scholarly journals Editorial on the Special Issue “The identification of causal mechanisms in demographic research”

2021 ◽  
Vol 46 ◽  
Author(s):  
Johannes Huinink ◽  
Josef Brüderl
Keyword(s):  
Special Issue ◽  
Demographic Research ◽  
Causal Mechanisms

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".

scholarly journals Introduction to the special issue: Elections, rhetoric and American foreign policy in the age of Donald Trump

Politics ◽  
2020 ◽  
pp. 026339572093537 ◽  
Author(s):  
Corina Lacatus ◽  
Gustav Meibauer
Keyword(s):  
Foreign Policy ◽  
American Foreign Policy ◽  
Special Issue ◽  
Donald Trump ◽  
Causal Mechanisms ◽  
Electoral Rhetoric

This introduction presents the special issue’s conceptual and empirical starting points and situates the special issue’s intended contributions. It does so by reviewing extant scholarship on electoral rhetoric and foreign policy and by teasing out several possible linkages between elections, rhetoric and foreign policy. It also discusses how each contribution to the special issue seeks to illuminate causal mechanisms at work in these linkages. Finally, it posits that these linkages are crucial to examining the changes brought about by Trump’s election and his foreign policy rhetoric.

scholarly journals Introduction: Intergenerational Transmissions of Infant Mortality using the Intermediate Data Structure (IDS)

2018 ◽  
Vol 7 ◽  
pp. 1-10
Author(s):  
Luciana Quaranta ◽  
Hilde Leikny Sommerseth
Keyword(s):  
Data Structure ◽  
Infant Mortality ◽  
Empirical Studies ◽  
Special Issue ◽  
Maternal Line ◽  
Demographic Research ◽  
Intermediate Data ◽  
Current Article ◽  
Death Clustering ◽  
Intergenerational Transmissions

It has previously been shown that infant mortality clusters in a subset of families, a phenomenon which was observed in historical populations as well as contemporary developing countries. A transmission of death clustering across generations has also been shown in Belgium, but it is unknown whether such effects are specific to the studied context or are also found in other areas. The current article introduces a special issue devoted to analysing intergenerational transmissions of infant mortality across the maternal line in Belgium, the Netherlands, northern and southern Sweden, and Norway. Taking advantage of the Intermediate Data Structure (IDS), the five empirical studies created datasets for analysis and ran statistical models using exactly the same programs, which are also published within the special issue. These works are the first set of studies using the IDS on several databases for comparative purposes. Consistent results across the studied contexts were shown: transfers of infant mortality across the maternal line were seen in all five areas. In addition, the works have shown that there are large advantages of adopting the IDS for historical demographic research. The structure has in fact allowed researchers to conduct studies which were fully comparable, transparent and replicable.

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".

scholarly journals Approaches and Methods for Causal Analysis of Panel Data in the Area of Morbidity and Mortality

2021 ◽  
Vol 46 ◽  
Author(s):  
Rasmus Hoffmann ◽  
Gabriele Doblhammer
Keyword(s):  
Panel Data ◽  
Research Question ◽  
Causal Analysis ◽  
Causal Effects ◽  
Morbidity And Mortality ◽  
General Definition ◽  
Demographic Research ◽  
Advantages And Disadvantages ◽  
Causal Mechanisms ◽  
Relevant Factors

We aim to give an overview of the state of the art of causal analysis of demographic issues related to morbidity and mortality. We will systematically introduce strategies to identify causal mechanisms, which are inherently linked to panel data from observational surveys and population registers. We will focus on health and mortality, and on the issues of unobserved heterogeneity and reverse causation between health and (1) retirement, (2) socio-economic status, and (3) characteristics of partnership and fertility history. The boundaries between demographic research on mortality and morbidity and the neighbouring disciplines epidemiology, public health and economy are often blurred. We will highlight the specific contribution of demography by reviewing methods used in the demographic literature. We classify these methods according to important criteria, such as a design-based versus model-based approach and control for unobserved confounders. We present examples from the literature for each of the methods and discuss the assumptions and the advantages and disadvantages of the methods for the identification of causal effects in demographic morbidity and mortality research. The differentiation between methods that control for unobserved confounders and those that do not reveal a fundamental difference between (1) methods that try to emulate a randomised experiment and have higher internal validity and (2) methods that attempt to achieve conditional independence by including all relevant factors in the model. The latter usually have higher external validity and require more assumptions and prior knowledge of relevant factors and their relationships. It is impossible to provide a general definition of the sort of validity that is more important, as there is always a trade-off between generalising the results to the population of interest and avoiding biases in the estimation of causal effects in the sample. We hope that our review will aid researchers in identifying strategies to answer their specific research question. *  This article belongs to a special issue on "Identification of causal mechanisms in demographic research: The contribution of panel data".

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