Written by: Timothy Grall, Survey Statistician, Program Participation and Income Transfers Branch
Raising children can be an expensive endeavor. A child recently born and raised to adulthood in the United States can cost almost $250,000, according to the Department of Agriculture’s Center for Nutrition Policy and Promotion.
For many families, receiving cash and noncash assistance from the noncustodial parent is a critical source of supporting income. In 2014, about one-quarter of children living in families, or 22.1 million children under age 21, lived with only one of their parents. About five in six of these 13.4 million custodial parents were mothers (82.5 percent).
Not all of these custodial-parent families received child support. In fact, only about half (48.7 percent) had court orders or other financial agreements in place obligating the absent parent to provide financial support. Of the 5.7 million custodial-parent families that were due child support in 2013, just 45.6 percent received all payments that were due. This was an increase from 1993 when just 36.9 percent received every payment.
In terms of noncash support received, about 61.7 percent of custodial parents received at least one type, such as gifts, clothes, diapers, food, etc., from the absent parent(s).
For the custodial parents who did receive financial child support, the annual average amount received amounted to $3,950, or approximately $330 per month. The annual average amount due was $5,770, or $480 per month. Overall, about two-thirds (68.5 percent) of the child support that was due in 2013 was received.
Child support represents a sizable proportion of personal income for custodial-parent families, ranging from 7.7 percent for parents who received a portion of the support, to 17.7 percent for those who received all child support they were due in 2013. It can be especially important for those with lower incomes. For the group of custodial-parent families with incomes below poverty and who received all support they were due, child support represented 70.3 percent of their average personal income.
Written by Lucinda Dalzell, Sara Stefanik and David Powers
Today, the U.S. Census Bureau released the 2014 Small Area Income and Poverty Estimates (SAIPE) for all school districts, counties, and states. These estimates are used to allocate federal funds to school districts for the next school year. Also released today were counts of Supplemental Nutrition Assistance Program participants at the county and state levels for most years between 1989 and 2013. These counts are drawn from the source data of the SAIPE, and are the only source of SNAP participant total all U.S. counties.
Formerly known as the Food Stamp Program, SNAP is a nutrition assistance program for low-income individuals and families administered by the U.S. Department of Agriculture’s Food and Nutrition Service. The Food Stamp Act of 1977 requires that states report the number of SNAP participants by program area to USDA each year. USDA receives and makes available county-level SNAP data from roughly half of all states. In order to obtain and validate county-level data for the remaining states, each year a team of Census Bureau staff members collaborates with USDA and state government agencies.
This multi-agency collaboration allows the Census Bureau to publish a full county-level SNAP data set that otherwise would not be available. This unique SNAP data set is also an important input for the SAIPE program.
By utilizing other Census Bureau population data, data users can create SNAP statistics by metropolitan statistical area status and by census region. Table 1 presents SNAP participation rates, shares of SNAP participants, and shares of population by metropolitan statistical area status and by census region. We compute the SNAP “participation rate” as the number of SNAP participants divided by the population size.
Table 1. SNAP and Population Data by Metro Area Status and by Region
The 2013 SNAP participation rate is 14.4 percent in metropolitan areas and 17.3 percent in non-metropolitan areas. At the regional level, the 2013 SNAP participation rate is 13.9 percent in the Northeast, 14.4 percent in the Midwest, 16.7 percent in the South, and 12.7 percent in the West.
The SNAP data are also available in our new interactive treemap web tool for years 2013 back through 2000.
Figure 1. Screenshot of Treemap of 2013 SNAP Participation Rates
Source: U.S. Census Bureau, Small Area Income and Poverty Estimates (SAIPE) program
Figure 1 displays a screenshot of the interactive treemap of the 2013 SNAP participation rates. The treemap allows the data user to click on an individual box to view the specific county-level data. The larger the box for a given county, the greater the number of people or SNAP participants (depending on the selection) who reside there. Also, each box is color-shaded to indicate the SNAP participation rate range for the corresponding county.
The county and state SNAP data sets we have discussed here are available for download from the data input area of the SAIPE program website. Data users can reach our office with any questions or comments at firstname.lastname@example.org or at 301-763-3193.
Written by: Deborah Stempowski, Chief, American Community Survey Office
Since 2005, the American Community Survey has provided annual data on more than 40 topics for our nation’s communities. Over the past 10 years, we have seen the many ways local communities, businesses, policymakers and researchers use these statistics to guide decisions that affect the daily lives of the American people.
How have you used #ACSdata over the last 10 years? We invite you to share your experiences in the comments or on Twitter. Here’s just a few examples:
1. Disability Statistics The Gwinnett County Department of Fire and Emergency Services used American Community Survey statistics on disabilities to justify the need for the 2015 Chesney Fallen Firefighters Memorial Grant to purchase bedside alarms for deaf/hard of hearing individuals.
2. Poverty Statistics
Information on income and poverty is used to allocate funding to areas with the greatest need. Philabundance, a hunger relief organization serving the greater Philadelphia area, used poverty and Supplemental Nutrition Assistance Program statistics from the American Community Survey to map where people in need lived. Through the visualization, they found that hunger was not confined to the city limits; it had expanded into the suburbs and rural areas. Philabundance uses these statistics to guide decisions regarding hunger relief efforts.
3. Commuting Statistics
What time do you leave for work? That’s one of the questions we ask to help both local and federal transportation planning. The South Carolina Department of Transportation uses the American Community Survey to determine the potential impact of a transportation project — such as a new road — on the local community, residents and environment.
4. Education Statistics Statistics help us measure changes in education over time, evaluate the educational attainment of the workforce and to identify the educational and training needs of adults. The Washington Workforce Training and Education Coordinating Board uses education and unemployment data at the local level to track employment of young workers (ages 18-24). It uses the American Community Survey as an ongoing measure to track how policies are affecting this group’s socio-economic status.
5. Veterans Statistics Though the Department of Veterans Affairs maintains veterans’ records, American Community Survey statistics provide federal and local program planners, policymakers and researchers with additional statistics about all veterans, regardless of whether they utilize VA services. Veterans Affairs uses the American Community Survey for a variety of reasons, including assessing the eligible population for federal programs benefiting veterans, such as health care and job training. Along with other data sources, Veterans Affairs also uses the service-connected disability statistics along with veteran population numbers to assess veteran population needs and estimate usage and demand for future medical care facilities.
6. Business Creation Census Business Builder: Small Business Edition combines both demographic data from the American Community Survey and Census Bureau economic data to help small-business owners’ make data-driven decisions when looking into opening or expanding their businesses.
7. Emergency Planning
Many questions on the American Community Survey help communities prepare and respond to disasters. Scientists from the National Institutes of Health use the statistics to simulate the spread of disease, allowing decision-makers to prepare for the next potential outbreak. They use the data to create “synthetic” populations and determine the effect of disease transmission.
8. Housing Statistics
Zillow uses American Community Survey statistics to add neighborhood-level characteristics to its database of more than 110 million U.S. homes. Statistics like commute times, median income and home value allows Zillow to empower consumers with knowledge about the place they call home.
9. Personal Finance
Nerd Wallet uses American Community Survey statistics to “provide clarity for life’s financial decisions.” Using hyperlocal American Community Survey statistics like age, housing cost and home value, the company creates accessible online tools and provides research to help consumers with personal finance decisions.
10. Income and Age Statistics
KaBOOM! uses American Community Survey statistics, such as median household income and the number of children 12 and under, to identify areas in need of playground equipment. It uses these statistics as a baseline to promote the concept that “Play matters for all kids.” KaBOOM! has created over 2,500 playgrounds across the country to serve more than 6.5 million children.
There is also a considerable gap when focusing on men and women living in the same household. Currently, the median earnings of wives is $12,154, or 33 percent of the median earnings of husbands, $37,363.
The gap is substantially smaller among unmarried, or cohabiting, couples. Cohabiting women earn a median of $18,350, or 67 percent of the median for cohabiting men, $27,352. Note that these data measure income earned in 2014.
It is also important to note that these values for median earnings compare all spouses and all unmarried partners, including those without any earnings. Although the earnings gap is often calculated only for those working full time, year-round, looking at all couples is valuable when using a family perspective. Many family factors affect men’s and women’s opportunities and decisions regarding whether and how to pursue a career. See the second figure for values comparing only those who worked full time, year-round. Wives working full time, year-round earn a median of $42,069, or 73 percent of the median earnings of similarly employed husbands, $57,416. The median earnings of cohabiting women working full time, year-round is $33,727, or 86 percent of the median earnings of similarly employed cohabiting men, $39,057.
Indeed, employment status is a key factor for understanding the earnings gap within couples. One reason that women earn less, on average, than their husbands or male partners is that they more frequently take time away from the workforce. Although both the wife and husband are employed in nearly half (48 percent) of married couples, it remains more common for the husband to be employed and the wife to be out of the labor force (22 percent) than vice versa (7 percent). Indeed, 96 percent of stay-at-home married parents are mothers.
Employment patterns also help explain why earnings are more comparable between male and female cohabiters than between husbands and wives. Compared with married couples, both partners are employed in a larger share of cohabiting couples, at 57 percent. Similar to what was observed for married couples, it is more common for men in unmarried couples to be employed and for women to be out of the labor force (17 percent) than for women to be employed and for men to be out of the labor force (8 percent). However, the gap is smaller than for married couples.
Given the amount of effort and planning required to move, it should come as no surprise that the vast majority of people in the United States do not move over a one-year period.
According to the U.S. Census Bureau’s 2015 Annual Social and Economic Supplement (ASEC) of the Current Population Survey (CPS), only 11.6 percent of the population age 1 and over moved between 2014 and 2015. This is not statistically different from the 2014 mover rate of 11.5 percent.
The CPS first asked about migration in 1948, a few years after the end of World War II. Historical migration rates dating back to 1948 are shown in Figure 1. The annual mover rate for 1947-1948 was 20.2 percent. Over time, the mover rate gradually declined. Rates hovered around 16 percent in the late 1990s but quickly fell to around 14 percent by the early 2000s.
Looking at the mover rate by type of move provides valuable insight into why the mover rate has declined. As Figure 2 shows, all types of domestic moves have declined since 1948. In 1948, 13.6 percent of the population moved within the same county. Movement within the same county fell below 10 percent in 2001 and has remained below 10 percent since 2003. Recent estimates show it hovering between 7 and 8 percent. Movement to a different county within the same state was 3.3 percent in 1948 and was down to 2.1 percent in 2015. Moves to a different county in a different state were about half in 2015 from what they were in 1948 (1.6 percent and 3.1 percent, respectively). The 1948 rates for different county within the same state and different county in a different state were not significantly different. Interestingly, the percent of movers from abroad has not changed considerably over the 67 years of data collection (it was 0.3 percent in 1948 and 0.5 percent in 2015).
In 1999, ASEC began asking movers for their main reason for moving. While this information does not help explain the decrease in the mover rate over time, it provides valuable insight into motivating factors behind moves and how these factors have changed for the years available. For this example, we compared 2009 data when the U.S. was deeply embedded in the Great Recession with estimates from 2015. According to Figure 3, “to establish own household” and “new job or job transfer” were both more common in 2015 than 2009. “To look for work or lost job,” “wanted a better neighborhood/less crime” and “wanted cheaper housing” were more common in 2009 than 2015.
The 2015 ASEC asked additional migration questions using a five-year reference period. These five-year questions have been added every five years since 2005. Prior to 2005, they were mostly asked on years ending in “5” to serve as midway points to five-year migration estimates from decennial censuses.
Five-year migration data from the CPS provide information over a longer period, hence the rates are typically higher than one-year migration rates and show the same decline in migration that we see in the one-year question (Figure 4). The 1975 rate was 45.6 percent, the 2005 rate was 39.1 percent, and the 2015 rate was 33.7 percent.
The one-year geographical mobility detailed tables for 2015 will come out later this year. They include information on selected characteristics, such as educational attainment, labor force status, distance moved and reason for move.