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Wage inequality in developing countries: South–South trade matters


The relationship between trade liberalization and inequality has received considerable attention in recent years. The major purpose of this study is to present new results on the sources of wage inequalities in manufacturing taking into account South–South (S–S) trade. Globalization has not only lead to increasing North–South (N–S) trade, but it has also changed the direction and composition of trade as more trade is carried out among developing countries. In this study, we find that increasing wage inequality is associated more with the South–South trade liberalization than with the classical trade liberalization with northern countries. A part of this increasing wage inequality due to S–S trade comes from the development of N–S trade relationship in S–S trade that increases wage inequality in middle-income developing countries. This study also seeks to shed some light on the link between the direction of trade and technological change. We explore the fact that S–S trade leads to a technological change biased toward skill-intensive sectors more than N–S trade. This indirect effect increases wage inequality for all developing countries, but it is more important in low-income countries.

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  1. It is notable that around 70% of tariffs faced by developing countries are levied by other developing countries.

  2. Here, we restrict globalization to trade liberalization, outsourcing, immigration, and capital account openness, as they affect trade flows in goods. A measure enabling the distinction between trade liberalization with a northern partner and trade liberalization with a southern partner does not exist (the tariffs by partner’s country are available on TRAINS since 1989). So we mainly use a ratio of trade flows on output.

  3. In addition, we replicate this test in using two indices of trade policy openness for developing countries obtained from a gravity model of bilateral trade data.

  4. A variation on this theme is the conjecture that, even if the technology to be transferred is neutral, the transitional process of transferring and installing new technologies may be skill-biased (Pissarides 1997). In this case, the effect on the returns to human capital will be temporary and skilled workers benefit only during the transition period to the new, higher, technological level. Goldin and Katz (1998) reach a similar conclusion. They argue that the demand for skilled workers can follow a technological cycle. The demand rises when new technologies and machinery are introduced, but it declines once the other workers have learned to use the new equipment.

  5. This argument is also related to the wage industry premium explanation mentioned earlier and used in several studies on Latin American countries to explain wage inequality (Goldberg and Pavcnik 2005). If N–S trade leads to tariff cuts and increased imports in the highly skilled labor industries and that S–S trade leads mainly to tariff cuts and increased imports in the low-skilled labor industries, this could explain why S–S trade could increase inter-industry wage inequality more than N–S trade.

  6. Theil index is calculated using the following formula: \( T = \frac{1}{n}\sum\nolimits_{i = 1}^{n} {\frac{{x_{i} }}{x}} \ln \left( {\frac{{x_{i} }}{x}} \right) \), where x i is the wage in industry i and \( \overline{x} \) is the mean wages in industry, and n is the number of ISIC 3 digit sector.

  7. The indirect effect is calculated using value (in log) of TSS/TNS multiplied by its coefficient in the Eq. 3 and by the coefficient in front of USBTC in the Eq. 4. We calculate the direct effect as the value (in log) multiplied by its coefficient in the Eq. 4 as direct effect. For example, in the first column (all developing countries) with a log value of 1.10, the indirect effect is 1.10 × (−0.078) × (−0.075) = 0.006 and the direct effect is 1.10 × 0.012 = 0.013 meaning a global effect of 0.020.

  8. We assume that FDI and Trade orientation might be jointly determined with wage inequality since they are all dependant from international factors whereas Education and GDP might have an impact on future values of wage inequality since for instance the level of education will determine the amount of skilled and unskilled labor force forthcoming.

  9. An inter-quantile regression shows that a 1% increase in the share of south trade relative to north trade increases the difference in wages between the 25th and 75th quantile by 0.029%.

  10. The interquantile regressions show that a 1% increase in the share of south trade relative to north trade increases the difference in wages between the 25th and 75th quantile by 0.050 and 0.048%, respectively.

  11. An inter-quantile regression shows that a 1% increase in the share of south trade relative to north trade decreases difference in wages between the 25th and 75th quantile by 0.047%.

  12. The author is grateful to Marcelo Olarreaga and Mathias Thoenig for this comment.


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The author thanks Olivier Cadot, Jaime de Melo, Marcelo Olarreaga and Lionel Fontagné as well as two anonymous referees for helpful comments on earlier drafts.

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Correspondence to Julien Gourdon.



See Tables 9, 10 and 11.

Table 9 List of countries included in the sample 1976–2000
Table 10 Classification of Isic industry according to skill intensity
Table 11 List of variables

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Gourdon, J. Wage inequality in developing countries: South–South trade matters. Int Rev Econ 58, 359–383 (2011).

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  • International trade
  • Wage inequality
  • Skill-biased technical change

JEL Classification

  • F1
  • J3
  • O3