In many markets we find ‘institutional biases’ – consistent patterns of behaviour among businesspeople, consumers or governments which affect how the market works [1]. Traders and farmers regularly cheat each other out of income. Businesses use networks of friends and family to side-step regulations or bring down competitors. These and many other negative behaviour patterns are important causes of underdevelopment. In May the BEAM Exchange released a paper highlighting this, written by Eric Derks and Michael Field. In this article I review the paper and offer some additional guidance on the topic.

Let me begin by summarising, and slightly simplifying, the authors’ main argument. The paper argues that institutional biases are an important cause of under-development. That correcting them is an important task of development programmes, yet few development programmes do this. And that one explanation is programme staff’s frequent failure to spot institutional biases. The paper therefore introduces concepts from the literature on Value Chain Governance (VCG), aiming to clarify how we can identify institutional biases. The paper also illustrates how several real-life programmes intervene to address the behaviour patterns holding back inclusive development.

In my view, the paper achieves its first aim: it attracts attention to an under-rated topic. The authors highlight several widespread patterns of behaviour which greatly influence market systems. One such behaviour pattern is biased relationships, where people favour their friends, family or networks, instead of dealing with the most appropriate or talented people. A second important institutional bias is ‘extractive’ behaviours, where businesses seek the maximum short-term gain from a deal, even when it harms their client’s business. ‘Extractive’ businesses ignore ways of working with their clients which would benefit both far more in the long-run.

A second aim of the paper is to offer practical guidance on how to spot institutional biases using concepts from the value chain governance literature. The central idea is that, in many markets, powerful firms and regulators set parameters which other firms must follow. These parameters include grades, standards and order volumes. Value chain governance prompts us to pay attention to companies’ and regulators’ power to shape the business decisions of others in the value chain. Equally, how organisations choose to use their power.

Readers familiar with VCG will probably appreciate the paper’s explanation of how VCG can cause and be affected by institutional biases. I would add two notes of caution, however.

Firstly, to spot many of the institutional biases which affect our target markets, we must look beyond the market’s core value chain and analyse connected markets. Imagine a programme which aims to improve the incomes of maize farmers. Imagine we diagnose that what most holds back maize farmers is that they sow low quality seed. To understand why this happens, we diagnose why the seed sector isn’t working better for farmers. We trace back farmers’ low quality seed to institutional biases in the seed sector. For example, shopkeepers recommending the highest-margin maize seed instead of the highest-yielding one. In this example, unless we look beyond the core maize value chain and understand the seed sector, we would overlook the most important problems.

Secondly, as the paper notes, analysing VCG is only one of several ways in which programmes can identify institutional biases. To spot certain types of institutional bias, other types of analysis are better. For example, whereas VCG focuses on how one organisation treats others along the value chain, by analysing firms’ organisational cultures we can identify bad business practices which commonly occur within firms. Equally, whereas VCG focuses on intra-firm behaviour, consumers’ and workers’ behaviour patterns often affect markets as well, and are worth understanding [2].

On a separate note, readers less familiar with VCG concepts may well find the paper’s guidance hard to follow. For those readers, I offer the following straight-forward tips, as a simpler guide to finding and addressing institutional biases. Some are borrowed from the paper, others are my own.

1. Look out for the most common bad business practices as you search for causes of underperformance. Both in your target sector and connected markets. Do businesses in the sector ignore long-term growth opportunities? Do they fail to cooperate, even when it would benefit them? Do they lack interest in finding solutions to the sector’s problems? Are they complacent about innovation?

2. Keeping asking “why?” Once you’ve identified areas of low performance in the market system you’re analysing, ask “why haven’t business, government or civil society addressed this?” Problem Trees can help you here; as you dig deeper they assist in keeping your thinking structured. Page 59 of this market assessment offers an example.

3. Think twice before assuming a ‘lack of capacity’. Consider the behaviours, incentives and attitudes among people who appear to lack capacity. Why haven’t they acquired that capacity?

4. When you find negative business practices, understand and address their causes. Often they are the result of market conditions, such as when firms pursue extractive short-term goals because uncertainty makes long-term planning impossible. Other potential causes include cultural, social and political influences, and the concerns, ideas and memories which dominate local business thinking. Even a simple lack of information can cause institutional biases, such as when business leaders fail to spot inclusive growth opportunities. Here, programmes can help to fill firms’ information gaps, as in this example from Nigeria.

5. When you find negative institutional biases, avoid assuming that they are the only thing holding back development. Other problems often exist alongside institutional biases. For example, high transaction costs, information failures and outdated regulations often prevent markets from working better for poor women and men.

Overall, I find that Mike Field and Eric Derks’ paper makes a valuable contribution. It highlights how common bad business practices are, how these practices hold back development, and that programmes should do more to address them. The paper’s VCG guidance can help us to find certain institutional biases which hold back development. Especially if we look beyond the immediate value chain and analyse how connected markets, culture, social norms and politics influence our target market. Some practitioners may find the paper difficult to interpret. The tips above are intended to help, by summarising some key implications.

Finally, I recommend reading the paper’s examples of institutional biases and ways to address them. I found these real-life cases inspiring. They highlight how feasible it is for M4P programmes to tackle institutional biases, for the benefit of poor women and men.

[1] In the M4P Operational Guide and elsewhere, ‘institutional biases’ are referred to instead as ‘informal norms’.

[2] The Financial Diaries studies are a great example of research into poor women and men’s consumer behaviour. Programmes like Financial Sector Deepening Kenya use the Financial Diaries research to inform intervention design.

Leave a Reply