The Determinants of Health Expenditure in the OECD Countries: A Pooled data Analysis

Gerdtham, Ulf; Jönsson, Bengt; MacFarlan, Maitland; Oxley, Howard

doi:10.1007/978-1-4615-5681-7_6

Cited by 120 publications

(172 citation statements)

References 13 publications

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“…For inclusion in our EBA, we aim at complete coverage of the determinants of health care expenditure (HCE) that have been suggested in the literature. Gerdtham et al (1998) deliver a comprehensive list of potential determinants that were suggested prior to 1998 and Martín et al (2011) update this list based on a systematic search for literature on the determinants of HCE in medical databases and principal health economics journals over the period 1998-2007. 6 We include in our analysis socio-demographic, economic, and technological factors as well as a number of institutional variables (mostly dummy variables) pertaining to specifics of the national health systems.…”

Section: The Grossman Modelmentioning

confidence: 99%

“…For the reproduction of the institutional dummy variables we rely on the information in Gerdtham et al (1998) instead of OECD countries, however. This means that for the OECD countries that are also EU 5 Grossman suggests adding (initial) wealth to the regressors in the demand functions for medical care in order to discriminate between the PI and the PC model.…”

Section: The Grossman Modelmentioning

confidence: 99%

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Testing the Grossman model of medical spending determinants with macroeconomic panel data

Hartwig

Sturm

2018

Eur J Health Econ

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in

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Section: The Grossman Modelmentioning

confidence: 99%

Section: The Grossman Modelmentioning

confidence: 99%

Testing the Grossman model of medical spending determinants with macroeconomic panel data

Hartwig

Sturm

2018

Eur J Health Econ

View full text Add to dashboard Cite

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“…Hitiris and Posnett (1992) and Gerdtham et al (1998) belong to the category of panel data studies at the international level, while Di Matteo and Di Matteo (1998) and belong to the more recent category of comparative regional studies. …”

Section: Crivelli Filippini and Mosca (2003) Investigated The Differmentioning

confidence: 99%

Federalism and regional health care expenditures: an empirical analysis for the Swiss cantons

2006

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Switzerland (7.2 million inhabitants) is a federal state composed of 26 cantons. The autonomy of cantons and a particular health insurance system create strong heterogeneity in terms of regulation and organisation of health care services. In this study we use a single-equation approach to model the per capita cantonal expenditures on health care services and postulate that per capita health expenditures depend on some economic, demographic and structural factors. The empirical analysis demonstrates that a larger share of old people tends to increase health costs and that physicians paid on a fee-for-service basis swell expenditures, thus highlighting a possible phenomenon of supply-induced demand. Copyright © 2005 John Wiley & Sons, Ltd.

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“…At the macro-economic level, the vast majority of studies find that age structure has a small or non significant impact on health care expenditures, whereas GDP has a sizeable and highly significant impact [1][2][3][4][5][6][7][8]. At the individual level, micro-economic studies find as well that the influence of age on health care expenditure is significantly reduced when proximity to death is taken into account [9][10][11][12][13] .…”

Section: Introductionmentioning

confidence: 99%

Health expenditure growth: reassessing the threat of ageing

2006

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In this paper we evaluate the respective effects of demographic change, changes in morbidity and changes in practices on growth in health care expenditures. We use microdata, i.e. representative samples of 3441 and 5003 French individuals observed in 1992 and 2000. Our data provide detailed information about morbidity and allow us to observe three components of expenditures: ambulatory care, pharmaceutical and hospital expenditures. We propose an original microsimulation method to identify the components of the drift observed between 1992 and 2000 in the health expenditure age profile. On the one hand, we find empirical evidence of health improvement at a given age: changes in morbidity induce a downward drift of the profile. On the other hand, the drift due to changes in practices is upward and sizeable. Detailed analysis attributes most of this drift to technological innovation. After applying our results at the macroeconomic level, we find that the rise in health care expenditures due to ageing is relatively small. The impact of changes in practices is 3.8 times larger. Furthermore, changes in morbidity induce savings which more than offset the increase in spending due to population ageing. Copyright © 2006 John Wiley & Sons, Ltd.

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The Determinants of Health Expenditure in the OECD Countries: A Pooled data Analysis

Cited by 120 publications

References 13 publications

Testing the Grossman model of medical spending determinants with macroeconomic panel data

Testing the Grossman model of medical spending determinants with macroeconomic panel data

Federalism and regional health care expenditures: an empirical analysis for the Swiss cantons

Health expenditure growth: reassessing the threat of ageing

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