Just Published: Measuring African Development: Past and Present

Very happy to the see Measuring African Development: Past and Present in print. You can read an open access of the introduction to the book here.  The volume takes you through the very first household budget surveys and national account estimates during colonial rule up through contemporary efforts of improving data with mobile phones, GPS and handheld devices.

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Posted in Africa's Statistical Renaissance, Africa's Statistical Tragedy, African Economic History, Agriculture, Canadian Journal of Development Studies, Christopher Cramer, Gerardo Serra, Ghana, IMF, Measuring African Development: Past and Present, Population, Routledge, Sudan, Tanzania | Tagged , , , , , , , , , , , , , , , , | Leave a comment

IMF and Ebola: why we don’t have any good answers

The debate on whether IMF has any blame for Ebola has been frustrating, even infuriating. It is going to continue to be frustrating largely because: “Who is to blame for Ebola?” is really not a good question. In turn, we have not got good answers.

  1. IMF is to blame.

That’s a bad answer. And that’s the how many, including Chris Blattman summarizes the view taken by the authors of the Lancet piece.  But Kentikelenis, King, McKee and Stuckler carefully stated “it could be that the IMF had contributed to the circumstances that enabled the crisis to arise in the first place”. It should be noted that it was provoked by the IMFs announcement of US$ 430 million of funding to fight Ebola. The IMF was selling itself as the solution to Ebola. But you could plausibly argue that IMF was part of the problem as well.

  1. History and Institutions is to blame.

That’s the view taken by Blattman in his first response. It is about state capacity, and it is wrong to assume that Africa is a blank slate. He is implicitly evoking the economic growth and political science literature that argues that the reasons why ‘good’ or ‘bad’ external policy advice makes little sense in countries like Sierra Leone, Liberia and Guinea. I am not that impressed with the research this argument relies on. Sure, according to the regression results – high ethnic fragmentation, colonial institutions, geography, slave trade, just to mention a few – is correlated with ‘bad’ institutions, ‘bad’ policy and low income per capita today.

I got two main problems with this. One is that it carries the ‘why aren’t you more like Denmark’ policy implication. Second, it is often forgotten that if you read the regression results there is only a fraction of the development outcomes today that is explained by ‘historical’ factors. I do sympathise with the view that is wrong to ignore politics and history – but I think that the literature that warns against the blank slate approach is equally wrong about ignoring the fact that recent history and current actions do actually have decisive impacts. The causality is messy, and because it is cleaner econometrically, messy current day politics is treated as an outcome. Yes, the answer is in politics and history, but IMF plays a role in that history.

  1. IMF is not to blame.

I could not make up a weaker response for the IMF than the one they came up with. They ran some econometric tests (with the fiscal data they themselves help country authorities to aggregate by the way) – et voilà: “we find an increase in health spending as a percent of GDP. In Guinea, spending increased by 0.7 percentage points, in Liberia by 1.6 points and in Sierra Leone by 0.24 points (from 2010 to 2013).”

0.24 percentage points? Really? So in Sierra Leone, according to World Bank statistics, total health spending as a share of GDP (note that this is not public, but total) was 15.24 percent in 2010 and it was 15.08 percent in 2012, the World Bank does report 2013 yet. In my experience with error margins in official statistics like this (remember, the denominator is GDP which can be a bit wobbly), that is not enough to assert direction of change. Look here, what happens to health spending as a share of GDP according to World Development Indicators.

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The pattern is stagnation. We start at 15 percent in Sierra Leone and end up at 15 percent. We start at 6 percent in Guinea and we end up with 6 percent. That’s the end of the civil war. So to find a percentage fraction in this history and say: ‘look that’s me’, is not a good answer.

Towards a better question:

Obviously I agree with Blattman in that one needs to take into the opportunity costs when making statements about how much should be spent. I also agree that we need better research to get better answers. The ones I reviewed here are all pretty sloppy, but the original question that actually should be posed is whether the Ebola crisis have shown that over the past decades IMF focus a bit too much on fiscal discipline and too little on the importance of capacity in education and health. Most agree that too much was cut in the 1980s and 1990s, and there bits and pieces of evidence that Kentikelenis, King, McKee and Stuckler can point to show that the priority given to increasing capacity to deliver health services Sierra Leone, Liberia and Guinea is not high enough.

Final note: In all this I was reminded by the excellent book by Griffiths on Sierra Leone in 1986. Worth a read.

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Writing about a data revolution: A critique in four venn diagrams

The UN Secretary-General’s Independent Expert Advisory Group on a Data Revolution for Sustainable Development (IEAG) have completed their report.  “A world that counts” is a cleverly crafted motivational manifest.  But it is not a practical roadmap on how to apply a ‘data revolution’ to the sustainable development goals agenda. The report’s key weakness is that it conflates a lot of terms, and assumes automatic relationships between things like ‘counting’ and ‘knowing’. I write about it in more detail here.  The mistakes made in the report are fundamental. Here I show some of the basic conflations in terms in four diagrams.

Not everything that counts can be counted

stuff that counts

The report strongly suggests that everything that matters can be counted. We know that this is not true. If the guiding principle for the Sustainable Development Goals is to make decisions ‘as if’ everything can be counted, the end result will be very misleading.

Data is not the same as statistics

 

stuff that counts

The data revolution hype is just one of many places where ‘statistics’ and ‘data’ are conflated. Data is not the same as numbers. Data literally mean ‘what is given’, so when we speak of data we are talking about observations – quantitative or qualitative, even figurative that can be used to get information.

To keep talking about data when we mean statistics may sound better, but it only contributes to confusion. The report suggests that the UN should develop a ‘global consensus on data’. What is that supposed to mean? That statement is meaningless if you exchange the word ‘data’ with ‘observations’, ‘knowledge’ or ‘evidence’. It can however make sense if you talk about ‘statistics’.  International organizations do have a natural role when it comes to developing global standards for official statistics.  Reaching a global consensus on how observations and evidence constitute knowledge is futile.

There are more methods to knowing than through counting

stuff that counts 3

The report says (p. 2-3): “whole groups of people are not being counted and important aspects of people’s lives and environmental conditions are still not measured” and then that “Never again should it be possible to say “we didn’t know”. No one should be invisible. This is the world we want – a world that counts.”

I understand the enthusiasm, but I want to warn against hubris. This is certainly not the world I want. I think it should always be possible to say ‘we didn’t know’. Numbers, or the act of counting does not guarantee objectivity nor does it always make us wiser.  It is a testament to the richness of life, and the poverty of numbers that all things cannot be counted.

More data does not mean better decisions

stuff that counts 4

The report says that demanding more data will lead to better decisions. That is a statement of belief, not a a theory of of change. What is often thought of as ‘evidence-based policy’ turns out to be ‘policy-based-evidence’. In short, predominantly the causality runs from policy to statistics. And a lot of evidence does not inform policy at all – whereas a lot of decisions are made without or despite data. That begs the question - how much resources should one advocate that is spent on a global development monitoring framework?

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Africa: Why Economists Get It Wrong

My latest book is available for pre-order:

For the first time in generations, Africa is spoken of these days with enthusiastic hope: no longer seen as a hopeless morass of poverty, the continent instead is described as “Africa Rising,” a land of enormous economic potential that is just beginning to be tapped.

With Africa: Why Economists Get It Wrong, Morten Jerven offers a bracing corrective. Neither story, he shows, is accurate. In truth, most African economies have been growing rapidly since the 1990s—and, until a collapse in the ’70s and ’80s, they had been growing reliably for decades. Puncturing weak analysis that relies too much on those two lost decades, Jerven redraws our picture of Africa’s past, present, and potential.

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How much will the Data Revolution cost?

What used to be 8 Millennium Development Goals are now 17 Sustainable Development Goals. The list of targets has ballooned from 18 to 169. The final list of indicators has not yet been determined. My estimate suggest that just the monitoring such a list would cost about $250 billion over 15 years — or twice what is spent annually on Official Development Assistance globally. In other words, if the list is adopted as suggested and it was to be accompanied be serious measurement, we would have to set aside all ODA to measurement in the first and the second year and then only in the third year would there be any funds to even begin to vaccinate, feed and school children. It is very likely that success and failure in the post-2015 agenda will be measured with deficient and bad data unless the list of targets is radically shortened.

The full paper is here.

Bjorn Lomborg reacted to the cost estimate in the Guardian.

Cheryl Doss, Senior Lecturer in African Studies and Economics at Yale University, argued that we should ask for more data.

I responded:

When Rabbit asked Winnie the Pooh whether he wanted honey or condensed milk with his bread Pooh excitedly answered, “Both.” But then not to seem greedy, he added, “But don’t bother about the bread, please.”

The debate on Sustainable Development Goals (SDGs), which are to replace the Millennium Development Goals (MDGs) in 2015, is reminiscent of a Winnie-the-Pooh-approach to measuring development. Do you want your poverty measure disaggregated by region or by gender? Both, and don’t bother worrying about the measurement please.

Read the full  response here.

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Africa by numbers: Keynote at Nordic Africa Days 2014

The Nordic Africa Days 2014 took place 26-27 September 2014 in Uppsala, Sweden. I gave the keynote lecture.

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Measuring African Development: Past and Present

Will be published as a book by Routledge. Estimated publication date January 8th 2015. My fourth book will be published by Zed in May 2015. Title: Africa: Why Economists Get it Wrong.

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The renaissance of African economic history

That is the promising title of the introduction to a special issue by Gareth Austin and Steven Broadberry soon to be published by Economic History Review.  The special issue will be launched at the LSE  25-26 October at the African Economic History Workshop. The program (put together by Leigh Gardner) looks very promising. For those who are interested in joining future events and otherwise following and contributing to the African Economic History Network should become members. In the special issue I got two articles. One with Ewout Frankema, with the title: Writing history backwards or sideways: towards a consensus on African population, 1850–2010. The second one  is perhaps even more ambitious and is called: A West African experiment: constructing a GDP series for colonial Ghana, 1891–1950.

Posted in Economic Growth, Economic History, Population | Tagged , , , , , | Comments Off

Overcoming Obstacles to Doing Business in Sub-Saharan Africa

I am invited to offer my comments by Aubrey Hubry who argues:

that inadequate infrastructure, lack of market data, and poor policy implementation impede investment in Africa, despite growing opportunities to do so profitably

The event takes place at the Atlantic council (details here) in the context of the US-Africa Leaders Summit. My book, Poor Numbers, was not really written with the concern of how the lack of data affects business decisions, but I enjoy the widening of the conversation of how measurement matters. I am preparing my notes for the discussion.

Posted in Africa Rising, Poor Numbers | Tagged , , , , , | 2 Comments

Towards a History of Economic Growth in Africa

From the OUP blog, the summary of the key findings of my latest book:

The book offers a reconsideration of economic growth in Africa in three respects. First, it shows that the focus has been on average economic growth and that economic growth has not failed. In particular, the gains made in the 1960s and 1970s have been neglected. Second, it emphasizes that for many countries the decline in economic growth in the 1980s was overstated, as was the improvement in economic growth in the 1990s. The coverage of economic activities in GDP measures is therefore inaccurate. In the 1980s, many economic activities were increasingly missed in the official records thus the decline was overestimated (resulting from declining coverage), and the increase in the 1990s was overestimated (resulting from increasing coverage). The third important reconsideration is that there is no clear association between economic growth and orthodox economic policies. This is counter to the mainstream interpretation, and suggests that the importance of sound economic policies has been exaggerated, and that the importance of the external economic conditions have been devalued in the prevailing explanation of African economic performance.

The OUP also offer the first chapter for free.

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