Developing a comprehensive time series of GDP per capita for 210 countries from 1950 to 2015

Population Health Metrics

Emmanuela Gakidou¹  Christopher JL Murray¹  Paul Gubbins¹  Spencer L James¹ 

[1] Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA

关键词: Indicator;    Covariate;    Social determinants;    Income;    GDP per capita;    GDP;   

Others  :  806112
DOI  :  10.1186/1478-7954-10-12

 received in 2011-08-19, accepted in 2012-06-11,  发布年份 2012

【 摘 要 】

Background

Income has been extensively studied and utilized as a determinant of health. There are several sources of income expressed as gross domestic product (GDP) per capita, but there are no time series that are complete for the years between 1950 and 2015 for the 210 countries for which data exist. It is in the interest of population health research to establish a global time series that is complete from 1950 to 2015.

Methods

We collected GDP per capita estimates expressed in either constant US dollar terms or international dollar terms (corrected for purchasing power parity) from seven sources. We applied several stages of models, including ordinary least-squares regressions and mixed effects models, to complete each of the seven source series from 1950 to 2015. The three US dollar and four international dollar series were each averaged to produce two new GDP per capita series.

Results and discussion

Nine complete series from 1950 to 2015 for 210 countries are available for use. These series can serve various analytical purposes and can illustrate myriad economic trends and features. The derivation of the two new series allows for researchers to avoid any series-specific biases that may exist. The modeling approach used is flexible and will allow for yearly updating as new estimates are produced by the source series.

Conclusion

GDP per capita is a necessary tool in population health research, and our development and implementation of a new method has allowed for the most comprehensive known time series to date.

【 授权许可】

   
2012 James et al.; licensee BioMed Central Ltd.

【 图 表 】

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