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Contributed by William D. Nordhaus, December 2, 2005
Abstract
The linkage between economic activity and geography is obvious: Populations cluster mainly on coasts and rarely on ice sheets. Past studies of the relationships between economic activity and geography have been hampered by limited spatial data on economic activity. The present study introduces data on global economic activity, the G-Econ database, which measures economic activity for all large countries, measured at a 1° latitude by 1° longitude scale. The methodologies for the study are described. Three applications of the data are investigated. First, the puzzling “climate-output reversal” is detected, whereby the relationship between temperature and output is negative when measured on a per capita basis and strongly positive on a per area basis. Second, the database allows better resolution of the impact of geographic attributes on African poverty, finding geography is an important source of income differences relative to high-income regions. Finally, we use the G-Econ data to provide estimates of the economic impact of greenhouse warming, with larger estimates of warming damages than past studies.
Footnotes
- *E-mail: william.nordhaus{at}yale.edu
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This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected on May 1, 2001.
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Author contributions: W.D.N. designed research, performed research, and wrote the paper.
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Conflict of interest statement: No conflicts declared.
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↵ † The gridded population data are available online at http://sedac.ciesin.columbia.edu/plue/gpw with full documentation in ref. 8 and updated in ref. 9.
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↵ ‡ The precise specification in Eq. 2 contains 72 country effects plus nine polynomial terms in temperature and precipitation, six statistics on extremes and higher moments in temperature and precipitation, the first and second moments of elevation, three variables for distance from coast (<50 km, <100 km, and <200 km), and 27 soil types. The equation has 17,305 degrees of freedom, although that is probably overstated because of spatial correlation. Undertaking further analysis of these data by using the techniques of spatial statistics is an important area of research. All results are described in detail in the background documentation available upon request.
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↵ § The equation used for the global warming equation is the log of output density as a dependent variable and, as independent variables, mean and squared temperature, mean and squared precipitation, elevation, roughness, roughness squared, the three distance-from-coast variables, country effects, and linear temperature effects by country.
- Abbreviation:
- GCP,
- gross cell product.
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Freely available online through the PNAS open access option.
- © 2006 by The National Academy of Sciences of the USA
Geography and macroeconomics: New data and new findings
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