In this article we explore methods for using mothers’ interviews to gather data on their children’s family formation experiences. These methods constitute a cost-efficient means of gathering data for models of family background that include both intergenerational and sibling influences. To judge the utility of these methods, we examine the quality of mothers’ reports across a range of their children’s family formation behaviors. The dimensions of reporting quality we analyze include completeness, precision, and accuracy of mothers’ reports. We use unique data from personal interviews with mother– child pairs to test the accuracy of these mothers’ reports. The results demonstrate that, with some behaviors, a flexible data collection approach can gather complete, precise, and accurate information on an entire sibling set by interviewing mothers. Our examination of data quality also suggests important limits on the use of this approach. The quality of mothers’ reports depends on the subject matter, with mothers providing lower quality reports of their children’s cohabitation behavior compared to their children’s marital, childbearing, and divorce behavior.
Demographic models can have two meanings, one broad and one narrow. In its broad meaning, demographic models refer to all mathematical, statistical, forecast, and microsimulation models that are applied to studies of demographic phenomena. In its narrow meaning, demographic models refer to empirical regularities in age patterns of demographic events. This article is concerned with demographic models in the narrow definition. Demographic models are widely used (a) to improve data quality and (b) to compare demographic outcomes and processes across populations or subpopulations. Both parametric and semiparametric specifications have been proposed for modeling age patterns of demographic events, giving rise to parametric models and semiparametric models. Successful applications of both types of models are found in research on mortality, nuptiality, and fertility. As an integral part of formal demography, demographic models have been linked closely to mathematical demography. In recent decades, however, statistical demography has played an increasingly important role in demographic models.
This article studies the regional variation in earnings inequality in contemporary urban China, focusing on the relationship between the pace of economic reforms and earnings determination. Through a multilevel analysis, it shows that economic growth depresses the returns to education and work experience and does not affect the net differences between party members and nonmembers and between men and women. Overall earnings inequality remains low and only slightly correlated with economic growth because, in faster-growing cities, the tendency toward higher levels of inequality is somewhat offset by the lower returns to human capital. A plausible interpretation is that these results are largely due to the lack of a true labor market in urban China.
In this paper, the author develops a new class of discrete-time, discrete-covariate models for modeling nonproportionality in event-history data within the log-multiplicative framework. The models specify nonproportionality in hazards to be a log-multiplicative product of two components: a nonproportionality pattern over time and a nonproportionality level per group. Illustrated with data from the U.S. National Longitudinal Mortality Study (Rogot et al. 1988) and from the the 1988 June Current Population Survey (Wu and Tuma 1990), the log-multiplicative models are shown to be natural generalizations of proportional hazards models and should be applicable to a wide range of research areas.