Changes Seen in Income Distribution for Women Ages 15 to 50 With Recent Births

By: Lindsay Monte, Fertility & Family Statistics Branch

Despite the recent economic downturn, the proportion of women with a birth in the previous 12 months who reported the highest annual income per household member grew between 2006 and 2014. This is in contrast to the patterns for all women, for whom the percentage at the higher end of the income scale declined during this same period.

Today, the U.S. Census Bureau released new tables showing household income distributions for different subsets of women ages 15 to 50, focusing on trends for women with a birth in the previous 12 months.

Comparisons of individual income are problematic for women with a recent birth because many women take time out of the labor force around the time of a birth. Even household income measures are complicated due to the fact that while household income may not change in the context of a birth, the number of people who live on that income likely increases. Therefore, our tables do not include individual income and while they do include a measure of household income, we also include income per household member as an additional measure of well-being.

We look at income per household member in order to account for household size. A total household income of $100,000 per year, for example, means very different things if you are talking about a household of one person than it does if you are talking about a household of 10 people. Therefore, in order to compare the relative well-being of different households, we take the total household income and divide it by the number of people who live in that household to get the income per household member. This gives us a measure of income for each person and allows us to compare women across different household sizes and income levels – an income ratio.

Of course, women who had a birth in the previous 12 months are a minority of all women ages 15 to 50 and they generally live in lower income households and have lower income per household member than do women as a whole. However, when we look at change over time and differences between all women and the subset of women with recent births, the declines seen for all women over the period of 2006 to 2014 are not mirrored for women with a birth in the previous 12 months (see figure below). Recent mothers have a smaller proportional growth in the lowest income ratio categories than did women as a whole. Additionally, recent mothers saw an increase in the percentage who were in the uppermost income ratio.

monte 3

There are several possible reasons for this. For example, the teen birth rate has fallen precipitously since the early 1990s.  This decline in births among women who generally live in lower income households likely shifts the household income of women with recent births upward. Similarly, the National Center for Health Statistics reported an unprecedented rise in the age at first birth between 2009 and 2014. With fewer teen births, and first births happening later, the age of women with any birth is also likely older. As older women tend to live in higher income households than their younger counterparts, the changing demographics of women who are recent mothers could also explain the higher income per household member over time.

Additionally, a report from the Pew Research Center found that women’s fertility was affected by the recession that began in December of 2007, with fertility rates falling the most in states hardest hit by the recession. It may be that the women who reported a recent birth in 2014 opted into childbearing because they were more financially secure than the women who reported a recent birth in 2006.

For more information on household income and income per household member of women with a recent birth, please refer to the fertility tables at <>.

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Using Census Bureau Data Made Easier: New Statistical Testing Tool Answers the Question “Is This Comparison Statistically Significant?”

Written by: Sirius Fuller, Decennial Statistical Studies Division

American Community Survey data can help you find quick answers on a variety of demographic and economic topics. For example, you might need to know “What’s the unemployment rate where I live?” A natural follow-up question might be “How does my town compare to a neighboring one?”

If you are using survey data to compare estimates, you must perform a statistical test to answer this type of question correctly. While it is easy to compare two estimates, survey data are based on a sample of the population — not the entire population — so it has statistical uncertainty. In the case of American Community Survey data, the margin of error is one type of statistical uncertainty. If the uncertainty is too large, then two estimates may appear different, but may not actually be statistically different. In that case, claiming there is a difference between them would not be accurate.

With the release of the U.S. Census Bureau’s new statistical testing spreadsheet tool, we are making it easier for just about anyone to carry out statistical testing correctly. The tool handles the testing behind the scenes for you. American Community Survey data may be downloaded or copied directly from the Census Bureau’s website. No special reformatting is necessary.

Data from the American Community Survey

Let’s say you want to compare the data for households without a computer in the three counties (see Figure 1). The data are from the newly released 2014 American Community Survey 1-Year Supplemental Estimates. The three counties were chosen because their populations were all roughly the same (about 20,000 people).

Stat 1

Statistical Testing for Two Estimates

To compare one estimate to another, copy the estimates and their margins of error from the table on the Census Bureau’s website and paste them into the spreadsheet. The result of the statistical test will appear on the right as a “Yes” or “No.” Figure 2 gives an example of the test in action. The example gives three different instances comparing one city to another at a time.

Stat 2

Note that you do not need to remove the “+/-” that precedes the margin of error in published American Community Survey products. The tool handles this and other special characters. In addition, the tool supports up to 3,230 possible comparisons of two estimates simultaneously. Thus, you may use it to test a large number of estimates at the same time. For example, you may test change over time for every county in the United States by comparing two sets of estimates for the same characteristic published a year apart.

While Saluda County, S.C., appears to have the highest number of households without computers, looking at the testing results (the red “No”), we see that it is not statistically different from Scott County, Ind. However, it is statistically different from New Kent County, Va. (the “Yes”), as seen in the second comparison.

Statistical Testing for Multiple Estimates

Additionally, the tool allows you to easily compare multiple estimates. This comes in handy when you want to test multiple geographies with each other simultaneously. Due to the large number of calculations done behind the scenes, the tool can compare up to 150 estimates. This keeps the display manageable in size while providing fast results. If you have between 10 and 150 estimates, using this method would require less cutting and pasting to achieve the same results.

In figure 3, we see the same results as in the previous example in a different format. Again, the results are shown as “Yes” and “No.” In addition, estimates compared to themselves have an “X” in the results.

Stat 3

Although it was designed with the American Community Survey in mind, other Census Bureau survey data may be used with the statistical testing spreadsheet tool. The tool is designed to be intuitive to use. It provides data users with an easy method to conduct statistical testing and assumes independence between estimates.

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Bigger Houses, Smaller Lots

The housing crisis of the last decade has not slowed the steady trend towards bigger houses with more bathrooms and multicar garages but these more spacious new homes are now on smaller lots.

The U.S. Census Bureau collects data on characteristics of new housing for the Department of Housing and Urban Development using the Survey of Construction. Annual data from the survey show that the proportion of single-family homes completed in 2015 with four or more bedrooms and three or more bathrooms has been on the rise since 1987. The share of new homes that are 3,000 square feet or more has been increasing since 1999. The same upward pattern applies to homes that are even larger — 4,000 square feet or more.

The survey shows that the median size of the 648,000 single-family homes completed in 2015 was 2,467 square feet. Of those new homes, 47 percent had four or more bedrooms compared to 35 percent five years earlier and 38 percent had three or more bathrooms compared to 25 percent.

Despite the trend towards living large, homes are going up on smaller lots. The share of homes completed on lots under 7,000 square feet has been growing since 1999. The vast majority of homes are built inside metropolitan areas.

Check out this interactive graphic on new single-family homes in 2015.

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America’s Age Profile Told through Population Pyramids

Author: Luke T. Rogers, Statistician/Demographer, Population Estimates Branch

Today, the U.S. Census Bureau released population estimates by age, sex, race and Hispanic origin for the nation, states and counties. These data enable us to learn about the U.S. population, including its age structure. Age structure is often displayed using a population pyramid. You can learn about the makeup of the U.S. population as a whole by looking at its population pyramid, below.

Chart 1

An examination of this population pyramid reveals peaks and valleys. Why do the age groups have different size populations? Let’s examine the baby boom generation (50- to 69-year-old population). During the baby boom, the U.S. population rapidly grew because of high fertility rates following World War II. This population surge is reflected in the U.S. population pyramid as an outward bump in those baby boomer age groups. As the baby boomers grew up, many had kids of their own. The children of these baby boomers, frequently called the echo boomers or millennials, can be seen as a similar bulge in the 15- to 34-year-old population. Even with lower fertility among baby boomers compared to their parents’ generation, the birth of the millennials still represented a mini population boom, simply because there were so many potential boomer parents.

Looking at the U.S. population pyramid, we also see how noticeably larger the older female population (age 80 and over) is when compared to the male population at the same ages. This size differential stems from the fact that, generally, women live longer than men do. As a result, older women tend to outnumber older men. Women’s higher share of the older population is one of the more consistent features in almost all population pyramids, regardless of region or level of geography.

We can see more detail when we study population pyramids for smaller geographies, such as states, metropolitan areas or counties. Let’s take a tour of a few interesting examples. We start with West Virginia, which experienced natural decrease (more deaths than births) between 2014 and 2015.

chart 2

West Virginia’s aging population is visible in the shape of the population pyramid above. Since some of the age groups near the top are wider than those at the bottom, we can tell that there are more older people than younger people. Similar to what was seen in the U.S. population pyramid, the baby boom generation is visible and distinct from the rest of the population. Meanwhile, the number of people of childbearing age (those roughly between the ages of 15 and 49) is comparatively smaller. This shape often leads to natural decrease because of deaths to the larger older population and lack of births from a smaller young population. Many rural areas have long-standing trends of natural decrease and a loss of people through migration, making age structures like this one common. In West Virginia, there have been seven consecutive years of natural decrease and three consecutive years of net migration loss.

chart 3

On the other end of the population pyramid spectrum is the Salt Lake City metropolitan area in Utah. Here we see growth from both positive net migration and natural increase (where more people are being born than are dying). Looking at Salt Lake City’s population pyramid, you can see that there are relatively more people that are of childbearing age, especially when compared to the older population. Additionally, a long history of natural increase is evident in Salt Lake City’s age structure, since it gradually gets wider toward the bottom. As high fertility levels and net migration gains persisted, the base of Salt Lake City’s population pyramid widened over the years, resulting in the shape it has now.

chart 4

Births and deaths aren’t the only components that influence the shape of a population’s age structure. Net migration (both domestic and/or international) can play a major role as well. Ouray County, Colo., for example, is defined by the Economic Research Service at the U.S. Department of Agriculture as a recreation county. Roughly speaking, being a recreation county means that the number of people who make their livings through recreational activities and the number of seasonal housing units are both relatively high. Recreation counties generally have similar age structures to one another. In Ouray County’s case, you can see the proportionately large share in the 50- to 74-year-old age groups. Like many recreation counties, in Ouray County, the primary driver of population growth is net domestic migration. Much of that growth was in the 65-and-older population, which implies that a substantial number of people are retiring in Ouray County.

chart 6

We can contrast the “older” age structure in Ouray County, Colo., with Centre County, Pa., which contains a large university. As can be seen in many college counties, Centre County has a noticeable spike in the 15- to 24-year-old population. Centre County gained residents between 2014 and 2015 from both natural increase and net migration, but — like Ouray County — net migration was the primary driver of its population increase. In the last year, Centre County experienced a decrease in the population under age 25 and an increase in the 25-and-older population. Despite these changes, the college-age population is a distinctive feature in Centre County’s population pyramid.

chart 6a

Areas with large military installations can also have unusual age structures. Take, Christian County, Ky., for example. Christian County is the location of part of a large military installation. We see the impact on the population pyramid as a disproportionately large male 20- to 29-year-old population relative to all other age and sex groups. In Christian County’s case, the resident population decreased slightly in the last year due primarily to a net loss of domestic migrants. Specifically, while the 18-to 24-year old and 65-and-older age groups increased slightly in the last year, this increase was offset by the loss of people ages 25 to 64 and children under the age of 18. As a side note, a similar kind of age structure to Christian County’s is seen often, but in a very different kind of place. Aside from the bulge of young children (less than 10), the age structure seen in Christian County would resemble the shape of counties with proportionally large incarcerated population.

As you can see in Christian County, and the rest of the U.S., age structure tells us a lot about an area’s population and how it changes over time.

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Majority of Workers Take Health Insurance Offered by Their Employers

Written by: Joelle Abramowitz, Economist, Social, Economic and Housing Statistics Division

New statistics from the U.S. Census Bureau show that in 2015, 78.8 percent of employees worked for an employer who offered insurance to any of its employees, 71.0 percent of workers were eligible to take offered coverage, and 54.3 percent took the coverage offered by their employers.

Employer-Sponsored Insurance

The most common reason cited for not taking employer-sponsored coverage was being covered by another insurance plan (11.5 percent of workers), followed by not working enough hours per week or weeks per year to be eligible for coverage (5.3 percent) and the coverage being too expensive (5.0 percent). The percent of respondents who reported not working enough hours per week or weeks per year to be eligible for coverage and percent of respondents reporting the coverage being too expensive as the reasons for not taking employer-sponsored coverage are not statistically significantly different from each other.

The new data on the offer and take-up of employer-sponsored insurance were collected as part of the 2014 and 2015 Current Population Survey Annual Social and Economic Supplement (CPS ASEC). The 2014 and 2015 research data files are available here.

The new questions are asked of respondents who were employed but did not have employer-sponsored coverage. The questions ask: 1) whether their employer offered coverage to any of its employees, 2) whether they were eligible for that coverage, if offered, 3) why they were ineligible, if offered and ineligible, and 4) why they chose not to take the coverage, if eligible. The questions refer to current coverage at the time of interview, covering February through April of the survey year.

Comparing estimates over early 2014 and 2015 shows an increase in the proportion of workers offered coverage by their employers (0.5 percentage points), as well as in the proportion of workers who were eligible to take offered coverage (0.9 percentage points).The increase in the proportion of workers offered coverage by their employers is not statistically significantly different from the increase in the proportion of workers who were eligible to take offered coverage. However, the data also show a decrease in the proportion of eligible workers who took offered coverage (1.5 percentage points), and, as a result, the proportion of all workers taking coverage remained stable over the period. For workers who reported that they did not take employer-sponsored coverage because they had coverage through another plan, the proportions with Medicaid, direct purchase, and a combination of private coverage types increased, while the proportions with military coverage and with dependent employer-sponsored coverage decreased.

The new questions on the offer and take-up of employer-sponsored insurance were added to the CPS ASEC as part of the 2014 redesign of its questions on health insurance. These new questions represent a significant data development, as they provide a large sample of employee-level data, complementing firm-level data available from other outlets. Similar offer and take-up questions were originally asked by the Bureau of Labor Statistics in the CPS Contingent Worker Supplement, but have not been asked since 2005.

For more information on these estimates and changes in the offer and take-up of employer-sponsored insurance, see New Estimates of Offer and Take-up of Employer-Sponsored Insurance. For more information on health insurance coverage in general, see the Census Bureau’s Health Insurance website.


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