Arbeitspapier

The relationship between nurse staffing levels and objective and subjective quality of care: A panel data approach for Germany

In this study we investigate the relationship between nursing staffing levels and hospital quality in Germany. We use administrative data from almost all German hospitals from 2002 to 2013 and link it to mortality rates and patient satisfaction measures. To analyze the association between nursing staffing levels and hospital quality indicators empirically, we estimate linear regression models and control for a wide range of hospital and patient characteristics that might bias the results. In addition, we exploit the longitudinal structure of the data and rule out potential bias due to time-invariant unobserved heterogeneity. The estimation results indicate a positive relationship between nurse staffing levels and hospital quality for both subjective and objective quality measures. Increasing nurse staffing levels by 10 percent reduces the mortality rate by 0.05 percent and increases patient satisfaction by around 0.7 percent, on average. Although we find some of these relationships to be statistically significant, at least marginally, the absolute magnitudes of the estimated coefficients are rather small.

ISBN
978-3-86788-844-8
Language
Englisch

Bibliographic citation
Series: Ruhr Economic Papers ; No. 724

Classification
Wirtschaft
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
Analysis of Health Care Markets
Health: Government Policy; Regulation; Public Health
Subject
Hospital quality
nurse staffing
patient satisfaction
mortality rate

Event
Geistige Schöpfung
(who)
Augurzky, Boris
Bünnings, Christian
Wübker, Ansgar
Event
Veröffentlichung
(who)
RWI - Leibniz-Institut für Wirtschaftsforschung
(where)
Essen
(when)
2017

DOI
doi:10.4419/86788844
Handle
Last update
10.03.2025, 11:43 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Augurzky, Boris
  • Bünnings, Christian
  • Wübker, Ansgar
  • RWI - Leibniz-Institut für Wirtschaftsforschung

Time of origin

  • 2017

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