Diversity

Part
01
of two
Part
01

Diversity: College Degrees in STEM

By race, the maximum number of STEM degrees were awarded to White in 2017-2018. We have updated the required information in rows 2-6 of the attached spreadsheet. All the data has been garnered from the National Center for Education Statistics (NCES).

Selected Findings

Calculations for STEM Degrees awarded to Other race/ethnicity People

Total

  • Number of STEM degrees awarded to Asian/Pacific Islander=77,134
  • Number of STEM degrees awarded to American Indian/ Alaska Native=3,149
  • Number of STEM degrees awarded to 'Two or more races'=22,471
  • Thus, total STEM degrees awarded to Other race/ethnicity people= 77134+3149+22471=102,754

Associate

  • Number of STEM degrees awarded to Asian/Pacific Islander=7,236
  • Number of STEM degrees awarded to American Indian/ Alaska Native=753
  • Number of STEM degrees awarded to 'Two or more races'=2,595
  • Thus, total STEM degrees awarded to Other race/ethnicity people=7236+753+2595=10,584

Bachelor's

  • Number of STEM degrees awarded to Asian/Pacific Islander=51,931
  • Number of STEM degrees awarded to American Indian/ Alaska Native=1,394
  • Number of STEM degrees awarded to 'Two or more races'=14,685
  • Thus, total STEM degrees awarded to Other race/ethnicity people=51931+1394+14685=68,010

Master's

  • Number of STEM degrees awarded to Asian/Pacific Islander=11,010
  • Number of STEM degrees awarded to American Indian/ Alaska Native=181
  • Number of STEM degrees awarded to 'Two or more races'=2,298
  • Thus, total STEM degrees awarded to Other race/ethnicity people=11010+181+2298=13,489

Advanced Graduate/Doctor's

  • Number of STEM degrees awarded to Asian/Pacific Islander=2,086
  • Number of STEM degrees awarded to American Indian/ Alaska Native=38
  • Number of STEM degrees awarded to 'Two or more races'=493
  • Thus, total STEM degrees awarded to Other race/ethnicity people=2086+38+493=2,617

Research Strategy

We were able to garner the required data from the National Center for Education Statistics (NCES), a part of the United States Department of Education's Institute of Education Sciences. However, NCES provides aggregated data for all STEM fields and does not bifurcate it by individual STEM disciplines. Thus, the data provided in the spreadsheet is inclusive of all STEM fields (science, technology, engineering, and mathematics). Also, the most recent data available is for the 2017-2018 academic year, which was published in 2019.
Part
02
of two
Part
02

Diversity: IT Hiring

The average salary offer for men from the Asian race in the technology industry in the U.S. is $137K per year. By estimation, the team determined that on average, a woman from the Asian race in the U.S. technology industry earns about $131,520 per year. Rows 7-10 of the attached spreadsheet have been updated with our findings.

Average Salary in the Technology Industry

  • A publication by Diversity Best Practices opined that in the technology industry, women earn 4% less than men on average for similar roles.
  • Based on this information, the following triangulation was made:
    • Black: Average salary offer for men in 2019 was $124K per year. 4% is $4,960 (i.e $124K*4%). Therefore, for women, it becomes ($124,000- $4,960) = $119,040.
    • White: Average salary offer for men in 2019 was $135K per year. 4% is $5,400 (i.e $135K*4%). Therefore, for women, it becomes ($135,000- $5,400) = $129,600.
    • Hispanic: Average salary offer for men in 2019 was $128K per year. 4% is $5,120 (i.e $128K*4%). Therefore, for women, it becomes ($128,000- $5,120) = $122,880.
    • Others (Asian): Average salary offer for men in 2019 was $137K per year. 4% is $5,480 (i.e $137K*4%). Therefore, for women, it becomes ($137,000- $5,480) = $131,520.

Research Strategy

To provide a breakdown of earnings for IT positions in the U.S., the team commenced with an exhaustive search through government databases, research and survey reports by reputable organizations, and third party databases. While we found data on the average earnings of employees in the U.S. broken down by gender, race, and age, this was a generalized data and was not differentiated by industry or position. We also found data on the average earnings of employees from different occupations. Unfortunately, IT-related occupations were not included. Furthermore, we tried to utilize advanced search to find useful data broken down by race, gender, and possibly years of experience. However, for positions related to IT, such as engineering, the available data points were not broken down by race and gender. Hence, we have used earnings data for the technology industry as a proxy to estimate the average earning for positions in IT companies.
Sources
Sources