What you need to know when estimating impact functions with panel data for demographic research

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 func...

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Vydané v:Comparative population studies Ročník 46; s. 453 - 486
Hlavní autori: Ludwig, Volker, Brüderl, Josef
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Wiesbaden Bundesinstitut für Bevölkerungsforschung (BiB) 01.01.2021
Federal Institute for Population Research (BiB)
Federal Institute for Population Research
Predmet:
Bias
Causality
Demographie
Demography
Divorce
Estimates
Fixed effects regression
German Family Panel (pairfam), release 11.0
Impact functions
Life satisfaction
Longitudinal studies
Marriage
Motherhood
Mothers
Negative weighting bias
pairfam
Panel data
Panel data analysis
Parents & parenting
Social sciences
Well being
ISSN:1869-8999, 1869-8980, 1869-8999
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Abstract 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.
AbstractList 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”.
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”.
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”.
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.
Author Brüderl, Josef
Ludwig, Volker
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SubjectTerms Bias
Causality
Demographie
Demography
Divorce
Estimates
Fixed effects regression
German Family Panel (pairfam), release 11.0
Impact functions
Life satisfaction
Longitudinal studies
Marriage
Motherhood
Mothers
Negative weighting bias
pairfam
Panel data
Panel data analysis
Parents & parenting
Social sciences
Well being
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