doorstep of the workplace. So I’ll give two possible policy ideas that are totally outside workplaces: if we had a 90% marginal tax rate on upper incomes, you might say, “who cares about inequality within organizations?” You get rich, and the government takes your money and gives it to poorer people. And so that lowers the stakes. And partly they focus on organizations because in the United States we don’t do that. And so that question of how much empirically are organizations creating of the system of inequality, is partly that number is higher because we don’t have that kind of society. So it’s not a statement about how inequality will always forever work, it’s really driven by the reality that we have now. And the other policy challenge to thinking organizationally is reparations. If the government stepped in and had a big reparations program and orientation, that is totally outside of individual workplaces, what would that do? So those are just things to think about.

Education

Their attitude toward education is interesting. And it’s – what do you call that when it’s not traditional, it’s not “heretic,” it’s very challenging. [The word I was looking for is “heterodox.”] They basically treat education as a proxy for claims-making resources. So the amount of education people have, when they get to the workplace, allows them to essentially bargain for or demand more or less money. Which, if you’ve ever had surgery, from a doctor, you want your surgeon to have gone to medical school. [Don: “You want your surgeon to be a good surgeon.”] Right, exactly. In our system, the proxy for that is that they’ve gone to medical school, and the board certifying and all that. So their issue is how much doctors are paid, not who gets to be a doctor. They’re not talking about inequality in the education system, all the things that create the unequal distribution of medical education.

Consider this also: there are limits to the organizational variation in this. There are no organizations in the United States that let people perform surgery without medical degrees. So that’s something very strong coming from the external reality that workplaces have to deal with. They can only hire people with medical degrees to do surgery, and surgery is very valued, it commands a lot of money in the market. So if they’re going to say “wages and jobs are organizational phenomena,” which they say, and education is this way of making claims on those things, then it’s interesting to push them on this issue of who gets to have the education. They say, sort of grudgingly in my opinion, yes, sometimes educational credentialing has to do with the skills required to do the job, but basically it’s about how much money you can extract from your employer. That’s why I focus on surgery, because lots of other education is just a cruder proxy for particular skills and whatnot.

They review literature on how factories work in Mexico and the U.S., including within the same multinational company, and the gender difference between maquiladoras. But if you think globally, the difference between a doctor in the U.S. and a factory worker in Mexico, and the vast inequality in resources they command, is not determined by the practices of their organizations, right? And an interesting thing about doctors in particular, is we pay a fortune in this country because the government (because of doctors) doesn’t let foreign doctors come practice here. Our doctors get paid ridiculously high amounts (Dean Baker, the economist, has written very compellingly about this). If we allowed foreign doctors to come here, foreign doctors would make a lot more money than they’re making, our doctors would make less money, and we would all pay less for equally good healthcare. So that’s a state policy, and not something that the hospitals can address.

While we’re thinking about the external factors, and I’m pushing them on this, they do a little review of Devah Pager’s work, “the mark of a criminal record” – employers don’t hire people with criminal records – so is that a problem of employer practices or is that a problem of mass incarceration and the distribution of criminal records? It’s both, but you couldn’t understand it by only studying the practices of employers, because that’s not a fixed quantity of a randomly distributed stigma.

So when you get to the intersectional stuff – consider race, class, and gender in our system of inequality. They point out gender and race integration in education “led to a weakening of gender and race based closure” (and that shows up in Don and Kevin’s previous book, and that’s reviewed here). So there’s less job segregation by race and gender than there used to be, and less exclusion, “while leaving unchallenged, or perhaps even strengthening, education based closure.” Well, by one way of thinking, of course, if race and gender are becoming less determinative of workplace outcomes, and education is becoming more determinative, that’s literally the goal of rational modern society, is to stop with the ascriptive criteria, and start using rational educational criteria, for skills and productivity. So they’re all up in arms about this, but it’s interesting to say, well, wait a second isn’t that kind of the point, like meritocracy. “There is an intersectional reality weakening closure on the basis of race and gender even as closure rules around education remain hegemonic.” So it would be worth it to explain, and I guess they do explain, why they think this is not the definition of progress. I’m being provocative. It’s not like education is fairly distributed, so it’s still all about ascriptive inequalities through the education system.

Between and within organizations

So what about inequality between and within organizations. And here it’s interesting because the world has changed while they were writing this book. In making their case for why organizations are so important, they write, “We are born and die in organizations.” OK, I like that, they obviously think it’s very important. “We spend a great deal of our lives working alongside others in organizations” – and then listen to this list of sort of other things: “We go to one organization to be educated (schools), to another to get income (workplaces), which we then spend in another (stores), in order to bring food and clothing to a fourth (households).” So they’re telling your other organizational fields. What’s interesting is that in schools, stores, and households, there’s more inequality between than within organizations. And so they’re very focused on workplaces, where probably you find more inequality within the organizations. They’re interested in those dynamics: What causes inequality within organizations, why do CEOs make so much, why is there gender segregation in the division of labor, and so on. Interestingly, and the trend over time is probably toward more inequality between. And if you think about families, in the old days, if you had an employed man and three children and a woman who had no income, then you have a tremendous amount of inequality within that organization, within that family. Nowadays if you have two children and the parents both have jobs, you have fewer people with no income and more people with income, and so there’s less within-household inequality, and that’s a trend over time.

In their second-to-last chapter they have a very good discussion about how this is also happening with firms and workplaces in the U.S. So if General Motors outsources their custodial service (I’m just making this up), some big company outsources lower status, or higher status, work, there’s a firm that is less hierarchical somewhere, that’s just all custodians. And there’s a firm that’s just all engineers. And General Motors is like bundling those services. So the inequality is increasingly between organizations there, rather than within. So instead of hierarchy within Amazon being from Bezos to the drivers, the drivers are all contracted, and so on. And Uber, and self-employment, and the gig economy, and all that stuff is sort of like if every Uber driver is an organization the way Uber thinks they are, then the inequality is all between organizations.

And so that’s the direction of social change, and it’s a challenge for their theory. If their theory is focused on inequality within firms, and organizations, then what’s happening in world, and how does their theory address this? And they say, “even if there were no internal inequalities within firms, there still might be considerable inequality between firms, as a function of firm resource inequality.” So they’re sort of already projecting to a world where every company had no inequality within it. We’re not there at all, but their answer to that is maybe more aspirational than empirical, and I think it’s debatable, and it’s worth debating, it’s: “The processes governing inequality between organizations is fundamentally the same as that governing inequality within organizations: relational claims-making, exploitation, and social closure.” OK, that’s a very strong statement. It says we’ve sketched out this whole theory about how inequality works within organizations, we see that the world is moving toward inequality between organizations, and we’re going to apply the concepts that we’ve developed to this new reality also. And that is a challenge for future work in this area. And so I’m not expecting them to have established this empirically before they do it, but that’s their case.

That’s one of the many examples of the great research agenda that comes out of this really interesting and important work. And with that I close. Thank you.

Equal-education and wife-more-education married couples don’t have sex less often

In my review of Mark Regnerus’s book, Cheap Sex, I wrote: “The book is an extended rant on the theme, ‘Why buy the cow when you can get the milk for free?’ wrapped in a misogynist theory about sexual exchange masquerading as economics, and motivated by the author’s misogynist religious and political views.”

Someone just reposted an old book-rehash essay of Regnerus’s called, “The Death of Eros.” In it he links to my post documenting the decline in sexual frequency among married couples in the General Social Survey. In marriage, Regnerus writes, “equality is the enemy of eros,” before selectively characterizing some research about the relationship between housework and sex. (Here’s a recent analysis finding egalitarian couples don’t have sex less.)

But I realized I never looked at sexual frequency in married couples by the relative education of the spouses, which is available in the GSS. So here’s a quick take: Married man-woman couples in which the wife has equal or more education don’t have sex less frequently.

I modeled sexual frequency (an interval scale from “not at all” = 0 to “4+ times per week” = 6 as a function of age, age-squared, respondent education, respondent sex, decade, and relative education (wife has lower degree, wife has same degree, wife has higher degree). The result is in this figure. Note the means are between 3 (“2-3 times per month”) and 4 (“weekly”). Stata code for GSS below.

OK, that’s it. Here’s the code (I prettied the figure a little by hand afterwards):

*keep married people
keep if marital==1

* with non-missing own and spouse education
keep if spdeg<4 & degree<4
recode age (18/29=18) (30/39=30) (40/49=40) (50/59=50) (60/109=60), gen(agecat)
recode year (1970/1979=1970) (1980/1989=1980) (1990/1999=1990) (2000/2008=2000) (2010/2016=2010), gen(decade)
gen erosdead = spdeg>degree
gen equal=spdeg==degree

gen eros=0
replace eros=1 if spdeg<degree & sex==1
replace eros=2 if spdeg==degree
replace eros=3 if spdeg>degree & sex==1

replace eros=1 if spdeg>degree & sex==2
replace eros=3 if spdeg<degree & sex==2

label define de 1 "wife less"
label define de 2 "equal", add
label define de 3 "wife more", add
label values eros de

reg sexfreq i.sex i.agecat i.decade i.degree i.eros [weight=wtssall]
reg sexfreq i.sex c.age##c.age i.degree i.eros##i.decade [weight=wtssall]
margins i.eros##i.decade
marginsplot, recast(bar) by(decade)

Note: On 25 Dec 2018 I fixed a coding error and replaced the figure; the results are the same.

New findings: The widening political divide over science

Gordon Gauchat published a paper in American Sociological Review in 2012 (paywalled), reporting that political conservatives in the United States lost confidence in the scientific community from the 1970s to 2010, while political liberals and moderates did not. As a result, a political divide opened up so that by 2010 conservatives had the lowest level of confidence in science.

I’ve written a short working paper that extends the trends through the 2016 General Social Survey. I find that political conservatives, Republicans, and Americans who attend religious services regularly, all demonstrate falling levels of confidence in the scientific community. Further, for the most recent period (2012-2016) educational attainment, for conservatives, is not associated with increased levels of confidence in science, except at the graduate degree level (and even there confidence is lower among conservatives). It’s as if conservative views on science are impervious to education.

I have only a few sentences about politics in the paper, but the gist of it is that this divide is bad, it undermines democracy, and plays into Trump’s authoritarian hands.

Here’s the figure showing the Republicans falling off the trust cliff: the trend in expressing “a great deal of confidence” in the scientific community, with controls, by party identification:

 And here’s the figure from the recent period, showing that conservatives have lower levels of confidence regardless of education level — and education has no relationship to confidence in science for them, up to the level of graduate degree at least.

I welcome your feedback.

The paper is here on SocArXiv, and the Stata code is here.

Decadally-biased marriage recall in the American Community Survey

Do people forget when they got married?

In demography, there is a well-known phenomenon known as age-heaping, in which people round off their ages, or misremember them, and report them as numbers ending in 0 or 5. We have a measure, known as Whipple’s index, that estimates the extent to which this is occurring in a given dataset. To calculate this you take the number of people between ages 23 and 62 (inclusive), and compare it to five-times the number of those whose ages end in 0 or 5 (25, 30 … 60), so there are five-times as many total years as 0 and 5 years.

If the ratio of 0/5s to the total is less than 105, that’s “highly accurate” by the United Nations standard, a ratio 105 to 110 is “fairly accurate,” and in the range 110 to 125 age data should be considered “approximate.”

I previously showed that the American Community Survey’s (ACS) public use file has a Whipple index of 104, which is not so good for a major government survey in a rich country. The heaping in ACS apparently came from people who didn’t respond to email or mail questionnaires and had to be interviewed by Census Bureau staff by phone or in person. I’m not sure what you can do about that.

What about marriage?

The ACS has a great data on marriage and marital events, which I have used to analyze divorce trends, among other things. Key to the analysis of divorce patterns is the question, “When was this person last married?” (YRMARR) Recorded as a year date, this allows the analyst to take into account the duration of marriage preceding divorce or widowhood, the birth of children, and so on. It’s very important and useful information.

Unfortunately, it may also have an accuracy problem.

I used the ACS public use files made available by IPUMS.org, combining all years 2008-2017, the years they have included the variable YRMARR. The figure shows the number of people reported to have last married in each year from 1936 to 2015. The decadal years are highlighted in black. (The dropoff at the end is because I included surveys earlier than those years.)

Yikes! That looks like some decadal marriage year heaping. Note I didn’t highlight the years ending in 5, because those didn’t seem to be heaped upon.

To describe this phenomenon, I hereby invent the Decadally-Biased Marriage Recall index, or DBMR. This is 10-times the number of people married in years ending in 0, divided by the number of people married in all years (starting with a 6-year and ending with a 5-year). The ratio is multiplied by 100 to make it comparable to the Whipple index.

The DBMR for this figure (years 1936-2015) is 110.8. So there are 1.108-times as many people in those decadal years as you would expect from a continuous year function.

Maybe people really do get married more in decadal years. I was surprised to see a large heap at 2000, which is very recent so you might think there was good recall for those weddings. Maybe people got married that year because of the millennium hoopla. When you end the series at 1995, however, the DBMR is still 110.6. So maybe some people who would have gotten married at the end of 1999 waited till New Years day or something, or rushed to marry on New Year’s Eve 2000, but that’s not the issue.

Maybe this has to do with who is answering the survey. Do you know what year your parents got married? If you answered the survey for your household, and someone else lives with you, you might round off. This is worth pursuing. I restricted the sample to just those who were householders (the person in whose name the home is owned or rented), and still got a DBMR of 110.7. But that might not be the best test.

Another possibility is that people who started living together before they were married — which is most Americans these days — don’t answer YRMARR with their legal marriage date, but some rounded-off cohabitation date. I don’t know how to test that.

Anyway, something to think about.