Fig 1.
The binary matrix of countries and products built from the worldwide export flows of BACI data-set for 1998, with Ncountries = 147 and Nproducts = 1131 [9].
Products are categorized according to the Harmonized System 2007 at 4-digit coarse-graining and the adopted digitization criterion is Balassa’s Revealed Comparative Advantage. By sorting the columns of the matrix by increasing Fitness and the rows by increasing Complexity, the matrix acquires a triangular-like shape. As it turns out, countries with more diversified export baskets are more competitive, while countries specialized in a few products—which generally are also exported by every other country—are the less competitive.
Fig 2.
The three figures refer to the US counties in 1998 and are realized through considering data on wages and employment from the QCEW data-set.
In this case, sectors are categorized according to the NAICS industry classification at 3-digit aggregation level, with Ncounties = 2805 and Nsectors = 89. (a): The binary county-sector matrix is built from the volume of sectoral wages. By ranking the columns of the matrix by increasing County Fitness and the rows by increasing Sector Complexity,
assumes a triangular-like shape, as it did in the analysis of international export. Hence, the path towards diversification in the production of goods and services is not only taken, as we have seen in Fig 1, by high Fitness countries but also by the most complex and diversified US counties. (b): The relation between the average wage and the Fitness of counties. The red line represents a kernel estimation of Relative County Average Wage versus FCOUNTY and the green shadowed area shows a 90% confidence interval of the expected value computed with bootstrap. On a country level the monetary counterpart of Fitness was the GDP per capita, while here we adopt the average aage as a proxy of the wealth produced by labor in a county. The trend is concordant with the one found comparing countries, in the sense that a non-linear relationship holds between the two variables: as FCOUNTY grows the Average Wage of the county increases; except for some deviations of Fitness from the monetary metric which are highly informative on a the potential performances of counties. (c): The relationship between national Sector Average Wage and Sector Complexity. As in the previous case, the red line is a kernel estimation of Sector Average Wage versus QSECTOR and the green area is a 90% confidence interval computed with bootstrap. As it seems reasonable, Sector Average Wage grows with Sector Complexity.
Fig 3.
Pooling of all countries and years for a total of 936 observations, over the time interval 1990–2008 and for a number of countries varying between 145 and 148.
The red line shows a non-parametric kernel estimation of the UTIP-UNIDO coefficient versus Relative GDP per capita. The green shadowed area represents a 90% confidence interval of UTIP-UNIDO expected values and has been computed with bootstrap. The negative relation reflects the one foreseen by the second half of the Kuznets curve: industrially advanced economies with high GDP per capita have low UTIP-UNIDO and vice versa.
Fig 4.
Pooling with the same features of Fig 3.
(a): A tridimensional study of UTIP-UNIDO coefficient as a function of the Ranking of country Fitness and Relative GDP per capita. The color-map, obtained with a multivariate non-parametric kernel estimation, is a smoothed graphical representation of the UTIP-UNIDO coefficient for different values of the country Fitness and Relative GDP per capita. The diagonal variability of the color suggests that wage inequality between sectors, at this scale, is determined both by Relative GDP per capita and Fitness ranking and follows a pattern similar to the one predicted by Kuznets. (b): The relationship between UTIP—UNIDO coefficient and CRRD index. The red lines show a non-parametric kernel estimation and the green shadowed area represents a 90% confidence interval of UTIP—UNIDO expected values and have been computed with bootstrap. By employing the CRRD as an industrialization proxy, we recover the entire Kuznets curve not only its downward part.
Table 1.
Wage inequality: log(UTIP—UNIDO).
OLS estimation, different model specifications.
Table 2.
Wage inequality: log(UTIP—UNIDO).
Fixed Effect estimation, different model specifications.
Fig 5.
All years and counties are pooled over 1990–2014, for a number of counties spanning from 2700 to 3167, and 69663 total observations.
(a): Theil index color-map, obtained with a multivariate non-parametric kernel estimation, a smoothed graphical representation of the variation of . From the diagonal green band it is clear that the highest Fitness counties are the most unequal. (b): Between-sector Theil component versus Complex Relative Ranking Development index. We study the relationship with a non-parametric kernel regression: the red line depicts the kernel estimation of
versus CRRD and the green shadowed area shows a 90% confidence interval of the expected value. The relationship is positive-sloping: as industrial development increases so does wage inequality, which then shows a plateau for high FC values.
Fig 6.
In this pooled analysis we relate the Herfindahl Index to the Complex Relative Ranking Development index, and the Coefficient of Variation of sectoral wages to Sector Complexity.
(a) County Herfindahl Index versus County Complex Relative Ranking Development index. The Herfindahl Index is computed with the employment shares of 3-digit NAICS industries and, being a concentration measure, it shows that as industrial diversification increases so does wage inequality. (b) Sector Wage Coefficient of Variations versus Sector Complexity. The Coefficient of Variation is here computed by considering the variability of wages at 6-digit aggregation level within every 3-digit NAICS sector at which the complexity on the abscissa refers. The relation is approximately positive and, on average, in the most complex sector wages vary ∼15% more than in the least complex one. (c) Sector Wage versus Sector Complexity. National sectoral wages increase as the complexity level of the sector grows. Thus, not only average retributions rise sharply for growing complexity, but within complex sectors wage variability is also higher.
Fig 7.
UTIP—UNIDO inequality measure versus CRRD.
Pooling of countries and years for the four time intervals: 1995–1997, 1998–2000, 2001–2004 and 2005–2008. The colored lines show a non-parametric kernel estimation of UTIP-UNIDO expected values. The shape shown in Fig 4 panel (b) is preserved over the chosen time intervals.
Fig 8.
Relation between wage inequality and development at a county level, for 2014 in the top half and 1990 in the bottom half of the figure.
Wage inequality is measured by the between-sector Theil component calculated from the distribution of sectoral wage at 3-digit NAICS aggregation level. The relations are analyzed with the same methods for both years. (a) and (c): versus CRRD. (b) and (d): Color-map of the variation of
as a function of the Fitness and the Relative Average Wage of counties. In 2014, it is clear that as industrial development increases so does wage inequality. Differently from 2014, in 1990 counties’ wage inequality grows until a certain level of CRRD and then, after a plateau, starts to decrease.
Fig 9.
as a function of CRRD for US counties in the years 1990, 1998, 2006 and 2014.
We observe two main features: (i) wage inequality increases over time; (ii) in 1990 we found a non monotonous behavior, while in the following years the second half of the curve experiences a turnaround, and as CRRD increases, inequality in the wage distribution among counties soars.
Fig 10.
Evolution of sector employment.
From (a) and (b) we can clearly observe the migration of labor force out of manufacturing (NAICS codes starting with 3) and the notable increase of employment in professional services (NAICS codes starting with 5).
Fig 11.
The macro-sectoral contributions to wage inequality computed with the Shapley value over the time interval 1990–2014.
Where the macro-sectors depicted in the figure are: 1 = agriculture, 2 = extraction, 3 = manufacturing, 4 = trade, 5 = professional services, 6 = education and health services, 7 = leisure industry, 8 = other services. The major effect on inequality is given by the service industries.