International Trade, Factor Endowments, and Income Inequality: Evidence from Chinese Regional Data

ABSTRACT

This study estimates the effects of international trade on income distribution within Chinese provinces. Dynamic panel data from a household survey and provincial statistics from 1988 to 2009 were used. Bias-corrected least square dummy variable estimations were employed. The results show that the distribution effects of exports and imports offset each other. Exports tend to reduce income inequality, but its effect varies depending on the distribution of factor endowments across regions. Imports tend to increase income inequality, mainly due to the positive relationship between imports and inequality in skill-intensive regions.

KEYWORDS:

• Factor endowment
• international trade
• income inequality
• China

JEL CLASSIFICATION:

• F16
• D31
• O15

Acknowledgments

The author thanks the two anonymous reviewers for their valuable comments.

Notes

1. Gini coefficient is 0 if there is perfectly equal income distribution and 1 otherwise. Therefore, larger Gini coefficients indicate greater inequality in income.

2. Data on Gini coefficients from 1980 to 2002 are collected from the Poverty and Equity Database of the World Bank and those from 2003 to 2014 are collected from the National Bureau Statistics of China. The World Bank reported estimated Gini coefficients every two years after 2003; these are roughly 10% lower than those in the official Chinese statements. However, the trends of the Gini coefficients from the two sources are almost the same.

3. This is indeed a strong assumption. The returns to skilled and unskilled labor may not be the same. Many empirical studies have documented a significant wage premium for skilled workers. Those topics are extremely interesting, but beyond the scope of this study. For simplicity, we consider w here as the average return to labor, which does not vary across individuals.

4. Although the hypotheses of the Hecksher-Ohlin model on the origin of international trade are often rejected by empirical studies, it can still be applied to link trade with overall income distribution through factor endowments.

5. More precisely, E_it,j should be measured as the per worker endowment of factor j of economy i divided by the per worker endowment of factor j in the world at time t, which indicates the relative abundance of factor j in economy i. However, since we use the logarithmic form of E_it,j, the world’s per worker endowment of factor j in the denominator can be merged into the error term, which will be removed in the bias-corrected LSDV estimation. The estimated β and θ are not biased because of our measurement for E_it,j.

6. The GDP deflator (2009 = 100) is employed as the price index.

7. The sample regions are representative, reflecting the unbalanced development in China.

Additional information

Funding

This work was supported by The National Social Science Fund of China (Project ID 18BJY051).