The descriptive statistics for four variables are shown in Table 3, Gender Wage Gap, Labor Force Participation, Education, and BRI Initiative Trade Openness. These variables are indicators of a country's gender inequality, economic participation, education level, and trade openness. The mean value for the Gender Wage Gap variable is 15.41%, indicating that women earn 15.41% less than men in the countries. This is a relatively large gender wage disparity, indicating significant wage inequality between men and women. The Labor Force Participation variable has a mean value of 52.14%, indicating that slightly more than half of the population aged 15 and up in these countries is economically active on average. This implies that there are still a significant number of people, particularly women, who are not working, which could be due to a variety of factors such as cultural norms, discrimination, and a lack of economic opportunities.
The Education variable has a mean value of 7.07%, indicating that only a small proportion of the population in these countries has completed tertiary education. This indicates a relatively low level of educational attainment, which may have implications for overall economic development and gender inequality in these countries. The mean value of the BRI Initiative Trade Openness variable is 34.77%, indicating that these countries have a moderate level of trade openness on average. This implies that they are not completely closed to international trade, but are also not completely open, which may have implications for economic growth and development. Overall, descriptive statistics indicate that the countries studied have significant gender inequality, low levels of education, and moderate levels of economic participation and trade openness. These findings provide a foundational understanding of the variables and their distribution in the sample, which will aid in the analysis and discussion of the variables' relationship.
Table 3
Descriptive statistics of variables
Variable | Mean | Standard deviation | Maximum | Minimum |
Gender Wage Gap (%) | 15.41 | 7.94 | 0.45 | 29.02 |
Labour force Participation (%) | 52.14 | 11.89 | 19.82 | 75.29 |
Education (%) | 7.07 | 4.88 | 1.52 | 15.89 |
BRI Initiative Trade Openness (%) | 34.77 | 11.58 | 9.53 | 63.25 |
Table 4 displays the outcomes of OLS regression models of earnings and returns to education and occupation for men and women with secondary education, undergraduate, graduate, and postgraduate degrees and having professions such as sales, administration, technical and executives. Males had higher returns to education than females at all levels of education, with the differences becoming more pronounced at higher levels of education. Particularly, males have higher returns to education of 0.55 for secondary education, 0.60 for undergraduate education, 0.63 for graduate education, and 0.69 for postgraduate education, whereas females had returns of 0.45, 0.50, 0.52, and 0.58, respectively. These findings suggest that males gain greater from educational attainment than females, however, the gap narrows at lower levels of education. Furthermore, the R-squared values show that the regressions disclosed between 60 and 74% of the variance in earnings. Additionally, higher R-squared values indicate that educational attainment contributes a more significant role in assessing earnings at higher levels of education. It indicates males and females in BRI countries, with higher levels of education are more likely to benefit from their education compared to those with fewer years of education.
These findings are significant because it demonstrates that investments in education could contribute to higher earnings for men and women in these countries' labor market. Policymakers should keep this in mind as they develop policies that decrease disparities between genders and encourage greater economic equality for women. Additionally, these results confirmed that policies aimed at reducing gender inequality should prioritize improving women's ability to obtain higher education to close the gender wage gap.
Table 4
Earnings and Returns to Education and Occupation for Males and Females
Variables | Male | Female |
Factors | Coefficient | Return to Education | Coefficient | Return to Education |
Secondary Education | 1.58** | 0.55 | 1.26** | 0.45 |
Undergraduate | 2.06** | 0.60 | 1.71*** | 0.50 |
Graduate | 2.64*** | 0.63 | 2.18 | 0.52 |
Postgraduate | 3.45** | 0.69 | 2.80*** | 0.58 |
Sales | 1.49 | 0.53 | 1.31 | 0.48 |
Office and Administration | 1.85 | 0.57 | 1.65*** | 0.53 |
Technical | 2.45** | 0.63 | 2.13 | 0.54 |
Executives | 3.13** | 0.69 | 2.75*** | 0.60 |
R-square | 0.60 | 0.75 |
Note: *** p < .01, ** p < .05, * p < .1 |
Table 5 presents the Blinder-Oaxaca decomposition describing the gender wage gap around the Belt and Road Initiative countries. According to the findings, the average wage disparity between men and women in each of these nations was 16.45 percent. This figure was divided into three categories: endowment differences (10.09 percent), return differences (2.80 percent), and unexplained differences (3.56 percent). Endowment differences indicate the variations in the disparity in wages between genders can be explained by distinctions between the characteristics that each demographic (men and women) brings to the job marketplace. Job tenure, level of education, and work experience are examples of these characteristics. This accounts roughly 61% of total variation. The discrepancy in the gender wage gap can be linked to differences in how these attributes are compensated in the labor market, based on to the differences in returns. Finally, the unexplained differences indicate that there are factors not accounted for in this analysis that contribute to the gender wage gap in these countries.
Table 5
Blinder-Oaxaca Decomposition of Gender Wage Gap, BRI Countries
Category | Share of Average Gap (%) |
Differences in Endowments | 10.09 |
Differences in Returns | 2.80 |
Unexplained Differences | 3.56 |
Average Gender Wage Gap | 16.45 |
Endowments (%) | 61 |
Differences in Returns (%) | 17 |
Unexplained Differences (%) | 21 |
The outcomes of the Blinder-Oaxaca decomposition of the wage disparity between men and women in BRI countries are shown in Table 6. The findings show that, when examined alongside countries with low trade openness, countries that have greater trade openness have disparities in endowments accounted for 10.48 or 75 percent of the average gender wage gap and differences in returns which reflect about 3.09 or 22 percent. Furthermore, countries with a high level of trade openness have significantly fewer unexplained differences (0.22) than countries with a low level of trade openness (3.56). The results suggest that in countries with high levels of trade openness, differences in endowments and returns between men and women are larger than in countries with low levels of trade openness.
This is a critical finding because it demonstrates that trade in certain countries could increase gender inequality. The findings additionally indicate that countries with high trade openness have more effective labor markets, as the unexplainable portion of the gender disparity in wages is significantly lower in these countries. This suggests that, while differences in endowments and returns account for a portion of the gender wage gap, other variables additionally play an essential part in determining the wage gap. Overall, the results presented demonstrate that policymakers should prioritize closing the gender wage gap in those countries by improving education and employment opportunities for women. On top of that, policymakers should consider the expected effect of trade on gender pay disparities and take steps to mitigate any negative effects.
Table 6
Blinder-Oaxaca Gender Wage Gap Decomposition by Trade Openness
Factors | Low Trade Openness (%) | High Trade Openness (%) |
Differences in Endowments | 10.09 | 10.48 |
Differences in Returns | 2.80 | 3.09 |
Unexplained Differences | 3.56 | 0.22 |
Average Gender Wage Gap | 16.45 | 13.79 |
Endowments (%) | 61 | 75 |
Differences in Returns (%) | 17 | 22 |
Unexplained Differences (%) | 21 | 1.59 |
Table 7 illustrates the outcomes of the Blinder-Oaxaca decomposition of the gender wage gap in Belt and Road Initiative (B&R) countries. The average gender wage gap in B&R countries was 25.67 percent, slightly higher than the average in all BRI countries (16.45 percent). Based on the decomposition results, differences in endowments constituted 17.13 percent of the gap in B&R countries, differences in returns explained for 4.66 percent, and unexplained differences constituted for 3.87 percent of the gap. The results presented reveal that, when compared to the overall BRI average, differences in endowments and returns between men and women are greater in B&R countries. This is a significant finding because it implies that participating in the Belt and Road Agreement could be leading to greater degrees of gender disparity in some countries. In addition, the findings demonstrate that policies focused on reducing the influence of the gender pay disparity should consider besides developing opportunities in education and employment for women, but also the potential effect of trade on the gender wage gap and take steps to mitigate any negative effects.
Table 7
Blinder-Oaxaca Decomposition of Gender Wage Gap, B&R Countries
Category | Share of Average Gap (%) |
Differences in Endowments | 17.13 |
Differences in Returns | 4.66 |
Unexplained Differences | |3.87 |
Average Gender Wage Gap | 25.67 |
Table 8 depicts the influence of trade on the gender wage gap in Belt and Road Initiative (BRI) countries. The outcomes are based on a decomposition analysis, which divides the overall impact of trade on gender wage disparities into two elements: the direct effect of trade openness and the indirect impact via educational institutions and occupation.
The coefficient for trade openness in low trade openness countries is -0.251, indicating that trade has a slight but negative effect on the gender wage gap. Therefore, as trade openness grows, the pay gap between men and women reduces slightly at 10% significance level. In in contrast, the value of the coefficient of trade openness in high trade openness economies is substantially higher, at -4.61. This suggests that trade has a significant and negative impact on the gender wage gap in these economies. The gender wage disparity decreases as trade openness increases in size. The sum of the trade openness coefficient is -2.431, which combines the impact of both high and low trade openness economies. This means that trade has a significant negative impact on the gender wage gap in the countries of the BRI.
In low trade openness countries, the education coefficient is 0.272, demonstrating that higher levels of education are linked with a smaller gender wage gap. The positive association is also observed in countries with high trade openness, where the coefficient is 0.671. It indicates that, regardless of trade openness, education is an important factor in closing the gender wage gap. The occupation coefficient is positive in both low and high trade openness economies, showing that specific professions are associated with an increased gender wage gap. However, the association is not statistically significant in either case, implying that profession may not have a significant effect on the gender wage gap in the countries of the BRI.
Overall, the findings indicate that trade openness has a significant negative impact on gender pay gaps in BRI countries. The indirect effect of trade on education is primarily responsible for this effect, as higher levels of education are linked with a smaller gender wage gap. However, the indirect effect through occupation is not significant, implying that the type of occupation may not be a significant factor in these countries' gender wage disparities. These findings imply that the BRI initiative may be acting as a gender equalizer in participating countries by promoting education and closing the gender wage gap.
The results presented in Table 8 support the liberal feminist viewpoint that trade can improve the economic prospects for women and ultimately close the gender pay gap. The negative coefficient for trade openness in high trade openness countries reveals that trade has a significant and negative influence on the gender wage gap in these countries. This result in line with the findings of Hannah et al. (2021), who found that trade has a positive impact on gender equality. In addition, the positive coefficient for education suggests higher levels of education are linked with a lower gender wage gap across low and high trade openness economies. The outcome of this study supports support to the socialist feminist viewpoint that education is critical for fostering gender equality.
However, these findings are opposite to some previous research conducted in other countries that discovered a positive relationship between trade openness and the gender wage gap. According to Orkoh et al. (2022), countries with higher levels of trade openness tend to have larger gender wage gaps. This could be because trade liberalization frequently leads to the expansion of export-oriented industries, which typically pay lower wages to women. Similarly, Benguria & Ederington, (2023) discovered that increased trade openness is linked to an increase in the gender wage gap in both developed and developing countries. The outcomes of this study show that trade openness is an important factor in explaining the gender wage gap in BRI countries. As trade openness grows, so does the gender wage gap, emphasizing the need for policies and initiatives to address this issue and promote gender equality in the labor market.
Table 8
Effect of Trade in BRI countries on Gender Wage Gap in BRI Countries
| Low Trade Openness (%) | High Trade Openness (%) | Total Trade Openness |
Variable | Coefficient | Coefficient | Coefficient |
Trade Openness | -0.251* | -4.61*** | -2.431** |
| (0.101) | (2.587) | (1.141) |
Education | 0.272* | 0.671** | 0.474* |
| (0.109) | (.425) | (0.224) |
Occupation | 0.168 | 0.593* | 0.381 |
| (0.262) | (0.279) | (0.425) |
Constant | 5.769 | 16.72 | 11.24 |
| (6.492) | (21.265) | (9.142) |