Tag Archives: GDP

Why economists fail in Africa

A great new book is just out by Morten Jerven called Africa: Why Economists Get it Wrong. It is a follow up to his excellent 2013 book, Poor Numbers: How We Are Misled by African Development Statistics and What to Do about It that I featured several times in this blog.

He argues: “There has been a chronic failure among economists to explain growth in Africa. The methods and analytical angles they have used to explain relative failure in Africa were conceived in the 1990s, but these were unsuitable for explaining growth in the 1960s or growth since the 2000s”.

Jerven does not deny that there has been economic failure in Africa. Zimbabwe is of course a case in point. But this was not generic failure, over the whole ‘post-colonial’ period across a whole continent. Rather there have been variations: growth in fits and starts, cycles of successes and failures, often with success being hidden by the aggregate statistics given the informal nature of much economic activity (certainly the case for Zimbabwe, as I’ve argued many times).

But what he calls this “erroneous stylised fact” of generic failure over a long period has been the basis for an ahistorical, decontextualised analysis of African growth patterns, which attempt to explain the African “shortfall” through cross-country or cross-continental comparison. The assumption is that it was a set of “initial conditions” that created the African predicament. Conditions such as environmental factors, ethnic fragmentation and a lack of social capital have all been suggested to have a direct role in the failure of economic growth. Just being African seems to be a problem according to some of this analysis.

However, Jerven argues “the causality story – initial conditions causing slow growth – is wrong and therefore not useful for policy advice”. Moreover, he argues “policy typologies such as the distinction between “closed” and “open” economies, or the related “bad” and “good” policies, do not correlate coherently with episodes of economic growth in African countries”.

The title of this blog is a reference to the much-lauded book, ‘Why Nations Fail’ , that I have reviewed before on this blog’. Jerven also takes this argument to task as it offers a far too simplistic and functionalist a view of institutions and governance, and, he argues, gets causality back-to-front. Effective institutions and ‘good’ governance emerge from development, and are not so much its precursors, he suggests. As he notes: “several decades were wasted putting a lot of effort into curing symptoms that were thought to be causes”.

So what are the key complaints Jerven has against economics as applied to Africa? It’s of course not all economics and economists that his ire is focused on, but a certain style of aggregated economic reasoning derived from comparative cross-country econometrics. Why has this approach been so problematic, and what can be done about it?

The problems Jerven outlines are multiple. The data that are used is often very shaky and patchy. Models derived from such data are inevitably suspect: garbage in, garbage out, as the adage goes. Aggregation across countries, and comparisons with patterns elsewhere miss out on the particularities of different economies and their histories, and so end up offering false or at least highly simplistic explanations. Africa, of course is not a country, but many and diverse nations, regions and economies with complex histories. But simple narratives prevail and are reinforced by aggregate economic analysis. Jerven identifies a few choice media quotes from The Economist over time that regurgitate the narrative that ‘Africa’ is a disaster, or alternatively today, ‘Africa’ is rising; statements that are almost completely meaningless and not supported by solid data.

The consequences of these faulty analyses – and the media tropes that follow on – are of course very real, as the book points out. Decades of structural adjustment policies were pushed across Africa on false premises, and with disastrous consequences. The arguments for institutional reform and good governance as preconditions for development may fail, as these new institutional forms may have to emerge from developmental processes, and be appropriately adapted to contexts (just as happened in Europe or the US). And, of course, the generic prescriptions for a whole continent fail to pay attention to location and specificity, and of course political economy and history.

So what to do? There are clearly a number of important challenges. One of course is to improve the data that analyses are based on. If we are relying only on very poor numbers, then it’s difficult to expect anything other than the garbage that is currently churned out. With better spatial differentiation, improved time series and so on, we can get to grips with variation and pattern, and offer greater nuance in our analyses. Good numbers really do matter. If growth pathways are so much to do with context – of politics, history, and so on – then cross-country econometric comparisons, especially with massively unlike settings (say comparing Asia or Europe with Africa) are really largely a waste of effort. Instead, Jerven argues, we need to move from cross-country econometrics to understanding particular economies in context, and understanding how African economies actually work. This means a focus on real markets, not the abstractions of models; informal and formal economic activity and the interactions between, not just what is in the formal statistics; and the historical and political factors that frame and shape options for the future.

The profession of economics with its current false scientism and its obsession with quantitative method has, over time, distanced itself from the complexities on the ground. The search for grand, universalising explanations for growth, poverty, inequality or whatever, has lost sight of the particular, contingent, conjectural conditions that create change and transformation. This is a profound methodological point. The book hints at the need for a revolution in development economics that brings back the older traditions of political economy and economic history. Such analyses must be focused not on assumed or inferred economic rules or an obsession with initial conditions driving uniform change, but the particular operations of particular economies – of nations, regions in particular settings.

Zimbabwe has a proud history of this type of economics (alongside some of the other more problematic sort). The Department of Economic History at UZ has long been an important source of insight into economic change over time, rooted in particular locations. The Zimbabwe Institute of Development Studies, sadly no more, was a focus for political economy analysis of labour, land, industrial change and more by many key scholars. Today, work at institutes such as the African Institute for Agrarian Studies, the Centre for Applied Social Sciences or the Labour and Economic Development Research Institute continue this work. Sustaining these intellectual traditions – rooted in place, context and history – will be important, as Zimbabwe seeks an alternative growth path into the future. Such analyses should help resist the more simplistic and often dangerous prescriptions from the flawed economics of the mainstream.

This post was written by Ian Scoones and first appeared on Zimbabweland

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Spurious statistics: why figures on Zimbabwe’s ‘lost growth’ mislead

There is a lot that is written about Zimbabwe that is misleading. But sometimes a piece appears that really beats the field. This week a blog from the Centre for Global Development in Washington joins that category. This claims that ‘misrule’ has cost Zimbabwe US$96 billion.

I would normally ignore such articles, but the CGD regularly produces some quite good material, if a bit close to the Washington view of the world on occasions. I also have been sent this article several times by my regular ‘correspondents’ to show (again) how wrong I am about Zimbabwe. So I thought it deserved a bit more attention, and now a blog, as I think it illustrates rather well a wider problem of the use of statistics in misleading ways.

This is not exclusive to this piece. Far from it. For example, a few weeks back when I was in South Africa I was reading the Cape Times over breakfast and was confronted by a whole page on Zimbabwe (the hook was Mugabe’s birthday) written by Professor Robert Rotberg from the Harvard Kennedy School.  This purported to show how disastrous things were through ten points. I was so flabbergasted by the content that I totted up the ‘facts’ that were presented that could be challenged with real field data that I and others had collected. There were 12 – one for each of the ten points made and two more besides. It was quite extraordinary how an author (nay illustrious ‘expert’) and an editor (of a perfectly respectable paper) could get away with it. But sadly it happens nearly every day, and most such interventions go completely unchallenged.

Anyway, the point is that in writing this blog each week I have plenty of material to reflect on, but most is not worth the time of day. However, I thought I should offer some response to the CGD piece, given its provenance and the way it illustrates a wider problem. The blog is written by Todd Moss who is COO and Senior Fellow at CGD, and was formerly Deputy Assistant Secretary in the Bureau of African Affairs at the U.S. Department of State and previously advisor to the Chief Economist in the Africa Region at the World Bank. He certainly has impressive credentials, and has written other material on Zimbabwe, but I cannot see from the website whether he has actually done field research in the country.

So where does the $96 billion figure come from? The blog presents the sorry story of Zimbabwe’s collapse in the formal economy from the early 2000s to 2009 and its slow and weak recovery since. The indicator used is the standard GDP measure. This is compared with a ‘what if?’ argument. What if Zimbabwe instead of declining grew at the rate seen in Zambia? The difference between the two scenarios is presented as the ‘loss’ that Zimbabwe has suffered.

The main argument is encapsulated in a graph, with the large deficit highlighted. The blog urges readers to tweet the graph to the world. Here is a very explicit and in some ways quite effective attempt at creating a ‘killer fact’, one that will become a focus for media articles, and a hook in the wider discourse (a phenomenon that Duncan Green from Oxfam has written on).

So why is this ‘fact’, and its wider narrative problematic? There is no denying the catastrophic collapse of the formal economy in the 2000s, and also the weakness of the recovery since, now faltering once again. Equally, the scale of graft and unaccountability was recently illustrated in the media exposes of highly paid parastatal officials, although these have now been capped. But what else needs to be taken into account when making an assessment? Here are four points.

  • First is the problematic statistic of GDP, particularly in African contexts. Morten Jerven has written lucidly about this issue in his fantastic book Poor Numbers; a book I highly recommend to Dr Moss, and anyone else thinking about African economies. GDP numbers are usually fabrications with little basis in reality, and they shift dramatically depending on the assumptions made and the data collection techniques used. They show something about the formal economy, at least in terms of trends (no denying that for Zimbabwe), but they need to be viewed with very large pinches of salt.
  •  Second the official statistics only pick up a fraction of the range of economic activity, especially in economies that have large informal sectors. With the restructuring of the economy since 2000, the informal sector in Zimbabwe has grown massively. Tendai Biti, the former MDC Finance Minister, argued recently that it represented most of the economy, perhaps over 80%. If so then the recent figures in the CGD graph represent only represent a small proportion of total economic activity and should be multiplied many times – in which case the disparity with Zambia would shrink dramatically. Of course this would be equally spurious, as Mr Biti’s guess is just that, and in fact we have no idea what the scale of economic activity is, as the standard statistics do not tell us, as statistical services measure only a fraction of the ‘informal sector’; a point made forcefully by Professor Jerven.
  •  Third, Zambia’s economy has certainly grown but from a low base. In the 1980s and 90s in particular the economy was in dire straits. So the growth rate that has been used in the projection is to some degree a bounce back, driven in large part by the growth of commodity prices internationally. As a resource dependent economy, the dramatic growth is highly dependent on the price of copper, for example. And this has accelerated, in turn driving growth. There are of course other vibrant sectors, including tourism, but Zambia’s economic growth, and its projection into middle income status, is based on quite fragile and narrow foundations, with question marks being raised about job creation.
  • Fourth, we have to ask how economic activity is distributed to make any useful assessment in relation to development. The benefits of growth in Zambia is massively concentrated. The bigger winners are international mining capital and South African retail and services. Of course this generates some jobs and tax revenues, but the distributive effects of such forms of growth have to be questioned. A broader based growth grounded in redistributive policies is perhaps more sustainable, and certainly more equitable in the longer term. Zimbabwe has certainly not got there yet, but the land reform for example has laid the foundations for this in the agricultural sector.

I could go on. If we probe a bit we can see that the ‘killer fact’ loses its shine quite dramatically. Its construction and deployment in an essentially political argument is clearly problematic. It would be just as problematic for example if Professor Jonathan Moyo – Zimbabwe’s Minister of Information and spin doctor extraordinaire – used the same figure to argue that this was the cost of international ‘sanctions’ on the country in the same period. Both Moss and Moyo would be using a spurious statistic to bolster a political narrative that is far too simple an explanation for a complex and evolving process.

So if you hear this figure again, or any other presented in this sort of way, think twice. More likely than not the statistics will have been conjured up to a suit a predefined narrative. Ask about its source, and whether real field research underpins it. More questioning and critique of such statistics and the narratives that they give rise to is essential to pick apart complex realities from dubious myth making.

This post was written by Ian Scoones and originally appeared on Zimbabweland

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Dodgy data and missing measures: why good numbers matter (part I)

Earlier this year, an excellent short book, “Poor Numbers: How we are misled by African development statistics, and what to do about it” by Morten Jerven from Simon Fraser University in Canada was published (see this African Arguments piece for a summary). It makes the case that African statistics are often worse than useless, and decisions, rankings and other assessments made based on such poor numbers are usually grossly misleading. Jerven comments (page xi):

“…the numbers are poor. This is not just a matter of technical accuracy. The arbitrariness of the quantification process produces observations with very large errors and levels of uncertainty. This numbers game has taken on a dangerously misleading air of accuracy, and the resulting numbers are used to make critical decisions that allocate scarce resources. International development actors are making judgments based on erroneous statistics. Governments are not able to make informed decisions because existing data are too weak or the data they need do not exist”.

He argues that this appalling state of affairs came about through a long neglect of statistical services in Africa, made worse by the withdrawal of state support during the structural adjustment period. He focuses in on the iconic statistic, the gross domestic product (GDP), and a few countries, including Nigeria, Malawi, Zambia and Tanzania. GDP figures are made up of various elements, and in many countries in Africa, agricultural income is crucial. Yet, as Jerven shows for Malawi, there are all sorts of reasons not to believe the figures, as political incentives in particular result in distortions (in the case of Malawi massively upwards to ‘prove’ the ‘success’ of the politically driven fertiliser subsidy policy). Also, in much of Africa, the informal economy is massive, and very poorly understood. There are ways of assessing informal economic activity, such as through assessing expenditures, but understandings remain often very limited. The result is that in countries where the informal economy is significant (most of Africa), there are large under-estimates in national income.

The consequences of all this are severe, the book argues. Planning and budget allocations are carried out on the basis of flimsy evidence, distortions arise as statistics are influenced by political interests, successes much hailed may be far from such, and in the endless pursuit of targets (driven for example by the Millennium Development Goal process), indicators may be meaningless, or the data simply made up or guessed. The highly popular country rankings on everything from GDP to good governance – including the latest offering coming from IDS (where I work), the Hunger and Nutrition Commitment Index (HANCI) – thus create their own political economy. Informed by dodgy data and the even more dubious process of ‘expert judgement’, many rankings may be worthless. Dudley Seers (quoted by Jerven, p. 36), who went on to become the founding director of IDS, had this to say 60 years ago:

“In the hands of authorities, such international comparisons may yield correlations which throw light on the circumstances of economic progress, and they tell us something about relative inefficiencies and standards of living, but they are very widely abused. Do they not on the whole mislead more than they instruct, causing a net reduction in human knowledge?”

A key complaint Seers was the lack of attention to the ‘subsistence economy’. This he referred to as the “well-known morass which those estimating national income of underdeveloped areas either skirt, rush across or die in” (again quoted by Jerven, p. 37).

Yet such measures and rankings inform opinion, resource disbursement and provide competitive league tables to which governments respond, often exacerbating the poor numbers problem, as yet more dodgy data is conjured up, combined and ranked in ways that make little sense.

Zimbabwe is not covered by the book, but the core argument still holds, as I will explore further next week. The Central Statistics Office, now ZIMSTAT, has been the main source of government data since the colonial era. Compared to many countries, it has impressive capacity and a very strong track record. One thing that could be said of the colonial and Rhodesian authorities is that they were very keen on data. From the Rhodesian Yearbooks to the regular national income and expenditure surveys, data was collected, collated and compiled rigorously and consistently.

Statistics are after all about measurement and control – they are the very essence of the state, as the term suggests. In his brilliant history of statistics, The Taming of Chance, Ian Hacking relates how states were developed alongside statistical services, including cadastral surveys, taxation systems and population counts. In Jim Scott’s terms the ordered, controlling and regulated way of ‘seeing like a state’, is very much wrapped up in counting, surveying and so being able to control, through a form of Foucauldian governmentality at the core of modern states.

While there are clearly negative aspects to this form of state capacity, there are also positive attributes. A committed developmental state cannot allocate funds, direct energies and plan for the future without a good statistical base. Negotiations with donors, steering of investments and prioritisation of expenditures are impossible. Equally, without solid data, political biases, bureaucratic whims and donor influence can overtake planning and budgeting to the detriment of developmental objectives.

Jerven concludes on the state of African statistics: “…the data are based on educated guesses, competing observations, and debateable assumptions, leaving both trends and levels open to question and the final estimates malleable (p. 108)… He continues: “Decisions about what to measure, who to count, and by whose authority the final number is selected do matter” (p.121). Which is why he recommends the revitalisation of African statistical services and, perhaps just as importantly, the improvement of capacity to interrogate and interpret data, including from qualitative insights.

Next week, I will turn to the implications for Zimbabwe more specifically.

This post was written by Ian Scoones and originally appeared on Zimbabweland

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