By Jason Donovan, Dietmar Stoian, and Evgeniya Anisimova
There has been a lot of attention to the value chain development (VCD) approach in programs aimed at poverty reduction and rural development over the past two decades. This has led to production of numerous guides, diagnostic tools, and applications. While offering useful snapshots of current structure and performance, many of these guides fail to account for a highly adaptive and changing nature of smallholder value chains and pay little attention to the dynamic forces affecting these chains or to adaptation. Yet, understanding these dynamics and adaptation is essential for these value chains to remain competitive in turbulent markets.
Some of the crucial factors we need to take into account when analyzing smallholder value chains and their ability to adjust to changes over time include:
- Various risks and dangers present in the political and marketing environment in which the value chain operates;
- Conflicting interests among value chain actors which are likely to become more pronounced in the face of negative shocks;
- Alternative economic activities smallholders could engage in if the shocks affecting their current value chain are too big to deal with;
- Resources needed and available for smallholders and other value chain actors to make such adaptations.
Our work on these issues, summarized in this paper, aimed to have a fresh look on value chains involving smallholders from the perspective of complex adaptive systems.
- Time (which implies the need to see value chain performance in historical perspective, analyzing the sequence of events that created shocks and led to adaptation by one or more chain actors);
- Sensitivity to (changes in) initial conditions (a.k.a. “Butterfly Effect” – when a relatively small change in, for example, a tax rate or quality certification affects a value chain that might be even indirectly linked to this change. The effect may be small, but it can also be very significant)
- Endogenous shocks (shocks generated from within the system)
- Sudden change (shocks coming from outside, such as, for example, the loss of a major buyer, a sudden pest outbreak, or a policy U-turn that can destroy a value chain overnight)
- Uncertainty (as if those external and internal shocks were not bad enough, they are hard or impossible to predict!)
- Interacting agents (value chain actors might have different functions and different, sometimes conflicting, goals and preferences – which affects how the chains performs)
- Adaptation (ability to evolve and learn – which success for the value chains involving smallholders often depend on an appropriate business model, support service providers, and government policy (a.k.a “enabling environment”)
We suggest that this framework can have two main applications: to ask new research questions (see table of diagnostic questions) and to analyze case studies (see an example in Box 1)
Time: between 2013-2017 Kenya’s exports of khat (Catha edulis) have grown by 10% per year, earning $232 million and making khat the country’s most valuable regional export. In February 2015, however, this expanding and highly lucrative value chain suddenly collapsed.
Uncertainty: although legal in Africa, khat is banned as a harmful drug in the USA, Canada, China, and most European countries. An influential Somali lobby group campaigns against trade in khat on the grounds that addiction causes unemployment and family breakdown. The export market also depends on efficient air cargo services since khat has to be consumed within three days.
Sensitivity to initial conditions: about 40% of the Kenyan crop is exported, with two thirds of exports going to Somalia, and one-third to the Somali diaspora in Europe. A Europe-wide trade ban on khat would therefore have a significant impact on the performance of the khat value chain.
Shocks: following a ban on khat imports by the Netherlands in 2012, the UK became the hub for illegal trade in khat to Europe and the USA. In June 2014, supported by Somali lobbyists, the UK declared khat a Class C drug, effectively closing the European market to imports from Kenya.
Interacting agents: the Europe-wide trade ban led to oversupply in the regional market for khat, which resulted in falling prices in Somalia. Farmers in Kenya saw the price of khat (locally known as miraa) fall by one-third. At the same time, the Somali government increased taxes by 100% to $200 per bag. This reduced demand from khat traders in Somalia who believed that consumers were unwilling to pay higher prices. Middlemen in Kenya responded by suspending the 16 daily flights from Nairobi to Mogadishu needed to supply the Somali market.
Adaptation: the Kenya Miraa Farmers and Traders Association (KMFTA) has held discussions with the British opposition Labor party and hopes that the ban will be lifted in case of a change of government. The association plans to engage the European Court of Justice to challenge the UK ban. Since the ban, government officials in Kenya have been meeting regularly with growers to discuss the latest developments and the possibility of growing alternative crops.
Conclusion: the experience of the khat value chain in Kenya makes sense when analyzed as a complex system where shocks produced sudden and unpredictable outcomes, where interacting agents created a “cascade” that closed down the value chain, and where asymmetric power between value chain actors and nation states prevented successful adaptation.