Several
weeks ago we released a short post announcing the release of a fresh dataset from the World
Bank’s Independent Evaluation Group (IEG), containing assessments of almost 10,000 World Bank
development projects. In that post, we examined some basic descriptive
statistics, breaking down project success by region and by year. Here we will
delve a bit deeper and explore the possible linkages between Quality of Monitoring
and Evaluation (QME), the Quality of Project Supervision (QPS), and project
success. “QPS” measures the intensity
of staff oversight during project implementation, while “QME” assesses the
credibility of the project's performance indicators and data.
To
assess project success, we convert the IEG's six-point measure to a binary
variable, with one adjustment from our previous post. Instead of assigning
projects rated by the Bank as 'moderately successful' to the satisfactory category, we assigned them to
the unsatisfactory category. This
procedure was undertaken to mitigate a potential upward bias in how the Bank evaluates its own projects.
The
QME variable is divided into four categories in the IEG dataset: high,
substantial, moderate, and negligible. Over two-thirds of projects were rated
in the bottom two categories, indicating substantial room for improvement in
QME. The graph provided below demonstrates
a strong positive correlation between QME and project success.
Projects
with high QME ratings were successful 93% of the time, while projects with
negligible QME ratings were successful only 3% of the time. Further analysis might
shed light on the nature of this relationship. For example, it may be the case
that donors find it more difficult to create strong performance indicators and
incrementally monitor project performance in countries with ineffective
governance or deficient infrastructure. And this may, in turn, affect project
performance.
The
QPS indicator is measured on the same six point scale as the project outcome
indicator, so we perform a similar process to transform it into a binary
variable. We classify projects rated 'highly successful' and 'successful' as satisfactory, while we classify projects
rated 'moderately successful', 'moderately unsuccessful', 'unsuccessful', and
'highly unsuccessful' as unsatisfactory.
Overall, projects received high scores on the QPS indicator: 75% of projects
qualified as satisfactory. This circle graph provided below compares (a) the
number of cases in which a project's QPS score and final outcome measure
corresponded, with (b) the number of cases in which these two indicator values
disagreed.

Only 3% of projects with low levels of project supervision had a final
outcome rating of 'satisfactory'. However, 27% of projects that received a 'satisfactory'
rating on the QPS indicator received a final outcome rating of 'unsatisfactory'. This pattern suggests that
effective supervision is a necessary, but insufficient, predictor of project
success.
A more thorough analysis is needed to determine the precise
linkages between the quality of monitoring and evaluation, the quality of
supervision, and project success, but our preliminary results support the
current emphasis on strengthening monitoring and evaluation systems and improving
project supervision.
This post was written by Ben Buch and
Doug Nicholson. Ben and Doug are AidData Research Assistants at the College of
William and Mary.