Non-familial activity and socio-economic differentials in fertility

The relationship between socio-economicstatus and fertility among married women is examined, using data from the 1/1,000 sample from the 1960 United States Census of Population and the 1960 Growth of American Families Study. Both sets of data indicate that the negative relationship between socio-economic status and fertility is still prevalent but may reflect different patterns of child-spacing rather than completed fertility. Labor force participation among these women is found to be negatively related to the number of children ever born. To determine the degree of involvement in this type of non-familial role, the work index or proportion of one’s married life engaged in the labor force is developed. The work index is found to be a particularly sensitive measure of involvement in the worker role vis-a-vis their fertility. The working hypothesis of this study, that such non-familial activity has a different effect according to one’s socio-economic status, is borne out. Participation in the labor force results in a relatively larger reduction in the fertility of upper status women than for those of lower status. However, this relationship apparently holds true only for those women from rural backgrounds but not for those from large urban areas.     

Precision,bias, and uncertainty for state population forecasts: an exploratory analysis of time series models

Many researchers have used time series models to construct population forecasts and prediction intervals at the national level, but few have evaluated the accuracy of their forecasts or the out-of-sample validity of their prediction intervals. Fewer still have developed models for subnational areas. In this study, we develop and evaluate six ARIMA time series models for states in the United States. Using annual population estimates from 1900 to 2000 and a variety of launch years, base periods, and forecast horizons, we construct population forecasts for four states chosen to reflect a range of population size and growth rate characteristics. We compare these forecasts with population counts for the corresponding years and find precision, bias, and the width of prediction intervals to vary by state, launch year, model specification, base period, and forecast horizon. Furthermore, we find that prediction intervals based on some ARIMA models provide relatively accurate forecasts of the distribution of future population counts but prediction intervals based on other models do not. We conclude that there is some basis for optimism regarding the possibility that ARIMA models might be able to produce realistic prediction intervals to accompany population forecasts, but a great deal of work remains to be done before we can draw any firm conclusions.