Engelhardt / Kohler / Fürnkranz-Prskawetz Causal Analysis in Population Studies

Concepts, Methods, Applications
1. Auflage 2009
ISBN: 978-1-4020-9967-0
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 23, 252 Seiten

Reihe: The Springer Series on Demographic Methods and Population Analysis

The central aim of many studies in population research and demography is to explain cause-effect relationships among variables or events. For decades, population scientists have concentrated their efforts on estimating the 'causes of effects' by applying standard cross-sectional and dynamic regression techniques, with regression coefficients routinely being understood as estimates of causal effects. The standard approach to infer the 'effects of causes' in natural sciences and in psychology is to conduct randomized experiments. In population studies, experimental designs are generally infeasible. In population studies, most research is based on non-experimental designs (observational or survey designs) and rarely on quasi experiments or natural experiments. Using non-experimental designs to infer causal relationships-i.e. relationships that can ultimately inform policies or interventions-is a complex undertaking. Specifically, treatment effects can be inferred from non-experimental data with a counterfactual approach. In this counterfactual perspective, causal effects are defined as the difference between the potential outcome irrespective of whether or not an individual had received a certain treatment (or experienced a certain cause). The counterfactual approach to estimate effects of causes from quasi-experimental data or from observational studies was first proposed by Rubin in 1974 and further developed by James Heckman and others. This book presents both theoretical contributions and empirical applications of the counterfactual approach to causal inference.

Autoren/Hrsg.

Engelhardt, Henriette

Kohler, Hans-Peter

Fürnkranz-Prskawetz, Alexia

Inhaltsverzeichnis

1;Contents;6
2;Contributors;8
3;Causal Analysis in Population Studies;10
3.1;1.1 Introduction;10
3.2;1.2 Structure of the Volume;13
3.3;References;16
4;Issues in the Estimation of Causal Effects in Population Research, with an Application to the Effects of Teenage Childbearing;17
4.1;2.1 Introduction;17
4.2;2.2 The Basic Causal Model;18
4.3;2.3 Instrumental Variables;21
4.4;2.4 Types of Instrumental Variables;26
4.5;2.5 Additional Issues;31
4.6;2.6 Summary and Conclusions;35
4.7;References;36
5;Sequential Potential Outcome Models to Analyze the Effects of Fertility on Labor Market Outcomes;38
5.1;3.1 Introduction;38
5.2;3.2 The Dynamic Causal Model - Notation, Effects, and Identification;41
5.3;3.3 Estimation;46
5.4;3.4 Specifying Causal Parameters of Interest;52
5.5;3.5 Data;54
5.6;3.6 Estimation Results;57
5.7;3.7 Conclusions;59
5.8;References;62
6;Structural Modelling, Exogeneity, and Causality;65
6.1;4.1 Causal Analysis in the Social Sciences;65
6.2;4.2 Structural Modelling;70
6.3;4.3 Conditional Models, Exogeneity and Causality;73
6.4;4.4 Confounding, Complex Systems and Completely Recursive Systems;77
6.5;4.5 Partial Observability and Latent Variables;82
6.6;4.6 Discussion and Conclusion;85
6.7;References;87
7;Causation as a Generative Process. The Elaboration of an Idea for the Social Sciences and an Application to an Analysis of an Interdependent Dynamic Social System;89
7.1;5.1 Introduction;89
7.2;5.2 Models of Causal Inference;90
7.3;5.3 Parallel and Interdependent Processes;94
7.4;5.4 An Application Example;103
7.5;5.5 Substantial Explanations;106
7.6;5.6 Summary and Concluding Remarks;111
7.7;References;112
8;Instrumental Variable Estimation for Duration Data;116
8.1;6.1 Introduction;116
8.2;6.2 Endogenous Covariates in Duration Models;119
8.3;6.3 Instrumental Variable Linear Rank Estimation;125
8.4;6.4 Application to the Illinois Re-employment Bonus Experiment;131
8.5;6.5 Conclusion;138
8.6;References;140
8.7;Appendix 1: Identification of the GAFT Model;141
8.8;Appendix 2: Counting Process Interpretation;143
8.9;Appendix 3 Asymptotic Properties of the IVLR;146
8.10;Appendix 4 Additional Tables for the IVLR of Reemployment Bonus Experiment;150
9;Female Labour Participation with Concurrent Demographic Processes: An Estimation for Italy;154
9.1;7.1 Introduction;154
9.2;7.2 Background;154
9.3;7.3 Model Specification: Theoretical and Methodological Issues;156
9.4;7.4 The Data;163
9.5;7.5 Results;164
9.6;7.6 Discussion;166
9.7;References;169
10;New Estimates on the Effect of Parental Separation on Child Health;171
10.1;8.1 Introduction;171
10.2;8.2 Background;173
10.3;8.3 Statistical Framework and Estimation Strategy;175
10.4;8.4 Data, Sample, and Descriptive Evidence;182
10.5;8.5 Estimation Results;187
10.6;8.6 Conclusion;196
10.7;References;198
10.8;Appendix;202
11;Assessing the Causal Effect of Childbearing on Household Income in Albania;204
11.1;9.1 Introduction;204
11.2;9.2 The Albanian Background;206
11.3;9.3 The Albania Living Standards Measurement Study;207
11.4;9.4 A Measure of Well-Being;208
11.5;9.5 Descriptive Statistics;212
11.6;9.6 Identifying the Causal Effect of a New Birth;214
11.7;9.7 Results;220
11.8;9.8 Conclusions;232
11.9;References;233
12;Causation and Its Discontents;235
12.1;References;241
13;Index;245