Problem Set 7

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Economics

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Feb 20, 2024

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Problem Set 7 (Due November 21, 11:59 pm) In the problem set, you will need to accomplish the following tasks: 1. Propose a topic for your project (1.5 points) Topic: Gender wage gap by industry in the United States 2. Find a data set of your interest and download it (1.5 points) 3. Develop at least three graphs/plots to deliver interesting stories about your data (3 points)

Animated scatter plot: http://localhost:28731/session/file5b711283b219.gif

Animated graph: http://localhost:28731/session/file5b71678dc0e.gif

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4. Conduct OLS regression analysis (including hypothesis testing) using your data set (2 points) 5. Write a short data analysis report, the report should include the following items  A title for your project (1 point):

 A brief description of the project (1 point)  Major stakeholders who would use the information that would be generated from your analysis and how they would use/benefit from that information (1 point)  A description of the dataset you will use for your project (1 point) o You can list the names of the variables, the year of the dataset, where did you find the data, the data type, etc.  Interesting stories about your data that you can tell from the graphs/plots that you developed in question #3. ( You can include your graphs/plots in this section ) (3 points)  Explain the results from the OLS regression analysis that you conducted in question #4 ( You can include your R OLS regression output in this section ) (3 points)  A summary of your findings (2 points) U.S. Gender Age Gap by Industry (2021) Description: My project analyzes gender wage gaps across different industries in the United States. It depicts a glimpse into gender-based earnings and wage disparities in the country, across various industries. To study this topic, I began by researching average income of men and women across the United States and noticed that there was a tendency towards a discrepancy in the wages that women and men received, on average. Thus, I dove deeper into this topic and researched the average income of both men and women across various industries, to analyze if such apparent discrepancies were constant or different across industries. Stakeholders: The main stakeholders involved in the topic of discussion are men and women in the United States who are part of the workforce, specifically those who work in any of the twenty industries mentioned in the data set. Men and women who work in the United States could use the information gathered in this analysis by determining where they fall in the earnings scale, and comparing if their earnings are below, at, or above average, and comparing it to their male/female counterparts. Another group of stakeholders are the leaders and executives that are responsible for paying their employees, and who have a duty to treat their workers equally and fairly. Leaders and executives could use this information to determine if they are compensating their male and female workers fairly and equally. The last group of stakeholders that my project will focus on is the United States Department of Labor, which is an agency within the Federal Government that focuses on administering federal labor laws to “guarantee workers’ rights to fair, safe, and healthy working conditions.” (U.S. Department of Labor, 2023). This agency should be held accountable for evident inequalities within the workforce that place women in a

disadvantaged position, and could write and implement laws and regulations to address the problem posed in my analysis. Description of Data Set: For my project, I used a data set that studies gender wage gaps by industry in the United States in 2021, by median weekly earnings. The data is from Statista, and was published in 2021, with data from that year. Itincludes three columns, one that states the industry in question, another that shows men’s earnings, and another one showing women’s earnings. The data is organized in descending order, and it includes data or 20 industries in the United States. My project includes two variables, industry and gender, and gender is divided among men and women. Findings from data and OLS regression analysis: The animated scatter plot below provides a visual representation of the relationship between men’s and women’s earnings in their respective industries in the United States. Based on observations from the graph, there is a strong positive correlation among the variables in the data, which means that as women’s earnings increase, men’s earnings increase as well, following the same trend. The animated transitions highlight industry-specific nuances, highlighting sectors where gender-based earnings disparities are more evident or less evident. Additionally, the bar chart provides a side-to-side depiction of earnings gaps among women and men, which further support my project’s conclusion of the clear difference in wages among men and women. In this graph, there is an indisputable gap between the wages documented for women and men in every industry, showing women’s earnings as being less than those of their respective male counterparts. Not all gaps are of equal sizes, some are smaller than other, nevertheless, all depict women’s earnings as lower.

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Furthermore, the OLS regression analysis depicts the relationship of earnings based on gender. The slope of the line represents the change in men’s earnings for a unit increase in women’s earnings, and further supports the previous findings, as it shows that as women’s earnings increase, men’s earnings increase simultaneously, hence, depicting a statistically significant positive coefficient for women’s earnings. The y-intercept of the line represents men’s earnings when women’s earnings equate to zero. Finally, this analysis contributes valuable insights for policymakers, organizations and their leaders, and researchers seeking to understand and address gender-based wage inequalities in the labor-force.

In conclusion, my analysis of the data on gender-based earnings across various industries in the United States revealed that there are clear nuances in earnings among men and women. Through my project, I analyzed and compared the data by using scatter plots, bar graphs, an OLS regression graph, and animated visualizations, which further supported my findings. As a result, the data confirmed that gender-based wage disparities are not only present, but quite common in the United States, and exist in several industries. As a result, it is essential to address the inequalities in the workplace and the labor force, and these findings can be used to target such issues and strive for a more equitable workplace.

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