Arbeitspapier

Examining the structure of spatial health effects in Germany using Hierarchical Bayes Models

This paper uses Hierarchical Bayes Models to model and estimate spatial health effects in Germany. We combine rich individual-level household panel data from the German SOEP with administrative county-level data to estimate spatial county-level health dependencies. As dependent variable we use the generic, continuous, and quasi-objective SF12 health measure. We find strong and highly significant spatial dependencies and clusters. The strong and systematic county-level impact is equivalent to 0.35 standard deviations in health. Even 20 years after German reunification, we detect a clear spatial East-West health pattern that equals an age impact on health of up to 5 life years for a 40-year old.

Language
Englisch

Bibliographic citation
Series: SOEPpapers on Multidisciplinary Panel Data Research ; No. 620

Classification
Wirtschaft
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Bayesian Analysis: General
Health Behavior
Health and Inequality
Health: Government Policy; Regulation; Public Health
Subject
Spatial health effects
Hierarchical Bayes Models
Germany
SOEP
SF12

Event
Geistige Schöpfung
(who)
Eibich, Peter
Ziebarth, Nicolas R.
Event
Veröffentlichung
(who)
Deutsches Institut für Wirtschaftsforschung (DIW)
(where)
Berlin
(when)
2013

Handle
Last update
10.03.2025, 11:45 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Eibich, Peter
  • Ziebarth, Nicolas R.
  • Deutsches Institut für Wirtschaftsforschung (DIW)

Time of origin

  • 2013

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