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

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

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

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

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.

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 overnight. • Undoubtedly – This good news, but a knowledge problem emerges.

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.

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…

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?

Where does the data come from? • Where does the international databases get their data from?

Field Work, West, E ast and Central Africa 2007-2010 • Interviews and Archival Accra, Abuja, work Ghana Nigeria • Visiting Central Statistical Kampala, Offices Uganda Nairobi, Kenya Research questions: Dar es Salaam, 1. How is national income Tanzania measured? Lusaka, 2. How does it affect Zambia prevailing judgments on Gaborone, Lilongwe, Botswana African Growth Malawi + Archival Work + Email survey: Burundi, Cameroon, Cape Verde, Guinea, Lesotho, Mali, Mauritania, Mauritius, Morocco, Namibia, Mozambique, Niger, Senegal, Seychelles and South Africa

Base Year Planned Revision Years Btw Revisions Country 1987 2002 (2013) 15 Angola 1996 2005 (n/a) 10 Burundi 1985 1999 (2014) 14 Benin 2006 Burkina Faso 2006 10(1996-06) Botswana 1985 2005 (2014) 20 Central African Republic 1996 Cote D'Ivoire 2000 Cameroon 1987 2002 (2014) 15 DRC 1990 2005 (2013) 15 Republic of the Congo 1999 2007 (2013) 17 Comoros 2007 28 (1980-07) Cape Verde 2004 Not compiled after 2005 Eritrea 2000/01 2010/11 (2013) 10 Ethiopia 2001 Gabon 2006 13 (1993-06) Ghana 2003 2006 (2013) 3 Guinea 2004 28 (1976/77-2004) Gambia 2005 19 Guinea-Bissau 1985 2007 (2013) 22 Equatorial Guinea 2001 2009 (2013) 8 Kenya 1992 2008 (2015) 16 Liberia 2004 2013 (2015/16) 10 Lesotho 1984 Madagascar 1987 1997 (2013) 10 Mali 2003 2009 (2013) 6 Mozambique 2007 2012 (2015) 5 Mauritius 2009 2014 5 (2002-07) Malawi 2004 2009(2013) 6 Namibia 2006 19 Niger 1990 2010 (2013) not known Nigeria 2006 2011 (2013) 5 Rwanda 1999 2010 (2014) 11 Senegal 2006 5 (2001-06) Sierra Leone 2009 South Sudan 1996 2008 (na) 12 Sao Tome and Principe 1985 2011 (2014) Swaziland 2006 Seychelles 1995 2005(2014) 10 Chad 2000 22 Togo 2001 2007 6 Tanzania 2002 2009/10 (2013) 8 Uganda 2005 2010 (2014) 5 South Africa 1994 2011 (2013) Zambia 1990 Zimbabwe Source: International Monetary Fund 2013; 21

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 5 th 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 old. • A problem of comparable metadata too!

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.

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 of the issue of seeing like a state – measurement is not only knowledge, it is governance, accountability and policy circles.

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.

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.

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.