Female Labor Force Participation in Urban and Rural China1

Abstract The objective of this study is to examine female labor force participation and its determinants in rural and urban China. The sociological literature has demonstrated that participation tends to increase in urban and industrialized places where women have higher levels of education and fewer children, where more workers are engaged in service pursuits, and where family structure is less traditional. With the use of data on counties and cities (N = 2,377) from the I-percent sample of the 1982 census of the People's Republic of China, it was found that female labor force participation is likely to rise in areas with increased agricultural employment, educational levels, proportion of female-headed households, and higher male-to-female sex ratios. Both the size of the service sector and the fertility rate had negligible effects on female labor force participation. Although, on average, rural places have slightly higher levels of female labor force participation, when other variables are controlled, urban places have a higher rate of female participation. In addition, the findings suggest that market factors (i.e., education) are more likely to determine the rate of female labor force participation in urban areas; whereas demographic and social factors (i.e., sex ratio and household structure) play a more important role in explaining the female labor force participation in rural counties.     

A note on LeCam’s bound for the distance between the Poisson binomial and the Poisson distribution

n possibly different success probabilities p ₁, p ₂, ..., p _n is frequently approximated by a Poisson distribution with parameter λ = p ₁ + p ₂ + ... + p _n . LeCam's bound p ² ₁ + p ² ₂ + ... + p _n ² for the total variation distance between both distributions is particularly useful provided the success probabilities are small. The paper presents an improved version of LeCam's bound if a generalized d-dimensional Poisson binomial distribution is to be approximated by a compound Poisson distribution. Received: May 10, 2000; revised version: January 15, 2001     

Tree health mapping with multispectral remote sensing data at UC Davis, California

Tree health is a critical parameter for evaluating urban ecosystem health and sustainability. Traditionally, this parameter has been derived from field surveys. We used multispectral remote sensing data and GIS techniques to determine tree health at the University of California, Davis. The study area (363 ha) contained 8,962 trees of 215 species. Tree health conditions were mapped for each physiognomic type at two scales: pixel and whole tree. At the pixel scale, each tree pixel within the tree crown was classified as either healthy or unhealthy based on vegetation index values. At the whole tree scale, raster based statistical analysis was used to calculate tree health index which is the ratio of healthy pixels to entire tree pixels within the tree crown. The tree was classified as healthy if the index was greater than 70%. Accuracy was checked against a random sample of 1,186 trees. At the whole tree level, 86% of campus trees were classified as healthy with 88% mapping accuracy. At the pixel level, 86% of the campus tree cover was classified as healthy. This tree health evaluation approach allows managers to identify the location of unhealthy trees for further diagnosis and treatment. It can be used to track the spread of disease and monitor seasonal or annual changes in tree health. Also, it provides tree health information that is fundamental to modeling and analysis of the environmental, social, and economic services produced by urban forests.     

Opposition to Solid Waste Incineration: Pre- Implementation Anxieties Surrounding a New Environmental Controversy*

Despite sociopolitical controversy surrounding the solid waste crisis, little sociological attention has focused on this crucial environmental issue. Drawing on research from the technological controversies, environmental concern, and solid waste literatures, this study examines the pre-implementation anxieties of a Louisiana community faced with a proposal to build an incinerator in its backyard. Utilizing survey data, a causal path model is developed in which the perceived advantages of the technology, general environmental concern, and support for recycling initiatives are identified as explanatory variables of incinerator opposition. Implications of these findings for the study of technical controversies and the larger solid waste crisis also are suggested.     

Ninth-Grade Students' Attitudes Toward Nontraditional Occupations

This study explored how students choose careers, their attitudes toward vocational education, and whether or not they would consider enrolling in a nontraditional vocational education program.     

Modelling map positional error to infer true feature location

The authors consider the issue of map positional error, or the difference between location as represented in a spatial database (i.e., a map) and the corresponding unobservable true location. They propose a fully model‐based approach that incorporates aspects of the map registration process commonly performed by users of geographic informations systems, including rubber‐sheeting. They explain how estimates of positional error can be obtained, hence estimates of true location. They show that with multiple maps of varying accuracy along with ground truthing data, suitable model averaging offers a strategy for using all of the maps to learn about true location.     

SUNK COSTS, PROFIT VARIABILITY, AND TURNOVER

Dynamic competitive models of industry evolution suggest that firm profit will be more volatile, and turnover lower, in industries with higher sunk costs. These implications are consistent with empirical observation. (JEL L00 )