The American Community Survey in counties with “seasonal” populations

The U.S. Census Bureau designed the American Community Survey (ACS) to provide annual estimates of social and economic characteristics for states, counties, municipalities, census tracts, and block groups. Because of its April 1 reference date, in northern nonmetropolitan counties with substantial seasonal population fluctuations the decennial census provides a statistical representation of the demographic and social characteristics of the population at a time when the population is close to its annual minimum. The year-round monthly ACS sample survey has the potential to provide local communities with an unprecedented understanding of the average population characteristics over the course of a year. In the future, the ACS even has the potential for providing social and economic characteristics of the population by season. This paper examines four ACS pilot data collection counties, Oneida and Vilas Counties in northern Wisconsin, and Lake and Flathead Counties in northwest Montana. We hypothesize that the ACS will reflect a resident population over the course of the year that is different from the traditional April 1 decennial census population. While the ACS holds much promise, our research uncovered some sampling problems that are not yet fully resolved. In addition, our analysis was not able to examine ACS estimates for minor civil divisions (MCDs), which are functioning governmental units in many states. The fact that these MCDs often have very small populations, together with the fact that estimated standard errors at the much larger census tract level in these counties are disconcertingly large, raises (currently unanswerable) questions concerning the eventual statistical quality of ACS estimates for small MCDs. Consequently, the adequacy of the ACS as a replacement for the census long form may depend on the ability of the Census Bureau to effectively address the concerns presented in this analysis.     

Revising Infant Mortality Rates for the Early Twentieth Century United States

Accurate vital statistics are required to understand the evolution of racial disparities in infant health and the causes of rapid secular decline in infant mortality during the early twentieth century. Unfortunately, U.S. infant mortality rates prior to 1950 suffer from an upward bias stemming from a severe underregistration of births. At one extreme, African American births in southern states went unregistered at the rate of 15 % to 25 %. In this study, we construct improved estimates of births and infant mortality in the United States for 1915–1940 using recently released complete count decennial census microdata combined with the counts of infant deaths from published sources. We check the veracity of our estimates with a major birth registration study completed in conjunction with the 1940 decennial census and find that the largest adjustments occur in states with less-complete birth registration systems. An additional advantage of our census-based estimation method is the extension backward of the birth and infant mortality series for years prior to published estimates of registered births, enabling previously impossible comparisons and estimations. Finally, we show that underregistration can bias effect estimates even in a panel setting with specifications that include location fixed effects and place-specific linear time trends.     

Assessing the devolution revolution: How accurate are state-level estimates from the current population survey

The devolution of many social policy responsibilities from the Federal government to states has prompted increased interest in state-level measures of need. One data source that could be used to provide more state-level information on a variety of topics is the Current Population Survey (CPS). During the past ten years the CPS has been used to produce state-level estimates on a variety of measures. However, there has been little systematic evaluation of these data. This paper provides measures of accuracy for several state-level estimates derived from the CPS. These include standard errors for single-year estimates, three-year averages, and five-year averages of the March CPS measures; standard errors for three-year averages of 12-month CPS files; and comparison of CPS-based estimates to data from the Decennial Census. The paper also examines the relative accuracy of CPS estimates based on states' size. The information in this study will help analysts better understand the tradeoffs between timeliness and accuracy to be considered when using state-level estimates derived from the CPS.     

State-Level 2010 Census Coverage Rates for Young Children

The Census Bureau’s demographic analysis (DA) shows that the net undercount rate for children aged 0–4 was 4.6 percent in the 2010 U.S. Decennial Census while adults (age 18 and older) had a net overcount rate of 0.7 percent. For the population aged 0–4, DA estimates are seen as more accurate than the U.S. Decennial Census because the estimates for this young population rely heavily on highly accurate birth certificate data. Given the relatively high net undercount rate for young children, it would be useful to examine census coverage rates for this population in subnational geographic units. In this study, the 2010 U.S. Decennial Census counts of children aged 0–4 are compared to the corresponding figures from the Census Bureau’s Vintage 2010 Population Estimates in each state. Differences between the 2010 U.S. Decennial Census count and the Vintage 2010 Population Estimates for the population aged 0–4 range from an estimated net undercount of 10.2 percent in Arizona to an estimated net overcount of 2.1 percent in North Dakota. Larger states tended to have higher net undercounts than smaller states. The ten largest states account for about 70 percent of the national net undercount of the population aged 0–4. Of all the factors examined here, the relative size of the Blacks Alone or in Combination plus Hispanics population is most highly correlated with the estimated net undercount of the population aged 0–4. Other measures that were highly correlated with net undercount rates for the population aged 0–4 were linguistic isolation, percent of adults without a high school degree, and the unemployment rate. In general, characteristics of people are more highly correlated with the net undercount rates of young children than the characteristics of housing units.     

Comparing the U.S. Decennial Census Coverage Estimates for Children from Demographic Analysis and Coverage Measurement Surveys

Following every U.S. decennial census since 1960, the U.S. Census Bureau has evaluated the completeness of coverage using two different methods. Demographic analysis (DA) compares the census counts to a set of independent population estimates to infer coverage differences by age, sex, and race. The survey-based approach (also called dual system estimation or DSE) provides coverage estimates based on matching data from a post-enumeration survey to census records. This paper reviews the fundamentals of the two methodological approaches and then initially examines the results of these two methods for the 2010 decennial census in terms of consistency and inconsistency for age groups. The authors find that the two methods produce relatively consistent results for all age groups, except for young children. Consequently, the paper focuses on the results for children. Results of the 1990, 2000, and 2010 decennial censuses are shown for the overall population in this age group and by demographic detail (age, race, and Hispanic origin). Among children, the DA and DSE results are most inconsistent for the population aged 0–4 and most consistent for ages 10–17. Results also show that DA and DSE are more consistent for Black than non-Black populations. The authors discuss possible explanations for the differences in the two methods for young children and conclude that the DSE approach may underestimate the net undercount of young children due to correlation bias.     

Estimation of interprovincial migration for Canada from place of birth by residence data, 1951–1961

In Canada, unlike many other countries, birth-residence data by age and sex are available in each of the decennial censuses from 1931 to 1961 which permit the estimation of intercensal net migration for the provinces and regions. After a brief discussion of the basic measures of migration from birth-residence data the paper focusses on the problems and procedures in estimating interprovincial net migration, 1951–1961 for Canada using “the place of birth survival ratio method, ” and it evaluates the estimates thus obtained. The evaluation of the estimates, taking into consideration the inherent limitations of the method and its merits compared with period migration estimates by the census survival ratio method and life table survival ratio method, suggests that the net migration estimates for the Canadian born by the place of birth survival ratio method are probably more reliable than those by the other two methods. One striking finding was that the net migration curves by age obtained from the census survival ratio and place of birth survival ratio estimates were smoother than the curve obtained with the use of the more accurate life table survival ratios. Furthermore, whatever the relative accuracy of net migration may be, the birth-residence approach is capable of furnishing more details about the net migration of the native born than by the standard survival-ratio methods. For the population under age 10 intercensal estimates were directly derived from the place of birth and residence distributions by age.     

Drawing statistical inferences from historical census data, 1850–1950

Virtually all quantitative microdata used by social scientists derive from samples that incorporate clustering, strati cation, and weighting adjustments (Kish 1965, 1992). Such data can yield standard error estimates that differ dramatically from those derived from a simple random sample of the same size. Researchers using historical U.S. census microdata, however, usually apply methods designed for simple random samples. The resulting p values and confidence intervals could be inaccurate and could lead to erroneous research conclusions. Because U.S. census microdata samples are among the most widely used sources for social science and policy research, the need for reliable standard error estimation is critical. We evaluate the historical microdata samples of the Integrated Public Use Microdata Series (IPUMS) project from 1850 to 1950 in order to determine (1) the impact of sample design on standard error estimates, and (2) how to apply modern standard error estimation software to historical census samples. We exploit a unique new data source from the 1880 census to validate our methods for standard error estimation, and then we apply this approach to the 1850–1870 and 1900–1950 decennial censuses. We conclude that Taylor series estimation can be used effectively with the historical decennial census microdata samples and should be applied in research analyses that have the potential for substantial clustering effects.     

The effect of undercount on the accuracy of small-area population estimates: Implications for the use of administrative data for improving population enumeration

The effects of underenumeration on the accuracy of alternative methods of population estimation have not been sufficiently analyzed. Although the US Bureau of the Census has decided not to adjust either the counts or its estimates for underenumeration in 1990, the extent to which local population estimates may account for underenumeration is of importance both for those who may wish to adjust existing estimates and in anticipation of future census adjustments. This paper examines the accuracy of small-area population estimation methods with and without adjustment. Mean Percent Errors, Mean Absolute Percent Errors, and Mean Percent Absolute Differences between local estimates for 1990 and 1990 adjusted and unadjusted census counts are computed. Population estimates for 1990 made using housing unit, ratio correlation, and component methods are compared for 451 counties and 2,633 places in the states of California, Florida, Texas, and Wisconsin. An analysis of the data for counties shows little indication that local estimates more accurately estimate the adjusted than the unadjusted population counts. The results for places show clear improvements in accuracy for places in Florida and Texas. Implications of the findings for issues related to undercount adjustment and local population estimates are discussed.     

The role of demographic data in the operation of public policy: a case study of the job training partnership act

To work at the state and local levels, the Job Training Partnership Act (JTPA) requires specific kinds of demographic data. Unemployment figures, poverty information, occupational supply and demand data, wage structure, and many other types of data are used in planning and operating JTPA programs, and in modifying the performance standards by which the programs are evaluated. The decennial census provides some key data and thus some significant types of information become less timely as the decade progresses. Furthermore, the framers of the Act had little or no knowledge of the forms in which data are collected and thus definitions given in the Act do not always agree with definitions used in the demographic data which are available. The policymakers for future government benefit programs could learn from this mistake: before writing legislation which is so heavily dependent on demographic data, they should ascertain what types of data are available or can be generated for use in the program.     

A Comparative Evaluation of Error and Bias in Census Tract-Level Age/Sex-Specific Population Estimates: Component I (Net-Migration) vs Component III (Hamilton–Perry)

While the housing-unit method continues to be the preferred method nationwide for producing small-area population estimates, this procedures lacks a method for making age/sex-specific estimates. This paper reports evaluation research on implementation of component-based methods for estimating census tract populations with age/sex detail. Two alternatives are explored: (1) the Component I method relying upon estimates of births, deaths, and net-migration and (2) the Component III method relying solely upon 1990 and 2000 Census counts. From an April 1, 2000 base, each method is used to make estimates moving forward to an April 1, 2010 estimate that is compared to the results of the 2010 Census. The two methods are compared in terms of accuracy and bias using both absolute and algebraic mean and median percentage errors. Results are reviewed and discussed in light of their implications for applied demographers tasked with making small-area demographic estimates.     

Assessing poverty: some reflections on the literature

This paper examines the quantitative poverty measurement literature. After describing the literature a number of issues in poverty research are discussed. It can be concluded that the framework for poverty assessments is not always fixed. Much has been written about the poverty line but the issue of updating it seems to have attracted less attention than deserved. Substantial advancements in poverty research have been gained by fuller reports on the extent of poverty through the use of poverty indices and because of increased availability of panel data.Revised version of an invited lecture presented at the Eighth Annual Conference of the European Society for Population Economics, Tilburg, The Netherlands, 1994. 1 thank three referees for useful suggestions, Ludmila Nivorozhkina for supplying information on the development of poverty in Russia and Håkan Nyman for producing the figures. The content draws on research conducted with financial support from the Swedish Council of Social Research (SFR).     

The American Community Survey: general commentary on the findings from external evaluations

As part of the Census Bureau’s effort to prove the operational feasibility of the American Community Survey (ACS), the Bureau contracted with local experts to conduct comparative analyses of the 1999–2001 ACS estimates with the 2000 Census data for various test counties. One of the goals of the ACS is to replace the decennial census long form. The resulting research papers analyzed various quality measures (response rates, allocation rates, and so on), conceptual differences in the two instruments, and statistical reliability in an effort to add to our assessment of the ACS as an adequate replacement instrument for collecting long form data. This paper discusses the results of these four research efforts and presents conclusions and recommendations for further Census Bureau action and research. The quality of traditional long form data, the importance of accurate population estimates and an accurate Master Address File (MAF), and continued research on the quality of small area data are discussed.     

Population research and education in Vietnam

Population research and education in Vietnam have expanded in their functions since the early 1980s. There are currently 5 departments conducting demographic studies: the National Committee for Population and Family Planning within the Ministry of Health, the Ministry of Education, and the Institute of Pedagogical Sciences, the Ministry of Labor Fore, the Vietnam Women's Union, and the Youth Union. An overview is provided of each department and its research and educational activities as well as the General Statistics Office (GSO) and the Institute of Sociology. GSO provides statistics on population annually from a variety of sources including the decennial census. The last census was conducted in 1989 and is useful for understanding general demographic trends. Vital rate data are considered too low. The Institute of Sociology uses the sample survey and focus groups to examine social and economic conditions and their effects on norms and values about reproduction. The Institute conducted a knowledge, attitudes, and prevalence (KAP) survey in 1984. Studies have focused on the commune level and the role of government in population regulation. Findings have supported a position that the goal of a 2-child family is not attainable without social and economic change as well as family planning. Migration studies have also been undertaken with the Ministry of Labor. The National Committee for Population and Family Planning is concerned with studies on the biotechnology of contraceptive methods and abortion. A study in 1987 examined health workers attitudes toward and knowledge of a variety of contraceptive methods. In 1988 the Demographic and Health Survey was conducted and data were comparable internationally. Studies have been helpful in designing training programs and in informing decision makers. Forecast data in 1990 were useful in setting targets for the coming decade. The Ministry of Education targets the general public and students. Materials aim to create positive attitudes to family planning and to inform about general population issues. Special groups such as the Women's Union have conducted KAP studies and the Center for Women's Studies has focused on the problems of women in development.     

Estimating historic population data for small geographies using census housing information

Historical population data for small geographies (e.g. blocks, block-groups, and census tracts) are not available for periods earlier than 1980. In this research note, we propose a geographically-constrained housing unit method (GHUM) to estimate historic population for small geographies using housing age data available in the 1980–2000 censuses. The GHUM is a two-stage method. The first stage follows a traditional housing unit method and provides initial household and group quarter population estimates for small geographies. The second stage takes advantage of the availability of historic data for larger geographies (e.g. counties, states) to adjust the first stage estimates and to provide final estimates. The GHUM is used to estimate 1940–1990 county population and census tract population in Kentucky. The quality of the population estimates is assessed. A two-sample Kolmogorov–Smirnov test indicates that these estimates are statistically reliable at the 10 % significance level.     

Estimation of Disaggregate-Level Poverty Incidence in Odisha Under Area-Level Hierarchical Bayes Small Area Model

Sustainable development goal-1 of the United Nations is to end poverty in all its forms everywhere. The estimates of poverty related parameters obtained from large scale sample survey are often available at large domain level (e.g. state level). But, poverty rates are not uniformly distributed across the regions. The regional variations are masked in such large domain level estimates. However, for monitoring the progress of poverty alleviation programmes aimed at reduction of poverty often require micro or disaggregate level estimates. The traditional survey estimation approaches are not suitable for generating the reliable estimates at this level because of sample size problem. It is the main endeavor of Small Area Estimation (SAE) approach to produce micro level statistics with acceptable precision without incurring any extra cost and utilizing existing survey data. In this study, the Hierarchical Bayes approach of SAE has been applied to generate reliable and representative district level poverty incidence for the State of Odisha in India using the Household Consumer Expenditure Survey 2011–2012 data of National Sample Survey Office and linked with Population Census 2011. The results show the precise performance of model based estimates generated by SAE method to a greater extent than the direct survey estimates. A poverty map has also been produced to observe the spatial inequality in poverty distribution.

     

How surveys are changing at the U.S. Bureau of the Census

"This article describes the survey activities of the U.S. Census Bureau and recent efforts by the bureau to move toward a more fully automated environment....[It] focuses on four areas in which the Census Bureau is changing its approach to surveys: (1) redesigning the questionnaire for the Current Population Survey to better reflect current labor force conditions; (2) redesigning the same questionnaire to gain advantages from computer-assisted interviewing; (3) redesigning the samples for household surveys to be used during the next decade, a task undertaken after every decennial census; and (4) progress in changing to a computer-assisted survey information collection (CASIC) system and developing a data management network for all Census Bureau surveys."     

Empirical Prediction Intervals for County Population Forecasts

Population forecasts entail a significant amount of uncertainty, especially for long-range horizons and for places with small or rapidly changing populations. This uncertainty can be dealt with by presenting a range of projections or by developing statistical prediction intervals. The latter can be based on models that incorporate the stochastic nature of the forecasting process, on empirical analyses of past forecast errors, or on a combination of the two. In this article, we develop and test prediction intervals based on empirical analyses of past forecast errors for counties in the United States. Using decennial census data from 1900 to 2000, we apply trend extrapolation techniques to develop a set of county population forecasts; calculate forecast errors by comparing forecasts to subsequent census counts; and use the distribution of errors to construct empirical prediction intervals. We find that empirically-based prediction intervals provide reasonably accurate predictions of the precision of population forecasts, but provide little guidance regarding their tendency to be too high or too low. We believe the construction of empirically-based prediction intervals will help users of small-area population forecasts measure and evaluate the uncertainty inherent in population forecasts and plan more effectively for the future.     

Establishing trends in atsic regional council populations using census data: A cautionary note

This paper points out that limitations in official census data for Aborigines and Torres Strait Islanders draw into question the validity of trend analysis based on time series data for Aboriginal and Torres Strait Islander Commission (ATSIC) regional council areas. Accordingly, the meaningful application of projection techniques to estimate future population profiles using existing census data is severely restricted. Among the difficulties encountered in reconstructing council area populations are census boundary changes over time, changes in enumeration techniques and coverage, the problems posed by self-identification and associated population growth, and, in some cases, the difficulty of matching ATSIC regional council boundaries with census geography. Following discussion of these problems, detailed figures showing changes in the size of the Aboriginal and Islander populations and labour force in each council area are presented using 1976 as the base year. As expected, geographic patterns of population and labour-force change are difficult to discern and exact reasons for comparative growth or decline are impossible to determine. The paper concludes that reverse projections for regional council areas using 1991 Census data would provide a more reliable basis for establishing demographic trends. Although not entirely adequate, these reconstructions for ATSIC regional councils are the only estimates of these populations that have been undertaken to date.     

Criteria for selecting a suitable method for producing post-2000 county population estimates: A case study of population estimates in Illinois

We compared 2000 county population estimates for Illinois against 2000 census counts. Administrative records (ADREC) and ratio correlation (Ratio-CORR) methods were used to produce two sets of controlled county estimates for 2000; a third set represented an average of the estimates reached using these methods. Another set using the ADREC method was not controlled to any estimate. Also, the 2000 estimates were adjusted for undercount in the 1990 census. We compared performance of these methods with the performance of two naive models: (i) do nothing and (ii) constant growth rate. ADREC estimates were more accurate than estimates from the Ratio-CORR or Average method in terms of Mean Absolute Percent (MAPE) or weighted MAPE. Undercount adjustment in general improved the accuracy of the estimates for all three methods. A top-down or bottom-up approach worked equally well. As a single method, ADREC performed best.     

New Estimates of Undocumented Mexican Migration and the Probability of Apprehension*

Using a new source of data, we estimate the probability of apprehension among Mexican migrants attempting to cross into the United States without documents. Over the period 1965-1989 we found an average apprehension probability of .35, confirming earlier estimates. We then applied annual probabilities to estimate the gross volume of undocumented Mexican migration and adjusted these figures to derive estimates of the net undocumented inflow.