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Record W1535422005

Marriage, Fertility And Divorce: A Dynamic Equilibrium Analysis Of Social Policy In Canada

2000· preprint· en· W1535422005 on OpenAlexaboutno aff
Nezih Guner, John Knowles

Bibliographic record

VenueRePEc: Research Papers in Economics · 2000
Typepreprint
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsnot available
Fundersnot available
KeywordsFertilityDemographyMarital statusPovertyAffect (linguistics)Total fertility rateMarriage marketDemographic economicsEconomicsPsychologyPopulationFamily planningSociologyEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

Elderly women are much more likely to be poor than the elderly men. In 1992, 15.7 percent of elderly females, those over age 65, were in poverty, in contrast to 8.9 percent of elderly males. A nonmarried women who is on the verge of retirement has about 20 percent less wealth than a nonmarried women. Since women are expected to live about 4 years longer than men at age 65, these wealth differences imply even bigger income differences at older ages. Old men and women differ dramatically in their marital status as well. In 1990, 76.5 percent elderly men were married, while only 41.5 percent of elderly women were. Most of the elderly women, 49 percent, were widows. Some questions naturally arise: Why does elderly men and women differ in their old age incomes? Are these differences simply a result of widowhood or do they reflect differences in labor market experiences of men and women? How will the future of elderly women and men will be effected by the changes in US family structure and female labor for ce participation patterns that took place since 1950s? How do public policies affect the lives of elderly women?US family structure has changed dramatically between 1950 and 1990. As a result of lower marriage rates, doubling of divorce rates and a fourfold increase in the rate of extramarital fertility, the proportion of singleparent households increased nearly three-fold during this period, while the proportion of married-couple households fell to 56% in 1990. Although these changes and their macroeconomic effects have received much attention, there hasn't been any work that focuses on the implications of these changes for the lives of the elderly. The first of the cohorts to come of age during this regime change, however, is now entering their old age, and they will have very different life-cycles than their predecessors. Until now, the majority of single elderly women have been widows. This is certain to change with much more women entering their old age as singles. During their adulthood, they are more likely to experience divorce, single motherhood, remarriage, and redivorce, creating an unprecedented pattern of marital histories.In this paper, we develop a dynamic general equilibrium model of economic interaction among generations in a world characterized by marital instability. Young adults marry, work and invest in their children; old adults live off their savings and human capital accumulated while young. Young women face a trade-off between investment in children and acquiring human capital by working. Before entering old age, marriages may dissolve either through divorce or the death of the husband. The basic goal of the paper is to contribute to the understanding of implications of recent changes in the patterns of marital stability and labor force participation for the future income distribution of the elderly population. Such an exercise will also help us to understand the emergence of marital instability in the US, by comparing the long-run implications of competing explanations. Finally, the project will contribute to the analysis of social policy, both on the intensive margin, by incorporating marital decisions into the an alysis of policy issues, such as transfers to the old and welfare payments to the young, that have already received considerable attention, and on the extensive margin, by identifying policy questions, such as the division of wealth in divorce, that are likely to have a strong impact on the economic status of the elderly.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.065
GPT teacher head0.394
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2000
Admission routes1
Has abstractyes

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