Professor Morten Jerven of Simon Fraser University has written an intriguing book about Africa’s overlooked statistical crisis. Called Poor Numbers: How we are misled by African development statistics and what to do about it, Prof. Jerven’s book delves into the economic growth statistics produced by weak, underfunded statistical offices across the continent, which nevertheless underpin policy making decisions in western capitals, international development agencies and elsewhere. Drawing on the text of his book, Prof. Jerven explains to Iain Marlow, editor of the Toronto Review, why these numbers, for many reasons and in many cases, are wrong — and might be leading donors and governments astray.
You’ve spent a lot of time in and around statistical offices in sub-Saharan Africa. Can you describe in detail what some of these offices are like compared to their institutional counterparts in Western countries, what tools and resources they have to work with and what scenes or situations have surprised you over the course of your research?
The statistical offices across sub-Saharan Africa are generally in poor condition. Also, relative to their own surroundings, the statistical offices often seem to be in a particularly derelict state. Perhaps what is particularly striking is the contrast of this situation with that of another pivotal stakeholder in the economic policy process with much higher resource endowments: the central bank. While the statistical offices are located in rundown offices, often with limited computer facilities — such as in Ghana, Malawi, Nigeria, Kenya, Tanzania and Zambia — the central banks of these countries are located in new high rise buildings with modern facilities. Furthermore, employment in the central banks command higher salaries and prestige, and central bank employees are thus in a better position, both symbolically and physically, in terms of providing timely and useful advice in the policy making process.
Most of the focus I’ve seen on untrustworthy economic growth numbers has been around China, where the gradual betterment of many people’s daily lives is an underpinning of the Communist Party’s legitimacy. In sub-Saharan Africa, what reasons for altering the stats have you come across, or is it simply a lack of detailed stats that leads to poor numbers?
Well, the main problem when compiling GDP estimates at statistical offices in most African economies is that of data availability. The offices who are putting together the national accounts which produce the GDP estimates have very little information about the economy they are supposed to measure. In particular, while they may have very little information about food production, they may have more information about export crops. They know a little bit about some manufacturing. The may have some information on the larger operators. They know about government activities. But there are huge gaps in the information relating to what we call ‘the informal sector‘ or the unrecorded economy. There is little or no data on food production, transport, trade and a range of small scale services, crafts and other activities that provides livelihood for the majority of the population. As a result, the growth and GDP numbers are guesses. And this large band of uncertainty means that it is easy to tamper with the numbers if it is politically convenient.
Many in the West take generally accurate national statistics for granted, but many would probably assume that stats — like access to healthcare or other indicators — would be worse in Africa. Why should people who care about international development and poverty allievation care about Africa’s poor numbers?
Reliable statistics, including estimates of economic growth rates and per-capita income, are basic to the operation of governments in developing countries and vital to non-governmental organizations and other entities that provide financial aid to them. Rich countries and international financial institutions such as the World Bank allocate their development resources on the basis of such data. The paucity of accurate statistics is not merely a technical problem; it has a massive impact on the welfare of citizens in developing countries. Where do these statistics originate? How accurate are they? My research shows how the statistical capacities of sub-Saharan African economies have fallen into disarray. The numbers substantially misstate the actual state of affairs. As a result, scarce resources are misapplied. Development policy does not deliver the benefits expected. Policymakers’ attempts to improve the lot of the citizenry are frustrated. Donors have no accurate sense of the impact of the aid they supply. In sum, as I argue in the book, poor numbers are too important to be dismissed as just that.
Is there a country or two that have particularly poor numbers?
There is a lot of variation, and it is very important that any reform or policy initiative to rectify this problem recognizes this variation. For the research in this book I have done interviews with statistical officers and important stakeholders in offices in Ghana, Nigeria, Uganda, Kenya, Tanzania, Zambia and Malawi, between 2007 and 2010. In addition structured interviews have been conducted in a survey of national income accountants in Burundi, Cameroon, Cape Verde, Guinea, Lesotho, Mali, Mauritania, Mauritius, Namibia, Mozambique, Niger, Senegal, Seychelles, Sierra Leone and South Africa. That means that direct contact has been established with 23 of 48 countries in sub-Saharan Africa. Of these, I would say that the GDP statistics in Zambia and Nigeria are particularly worrisome. Note that the countries that did not respond to my queries or the countries that I did not visit most probably are in even worse state. In a country like Democratic Republic of Congo, or Somalia, there is no, and has been no data produced for many decades now. The IMF and World Bank may still be producing and publishing numbers on these countries but these are largely made up.
You trace the lack of high quality statistics in modern day Africa back to the region’s general historical tendency to shy away from private notions of property; by not collecting taxes, no one really collected any information. What is it about African economic history that sets up the poor numbers of the present day?
That’s correct. The work of economic historians has emphasised that, historically, African polities were typically land abundant and that labour was relatively scarce.This has implications for the property rights regime. The way this works out is that when land is abundant, it is also free — not subject to political taxation. Therefore land has typically not been subject to private property rights, and states have not collected taxes on land holdings. Today, land is not abundant, but because most contemporary African states are based on borders drawn up by colonial powers, this pattern of state control has prevailed. States typically control mines and ports, but not land, and taxes are collected through marketing and sales, not on land or production.
Those are the initial conditions — or structural constraints, if you like. But there were also more recent historical conjectures that has had a massive impact.
The statistical capacity of African states was greatly expanded in the late colonial and early postcolonial period, but it was greatly impaired during the economic crisis of the 1970s. The economic crises of the 1970s and 1980s hit African economies particularly hard. The importance of statistical offices were neglected in the decades of liberal policy reform that followed — the period of “structural adjustment” in the 1980s and 1990s. In retrospect it may be puzzling that the International Monetary Fund (IMF) and the World Bank embarked on growth oriented reforms without ensuring that there were reasonable baseline estimates that could plausibly establish whether the economies were growing or stagnating. For statistical offices, structural adjustment meant having to account for more with less: Informal and unrecorded markets were growing, while public spending was curtailed. As a result, our knowledge about the economic effects of structural adjustment is limited. More generally, the economic growth time series, or the cumulative record of annual growth between 1960 and today, for African economies does not appropriately capture changes in economic development.
You describe a sort of unholy alliance between data-creators in international institutions and researchers and others who rely on them. Can you explain how the situation came about — and how it perpetuates itself?
Unfortunately, the scholars best equipped to analyse the validity and reliability of economic statistics are often data users themselves, and thus reluctant to undermine the datasets that are the bread and butter of scholarly work. International institutions are not only the main providers and disseminators of the data, but their programmes and plans are often tied to targets and indicators, and therefore they accept the data at face value in the public sphere. Privately, or in technical consultations, advice may be given, or direct pressure applied during the process of producing the data. Finally, on the domestic political scene, there is little to no transparent debate concerning the issue.
As I describe in the book, African statistical offices were increasingly unable to deliver the numbers required for the policy reports and decision models in the 1970s and 1980s. At this point the emphasis shifted towards agreeing upon some numbers, and actual measurement took the backseat. The important stakeholders in this process, such as the World Bank data group, have so far been unwilling to publicly admit these problems, because they throw into doubt the very numbers that are downloadable as evidence of development from their own databases. However, when confronted with some of the research presented in this book, the chief economist for Africa at the World Bank has now declared Africa’s statistical tragedy. Thus there is some hope that the problem of poor numbers gets the attention it deserves.
Photos courtesy of Morten Jerven