Nepal's Safe Motherhood Programme has failed to deliver expected gains in maternal and child health. Nepalese mothers and their children continue to dispense with (or be denied) antenatal care, experience high maternal mortality rates and suffer chronic malnutrition. We address the correlates and consequences of antenatal care utilization in Nepal by applying two-stage least squares, binomial logit and Heckman selection bias estimates to data drawn from the Nepal Health and Demographic Surveys of 1996 and 2001. Results indicate that maternal education, even at low levels, significantly increases the use of antenatal care; paternal education plays a more important role in the use of routine antenatal care than the conventional wisdom suggests; and when mothers use routine professional antenatal care and maintain good health their children tend to stay healthy through infancy and early childhood. Since health-seeking behaviour is circumscribed by patriarchal gender norms in Nepal, health policies should not only focus on female education and women's status, but also involve husbands in the process of maternal care utilization.

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Accurate description of the distribution of housing units within sub-County geographies is an important component of small-area population estimation. This paper pilots the use of the Pearl-Reed logistic model to predict housing unit growth in urban Census tracts in Bernalillo County, New Mexico for 2007. The model is based upon 1990 to 2000 growth rates, constrained with respect to a priori estimates of an upper-limit of housing units that could potentially be built within a tract based on its land area. In spite of the simplistic nature of this model, it is found to perform quite well. Further development based on incorporation of additional economic, demographic, and sociologic data would likely improve the model substantially; however, in this study the model out-performed standard trend extrapolation procedures for the study area and displayed error measures comparable to those reported in the literature for extrapolation methods in general.

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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 addressbased 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.