29 research outputs found

    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.

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    <p>(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.</p

    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.

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    <p>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.</p

    <i>UTIP</i>β€”<i>UNIDO</i> inequality measure versus <i>CRRD</i>.

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    <p>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 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182774#pone.0182774.g004" target="_blank">Fig 4</a> panel (b) is preserved over the chosen time intervals.</p

    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.

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    <p>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 <i>CRRD</i>. (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 <i>CRRD</i> and then, after a plateau, starts to decrease.</p

    All years and counties are pooled over 1990–2014, for a number of counties spanning from 2700 to 3167, and 69663 total observations.

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    <p>(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 <i>CRRD</i> 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 <i>F</i><sub><i>C</i></sub> values.</p

    Pooling with the same features of Fig 3.

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    <p>(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 <i>UTIP</i>β€”<i>UNIDO</i> coefficient and <i>CRRD</i> index. The red lines show a non-parametric kernel estimation and the green shadowed area represents a 90% confidence interval of <i>UTIP</i>β€”<i>UNIDO</i> expected values and have been computed with bootstrap. By employing the <i>CRRD</i> as an industrialization proxy, we recover the entire Kuznets curve not only its downward part.</p

    The macro-sectoral contributions to wage inequality computed with the Shapley value over the time interval 1990–2014.

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    <p>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.</p

    Wage inequality: log(<i>UTIP</i>β€”<i>UNIDO</i>).

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    <p>OLS estimation, different model specifications.</p

    Evolution of sector employment.

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    <p>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).</p

    as a function of <i>CRRD</i> for US counties in the years 1990, 1998, 2006 and 2014.

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    <p>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 <i>CRRD</i> increases, inequality in the wage distribution among counties soars.</p
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