Gender gap and Gender inequality in China and USA
Introduction
Measures of gender inequality are considered to acknowledge that men and women are not equal and that gender is a significant factor that influences an individual’s life experiences (Blau, 2016). The gender differences may arise from distinctions in biology, cultural norms, and psychology. Research has indicated that some of the distinctions between genders are empirically grounded while others are socially constructed (David, Manning, & Smith, 2016).
Equal rights between men and women are guaranteed by various countries’ constitutions across the world (Gressard, Swahn, & Tharp, 2015). In China, the law prohibits gender discrimination, although these laws are rarely enforced (Fincher, 2016). This study attempted to motivate the Chinese government to take gender equality measures seriously. The motivation was expected to be initiated by the comparative analysis of the Chinese gender gaps and those of the US. According to statistics, gender inequality in the US has been diminishing throughout the country’s history and significant advancements towards equality have been witnessed beginning mostly in the early 1900s (David, Manning, & Smith, 2016).
Research Aim
The aim of the study is to explore the gender gap and gender inequality in China and compare them to those of the US.
Method
The study adopted a quantitative research method where quantitative data was gathered from the World Bank’s official website, analyzed using statistical methods and conclusions made based on the analysis results. Secondary data was used.
The study considered the overarching objectives and statistics where the researcher compared six variables including females’ labor force participation rate, females’ school enrollment at tertiary level, females’ education attainment for at least Bachelor’s degree or equivalent, secondary school enrollment by females, primary school enrollment by females, and females’ unemployment rate (Ji, Wu, Sun, & He, 2017).
Descriptive statistics methods were used where graphs and measures of central tendencies were used to compare the two countries. Presentation of the results was made using charts and statistical tables. Inferential statistics were also used to enhance mean comparisons where data was available for similar years in the two countries. For such cases, the study used paired two samples T-test to compare the means at 5% level of significance.
Findings
The analysis observed that the females’ labor force participation in China follows a downward trend from 1990 to 2018. The results implied that the gender gap in China with respect to labor force participation has worsened with time. To the contrary, the labor force participation in the US follows an upward trend indicating gradual improvement with time.
A further analysis using the T-test technique revealed that the USA’s female labor force participation rate was significantly higher than that of China. The study obtained mean female labor force rates equal to 45.58% and 44.55% for the USA and China respectively. The t-statistics associated with the mean difference was equal to 5.665 with a probability value <0.00. Apparently, the probability value was less than 0.005 implying that the test rejected the null hypothesis of equality of means in favor of the alternative hypothesis which stated that the means were different.
| t-Test: Paired Two Sample for Means | ||
| USA females’ Labor force (percentage of total labor force) | China’s females’ Labor force (percentage of total labor force) | |
| Mean/average | 45.58019 | 44.54552 |
| Variances | 0.258924 | 0.292584 |
| Observations | 29 | 29 |
| Pearson Correlations | -0.75518 | |
| Hyp. Difference of the Mean | 0 | |
| D F | 28 | |
| T-Statistics | 5.665475 | |
| one-tail Pr(T< =t) | 2.26E-06 | |
| one-tail T-Critical | 1.701131 | |
| two-tails Pr(T< =t) | 4.52E-06 | |
| two-tails T-Critical | 2.048407 | |
The analysis of the females’ school enrollment at tertiary level produced mean values equal to 69.009 and 19.767 for the USA and China respectively. The comparison of the mean rates was done using a comparative bar graph which revealed a significant difference in the two countries.
Similarly, the analysis of the females’ education attainment with at least Bachelor’s degree or equivalent produced mean values equal to 32.44% and 2.998% for the USA and China respectively. The comparison of the mean rates was done using a comparative bar graph which revealed a significant difference in the two countries.
Also, the analysis of the females’ school enrollment rate at secondary level produced mean values equal to 94.38% and 49.98% for the USA and China respectively. The comparison of the mean rates was done using a comparative bar graph which revealed a significant difference in the two countries.
Further, the analysis of the females’ school enrollment rate at pre-primary level produced mean values equal to 64.42% and 41.39% for the USA and China respectively. The comparison of the mean rates was done using a comparative bar graph which revealed a significant difference in the two countries.
A time series analysis was done for the females’ unemployment rate between the years 1991 and 2018 for the two countries under investigation, the US and China. The analysis revealed that the females’ unemployment rate for China was increasing gradually. Conversely, the females’ unemployment rate for the US was varying significantly with a decreasing trend in the long run. The variations in the US’s females’ unemployment rate would suggest possibilities of efforts to minimize the gender gap. On the other hand, the gradual increase in the females’ unemployment rate in China indicated possibilities of lack of sufficient commitments towards gender equality.
Nonetheless, the T-test analysis revealed that the rate of females’ unemployment in the US was higher than that of China. The analysis produced mean values equal to 3.4020% and 5.729% for China and US females’ unemployment rates respectively. The t-statistic associated with the observed mean difference was equal to -8.523 with a probability value less than 0.05. Hence, the test rejected the null hypothesis in favor of the alternative hypothesis leading to a conclusion that the females’ unemployment rate in the US was higher than that of China.
| t-Test: Paired Two Sample for Means | ||
| China’s females’ Unemployment rate (percentage of females’ labor force) | USA’s females’ Unemployment rate (percentage of females’ labor force) | |
| Mean/average | 3.402036 | 5.729214 |
| Variances | 0.455277 | 1.793897 |
| Observations | 28 | 28 |
| Pearson Correlations | 0.08946 | |
| Hyp. Difference of the Mean | 0 | |
| D F | 27 | |
| T-Statistics | -8.5231 | |
| one-tail Pr(T< =t) | 1.94E-09 | |
| one-tail T-Critical | 1.703288 | |
| two-tails Pr(T< =t) | 3.89E-09 | |
| two-tails T-Critical | 2.05183 | |
Conclusion
The study concluded that the Chinese gender gap is worsening with time while there were notable improvements in the US. The study revealed that the percentage of females in the US’s labor force was in the decline while that in China’s labor force was in the rise. Also, the study concluded that there was a higher enrollment of females in the tertiary, secondary and pre-primary education levels in the US than in China. Further, the study revealed that the proportion of females with at least a Bachelor’s degree in the US was higher than that in China. However, the study revealed that unemployment among females was higher in the US than in China, although the rate was declining in the US while it was rising in China.
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