Synthetic Work-life Earnings are a way of looking at how small differences in earnings can add up to big differences over an extended period of time. For example making $12 per hour more adds up to $480 per week or $25,000 per year or $1,000,000 in a 40-year work-life. While this way of looking at earnings is very useful one should note that these estimates are based on many assumptions, discussed below, and they should be used for illustration purposes rather than an exact projection of a persons earnings.

We start by using data from the American Community Survey (ACS). A single year of data collection in the ACS has about 4.5 million respondents so the sample is large enough to look at very small groups of people.

These are the general steps:

1. Limit the sample
   1. Age 25-64
   2. Employed for at least 50 weeks in the last year
   3. Worked at least 35 hours per week
   4. Bachelor’s degree or higher
2. Split the sample into groups
   1. Age: 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64
   2. Highest degree earned: Bachelor’s degree, Master’s degree, Professional degree, Doctorate degree
   3. Field of Bachelor’s degree: 15 major groups
   4. Detailed occupation category
3. Calculate median earnings for each group at each age level.

   Example: 25-29 year-olds with a Master’s degree, who majored in Communications for the bachelor’s degree, and who work as elementary school teachers.

4. Add the median earnings at each age level together.
5. Multiply the total by 5 since we used five-year age groupings.

Many of the most common occupations for a field of degree are displayed, provided a Synthetic Work-life Estimate could be calculated that met certain conditions:

1. The underlying population for the group (regardless of age) was at least 10,000. (e.g. In 2010 in the whole United States there were at least 10,000 business majors who did not go beyond a bachelor’s degree and were employed full-time, year round and held jobs as accountants and auditors at the time of interview)
2. Each age level within the group had enough un-weighted cases to produce a median.
3. The Coefficient of Variation for the SWE estimate was not greater than .1—meaning that the error associated with the estimate is not more than 10% of the total estimate. In other words all the estimates presented have a relatively small Margin of Error.
   
   The coefficient of variation is calculated using the following Equation.
   
4. For the infographics, miscellaneous groupings of occupations are not shown (e.g. “Managers, all other” and “Engineers, all other”). These occupations are included in the tables

• Because the ACS collects information about respondents only one time, we have no way of knowing their job history, or what changes they might make in their future careers. This estimate assumes that a person will stay in the same occupation for 40 years, full-time, year-round without changing their level of education. It also assumes the economy will stay the same for the entire time without inflation.
  
  
• In reality, the economy is constantly changing. Educational and experiential requirements for an occupation may change, the occupation may become less common or come into sudden demand. There may be recessions and periods rapid economic growth that affect occupational and educational groups differently.
  
  
• Employees may also receive valuable benefits other than salary earnings not measured here for example—stock options, time off, health care benefits, transit subsidies, childcare, or flexible work hours.
  
  
• One person may enter the work force at age 16 while another may delay working until age 30. Likewise one may retire at age 60 while another continues working until age 80. Some workers may reduce their work hours or completely exit the workforce for a period of time to take care of children, go back to school, or because of an extended illness. The likelihood of working for an extended number of years may also be affected by the nature of the job.
  
  
• Because of these limitations, data-users should not expect to earn the exact amounts published here. Instead, the information should be used to give the data a richer perspective. The estimates can be used in combination with other career projection data that are available such as Bureau of Labor Statistics.
  
  
• The ACS does not collect data on professional training and certifications that may impact earnings such as board certifications, software training, and certificates awarded by universities.
  
  
• The ACS does not collect data on field of degree for degrees other than the bachelor’s degree. Although we show earnings for advanced degrees, we have no way of knowing the subject area of that advanced training. A person may major in any subject and then go on to get a Master’s in Business Administration (MBA), Masters of Teaching (MAT) or law degree (JD), a medical degree (MD) or a doctorate (PhD) which often lead to certain types of professions. These subject areas of the advanced degrees are likely to impact earnings more than the bachelor’s field. Additionally, one could go on to an academic master’s or doctorate degree in an unrelated area or in their minor area and then go on to an occupation seemingly unrelated to either degree, the data don’t allow us to know this kind of detail.