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Zimbabwe is food secure this season, but more questions raised

The annual ZimVac assessment based on a national sample survey of over 10,000 households and carried out in May came out a month or two back. Unlike last year, when alarm bells were rung over a potential food security catastrophe, this year the prognosis was good. Excellent rains, including in some of the drier and usually more food insecure parts of the country, resulted in a bumper harvest.

Last year I critiqued the use of the headline figure from the assessment as potentially misleading. The same limitations of the survey apply, but the media reporting is more balanced this year (with some extreme exceptions – see comment string in an earlier blog). The survey is based on the 2012 Zimstat sampling frame and covers a large number of enumeration areas across the country, sampled proportional to population densities. Annoyingly the report still doesn’t separate out communal areas and resettlement areas, and my guess is that there remains some sampling bias. More on this below. Last year fortunately the dire predictions were not borne out. In part this was because the rains came, and a green crop filled the hunger period, but also I hypothesised in an earlier blog that the production from new resettlement areas was being undercounted. I suspect this remains the case.

Anyway, I thought blog readers would like a quick summary of the report, as without an impending disaster the media has largely ignored it. You can read the powerpoint report in full, which covers all sorts ranging from nutrition to sanitation. I will concentrate on agricultural production and food security, and draw text directly from the report.

The Ministry of Agriculture, Mechanisation and Irrigation Development estimates that the country will have a cereal harvest surplus of 253,174 MT in the 2014/15 consumption year from a total cereal harvest of 1,680,293MT.

Maize remained the major crop grown by most households (88%) compared to 80% for 2012/13, while groundnuts were the second most grown crop. Generally, the proportion of households growing crops increased except for cotton which showed a decline (due to the collapse in prices) and soya beans which remained unchanged.

Nationally, average household cereal (maize and small grains) production was 529.5kg. This was higher than last season (346kg). In Masvingo maize production averaged 339.7 kg and small grains 126 kg, given a total of 525.7 kg per household. Overall, average household cereal production was highest in Mashonaland West and lowest in Manicaland, and the contribution of small grains to total household cereal production was significant in Masvingo, Matabeleland North and Matabeleland South.

While improvements, these average figures are still low. And compared to the production levels from new resettlement households minute. Our studies in Masvingo, even in the poor rainfall years of between 2010 and 2012, show much higher averages (although with variations). Gareth James’ studies from Mashonaland shower higher outputs still. Again in the poorer rainfall years, he recorded average outputs of maize some 12 times these average national figures for all cereals for the good rainfall year of the past season. Of course the new resettlements have proportionately fewer people and so appropriately in a national representative sample this should be reflected. But, without data broken down and without indications of variation, the ZimVac study still fails to capture this story. As I have argued before (many times!), this is important for policy, and for thinking about national food security.

The ZimVac survey showed that for the 2013/2014 agricultural season approximately 45.2% of the households benefited from the Government Input Support Scheme, which was the main source of inputs. The proportion of households accessing maize inputs through purchase remained unchanged (39%) from 2013. About 2.3% of the households accessed their maize inputs from NGOs which was a decrease from 4.0% in the 2012/13 season.

Given the higher levels of production, the national average maize price was $0.37/kg down from $0.53/ kg during the same period last year. This pattern was also reflecting at the provincial level. Matabeleland South recorded the highest maize price ($0.65/kg). This was the same pattern during the same period last year.

Livestock (cattle, sheep and goats) were in a fair to good condition when the survey took place. Grazing and water for livestock were generally adequate in most parts of the country save for the communal areas, where it was, as is normal, generally inadequate. However, the report notes, there are marginal parts of Matabeleland North and South, Midlands, Manicaland and Masvingo provinces which had inadequate grazing which may not last into the next season.

According to the report, around 60% of the households reported not owning any cattle. Mashonaland East had the highest proportion of households not owning any cattle and Matabeleland South had the least. Nationally, only 14% of the households owned more than 5 cattle with Matabeleland South and Matabeleland Matabeleland North having a higher proportion of households owning more than 5 cattle.

Like the cereal production data, these national and provincial figures are very different to what we have found (and Gareth and others) in the new resettlements. Here cattle ownership is far higher, reflecting the richer, more capitalised form of farming found. Of course the ZimVac study may suffer from under-reporting, as in many large-scale surveys with huge samples, but the contrasts are interesting – and again potentially important.

In terms of food consumption, Masvingo had the highest proportion of households consuming an acceptable diet (75%) and Matabeleland North had the lowest (54%). This showed increased local availability of foodstuffs, and improved off-farm opportunities. However, nutritional indicators remained low, including a high prevalence of stunting. As commented on before, this mismatch between food intake and nutritional indicators remains puzzling.

So, following the food balance methodology the assessment adopts (see discussion of the methodology and its limitations in an earlier blog), the report estimates that for the 2014/15 consumption year at peak (January to March next year) is projected to have 6% of rural households food insecure. This is a 76% decrease compared to the (disputed) estimate the previous consumption year.

This proportion represents about 564,599 people at peak (which may of course be people suffering deficits for only a few days), not being able to meet their annual food requirements. Their total energy deficit is estimated at an equivalent of 20,890MT of maize; actually a very small amount, and not suggesting any urgent need for food aid, given the margins of error in the estimates. Matabeleland North (9.0%), Matabeleland South (8.3%) and Mashonaland West (7.7%) were projected to have the highest proportions of food insecure households. By contrast, Manicaland (2.7%) and unusually Masvingo (3.4%) provinces were projected to have the least proportions of food insecure households.

So in sum, a good harvest results in a good food security situation. This is of course good news, and no surprise. But the report and the analysis still raise many questions. I hope that those working on food and farming in Zimbabwe can join forces and think harder about questions of sampling, the contributions of the new land reform areas to production, and the complex dynamics at the heart of the food economy that underpins food insecurity prevalence and distribution. The ZimVac annual survey is a major contribution, but with some thought and adaptation it could be contributing much more to our understanding of changing livelihoods and food economies in the post-land reform era.

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

 

 

 

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The new farm workers: Changing agrarian labour dynamics following land reform in Zimbabwe

Farm labour has been highlighted by many as one of the big losers from land reform. Certainly, the post-2000 land reform in Zimbabwe has resulted in a significant displacement of farm workers from former large-scale commercial farms. However, the scale and implications of this are much disputed, and poorly understood. In assessing the implications for employment, livelihoods and agrarian relations, it is critical to have a proper assessment of what has happened since 2000. Unfortunately, as with so much in the Zimbabwe land debate, this discussion is coloured by inaccurate figures and ideological positions, unsupported by empirical data.

Fortunately, a new paper by Walter Chambati, one of Zimbabwe’s leading researchers on agrarian labour, has just been published the Future Agricultures Consortium in association with AIAS. This helps move the debate forward by providing a detailed examination of changing agrarian labour relations based on detailed research from Goromonzi district, one of the high potential farming areas of Zimbabwe influenced by land reform.

One of the big problems with the debate about farm labour since 2000 has been (once again) the lack of data on what happened to farm workers following land reform. The figures regularly trotted out in the media, and by many others too are usually wildly inaccurate. For example the MDC in their recently launched policy paper claimed (p. 44) that “some 400 000 farm workers have been displaced with their families plunging nearly 2 million people into destitution and homelessness” due to what they term the ‘chaotic’ land reform. This is way off the mark, and inevitably colours the analysis and the policy conclusions reached.

When we were putting together our book in 2010, we searched across the available data and tried to triangulate between sources. Our best estimates (based on CFU, GAPWUZ, CSO/Zimstat and other sources) were that before land reform in the late 1990s, there were between 300000 and 350000 [ammended, 29 May – see comments] permanent and temporary farm workers working on large-scale farms and estates. Of these 150000-175000 (169000 in 1999 according to the CSO) were permanent workers, making up a total population of around one million, including any dependents. In the new settlements established after 2000, around 10000 households were established by those who were formerly permanent farm workers, along with others who were temporary farm workers and joined the land invasions. A further 70000 permanent worker households remained in work on estates, state farms and other large-scale farms. There were also substantial numbers of in situ displaced people still on farms living in compounds, seeking work on the new farms and perhaps with access to a small plot – perhaps around 25000 households. These were predominantly in the Highveld areas where significant farm worker populations resided on the farms, many without connections elsewhere and originally migrants from elsewhere in the region. Thus nationally this all means around 45000-70000 permanent farm worker households were displaced and had to move elsewhere – to other rural areas or towns – while others who were temporary workers had to seek new sources of income but remained based at their original homes, some continuing as labourers on the new farms.

These figures suggest a very different pattern to that suggested in many media commentaries, donor reports, and policy documents. It is disappointing that some more thorough cross-checking does not take place before these are published. Of course such patterns of displacement and resettlement vary dramatically across the country. In the Highveld areas where highly capitalised farms required large amounts of labour – for instance for tobacco or horticulture operations – displacements were significant. Outside these areas, the pattern was different. This was the case in Masvingo province, where land reform displaced largely ranch operations which offered limited employment. However, while there has undoubtedly been displacement and associated hardship, the scale and implications are very different to what is often suggested.

And what has replaced the former pattern of farm employment? Again this varies significantly, depending on the intensity of the farm operations, type of crops and the type of labour required. Across the farms in our study sample in Masvingo, we found that in the late 2000s on average 0.5 and 5.1 permanent workers were employed in A1 and A2 farms respectively, while 1.9 and 7.3 temporary workers were employed. This is shown in Chambati’s studies of labour in Goromonzi, and his earlier studies in Chikomba and Zvimba , and is confirmed by the AIAS six district study that showed how the average number of permanent workers increased from 1.28 on the smallest farms (up to 5 ha arable area – largely A1) and increased to 4.87 labourers for farms with arable areas of above 40 hectares (largely A2). The number of casual workers increased from 5.43 to 10.69 labourers across these ranges (p.118). Aggregating such figures up across new resettlement farms nationally, this represents a considerable amount of employment generated, including for many women.

When the new farms replaced low employment operations such as ranching as in many of our Masvingo sites, the amount of employment available now far exceeds what was there before. However farms that employed greater amounts of labour before, the opposite may well be true. And in addition to the numbers of jobs, there is of course the question of pay, conditions, and the type of skills required. This again is highly variable.

Chambati explains how the labour regime has continued to evolve, especially following the dollarization of the economy:

“Non-wage labour such as sharecropping and labour tenancies are emerging in response to shortages of finance to hire labour. The integration of farm labour and land beneficiary communities through familial relations and other social networks provides prospects for the improved social reproduction of labour. The new form of social patronage based on kinship ties is being extended as more relatives are brought in for farm work to minimise cash outlays on the dollarized farm wages”.

It is important to keep up with these changes, and understand the transformations in labour regimes that are occurring, with their implications for wages, rights, gender access, skill requirements and overall employment levels. With small-scale, medium scale and large-scale farms competing for labour in a particular area, there is a range of new dynamics of play. In the Highveld, the farm compound, although transformed from the past, remains a site of contestation, as Chambati explains for Goromonzi.

The social relations of labour on the new farms are of course a far cry from the exploitative residential tenancy system of the old large-scale commercial farms, with a diversity of new arrangements seen. But this does not mean that exploitation has disappeared. The new farm workers, dispersed over many more farms and often embedded in kinship networks, are poorly organised and unable to articulate demands effectively, and there has been downward pressure on wages. The organisations that once assisted farm workers on large-scale farms have yet to re-orientate their activities towards these new vulnerable groups.

With the debate still focused on the discussion of displacement, and informed by a poor understanding of what happened, a thorough reappraisal of rural labour regimes and their implications following land reform has yet to happen. The paper by Chambati offers a further useful case study to complement others, and suggests some important new directions. Sadly the policies of Zimbabwe’s farm worker labour unions and all the political parties remain largely silent on this issue, stuck in old debates informed by poor data. The same is true of much of the policy and media commentary.

Farm labour on the new resettlement farms generates a considerable number of livelihoods. As a source of employment, this sector is underestimated and poorly understood, yet is highly significant in the rural economy. As a group with poor labour rights and in need of organisation and support, the new farm workers are also an important constituency for unions, support groups and others. Hopefully those thinking about future policy frameworks will read Chambati’s paper – and indeed all the other studies on the subject – and think harder about rural labour issues, before pronouncing a standard, but now thoroughly disputed, narrative.

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

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Why good numbers matter in Zimbabwe (part II)

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.

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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