CREAT: Census Research Exploration and Analysis Tool

When It Rains It Pours: Under What Circumstances Does Job Loss Lead to Divorce

December 2013

Written by: Melissa Ruby Banzhaf

Working Paper Number:

CES-13-62

Abstract

Much of the previous research that has examined the effect of job loss on the probability of divorce rely on data from the 1970s-80s, a period of dramatic change in marital formation and dissolution. It is unclear how well this research pertains to more recent trends in marriage, divorce, and female labor force participation. This study uses data from the Survey of Income and Program Participation (SIPP) from 2000 to 2012 (thus including effects of the Great Recession) to examine how displacement (i.e., exogenous job loss) affects the probability of divorce. The author finds clear evidence that the effects of displacement appear to be asymmetric depending upon the gender of the job loser. Specifically, displacement significantly increases the probability of divorce but only if the husband is the spouse that is displaced and his earnings represented approximately half of the household's earnings prior to displacement. Similarly, results show that the probability of divorce increases if the wife is employed and as her earnings increase. While the mechanism behind these asymmetric results remains unclear, these results are consistent with recent research that finds a destabilizing effect on marriages when a wife earns more than her husband.

Document Tags and Keywords

Keywords Keywords are automatically generated using KeyBERT, a powerful and innovative keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant keywords.

By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the text, highlighting the most significant topics and trends. This approach not only enhances searchability but provides connections that go beyond potentially domain-specific author-defined keywords.
:
employ, employed, recession, earner, unemployed, marriage, divorced

Tags Tags are automatically generated using a pretrained language model from spaCy, which excels at several tasks, including entity tagging.

The model is able to label words and phrases by part-of-speech, including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are identified to contain references to specific institutions, datasets, and other organizations.
:
Federal Reserve Bank, National Longitudinal Survey of Youth, Chicago Census Research Data Center, Survey of Income and Program Participation, Journal of Economic Literature, PSID

Similar Working Papers Similarity between working papers are determined by an unsupervised neural network model know as Doc2Vec.

Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the capture of semantic meaning in a way that relates to the context of words within the document. The model learns to associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as document classification, clustering, and similarity detection by preserving the order and structure of words. The document vectors are compared using cosine similarity/distance to determine the most similar working papers. Papers identified with 🔥 are in the top 20% of similarity.

The 10 most similar working papers to the working paper 'When It Rains It Pours: Under What Circumstances Does Job Loss Lead to Divorce' are listed below in order of similarity.