Job creation is often seen as a major measurement challenge. There are many suggested reasons for this. Some people complain that jobs are hard to define. Others worry that they’re hard to measure. But the real reason why measurement is challenging is simple; most development programmes don’t create jobs, and it’s very hard to measure what isn’t there. 

But were they attributable jobs?

The reason is that most private sector development programmes bring about incremental change. A successful programme might increase productivity by 25% or make it quicker to get a business license. Job creation, however, isn’t incremental. A factory that costs ten million dollars might employ a thousand people. But an investment of ten thousand dollars doesn’t employ one person; you need the whole factory, or none of it. Unless a programme is working at massive scale, it’s very hard to influence this kind of investment decision.

To address this challenge, most market systems development projects measure ‘net additional income’, or ‘full time equivalent (FTE) jobs’, which are typically informal jobs on smallholder farms. This is easier to create (and so measure) because net income and FTE jobs are incremental. A project can promote a new variety of seed, or a new type of business relationship, and will have a small, attributable impact on many people. These indicators aren’t very helpful, however, for programmes working in manufacturing or formal sector job creation.

If you do work in a programme that really is creating formal jobs, then measuring them can be relatively simple, compared to measuring additional income. If you want to know whether a smallholder farmer has increased their income, you might need to take GPS measurements of the land, weigh the crop produced, or teach the farmer to keep records. To measure job creation in factories, you just need to buy the HR Manager a drink after work. Building trust of respondents is crucial, but once you have a good reputation, the number of jobs can be read off a database.

Even if your programme doesn’t create jobs directly, you may reasonably think that it does so indirectly. For example, perhaps your programme helps small businesses to increase their sales or raises the incomes of smallholder farmers – which perhaps creates jobs up and down the value chain. This kind of job creation is difficult to measure directly but can be modelled through abstruse mathematical formulas – such as a ‘Social Accounting Matrix’ (SAM) model, which is even less exciting than the name suggests. These models, of course, rely on wild assumptions and have huge margins of error. For most job creation programmes, however, this might be the best you can do.