This blog post was written by the Monitoring and Evaluation Technical Assistance (META) Project and is included as an archived post on the Switchboard blog.
If you’re a refugee employment specialist, you’re probably tracking your clients’ income. But of all the data you could collect, why measure this? And how exactly should you do it? The answers will depend on your project, but when measuring income as part of a data-driven job upgrade program, META proposes asking yourself two questions:
- What do we want to learn, and why?
In a job upgrade program, measuring income may seem like a given. But too often we collect data without a clear plan for its use. All measurement should be purposeful: if we spend the time to think through the what and the why before focusing on the how, we help ensure we get the information we need (and we don’t burden staff and clients with unnecessary data collection). So carefully consider what you want to learn and how you’ll use the information once you have it! For example:
|We need to learn…||In order to…|
|Do clients have employment income that exceeds their basic needs by the end of the job upgrade program period?||Help understand if our program is effective|
|Are there differences between male and female clients in the average time it takes to move beyond the survival job?||Help understand if our program is gender-responsive|
|Are employers satisfied with the clients they hire? Do more satisfied employers offer our clients more opportunities for career growth?||Help build productive relationships with new partners and strengthen existing partnerships|
Keep in mind that the question “Do clients have employment income that exceeds their basic needs by the end of the program period?” relates to an outcome that is quite different from, and more meaningful than, “Do clients earn more income in Job B than they previously earned in Job A?” Figuring out what outcomes we want to achieve and what we need to know (or the story we want to tell) will directly inform our measurement plan.
- What data will help us learn this, and where can we get it?
Now we can consider indicators, the variables we use to measure change. At this point, it pays to be specific. Ask yourself: What do we mean by “employment income,” “basic needs,” and “program period”? Will we disaggregate by gender? Where will we actually get this data (is it realistically measurable given our human and financial resources)? An indicator matrix is a useful tool to map out this and more. See the partial example below:
|Question||Indicator||Calculation||Disaggregation||Source of Data (Means of Verification)|
|Do clients have employmentincome that exceeds their basic needs by the end of the job upgrade program period?||% of clients whose income exceeds their basic expenses within six months of enrollment||Numerator
# of clients whose income is greater than their basic expenses (sum of all employment income minus sum of all basic expenses) within six months of enrollment
Total # of clients served
|Disaggregate by client gender||Numerator Source
Household budget form completed with the client at the end of program period (six months or earlier)
Job upgrade program enrollment spreadsheet
Note that this isn’t the only way to answer this question! For example, your needs may lead you to measure income on the household level, rather than the individual. Or your question may be better answered by tracking all sources of income, not just employment income. To sum up, how you measure changes should correspond directly to why you measure: what do you plan to do with this data?