Section 2 The Challenges

An intro paragraph that explains that difficulties arise because of challenges in measurement, attribution, and other factors.

A section that discusses the challenges of job creation measurement. Topics for discussion:

direct measurement of jobs not always feasible (perhaps the beneficiaries are not identified, or there is a scale/resource limitation).
concept specification: differences between definitions between major stakeholders
estimation methods
models
rampant assumptions and the propagation of error
typically there is no randomisation of beneficiaries, and there is undoubtedly is self-selection bias. The extend of this bias on the measured results is not clear.

Talk about the challenges of data-driven development:

Development is active: it often cannot wait for the research
There are often resource contraints on research
the incentives structure rewards nice-looking numbers, not necessarily the truth. Those who implement are typically the same as those who measure their own performance.
little appreciation for good data
lack of accountability means there is no transparency: much data is proprietary.

Talk about:

Institutional momentum: it is hard to drive mentality shifts. Adopting new practices is slow and can often be hindered because of institutional rules that favor the status quo and the past.
skills gap: good M&E analysts and researchers are hard to come by