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Since 1950 the sources of the gains from marriage have changed radically. As the educational attainment of women overtook and surpassed that of men and the ratio of men's to women's wage rates fell, traditional patterns of gender specialization in work weakened. The primary source of the gains to marriage shifted from the production of household services and commodities to investment in children. For some, these changes meant that marriage was no longer worth the costs of limited independence and potential mismatch.Cohabitation became an acceptable living arrangement for all groups, but cohabitation serves different functions among different groups. The poor and less educated are much more likely to rear children in cohabitating relationships. The college educated typically cohabit before marriage, but they marry before conceiving children and their marriages are relatively stable. We argue that different patterns of childrearing are the key to understanding class differences in marriage and parenthood, not an unintended by-product of it. Marriage is the commitment mechanism that supports high levels of investment in children and is hence more valuable for parents adopting a high-investment strategy for their children.

We treat health as a form of human capital and hypothesize that women with more human capital face stronger incentives to make costly investments with future payoffs, such as avoiding abusive partners and reducing drug use. To test this hypothesis, we exploit the unanticipated introduction of an HIV treatment, HAART, which dramatically improved HIV + women's health. We find that after the introduction of HAART HIV + women who experienced increases in expected longevity exhibited a decrease in domestic violence of 15 percent and in drug use of 15–20 percent. We rule out confounding via secular trends using a control group of healthier women. [ABSTRACT FROM AUTHOR]

Herein, the establishment of data‐driven prediction and explanation models for three essential process variables in ironmaking blast furnace processes, namely hot metal temperature, silicon concentration, and cooling capacity is demonstrated. Aside a reliable prediction quality of the models with sufficient prediction horizon, an additional main goal has been to establish interpretable and revealing models. To support (linguistic) interpretability, the main focus has been set on the extraction of rule‐based models from a large database collected at a particular blast furnace process located at the partner company's site. Due to expected uncertainty in the data, due to, e.g., measurement noise, fuzzy systems are an adequate architectural choice for achieving models in a robust rule‐based form. For a fully automatic training of the predictive fuzzy systems new feature ranking methods have been performed on the one side and a special granular rule extraction procedure on the other side. Testing the obtained models on two separate test datasets from consecutive years shows a stable prediction performance without any error drifts and a higher performance than other related machine learning methods including deep neural networks, and others. Moreover, the final models, turned out to have maximal 4–5 inputs and just a couple of rules and allowed to gain new insights into the processes. [ABSTRACT FROM AUTHOR]