Wage Inequality in the X City of China - Case Study Example

WAGE INEQUALITY REPORT IN THE X OF CHINA Number The X in the Y province of China has experienced a drastic increase in wage inequality in the recent year. Using a sample of 359 employees from manufacturing, construction and other sectors, we investigate the role of race (Hukou), party membership affiliation, age and experience in determining the X’s wage inequality. We find evidence that level of education, age, experience and sector of employment poses the greatest variation in determining the wage limits in the city. The presence of trade liberization and international foreign investment policy imparts varying levels of exposure to some Chinese cities more than others. While the presence international firms operating in the X city do not have a direct effect on wage equality, a major difference is evident between the majority and minority foreign-owned firms. Majority foreign-owned firms exhibit skilled-biased changes that adversely increase wage inequality. INTRODUCTION. The unequal distribution of individual or household wage across various sectors in the economy is referred as wage inequality. It can be presented as a percentage of wages to percentage of population. China has witnessed rapid growth in national income, foreign investment and export volume in the last few decades. However these economic improvement has been accompanied by income inequality. The wage inequality coefficient of China has steadily increased from 0.33 in the 80s to 0.46 in the year 2000 according to government statistics. These signify a 2-3% growth rate per year, alarmingly one of the fastest in ever recorded. (Yunbo Zhou, 2012) Investigated the causes of the disparities in the wage inequality in urban and rural areas and found that, in rural areas, it is explained by an increase in the wage earning jobs in poorer regions in the end of the 20th century and decrease in regressive taxes. There are allegations of wage inequality in the X state of China. Using the provided data we investigate it basing our research on three divisions we carry out data analysis and provide the results to the Bureau of Human Resources and Social Security; Gender Affiliation and membership in Communist party Local (Hukou) and non-local workers Given the data we determine the correlation coefficients between wage rates and the various variables. This will enable us to deduce whether to use the variables in our regression analysis. Table 1 summary statistics of wage rates by sector and by gender Manufacturing sector Construction sector Others All Male Female All Male Female All Male Female Mean 2.300194 2.389804 2.082571 2.091158 2.077667 2.334 2.24447 2.385095 2.071033 S.d. 0.106617 0.129925 0.180342 0.118321 0.124271 0 0.06246 0.084603 0.090478 No. obs 72 51 21 19 18 1 268 148 120 Table 2 t-Test results for male and female workers H0: μ1-μ2=0 vs HA: μ1-μ2≠0 Manufacturing sector Construction sector Others Assuming σ1=σ2 t statistic 1.316535 -0.47321 2.525396 t critical 1.994437 2.109816 1.968922 Assuming σ1≠σ2 t statistic 1.382249 -1.75867 2.535402 t critical 2.018082 - 1.969201 The research conducted examines the phenomena of nature of two variables and their degree of relatedness. Altering the level of one variable will automatically affect the other. The concept behind the t - test is to determine the difference in the statistic means of two variables relative to the spread or variability of the wage. The purpose of statistical tests is fundamentally meant to test null hypothesis. The results in the Tables 1 and 2 can be used to draw the following conclusions; The wage earned by male workers in the manufacturing sector is significantly higher than what is earned by female workers. The same is also true in the other sectors. However female workers in the manufacturing sector earn more than their male counterparts. This deviation is attributed to the less number of female workers in the construction sector. Thus we can conclude that wage inequality is evident in the X town of China based on gender. There is a slight increase in the wages of Communist party affiliated members as compared to non members in all the sectors. The mean wage value for members and non-members are approximately 2.3 and 2.2 respectively. Table 4: summary statistics of wage rates by sector and by Communist Party Membership Manufacturing sector Construction sector Others All Non-Party Party All Non- Party Party All Non party Party Mean 2.300 7 2.25 2.31570 2.091158 1.927455 2.31625 2.24447 2.218144 2.33573 S.d. 0.10661 0.12662 0.195882 0.118321 0.153609 0.163241 0.06246 0.073117 0.116844 No. obs 72 55 17 19 11 8 268 208 60 Table 5: Two sample t-Test results for Communist and non-Communist party members H0: μ1-μ2=0 vs HA: μ1-μ2≠0 Manufacturing sector Construction sector Others Assuming σ1=σ2 t statistic 0.260016 -1.70634 -0.7842 t critical 1.994437 2.109816 1.968922 Assuming σ1≠σ2 t statistic 0.281719 -1.73453 -0.85311 t critical 2.039513 2.119905 1.981967 Table 6 summary statistics of wage rates by sector and by local/non-local (Hukou) Manufacturing sector Construction sector Others All Local Hukou Hukou not in X All Local Hukou Non-local All Local Hukou Non local Mean 0.805556 2.408069 1.85328 2.091158 2.157333 5 1.977714 2.24447 2.252772 2.219061 S.d. 0.04697 0.122182 0.17046 0.118321 0.1570 0.181891 0.06246 0.072293 0.124884 No. obs 72 58 14 19 12 7 268 202 66 Table 7; Two sample t-test results for local Hukou workers and non Huoku-workers H0: μ1-μ2=0 vs HA: μ1-μ2≠0 Manufacturing sector Construction sector Others Assuming σ1=σ2 t statistic -2.1088 -0.72249 -0.23212 t critical 1.994437 2.109816 1.968922 Assuming σ1≠σ2 t statistic -2.64589 -0.74745 -0.23362 t critical 2.048407 2.144787 1.981372 Table 8: Correlation Matrix    Wage Education Age Sex Sector Marriage Party Mem Hukou Occupation Experience Wage 1 Education 0.356721 1 Age 0.10746 -0.15326 1 Sex -0.14318 0.0903 0.033997 1 Sector -0.01087 -0.21837 0.096619 -0.20707 1 Marriage 0.036329 -0.04725 0.253103 -0.05549 0.071799 1 Party Mem 0.039459 -0.07743 0.080112 -0.14233 0.080774 0.085917 1 Hukou 0.06179 0.179301 0.00701 0.098543 -0.01547 -0.05157 0.055034 1 Occupation -0.12082 -0.20365 -0.10479 -0.2117 0.323502 0.035589 0.249155 -0.02874 1 Experience 0.02142 -0.36687 0.975334 0.013076 0.139345 0.247758 0.092294 -0.03289 -0.05288 1  The correlation between wage and education is relatively high as compared to other variables (0.357). Wage and sex are negatively correlated and the same applies to occupation and sector. Analysis has shown a weak negative correlation between wage and sector, indicating that the wage rate does not directly depend on any sector of the economy. Likewise the same applies to occupation, marriage and party membership. Age and experience are highly correlated. The correlation coefficient is 0.97. Having more years working for an organization is essential for delivering quality work. SUMMARY OUTPUT Regression Statistics Multiple R 0.434872 R Square 0.189114 Adjusted R Square 0.175292 Standard Error 0.888545 Observations 359 ANOVA   df SS MS F Significance F Regression 6 64.81336 10.80223 13.68215 5.61E-14 Residual 352 277.9084 0.78951 Total 358 342.7217         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -0.38885 1.376165 -0.28256 0.77768 -3.09539 2.3176 -3.09539 2.31769 Education 0.128845 0.224509 0.57389 0.56640 -0.3127 0.57035 -0.3127 0.57039 Age 0.03766 0.223857 0.16823 0.86649 -0.40261 0.47792 -0.40261 0.47792 Sex -0.36185 0.097988 -3.69285 0.00025 -0.55457 -0.1691 -0.55457 -0.16914 Experience -0.0223 0.224226 -0.09945 0.92083 -0.46329 0.4186 -0.46329 0.41869 Hukou 0.011308 0.112082 0.10088 0.91969 -0.20913 0.2317 -0.20913 0.23174 Party Membership 0.070201 0.112395 0.62459 0.53264 -0.15085 0.2912 -0.15085 0.29125 Table 9 : Regression Result Interpretation of coefficients Education From the results above , wage and education are negatively correlated. An increase in the level of education do not directly results in a wage increase if all the other factors are kept constant. Rather educated people are three times more likely to get a lower wage as compared to their uneducated colleagues. Age and experience Age and experience is highly correlated at 97%. However, age and the amount of earning is negatively correlated. A unit increase in the age of a person (say 10 years) will significantly decrease the wage limit by almost 10 times. This is almost true since older workers tend to be unproductive compared to younger ones. Party Membership Registered workers receive more earnings as compared to unregistered ones. Likewise Hukou (household registered) members are more likely to earn more as compared to non registered members in city X. The relation between age and experience is directly proportional. A unit increase in the age of a worker (1 year) results in an almost directly proportional level of experience (97%). Sex Given that all the other factors are held constant, wage and sex are directly proportional. A unit wage category applies to both male and female workers. Hypothesis Testing on coefficients From the P values only gender has a significant effect on the wage value at coefficient βi equals to 0.00025. Regression Model The Multiple Coefficient of Determination (R2) investigates the proportion in variation in wage that is affected by the variations in education, sex, experience and age. The value is significantly lower at 0.17 implying that only 17% variation in the wage value of workers is determined by the variation in these independent variables. The Significant F statistic in the ANOVA table is 5.61E-14. This value is nearly equal to zero thus the null hypothesis is accepted. The model has a fairly good insight on the interpretation of the causes of wage inequality. The existence of wage inequality can be attributed to factors such as the level of education, gender and Communist membership affiliation since all of them pass the t-Test at the 10% level. The causes of wage disparities in different departments cannot be attributed to the above stated factors alone. Skilled-biased technical progress, technology and globalization have been proven to cause wage inequality. Skilled-biased technical progress arises when skilled labor is favored over unskilled workers. Skilled labor is directly related to high level of education. According to recent studies, (Li Shi, 2006) identifies skilled-biased change as the main driver for foreign investment and importation of technology. Examples from ….. On the use of firm level data to investigate the relationship between foreign market exposure and the use of technology in China confirmed a positive correlation especially after the trade liberalization and WTO entry in the 21st century. The investigation pertaining the causes of increased wage inequality in China has not yielded lots of of results due to the availability of government-controlled economic segments. Such characteristics affect wage inequality to a great extend. The entry of foreign firms has tilted wage inequality out of proportion due to firm ownership and even to some extend party alienation. In addition, there exist an empirical model that suggests evidence that as China opens up its foreign investment, less educated workers pooled in zones considered as local (Hukou) would enjoy increased relative wage than non-local workers. THE MODEL Lets consider a 5 factor model of the wage distribution. Let Wagei=+eductioni+sexi+agei+I where  -denote sector of the economy eductioni -denote labor ( skilled / unskilled) sexi- denote gender variable agei- denote age and level of experience I- denote a fuction variable dependent on technology and ownership of the firm. When estimating the wage researchers argue that cross-firm variations provide little information on the wage rates thus firms with a high wage bill will not economize on its skilled labor. Therefore I will not cause an impact to a greater extend and is assumed to be a constant across all firms. Thus Wagei=+eductioni+sexi+agei+I The coefficient eduction shows the effect of relative skilled labor on wage rate. The value can be positive, negative to imply skilled or unskilled workforce respectively. sexi is a coefficient that determines the ratio of male to female workers in a sector of the economy while agei captures the effect of age and experience to the wage rate. If firms in the X city adopt younger but experienced workforce, then agei >0 and vice versa. CONLUSION The X city of China has evidenced a drastic increase in the wage inequality caused by a number of variables. In this paper we have used a sample of 359 workers drawn from various sectors of the economy to investigate these cases. An analysis of the data yields the following results. First, we found that the rate of wage inequality in the city is dependent on factors such parties; level of education, gender ratio in a firm, age and experience, membership alienation to Communist party as well as identity with local groupings ( Hukou). Moreover, the study found more extensive evidence to support wage inequality. Government controls and skilled-based change top the list. Others include trade liberization and foreign firm ownership which had a negative effect on the city’s wage rate. References Li Shi, H. S., 2006. Unemployment, inequality and poverty in urban China. s.l.:Routledge. Richard A. DeFusco, C. D. W. M. C. J. E. P. C. D. E. R. C., 2011. Quantitative investment analysis. s.l.:John Wiley & Sons. Siegel, A. F., 2011. Practical business statistics. s.l.:Academic Press. Yan, S., 2008. Real wages and wage inequality in China. s.l.:ProQuest. Yunbo Zhou, Y. Q., 2012. Empirical analysis on income inequality of Chinese residents. s.l.:Springer. Read More