scholarly journals Does Marriage Increase Couples’ Life Satisfaction?

2021 ◽  
Vol 46 ◽  
Author(s):  
Alexander Gattig ◽  
Lara Minkus
Keyword(s):  
Life Satisfaction ◽  
Panel Data ◽  
Fixed Effects ◽  
Unobserved Heterogeneity ◽  
Married Couples ◽  
Social Mechanisms ◽  
Special Issue ◽  
Demographic Research ◽  
Analytical Strategies ◽  
Lower Effect

Many contemporary studies find that married couples are more satisfied with life than unmarried people. However, whether marriage makes people more satisfied with life or whether more satisfied couples are more likely to marry remains a debated question. We reassess this relationship with panel data from the German Family Panel (pairfam) and extend previous analyses by adding individual trajectories (slopes) to standard fixed-effects regressions (FEIS). We are thereby able to distinguish – controlling for time-constant unobserved heterogeneity – whether there is in fact an effect of marriage on life satisfaction, whether people who are simply happier in their relationship are more likely to get married, or whether people whose development in life satisfaction is more positive are more likely to get married. We translate these different social mechanisms into different analytical strategies and find that OLS regression – due to its confounding effects between and within persons – overestimates the effect of marriage on life satisfaction. A fixed-effects estimator reveals a much lower effect of marriage on life satisfaction for couples who marry compared to those who continue to live apart together or cohabitate. Additionally, using a FEIS estimator and adjusting for – non-linear – development of individual life satisfaction over time, suggests that this effect is in fact causal. * This article belongs to a special issue on "Identification of causal mechanisms in demographic research: The contribution of panel data".

Limitations of Fixed-Effects Models for Panel Data

2019 ◽  
Vol 63 (3) ◽  
pp. 357-369 ◽  
Author(s):  
Terrence D. Hill ◽  
Andrew P. Davis ◽  
J. Micah Roos ◽  
Michael T. French
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Statistical Power ◽  
Unobserved Heterogeneity ◽  
Critical Discussion ◽  
Causal Inferences ◽  
Cross Sectional ◽  
Fixed Effects Models ◽  
Time Invariance ◽  
Type Ii Errors

Although fixed-effects models for panel data are now widely recognized as powerful tools for longitudinal data analysis, the limitations of these models are not well known. We provide a critical discussion of 12 limitations, including a culture of omission, low statistical power, limited external validity, restricted time periods, measurement error, time invariance, undefined variables, unobserved heterogeneity, erroneous causal inferences, imprecise interpretations of coefficients, imprudent comparisons with cross-sectional models, and questionable contributions vis-à-vis previous work. Instead of discouraging the use of fixed-effects models, we encourage more critical applications of this rigorous and promising methodology. The most important deficiencies—Type II errors, biased coefficients and imprecise standard errors, misleading p values, misguided causal claims, and various theoretical concerns—should be weighed against the likely presence of unobserved heterogeneity in other regression models. Ultimately, we must do a better job of communicating the pitfalls of fixed-effects models to our colleagues and students.

scholarly journals ESTIMATING PANEL DATA DURATION MODELS WITH CENSORED DATA

2008 ◽  
Vol 24 (5) ◽  
pp. 1254-1276 ◽  
Author(s):  
Sokbae Lee
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Unobserved Heterogeneity ◽  
Right Censoring ◽  
Duration Models ◽  
Finite Sample ◽  
Asymptotically Normal ◽  
Monte Carlo Studies

This paper presents a method for estimating a class of panel data duration models, under which an unknown transformation of the duration variable is linearly related to the observed explanatory variables and the unobserved heterogeneity (or frailty) with completely known error distributions. This class of duration models includes a panel data proportional hazards model with fixed effects. The proposed estimator is shown to be n1/2-consistent and asymptotically normal with dependent right censoring. The paper provides some discussions on extending the estimator to the cases of longer panels and multiple states. Some Monte Carlo studies are carried out to illustrate the finite-sample performance of the new estimator.

scholarly journals Analysing Transitions in Intimate Relationships with Panel Data

2021 ◽  
Vol 46 ◽  
Author(s):  
Michael Feldhaus ◽  
Richard Preetz
Keyword(s):  
Panel Data ◽  
Intimate Relationships ◽  
Union Formation ◽  
Special Issue ◽  
Cross Sectional ◽  
Demographic Research ◽  
Longitudinal Methods ◽  
Causal Mechanisms ◽  
Over Time

Panel data on intimate relationships are becoming increasingly available, enabling a closer examination and deeper understanding of why and how they develop over time. The aim of this review is to illustrate to what extent demographic research has made progress in understanding the dynamics of intimate relationships by examining panel data. We focus on hypotheses about key transitions throughout the progression of intimate relationships, ranging from union formation up to cohabitation, marriage, divorce and repartnering. For every hypothesis, we will present findings from cross-sectional data and illustrate whether the use of panel data and longitudinal methods modified the previous understandings of transitions in intimate relationships. * This article belongs to a special issue on "Identification of causal mechanisms in demographic research: The contribution of panel data".

Charitable Giving in Married Couples: Untangling the Effects of Education and Income on Spouses’ Giving

2021 ◽  
pp. 089976402110089
Author(s):  
Debra J. Mesch ◽  
Una Okonkwo Osili ◽  
Elizabeth J. Dale ◽  
Jacqueline Ackerman ◽  
Jon Bergdoll ◽  
...  
Keyword(s):  
Charitable Giving ◽  
Fixed Effects ◽  
Unobserved Heterogeneity ◽  
Panel Study ◽  
Married Couples ◽  
Women's Labor ◽  
Earned Income ◽  
Household Resources ◽  
Educational Differences ◽  
Household Model

This research note looks beyond the unitary household model and analyzes the influence of household resources by gender on charitable giving. We investigate the intrahousehold variables of income and education and their effects on giving behaviors in married couples. We use data from the longitudinal Philanthropy Panel Study (2005–2017) to examine how spouses’ income and educational differences affect charitable giving behaviors and introduce fixed effects to control for unobserved heterogeneity. Initially, we find a positive relationship between both the husband’s and wife’s earned and unearned incomes and the likelihood and amount of giving by married couples. However, when fixed effects are used, we find women’s earned income to be significantly associated with all forms of giving, showing that women’s labor market earnings disproportionately influence giving behavior. Education is less of a factor in whether couples give and influences giving only when the husband has more education than the wife.

scholarly journals What You Need to Know When Estimating Impact Functions with Panel Data for Demographic Research

2021 ◽  
Vol 46 ◽  
Author(s):  
Volker Ludwig ◽  
Josef Brüderl
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Causal Effect ◽  
Standard Procedure ◽  
Well Being ◽  
Demographic Research ◽  
Impact Function ◽  
Using Data ◽  
The Impact ◽  
Biased Estimates

The estimation of impact functions – that is the time-varying causal effect of a dichotomous treatment (e.g., marriage, divorce, parenthood) on outcomes (e.g., earnings, well-being, health) – has become a standard procedure in demographic applications. The basic methodology of estimating impact functions with panel data and fixed-effects regressions is now widely known. However, many researchers may not be fully aware of the methodological subtleties of the approach, which may lead to biased estimates of the impact function. In this paper, we highlight potential pitfalls and provide guidance on how to avoid these in practice. We demonstrate these issues with exemplary analyses, using data from the German Family Panel (pairfam) study and estimating the effect of motherhood on life satisfaction.   * This article belongs to a special issue on “Identification of causal mechanisms in demographic research: The contribution of panel data”.

Rank Status and Happiness in India: A Panel Ordered Probit Estimation of the Effect of Ordinal Relative Comparison on Well-being

2020 ◽  
Vol 11 (01) ◽  
Author(s):  
T. Lakshmanasamy ◽  
K. Maya
Keyword(s):  
Life Satisfaction ◽  
Fixed Effects ◽  
Reference Group ◽  
Well Being ◽  
Ordered Probit ◽  
Probit Regression ◽  
Differential Effects ◽  
Comparison Income ◽  
Average Reference ◽  
The Rich

Most often the social comparison or relative income hypothesis has been used as an explanation for the lack of systematic relationship between income and happiness, using the ordered probit regression method. The identification of relevant reference group and the estimation of the differential effects of comparison income have been controversial. To overcome these twin issues, this paper uses an ordinal comparison income approach based on rich/poor dichotomy and rank income. The rank income of an individual is defined as his relative position in the income distribution within the reference group and the average income of the reference group is used to define the rich/poor classification. The differential effects of ordinal incomes across life satisfaction distribution is estimated by the panel fixed effects ordered profit regression model using the WVS data for India. The estimated results show that ordinal income comparison, rather than cardinal average reference income, is a better predictor of life satisfaction levels. Raising income level is relatively important for less satisfied people while increasing rank status is important for highly satisfied people in India.

Empathy in context: Socioeconomic status as a moderator of the link between empathic accuracy and well-being in married couples

2021 ◽  
pp. 026540752199075
Author(s):  
Emily F. Hittner ◽  
Claudia M. Haase
Keyword(s):  
Socioeconomic Status ◽  
Life Satisfaction ◽  
Depressive Symptoms ◽  
Marital Satisfaction ◽  
Negative Affect ◽  
Marital Conflict ◽  
Married Couples ◽  
Well Being ◽  
Empathic Accuracy ◽  
Low Levels

The present laboratory-based study investigated socioeconomic status (SES) as a moderator of the association between empathic accuracy and well-being among married couples from diverse socioeconomic backgrounds. Empathic accuracy was measured using a performance-based measure of empathic accuracy for one’s spouse’s negative emotions during a marital conflict conversation. Aspects of well-being included well-being (i.e., positive affect, life satisfaction), ill-being (i.e., negative affect, anxiety symptoms, depressive symptoms), and marital satisfaction. SES was measured using a composite score of income and education. Findings showed that SES moderated associations between empathic accuracy and well-being. Empathic accuracy was beneficial (for well-being and ill-being) or not harmful (for marital satisfaction) at low levels of SES. In contrast, empathic accuracy was not beneficial (for well-being and ill-being) or harmful (for marital satisfaction) at high levels of SES. Results were robust (controlled for age, gender, and race). Findings are discussed in light of interdependence vs. independence in low- vs. high-SES contexts and highlight the importance of socioeconomic context in determining whether empathic accuracy benefits well-being or not.

scholarly journals Are the Self-Employed Mentally Healthier Than Salaried Workers? Evidence From Korea, Mexico, and the United States

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 40-41
Author(s):  
Hankyung Jun
Keyword(s):  
Fixed Effects ◽  
Mental Health Problems ◽  
Unobserved Heterogeneity ◽  
The United States ◽  
Fixed Effects Model ◽  
Reverse Causality ◽  
Positive Health ◽  
Self Employment ◽  
Salaried Workers ◽  
The U.S

Abstract Self-employed workers are often reported to have better health than salaried workers. Whether this is because self-employment has health benefits or healthier workers are self-employed is not clear. Self-employed workers may have higher job satisfaction due to higher levels of self-efficacy and autonomy, but may also experience higher job stress, uncertainty, and lack of health insurance leading to mental health problems. Self-employed workers in the U.S. may have different characteristics than those in Mexico and Korea given different working and living environments as well as different institutional arrangements. This study will examine the association between self-employment and mental and cognitive health for older adults in the U.S., Mexico, and South Korea. It uses harmonized panel data from the Health and Retirement Study, the Korean Longitudinal Study of Aging, and the Mexican Health and Aging Study. We compare the health and selection effect of self-employment using a pooled logistic model, fixed-effects model, and a bivariate probit model. In addition to comparing self-employed and salaried workers, we analyze differences between self-employed with and without employees. By using rich data and various models, we address reverse causality and estimate the relationship between self-employment and health. We show that the positive health effects of self-employed workers in the U.S. disappear once controlled for unobserved heterogeneity, indicating the possibility of healthier workers selecting into self-employment. Interestingly, for Korea and Mexico, healthier individuals seem to select into wage work which reflects the difference in working conditions across countries. Further analysis will show effects by business size.

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