The ETE (Education through Employment) Data System is a secure, longitudinal data system that links information from DC public and public charter schools with social services, postsecondary education, and employment records. The system measures the impact of PK-12 education, higher education, public workforce, social services, and justice programs on access to family-sustaining careers and economic mobility.

The public dashboards show high-level, aggregate results to help policymakers, practitioners, and community members understand how DC students are doing after high school in terms of education, employment, and wages.

The dashboards focus on a few key questions:

• Wages: How much are former DC students earning at different points after high school, by education pathway?
• Industries of employment: In what industries are they working, and does that differ by education level?
• Education and work together: How do wages compare for people with different levels of education (for example, high school diploma, some college, degree completion)?
• Equity: How do outcomes look for specific groups, such as foster youth or students with particular characteristics?

Importantly, the employment and wage outcomes shown reflect only jobs covered by DC's Unemployment Insurance (UI) system - that is, work for DC UI-covered employers. They are not a complete picture of every job a DC student holds. Earnings from jobs in other states, most federal employment, self-employment, and gig work are not captured. See the Wage Data FAQs for details.

It is normal for the numbers in these dashboards to differ from:

• District reports
• DC partner agency-specific reports
• National statistics

Reasons include:

• Different cohorts (who is included)
• Different timeframes (for example, calendar year vs. school year)
• Different definitions (for example, how "employed" or "enrolled" is defined)
• Different data snapshots (newer or updated information)
• Identity resolution: to follow a person across schools, colleges, and jobs, the ETE Data System matches records from different agencies to figure out which ones belong to the same person. Because there is no single perfect ID shared across all sources, this matching is done by comparing information and is not perfect. A small number of records may be matched or missed differently than in an individual agency's own count, which can make totals differ slightly.

No. The dashboards show patterns and associations - for example, that people with higher levels of education tend to have higher median wages - but they do not prove that education alone caused wage differences.

Many factors can influence outcomes, including:

• Program and field of study
• Work experience
• Local and national economic conditions
• Personal circumstances and opportunities

The dashboards are a starting point for questions and conversations, not a definitive statement about cause and effect.

No. The ETE Data System's public dashboards show only aggregate statistics, such as percentages and medians for groups of people. Individual records are never visible on ETE public dashboards. The system is designed to protect privacy and comply with applicable laws and data-sharing agreements.

The dashboards are updated annually (typically in the second quarter of the calendar year), when:

• New education records (graduation, earned degree or certificate, etc.) become available.
• New wage data become available for additional quarters or years.

To protect privacy and comply with data-sharing agreements and laws, the ETE Data System has a strict data governance policy of not showing results for very small groups (fewer than 10 individuals, including zero). 

When the number of people in a particular cell (for example, a specific combination of subgroup, industry, and year) is too small, the value is hidden. 

You may see this as: 

• The notation “Suppressed” 
• Some categories and subpopulations missing from filter dropdowns when their values are too small to display safely 
• Visualizations may show partial or fragmented lines to indicate suppression  These rules protect individuals from being identifiable.  The dashboards do not distinguish a suppressed value from a genuine zero (a category that truly has no one). Both are simply not shown - as a gap in a Dashboard’s line chart, or as “Suppressed” in a Relative view. A missing or “Suppressed” value means the result is not available to display, not that it equals a particular number.  

These rules protect individuals from being identifiable.  The dashboards do not distinguish a suppressed value from a genuine zero (a category that truly has no one). Both are simply not shown - as a gap in a Dashboard’s line chart, or as “Suppressed” in a Relative view. A missing or “Suppressed” value means the result is not available to display, not that it equals a particular number.  

Results for small groups (for example, a specific race within a narrow program type) can be:

• Unstable from year to year, and 
• More likely to be suppressed for privacy reasons 

When viewing small groups:

• Look at patterns across multiple years, not just a single point in time.
• Treat small differences or sudden jumps with caution, as they may reflect small sample sizes rather than meaningful change. 

Note: Actual counts for very small groups are never displayed. If a group appears in a disaggregated view, privacy protections are applied, which may result in missing data or partial lines in charts.   

The dashboards do not show the exact number of people behind each value. Instead, each value carries a data-reliability indicator - a broad range describing how many individuals it is based on (roughly 10-24, 25-50, or more than 50; fewer than 10 is suppressed). 

This indicator is built into the visuals so you can judge reliability at a glance: 

• All wage trend charts utilize a range in size of the markers (the points plotted along each line) - smaller points mean a smaller group with a less certain estimate.
• Industry charts reflect data reliability using the bar color.
• In all charts, the range appears in the tooltip when you hover over a point or bar. 

A value based on a larger group should be given more weight than one based on a group near the suppression threshold. This lets you see how much data supports a number without revealing exact small counts.

Most dashboards focus on:

• Students who attended DC public or public charter schools, and
• Graduated (or expected to graduate) in 2016 through the latest year of outcome data for a given relative year. 

Some dashboard visualizations focus on specific groups, such as:

• Students who attended the University of the District of Columbia (UDC) 
• CTE (Career and Technical Education) concentrators 

The dashboards use relative years to show how long it has been since a student was expected to graduate from high school. Expected graduation year is calculated as the student’s first ninth-grade year plus three years. 

For example:

• Relative Year 1: approximately one year after expected high school graduation 
• Relative Year 6: approximately six years after expected high school graduation 

Using relative years allows us to compare different high school cohorts on the same timeline, even if they finished in different calendar years.

Yes. Beginning with the 2026 ETE Data System refresh, a small subset of students is excluded from the dashboard cohort because their educational outcomes cannot be reliably observed. 

Specifically, a student is excluded when all the following are true:

• Their last recorded DC public or public charter school enrollment ended with an exit code indicating they: 
  • Transferred to a school in another state, 
  • Transferred to a private DC school, 
  • Moved out of the country, 
  • Are deceased, or 
  • Moved to homeschool.
• They have no record of earning a high school credential in DC, and 
• They have no record of CTE concentration, postsecondary enrollment, or other higher educational outcome in DC-linked data.

Once a student exits the DC system without a credential, we lose visibility into what they accomplished afterward. Including them as “Some High School, No Diploma” would unfairly understate outcomes for that group, so they are removed from the analysis entirely. This is consistent with the OSSE Adjusted Cohort Graduation Rate methodology. 

Students who exited under these conditions, but who did go on to earn a CTE credential, enroll in postsecondary education, or otherwise achieve a higher educational outcome through DC-linked records remain in the dashboards under their highest observed outcome.

Education level is based on the highest level of education we can observe in secondary, postsecondary, and other credential data as of a given relative year. The full set of categories includes:

• Some High School, No Diploma
• GED
• IEP Certificate 
• Graduated High School in More than 4 Years
• Graduated High School in 4 Years or Less 
• CTE Concentrator 
• Work-Based Learning Participant
• Industry Recognized Credential
• Workforce Credential
• Certificate
• Some College, No Degree 
• Still in College 
• Certificate 
• Associate degree 
• Bachelor’s Degree
• Post-Baccalaureate Degree (Master’s or Doctoral) 

Different dashboards group or display these categories differently depending on the question being asked. 

The dashboards organize education outcomes into five pathways so that a single individual can appear under more than one view depending on what they accomplished:

• Overall - everything combined into the single highest level attained 
• High School - diploma, GED, IEP certificate, or no credential 
• Career and Technical Education (CTE) - concentrator status, work-based learning, industryrecognized credentials 
• Post-Secondary - certificates, associate, bachelor’s, and graduate degrees 
• University of DC (UDC) - UDC-specific subset of post-secondary

The ward shown is the student’s ward of residence in their final year of high school, based on their last recorded DC public or public charter school enrollment. 

We do not update ward using addresses after high school; the dashboards use the latest known ward from high school enrollment only. hey are not a complete picture of every job a DC student holds. Earnings from jobs in other states, most federal employment, self-employment, and gig work are not captured. See the Wage Data FAQs for details.

This refers to individuals who:

• Did not complete any form of high school degree or equivalent (no DC diploma, GED, IEP certificate, or other recognized high school credential), and 
• Did not achieve CTE concentrator status, or 
• Did not complete a non-credit bearing workforce credential, or
• Have no record of enrollment in postsecondary education 

Beginning with the data 2026 refresh, students who also have a confirmed exit from the DC system (transfer out of state, transfer to private DC school, out of country, death, or homeschool) without a DC credential are excluded from the dashboard cohort rather than counted under “Some High School, No Diploma.” 

“Some College, No Degree” represents individuals who:

• Enrolled in a postsecondary program at least once after high school, and 
• Have not earned a postsecondary degree or certificate by the time outcomes are measured 

In other words, they started college at some point but, as of the selected relative year (for example, Relative Year 6), do not have a recorded completion. Students who are actively enrolled in a postsecondary program during the given Relative Year are not included in this category; they appear under “Still in College” instead.

Wage data come from Unemployment Insurance (UI) wage records reported by employers in the District of Columbia.

That means: 

• Wages in these dashboards include earnings from DC UI-covered jobs only.
• They do not include:
  • Jobs in other states 
  • Some types of federal employment 
  • Self-employment or gig work
  • Tips not reported in wage records 

As a result, wages shown in the dashboards may underestimate total earnings for some individuals. 

There are several reasons: 

• Many young adults are students and workers at the same time, often in part-time jobs.
• Jobs where tips are a major source of income (for example, restaurants, hospitality) may have wages under-reported in UI records.
• Out-of-state jobs, self-employment, and federal jobs are not captured in DC’s UI wage data. 
• The dashboards use a consistent method to annualize partial-year wages and adjust for inflation, but they are still best understood as indicators of earnings, not a complete picture of all income.

Some wage dashboards include a horizontal line representing a “living wage” for a single adult in DC. The benchmark comes from the Living Wage Calculator (originally developed at MIT by Dr. Amy K. Glasmeier and now maintained by the Living Wage Institute). For the 2026 refresh, it is approximately $55,577 annualized. 

Living wage data sourced from the Living Wage Institute via https://livingwage.mit.edu/states/11. 

This line is: 

• A contextual benchmark 
• Helpful for seeing whether typical wages for a group are above or below a basic cost-of-living estimate 
• It does not mean that everyone below the line is necessarily struggling or that everyone above it is fully secure; it is a reference point to aid interpretation. 

In brief:

• Quarterly wages are reported by DC employers to the UI system.
• These quarterly amounts are annualized (multiplied by a variable rate based on how many quarters in a year the person worked). 
• Annual wages are then adjusted for inflation so that wages from different years are expressed in constant dollars, making them comparable over time.

The dashboards distinguish between all employed individuals and individuals with stable employment. 

An individual is considered to have stable employment in a given year if they have UI wages reported in three consecutive quarters (the prior quarter, the current quarter, and the next quarter). The threeconsecutive-quarter rule is a simple way to flag sustained attachment to a DC-covered job rather than incidental short-term work. 

• The individual count reflects everyone with at least one UI wage record in the relative year. 
• The stable employment individual count is the subset of those individuals who meet the threeconsecutive-quarter rule. 

Median wage figures are calculated using the stable employment subset, so they reflect ongoing, sustained earnings rather than short bursts of employment. Exact counts are not displayed; the stable employment count instead determines the group’s data-reliability indicator (see What do the different marker sizes and bar colors mean?), which is shown as a range in the tooltip and reflected in marker size and bar color.

Wages are tied to the highest level of education attained as of a uniform relative year. 

For each individual: 

• We determine their highest completed education level as of the Relative Year (for example, Some High School/No Diploma, High School Diploma, Associate Degree, Bachelor’s Degree, Post-Baccalaureate Degree). 
• We then use their annualized, inflation-adjusted wages from the Relative Year and report those wages under that highest education level. 

This means: 

• If someone completes a bachelor’s degree and is now working while pursuing a master’s degree, their wages at Relative Year 6 will be reported under “bachelor's degree” (their highest completed education at that time). 
• The degree completion date can occur at any point prior to or during the years leading up to the Relative Year. 
• We do not wait an extra year beyond degree completion to begin counting wages; wages are tied to the relative year itself, not “after a full year post-completion.”  

Individuals with no UI-covered wages in the Relative Year are not included in the wage or employment calculations. 

If someone completes a degree but does not work in a DC UI-covered job in the Relative Year (for example, individuals who are unemployed, working out of state, self-employed, or in non-covered employment), they are not counted in the wage medians. 

The median wages you see are calculated only among those who both:

• Have the relevant education level, and 
• Have at least one stable UI wage record in the Relative Year.  

The dashboards are limited to wage records from DC UI-covered jobs. If someone: 

• Moves out of DC and is employed in another state 
• Works only non-covered jobs 
• Is self-employed or in certain federal roles

their wages may not appear on these dashboards. 

The median wage calculation uses a single point in time for each dashboard view - the wages from the relative year being displayed. 

So, if a person earned $30,000 in Relative Year 1 and $100,000 in Relative Year 6, only the Relative Year 6 wage (inflation-adjusted and annualized) is used in the Relative Year 6 view. 

Implications: People may be at different ages and stages in their careers when they reach Relative Year 6 (depending on when they completed their education and how long they have been in the workforce), but everyone is measured at the same relative timepoint.  

Industries are assigned to each employer based on the employer’s name as reported in DC UI wage records, using a layered classification process. Each employer name is checked against the following steps in order, and the first confident match is used: 

1. A curated list of recognizable companies and DC-area employers mapped directly to their industry
2. A purchased business-name reference database (NAICS Association) matched on the exact business name
3. Pattern matching on industry-specific keywords in the employer name (for example, “Restaurant” maps to Food Services; “Hospital” maps to Health Care) 
4. Business-suffix clues (for example, certain professional-firm designations) as a last resort 
5. Detection of personal-name registrations, which are classified as household employment because DC requires individuals who employ domestic workers (such as nannies, housekeepers, and home health aides) to register under their own name 

For the 2026 data refresh, the known-employer list was expanded, and the classification rules were enhanced to improve accuracy. 

The vast majority of wage records can be linked to a classified industry. A small share remain “Industry Unknown” when the employer name does not match any of the above patterns. 

When you hover over an industry label in the chart, a short definition appears describing that industry (based on NAICS groupings). These descriptions provide plain-language explanations of sectors such as “Health Care,” “Public Administration,” or “Professional Services,” without requiring users to know NAICS codes. 

Many people work for more than one employer in a year. For the dashboards: 

• All UI-covered wages for the year are combined and annualized to estimate total annual earnings. 
• For industry-based dashboards, a person is included under each industry they worked in. An individual may therefore be counted in two industries in a given year if they were employed in two different industries within that year.  

Yes, in the industry dashboards. 

• An individual may be represented in each unique industry they worked in during the Relative Year, so a person holding jobs in two industries (including “Industry Unknown”) could be counted in both industries’ wage distributions. 
• If an employer’s industry cannot be determined from its name, the person is assigned to “Industry Unknown” for that particular job in the relative year. 
• Because individuals may appear in more than one industry, percentages across industries can sum to greater than 100%.