Diffusion processes and event history analysis

Several authors (e.g., Brüderl, Diekmann, Yamaguchi) derive hazard rate models of event history analysis from social diffusion processes. This paper also focuses on the integration of diffusion research and survival analysis. After a discussion of Diekmann's flexible diffusion model, we present an alternative approach which clarifies theoretical differences between popular rate models (e.g., the exponential model, log-logistic model, sickle model). Specifically, this approach provides a new rationale for the generalized log-logistic model in the sense of a flexible infection process. In cases with bell-shaped duration dependence, it thus allows a test for social contagion as a result of random contacts between actual and potential adopters. An application to divorce data serves as an illustration.

Diffusion Processes and Event History Analysis

Several authors (e. g., Brüderl, Diekmann, Yamaguchi) derive hazard rate models of event history analysis from social diffusion processes. This paper also focuses on the integration of diffusion research and survival analysis. After a discussion of Diekmann's flexible diffusion model, we present an alternative approach which clarifies theoretical differences between popular rate models (e. g., the exponential model, log-logistic model, sickle model). Specifically, this approach provides a new rationale for the generalised log-logistic model in the sense of a flexible infection process. In cases with bell-shaped duration dependence, it thus allows a test for social contagion as a result of random contacts between actual and potential adopters. An application to divorce data serves as an illustration.

A Pooled Time-Series Analysis on the Relation Between Fertility and Female Employment.

Various authors find that in OECD countries the cross-country correlation between the total fertility rate and the female labour force participation rate turned from a negative value before the 1980s to a positive value thereafter. Based on pooled time series analysis the literature seems to agree that this change is due to unmeasured country and time heterogeneity with respect to female employment. However, the role of female employment for time and country heterogeneity remains unclear. Using data of 22 OECD countries from 1960-2000 we estimate pooled time series models of fertility and female labour force participation by applying random effects and fixed effects panel models as well as Prais-Winsten regressions with panel-corrected standard errors and autoregressive errors. Proceeding with Prais-Winsten regressions our empirical findings reveal substantial differences across countries and time periods in the effects of female employment on fertility. Initial increases in female employment strongly lowers fertility, but continued increases have a progressively less negative effect. The country heterogeneity in the effect of female employment can also be confirmed for different regions as well as for varying welfare and gender regimes.

On the changing correlation between fertility and female employment over space and time: a pooled time-series analysis on the impact of social indicators

"In OECD countries, the cross-country correlation between the total fertility rate and the female labour force participation rate turned from a negative value before the 1980s to a positive value thereafter. Based on pooled time-series analysis, the literature seems to agree that this change is due to unmeasured country and time heterogeneity with respect to female employment. However, the determinants of this heterogeneity remain unclear. Using data of 16 European countries from 1960-2005, the author estimates pooled time-series models of fertility and female labor force participation by applying Prais-Winsten regressions with fixed country and time effects and investigate the changing effect of female labor participation for a set of labor market, educational and demographic variables and indicators of social policy. The empirical findings reveal that the change in the correlation seems to be due to the increasing proportion of females employed part-time, increasing educational attainment of females, increasing age at first birth and increasing gross enrollment ratio of children in pre-primary education." (author's abstract

Estimating Causal Effects with Matching Methods in the Presence and Absence of Bias Cancellation

This paper explores the implications of possible bias cancellation using Rubin-style matching methods with complete and incomplete data. After reviewing the naïve causal estimator and the approaches of Heckman and Rubin to the causal estimation problem, we show how missing data can complicate the estimation of average causal effects in different ways, depending upon the nature of the missing mechanism. While - contrary to published assertions in the literature - bias cancellation does not generally occur when the multivariate distribution of the errors is symmetric, bias cancellation has been observed to occur for the case where selection into training is the treatment variable, and earnings is the outcome variable. A substantive rationale for bias cancellation is offered, which conceptualizes bias cancellation as the result of a mixture process based on two distinct individual-level decision-making models. While the general properties are unknown, the existence of bias cancellation appears to reduce the average bias in both OLS and matching methods relative to the symmetric distribution case. Analysis of simulated data under a set of difference scenarios suggests that matching methods do better than OLS in reducing that portion of bias that comes purely from the error distribution (i.e., from "selection on unobservables"). This advantage is often found also for the incomplete data case. Matching appears to offer no advantage over OLS in reducing the impact of bias due purely to selection on unobservable variables when the error variables are generated by standard multivariate normal distributions, which lack the bias-cancellation property.

Estimating causal effects with matching methods in the presence and absence of bias cancellation

This paper explores the implications of possible bias cancellation using Rubin-style matching methods with complete and incomplete data. After reviewing the naïve causal estimator and the approaches of Heckman and Rubin to the causal estimation problem, we show how missing data can complicate the estimation of average causal effects in different ways, depending upon the nature of the missing mechanism. While - contrary to published assertions in the literature - bias cancellation does not generally occur when the multivariate distribution of the errors is symmetric, bias cancellation has been observed to occur for the case where selection into training is the treatment variable, and earnings is the outcome variable. A substantive rationale for bias cancellation is offered, which conceptualizes bias cancellation as the result of a mixture process based on two distinct individual-level decision-making models. While the general properties are unknown, the existence of bias cancellation appears to reduce the average bias in both OLS and matching methods relative to the symmetric distribution case. Analysis of simulated data under a set of difference scenarios suggests that matching methods do better than OLS in reducing that portion of bias that comes purely from the error distribution (i.e., from “selection on unobservables”). This advantage is often found also for the incomplete data case. Matching appears to offer no advantage over OLS in reducing the impact of bias due purely to selection on unobservable variables when the error variables are generated by standard multivariate normal distributions, which lack the bias-cancellation property. (AUTHORS)

Differences in family policy and the intergenerational transmission of divorce: a comparison between the former East and West Germany

The intergenerational transmission of the risk of divorce is a well-known long-term effect of divorce that has been found in many Western societies. Less known is the extent to which different family policies and divorce laws have an effect on the intergenerational transmission of divorce. In this paper, the division of Germany into two separate states from 1949 until 1990, with the consequent development of two very different family policies, is regarded as a natural experiment that enables us to investigate the effect of family policy on the mechanisms underlying the social inheritance of divorce. Data from respondents from the former East and West Germany participating in the German Life History Study are analyzed, using multivariate event-history methods. The results indicate that the strength of the intergenerational divorce transmission, when adjusted for differences in the divorce level, was lower in the East than in the West. Differences in marriage age and the timing of first birth, which are partial indicators of family policy, as well as differences in religion, could explain this effect. Furthermore, we found a tendency towards a reduction in the dynamics of divorce transmission over time, both in East and West Germany.

Differences in Family Policies and the Intergenerational Transmission of Divorce

The intergenerational transmission of the risk of divorce is a well-known long-term effect of divorce that has been found in many Western societies. Less known is what effect different family policies and divorce laws have on the intergenerational transmission of divorce. In this paper, the division of Germany into two separate states from 1949 until 1990, with the consequent development of two very different family policies, is regarded as a natural experiment that enables us to investigate the effect of family policy on the mechanisms underlying the social inheritance of divorce. Data from respondents from the former East and West Germany participating in the German Life History Study are analyzed using multivariate event-history methods. The results indicate that the strength of the intergenerational divorce transmission, when adjusted for differences in divorce level, was lower in the East than in the West. Differences in religion, marriage age and timing of first birth, which are partial indicators of family policy, could explain this effect. Furthermore, we did find a tendency towards a reduction in the dynamics of divorce transmission over time, both in East Germany and in West Germany.divorce risk, family policy, intergenerational transmission of divorce risk

His and her working hours and well-being in Germany: a longitudinal crossover-spillover analysis

This paper investigates the relationship between work time arrangements and personal well-being in married and cohabiting couples. Using data from the German Socio-Economic Panel Survey (SOEP), we study how the number of hours worked by the survey respondents and their partners influenced their own well-being. We also investigate possible transmission mechanisms between the two variables, namely income, hours spent in homemaking and care activities, and possible mismatch between desired and actual hours. Using Hybrid panel models we find evidence of different relations according to the respondent's gender: Women report higher satisfaction with the increase of their partner’s working hours, while the opposite is true for men. At the same time, own hours have a positive effect on men’s life satisfaction, while they have the opposite effect for women. The presence of young children in the household further amplifies these results. Our conclusion is that respondents are happier when their and their partner’s behavior conforms to the roles of female homemaker and male breadwinner. Considering the absence of a strong mechanism related to time needs and time desires, we suggest those results are related to strong traditional attitudes towards gender roles and female labor force participation in the country considered