This week’s blog follows on directly from last week, when I introduced the excellent new book, Poor Numbers, by Morten Jerven. This week we move from the general argument to the Zimbabwe case.
Let me offer three examples – each of which have been mentioned in this blog before – that complement Jerven’s cases, and contribute to the same bigger point that good numbers matter.
Agricultural output data: Zimbabwe’s agricultural data comes from a variety of sources, including annual crop surveys, market surveys and assessments of throughput at marketing depots. In the past, when the sector was dominated by a few large farms, it was relatively easy to get a picture of production each year. Output from the communal areas was assessed through state marketing channels through marketing boards for most of the agricultural commodities, especially maize (but also cotton, tobacco and beef). While statistics on cotton and tobacco remain reasonably good, as their marketing is channelled through few players, the production and marketing of maize and beef, by contrast, has changed dramatically since land reform.
Today there are diverse marketing channels, including much locally-focused marketing and little reliance on the old marketing board routes. And with many more farms across the country (around 150,000 new units in the A1 schemes alone), field-level monitoring by extension agents is nigh on impossible. For important crops such as the small grains (millets, sorghum), groundnuts, many oilseeds and beans, as well as smallstock, we know virtually nothing about total production and marketing.
The bottom line is that we don’t know how much food is produced and where, nor do we know how much is stored and marketed. Despite the attempts of Fewsnet, ZimVAC and others, the estimates are increasingly guesswork, especially as sampling frames and data collection protocols have not changed sufficiently to respond to the dramatically reconfigured agrarian structure.
Each year we get conflicting estimates of how dire the harvest is going to be, and the consequences this will have for food imports, and food aid. With such uncertainties, this becomes a critical area of political contestation: between government and the donors, and even between international agencies. Claiming a food ‘crisis’ may be the only way of securing international funds, as sustaining an ‘emergency’ has been essential to continued international engagement through ‘humanitarian’ aid. Such a response may well be justified; but it may be not. The problem is often we don’t know.
Migration data: Similar uncertainties centre population data and migration-related demography. While we know that migration, particularly to South Africa, has increased, we have absolutely no idea how many people have moved permanently there (or indeed to other destination countries, although the data for the UK, for example, is better). Large numbers are bandied around, which serve particular politically purposes; in South Africa (linked to xenophobic, anti-immigrant rhetoric) and in Zimbabwe and internationally (supporting the narrative that people are ‘fleeing’).
But the figures of course don’t take into account the long-term pattern of circular migration whereby people move temporarily, or indeed increasingly seasonally. If we were to believe the figures, there would be far fewer people in Zimbabwe than there seem to be. For example, the preliminary results for the 2012 census show that the population has increased by 1% over a decade and stands at nearly 13m. Even within the country we don’t know where people are living. There is an assumption that the urban areas are growing, as people flood to the cities. But is this the case? Debbie Potts doubts this data for sub-Saharan Africa generally, but until we get better locational census data that accounts for regular movement, we will not know.
Land ownership data: This is perhaps the most contested, and in the absence of a proper land audit, we cannot know. But when ‘surveys’ purport to present data that show that “40% of the land was seized by Mugabe and his cronies”, and these figures get reported in the international media as fact, we are in trouble. This most recent examples of this short-cut journalism and recycling of ‘facts’ are from the BBC (on the Hard Talk show with Patrick Chinamasa) and the UK Guardian (in a link put in by the paper in an otherwise good piece by Simukai Tinhu). The earlier land audits by Utete and Boka have shown categorically the problem of elite capture in the A2 sites, and our detailed province-specific work in Masvingo supports this. But the scale is nothing like that claimed.
This poverty of data leads to a poverty of understanding, and so a distortion of debate. We should not be ignoring the abuse of the land reform programme by some politically-military connected elites, and the ownership of multiple farms is clearly contrary to any regulation, but our focus should equally not be only on this issue, and the wider picture, based on realistic data, needs to be central. This is why, in terms of the GPA and in line with the now agreed constitutional commitments, a proper land ownership and use survey (an audit) is critical.
If you don’t know how much food is being produced, how many people are in the country or have left and who owns what land, then how can you begin to make plans for the future? As contributors to other headline statistics, including GDP, such figures may result in major distortions.
For example, in Zimbabwe, GDP figures have been used to show the dramatic decline, and then impressive recovery in the formal economy (see the shower of graphs in the most recent budget statement), yet, as I have argued before, even in the depths of the crisis in the late 2000s, economic activity was far higher than measured. The ‘real economy’ – informal, often based on barter exchanges, sometimes illegal, much of linked to cross-border trade – was thriving, despite the collapse in the core, formal economy. It had to: this is how people survived. If you believed the figures on the formal economy, where the numbers were collected, people would have been suffering far more than they did.
As the formal economy has recovered, this has been registered in the statistics, but the informal economy still exists, and indeed the 2000s saw a massive restructuring of economic activity, not only in the agricultural sector, but across the economy towards more small-scale, informally-based enterprises. This is not a bad thing, as it provides the basis for more inclusive, employment generating, broad based growth. But if it is not understood, measured and recorded, it does not feature in planning and crucially budget allocation discussions. ZIMSTAT has recently published the 2011/12 Poverty, Income Consumption and Expenditure survey, and in a future blog I will review its findings, and the degree to which it has been able to respond to the changed post-2000 context.
While it may seem that a focus on statistical services is a rather dry and dull subject, it is in fact essential. ZIMSTAT has a small ‘did you know?’ box on their website’s front page. It says: “The likely success of development policies in achieving their aims will be improved by the use of statistics”. They are right. Revitalising statistical services, and improving their capacity to carry out national-level, macro-census type work, as well as smaller, more focused surveys, complemented with qualitative insights, is vital.
If development is to be successful, a thorough-going and honest debate on the quality of data and how to improve it is essential. Jerven’s superb book discusses an important topic with clarity and honesty; and for donors thinking of investing in government capacities in Zimbabwe again, it is well worth a read.