[PPT] - Africa African Gro n Growth th Mira Miracle le or r PowerPoint Presentation

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Africa African Gro n Growth th Mira Miracle le or r Statistical T Statistical Tra ragedy? y?

Interpretin ing tren ends i in th the e data o a over th the e pas ast two wo decad ades.

By Morten Jerven School for International Studies Simon Fraser University

www.mortenjerven.com

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Full Disclosure

Director of Statistics in Zambia: "It is clear from the asymmetrical information that he had collected that Mr. Jerven had some hidden agenda which leaves us to conclude that he was probably a hired gun meant to discredit African National Accountants and eventually create work and room for more European based technical assistance missions.“ Pali Lehohla, South African Statistician General: “Morten Jerven will highjack the African statistical development programme unless he is stopped in his tracks.” UNECA SPEECH CANCELLED

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Poor Numbers. How We Are Misled by African Development Statistics and What to Do about It

1. What Do We Know about Income and Growth in Africa? 2. Measuring African Wealth and Progress 3. Facts, Assumptions, and Controversy: Lessons from the Datasets 4. Data for Development: Using and Improving African Statistics

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Outline

GDP in Africa: diagnosing the knowledge

problem.

GDP by Proxy: Rainfall, Luminosity and

Assets.

Filling the data gaps – Poverty, Inequality and

Growth.

Lessons – data for development.

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The symptom of a problem

On the 5th of November, 2010, Ghana Statistical

Services announced that its GDP for the year 2010 was revised to 44.8 billion cedi, as compared to the previously estimated 25.6 billion cedi.

This meant an increase in the income level of Ghana by

about 60 percent and, in dollar values, the increase implied that the country moved from being a low income country to a middle lower income country

vernight.
Undoubtedly – This good news, but a knowledge

problem emerges.

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Reactions

Todd Moss at CGD: Boy we really don’t know

anything!

Andy Sumner and Charles Kenny in the Guardian:

Ghana escapes the ‘poverty trap’. Paul Collier and Dambisa Moyo are wrong!

UNDP in Ghana: It is a statistical illusion.
Shanta Devarajan, World Bank Chief Economist for

Africa: declares Africa’s statistical tragedy.

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Is Africa much richer than we think?

Nigeria is expected to announce the new rebased gross domestic product (GDP) figures… …Nigeria’s GDP per capita may double… If it does, it implies a 15 percent total increase in SSA GDP, and that about 40 ‘Malawis’ are currently unaccounted for inside Nigeria…

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What do we know about Income and Growth in SSA?

In the world bank data base you find

annual GDP estimates for all countries from 1960 until 2012.

Some countries have not yet published

their own numbers.

There are breaks in the series..
How do they come up with these

numbers?

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Where does the data come from?

Where does the international databases get

their data from?

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Field Work, West, E ast and Central Africa 2007-2010

Interviews and Archival

work

Visiting Central Statistical

Offices Research questions:

1. How is national income

measured?

2. How does it affect

prevailing judgments on African Growth

Gaborone, Botswana Lusaka, Zambia Dar es Salaam, Tanzania Kampala, Uganda Accra, Ghana Nairobi, Kenya Lilongwe, Malawi Abuja, Nigeria

+ Archival Work + Email survey: Burundi, Cameroon, Cape Verde, Guinea, Lesotho, Mali, Mauritania, Mauritius, Morocco, Namibia, Mozambique, Niger, Senegal, Seychelles and South Africa

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Country

Base Year Planned Revision Years Btw Revisions

Angola

1987 2002 (2013) 15

Burundi

1996 2005 (n/a) 10

Benin

1985 1999 (2014) 14

Burkina Faso

2006

Botswana

2006 10(1996-06)

Central African Republic

1985 2005 (2014) 20

Cote D'Ivoire

1996

Cameroon

2000

DRC

1987 2002 (2014) 15

Republic of the Congo

1990 2005 (2013) 15

Comoros

1999 2007 (2013) 17

Cape Verde

2007 28 (1980-07)

Eritrea

2004 Not compiled after 2005

Ethiopia

2000/01 2010/11 (2013) 10

Gabon

2001

Ghana

2006 13 (1993-06)

Guinea

2003 2006 (2013) 3

Gambia

2004 28 (1976/77-2004)

Guinea-Bissau

2005 19

Equatorial Guinea

1985 2007 (2013) 22

Kenya

2001 2009 (2013) 8

Liberia

1992 2008 (2015) 16

Lesotho

2004 2013 (2015/16) 10

Madagascar

1984

Mali

1987 1997 (2013) 10

Mozambique

2003 2009 (2013) 6

Mauritius

2007 2012 (2015) 5

Malawi

2009 2014 5 (2002-07)

Namibia

2004 2009(2013) 6

Niger

2006 19

Nigeria

1990 2010 (2013) not known

Rwanda

2006 2011 (2013) 5

Senegal

1999 2010 (2014) 11

Sierra Leone

2006 5 (2001-06)

South Sudan

2009

Sao Tome and Principe

1996 2008 (na) 12

Swaziland

1985 2011 (2014)

Seychelles

2006

Chad

1995 2005(2014) 10

Togo

2000 22

Tanzania

2001 2007 6

Uganda

2002 2009/10 (2013) 8

South Africa

2005 2010 (2014) 5

Zambia

1994 2011 (2013)

Zimbabwe

1990

Source: International Monetary Fund 2013; 21

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A problem of comparable data

Different response rates and different time of

survey in Jerven (2013), IMF (2013) and AfDB (2013) – 37, 45 and 34 countries

IMF Recommendation: Base year every 5th year

– 7 countries met this in 2011. (According to latest IMF REO, only 4, AfDB reports 9).

Mean base year 2000 – 8 (AfDB) or 13 (IMF)

countries have base years more then two decades

ld.
A problem of comparable metadata too!

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Implications for the Growth E vidence

Any ranking of African countries according to GDP is

going to be misleading, given the uneven use of methods and access to data.

Any statement of growth over a short or medium term

period is likely to be affected.

Very recent growth data: likely to be overestimated.

The GDP per capita of many countries are now underestimated.

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GDP by Proxy

Rainfall? Too narrow and replicates methods of the

NSO for estimating agricultural output.

Assets? Stock not flow. Changes in prices, demand

preference and location specific preference. DHS sample bias.

Luminosity? Poor proxy for historians – not suitable for

governance.

None give predictable outcome – and all sidesteps issue
f the issue of seeing like a state – measurement is not
nly knowledge, it is governance, accountability and

policy circles.

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Filling the data gaps – Poverty, Inequality and Growth.

‘African Poverty is Falling...Much Faster than You Think!’ (Sala-i-

Martin and Pinkovskiy, 2010) - Claim: Since 1995, African poverty has been falling steadily. MDG will be reached by 2013. Method: Matching GDP growth (PWT) with inequality data (WIDER-DS) Data: “118 surveys for 48 African countries considered” – but many countries do not have data: Angola, DRC, etc Nigeria not since

1996. 1610 gaps in the annual country time series.
‘The making of the middle class in Africa’ (Ncube and Shimeles,

2013) – Claim: 1990-2011 Middle Class in Africa increased from 11 to 15 percent. Method: Asset index, Synthetic Panel, Middle class defined: households within the bounds of 50 percent to 125 percent of the median. Data: 84 observations from 35 countries. 19 countries missing, and 1050 gaps in the annual country time series.

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Conclusion

Our current estimates are doubly biased. The

knowledge problem stands in striking contrast with the demand for numbers in the development community.

Numbers matter: any evaluation of Africa's

development must begin and end with a careful evaluation of the growth and income evidence. Without such analysis, one runs the risk of reporting statistical fiction.

Poor numbers are too important to be dismissed as

just that.

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Lessons

What to do about it? Data users – question your evidence! Data disseminators – label your product correctly! Donors – coordinate! Data producers –find and align your priorities! A new agenda for data for development in SSA is required – where local demand, incentives and applicability is at the center.