The Census Bureau gathered fertility by asking a “children ever born” question from 1940 to 1990 in the decennial census. The 2000 Census did not ask a fertility question at all. With the advent of the American Community Survey, fertility was covered but with a different question. It asked if a woman had given birth to a child in the past year. This allows researchers to compute a total fertility rate. It performs reasonably well against the measure produced from the vital statistics system. And, given that geography is not readily available with the natality detail files anymore, this is a welcome solution. The main drawback to the ACS question is that the reference year will not span the calendar year that the vital statistics system is based on. Only the December respondents are referencing a calendar year. See the Background section below for a further discussion of this.
However, recently, the Census Bureau noticed some anomalies in the data for selected areas and determined that some interviewers had been sloppy and asked “Have you given birth” rather than “Have you given birth in the last year.” Many more women will answer yes to the former and inflate the numerator. This is a good illustration of how much effort the Census Bureau goes to for producing accurate and robust statistics.
Addressing Data Collection Errors in the Fertility Question in the American Community Survey
Tavia Simmons | Census Bureau
In recent years, a few geographic areas in the American Community Survey (ACS) data had unusually high percentages of women reported as giving birth in the past year, quite unlike what was seen in previous years for those areas. This paper describes the issue that was discovered, and the measures taken to address it.
Indicators of Marriage and Fertility in the United States from the American Community Survey: 2000 to 2004
T. Johnson and J. Dye | Census Bureau
Slides 23 to 26 discuss and illustrate how the ACS and Vital Statistics estimates diverge from each other.
A new report from the Bureau of Labor Statistics us a picture of minimum wage workers in 2015. Some highlights (from the report website):
Age. Minimum wage workers tend to be young. Although workers under age 25 represented only about one-fifth of hourly paid workers, they made up about half of those paid the federal minimum wage or less. Among employed teenagers (ages 16 to 19) paid by the hour, about 11 percent earned the minimum wage or less, compared with about 2 percent of workers age 25 and older. (See table 1 and table 7.)
Gender. Among workers who were paid hourly rates in 2015, about 4 percent of women and about 3 percent of men had wages at or below the prevailing federal minimum. (See table 1.)
Full- and part-time status. About 7 percent of part-time workers (those who usually work fewer than 35 hours per week) were paid at or below the federal minimum wage, compared with about 2 percent of full-time workers. (See table 1 and table 9.)
State of residence. The states with the highest percentages of hourly paid workers earning at or below the federal minimum wage were in the South: Alabama, Louisiana, Mississippi, and Virginia (all were about 6 percent). The states with the lowest percentages of hourly paid workers earning at or below the federal minimum wage were in the West: Alaska, California, Oregon, and Washington (all were about 1 percent). It should be noted that some states have laws establishing higher minimum wage rates than the federal minimum wage. (See table 2 and table 3.)
H/T Data Detectives
Nathan Yau has a nice visualization of the change in work hours of mothers and fathers from 1965 to 2014.
Jeff Guo of Wonkblog examines research showing trends in how children of mixed marriages report their own race to the Census Report.
In fact, new immigrants may be assimilating a lot faster than than we had ever thought. A new study this week from economists Brian Duncan, of the University of Colorado, and Stephen Trejo of University of Texas, Austin finds that the descendents of immigrants from Latin-American and Asian countries quickly cease to identify as Hispanic or Asian on government surveys.
The Duncan & Trejo paper can be found here.
Bloomberg Business has an interesting (and productivity vortex) interactive chart show who marries who based on profession.
Philip Cohen of Family Inequality charts the correlation between marriage and gender inequality:
I used data from this U.N. report on marriage rates from 2008, restricted to those countries that had data from 2000 or later. To show marriage rates I used the percentage of women ages 30-34 that are currently married. This is thus a combination of marriage prevalence and marriage timing, which is something like the amount of marriage in the country. I got gender inequality from the U.N. Development Programme’s Human Development Report for 2015. The gender inequality index combines the maternal mortality ratio, the adolescent birth rate, the representation of women in the national parliament, the gender gap in secondary education, and the gender gap in labor market participation.
Philip Cohen writes about a new paper by Raj Chetty, et al. and the role race plays, even while it is missing from the data:
The tricky thing with this data, and I don’t blame Chetty et al. for this, although I would like them to say more about it, is that they don’t know the race of the children. The data are from tax records, which allow you to know the income and marital status of the parents, but not the race. But they know where they grew up. So if they have a strong effect of the racial composition of the county kids grow up in, but they don’t know the race of the kids, you have to figure a big part of that is race of the kids — and by “you” I mean someone who knows anything about America.
Scott Stanley, writing for Family Studies, contrasts his own work with a study by Sarah Mernitz and Claire Kamp Dush which finds that people experience emotional gains when they move in together regardless of marital status. Stanley’s analysis finds that, for a variety of reasons, this isn’t necessarily true.
The big news from the National Vital Statistics Report, Births: Final Data for 2014, was that the general fertility rate increased in 2014 for the first year since 2007.
Anna Sutherland, writing for Family Studies, highlights some other findings: The U.S. Fertility Rate May (Finally) Be Recovering from the Recession.
Nathan Yau of Flowing Data created a beautiful visualization of how Americans spend an average day.
More specifically, I tabulated transition probabilities for one activity to the other, such as from work to traveling, for every minute of the day. That provided 1,440 transition matrices, which let me model a day as a time-varying Markov chain.