Table 4. The relationship between GVC participation and RTI, by sector and occupation type, standardized

|Dependent variable: worker-|Indu<br>|stry<br>|Business<br>|services<br>|Other s<br>|ervices<br>|
|---|---|---|---|---|---|---|
|level RTI|(1)<br>Non-<br>offshorable|(2)<br>Offshorable|(3)<br>Non-<br>offshorable|(4)<br>Offshorable|(5)<br>Non-<br>offshorable|(6)<br>Offshorable|
|Backward GVC participation|-0.004|0.074***|-0.038*|0.041|0.204***|0.353***|
|(GVCB) share in exports (std.)|(0.025)|(0.024)|(0.021)|(0.032)|(0.049)|(0.099)|
|GVCB share (std.) * [Ln(GDP pc)|0.009|-0.014|0.078***|-0.030|-0.221***|-0.259|
|–mean(Ln(GDP pc)]|(0.030)|(0.031)|(0.026)|(0.031)|(0.080)|(0.199)|
|Forward<br>GVC<br>participation|-0.002|0.092***|-0.015|0.044*|-0.057*|0.107|
|(GVCF) share in exports (std.)|(0.019)|(0.018)|(0.018)|(0.024)|(0.032)|(0.071)|
|GVCF share (std.) * [Ln(GDP pc)|-0.026|-0.080***|-0.039**|-0.083***|0.039|0.074|
|–mean(Ln(GDP pc)]|(0.020)|(0.021)|(0.017)|(0.028)|(0.039)|(0.079)|
|Ln(GDP<br>per<br>capita)<br>–|-0.124**|-0.139|0.017|-0.010|-0.037|-0.065|
|mean(Ln(GDP per capita))|(0.060)|(0.125)|(0.052)|(0.075)|(0.096)|(0.138)|
|Observations|21,023|8,364|44,351|7,578|34,540|2,326|

Note: ***p < 0.01, **p < 0.05, *p < 0.1. Standard errors in parentheses. Standardized weights are used that give each country equal weight. The standard errors are clustered at a sector × country level. Measures for GVCB share and GVCF share are standardized. All regressions include controls for technology (computer use, computer use squared), FDI, skills, education, age, gender, sector FE, and sector FE interacted with GDP per capita. The specification follows that in Table 2, but for consistency with Table 3, Canada, China, Hungary, Macedonia (FYROM), Peru, Serbia, Singapore, Sweden, and Türkiye (for which wage data are not available) are excluded. Source: Authors’ calculations based on PIAAC, STEP, CULS (tasks), World Bank (GDP), EORA data. and Borin and Mancini (2015, 2019) (GVC participation measures).

# c. Quantifying the direct and indirect contribution of GVC participation to wage inequality

In this subsection, we use the estimated models to quantify the contribution of GVC participation to wage inequality in the reduced country sample of 38 countries, both directly and indirectly. We find that the direct contribution of GVC participation to wage inequality is negative in most countries (Figure 5a). In other words, higher GVC participation is linked to reduced wage inequality within countries. Some notable exceptions include the US and small countries intensively integrated into GVC, such as Ireland (high backward GVC participation) or Norway (high forward GVC participation). The results suggest a U-shaped relationship between GDP per capita and the direct contribution of GVC participation to wage inequality (Figure 5a). That is, the reduction in wage inequality is the strongest in upper-middle-income and bottom-high-income countries, but the smallest in low-income countries (which are weakly integrated into GVCs) and high-income countries. The Mincerian wage regressions suggest that the direct contribution reflects the positive role of forward GVCs for workers' wages in non-offshorable occupations in the industrial and business services sectors (Table 3).

In sharp contrast, the indirect contribution of GVC participation to wages through its link with workers’ RTI widens wage inequality in most countries (Figure 5b). Contrasting relationships between GVC participation and RTI among different groups of workers drive this pattern. The Mincerian wage regressions suggest a negative relationship between the RTI of workers and individual wages in all sectors and occupation types (Table 3). So, a higher GVC integration is associated with wider within-country wage inequality through larger RTI gaps between workers in offshorable and non-offshorable occupations in different sectors. In most countries, the indirect contribution is smaller in absolute terms than the direct contribution.

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