Fig 1.
The binary matrix Msp of the year 2015.
Each row of the matrix represents a Brazilian state. States are ordered in terms of their Fitness from the smallest value (row 0) to the largest one (row 26). Analogously columns represent Products ordered in terms of their Complexity from the smallest value (column 0) to the largest one (column 1172). The matrix elements Msp are drawn in dark green and the others in white. In the figure we highlight high Fitness states such as São Paulo and Paraná, a middle rank State such as Ceará and a low Fitness state such as Roraima.
Fig 2.
Products spectroscopy of the years 2005 (dotted lines) and 2015 (filled colors) of the states: a) São Paulo, b) Paraná, c) Ceará, and d) Roraima.
The figures show the export volume (in US Dollars) of those states for each product with Mcp = 1 ordered according to their Complexity. Products are grouped in bins of 10 and the export volume in each bin are summed up.
Fig 3.
The binary matrix Mcp of the year 2015.
The rows of the matrix represent the World countries ordered according to their Fitness with row 0 for the country with the lowest Fitness and row 147 for the one with the highest Fitness. Analogously columns represent Products ordered in terms of their Complexity from the lowest one at column 0 to the highest one at column 1174. The elements Mcp = 1 are represented as blue dots.
Fig 4.
Dynamics of the World countries in the Fitness-Income plane.
The figure shows the dynamics (from the year 1995 to the year 2015) of World countries in the Fitness-Income plane in logarithmic scale. We emphasize the BRIC countries: Brazil in green, Russia in blue, China in red, and India in orange.
Table 1.
Fitness variation from 2003 to 2013 of BRIC countries.
Fig 5.
Products spectroscopy of the years 2005 (dotted lines) and 2015 (filled colors) of the countries: a) Brazil, b) Russia, c) China, and d) India.
The figures show the export volume (in US Dollars) of those states for each product with Mcp = 1 ordered in terms of their Complexity. The products have been grouped (10 for bin) and the export volumes of each product inside each bin have been summed.
Fig 6.
Time evolution of the ranking of Brazilian states according to the Exogenous Fitness algorithm.
The figure shows the time evolution of the ranking of the Brazilian states according to the Fitness obtained through the Exogenous Fitness algorithm applied to the time interval 2000-2015.
Fig 7.
Fitness map of the Brazilian states.
The colors in the map vary from green (high Fitness) to red (low Fitness) and they show the differences of the Fitness among the Brazilian states.
Table 2.
Fitness variation from 2003 to 2013 of the states: São Paulo, Paraná, Ceará, and Roraima.
Fig 8.
Dynamics of Brazilian states in the Fitness-Income plane.
a) The figure shows the evolution (from 2000 to 2015) of the Brazilian states in the Fitness-Income plane in logarithmic scale. The dotted black line in the figure shows the expected level of GDPp given the level of Fitness and it is the result of the minimization of the Euclidean distance of the states from the line, weighted by the states GDP. b) The figure shows the coefficient calculated considering a time window from 2003 to 2013. The color varies from green (where the versors of evolution tend to be parallel), to red (where the versors tend to be unevenly directed). c) The figure shows a grid where for each cell we calculate the versor of the sum vector. From the figure two regions appear: the first one where the versors tend to be parallel in the direction of a high GDPp (shown in green); and the second one where the versors tend to be unevenly directed (shown in red). Fig 8b and c together show that there is a region (green) of high predictability of motion in direction of a high GDPp; and a region (red) of low predictability of motion. d) The figure shows the dynamics (from 2000 to 2015) of the Brazilian states in the Fitness-Income plane highlighting in green the states in the high predictability region and in red the states in the low predictability one.
Fig 9.
Time evolution of the ranking of Brazilian states according to the (Endogenous) Fitness algorithm.
The figure shows the time evolution of the ranking of the Brazilian states in terms of the Fitness obtained through the (Endogenous) Fitness algorithm applied during the time interval 2000-2015.
Fig 10.
Time evolution of the ranking of Brazilian states according to the ECI algorithm.
The figure shows the time evolution of the ranking of the Brazilian states during the period 2002-2015 in terms of the ECI, directly downloaded by the Dataviva platform [27].
Fig 11.
ECI map of the Brazilian states.
The colors in the map vary from green (high ECI) to red (low ECI) and they show the variation of the ECI across the Brazilian states.
Fig 12.
Evolution of Brazilian states in the ECI-Income plane.
a) The figure shows the dynamics (from 2002 to 2015) of the Brazilian states in the ECI-Income plane, where the GDPp is in logarithmic scale. Only the state of São Paulo and the Distrito Federal appear to be clearly distinguishable from the rest of the states. All the others states are indeed concentrated in a small region of the graph. b) The figure shows the coefficient calculated considering the time interval 2003-2013. Colors vary from green (where the versors tend to be parallel), to red (where the versors tend to be unevenly directed). From the figure we can therefore verify that there is a low predictability of the evolution of all the states. c) Here we show a grid where for each cell we calculate the versor of the sum vector. From the figure we see that there is no privileged direction, indeed the vectors are unevenly directed.