Population Review: Published since 1957
Volume 58, Number 2, 2019 Call for Papers
Artificial neural network (ANN) models are rarely used to forecast population in spite of their growing prominence in other fields. We compare the forecasts generated by ANN long short-term memory models (LSTM) with population projections from the traditional cohort-component method (CCM) for counties in Alabama, USA. The evaluation includes projections for all 67 counties, which are diverse in population and socioeconomic characteristics. When comparing projected values with total population counts from the 2010 decennial census, the CCM used by the Center for Business and Economic Research at the University of Alabama in 2001 produced comparable or better results than a basic multi-county ANN LSTM model. Results from ANN models improve when we use single-county models or proxy for a forecaster’s experience and personal judgment with potential economic forecasts. The results indicate the significance of forecaster’s experience/judgment for CCM and the difficulty, but not impossibility, of substituting these insights with available data.
Is Fertility Preference Related to Perception of the Risk of Child Mortality, Changes in Landholding, and Type of Family? A Comparative Study on Populations Vulnerable and not Vulnerable to Extreme Weather Events in Bangladesh
This study addresses how perception of risk of child mortality, land ownership and household type influence fertility preferences. The study focuses on four distinct villages: two vulnerable to cyclones and floods and two not usually subject to the impacts of extreme weather events (EWEs). The study uses a mixed-methods approach in collecting relevant information from 759 randomly selected ever-married women at reproductive age who had at least one child and were living with their husband during the field survey. The descriptive findings demonstrate that fertility preferences vary regarding perceived risk of child death, land ownership and household type, and that the influences of these factors vary for areas vulnerable to EWEs and not vulnerable to EWEs. Binary logistic regression analysis reveals that perceived risk of child death from EWEs and land ownership are the significant covariates in areas vulnerable to EWEs. In contrast, experience with child death, land ownership and household type are the most influential covariates explaining variation in fertility preferences in the areas not vulnerable to EWEs. The findings of the study can inform policy recommendations in terms of effective disaster management programs and family planning initiatives during climate-related events.
In this paper, I take advantage of newly available data in the Survey of Income and Program Participation (SIPP) to document outcomes among individuals with deceased parents. I focus first on minors and find that about 2 million children in the United States have a biological mother or father who is deceased. This is the first direct estimate of the size of the orphan population in the United States. Relative to children with both parents living, these maternal and paternal orphans have less favorable educational and health outcomes but similar levels of economic well-being. I find the Social Security program provides extensive (but not universal) support to the child survivor population, with participation in the program potentially affected by the earnings of deceased parents prior to death and by awareness of benefit eligibility by adult members in the child’s household. Similar to outcomes for child survivors, I find adult respondents who have deceased parents at the time of the SIPP have less favorable educational and health outcomes. In contrast to child survivors, adults with deceased parents – across a wide range of age groups – are more likely to have low levels of economic well-being. I also find, by examining a past legislative change in Social Security student benefits that would have affected several cohorts in the SIPP, that financial resources available to young adult survivors have effects on educational attainment and effects on income much later in life.
Infant mortality is an important population health statistic that is often used to make health policy decisions. For a small population, an infant mortality rate is subject to high levels of uncertainty and may not indicate the “underlying” mortality regime affecting the population. This situation leads some agencies to either not report infant mortality for these populations or report infant mortality aggregated over space, time or both. A method is presented for estimating “underlying” infant mortality rates that reflect the intrinsic mortality regimes of small populations. The method is described and illustrated in a case study by estimating IMRs for the 15 counties in California where zero infant deaths are reported at the county level for the period 2009–2011. We know that among these 15 counties there are 50 infant deaths reported at the state level but not for the counties in which they occurred. The method’s validity is tested using a synthetic population in the form of a simulated data set generated from a model life table infant mortality rate, representing Level 23 of the West Family Model Life Table for both sexes. The test indicates that the method is capable of producing estimates that represent underlying rates. In this regard, the method described here may assist in the generation of information about the health status of small populations.
Ernesto Amaral is an assistant professor in the Department of Sociology at Texas A&M University. His research is related to social demography, migration, and public policy analysis. His teaching interests include demography, migration, methods, social statistics, and public policy analysis. He was an associate sociologist at the RAND Corporation from 2014 to 2017. He served as an assistant/associate professor at the Federal University of Minas Gerais, Brazil from 2009 to 2014. He received his PhD in sociology with a concentration in demography from the University of Texas at Austin in 2007. More information about his work can be found at www.ernestoamaral.com. RECENT ARTICLE (w/coauthors): Current and Future Demographics of the Veteran Population, 2014–2024
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