Category Archives: Research reports

Sociology: “I love you.” Economics: “I know.”

Sour grapes, by Sy Clark.

Sour grapes, by Sy Clark.

A sociologist who knows how to use python or something could do this right, but here’s a pilot study (N=4) on the oft-repeated claim that economists don’t cite sociology while sociologists cite economics.

I previously wrote about the many sociologists citing economist Gary Becker (thousands), compared with, for example, the 0 economists citing the most prominent article on the gender division of housework by a sociologist (Julie Brines). Here’s a little more.

It’s hard to frame the general question in terms of numerators and denominators — which articles should cite which, and what is the universe? To simplify it I took four highly-cited papers that all address the gender gap in earnings: one economics and one sociology paper from the early 1990s, and one of each from the early 2000s. These are all among the most-cited papers with “gender” and “earnings OR wages” in the title from journals listed as sociology or economics by Web of Science.

From the early 1990s:

  • O’Neill, J., and S. Polachek. 1993. “Why the Gender-gap in Wages Narrowed in the 1980s.” Journal of Labor Economics 11 (1): 205–28. doi:10.1086/298323. Total cites: 168.
  • Petersen, T., and L.A. Morgan. 1995. “Separate and Unequal: Occupation Establishment Sex Segregation and the Gender Wage Gap.” American Journal of Sociology 101 (2): 329–65. doi:10.1086/230727. Total cites: 196.

From the early 2000s:

  • O’Neill, J. 2003. “The Gender Gap in Wages, circa 2000.” American Economic Review 93 (2): 309–14. doi:10.1257/000282803321947254. Total cites: 52.
  • Tomaskovic-Devey, D., and S. Skaggs. 2002. “Sex Segregation, Labor Process Organization, and Gender Earnings Inequality.” American Journal of Sociology 108 (1): 102–28. Total cites: 81.

A smart way to do it would be to look at the degrees or appointments of the citing authors, but that’s a lot more work than just looking at the journal titles. So I just counted journals as sociology or economics according to my own knowledge or the titles.* I excluded interdisciplinary journals unless I know they are strongly associated with sociology, and I excluded management and labor relations journals. In both of these types of cases you could look at the people writing the articles for more fidelity. In the meantime, you may choose to take my word for it that excluding these journals didn’t change the basic outcome much. For example, although there are some economists writing in the excluded management and labor relations journals (like Industrial Labor Relations), there are a lot of sociologists writing in the interdisciplinary journals (like Demography and Social Science Quarterly), and also in the ILR journals.


Citations to the economics articles from sociology journals:

  • O’Neill and Polachek (1993): 37 / 168 = 22%
  • O’Neill (2003): 4 / 52 = 8%

Citations to the sociology articles from economics journals:

  • Petersen and Morgan (1995): 6 / 196: 3%
  • Tomaskovic-Devey and Skaggs (2002): 0 / 81: 0%

So, there are 41 sociology papers citing the economics papers, and 6 economics papers citing the sociology papers.

Worth noting also that the sociology journals citing these economics papers are the most prominent and visible in the discipline: American Sociological Review, American Journal of Sociology, Annual Review of Sociology, Social Forces, Sociology of Education, and others. On the other hand, there are no citations to the sociology articles in top economics journals, with the exception of an article in Journal of Economic Perspectives that cited Peterson and Morgan — but it was written by sociologists Barbara Reskin and Denise Bielby. Another, in Feminist Economics, was written by sociologist Harriet Presser. (I included these in the count of economics journals citing the sociology papers.)

These four articles are core work in the study of labor market gender inequality, they all use similar data, and they are all highly cited. Some of the sociology cites of these economics articles are critical, surely, but there’s (almost) no such thing as bad publicity in this business. Also, the pattern does not reflect a simple theoretical difference, with sociologists focused more on occupational segregation (although that is part of the story), as the economics articles use occupational segregation as one of the explanatory factors in the gender gap story (though they interpret it differently).


Previous sour-grapes stuff about economics and sociology:


* The Web of Science categories are much too imprecise with, for example, Work & Occupations — almost entirely a sociology journal –classified as both sociology and economics.


Filed under Research reports

Book review: One Marriage Under God

The following are notes for my remarks at an author-meets-critics session at the Social Science History Association yesterday in Baltimore. The book is One Marriage Under God: The Campaign to Promote Marriage in America, by Melanie Heath.


The book is well researched, elegantly argued, easily read, and deeply thought-provoking. I highly recommend it.In the study, Heath analyzes many aspects of the marriage promotion movement, including marriage classes and training and organizing, using participant observation, and interviews and focus groups, in Oklahoma.

I have forgotten that it was from this book that I learned that the welfare reform law of 1996 begins with the sentence, “Marriage is the foundation of a successful society.” This explains so much about why marriage promotion and welfare reform are one project, how futile they both are, and how reactionary, in my opinion. Heath makes this very clear when she describes the use of welfare money to teach marriage education to white, middle-class couples, ultimately probably widening the “marriage gap between lower and middle-class families.”

I usually criticize marriage promotion for spending poor peoples money on convincing them to get married, but it’s actually often spent helping middle-class people with their marriages altogether. But that makes perfect sense: welfare, just like welfare reform, is made to build up the normative white middle-class family. Thus, when, as Heath observes, poor single mothers resented their useless workshops on the importance of marriage, the program was actually serving its purpose.

Instead of a safety net, in the United States we have marriage – but we have less and less of it. That means it is a privilege and a necessity, and excluding people from it is a form of inequality.

And driving people toward marriage is what we substitute for welfare – how we give people a choice between conformity and destitution for them and their children – and justify that forced choice with Christian morality. 

The passages describing the presence of same-sex couples in marriage education classes are excruciating and extremely revealing. And yet she discovers that even the conservatives in these situations recognize the lesbian couples “have needs too” — a reality that necessitated additional boundary work to protect the core concept at hand. The lesbians literally had to play the roles of heterosexuals in class exercises.

Marriage promotion uses marriage to bolster the gender difference and its hierarchy simultaneously. And it elevates marriage through the contrast with welfare dependency which it sees as “an assault on freedom and responsible citizenship.” Both positions reinforce the gender hierarchy. And this helps to answer why the marriage promotion movement has never embraced same-sex marriage rights (despite a halfhearted and ultimately unsuccessful rearguard effort by David Blankenhorn and a few other washed up marriage promoters).

She presciently includes the campaign to ban same-sex marriage in the research. This is entirely fitting because these two movements have been united from the start — but that connection blossomed in the years since she wrote this book (published in 2012). We see this in the political history and the interlocking organizational leadership networks between marriage promotion and the movement against marriage equality: David Blankenhorn, Maggie Gallagher, Brad Wilcox, Mark Regnerus, the National Marriage Project, the National Organization for Marriage, the Institute for American Values, the Heritage Foundation, The National Fatherhood Initiative, the Family Research Council. (This movement, incidentally, and especially its research and public relations arms, formed the context in which the Council on Contemporary Families, of which I am now a board member, was organized.) Add William Galston, also Ron Haskins, Marco Rubio, now and the GOP debate over the larger child tax credit (and debate over its refundability).

Heath puts it well when she writes of their “shared ideology that relies on an ideal heterosexual family as a way to manage and organize the diverse and often contradictory threads of market fundamentalism, religion, and morality.”
An important original contribution of this book is Heath’s description of the nationalist and patriotic underpinnings of the marriage promotion movement, which I had not fully appreciated (something also seen in the marriage promotion efforts among American Indians in Oklahoma and the so-called Native American Healthy Marriage Initiative.) Fighting same-sex marriage, and fighting the culture of poverty, are both efforts to shore up the family bulwark of American citizenship.

Marriage promotion, as embodied in the trainings and educational materials that she studies, was built on the program to enhance inherent differences between men and women, which are of course also the pillars upon which opposition to marriage equality stands. And a basis for Christian morality and traditional nostalgic American patriotism — as well as capitalism, or more properly market fundamentalism, because this marriage structure stands in opposition to dependence on the welfare state and in support of the family wage and the patriarchal family economy.

She writes: “this punitive individualism, and the lack of an alternative narrative in the American ethos, enables coalitions of various stripes (conservative Christians, economic conservatives, and centrist liberals) to join together in promoting marriage. In this way marriage ideology connects Americas market fundamentalist corporate culture with moral/religious traditions.”

Marriage promotion — true to the long history of the American welfare system — becomes an inequality reproduction machine, serving race, gender and sexuality divides, and building the ideological supports for widening economic inequality. In the end they don’t increase the amount of marriage, or decrease the amount of poverty, and that does not mean they have failed.

One Marriage Under God belongs in the pantheon of classic historical work on marriage in the United States, including works by Nancy Cott and Stephanie Coontz, as well as Gwendolyn Mink, Linda Gordon, and Ruth Sidel — just off the top of my head. Now that marriage promotion has been demonstrated to be a failure on its own formal terms by the extensive and well-funded and well-conducted studies paid for by the welfare program, and now that the Supreme Court has effectively ended the movement against marriage equality, the book is thankfully more historical then it was just three years ago. But, as a reading of those historical works I just mentioned clearly shows, this thing just will not die. So this book remains essential.

1 Comment

Filed under Research reports

Age composition change accounts for about half of the Case and Deaton mortality finding

This paper by Anne Case and Angus Deaton, one of whom just won a Nobel prize in economics, reports that mortality rates are rising for middle-aged non-Hispanic Whites. It’s gotten tons of attention (see e.g., “Why poor whites are dying of despair” in The Week, and this in NY Times).

It’s an odd paper, though, in its focus on just one narrow age group over time. The coverage mostly describes the result as if conditions are changing for a group of people, but the group of people changes every year as new 45-year-olds enter and 54-year-olds leave. That means the population studied is subject to change in its composition. This is especially important because the Baby Boom wave was moving through this group part of that time. The 1999-2013 time frame included Baby Boomers (born 1945-1964) from age 35 to age 68.

My concern is that changes in the age and sex composition of the population studied could account for a non-trivial amount of the trends they report.

For example, they report that the increased mortality is entirely concentrated among those non-Hispanic White men and women who have high school education or less. But this population changed from 1999 to 2013. Using the Current Population Survey — which is not the authority on population trends, but is weighted to reflect Census Bureau estimates of population trends — I see that this group became more male, and older, over the period studied. That’s because the Baby Boomers moved in, causing aging, the population reflects women’s advances in education, relative to men, circa the 1970s. Here are those trends:


It’s odd for a paper on mortality trends not to account for account for sex and age composition changes in the population over time. Even if the effects aren’t huge, I think that’s just good demography hygiene. Now, I don’t know exactly how much difference these changes in population composition would make on mortality rates, because I don’t have the mortality data by education. That would only make a difference if the mortality rates differed a lot by sex and age.

However, setting aside the education issue, we can tell something just looking at the whole non-Hispanic White population, and it’s enough tor raise concerns. In the overall 45-54 non-Hispanic White population, there wasn’t any change in sex composition. But there was a distinct age shift. For this I used the 2000 Census and 2013 American Community Survey. I could get 1999 estimates to match Case and Deaton, but 2000 seems close enough and the Census numbers are easier to get. (That makes my little analysis conservative because I’m lopping off one year of change.)

Look at the change in the age distribution between 2000 and 2013 among non-Hispanic Whites ages 45-54. In this figure I’ve added the birth year range for those included in 2000 and 2013.


That shocking drop at age 54 in 2000 reflects the beginning of the Baby Boom. In 2000 there were a lot more 53-year-olds than there were 54-year-olds, because the Baby Boom started in 1946. (Remember, unlike today’s marketing-term “generations,” the Baby Boom was a real demographic event.) So there was a general aging, but also a big increase in 54-year-olds, between 2000 and 2013, which will naturally increase the mortality rate for that year.

So, to see whether the age shift had a non-trivial impact on the number of deaths in this population, I used one set of mortality rates: 2010 rates for non-Hispanic Whites by single year of age, published here. And I used the age and sex compositions as described above (even though the sex composition barely changed I did it separately by sex and summed them).

The 2010 age-specific mortality rates applied to the 2000 population produce a death rate of 3.939 per 1,000. When applied to the 2013 population they produce a death rate of 4.057 per 1,000. That’s the increase associated with the change in age and sex composition. How big is that difference? The 2013 death rate implies 118,313 deaths in 2013. The 2000 death rate implies 114,869 deaths in 2013. The difference is 3,443 deaths. Remember, this assumes age-specific death rates didn’t change, which is what you want to assess effects of composition change.

So I can say this: if age and sex composition had stayed the same between 2000 and 2013, there would have been 3,443 fewer deaths among non-Hispanic Whites in the ages 45-54.

Here is what Case and Deacon say:

If the white mortality rate for ages 45−54 had held at their 1998 value, 96,000 deaths would have been avoided from 1999–2013, 7,000 in 2013 alone.

So, it looks to me like age composition change accounts for about half of the rise in mortality they report. They really should have adjusted for age.

Here is my spreadsheet table (you can download the file here):


As always, happy to be credited if I’m right, and told if I’m wrong. But if you just have suggestions for more work I could do, that might not work.

Follow up: Andrew Gelman has three excellent posts about this. Here’s the last.


Filed under Research reports

No, you should get married in your late 40s (just kidding)

Please don’t give (or take) stupid advice from analyses like this.

Since yesterday, Nick Wolfinger and Brad Wilcox have gotten their marriage age analysis into the Washington Post Wonkblog (“The best age to get married if you don’t want to get divorced”) and Slate (“The Goldilocks Theory of Marriage”). The marriage-promotion point of this is: don’t delay marriage. The credulous blogosphere can’t resist the clickbait, but the basis for this is very weak.

Yesterday I complained about Wolfinger pumping up the figure he first posted (left) into the one on the right:

wolfbothToday I spent a few minutes analyzing the American Community Survey (ACS) to check this out. Wolfinger has not shared his code, data, models, or tables, so it’s hard to know what he really did. However, he lists a number of variables he says he controlled for using the National Survey of Family Growth: “sex, race, family structure of origin, age at the time of the survey, education, religious tradition, religious attendance, and sexual history, as well as the size of the metropolitan area.”

The ACS seems better for this. It’s very big, so I can analyze just the one-year incidence of divorce (did you get divorced in the last year?), according to the age at which people married. I don’t have family structure of origin, religion, or sexual history, but he says those don’t influence the age-at-marriage effect much. He did not control for duration of marriage, which is messed up in his data anyway because of the age limits in the NSFG.

So, in my model I used women in their first marriages only, and controlled for marriage duration, education, race, Hispanic ethnicity, and nativity/citizenship. This is similar to models I used in this (shock) peer-reviewed paper. Here are the predicted probabilities of divorce, in one year, holding those control variables constant.


Yes, there is a little bump up for the late 30s compared with the early 30s, but it’s very small.

Closer analysis (added to the post 7/19), generated from a model with age-at-marriage–x–marital duration interactions, shows that the late-30s bump is concentrated in the first five years of marriage:


This doesn’t much undermine the “conventional wisdom” that early marriage increases the risk of divorce. Of course, this should not be the basis for advice to people who are, say, dating a person they’re thinking of marrying and hoping to minimize chance of divorce.

If you want to give advice to, say, a 15-year-old woman, however, the bottom line is still: Get a bachelor’s degree. You’ll likely earn more, marry later, and have fewer kids. If you or your spouse decide to get divorced after all that, it won’t hurt that you’re more independent. For what it’s worth, here are the education effects from this same model:


(The codebook for my IPUMS data extraction is here, my Stata code is here.)

Anyway, it’s disappointing to see this in the Wonkblog piece:

But the important thing, for Wolfinger, is that “we do know beyond a shadow of a doubt that people who marry in their thirties are now at greater risk of divorce than are people who wed in their late twenties. This is a new development.”

That’s just not true. I wouldn’t swear by this quick model I did today. But I would swear that it’s too early to change the “conventional wisdom” based only on a blog post on a Brad-Wilcox-branded site.


One interesting issue is the problem of age at marriage and education. They are clearly endogenous — that is, they influence each other. Women delay marriage to get more education, they stop their education when they have kids, they go back to school when they get divorced — or think they might get divorced. And so on. And, for the regression models, there are no highly-educated people getting married at really young ages, because they haven’t finished school yet. On the other hand, though, there are lots of less-educated people getting married for the first time at older ages. Using the same ACS data, here are two looks at the women who just married for the first time, by age and education.

First, the total number per year:


Then, the percent distribution of that same data:

age-ed-mar-distInteresting thing here is that college graduates are only the majority of women getting married for the first time in the age range 27-33. Before and after that most women have less than a BA when they marry for the first time. This is also complicated because the things that select people into early marriage are sometimes but not always different from those that select people into higher education. Whew.

It really may not be reasonable to try to isolate the age-at-marriage effect after all.


Filed under Research reports

The latest get-married-young thing tells you all you need to know

Just a quick note for people wondering about this new thing by Nicholas Wolfinger on Brad Wilcox’s blog. He says it used to be (before 1995) that getting married young increased the odds of divorce. Since then, however, he says getting married either before or after age 32 raises the odds of divorce.

Why is that? His explanation — in his very own words, from his very own post: “my money is on a selection effect.” In other words, do not follow the advice in the headline, which is: “Want to Avoid Divorce? Wait to Get Married, But Not Too Long.” Because if the mechanism is selection, then changing your behavior to ride that curve will not work.

I’m not getting into the methods, which are not revealed, despite a link for “more information” — there is no paper, no tables, no code or data. However, something is off, and the post is off-gassing a discernible essence of Wilcox’s influence. In the new blog post, they show this graph:

wolfinger1Wow, that’s a pretty big boomerang effect. If it weren’t a selection effect, it might really be relevant for personal decision-making. But when you follow the link for “more information” you see this graph:


The upward swing here is hardly enough to get your marriage promotion lather up. Clearly, something had to be improved from Wolfinger’s post from April and his post for Wilcox’s site in July. That’s the kind of data leadership we expect from this site. (Also, get rid of those dots, which show you the all those people with really low divorce odds at higher ages.)



Filed under Research reports

The U.S. government asked 2 million Americans one simple question, and their answers will shock you

What is your age?

[SKIP TO THE END for a mystery-partly-solved addendum]

Normally when we teach demography we use population pyramids, which show how much of a population is found at each age. They’re great tools for visualizing population distributions and discussing projections of growth and decline. For example, consider this contrast between Niger and Japan, about as different as we get on earth these days (from this cool site):


It’s pretty easy to see the potential for population growth versus decline in these patterns. Finding good pyramids these days is easy, but it’s still good to make some yourself to get a feel for how they work.

So, thinking I might make a video lesson to follow up my blockbuster total fertility rate performance, I gathered some data from the U.S., using the 2013 American Community Survey (ACS) from I started with 10-year bins and the total population (not broken out by sex), which looks like this:


There’s the late Baby Boom, still bulging out at ages 50-59 (born 1954-1963), and their kids, ages 20-29. So far so good. But why not use single years of age and show something more precise? Here’s the same data, but showing single years of age:


That’s more fine-grained. Not as much as if you had data by months or days of birth, but still. Except, wait: is that just sample noise causing that ragged edge between 20 and about 70? The ACS sample is a few million people, with tens of thousands of people at each age (up age 75, at least), so you wouldn’t expect too much of that. No, it’s definitely age heaping, the tendency of people to skew their age reporting according to some collective cognitive scheme. The most common form is piling up on the ages ending with 0 and 5, but it could be anything. For example, some people might want to be 18, a socially significant milestone in this country. Here’s the same data, with suspect ages highlighted — 0’s and 5’s from 20 to 80, and 18:


You might think age heaping results from some old people not remembering how old they are. In the old days rounding off was more common at older ages. In 1900, for example, the most implausible number of people was found at age 60 — 1.6-times as many as you’d get by averaging the number of people at ages 59 and 61. Is that still the case? Here it is again, but with the red/green highlights just showing the difference between the number of people reported and the number you’d get by averaging the numbers just above and below:

totalsingleyearsflaggedhighlightProportionately, the 70-year-olds are most suspicious, at 10.8% more than you’d expect. But 40 is next, at 9.2%. And that green line shows extra 18-year-olds at 8.6% more than expected.

Unfortunately, it’s pretty hard to correct. Interestingly, the American Community Survey apparently asks for both an age and a birth date:


If you’re the kind of person who rounds off to 70, or promotes yourself to 18, it might not be worth the trouble to actually enter a fake birth date. I’m sure the Census Bureau does something with that, like correct obvious errors, but I don’t think they attempt to correct age-heaping in the ACS (the birth dates aren’t on the public use files). Anyway, we can see a little of the social process by looking at different groups of people.

Up till now I’ve been using the full public use data, with population weights, and including those people who left age blank or entered something implausible enough that the Census Bureau gave them an age (an “allocated” value, in survey parlance). For this I just used the unweighted counts of people whose answers were accepted “as written” (or typed, or spoken over the phone, depending on how it was administered to them). Here are the patterns for people who didn’t finish high school versus those with a bachelor’s degree or higher, highlighting the 5’s and 0’s (click to enlarge):


Clearly, the age heaping is more common among those with less education. Whether it’s really people forgetting their age, rounding up or down for aspirational reasons, or having trouble with the survey administration, I don’t know.

Is this bad? As much as we all hate inaccuracy, this isn’t so bad. Fortunately, demographers have methods for assessing the damage caused by humans and their survey-taking foibles. In this case we can use Whipple’s index. This measure (defined in this handy United Nations slideshow) takes the number of people whose alleged ages end in 0 or 5 and multiplies that by 5, then compares it to the total population. Normally people use ages 23 to 62 (inclusive), for an even 40 years. The amount by which people reporting ages 25, 30, 35, 40, 45, 50, 55, and 60 are more than one-fifth of the population ages 23-62, that’s your Whipple’s index. A score of 100 is perfect, and a score of 500 means everyone’s heaped. The U.N. considers scores under 105 to be “very accurate data.” The 2013 ACS, using the public use file and the weights, gives me a score of 104.3. (Those unweighted distributions by education yield scores of 104.0 for high school dropouts and 101.7 for college graduates.) In contrast, the Decennial Census in 2010 had a score of just 101.5 by my calculation (using table QT-P2 from Summary File 1). With the size of the ACS, this difference shouldn’t have to do with sampling variation. Rather, it’s something about the administration of the survey.

Why don’t they just tell us how old they really are? There must be a reason.

Two asides:

  • The age 18 pattern is interesting — I don’t find any research on desirable young-adult ages skewing sample surveys.
  • This is all very different from birth timing issues, such as the Chinese affinity for births in dragon years (every twelfth year: 1976, 1988…). I don’t see anything in the U.S. pattern that fits fluctuations in birth rates.

Mystery-partly-solved addendum

I focused one education above, but another explanation was staring me in the face. I said “it’s something about the administration of the survey,” but didn’t think to check for the form of survey people took. The public use files for ACS include an indicator of whether the household respondent took the survey through the mail (28%), on the web (39%), through a bureaucrat at the institution where they live (group quarters; 5%), or in an interview with a Census worker (28%). This last method, which is either a computer-assisted telephone interview (CATI) or computer-assisted personal interview (CAPI), is used when people don’t respond to the mailed survey.

It turns out that the entire Whipple problem in the 2013 ACS is due to the CATI/CAPI interviews. The age distributions for all of the other three methods have Whipple index scores below 100, while the CATI/CAPI folks clock in at a whopping 108.3. Here is that distribution, again using unweighted cases:


There they are, your Whipple participants. Who are they, and why does this happen? Here is the Bureau’s description of the survey data collection:

The data collection operation for housing units (HUs) consists of four modes: Internet, mail, telephone, and personal visit. For most HUs, the first phase includes a mailed request to respond via Internet, followed later by an option to complete a paper questionnaire and return it by mail. If no response is received by mail or Internet, the Census Bureau follows up with computer assisted telephone interviewing (CATI) when a telephone number is available. If the Census Bureau is unable to reach an occupant using CATI, or if the household refuses to participate, the address may be selected for computer-assisted personal interviewing (CAPI).

So the CATI/CAPI people are those who were either difficult to reach or were uncooperative when contacted. This group, incidentally, has low average education, as 63% have high school education or less (compared with 55% of the total) — which may explain the association with education. Maybe they have less accurate recall, or maybe they are less cooperative, which makes sense if they didn’t want to do the survey in the first place (which they are legally mandated — i.e., coerced — to do). So when their date of birth and age conflict, and the Census worker tries to elicit a correction, maybe all hell breaks lose in the interview and they can’t work it out. Or maybe the CATI/CAPI households have more people who don’t know each other’s exact ages (one person answers for the household). I don’t know. But this narrows it down considerably.


Filed under Research reports

The total fertility rate, with instructions, in 9 minutes

Maybe because I haven’t had a classroom full of students since December, I made an instructional video.

In 9 minutes I explain what the total fertility rate is and then illustrate how to get the data you need to calculate it using IPUMS’s American Community Survey analysis tool. In the dramatic last five minutes we calculate the TFR for the United States in 2013, and match the official number. Wow. And you thought your holiday weekend was going to be fun already.

I want more people to have a hands-on feel for basic demography, and to realize how easy it is, and how accessible, with the tools we have nowadays. So, this is for students, non-demographic researchers, and journalists.

The video:

And here’s the end product (a little touched up):

tfr2013Check it out if you’re having trouble sleeping.


Filed under Research reports