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Insuring against disaster: the politics of protection

One of the most popular responses to drought – and disasters more generally – by aid agencies today is insurance. This fits the current development mood, requiring market-based solutions that operate at a distance and work seemingly ‘efficiently’, offsetting the need for cumbersome, late responses of state or development agency delivered humanitarian aid.

It all sounds good in theory, but what is the reality? This was a theme we explored in Zimbabwe both in the field with farmers across our sites, as well as in Harare with some of those managing a new insurance approach for drought response, sponsored by the Africa Risk Capacity Group, and supported by multiple donors.

Many types of insurance  

Insurance comes in many shapes and forms. There’s classic indemnity insurance where if a disaster strikes an assessor will estimate the damage and pay out if you have a policy. This is the sort of insurance you have in case your house burns down or you crash your car.

Index-based insurance by contrast aims to pay out before the event happens. This provides early response and avoids the dangers either of later payment or of emergency responses that can undermine people’s livelihoods in the longer term. Here pay-outs occur if a threshold is crossed, say of rainfall or its proxy, such as vegetation cover. Index-based policies have become quite popular for agricultural and livestock insurance in developing countries, for instance.

A final type is sovereign insurance where a whole country takes out a policy against drought (or some other disaster) and the pay-out happens again if a threshold (the ‘attachment point’) is crossed (such as below average rainfall across the country, assumed to be affecting a certain number of people). This is aimed at ensuring ‘early action’ as part of an anticipatory approach to assistance, which again doesn’t have to wait for the mobilisation of international funds after the event.

Sovereign insurance: the Africa Risk Capacity model

This ‘sovereign insurance’ is what the government of Zimbabwe has recently bought from ARC (USD 2.5m) in partnership with the UN World Food Programme (USD 1.5m) and the START network (USD 2.5m) (a consortium led by international NGOs involved in humanitarian assistance with 20 members in Zimbabwe), who run ‘replica’ programmes paid for by international aid donors. The model will pay-out when an estimated 3.3 million people are affected (the attachment point), with a response cost of USD 154m, and so is designed for a major national disaster not for regular responses to food insecurity.

However, the ARC approach, which is an African Union initiative, with separate development and commercial arms, has had a chequered history. For example, in 2016 the insurance failed to pay out in Malawi during what was clearly on the ground a disastrous drought. In the end an ex gratia payment was made, but the approach was seriously critiqued – a damning Action Aid report argued that this was the wrong model for improving resilience. Despite its many promoters in the aid agencies, the ARC lost credibility and there was a period when it looked like it would collapse with insufficient country subscribers and too little funding to reinsure. A preliminary evaluation by OPM suggest some major flaws, particularly in the way the underlying ‘AfricaRiskView‘ model was constructed.

Since then, there have been multiple efforts at improving the system and customising the underlying model. Zimbabwe has bought into a very different operation, with the model being calibrated with local information through a committee, led by the Ministry of Finance, with many experts from the ministry of agriculture (Agritex) as well as aid agencies and NGOs. Although the details of the model are secret – they are proprietary information of the commercial arm of the ARC, and the basis on which it presumably gears a profit from its assessment of risks – there is clearly more local participation and transparency than before. Those who have premiums invested can monitor the progress through the season, assessing if a pay-out is likely.

The implementing agencies, whether government, the WFP or the START network, must come up with a contingency plan for how they will deliver assistance in case a pay-out is made. The current policy aims to reach up to 800,000 people in drought-prone districts if a full pay-out occurs. This is supposed to mean that things can happen quickly and before the worst impacts of a disaster strike.

Contingency plans currently involve the usual array of targeted interventions, with all the problems that these entail, but the principle of early action and rapid response is definitely a good one; although such plans need to be held in place and updated in all the years in between pay-outs (the ARC deal for Zimbabwe expects, but doesn’t guarantee, a one in four year pay-out; the assumed ‘return period’).

Practical concerns

How will it work it practice? This is the first year with the latest round of insurance, so the simple answer is we don’t know. Past experience is limited in Zimbabwe, as there has only been one payment from a previous round in mid 2020 of USD 1.4 million to the government and around USD 300k to WFP (as the replica premium holder) following the poor rains of 2019-20.

As one senior official from a humanitarian agency observed, such pay-outs are all well and good, but they are a drop in the ocean. When hundreds of millions of dollars are required across over 8 million food insecure people and maybe 1.8 million in dire need, then the premiums required to cover this would be enormous, and way beyond any agency or donor, let alone the government (even though of course the ‘food insecurity’ figures may be dubious). For example, under the new policy WFP’s maximum pay-out would only be USD 6.5 million; hence people referred to the approach as a ’boutique experiment’, with varying doses of scepticism.

Other commentators we talked to were intrigued by the system, desperate to find a new way of doing things given the failures of the standard approaches but wondered whether a profit motivated company could really deliver funding for humanitarian assistance through global reinsurance markets. Should insurance brokers be making money out of disasters? For the humanitarians this was a difficult one, even if it worked (which was not sure).

There were many other debates about the practicalities and questions were still raised about the underlying model. For example, it focuses on maize as the indicator crop, but what about in areas where sorghum or other crop mixes are important? What about livestock? It was unclear if the model differentiated between different soil types – the impact of rainfall deficits is massively different between sandy and heavy soils, for example. The idea of a ‘sovereign’ drought given the variability of patterns across the country seemed odd to others. How can a single-cut off for the whole of such a diverse country be decided? And how can a single point be defined, when drought always evolves through the season?

As the next seasons unfold (the premium has been paid over several years), we will see how it pans out. A key issue for any index type insurance (where predictive risk indicators are used and actual damage is not assessed post hoc) is the question of what is called ‘basis risk’: the difference between the predictions and the actual outcomes.

If this is large (as was the case in Malawi in 2016), then the insurance pay-outs are not geared to need, and the product becomes ineffective. Those who pay the premiums or expect pay-outs rightly object and in an increasing number of cases, ex gratia pay-outs are made, effectively acknowledging the failure of the calculative risk model.

Even though the ARC model has been improved, there are plenty of reasons to expect significant basis risk, given what we know about drought in Zimbabwe (see previous blogs here and here).

From risk to uncertainty: basic challenges to insurance as a solution to disasters

There are however some more fundamental issues with insurance as a response to uncertain disasters. The challenges are more than fixing the technical parameters and reducing basis risk and improving contingency planning and implementation.

Any insurance assumes you can calculate the probabilities of an event happening (or an index being triggered). This is the basis of setting premiums where the insurance company bets on incomes versus pay-outs based on estimates of the likelihood of trigger events/disasters happening. In other words, insurance is premised on assumptions about ‘risk’ – where you have a good idea of the probability of what is going to happen (a calculable, predictive approach) – but not ‘uncertainty’ – where you don’t know the likelihood of outcomes. Under conditions of uncertainty, insurance will not work.

By assuming risk, insurance creates particular political dynamics centred on strategies of management and control. As a result, most contemporary forms of insurance are based on a modernising, market-based vision of predictive control, generating forms of ‘governmentality’ over insurance ‘subjects’. In the case of the ARC’s sovereign insurance this is scaled across a whole nation, is exerted through an insurance company with links to the global insurance markets and is executed on the ground by the state, UN agencies or international NGO networks.

This is very different to the drought responses among farmers, discussed in previous blogs in this series, with radically different politics. As discussed in the previous two blogs, it’s uncertainty not risk that dominates the experience of drought in Zimbabwe, as elsewhere. This becomes more the case as climate change affects patterns of rainfall. Taking uncertainty seriously suggests a very different approach to disaster response.

An approach centred on uncertainty, rather than risk, would have to adopt the approaches used by farmers in the face of variable patterns of rainfall and uncertain drought conditions, described in the previous blogs. This would mean flexibility, adaptability, contingency planning and performative responses to unfolding circumstances (a theme picked up in the next blog).

This is less neat than the technocratic, market solution of insurance, and for large and cumbersome agencies may be impossible. But, in the end, an uncertainty-centred approach may be more effective and more attuned to the real world of uncertainty that characterises drought and disaster, as we argued in a recent paper in relation to humanitarian and social assistance more broadly.

Time will tell how the ARC sovereign insurance model fares in Zimbabwe. Maybe it can adapt and become the flexible, early response system taking account of uncertainties that is so needed. Watch this space for updates in the coming years.

This is the third blog in a short series on ‘drought’. See the first and second blog here and here.

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Is Zimbabwe food secure this year?

A bumper harvest has meant that Zimbabwe is largely food secure this season. Despite the fall armyworm outbreak, maize production was up to an estimated 2.1 million tonnes, thanks in large part to good rains. Government and donor support programmes supplying fertiliser helped too. Total cereal production – including small grains such as sorghum and millet – was estimated to be 2.5 million tonnes. Areas planted expanded significantly, with maize planted on 1.9 million ha, up from 1.2 million ha the previous year. Tobacco production – a key source of income for buying food for many – suffered a bit due to heavy downpours and waterlogging, but a good season was recorded with 186 million kg produced. Grain imports were expected to be minimal – perhaps 5,000 tonnes mostly through informal cross-border exchange – and the GMB and private producers were storing grain in large quantities. The Grain Marketing Board was expecting to purchase about half a million tonnes of maize.

So, with a good season, combined with effective supply of lots of fertiliser, Zimbabwe returned to its former ‘breadbasket’ status. Or so goes one narrative on last season. Certainly output last year was impressive. Everywhere you went was a maize field; green and productive. The government hailed the ‘command agriculture’ scheme as the basis for reviving commercial production (see next week’s blog). And the aid donors were thrilled with their food security programming. But without greater resilience in the agri-food system, this new success is fragile. What if the rains fail again, as they did in 2016 due to El Nino, when only 512,000 tonnes of maize were produced?

Vulnerabilities persist

Even in this year of apparent plenty, the ZimVac study, which looks at food security and livelihood vulnerability nationally, warned that some people, in some places, right at the end of the season were likely to be food insecure. The World Food Programme country director quoted the figure – 1.1 million people will be food insecure in Zimbabwe. As discussed many times on this blog, this sort of statement is dangerously misleading and irresponsible, but of course understandable, as it is wrapped up in the politics of food, and the positioning of large UN agencies, donors, relief NGOs and the state, each reliant on claims about food insecurity for their flows of income.

But there is an important point underlying the headline figure (which really is a distraction, but one the newspapers love each year when the ZimVac report comes out). As the detail of the report shows, vulnerabilities have not gone away. The cash crisis currently gripping the country, the stealth of rising inflation and parallel markets, and the lack of access to food or income to buy it is what is worrying. Pockets of vulnerability persist: on the margins of the country where market connections are poor; among highly marginalised groups (the unwell, disabled, aged, infirm, or child-headed households); and particularly in communal areas where access to productive assets (most notably land) is limited, or in urban settings where employment is fragile and connections to rural homes is weak.

Understanding food systems

With centralised food storage and a boosting of irrigation and production capacity in commercial farms (notably A2 land reform areas), the prospects of overall food balances being met at a national level are improving. But as Amartya Sen argued long ago, aggregate food availability is not the same as access and entitlement; and it is entitlement failure more often than not that causes food insecurity and famine. This is why the debate needs to shift to food systems – and the links between production, markets and provisioning. While getting estimates of total production through the annual crop assessments is vital, it is not enough. Even the relatively sophisticated vulnerability assessments that use this data do not capture everything, as I have discussed before.

The maps of food insecurity that the agencies put out do not reveal the social and political geography of the different colour shades. How are urban and rural areas linked? What is the relationship in the food system between communal areas and new resettlements? Where are markets and how are they linked to producers and consumers, by what infrastructure? And so on. This requires a more connected approach, one that perhaps looks at regional interactions, and especially links between areas.

Land reform areas: central to food security?

My hunch is that at the heart of the new agri-food system, and central to a new perspective on food security in Zimbabwe are the new resettlement areas – to date mostly the A1 areas, but increasingly A2 too. While not everyone by any means, our data from Mvurwi, Matobo and Masvingo shows that there are a significant group (ranging from 60% to 40%, depending on site and season) who are producing surpluses year on year, selling on through local markets, transferring to relatives in town, or storing for future years. More or less everyone produced surpluses this year, but even in bad years, like the last few, this is an unseen motor of the new food economy.

In the generic reports or undifferentiated maps, this dynamic is not revealed. Aggregate pictures do not tell the full story. There is a politics to keeping this from view of course, but also a lack of capacity in data sampling and analysis. We are currently extending our earlier studies that looked at communal areas near our A1 sites to look at links, and interesting stories are emerging, but these will inevitably remain case studies in need of locating in a wider national picture for planning and policy.

It is great news that Zimbabwe is (mostly) food secure this season, and such a massive harvest was reaped. But food and agriculture policy cannot rely on just hoping for a good rainfall season – especially with the heightened variability due to climate change – and must take on board a more nuanced perspective rooted in a deeper understanding of how the post-land reform agri-food system works in Zimbabwe. It is amazing to me that this has yet to happen, more than 17 years on.

This post was written by Ian Scoones and appeared on Zimbabweland

 

 

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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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Food crisis in Zimbabwe: 2.2 million at risk. But where do the figures come from, and what do they mean?

The newspapers have been full of commentary on a looming food crisis in Zimbabwe. This has followed from the World Food Programme’s press release that 2.2 million people will be in need of food aid in the coming months. The Commercial Farmers Union has called it a ‘man-made crisis’, the direct result of the ‘chaotic’ land reform, and a decade of inappropriate policies.

I wanted to find out a bit more about where the 2.2 million figure came from. It’s a big number, and would mean a lot of food imports, way beyond the means of the Finance ministry. After a bit of digging I eventually found the figure, buried on page 122 of the ZimVac (Zimbabwe Vulnerability Assessment Committee) livelihood assessment draft report for 2013.

Each year ZimVac, a coalition of NGOs, researchers and government agencies, undertake a major rural livelihood assessment, based on a sample of over 10,000 households across the country. The sample is drawn according to the latest ZIMSTAT ‘master sampling frame’, and the resulting data is aimed to be representative of the country as a whole. It’s an excellent and important initiative, but it has its deficiencies, as those involved readily admit.

The process for deciding the headline figure is complex. It involves assessing for each household all the cereal production, and then adding in income from employment, remittances, livestock sales, and other sources of income that could be used to buy food (p. 120). Assumptions on prices and market availability are used to translate income into food and in turn energy (p.121). The food security assessment is based on the household’s potential access to enough food from all sources, including purchases, to give each member a minimum of 2100 kilocalories per day in the consumption period 1 April 2013 to 31 March 2014 (p. 119). The total number in food deficit figure is then calculated as a sum of all of those experiencing any negative balance in the accounting period.

It’s a complicated procedure with lots of steps and plenty of assumptions. What the headline figure doesn’t indicate – although the report does, and the background documents for the ZimVac surveys over the years are quite transparent about this – is that the big number includes many people who may have a projected deficit for actually a very short period. Indeed, at the time of the survey in May 2013, over 80% of households surveyed had no hunger problems with only a very small proportion recording ‘severe hunger’ (p. 115). The report shows that there is a progression of food insecurity, with a peak of 2.2m people expected in January to March 2014 (p.124). 31% of the total (683,000 people) move into food deficit only in this crunch period before the next harvest; and some of whom may in fact be food insecure for only a very few days.

The 2.2 million figure is of course a good flag-waving number for the WFP to raise funds, and for the CFU to bash the government for the land reform (and even President Mugabe is now joining the critique of the ‘new farmers’), but the actual implications are more complex. Here are five reasons why we need to be cautious about the figures.

  • First, there’s geography: as the report shows the problems are concentrated in the dry south of the country which experienced the worst season in terms of rainfall and its distribution (p.125-6).
  • Second, there is almost certainly (as ever in surveys) an underreporting of income, and so purchasing power. Since in drought years, market purchases are essential for food entitlements, this is rather crucial.
  • Third, the assessment model allows for only limited sales of livestock to compensate for food deficits (households are assumed to retain a minimum of 5 goats and 3 cattle). Yet livestock is precisely the asset in the drier parts of the country that are used in times of drought to exchange for grain, and distress sales are common, and important for food security.
  • Fourth, remittances are especially important in drought-prone areas, yet the figures used in the model for this year are based on recall of last year’s receipts. Last year was of course a relatively good year for rural production, and so remittance flows inevitably dropped. But this year, you can be sure, they will increase in response to the shortfalls. For perfectly good reasons, the model does not account for this, but it’s another reason why we can expect things to be not as bad as predicted.
  • Finally, the assessment does not include early cropping – for example of green maize – which is often important in that crunch period before the ‘proper’ harvest.

For all these reasons and more, we should be cautious about the headline statistics, and understand in more detail what happens to whom and where.

One of the most striking figures in the report is the prediction that 98% of rural households nationally will hit a food deficit by next March if only cereal production and stocks were included (p. 123). Of course this includes those with no food production to speak of, such as farmworkers and other rurally-based non-farm households. But even discounting this group, this is striking, and does suggest a problem in agricultural production, as Charles Taffs of the CFU indicates. However, again we must be cautious in jumping to conclusions.

One big concern I have with recent national surveys is that they have been sampling according to old sample frames set before the land reform. This was the case for the 2011 PICES (Poverty, Income, Consumption and Expenditure Survey) study and the 2010-11 Demographic and Health Survey, both using the 2002 census sample frame. I have been assured that the ZimVac survey for 2013 used an updated sample, with ‘enumeration areas’ allocated proportional to population distribution derived from the 2012 census. If so, this would have included the significant populations, especially in A1 areas, who are – at least according to our data from Masvingo – producing more and doing better than their counterparts in the communal areas, where most the earlier rural samples are drawn from. And in our study areas on A1 sites we see between half and two-thirds of the households producing sufficient cereals for the year – not just 2%,

Following the 2012 census, ZIMSTAT is revising the national ‘master sample frame’, and hopefully from now on national surveys will be statistically more representative. Unfortunately it is still difficult to stratify the data according to land use types, and so distinguish between resettlement areas and others, so Taffs and co should probably hold off on their outright dismissal of land reform on the back of this data for now. As ever, it’s more complicated than it first seems.

That said, last season was unquestionably a worse one than experienced in the last few years, including in Masvingo. It also hit some higher potential areas hard, with a very unevenly spread rainfall. Despite improvements since 2009, input supply was again erratic and untimely last year. Also, maize area planted was again down, reflecting the shift from food crops to tobacco in some areas, perhaps especially in those food producing areas in the higher rainfall zones. This restructuring of the crop system is directly driven by incentives – tobacco, supported through contract arrangements – , is a much more profitable crop than maize, especially if marketed through the Grain Marketing Board. Over the last decade or more we have seen switches to small grains (although plantings were down this past year according to ZimVac), but these are still a small percentage of total crop output, and it remains maize that drives the food economy, although much of this circulates outside the formal channels, and so is difficult to capture in national statistics.

So what should we make of all this? Certainly there is going to be a problem of food deficits in the coming months. However, problems are going to be concentrated in a certain time period, and outside a few areas and for more vulnerable people, it’s not going to be as bad as the headline figure and the media commentary perhaps suggests. Imports will certainly be needed, and targeted food aid will be important, but other coping strategies will also come into play to offset the worst.

Indeed this seems to have been the pattern over many years now. There is a ritualised flurry of activity around this time of year, with the aid agencies calling for funds to support food aid, and those critical of land reform saying that this ‘proves’ that Zimbabwe has gone for food producer to ‘basket case’. Yet by the end of the season, the expected famine has not occurred and, although hardships unquestionably are faced, the scale and depth of the problem is not as expected. This can be explained due to both sampling and non-sampling errors inherent in the standard surveys; but also significantly because assessments have not got to grips with the new patterns of production (particularly in A1 areas) and marketing (mostly informal). This will require new, and better attuned, data collection techniques.

Unfortunately too often the emergency, humanitarian aid and disaster relief momentum overrides discussion of the developmental issues, and the scramble for food aid (and all the associated politicking) diverts attention and resources. As I have mentioned in this blog many times before, rural development challenges are many. They include the need to invest in irrigation to offset drought vulnerability, the importance of investment and reforms to ensure timely supply of inputs, a pricing and market policy to balance incentives between food and cash crops, a livestock policy that ensures such assets are secure and available in times of need, and, overall, more concerted support for the resettlement areas to ensure that they can indeed supply the nation with food.

Next week, I will continue this theme and look at the data on production and imports over time in a bit more detail. Since 2000 there is little doubt Zimbabwe is in a new era, and policy responses have to take this into account.

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

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