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The Racial Gap on Wages

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The Racial Gap on Wages

  1. 1. Leonardo Sprintzin The Racial Gap On Wages Econometrics 4140 Project Dr. Ryan Ruddy
  2. 2. 1) Introduction Perhaps one of the most common questions about today’s labor market is if a significant racial gap in wages exists in the United States, in other words, do White workers earn more than minorities? This paper’s goal is to answer this question taking into consideration the different levels of education, holding all else constant. Consequently, this is a relevant question because if proven that there is a significant gap in our collected data it opens room for a possible racial discrimination that employees are subject to when firms are determining their wages. The data evaluated in this study is a sub sample of the Current Population Survey 2014. The Analysis is based on a regression via OLS, which accounts for factors used to determine wage based of different levels of education, (high school, bachelor’s and masters), age and workers class and then factors that shouldn’t be relevant upon determining wages such as gender and race. After conducting this study and running several regressions it is safe to assume that the racial gap on wages still exists in the American society, subsequently it is easy to illustrate my statement by comparing the wages between Black and Whites workers. 2) Data The data set consists of 1,199 observations of working individuals between the ages of 23 and 59 Table 1: Mean Mean Age 41.598 (10.75785) Earnings per hour 22.65073 (13.89619) Female .5029191
  3. 3. (.5002001) White .8106756 (.3919296) Black .1067556 (.3089312) American Indian .0100083 (.0995813) Asian .058382 (.2345622) Mixed Races .0141785 (.1182756) Hispanic .1351126 (.3419864) High School .2710592 (.4446921) Bachelor's Degree .2335279 (.4232518) Master's Degree .0917431 (.2887834) Part time .0075503 (.0866003) Observations 1,199 Standard deviation in parentheses The average age in the sample is 41.59 years old. On average, individuals made $ 22.65 an hour with a standard deviation of $13.89. Approximately half of our sample was female. The majority was White (81.06%). 10.67% of the individuals were Black, and 14.17% were mixed races, it is worth mentioning that 13.51% of individuals in our
  4. 4. sample were Hispanics. 27.1% of our sample holds a High School diploma, 23.35% have a bachelor’s degree and 9.17% have completed a master’s degree. 0.755% of our sample have a part time job. Most of the data is located to the right side of the histogram and relies on the milestone years. 13, 14, 16 and 18 areas represent high school diplomas, associates, bachelors and master’s degrees. The gaps that exist in the graph represent areas where there are no people in our sample that can be classified in those groups. 3) Empirical Model To identify and compare racial differences in the returns on wages on different levels of education I ran the following linear regression on log wages:
  5. 5. Log (wage) = β0 + β1 Age + β2 Age 2 + β3 Years of Education + β4 Black + β5 American Indian + β6 Asian/PacificIslands + β7 Other + β8 Hispanic + β9 female + β10 part-time + ɛi I decided to use log wages since it makes it easier to read and interpret the results of the regression. It is also important to remember that log wage are more normally distributed and consequently it may increase the goodness of fit. Holding all else constant, based on my previous research, I expect to see a positive, yet diminishing, return to age. Therefore, I would expect β1 to be positive and β2 to be negative. I expect β3 to be a positive coefficient because the more education you have more skills and knowledge you will acquire and therefore increases your chances of having a higher paying job position. To match my hypothesis I expect the coefficients β4, β5, β6, β7 and β8 to be negative. I also expect B9 to be negative due to income inequality between men and women. Lastly I anticipate β10 to be negative since part time jobs pay less than full time jobs. Omitted bias variable may occur in this model, because there is a lack of geographical data in the model affecting the race variable. It is worth mentioning that there is a significant Black population living down South, also the economy in that part of the country is less developed than the Northeastern part of the United States, subsequently this factor could affect people’s wages. For example, an employee that works for a law firm in New York City, New York may earn more than an employee in that works for a law firm in Jackson, Mississippi since the cost of living in New York is higher than the cost of living in Mississippi due to the economic development of these two regions. Consequently, this would mean that the error term is negatively correlated
  6. 6. with wages causing a downward or upward biased in some of the estimate of the race coefficient. 4) Result Table 2: RegressionCoefficient (1) (2) (3) Model Log Wage Alternative Log Wage - White Alternative Log Wage - Black Education 0.0931*** 0.0969*** 0.0960*** (0.00674) (0.00648) (0.00647) Age 0.0471*** 0.0495*** 0.0471*** (0.0142) (0.0142) (0.0142) Age2 -0.000449*** -0.000479*** -0.000447*** (0.000172) (0.000173) (0.000172) Female -0.133*** -0.137*** -0.136*** (0.0356) (0.0358) (0.0357) Black -0.216*** -0.204*** (0.0596) (0.0590) American Indian -0.580*** (0.206) Asian -0.0424 (0.0720) Mixed 0.244* (0.140) Hispanic -0.0607 (0.0540) White 0.123*** (0.0454) Part- Time -0.419** -0.421** -0.420** (0.206) (0.207) (0.207) Constant 0.618** 0.390 0.565* (0.296) (0.298) (0.295)
  7. 7. Observations 810 810 810 R2 0.280 0.265 0.269 Adjusted R2 0.271 0.259 0.264 Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01 Table 2 presents 810 observations, the reason why it shows a lower number of observations than Table 1 is due to the fact that among 1,199 people in our sample only 810 individuals had a job. The first column in our table (Model Log Wage) reveals that the workers wage will increase on average by 9.31% for each additional year of education, all else held constant. The effect of one additional year age is an average increase in wages of 4.71% minus 0.0449% times current age, all else held constant. The returns to ages are decreasing. In comparison to male, females earn on average 13.3% less, all else held constant. In comparison to White workers, Black workers earn on average 21.6% less, all else held constant. Also in comparison to White workers, American Indian workers earn on average 58% less, all else held constant. Asian workers earn on average 4.24% less than White workers, all else held constant. Mixed races workers on average earn 24.4% more than White workers all else held constant. Hispanics earn on average 6.07% less than white workers all else held constant. Part time workers earn on average 41.9% less in comparison to full time workers. The R2 is 0.280 meaning 28% of the variation of the log wage is explained by the model. The variables that are statistically significant at a 5% level are: Education, Age, Age2, Part -time, Female, Black workers, and American Indian workers. Summing to the effect that if they didn’t have an effect on the wage the chances that we get the data as extreme as we did it will be less than 5%. I also conducted a T-test for the Black workers variable and I was to reject the null hypothesis and prove that White workers earn more than Black workers.
  8. 8. I conducted an F test, I excluded race dummy variables to build s my restricted model and I was able to reject the null, as the corresponding p-value is 0.0417. It can be concluded that the majority of the race coefficients are jointly significant in determining the log of wage. The regression’s result predicts that a 40-year-old White male with 10 years education working full time will get a log wage of 4.833. A Black male with the same exact level of education, the same age and working on a full time job will get a log wage of 4.617. Since the Black workers are earning less in comparison to the White workers the result matches with my predictions. To exemplify my thesis a little bit more I ran two other regressions with almost same variables, (with an exception of Hispanic), that I used on the Model Log Wage regression but I made some adjustments on the race dummy variables. In the Alternative Log Wage – White regression I kept the White variable and excluded all the other race dummy variables so I could have a better dimension of how much more White workers earn in comparison to other races, and I concluded that White workers make on average 12.3% more than other races, all else held constant. The R2 is 0.265 meaning 26.5% of the variation of the log wage is explained by the model. In the Alternative Log Wage – Black regression I kept the Black variable and also excluded all the other race dummy variables so I could have a better dimension of how much less Black workers earn in comparison to other races and I concluded that Black workers make on average 20.4% less than other races, all else held constant. The R2 is 0.269 meaning 26.9% of the variation of the log wage is explained by the model. Taking into consideration both of the R2 ‘s presented in the alternative models it is safe to assume that a model lacking any race
  9. 9. or ethnicity relatively does a poor job of explaining the variation in log wages when compared to the model with all the races and ethnicities. 5) Conclusion In today’s society higher education has a significant impact upon the determination of wages, the more years of education a worker has the more likely he will be compensated with a competitive salary. After all the highest paying jobs available in the market require many skills that for the most part can be acquired through education. Consequently it is understandable that a worker that has a master’s degree will be receiving a higher salary that a worker that only has his/her high school diploma. Although, according to my results, the racial gap is alive and well in the American society in the year of 2014 despite all our best efforts to eradicate racial based discrimination in the labor market. Thus minorities are still getting paid less than white workers even if they have the same amount of education.

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