Improving Our Nation’s Data on Race and Ethnicity

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Written by: Nicholas Jones, Director of Race and Ethnic Research and Outreach

Since the 1970s, the Census Bureau has conducted decennial content tests to research and improve the design and function of different questions, including questions on race and ethnicity. As our population grows more diverse, we want to ensure that the data we collect accurately reflects how the people living in our country identify themselves.

Recently, the Census Bureau undertook a critical middecade study to test alternative versions of the race and ethnicity questions. The goal is to improve question design and data quality for the 2020 Census. Building on the foundation and successful strategies of the 2010 Census Alternative Questionnaire Experiment Research on Race and Hispanic Origin, the 2015 National Content Test is helping refine our previous efforts to address race and ethnicity reporting issues and important racial/ethnic community concerns.

During the 2015 National Content Test, we are focusing on three crucial goals for race and ethnicity:

  • Increasing the accuracy and reliability of reporting in major race/ethnic categories.
  • Lowering item nonresponse rates.
  • Collecting detailed data for myriad groups.

This research will enable the 2020 Census to provide critical racial and ethnicity statistics about our nation’s population. To learn more about the ongoing research, click here.

Outreach and Engagement Over the Past Year                                   

Coinciding with this extensive research, the Census Bureau continues ongoing engagement and discussions about race and ethnicity with the U.S. Office of Management and Budget, federal statistical agencies and many stakeholder groups. Over the past year, our research team reached out to many stakeholders to make them aware of the 2015 National Content Test research plans, discussed their questions and obtained their feedback.

This ongoing outreach includes collaboration and dialogue with:

  • Office of Management and Budget and other federal agencies through interagency meetings.
  • Department of Justice, Office of Civil Rights about redistricting.
  • Equal Employment Opportunity Commission on comparing data trends.
  • White House Initiative on Asian Americans and Pacific Islanders on data disaggregation.
  • State Department on racial identity in the Americas.

Additionally, the agency engaged in dialogues with many other stakeholders, including:

  • The Census Bureau’s National Advisory Committee on Racial, Ethnic and Other Populations.
  • Conferences with Afro-Latinos.
  • Summits with the National Congress of American Indians.
  • Briefings with congressional caucuses for Blacks, Hispanics, Asians and Pacific Islanders.
  • Legislative briefings with Caribbean-Americans.
  • Conferences with Native Hawaiians and Pacific Islanders.
  • Forums with Middle Eastern and North African scholars.

We heard from thousands of individuals, organizations and community groups, and their feedback helped us with our research plans.

Next Steps

The results of the 2015 National Content Test, public feedback and continued dialogues with the Office of Management and Budget and external stakeholders and experts will guide the proposed race and ethnicity question design for the 2020 Census. The research seeks to improve data on race and ethnicity and inform recommendations to the Federal Interagency Working Group for Research on Race and Ethnicity, regarding the 1997 OMB Standards and Guidance on Race and Ethnicity. By early 2017, the 2020 Census topics must be submitted to Congress, with final question wording due the following year in April 2018.

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Census Bureau Opens 20th Research Data Center in Wisconsin

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Written by: Barbara Downs, Ph.D., Center for Economic Studies

As we celebrate World Statistics Day and the importance of access to sound data, it’s a good time to recognize the recent opening of the Federal Statistical Research Data Center at the University of Wisconsin-Madison. Research Data Centers like this give qualified researchers on approved projects access to restricted data that are vital to increasing our understanding of the U.S. population and economy.

In addition to its own onsite researchers, the Census Bureau draws upon the expertise of the research community throughout the United States via the Federal Statistical Research Data Center (FSRDC) network. Research from this network has led to new uses of existing Census Bureau data, improved content in existing Census Bureau surveys and the development of new surveys designed to capture the dynamic U.S. economy. All of these activities are in support of the Census Bureau’s mission to serve as the leading source of quality data about the nation’s people and economy. The results of this research have been published in many top peer-reviewed journals, including the American Economic Review, Demography, the Journal of Political Economy and the Quarterly Journal of Economics.

In existence since 1994, the network represents a partnership between participating federal agencies and non-profit research institutions. Research data centers are secure facilities that provide access to restricted-use microdata collected by the Census Bureau and other agencies such as the National Center for Health Statistics and the Agency for Healthcare Research and Quality.  Researchers with approved projects can conduct research that benefits the Census Bureau by improving measures of the economy and people of the United States.

Before gaining access to the FSRDC network, researchers must submit proposals to the Census Bureau. The review process ensures that proposed research is feasible, has scientific merit and benefits Census Bureau programs. In addition, the research data center network’s operating procedures, strict security and strong legal safeguards ensure the confidentiality of data, as required by law. Researchers, for instance, must pass a full background investigation and are sworn for life to protect the confidentiality of the data they access, with violations subject to significant financial and legal penalties.

The FSRDC network currently hosts about 700 researchers working on more than 200 different projects.  The Wisconsin Federal Statistical Research Data Center joins 19 other Census Bureau research data center locations in the United States. These facilities are located at 14 colleges and universities — Baruch College (New York City), Cornell University (Ithaca, N.Y.), Duke University (Durham, N.C.), University of Michigan (Ann Arbor), University of Minnesota (Minneapolis), Penn State University (University Park, Pa.), University of Southern California (Los Angeles), Stanford University (Palo Alto, Calif.), Texas A&M (College Station), UC-Berkeley (Calif.), UC-Irvine (Calif.), UCLA (Los Angeles), University of Washington (Seattle) and Yale University (New Haven, Conn.) — and in the cities of Atlanta; Boston; Chicago; Raleigh, N.C.; and Suitland, Md.

Centers expected to open soon will be in Kansas City, Mo., as well as the University of Maryland (College Park), the University of Missouri (Columbia) and the University of Nebraska (Lincoln).

You can learn more about the FSRDC program and our newest location, the Wisconsin Federal Statistical Research Data Center, at and

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Where Did All the Construction Workers Go?

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New Census Data on Job-to-Job Flows

By Hubert Janicki and Erika McEntarfer

 Unfilled job openings in the construction industry have risen steadily since 2009 (Job Openings and Labor Turnover Survey, Bureau of Labor Statistics). The rise follows a sharp fall due to the housing bust and the subsequent Great Recession (Job Openings and Labor Turnover Survey, Bureau of Labor Statistics). Anecdotally, many builders and contractors have reported difficulty finding new workers.  While not necessarily indicating a shortage in the market for construction workers, it is possible that tightening labor markets in construction are a drag on employment recovery in this sector.  This post sheds light on the labor market in construction using new Census Bureau Job-to-Job Flows data.

Specifically, we investigate the dynamics of worker flows in the construction industry in an attempt to examine some possible causes of this recent tightening in the labor market. We find that over 60 percent of construction workers displaced by the housing bust are employed in other industries or have left the labor market by 2013. We also find evidence of a persistent drop in hiring of younger workers into construction jobs over the last decade that is likely contributing to the current shortage of skilled workers in construction.

We first look at cross-industry job flows to see if construction workers moved into other industries after the housing boom. Figure 1 shows the sources of net employment growth in the construction industry from the second quarter of 2000 to the fourth quarter of 2013. Net employment growth in construction is employment gained or lost from workers moving in and out of construction (black line in Figure 1) due to workers switching industries (blue dashed line in Figure 1) and workers moving in and out of employment (red dotted line in Figure 1).

Employed workers moving from other industries into construction fueled a large part of construction employment growth during the housing boom. However, when the boom ended in 2006, there was no corresponding employment decline from workers moving into other industries; almost the entire employment decline is workers separating to nonemployment.

Which industries fueled employment growth in construction during the housing boom?

Figure 2 shows net flows into construction due to industry switching (blue dashed line in Figure 1) by industry. Workers moving from manufacturing, mining and especially leisure/hospitality jobs fueled much of the employment growth in construction between 2003 and 2005. Flows from leisure/hospitality jobs are especially large — about half of employment growth during the construction boom came from workers moving from leisure/hospitality into construction. These flows are likely driven by pay differentials between the two industries: average worker earnings in leisure/hospitality jobs are lower than in the construction sector. When the industry contracted sharply after the housing crash, that pipeline reversed (i.e. net flows between construction and leisure/hospitality became negative), but only for a few quarters.

Figure 1: Net Employment Change in Construction: Industry Switchers and Workers Moving In and Out of Employment: 2000Q2 – 2013Q4

Net Employment Change in Construction: Industry Switchers and Workers Moving in and out of employment: 2000Q2-2013Q4

Source: U.S. Census Bureau, beta Job-to-Job Flows (J2J) data, seasonally adjusted, 2014 Q4 release, set of 32 states with complete data from 2000 Q2 to 2013 Q4. “Employed Workers Switching Industries” shows the net job reallocation between construction and other industries from workers changing jobs with little to no employment gap (less than one quarter) between job spells. “Workers Moving In and Out of Employment” shows net employment change in construction from workers moving in and out of longer nonemployment spells (one quarter or more).

One hypothesis for the lack of more sustained worker flows from construction to leisure/hospitality jobs during the Great Recession is the differences in average workers earnings across these two industries. In particular, workers might conduct longer searches or wait out the housing crash rather than go “back down the job ladder” into lower paying work.

Another hypothesis is the relative lack of job security in leisure/hospitality jobs compared with jobs in other sectors. If service sector workers were more likely to lose their jobs in the housing crash and the Great Recession, then construction workers might have less of an incentive to look for jobs in that sector. Consequently, flows of former construction workers back to leisure/hospitality are not as large as one might expect given the long period of inflows from that sector into construction in the preceding years.

Figure 2: Net Employment Change in Construction from Workers Changing Industries: 2000Q2 – 2013Q4

Net Employment Change in Construction from Workers Changing Industries: 2000Q2 – 2013Q4

Source: U.S. Census Bureau, 2015Q2 beta Job-to-Job Flows (J2J) data, set of 32 states with complete data from 2000 Q2 to 2013 Q4. Data are seasonally adjusted. This graph shows net employment reallocation in construction from workers switching industries with little to no employment gap (less than one quarter) between job spells.

What happened to construction workers who entered long nonemployment spells during 2006 to 2009?

The Job-to-Job Flows public-use data do not release outcomes for workers who experienced employment gaps of longer than one-quarter, but we can look to the underlying longitudinal microdata for an answer. For construction workers who entered nonemployment spells of more than three months between 2006 and 2009:

  • About 40 percent were either recalled to their previous employer or eventually found another construction job.
  • Approximately one-third began work in another industry, typically after an employment gap of over a year. The most common destination jobs for these workers are in the trade/transportation and business services sectors. A look at destination jobs by detailed industry suggests many became general laborers, some landscapers, and some truck drivers. Despite the boom in mining during this period, mining accounts for less than 5 percent of new jobs for former construction workers exiting nonemployment.
  • Approximately one-quarter of displaced construction workers have no observed subsequent employment by the end of 2013, five to seven years after displacement. These individuals presumably have left the labor market, although they could be working informally or be self-employed.

Figure 3: Hires of Younger Workers as a Percent of All Hires in Construction 2000Q1 – 2013Q4

Figure 3: Hires of Younger Workers as a Percent of All Hires in Construction 2000Q1 – 2013Q4

Source: Author’s calculations from U.S. Census Bureau’s National Quarterly Workforce Indicators (QWI), 2015Q1 vintage. Data are seasonally adjusted and shares are smoothed using a centered moving average.

Another reason construction firms may have trouble finding skilled workers today is that hiring of young workers in the industry declined after the housing boom. Overall, hires of workers less than 45 years old fell from 73% of construction hires in 2000 to 63% in 2011.

To examine this reason in more detail, we turn to the Quarterly Workforce Indicators data. Figure 3 shows the composition of hires into construction jobs by worker age among younger workers. A striking feature of this figure is a marked decline in the share of total hiring from the 19-24 age group. In particular, the percent of hires accounted for by the 19-25 age group declined from approximately 18 percent at its peak before 2006 to 13 percent in 2012-2013. In comparison, the composition of hires of workers in the 25-34 and 34-44 age groups shows much more modest declines over this time period.

The age 19-24 demographic group of workers is particularly relevant since the participation of that age group in industries other than construction has remained constant at approximately 15 percent throughout this period (Figure 4). In other words, the decline in hires among younger workers in the construction industry is not because of a delayed entrance of 19- to 24-year-olds into the labor market, but rather the entrance of these workers into other industries. One reason for this decline could be unwillingness by construction companies to train relatively young workers relative to other industries in the economy. Another reason for this decline could be unwillingness to hire or work for short-duration contract jobs. Note in Figure 4 that young workers (19-24 age group) are being replaced with workers in the age 45-55 category. This increase in percent of 45- to 55-year-olds employed in the construction sector exceeds the employment share of this age group in all industries.

Figure 4: Decomposing Workforce by Age: Construction versus All Industries, 2000Q1 – 2013Q4

Figure 4: Decomposing Workforce by Age: Construction versus All Industries, 2000Q1 – 2013Q4

Source: Author’s calculations from U.S. Census Bureau’s National Quarterly Workforce Indicators (QWI), 2015Q1 vintage. Shares are smoothed using a centered moving average.

In short, our analysis of the data suggests that 60 percent of displaced construction workers have left the labor market or moved into other industries. Although some former construction workers transitioned quickly to other sectors, for most, a move into another industry occurred after a long spell of nonemployment. Also likely contributing to a shortage of experienced workers is a shift in hiring preferences — during the downturn construction firms hired fewer young workers, fewer young workers gained experience in the industry, and the share of older workers grew faster than in other industries.

You can use these data sources to carry out a more detailed analysis. Job-to-Job Flows Beta release data and Quarterly Workforce Indicators data are available for download through the Longitudinal Employer-Household Dynamics program at

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How Well Do You Speak English? Assessing the Validity of the American Community Survey English-Ability Question

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Written by: Erik Vickstrom, Social, Economic, and Housing Statistics Division

Using data from the American Community Survey, the Census Bureau estimated that 60.4 million, or 20.7 percent of the population, spoke a language other than English at home in 2013.

Of these people, 58.3 percent reported speaking English “very well.” The Census Bureau generates statistics on English ability by asking respondents who report speaking a language other than English at home to indicate how well they speak English: “very well,” “well,” “not well” or “not at all.” Federal programs use data from this English-speaking-ability question for three purposes:

  • To decide which localities are required to produce voting materials in minority languages.
  • To determine the number of school-age English-language learners in each state.
  • To determine which governmental programs should provide assistance to those who have limited ability to communicate in English.

A new working paper investigates whether this question and its self-assessment of English ability actually measure an individual’s real English ability.

Researchers at the Census Bureau have been interested in measuring English ability since the question first appeared on the 1980 Census questionnaire. In 1982, results from the Census Bureau’s English Language Proficiency Study showed a strong correlation between the English-speaking-ability question and English-proficiency test scores. In addition, results from the 1986 National Content Test found a positive correlation between spoken English ability and reading and writing ability in English. More recently, a National Research Council report called for additional research on the accuracy of this survey item.

My research seeks to provide insights into the validity of the Census Bureau’s American Community Survey English-speaking-ability question by comparing self-assessments of English ability with objective tests of English literacy. Using data from the National Assessment of Adult Literacy, conducted in 2003 by the National Center for Education Statistics, I find that self-reported English ability is consistent with objective measures of literacy.

Table 1: Descriptions of prose proficiency levels, National Assessment of Adult Literacy

Figure 1: Average prose literacy scores by self-reported English ability, National Assessment of Adult Literacy


Respondents to the National Assessment of Adult Literacy completed a literacy assessment that drew from 152 open-ended questions that simulated real-life situations. While these tests allowed the assessment of prose, document and quantitative literacy, my research focused only on prose literacy scores. The prose assessment, which sought to measure the language skills necessary for effective access to public service programs, seemed most comparable to the adult English proficiency tests on the English Language Proficiency Study.

The prose literacy assessment allowed the calculation of a prose score of between 0 and 500 for respondents. The literacy scores correspond to four performance levels: “below basic,” “basic,” “intermediate” and “proficient.” Table 1 shows these performance levels with the corresponding ranges of literacy scores, key abilities and sample tasks for each level. For example, respondents scoring in the “proficient” prose-score range are able to compare viewpoints in two editorials, while those scoring in the “intermediate” prose-score range are able to consult reference materials to determine which foods contain a particular vitamin.

Figure 1 shows the relationship between self-assessed English ability and prose scores among National Assessment of Adult Literacy respondents as measured by the literacy assessment. Respondents who reported speaking only English score 284 on the prose literacy assessment, indicating that they have, on average, intermediate proficiency. Those speakers of languages other than English who report speaking English “very well” have an average prose-literacy score of 269. This score falls in the intermediate performance level. In contrast, speakers of languages other than English who report speaking English “well” have an average score of 233, which puts them in the basic performance level. Those who reported speaking English “not well” or “not at all” scored 151 and 111, respectively, and both groups fall in the below-basic performance level.

Speakers of non-English languages who report speaking English “very well,” like English-only speakers, have average prose-literacy scores that fall into the intermediate performance level. Prose literacy at this level indicates abilities sufficient to read and understand moderately dense, less commonplace prose texts. Thus, the speakers with the best self-reported English ability can perform the same key tasks as the average English-only speaker. Speakers of languages other than English reporting an English ability of less than “very well,” in contrast, have lower language skills on average.

These results suggest that the English-ability question, despite being a self-assessment, does a good job of measuring English ability.

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Characteristics of Likely-Transgender Individuals in Administrative Records and the 2010 Census

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Written by: Benjamin Cerf Harris, Ph.D., Economist, Center for Administrative Records Research and Applications

Transgender issues and images are increasingly present in popular media, literature, journalism, and the United States legal system. Few data sets, however, include information on the transgender community.

In a new working paper, I evaluate first-name and sex-coding changes in administrative files from the Social Security Administration (SSA) to model which individuals are likely to be transgender. I then match these likely-transgender individuals to their responses in the 2010 Census to create a preliminary dataset for studying the characteristics of likely-transgender individuals.

It is crucial to note that the number of people I identify as “likely-transgender” is not an estimate of the number of transgender individuals in the population, or even in the Social Security data. Gender identity is too complex to be captured only by name changes and sex-coding changes in a particular file. Further, some transgender individuals may not have a Social Security number, may not change their names with the SSA, or may have gender-neutral names. While this approach does not provide exact estimates of who is and is not transgender, it does allow researchers to learn about relative changes in trends over time and cross-sectional differences in characteristics for a subset of likely-transgender individuals.

Beginning in 1936 (the first year Social Security numbers were issued), I find claims for male-to-female and female-to-male name changes, many of which include sex-coding changes in the same directions. Figure 1 shows these “transgender-consistent” claims make up just under 0.02 percent of all claims and remained roughly proportional as the number of people with Social Security numbers grew.

Next, I match likely-transgender individuals to their records in the 2010 Census to explore residential patterns and responses to the question on sex. Figure 2 shows that likely-transgender individuals in the Social Security files are most concentrated in western and northeastern states that had legal protections against discrimination based on gender identity or expression. They are least concentrated in states without such protections.RM2

I also find that likely-transgender individuals answer questions about sex differently than nontransgender individuals. Figure 3 shows likely-transgender respondents report both sexes, or leave the question blank, more often than nontransgender respondents. This is interesting because this question appears everywhere, from surveys to credit card applications to the forms we all fill out at the doctor’s office.


Together, these results help illuminate U.S. transgender history from the 1930s onward, and they help address questions about the characteristics and residential patterns of likely-transgender individuals. This work also demonstrates the potential for integrating data from existing sources, including administrative records, to produce new information products that provide deeper insights into the U.S. population. Despite several limitations to this approach, it is an important step toward learning more about transgender individuals’ experiences in the U.S.

The paper is available at

Note: Please also see the Census Bureau statement on transgender data collection:

Census Bureau Statement on Transgender Data Collection

June 3, 2015 — Content changes in the decennial census and surveys are managed through an interagency process by the Office of Management and Budget (OMB). Decisions on new content are reached through careful consideration and public input and linked to a federal, legislative or programmatic need for the data. At this time the Census Bureau does not have plans to test questions about gender identity or sexual orientation for the 2020 Census or American Community Survey.

The Census Bureau, however, is a member of the Office of Management and Budget’s Federal Interagency Group on Improving Measurement of Lesbian, Gay, Bisexual Transgender (LGBT) populations in Federal Surveys, and the Census Bureau continues to work with the Office of Management and Budget and other federal agencies to examine the changing requirements and data recommended for program implementation. Additionally, a measure of sexual orientation was recently included in the National Health Interview Survey (NHIS) conducted by the Census Bureau and sponsored by the National Center for Health Statistics (NCHS).

The Census Bureau is committed to reflecting the information needs of our changing society. The Census Bureau is constantly examining the effectiveness of census and survey questions to collect accurate data on families and people and works with the National Advisory Committee on Racial, Ethnic and Other Populations and the Census Scientific Advisory Committee on recommendations for changing population needs.

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Where Do We Go When We Retire? A Broader Look at Retirement Destinations

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Ben Bolender, Population Division, U.S. Census Bureau

Where do you plan to live when you or a loved one reaches the golden years? Do you plan to stay where you are now? Do you plan to move to some place warm and sunny? Our new research from the 2014 population estimates looks at where people age 65 and over are choosing to move.

People moving from place to place are reflected in the measures of migration created every year for the Census Bureau’s population estimates. We use primarily Medicare enrollment and group quarters data to calculate the movement of the population age 65 and over. People move for a variety of reasons. The tendency to migrate is highest for young adults. However, in the ages following retirement (commonly around 65), we see another spike in migration. With the baby boomers being the largest cohort yet to enter these ages, the question of where people move when they retire is becoming increasingly important.

Sumter County, Fla., has been one of the fastest-growing counties in recent years, at least partially because of its planned retirement destination status. Based on our research into older age migration patterns, we see that Sumter attracted just over 11,000 older people from April 1, 2010 (Census Day) to July 1, 2014, from other parts of the U.S. Another county with a strong draw was Maricopa, Ariz., which gained almost 18,000 older people during this same period through migration from other parts of the U.S.

While it is true that these areas do attract the most older-age migrants, they do not reflect the wide variety of places that attract retirement-age people. Examining numeric change tells one story, but it is often useful to look at other measures of change. The picture changes when we group counties by population size and look at percent change in the older population from migration. For example, Sumter County ranks high in this measure, showing an increase of 27.7 percent in the 65-and-older population through just domestic migration from 2010 to 2014. The comparable number for Maricopa County was only 3.9 percent.

For counties with a total population of 50,000 or more in 2014, Sumter leads the list for fastest-growing older population through domestic migration. This was followed by Broomfield, Colo., with a 17.6 percent increase, and Rockwall, Texas (part of the Dallas metro area), with a 16.9 percent increase. Here we see that both midsized mountain towns and suburban areas saw their older populations rise rapidly through migration.

When we look at counties with populations between 20,000 and 50,000, Jasper, S.C., leads the way at 28.1 percent, followed by Kendall, Texas, at 14.9 percent. Many older people choose to move to suburbs and areas adjacent to larger cities. Both of these counties are on the outskirts of larger metropolitan statistical areas.

For counties with fewer than 20,000 people in 2014, looking at percent growth starts to pick up much smaller numeric increases. Stark, Ill., leads the list at 17.3 percent growth, followed by Lake, S.D., at 13.9 percent and Sequatchie, Tenn., at 12.6 percent. These three areas represent different flavors of rural America in their plains, forests, valleys, and fields. Even with the variety of places where you could choose to retire, with the aging of the baby boomers it is likely that when you arrive you will find others who made the same choice.

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How Did People Experience Poverty from 2009 to 2012?

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Note: Census Bureau experts are presenting on a variety of topics at the Population Association of America annual conference. Follow the Research Matters blog or visit the press kit to learn more about their work.

Written by Ashley Edwards

Typical poverty statistics are a snapshot of the population in poverty at the time of the survey.  Are the poor young or old?  Single-parents, in large families, or childless?  New estimates  highlight an easy-to-overlook aspect of poverty:  It is not a constant and individuals move in and out of poverty.  Data from the Survey of Income and Program Participation allowed me to estimate the median length of time individuals spent in poverty between 2009 and 2012 as well as the number of times individuals shifted in and out of poverty.  The data highlight the diverse poverty experiences of different demographic groups in the early years of the current economic recovery.

Of individuals who entered poverty at some point between 2009 and 2012,  the median length of a poverty spell was 6.2 months. However, some individuals experienced repeated poverty spells, and measures based on spell length failed to account for the often cyclical nature of poverty. Over this four-year period, more than half (54.5 percent) of individuals who entered poverty experienced only a single spell, while 45.5 percent had two or more poverty spells.

My research shows children under the age of 18 and Hispanics tended to experience more poverty spells than the overall population experiencing poverty from 2009 to 2012. Individuals age 65 and older experienced fewer and longer poverty spells, but their median total time in poverty was not statistically different from the overall ever-poor population.

Blacks and individuals in female-householder families did not experience more frequent poverty spells, but their median spell lengths were longer. In contrast, Hispanics experienced more poverty spells, but their individual spell lengths were not any longer than the ever-poor.

Title of Graph – Selected Family and Demographic Characteristics by Poverty Spell Frequency and Duration: 2009 to 2012

poverty1Initiated in 1983, the Survey of Income and Program Participation provides a wealth of information to analyze the economic situation of people in the United States. It offers detailed information on cash and noncash income, while also collecting data on taxes, assets, liabilities and participation in government transfer programs. The data allow the government to evaluate the effectiveness of federal, state and local programs.

The 1982 annual poverty thresholds were used as the base to calculate monthly poverty thresholds during the reference period. These annual thresholds were then divided by 12 and adjusted using the consumer price index for a given reference month to provide a monthly poverty threshold by family size. For example, in January 2009, the monthly poverty threshold for a family of four with two children was $1,784, and in December 2012, the monthly poverty threshold for the same family was $1,940.

Note: The Census Bureau’s Trudi Renwick has conducted related poverty research on the supplemental poverty measure. A poster presented at the Population Association of America annual conference displays the results of using data from the Current Population Survey Annual Social and Economic Supplement to estimate the value of variables not included in the American Community Survey but critical to the production of supplemental poverty measure estimates from the American Community Survey. Her poster examines the effect of government noncash benefits and necessary expenditures on state level supplemental poverty measure rates.  See the results of this research at:

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Talkin’ ‘Bout Our Generations: Will Millennials Have a Similar Impact on America’s Institutions as the Baby Boomers?

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Note: Census Bureau experts are presenting on a variety of topics at the Population Association of America annual conference. Follow the Research Matters blog or visit the press kit to learn more about their work.

Written by Sandra Colby, Population Division

Over the next several years, baby boomers will continue transitioning into retirement and old age while millennials, many of whom are children of the baby boomers, pass through the traditional benchmarks of adulthood (e.g., finishing school, finding jobs and buying homes). Researchers and reporters, among others, have drawn comparisons between the experiences and behaviors of these two generations.

However, these comparisons often overlook one important difference between the generations — their memberships are not defined by the same metric. That is, while birth cohorts included in the baby boomer generation are associated with a rise in fertility, no similar demographic event can be used to distinguish the birth cohorts included in the millennial generation. Notably, shared experiences rather than demographics define the millennials.

Because of this, the birth years for this generation are not as distinguishable as those of the baby boomers. The Census Bureau does not provide guidance on which years are included in the millennial generation, and many definitions are used by the public. For the purposes of this blog, I use the term “millennials” to encompass those born between 1982 and 2000.

Figure 1 shows births for the years 1909 through 2013. A large increase in the number of births between 1945 and 1946 marks the start of the baby boom generation, but there is no corresponding increase to establish the start of the millennial generation. Gens1

Although the birth cohorts comprising the millennial generation were as large, and in some cases larger, than those of the baby boomer generation, the millennial generation differs from the baby boomers because these large birth cohorts are part of a broader trend that started in the previous generation and is continuing into the next.  In other words, the fertility trends associated with the beginning of the millennial generation are not exceptional.

One consequence of this difference relates to the impact that these generations have on societal institutions. Despite the similar size of the millennial generation relative to the baby boomers, their transition through life thus far has not introduced the same level of shock as the baby boomers caused.

To illustrate this point, I use the example of school enrollment for 5 and 6 year olds. Figure 2 shows the annual change in the number of 5 and 6 year olds enrolled in school. For the majority of years, the number of students remained relatively stable. One exception was the 1.4 million increase in students between 1953 and 1954. The growth in 1954 corresponds to children born between 1947 and 1949 (the first of the baby boomers) enrolling in school. This increase is nearly triple that experienced at any point during the millennial generation’s transition into school and represents an increasing demand on the education system not replicated by the millennials.


The goal here is not to minimize the millennial generation’s significance, only to highlight important differences in the origin of this generation and its impact on societal institutions as its members transition through the course of life.

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When Do Mothers Earn More? A Look at Fertility Timing and Occupation

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Note: Census Bureau experts are presenting on a variety of topics at the Population Association of America annual conference. Follow the Research Matters blog or visit the press kit to learn more about their work.

Written By: Liana Christin Landivar, Sociologist, Industry and Occupation Statistics Branch

Researchers have highlighted a consistent motherhood “wage penalty” of 6 to 7 percent for mothers of one child and 12 to 13 percent for mothers of multiple children.  Another  examination of the data, however, shows that there are certain circumstances where mothers outearn nonmothers.

Using data collected in the 2013 American Community Survey, I look at the earnings gap between mothers and nonmothers, and then examine how the earnings gap varies by occupation and age of children. My research shows that while mothers were more likely to be out of the labor force or work part time than nonmothers, mothers earned more than nonmothers among full-time, year-round workers. One explanation provided in prior research is that mothers with higher earnings potential may be more likely to remain employed full time.

Median earnings of women ages 18-50 employed full time, year-round, 2013


Fertility delay has been linked to higher earnings for mothers. The mean age at first birth in 2013 was 26 years old and a growing number of women are postponing children. When looking at earnings by age, we see that younger mothers earned less than nonmothers in the same age group, while older mothers earned more than nonmothers in the same age group. Mothers in the youngest age group, 18-29, experienced the largest earnings penalty relative to nonmothers. Mothers in the oldest age group, 40-50, experienced the largest earnings premium, particularly among mothers of preschool-age children (ages 0-5).

Median earnings by age of mother and age and presence of children, 2013


The earnings gap also varied by occupation. Mothers in the managerial and professional occupation group experienced the largest earnings penalties for young motherhood, but also the largest earnings premium for delayed fertility. In managerial and professional occupations, mothers of preschoolers earned $11,000 more than nonmothers of the same age if they had children between the ages of 40 and 50. Women in managerial and professional occupations with the earliest fertility, that is, mothers ages 18 to 29 with school-age children (ages 6 to 17), earned $9,000 less than nonmothers of the same age. Having children at older ages did not translate into an earnings premium for women in construction, production, agriculture, health care support, cleaning and maintenance, or food preparation, where mothers earned less than nonmothers or there is no statistical difference in their earnings.

Mothers earned more than nonmothers when they had children at older ages and they worked in managerial and professional, protective service or sales occupations. From an earnings point of view, delaying fertility (that is, putting off when they have their first child) may be particularly important for women in occupations requiring advanced degrees or longer tenure for career advancement, because younger mothers may never catch up to the earnings of women who wait to have children or who never have children.  In other occupations, however, delaying fertility is not associated with higher earnings for mothers.

Earnings penalty or premium by occupation and age among mothers of preschool children, 2013*

The estimates presented are based on responses from a sample of the population. As with all surveys, estimates may vary from the actual values because of sampling variation or other factors. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see:

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China Replaces Mexico as the Top Sending Country for Immigrants to the United States

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Note: Census Bureau experts are presenting on a variety of topics at the Population Association of America annual conference. Follow the Research Matters blog or visit the press kit to learn more about their work.

Written by Eric Jensen

Based on my research, in 2013, China replaced Mexico as the top sending country for immigrants to the United States. This followed a decade where immigration from China and India increased while immigration from Mexico decreased. Other top immigrant-sending countries in 2013 from Asia included Korea, the Philippines and Japan. This new pattern in the national origins of recent immigrants is a notable change from recent decades.

The racial and ethnic composition of immigration flows to the United States has also been shifting. In 2000, nearly half of all foreign-born immigrants, 41.2 percent, were Hispanic, compared with 23.6 percent for the non-Hispanic Asian alone population. Since 2009, a greater proportion of foreign-born immigrants have been non-Hispanic Asian alone (34.7 percent) than Hispanic (30.1 percent). By 2013, the percentage of non-Hispanic Asian alone had increased to 40.2 percent of the total immigration flow, while the percentage Hispanic had dropped to 25.5 percent.

The U.S. Census Bureau’s Population Estimates Program measures net international migration, including the foreign-born population whose residence one year ago was abroad. According to the 2013 American Community Survey, there were 1,201,000 immigrants. China was the top sending country with 147,000, followed by India with 129,000, and Mexico with 125,000. The numbers of immigrants from India and Mexico were not statistically different from each other. In 2012, the American Community Survey showed that Mexico and China were the top two sending countries with 125,000 and 124,000, respectively (which were not significantly different from each other).

Change in the racial and ethnic composition of immigrant flows contributes to the overall racial and ethnic makeup of the United States. While Hispanics are still the largest racial or ethnic minority group, a larger percentage of the Asian population was foreign-born (65.4) compared with the Hispanic population (35.2) in 2013. Given the numbers above, it is likely that the contribution of immigration to overall population growth will be greater for Asians than for Hispanics.

Historically, the national origins of immigrant flows have changed dramatically. The earliest waves of immigrants originated in Northern and Western Europe. Immigrants from Southern and Eastern Europe later predominated. The most recent wave of immigrants has largely been from Latin America, and to a lesser extent, Asia. Whether these recent trends signal a new and distinct wave of immigration is yet to be seen.

The figure below shows the foreign-born population whose residence one year ago was abroad for China, India and Mexico from 2000 to 2013.

Foreign-Born Population Whose Residence One Year Ago Was Abroad by Selected Places of Birth: 2000-2013



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