On the train to Manchester this morning I finished a terrific book I should really have read long ago. I’m very glad I finally have. It’s Morten Jerven’s [amazon_link id=”080147860X” target=”_blank” ]Poor Numbers: how we are misled by African development statistics and what to do about it.[/amazon_link] The title made me think it was only relevant to African statistics, when in fact anybody interested in GDP and national accounts should read it.
[amazon_image id=”080147860X” link=”true” target=”_blank” size=”medium” ]Poor Numbers: How We are Misled by African Development Statistics and What to Do About it (Cornell Studies in Political Economy)[/amazon_image]
The book is short and non-technical, but includes a number of important arguments and examples. Here are the conclusions I take from it:
1. Statistics are the ‘facts’ “states collect to get knowledge about their own economic or social conditions.” Having reliable statistics is a marker of an effective state – “the ability to collect information and taxes are closely related” – and the statistics chosen reflect the power structures and political priorities of states. African states are not effective, their statistics are not reliable. (But this also made me reflect that there is a lot happening in the developed economies for which we have no statistics – and no ability of the state to understand or influence change.)
2. African GDP statistics in the key online databases used by economists – the World Bank, the Penn World Tables, the Maddison database – are inconsistent because of different interpretations of the underlyaing national data, different base years, different price indices. The sources even rank African countries differently in terms of GDP per capita. Econometric work will get different results depending which is used.” Jerven argues that economists need to have a much more detailed understanding of both the data they download and the specifics of individual countries’ circumstances to be able to interpret the numbers.
3. The underlying national level data are unreliable because of a lack of resources and statistical capacity. Surveys are rarely carried out, there is much guesswork, base year changes happen too infrequently, there is political influence.
4. 2 and 3 together mean little reliance can be placed on the standard cross-country regressions using the standard data sets. “These problems undermine any general conclusions drawn about what stimulates or hinders economic development in Africa.’
5. The standard national accounts concepts don’t apply well to developing economies with a large informal sector. The distinction between production and consumption or working and not-working is not as clear. (And may be becoming less clear in developed economies too, as technology blurs these boundaries and working patterns change.)
The book argues that the standard outline of African growth – a dismal 1970s, a better outcome post- structural adjustment remedies, and a recent acceleration in growth is largely ‘illusory’. The recent uplift in particular comes from the World Bank/IMF splicing recent rebased GDP figures onto an earlier series, as Jerven describes it. He argues that more data needs to be collected, in regular surveys, to enable both good statistics and an effective state knowing what is happening in the economy and to its tax base. He also argues strongly for greater transparency by national statistical offices but especially by the international agencies such as the World Bank and IMF, whose say-so determines the methods used to create the statistics and the world’s interpretation of what is happening in each economy.
“Accounting for the national economy is fundamental for government accountability. Without reliable macro data, political transparency is hard to imagine. …. Numbers are too important to be ignored and the problems surrounding the production and dissemination of numbers too serious to be dismissed.”
So don’t make my initial mistake of thinking this is a bit of a specialist book. It’s a fascinating and important read.