Along with this broad similarity, some interesting differences are evident in Table 4. The residual in the equation for capital intensity is particularly large, as measured by the estimated standard deviation of the error. This leads to an interesting observation. The United States is an excellent example of a country with good social infrastructure, but its stock of physical capital per unit of output is not remarkable. While the U.S. ranks first in output per worker, second in educational attainment, and 13th in productivity, its capital-output ratio ranks 39th among the 127 countries. The U.S. ranks much higher in capital per worker (7th) because of its relatively high productivity level.

Y/L a

(K/Y) —

Observed Factor of Variation 35.1 4.5 3.1 19.9
Ratio, 5 richest to 5 poorest countries 31.7 1.8 2.2 8.3
Predicted Variation,

Only Measurement Error

38.4 2.1 2.6 7.0
Predicted Variation, Assuming rS S = .5 25.2 1.9 2.3 5.6

Table 5 summarizes the extent to which differences in true social infrastructure can explain the observed variation in output per worker and its components. The first row of the table documents the observed factor of variation between the maximum and minimum values of output per worker, capital intensity, and other variables in our data set. The second row shows numbers we have already reported in the interpretation of the productivity results. Countries are sorted by output per worker, and then the ratio of the geometric average of output per worker in the 5 richest countries to the 5 poorest countries is decomposed into the product of a capital intensity term, a human capital term, and productivity. The last two rows of the table use the basic coefficient estimates from Tables 2 and 4 to decompose the predicted factor of variation in output into its multiplicative components.One sees from this table that differences in social infrastructure are sufficient to account for the bulk of the observed range of variation in capital intensity, human capital per worker, and productivity. Interpreted through an aggregate production function, these differences are able to account for much of the variation in output per worker.