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.     

On the ratio-correlation and related methods of subnational population estimation

The ratio-correlation method is one of several methods currently being used in the United States for making postcensal estimates of subnational units such as states and counties. It seems to have been commonly presumed by those writing on the subject that the working of the ratiocorrelation method can be understood simply on the basis of the multiple regression theory. That this common notion may sometimes be untenable is demonstrated in this paper. It is pointed out that the ratio-correlation method of subnational population estimation has certain characteristic features that make its application fall sometimes outside the usual contexts in which the conventional multiple regression theory is applicable. A number of alternatives to the ratio-correlation method are suggested. Some of the alternatives suggested are shown to yield relatively more accurate results when used for estimating postcensal populations of North Carolina counties.     

Confidence intervals for postcensal state population estimates

This paper develops a methodology for constructing confidence intervals around postcensal state population estimates. Using regression equations, forecast intervals are derived around the average age-specific death rates over the postcensal estimation period. These results, combined with the number of postcensal deaths and the most current census counts, are translated into confidence intervals for the age structure. Two approaches are offered for constructing total population confidence intervals. One examines a simulated distribution while the other focuses on the mathematical derivation of population means and variances. The methodology is illustrated by deriving statistically defensible confidence intervals around the July 1, 1975 population of Florida.     

贫困地区劳动力迁移、农户收入与贫困的缓解——基于新疆农户面板数据的实证分析

本文基于新疆30个贫困县、3000个农户、2008-2010年的微观面板数据,在控制了家庭规模、劳动力数量等家庭特征变量和粮食播种面积等村庄特征变量的前提下．运用固定效应法与工具变量法分别考察了劳动力迁移规模、迁移方式与迁移区位对家庭收入的影响．并进一步分析了劳动力迁移对贫困的缓解效应。回归结果表明：劳动力迁移规模虽然对农户农业收入有负向影响．但显著提高了农户人均收入与利他性收入：自发性迁移与政府组织性迁移方式均能显著提高农户收入,且自发性迁移的作用更强;省内县外迁移对农户收入的提高最为显著,其次为县内乡外迁移．省外迁移不影响农户收入：劳动力迁移规模提高了中等收入农户的收入水平。但对贫困户的贫困无缓解效应．也不影响富裕户的收入水平。     

Household size and the poisson distribution

Household size distributions for 104 countries are examined. It is shown that a Poisson distribution truncated at zero can be used to derive models of household size distribution. An improved fit is obtained by adding a linear term to the truncated Poisson model. This distribution depends only on average household size which in turn is shown to be related to modified dependency ratios. This method can be used for comparisons of household size distributions across nations and for long-term forecasting.     

Correcting the Underestimation of Top Incomes: Combining Data from Income Tax Reports and the Brazilian 2010 Census

To deal with the problem of underestimation of top incomes in household surveys, we propose a methodology to combine the income distributions of the Brazilian 2010 Census (survey) and of 2010 DIRPF (personal income tax reports). The method consists in estimating a system of non-response weights that uses as frame the tax register and is applied to the top of the distribution. After applying this calibration methodology, we decompose inequality income sources. Correcting survey distributions with tax data increases the contribution of non-labor income to inequality, as the case of the Brazilian Census shows. Changes in the methodology do not affect the results substantially.     

The sectoral labor supply of married couples in Brazil: Testing the unitary model of household behavior

An assumption of the unitary model of household decision-making is that household members maximize one household utility function. This assumption implies that households pool their income and, therefore, the ownership of nonwage income has no effect on household demand. In this paper, this implication is tested by estimating multi-sector labor supply equations for men and women in Brazil. The results indicate that the unitary model is rejected in the informal and self-employment sectors for men and the formal and informal sectors for women; in these cases own nonwage income has a significantly negative effect on labor supply while spousal nonwage income has no significant effect. Received: 29 December 1997/Accepted: 9 December 1998     

Using administrative data to estimate population displacement and resettlement following a catastrophic U.S. disaster

After a large scale evacuation, authorities need to know the new and frequently changing population distributions in order to meet needs for housing, schools, health care, and other services. This paper reviews literature from the fields of demography and other disciplines to identify available administrative data sets that can form the basis of sound, relevant, and timely county-level population estimates following a catastrophic U.S. event. The most appropriate data to estimate population in damaged counties will be disaster-specific data such as housing damage estimates and FEMA applicant counts initially, and later electric accounts and USPS active residences. In heavily damaged counties, data on electric accounts and USPS active residences may not be consistently collected for many months, during which time sample surveys may be needed. For counties that receive an influx of population, school enrollment data provide the most appropriate basis for population estimates. Population estimates for large, heavily damaged counties are highly uncertain. Sensitivity analysis when using estimates for planning in these areas is recommended. The Census Bureau can build on this research by codifying recommendations to local authorities for developing frequent post-disaster population estimates.     

Cointegration modeling of fertility in the United States

This paper presents estimates of a multiple time series model of fertility, female labor force participation, women's wages, and the relative cohort size of younger to older adult males. Cointegration methods permit modeling of these nonstationary variables, yielding estimates of the long-run relation among the variables, and the dynamic response of each variable to displacements from the steady state. The estimated steady state relation between fertility and the other variables is consistent with economic models of fertility, with fertility negatively related to female wages and male relative cohort size. Fertility responds to cohort size in a manner that is consistent with Easterlin's relative income model of household behavior. Finally, both female labor market variables adjust significantly to departures from the steady state relation, implying that they cannot be treated as exogenous in time series models of fertility.     

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.     

Differences Between Household Income from Surveys and Registers and How These Affect the Poverty Headcount: Evidence from the Austrian SILC

We take advantage of the fact that for the Austrian SILC 2008–2011, two data sources are available in parallel for the same households: register-based and survey-based income data. Thus, we aim to explain which households tend to under- or over-report their household income by estimating multinomial logit and OLS models with covariates referring to the interview situation, employment status and socio-demographic household characteristics. Furthermore, we analyze source-specific differences in the distribution of household income and how these differences affect aggregate poverty indicators based on household income. The analysis reveals an increase in the cross-sectional poverty rates for 2008–2011 and the longitudinal poverty rate if register data rather than survey data are used. These changes in the poverty rate are mainly driven by differences in employment income rather than sampling weights and other income components. Regression results show a pattern of mean-reverting errors when comparing household income between the two data sources. Furthermore, differences between data sources for both under-reporting and over-reporting slightly decrease with the number of panel waves in which a household participated. Among the other variables analyzed that are related to the interview situation (mode, proxy, interview month), only the number of proxy interviews was (weakly) positively correlated with the difference between data sources, although this outcome was not robust over different model specifications.     

Incorporating Neighborhood Choice in a Model of Neighborhood Effects on Income

Studies of neighborhood effects often attempt to identify causal effects of neighborhood characteristics on individual outcomes, such as income, education, employment, and health. However, selection looms large in this line of research, and it has been argued that estimates of neighborhood effects are biased because people nonrandomly select into neighborhoods based on their preferences, income, and the availability of alternative housing. We propose a two-step framework to disentangle selection processes in the relationship between neighborhood deprivation and earnings. We model neighborhood selection using a conditional logit model, from which we derive correction terms. Driven by the recognition that most households prefer certain types of neighborhoods rather than specific areas, we employ a principle components analysis to reduce these terms into eight correction components. We use these to adjust parameter estimates from a model of subsequent neighborhood effects on individual income for the unequal probability that a household chooses to live in a particular type of neighborhood. We apply this technique to administrative data from the Netherlands. After we adjust for the differential sorting of households into certain types of neighborhoods, the effect of neighborhood income on individual income diminishes but remains significant. These results further emphasize that researchers need to be attuned to the role of selection bias when assessing the role of neighborhood effects on individual outcomes. Perhaps more importantly, the persistent effect of neighborhood deprivation on subsequent earnings suggests that neighborhood effects reflect more than the shared characteristics of neighborhood residents: place of residence partially determines economic well-being.     

Assessing How Participators Combine Acts in Their “Political Tool Kits”: A Person-Centered Measurement Approach for Analyzing Citizen Participation

The aim of this paper is to highlight some key issues and challenges in the analysis of poverty at the local level using survey data. In the last years there was a worldwide increase in the demand for poverty and living conditions estimates at the local level, since these quantities can help in planning local policies aimed at decreasing poverty and social exclusion. In many countries various sample surveys on income and living conditions are currently conducted, but their sample size is not enough to obtain reliable estimates at local level. When this happens, small area estimation (SAE) methods can be used. In this paper, a SAE model is used to compute the mean household equivalised income and the head count ratio for the 57 Labor Local Systems of the Tuscany region in Italy for the year 2011. The caveats of the analysis of poverty at the local level using small area methods are many, and some are still not so well explored in the literature, starting from the definition of the target indicators to the relevant dimensions of their measurement. We suggest in this paper that together with the universally recognized multidimensional, longitudinal and local dimensions of poverty, a new dimension must be considered: the price dimension, which should take into account local purchasing power parities to correctly compare the poverty indicators based on income measures.     

Estimating intrahousehold allocation in a collective model with household production

The purpose of this paper is to estimate the intra-family distribution of income and the individual demand for leisure and household production from Swedish cross-sectional household data. As a basis for the analysis, we use a collective model where each individual is characterized by his or her own utility function and divides total time between leisure, household production and market work. For the purpose of comparison, we also estimate a version that is consistent with a more traditional model of labor supply, the unitary model. Received: 14 April 2000/Accepted: 12 January 2001     

Income Affluence in Poland

This paper examines the evolution of income affluence (richness) in Poland during 1998–2007. Using household survey data, the paper estimates several statistical indices of income affluence including income share of the top percentiles, population share of individuals receiving incomes higher than the richness line, and measures that take into account both the extent and the intensity of affluence. Results show that over the period under study there was a statistically significant and socio-economically sizable rise in income affluence by between 9 and 50%, depending on the index used. The overall income distribution in the period has shifted in favour of the rich as relative poverty and relative size and income share of the middle class have declined.     

农民工入户意愿及其影响因素研究——基于2009年广东省农民工调查数据分析

本文运用logit统计模型,在广东省农民工入户政策的改革背景下,分析上层农民工群体的入户意愿及其影响因素。研究发现,入户意愿是农民工个体权利的合理化表达,其背后隐藏的是经济诉求、生活保障、权益表达等劳动力再生产的综合需求。是否拥有责任田、在本城市工作年限、是否有亲戚在工作地、对城市的归属感和生活的安定感是影响上层农民工入户意愿的主要因素,而受教育年限、收入和工作级别等因素对他们入户意愿的影响并不显著。     

Dads, disease, and death: determinants of daughter discrimination

Existing evidence suggests that girls are differentially affected by income shocks and changes in bargaining power. Most studies, however, ignore household production and confound differential opportunity costs with changes in income or bargaining power. I disentangle these determinants of gender discrimination??preferences, income and time allocation??by comparing households with varying degrees of parental involvement. Results indicate that, controlling for household fixed effects, reducing the time available for household production has a disproportionately negative effect on daughters. But, for a transitory income shock, daughters?? education is less income-elastic. Increasing mothers?? bargaining power is most effective in narrowing the gender gap.     

Minimal Household Units: A New Approach to the Analysis of Household Formation

In this paper a new method for the analysis of household formation and the composition of households is described. The components, or building blocks, which combine to form households are identified and these have been called ‘minimal household units’. The focus of the analysis is on whether a minimal household unit sets up as a separate household or, if not, with whom it is shared. An economic theory of household formation is outlined, and a probit model is used to guide the estimation of the effects of economic and demographic characteristics of the minimal household unit on the probability of being a separate household, economic factors, such as the unit's income, and other social and demographic characteristics of the unit's members are shown to have a significant influence on the probability of its being a separate household.     

Social and Economic Indicators of Household Income in Turkey: Does Ethnicity Matter?

In Turkey, the GDP per capita of eastern regions, where the Kurdish population is dense, is significantly lower than that of the rest of the country. The aim of this paper is to provide a quantitative analysis to investigate the contribution of household characteristics and regional disparities on racial/ethnic inequalities in household income, particularly across Turks and Kurds. Based on the results of regression-based decomposition analyses, there exist significant income differences between Turks and Kurds. However, this difference significantly diminishes if the household head is working. It is also observed that the household income increases with education, while decreases with migration and being settled in economically disadvantaged regions. Results also indicate that differential returns to Turks and Kurds on observable characteristics are lower in higher income quintiles