A Multilevel Analysis of the Compositional and Contextual Association of Social Capital and Subjective Well-Being in Seoul, South Korea

The purpose of this study is to examine the association between social capital and subjective well-being (life satisfaction) by using multilevel analysis considering both individual and area-level social capital while adjusting for various control variables at multiple-levels in Seoul, South Korea. The data was from the 2010 (Wave 2) Seoul Welfare Panel Study, conducted by Seoul Welfare Foundation. The final sample for this study consisted of 5,934 individuals aged 18 years or older in 2,847 households within 25 administrative areas. Three-level multilevel linear regression analyses, with random intercept models, were applied. Our results provide evidence that various dimensions of social capital both at the individual and area-level are positively associated with subjective life satisfaction, even after controlling for various factors at the individual, household, and area-levels. All of individual-level social capital variables including organizational participation, perceived helpfulness, trust in authorities were positively associated with subjective life satisfaction. Except for trust in authority, area-level organizational participation and perceived helpfulness were positively associated with subjective life satisfaction. These results suggest that decision makers should consider both individual and area-level social capital targeting to enhance one’s well-being.     

Rural–Urban Child Height for Age Trajectories and Their Heterogeneous Determinants in Four Developing Countries

The large literature on health differentials between rural and urban areas relies almost exclusively on cross-sectional data. Bringing together the demographic literature on area-level health inequalities with the bio-physiological literature on children’s catch-up growth over time, this paper uses panel data to investigate the stability and origins of rural–urban health differentials. Using data from the Young Lives longitudinal study of child poverty, I present evidence of large level differences but similar trends in rural versus urban children’s height for age in four developing countries. Further, observable characteristics of children’s environment such as their household wealth, mother’s education, and epidemiological environment explain these differentials in most contexts. In Peru, where they do not, children’s birthweight and mothers’ health and other characteristics suggest that initial endowments—even before birth—may play an important role in explaining "residual" rural–urban child height inequalities. These latter results imply that prioritizing maternal nutrition and health is essential—particularly where rural–urban height inequalities are large. Interventions to reduce area-level health inequalities should begin even before birth.     

The Association Between the Fear of Crime,and Mental and Physical Wellbeing in New Zealand

Researchers have provided clear evidence that the fear of crime can lead to various mental health-related issues including anxiety and psychological distress. However, studies on the effects of fear of crime on physical health are limited. Adding to and extending this literature, we evaluated the association between fear of crime and mental and physical health outcomes in a new setting (New Zealand) and at a national scale. As an added contribution to the literature, we examined whether the fear of crime is independently associated with mental and physical wellbeing, regardless of neighbourhood crime rates. Using data from the New Zealand General Social Survey, the 2006 census and the New Zealand Police, we fitted linear and two-level hierarchical linear models regression models to assess the impact of fear of crime on mental and physical health, at varying stages of individual and area-level confounder adjustment. Even after adjusting for a number of individual- and area-level factors that are related to social inequalities in health in the country, a significant effect of increased fear of crime on lower mental and physical wellbeing was detected. We did not, however, detect significant independent effects for neighbourhood crime rates for either outcome. Our findings indicate that fear of crime, rather than recorded crime rates, was associated with detrimental mental and physical health outcomes. As such, efforts to not only reduce crime but perceived risk of crime could yield public health and social wellbeing benefits.     

Are Area-Level Measures of Employment Associated with Health Behaviours and Outcomes?

Accessible employment suited to residents’ needs is an important equity issue and social determinant of health and wellbeing, and a balanced jobs-housing ratio within a region has the potential to provide health benefits. This paper aims to provide evidence on the associations between employment and health, and the potential pathways, as well as identifying spatial indicators that can be used to measure urban employment, a construct of liveability. We used 2011 census data to create and test two area-level spatial measures of urban employment with geo-coded population health behaviour and outcome data (2011 VicHealth survey) in 5206 employed adults living in urban Victoria, Australia. Those living in areas with higher levels of local employment had reduced odds of a longer commute (OR 0.87). The odds of a longer commute time was also greater for those who lived in an area where more people commuted to work by private vehicle (OR 1.20), and less for those who lived in local areas where more people travelled to work by public transport (OR 0.85) or active travel (OR 0.80). The odds of reporting longer sitting times was less for those who lived in a local area where more people commuted to work by private vehicle (OR 0.65). Those who had a longer commute times, regardless of travel mode, had greater odds of reporting more sitting during a typical weekday (OR 1.67). In turn, those who spent more time sitting had significantly greater odds of reporting poorer self-rated health (OR 1.34). Such work provides evidence to policy-makers to help build the argument for which area-level attributes are needed to support urban employment across a region.     

Urban Form,Heart Disease,and Geography: A Case Study in Composite Index Formation and Bayesian Spatial Modeling

Recent studies indicate a relationship between measures of urban form as applied to urban and suburban areas, and obesity, a risk factor for heart disease. Measures of urban form for exurban and rural areas are considerably scarce; such measures could prove useful in measuring relationships between urban form and both mortality and morbidity in such areas. In modeling area-level mortality, geographic relationships between counties warrant consideration because geographically adjacent areas tend to have more in common than areas farther from each other. We modify county-level indices of urban form found in the literature so that they can be applied to exurban and rural counties. We then use these indices in a Bayesian spatial model that accounts for spatial autocorrelation to determine if there is a relationship between such measures and cardiovascular disease mortality for white males age 35 and older for the time period 1999–2001. Issues related to the formation and usefulness of the indices, and issues related to the spatial model, are discussed. Maps of observed and expected relative risk of mortality are presented. Jimmie Givens retired from his service.     

我国未来城镇人口规模及人口结构变动预测

对城镇人口规模和结构变动给予合理预测,有助于政府预估社会的劳动力供给和养老压力,进而制定合理高效的社会保障政策和进行相应政策调整。本文从规范的人口学角度,采用队列要素法通过年龄移算,在考虑城镇人口基数、出生率、死亡率、性别比以及城市化背景下乡城人口的净迁移率等多个参数的情况下,通过编制我国城镇人口生命表．对从现在起至未来2030年的时间内我国城镇人口的规模和结构进行动态预测,从而为我国制定合理而有针对性的城镇人口政策和经济政策提供参考。     

How does POSSLQ measure up? Historical estimates of cohabitation

We use March Current Population Survey (CPS) data from 1977 to 1997 to produce a new historical series of indirect cohabitation prevalence estimates. We compare our new estimates with those produced by the traditional method and evaluate the new estimates. We then compare the indirect estimates with the new direct estimates to investigate whether biases exist in the indirect estimates. Our findings indicate that the traditional indirect method of estimating cohabitation prevalence underestimates cohabitors in different subpopulations, especially among those with children. We also find that the new indirect measure produces relatively unbiased estimates of cohabitors' characteristics.     

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.     

An Evaluation of Population Estimates in Florida: April 1, 2000

The housing unit (HU) method is the most commonly used method for making small-area population estimates in the United States. These estimates are used for a wide variety of budgeting, planning, and analytical purposes. Given their importance, periodic evaluations of their accuracy are essential. In this article, we evaluate the accuracy of a set of HU population estimates for counties and subcounty areas in Florida, as of April 1, 2000. We investigate the influence of differences in population size and growth rate on estimation errors; compare the accuracy of several alternative techniques for estimating each of the major components of the HU method; compare the accuracy of 2000 estimates with that of estimates produced in 1980 and 1990; compare the accuracy of HU population estimates with that of estimates derived from other estimation methods; consider the role of professional judgment and the use of averaging in the construction of population estimates; and explore the impact of controlling one set of estimates to another. Our results confirm a number of findings that have been reported before and provide empirical evidence on several issues that have received little attention in the literature. We conclude with several observations regarding future directions in population estimation research.     

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.     

New midyear age-sex-color-specific estimates of the U.S. population for the 1940s and 1950s: including a revision of coverage estimates for the 1940 and 1950 censuses

This paper describes new midyear (July 1) estimates of the "true" population of the United States by age, sex, and color (white, nonwhite) for the 1940s and 1950s. It also presents the corresponding implied coverage estimates for the 1940 and 1950 censuses. The new population estimates are calculated by combining the most recent figures on the 1960 population with estimates of the demographic components of change for the 1950s and 1940s in an iterative reverse cohort-component projection algorithm. Among the principal findings of the new estimates are: (a) existing midyear estimates of the "true" population in the 1950s are 450,000 to 500,000 too high; (b) existing age-specific estimates for the 1950s tend to underestimate the population at the older ages (55 years and over) and overestimate the population in the young and middle adult years (15 to 54 years); (c) estimates of the "true" population in the 1940s were too low except for nonwhites at ages 65 and over; (d) existing estimates of percentage net undercount and underenumeration for the 1950 and 1940 censuses tend to be too high, substantially so for nonwhites in the 1940 Census; and (e) nonwhites were more completely enumerated in 1940 than in 1950. Thus, in addition to being methodologically and temporally consistent with post-1960 estimates, the new population estimates described here imply some substantial revisions in demographic, social, and economic statistics for the two decades prior to 1960.     

Assessing post-census state poverty estimates

While the decennial census provides poverty figures for states and other subnational geographic units every ten years, their utility declines over the course of a decade. Consequently, there is growing interest in producing post-census estimates for a variety of indicators. This study extends recent efforts to estimate post-census poverty figures for states by producing such estimates using a multiple regression approach. The accuracy of the multiple regression estimates along with recently produced estimates from the Current Population Survey (CPS) are evaluated relative to the decennial census. The mean absolute percentage point error (MAPPE) using the ratio-correlation technique (1.56 percentage points) was somewhat higher than the MAPPE of 1989 CPS (1.37 percentage points) and an average of 1988–1990 CPS data (1.15 percentage points). However, a simple regression technique using data from 1979 to estimate poverty in 1989 produced a set of estimates where the MAPPE (1.37 percentage points) is nearly as accurate as the single-year CPS estimates. Estimates which average regression estimates and CPS-based estimates are more accurate than either regression or CPS estimates used alone. Several suggestions are offered for improving regression estimates.This article is a revised version of a paper presented at the annual meeting of the Population Association of America, 1–4 April 1993, Cincinnati, OH, USA.     

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).     

Estimates of coverage of the population by sex,race, and age in the 1970 census

Several estimates of total net underenumeration and of net census errors by sex, race (white, Negro-and-other-races, Negro), and age (five-year groups) in the 1960 and 1970 Censuses, for the total population of the United States, derived by the methods of demographic analysis, are presented. The different data, procedures, and assumptions employed in developing the various estimates are described briefly, and the findings are then discussed in terms of a”preferred” set of estimates. The preferred set of estimates of corrected population for 1970 combines estimates for persons under age 35 based directly on birth, death, and migration statistics, estimates for females aged 35 to 64 based on the Coale-Zelnik estimates (white) for 1950 or the Coale-Rives estimates (Negro) for 1960, estimates for males aged 35 to 64 based on the use of expected sex ratios, and estimates for the population 65 and over based on”Medicare” enrollments and expected sex ratios. These estimates indicate an overall net underenumeration of 5.3 million persons or 2.5 percent in 1970, as compared with 5.1 million or 2.7 percent in 1960, and a net underenumeration of 1.9 percent for whites and of 7.7 percent for Negroes in 1970, as compared with 2.0 percent and 8.0 percent, respectively, in 1960. As in 1960, undercoverage in 1970 was greatest for Negro males (9.9 percent); net error rates exceeded 12 percent in each age group 20 to 49 and reached 17 to 19 percent at ages 25 to 44. All sex-race groups showed marked increases between 1960 and 1970 for children under ten and marked declines at ages ten to 24. Equally reliable estimates of population coverage cannot be prepared for states and smaller geographic units or for the population of Spanish ancestry.     

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.     

Rethinking the “early” decline of marital fertility in the united states

In this article, I rely on new estimates of nineteenth-century mortality and the Integrated Public Use Microdata Series to construct new estimates of white fertility in the nineteenth-century United States. Unlike previous estimates that showed a long-term decline in overall fertility beginning at or before the turn of the nineteenth century, the new estimates suggest that U.S. fertility did not begin its secular decline until circa 1840. Moreover, new estimates of white marital fertility, based on "own-children" methods, suggest that the decline in marital fertility did not begin in the nation as a whole until after the Civil War (1861-1865).     

Recent net alien immigration to the United States: Its impact on population growth and native fertility

Estimates of the size and structure of recent alien immigration to the United States are made. Substituting these revised estimates in the Series II projections of the U.S. Bureau of the Census implies a future U.S. population smaller than that implied by the Census Bureau’s estimates of immigration. The analysis of Coale (1972)—which calculates the decline in native-born fertility required to accommodate immigration and, at the same time, maintain a stationary population—is replicated, using both the Census Bureau’s estimates and the revised estimates reported here. The revised estimates indicate a smaller reduction in native fertility and a smaller ultimate size of the stationary population than are implied by the Census Bureau’s immigration estimates. The importance of age structure in all of these calculations is demonstrated.     

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.     

A regression approach to estimating the average number of persons per household

In the housing unit method, population is calculated as the number of households times the average number of persons per household (PPH), plus the population residing in group quarters facilities. Estimates of households and the group quarters population can be derived directly from concurrent data series, but estimates of PPH have traditionally been based on previous values or estimates for larger areas. In our study, we developed several regression models in which PPH estimates were based on symptomatic indicators of PPH change. We tested these estimates using county-level data in four states and found them to be more precise and less biased than estimates based on more commonly used methods.     

An Evaluation of Persons per Household (PPH) Estimates Generated by the American Community Survey: A Demographic Perspective

The American Community Survey (ACS) is a U.S. Census Bureau product designed to provide accurate and timely demographic and economic indicators on an annual basis for both large and small geographic areas within the United States. Operational plans call for ACS to serve not only as a substitute for the decennial census long-form, but as a means of providing annual data at the national, state, county, and subcounty levels. In addition to being highly ambitious, this approach represents a major change in how data are collected and interpreted. Two of the major questions facing the ACS are its functionality and usability. This paper explores the latter of these two questions by examining “persons per household (PPH),” a variable of high interest to demographers and others preparing regular post-censal population estimates. The data used in this exploration are taken from 18 of the counties that formed the set of 1999 ACS test sites. The examination proceeds by first comparing 1-year ACS PPH estimates to Census 2010 PPH values along with extrapolated estimates generated using a geometric model based on PPH change between the 1990 and 2000 census counts. Both sets of estimates are then compared to annual 2001–2009 PPH interpolated estimates generated by a geometric model based on PPH from the 2000 census to the 2010 census. The ACS PPH estimates represent what could be called the “statistical perspective” because variations in the estimates of specific variables over time and space are viewed largely by statisticians with an eye toward sample error. The model-based PPH estimates represent a “demographic perspective” because PPH estimates are largely viewed by demographers as varying systematically and changing relatively slowly over time, an orientation stemming from theory and empirical evidence that PPH estimates respond to demographic and related determinants. The comparisons suggest that the ACS PPH estimates exhibit too much “noisy” variation for a given area over time to be usable by demographers and others preparing post-censal population estimates. These findings should be confirmed through further analysis and suggestions are provided for the directions this research could take. We conclude by noting that the statistical and demographic perspectives are not incompatible and that one of the aims of our paper is to encourage the U.S. Census Bureau to consider ways to improve the usability of the 1-year ACS PPH estimates.