Geography and macroeconomics: new data and new findings - PubMed (original) (raw)

Geography and macroeconomics: new data and new findings

William D Nordhaus. Proc Natl Acad Sci U S A. 2006.

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 degree latitude by 1 degree 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.

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Conflict of interest statement

Conflict of interest statement: No conflicts declared.

Figures

Fig. 1.

Fig. 1.

Economic map of Europe. This figure shows an economic topographical map of Europe with heights proportional to gross domestic product per area. Note how economic activity clusters in the core, whereas the periphery has much lower economic elevations. The observations measure economic activity in millions of 1995 U.S. dollars per km2 at a 1° latitude by 1° longitude scale.

Fig. 2.

Fig. 2.

Fractile plot for key geographic variables. The figure shows the fractile plots for key variables (mean temperature, mean precipitation, mean distance from coast, mean elevation, and absolute value of latitude). Fractiles rank each variable from lowest to highest cell observations. For each variable, we have fitted a kernel density function to the bivariate relationship between the log10 (output density) and the geographic variable. Zero values of output are included as equal 0 (n = 17,796).

Fig. 3.

Fig. 3.

Boxplot of output per capita and temperature. Earlier studies indicate that high-latitude countries have higher output per capita than those in low latitudes. This relationship is verified by using mean temperature as the geographic variable for grid cells. Coldest regions have an output per capita ≈12 times that of warmest regions. In boxplots, the means are the red circles, the medians are the heavy red horizontal line, the one-sigma ranges of the median are the blue shaded regions, and the interquartile ranges are the boxes. The width of the box is proportional to the square root of the number of observations in each bin. The bin is shown on the horizontal axis, but only every other bin can fit on this graph. The vertical scale is log10, so that each unit is a factor of 10. Zero observations are omitted (n = 15,755).

Fig. 4.

Fig. 4.

Boxplot of output density and temperature. This boxplot shows the distribution of output density by temperature. Output density varies by at least five orders of magnitude from cold to temperate region. For the explanation of the boxplots, see Fig. 3. Zero observations are set at log10 (x) = 0 (n = 18,995).

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