Review of Poor Numbers by Morten Jerven

Review #

Kpedekpo and Arya commented on the status of national accounts in Africa.

Focus on main tables and aggregates, especially GDP, which is reinforced by international agencies. The latter requests data long before preparation is defensible, resulting in figures that are a little better than random numbers.

World bank generates its own data.

Missing datasets is a rampant problem.

Data collection in Africa is just political negotiations. Hard to tell actual economic histories.

Economic rankings of counties may be pure guess work (until up to 2009). Somehow the world bank has a way to get these estimates.

The ability to collect information and yax are strongly correlated.

Counting people in Nigeria is so flawed, it is hard to get/know a good estimate fo the population's current size.

The neglect of the subsistence production can lead to serious misunderstandings fo the process of development and therefore to inappropriate policies and plans to accelerate development. (William F Stopler, planning without effects).

Case of Tanzania #

The analysis was using different time series.

Different results when reconciling the statistical data from countries.

Statistical data tends to vary across the years and as new information is gatherd, old data in the offices may be updated or discarded.

For development statisticians with little to 0 information on the ground, it is easy to use the outdated data and draw very wrong conclusions.

The Green Revolution in India may have occurred statistically rather than in real life.

Agricultural statistics is also very poor.

• Sometimes, if not all the time, the sampling methods are optimized for farming methods from the Northern hemisphere thus ignoring the local context.
• Those models could then be used with false statistical fundamentals and a lot of subjectivity.