In a study of the production and use of African economic development statistics I emphasized that this is just not 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 international development actors use the resulting figures to make critical decisions that allocate scarce resources. Governments are not able to make informed decisions because existing data are too weak or the data they need do not exist, and finally I argued that scholars are making judgments based on erroneous statistics.
I suggested that, since the 1990s, within economics there has been a tendency for an increase in the distance between the observed and the observer. When international datasets on macroeconomic variables became available, such as the Penn World Tables, and the workhorse of study of economic growth became the cross-country growth regressions. The trend turned away from carefully considered country case studies and then rather towards large in country studies interested in average effects. However, the danger of such studies is that it does not ask the right kind of questions of the evidence. As an economic historian, I approach the GDP evidence with the normal questions in source criticism – how good is this observation? Who made this observation? And under what circumstance was this observation made?
In most studies of economic growth, the downloaded data from international databases is treated as primary evidence, although in fact it is not. The data available from the international series have been obtained from governments and statistical bureaus and have then been modified to fit the purpose of the data retailer and its customers. These alterations create some problems, and as my research shows, the conclusions of any study that compares economic performance across several countries depend on which source of growth evidence is used. The international databases provide no proper sources for their data and no data that would enable analysts to understand why the different sources disagree about growth. See for example the disagreement in economic growth series reported by the national statistical office, from Penn World Tables, The World Bank and the Maddison dataset for Tanzania, 1961-2001 which I have published in an earlier article.
{mbox:lightbox/jerven-growth-rate-tanzania.jpg|width=300|height=194|caption=Click to enlarge|title=Annual range of disagreement in GDP growth rate, Tanzania 1961–2001}
The average annual disagreement between 1961 and 2001 is 6% (see the graph on the right). It is not evenly distributed; there is serious dissonance regarding growth in Tanzania in the 1980s and 1990s, and how the effects of economic crisis and structural adjustment affected to economy depends on which source you consult.
The problem is that growth evidence in the databases covers years for which no official data was available and the series are compiled from national data that use different base years. The only way to deal satisfactorily with inconsistencies in the data and the effects of revisions is to consult the primary source. The official national accounts are the primary sources. The advantage of using the national accounts as published by the statistical offices is that they come with guidelines and commentaries. When the underlying methods or basic data used to assemble the accounts are changed, these changes are reported. The downside of the national accounts evidence is that the data are not readily downloadable. The publications may have to be manually collected, and then the process of data entry and interpretation follows. When such studies of growth are done carefully, it offers reconsiderations of what used to be accepted wisdom of economic growth narratives.
My latest monograph does precisely that. Economic Growth and Measurement Reconsidered in Botswana, Kenya, Tanzania, and Zambia, 1965-1995, presents a study that is based on my research visits to the statistical offices of these four countries. In each country, I collected reports and handbooks on methodology. I have supplemented this with consultations with the representatives of the respective central statistical offices.
My findings on the measurement of performance have led me to reconsider some of the arguments about African economic growth. The study underlines the importance of looking beyond the averaged aggregate growth rates because of, rather than despite, the issues of data quality. I hope that my findings will stimulate and pave the way for new research that will suggest new evidence and methods to explain long-term economic and social change and (by implication) the current predicament of African economies.
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 there has been no failure of economic growth. 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 incomplete. In the 1980s many economic activities were increasingly missed in the official records thus the decline in the 1980s 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 overstated, and that the importance of the external economic conditions have been understated in the prevailing explanation of African economic performance.
