Fig 1.
Binary matrix identifying countries producing a specific product.
Countries (rows) are ordered according to their Fitness, Products (columns) are ordered according to their Complexity. A clear triangular structure emerges.
Fig 2.
Non parametric kernel estimation of growth rate of per capita GDP due to inputs versus relative per capita GDP.
The shadowing indicates 90% of confidence interval of the expected value, computed with bootstrap. Different countries-years in the range 1963–2000 have been pooled after removing the global trend. While the low performance of low GDP countries in increasing their input is clearly visible (left site of the figure), the slowing down of input growth expected after catching-up (right side) is modest.
Fig 3.
Non parametric Gaussian kernel estimation of growth rate of per capita GDP due to input versus per capita GDP for the lowest and the top tertile of the fitness distribution.
The shadowing indicate the 90% confidence interval of the expected value, computed with bootstrap. Different countries-years in the range 1963–2000 have been pooled after removing the global trend. Dividing the countries in three sets of the same numerosity depending on their fitness values highlights very different behaviors and reconciles the theory with the empirical observation.
Fig 4.
The color map represents the Per Capita GDP Growth due to inputs, for different values of Fitness and GDP per Capita.
Different countries-years in the range 1963–2000 have been pooled after removing the global trend. Both the role of the fitness of the country in lowering the threshold to enter in the high endogenous GDP growth regime (the blue band in the center) and the slowing down of the process for developed countries (the top-right corner) are evident.
Fig 5.
This plot present two bits of information on the expected GDP growth due to input growth in the plane fitness—relative GDP per capita.
i) in different shades of blue, the isolevels of GDP growth due to input growth, computed with a spacing of 0.4%; ii) in shades of grays, the estimation error of GDP growth due to input, where black means a standard error of 0.4% or more, and white a standard error of 0.2% or less. Different countries-years in the range 1963–2000 have been pooled after removing the global trend. The figure highlights the complementary role of Fitness and relative GDP per capita to identify the expected value of GDP growth due to input growth.
Fig 6.
In Fig (a), color map of the per capita GDP growth due to input in the Fitness—relative GDP per capita space, as in Fig 4. Superimposed to it, isolevel lines of CIRDc,t. In the figure we also highlight the origin, corresponding to an hypothetical country having average GDP per capita and Fitness. In Fig (b), non parametric kernel estimation of per capita GDP growth due to input versus CIRDc,t. The shadowing represent 95% confidence intervals. The blue lines correspond to the same lines of Fig (a).