Making the Housing Unit Method Work: An Evaluation of 2010 Population Estimates in Florida

The housing unit (HU) method—in which population estimates are derived from estimates of occupied HUs—is the most commonly used method for making small-area population estimates in the United States. It is widely used because it is conceptually simple, can utilize a wide variety of data sources, can be applied at virtually any level of geography, and often produces reliable estimates. Yet the HU method is more nearly a general approach to population estimation than it is a specific methodology. In this paper, we describe and evaluate several data sources and estimation techniques that can be used in applying the HU method. Using a set of county and subcounty estimates produced by the Bureau of Economic and Business Research (BEBR) at the University of Florida for April 1, 2010, we analyze errors by population size and growth rate, calculate errors for each of the three components of the HU method, and investigate the accuracy of estimates based on several different data sources and estimation techniques. We compare the accuracy of the 2010 estimates with previous BEBR estimates and estimates produced by the U.S. Census Bureau. We conclude that although some data sources and estimation techniques work better than others, the HU method can be tailored to produce reliable population estimates for a wide variety of geographic areas.     

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.     

Some new techniques for applying the housing unit method of local population estimation

The housing unit method of population estimation is often characterized as being imprecise and having an upward bias. We believe that the method itself cannot properly be characterized by a particular level of precision or direction of bias. Only specific techniques of applying the method can have such characteristics. In this paper we discuss several new techniques we have developed for estimating households and the average number of persons per household. Estimates produced by these techniques are compared to estimates produced by several other techniques. Special census results from Florida provide preliminary evidence that the new techniques produce more precise, less biased estimates than the other techniques.     

Assessing the accuracy of Australia’s small-area population estimates

The Australian Bureau of Statistics provides population estimates of Statistical Local Areas annually. The accuracy of these estimates can be assessed after population estimates are rebased after each quinquennial Census of Population and Housing, however there appears to be no straightforward method of assessing these estimates. Errors that occur with population estimates can be attributed to several factors, both broad and specific to individual areas. These factors include inherent characteristics of the region, such as population size and growth rate; changes in the geographic boundaries; quality of input data; estimation method; and adjustments to control totals (state populations).     

Precision,bias, and uncertainty for state population forecasts: an exploratory analysis of time series models

Many researchers have used time series models to construct population forecasts and prediction intervals at the national level, but few have evaluated the accuracy of their forecasts or the out-of-sample validity of their prediction intervals. Fewer still have developed models for subnational areas. In this study, we develop and evaluate six ARIMA time series models for states in the United States. Using annual population estimates from 1900 to 2000 and a variety of launch years, base periods, and forecast horizons, we construct population forecasts for four states chosen to reflect a range of population size and growth rate characteristics. We compare these forecasts with population counts for the corresponding years and find precision, bias, and the width of prediction intervals to vary by state, launch year, model specification, base period, and forecast horizon. Furthermore, we find that prediction intervals based on some ARIMA models provide relatively accurate forecasts of the distribution of future population counts but prediction intervals based on other models do not. We conclude that there is some basis for optimism regarding the possibility that ARIMA models might be able to produce realistic prediction intervals to accompany population forecasts, but a great deal of work remains to be done before we can draw any firm conclusions.     

A method for combining sample survey data and symptomatic indicators to obtain population estimates for local areas

Past efforts of statistical demographers to compute postcensal population estimates for local units have been hampered by the fact that they have had to rely completely on symptomatic information. In this paper, a new method of postcensal estimation is presented in which the symptomatic information is combined with sample data by means of a regression format. Combining symptomatic information on births, deaths, and school enrollment with sample data from the Current Population Survey, county estimates of population growth were computed by means of the new method for the postcensal period after 1960. These estimates were tested for accuracy by comparison with a set of special censuses which were conducted between 1964 and 1967 in 75 counties. The results of this test are promising, but not conclusive. A more conclusive test is currently underway using 1970 data. While the method has been tested as a means for estimating population growth, it is to be emphasized that it can be used to compute postcensal estimates for any variable for which the necessary symptomatic information and sample data are available.     

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.     

The impact of incomplete geocoding on small area population estimates

Small-area population estimates are often made using geocoded address data in conjunction with the housing-unit method. Previous research, however, suggests that these data are subject to systematic incompleteness that biases estimates of race, ethnicity, and other important demographic characteristics. This incompleteness is driven largely by an inability to complete georeference address-based datasets. Given these challenges, small-area demographers need further, and to date largely unavailable, information on the amount of error typically introduced by using incompletely geocoded data to estimate population. More specifically, we argue that applied demographers should like to know if these errors are statistically significant, spatially patterned, or systematically related to specific population characteristics. This paper evaluates the impact of incomplete geocoding on accuracy in small-area population estimates, using a Vintage 2000 set of block-group estimates of the household population for the Albuquerque, NM metro area. Precise estimates of the impact of incomplete geocoding on the accuracy of estimates are made, associations with specific demographic characteristics are considered, and a simple potential remediation based on Horvitz-Thompson theory is presented. The implications of these results for the practice of applied demography are reviewed.     

Bayesian population reconstruction of female populations for less developed and more developed countries

We show that Bayesian population reconstruction, a recent method for estimating past populations by age, works for data of widely varying quality. Bayesian reconstruction simultaneously estimates age-specific population counts, fertility rates, mortality rates, and net international migration flows from fragmentary data, while formally accounting for measurement error. As inputs, Bayesian reconstruction uses initial bias-reduced estimates of standard demographic variables. We reconstruct the female populations of three countries: Laos, a country with little vital registration data where population estimation depends largely on surveys; Sri Lanka, a country with some vital registration data; and New Zealand, a country with a highly developed statistical system and good quality vital registration data. In addition, we extend the method to countries without censuses at regular intervals. We also use it to assess the consistency of results between model life tables and available census data, and hence to compare different model life table systems.     

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.     

Some New Techniques for Applying the Housing Unit Method of Local Population Estimation: Further Evidence

The housing unit method of population estimation is often characterized as being imprecise and having an upward bias. In an earlier paper we argued that the method itself cannot be properly characterized by a particular level of precision or direction of bias. Only specific techniques of applying the method can have such characteristics. In that paper we presented several new techniques for estimating the number of households and average number of persons per household (PPH). However, the testing of these new techniques was limited by the lack of census results against which the estimates could be compared. Complete census data on population, households, and PPH are now available and can be used to test alternate estimation techniques. In this paper we replicate the tests reported in our earlier paper using 1980 census data for Florida’s 67 counties. These tests provide further evidence that the new techniques produce more precise, less biased estimates than previously used techniques.     

How Far Has Fertility in China Really Declined?

According to births in the last year as reported in China's 2000 census, the total fertility rate (TFR) in the year 2000 in China was 1.22 children per woman. This estimate is widely considered to be too low, primarily because some women who had out‐of‐quota births according to China's one‐child family policy did not report those births to the census enumerator. Analysis of fertility trends derived by applying the own‐children method of fertility estimation to China's 1990 and 2000 censuses indicates that the true level of the TFR in 2000 was probably between 1.5 and 1.6 children per woman. A decomposition analysis of change in the TFR between 1990 and 2000, based on our best estimate of 1.59 for the TFR in 2000, indicates that about two‐fifths of the decline in the conventional TFR between 1990 and 2000 is accounted for by later marriage and less marriage, and three‐fifths by declining fertility within marriage. The analysis also applies the birth history reconstruction method of fertility estimation to the two censuses, yielding an alternative set of fertility estimates that are compared with the set derived by the own‐children method. The analysis also includes estimates of trends in fertility by urban/rural residence, education, ethnicity, and migration status. Over time, fertility has declined sharply within all categories of these characteristics, indicating that the one‐child policy has had large across‐the‐board effects.     

Evaluating Population Forecast Accuracy: A Regression Approach Using County Data

Many studies have evaluated the impact of differences in population size and growth rate on population forecast accuracy. Virtually all these studies have been based on aggregate data; that is, they focused on average errors for places with particular size or growth rate characteristics. In this study, we take a different approach by investigating forecast accuracy using regression models based on data for individual places. Using decennial census data from 1900 to 2000 for 2,482 counties in the US, we construct a large number of county population forecasts and calculate forecast errors for 10- and 20-year horizons. Then, we develop and evaluate several alternative functional forms of regression models relating population size and growth rate to forecast accuracy; investigate the impact of adding several other explanatory variables; and estimate the relative contributions of each variable to the discriminatory power of the models. Our results confirm several findings reported in previous studies but uncover several new findings as well. We believe regression models based on data for individual places provide powerful but under-utilized tools for investigating the determinants of population forecast accuracy.     

An overview of demographic analysis as a method for evaluating census coverage in the United States

"Since the 1950 U.S. census, demographic methods based on the fundamental balancing equation of demography have played an important role in the evaluation of the census net undercount. Application of this set of methods, called demographic analysis, results in national estimates of the net undercount for age-race-sex groups. Although results of demographic analysis are readily available in Bureau of the Census publications, the procedures used to estimate each of the components of population change are less well-known. In this paper we review the historical foundation of demographic analysis, beginning with Coale's 1950 census evaluation project and concluding with the recent evaluation of the 1990 census. We examine each of the components of the method, how their estimation has changed over time, and how they were estimated for the 1990 census."     

Mixed estimation of old‐age mortality

The estimation of the mortality of the “oldest old”; is subject to considerable random error, but important prior information exists that can be used to make the estimates more robust. Mixed estimation is a method of incorporating auxiliary information into the statistical estimation of linear models. We extend the method to cover general maximum likelihood estimation, and show that the mixed estimator can be represented approximately as a weighted average of the purely data based estimator and the auxiliary estimator. The methods can be applied to the analysis of the old‐age mortality via logistic and Poisson regression. A major advantage of the mixed estimator is the simplicity with which it can incorporate partial prior information. Moreover, no special software is needed in the fitting. We show how the targeting methods of Coale and Kisker can be represented as mixed estimation in a natural way that is more flexible than the original proposal. We also derive empirical estimates of the target information based on pooled data from several countries with high quality data. We consider the mortality of Finland at ages 80 +, study the reliability of the evidence of mortality crossover, and derive estimates of life expectancy at age 100.     

Measuring Residential Segregation With the ACS: How the Margin of Error Affects the Dissimilarity Index

The American Community Survey (ACS) provides valuable, timely population estimates but with increased levels of sampling error. Although the margin of error is included with aggregate estimates, it has not been incorporated into segregation indexes. With the increasing levels of diversity in small and large places throughout the United States comes a need to track accurately and study changes in racial and ethnic segregation between censuses. The 2005–2009 ACS is used to calculate three dissimilarity indexes (D) for all core-based statistical areas (CBSAs) in the United States. We introduce a simulation method for computing segregation indexes and examine them with particular regard to the size of the CBSAs. Additionally, a subset of CBSAs is used to explore how ACS indexes differ from those computed using the 2000 and 2010 censuses. Findings suggest that the precision and accuracy of D from the ACS is influenced by a number of factors, including the number of tracts and minority population size. For smaller areas, point estimates systematically overstate actual levels of segregation, and large confidence intervals lead to limited statistical power.     

New Directions in the Development of Population Estimates in the United States?

The advent of a continuously updated Master Area File (MAF) following the 2000 census represents an information resource that can be tapped for purposes of developing timely, cost-effective, and precise population estimates for even the smallest of geographical units (e.g., census blocks). We argue that the MAF can be enhanced (EMAF) for these purposes. In support of our argument we describe a set of activities needed to develop EMAF, each of which is well within the current capabilities of the U.S. Census Bureau and discuss various costs and benefits of each. We also describe how EMAF would provide population estimates containing a wide range of demographic (e.g., age, race, and sex) and socio-economic characteristics (e.g., educational attainment, income, and employment). As such, it could largely negate and eliminate the need for many of the traditional demographic methods of population estimation and possibly reduce the number of sample surveys. We identify important challenges that must be surmounted in order to realize EMAF and make suggestions for doing so. We conclude by noting that the idea of the EMAF could be of interest to other countries with MAF files and strong administrative records systems that, like the United States, are facing the challenge of producing good population information in the face of increasing census costs.     

Coverage of the population in Census 2000: Results from Demographic Analysis

Since the 1960 Census, Demographic Analysis (DA) has been used by the Census Bureau to evaluate the coverage of the population. Administrative statistics on births, deaths, immigration and Medicare enrollments as well as estimates of legal emigration and net undocumented immigration are used to produce demographic analysis estimates of the population for the census date. These estimates are compared to the Census 2000 data to evaluate coverage in the census. The results are also compared to measures of undercount obtained from dual system estimation. The DA measures substantial reduction in net undercount in Census 2000 compared to 1990. The reductions occur among all demographic categories: all broad age groups, males and females, Blacks and Non-Blacks.     

Improvingaccuracy in multiple regression estimates of population using principles from causal modelling

This paper reports a mildly restricted procedure for using a theoretical causal ordering and principles from path analysis to provide a basis for modifying regression coefficients in order to improve the estimation accuracy of the ratio-correlation method of population estimation. The modification is intended to take into account temporal changes in the structure of variable relationships, a major element in determining the accuracy of post-censal estimates. The modification of coefficients is conservative in that it uses rank-ordering as a basis of change. Empirical results are reported for counties in Washington state that demonstrate the increased accuracy obtained using the proposed procedure.     

Levels of urbanization in the world’s countries: testing consistency of estimates based on national definitions

Estimates of urbanization levels for all countries, as published in the United Nations World Urbanization Prospects, are not comparable across countries because they are based on national definitions of urbanization, which vary greatly across countries. We construct alternative estimates of urbanization that are comparable across countries, to use these estimates to point out definitional problems or outlying countries with interesting urban characteristics. The alternative estimates are constructed using variables that are associated with urbanization, as well as a categorical variable that summarizes the urban definition used in the country. We find that among the 181 countries included in the analysis, 21 % have a restrictive definition, and 31 % a generous definition of urbanization. In 2000, the difference between the alternative estimate and the UN estimate was more than 10 % for 67 countries. An examination of six countries with surprisingly low or high reported levels of urbanization, as compared the alternative estimates, highlights issues with the plausibility of the official estimate. We conclude that comparison of urbanization estimates across countries should be treated with care, given the wide range of definitions used. To produce more informative estimates of urbanization, several countries would need to clarify or rethink their definition of urban.