Significance over scale when selecting sectors

When promoting territorial economic development from an innovation systems perspective it is important to find ways of increasing the use of knowledge and innovation in the region. However, in mainstream economic development there is a tendency to target the private sector based on scale. This means that practitioners look at quantitative measures such as jobs, numbers of enterprises, numbers of beneficiaries, etc. when deciding where to do analysis and focus support. This is common practice in value chain promotion, sub sector selection, etc. Many development programmes do this as well prioritizing scale measures such as jobs, women, rural individuals, etc.

From my experience of assisting development organisations to strengthen the economic resilience of regional economies (which means more innovation, more experiments, more diversity, increased use of knowledge, more collaboration between different technological domains), I have found that the scale argument is distracting and too focused on the beneficiaries (whatever is counted) and not focused enough on those indirect public or private agents that are significant and that enable a whole variety of economic activities to take place. With significant I mean that there could even be only one stakeholder or entry point (so the direct scale measure is low) but by addressing an issue it enables a whole variety of economic activities to take place.

Of course, scale is very important when a local politicians need votes. It is also important when you have limited budget and must try to achieve wide spread benefit. For this reason scale is very important for social programmes.

However, when local institutions are trying to strengthen the local innovation system, in other words improve the diversity technological capability of a region, then scale becomes a second priority. The first priority then becomes identifying economic activity that enables diversity or that reduces the costs for enterprises to innovate, use knowledge more productively should be targeted. The reason why this does not happen naturally is that these activities are often much harder to detect. To make it worse, “significance” could also be a matter of opinion (which means you have to actually speak to enterprises and their supporting institutions) while crunching data and making graphs often feel safer and appear to be more rigorous.

My argument is that in regions, the long term evolution and growth of the economy is based on supporting diversification and the creation of options. These options are combined and recombined by entrepreneurs to create new economic value in the region, and in so doing they create more options for others. By focusing exclusively on scale, economic actors and their networks increasingly behave in a homogeneous way. Innovation becomes harder, economic diversity is not really increased. I would go as far as saying that success becomes a trap, because once a recipe is proven it is also harder to change. As the different actors becomes more interdependent and synchronized the system becomes path dependent. Some systems thinkers refer to this phenomena as tightly coupled, meaning a failure in one area quickly spills over into other areas. This explains why whole regions goes into decline when key industries are in decline, the economic system in the region became too tightly coupled.

But I must contradict myself just briefly. When interventions are more generic in nature, meaning they address market failures that affect many different industries and economic activities, then scale is of course important.

The experienced development practitioners manage to develop portfolios where there are some activities that are about scale (for instance, targeting a large number of informal traders) and then some activities that are about significance (for instance ensuring that local conformity testing labs are accessible to local manufacturers).

The real challenge is to figure out what the emergent significant economic activities are that improves the technological capability in the region. New emergent ideas are undermined by market failures and often struggle to gain traction. Many new activities requires a certain minimum economic scale before it can be sustained, but this is a different kind of scale than when practitioners use scale of impact as a selection criteria. Many small but significant economic activities cannot grow if they do not receive public support in the form of promotion, awareness raising or perhaps some carefully designed funding support.

There are a wide range of market failures such as high coordination costs with other actors, high search cost, adverse selection, information asymmetry and public good failures that undermines emergence in local economies. It is exactly for this reason that public sector support at a territorial level (meaning sub national) must be sensitive to these market failures and how they undermine the emergence of new ideas that could be significant to others. The challenge is that often local stakeholders such as local governments have limited influence over public institutions in the region that are funded from other spheres of public administration.

Let me wrap up. My argument is that scale is often the wrong place to start when trying to improve the innovation system in a region. Yes, there are instances where scale is important. But my argument is that some things that could be significant, like the emergence of variety and new ideas often get lost when interventions are selected based on outreach. Furthermore, the focus on large scale impact draws the attention to symptoms of problems and not the the institutional or technological institutions that are supposed to address market failures and support the emergence of novelty.

I will stop writing now, Marcus always complains that my posts are too long!

Let me know if I should expand on the kinds of market failures that prevent local economies from becoming technologically more capable.



Systemic Insight – Economic development is about introducing options, not bringing solutions

Marcus and I have just posted a new article on the Systemic Insight website. The post is about our recent article that was published in the IDS Bulletin (Vol 46.3) and is titled “Explore, Scale Up, Move Out: Three Phases to Managing Change under Conditions of Uncertainty”. 

Please note that in future all my posts specifically about complexity and resilience will be published on our Systemic Insight website. To my regular blog readers it may be worthwhile to also subscribe to the feed on that site. We promise to write more often now that the foundation for our applied research theme on complexity in development has been created. We already have many customers using our approaches and this area of work is promising and rewarding. We have added a new page to the Systemic Insight site that explains some of our most frequently asked about services related to complexity thinking applied to development.

On this site I will keep on writing about Private Sector Development in general, and particularly on innovation and innovation systems.

Your feedback, comments, emails, phone calls, tweets and likes are appreciated. Let us know what you think and what you would like to discuss, read about or just “air”.

Best wishes,


The oblique search for new industrial opportunities

Industrial policy is typically set at national level. It is often aspirational and attempting to “stretch” an economy into new kinds of production and value addition. Programmes are designed, targets are set such as doubling manufacturing contribution of x% within 7 years. Therefore it is sometimes disconnected from the present as it seeks a new Status Quo, a different structure of production.

Yet the natural process under which new production activities are created is complex. It is not as simple as finding a market opportunity, finding the right production process, securing funding and launching a business. The economic context, the political climate, the entrepreneurs with the right levels of experience, backing and confidence are all needed. And don’t forget individuals with a desire to expand, take risks and try new things.

Danni Rodrik argues that Industrial Policy should be a search and learning process. Many centrally planned industrial policies even cite Rodrik as they then commence with outlining with great certainty what must be done, by whom, with which resources and to which effect. This logic completely ignores the importance of what exists, and what is possible from here. It ignores that fact that the past matters, and that the current structures are the result of a series of evolutionary steps. Complexity science teach us that these plans ignore the fitness landscape, a landscape that is dynamic and constantly changing. Any attempt to extend the horison further than what is within reach should be treated with great caution. One of the greatest obstacles is the attide towards risk and the optimism of enterprises. I don’t think Rodrik meant the ministers officials must do the search, rather, industry must do the search or at least be actively involved in the search in partnership with government and institutions.

But the search is not about answering a simple question. A more oblique approach is called for (see John Kay, Obliquity). Which means we should set aside targets and indicators, and focus on creating small experiments to introduce more variety and options into the system. It means that finding out that something is not possible is as valueble as figuring out that something else is indeed possible. Taking Rodrik literally, it would mean also giving much more attention to what entrepreneurs are searching for and experimenting with in the background. It requires that we recognise that the current economy is creating what is viable under the current dynamic circumstances, and that only strategies that recognise where we are and what is certainly within reach from here is in fact viable. The challenge for developing economies is that what is possible is typically limited and further constrained by strong ideological bias as to what is possible or desirable. For instance, many South African business owners are trying to shift out of price sensitive markets competing on a basis of low cost skills. Entrepreneurs are moving into knowledge and capital intensive production, with more focus on service and integration. Government is searching for a way to employ people with low skills because its own social programmes and service delivery is not a viable fall back for people with insufficient skills.

The search is not about analysis
Complexity describes a situation where the patterns of what exactly is going on is unclear or shifting. We cannot entirely figure out what is leading to what and what is reinforcing what. Due to the dynamism, we cannot really understand the situation better through analysis. Another way of explaining this, is that a situation is complex when more than one competing hypothesis can with some probability explain what is going on. The only way to make sense of complexity is to try something, actually, try many things. And then see what seems to work better. It means that we start with what we have and who we know (and can trust), and then try a range of things with the simple purpose of seeing what is possible within the current constraints of the economic system. Steps must be taken to reduce risks (for instance by ensuring that the costs of failure are small, or that the experiments try different ways of solving the same problem), but then this whole approach in itself must be recognised to be politically risky.

This is where donors and development partners come in. By assisting developing countries to conduct low key experiments in order to create variety is essential, as development partners can reduce the political risks of their counterparts. This approach will furthermore require the abondenment of targets and indicators as an attempt to measure accountability and progress. A more subjective approach that sets indicators that monitors the overall health or dynamism is needed so that the experimentors can sense when they are indeed making progress. Thus the indicators does not measure success, nor input.

Perhaps then a skunkwork approach to a more complexity sensitive industrial policy approach is needed. Let the normal industrial policy targets and rigmarole be there. Politicions and bureacrats like this sense of certainty and purpose. But allow for some experimentation on the side under the heading “industrial policy research”. Allow this team to work with private sector partners to conduct small experiments to try new business models in an incremental way. For instance, do incubation to try new ways of mineral beneficiation, but without investing in large buildings or expensive equipment. Use what is existing as far as possible, even if it means having the manufacturing done on a contract basis elsewhere in order to test if local demand for the outputs exist.

The future aint what it seems

I have written many times before about my serendipitous journey into the topic of complexity. One of the important insights for me is that we cannot really predict much of the future under conditions of uncertainty. While there are many things for which we know what the consequences are, we have to acknowledge that there are many situations where we simply don’t know how things will turn out.

As I became more sensitive of the consequence of the insight about unpredictability I realized how much of my work hinged on assisting customers to somehow plot and engineer a specific future path. I moderate at least one strategy session for some or other developmentally minded organization every month, sometimes many times more. All these organizations want to set their portfolio of interventions into motion, and want to make sure their plans are foolproof and environment proof – meaning that failure can be avoided somehow.

Recently I started following Dave Snowden’s advice, assisting customers to have much deeper conversations about what is going on NOW, and what is possible NOW. We’ve been using the 3 Criteria for Quick Wins for a while (see note below), but now I emphasize living in the NOW. At first I felt a bit insecure to insist that we stop trying to focus on the ideal future, but now my confidence has grown. The amazing thing is that many of my customers are responding positively to this focus on what is possible now. Maybe it is more intuitive to work from the current. Maybe South Africa has become so complex that we can actually not afford to spend much time in the future.

I must add, we do still look at the future. I am not promoting a junkie style of optimizing the current without a view of the future. There are some things that we know about the future. For instance, if a University decides to increase investment in post graduate research, they know they will increase revenue, increase research outputs, without necessarily increasing fixed overheads. But we don’t set a high goal, set milestones and lunge into action. We start by saying “how does post graduate research work now?”. We explore the options, the possibilities and the obstacles. We also look at what we’ve tried in the past and whether the context has changed so that we can try a small experiment again. Then we develop a portfolio of small low risk interventions that can be executed simultaneously.

I have been following this approach for just a few months and I must admit that I am pleasantly surprised by the outcomes. Of course it felt weird in the start to leave customers with a portfolio of experiments instead of a clearly developed log-frame like project plan full of milestones, champions, indicators and deliverables. But I can see how my customers’ organizations have become a more healthy, balanced and perhaps even more naturally innovative.

The future ain’t what it seems because we have so many things we can do in the present. It takes real leadership to work with what we have and it takes real courage to break with the typical management-style of detailed project plans, log frames, project charts and the like.


Note about quick wins.

As Mesopartner, we define a quick win activity as one where:

1) The resources are within our control. This includes funding, but also key resources, key people and willing champions

2) The results are easy to communicate. Preferably the results are visual so that the benefits of change are disseminated easily to others.

3) We can take the first steps of implementation very soon, within days or weeks.



Industry development under conditions of complexity

Most economic development projects have a tendency to separate analysis from intervention or implementation. This follows on an engineering approach where you must first understand a problem or issue before you can design interventions which is then logically followed by implementation and later on evaluation. I will not now go off on why this logic is questionable as I have written about this before and we have dedicated the website to this topic.

But complexity thinking is challenging this norm of separating analysis and intervention.

Auwhere to gothors such as Snowden argues that under conditions of complexity, the best approach is to diagnose through intervention, which means that there is no real separation between diagnosis and intervention. Practically, you might have to spend some days and a little bit of effort to analyze who is interested in a particular issue so that you know where to start, but you have to recognize that even asking some simple questions is in itself already an intervention. Furthermore, the objective of working under conditions of complexity is to introduce more variety so that different approaches to overcoming constraints can be tried out simultaneously. This means that small portfolios of experiments must be developed and supported, trying many different ways to solve a problem. Many of these are guaranteed to fail, but new novelty will also arise. The health of a system depends on more options being proven viable. Strong alignment of interests, priorities and interventions are actually unhealthy for a system in the long run.

I’ve had this discussion many times with fellow practitioners in the last years and usually at some point somebody would say “but not everything is complex”. I agree. They would argue that there are definite casual relations between for instance education and economic development. Well, this may be true in some places. However, whenever a government (or a donor) decides that a particular sector or industry requires support it should assume that the issue is much more complex than it may appear, otherwise the industry actors and supporting organizations and demanding clients would have sorted things out by themselves.

The idea that diagnosis takes place during intervention has many detractors, despite the fact that many strong economic development organizations intuitively follows this process logic of working with diverse stakeholders in an ongoing process. Here is a short list of some of the detractors and their main reason for resisting such a process approach:

  • Large consulting firms: They would fight this approach as processes are much more difficult to quote and manage than a clearly defined project. Furthermore, this kind of approach depends on more expensive multidisciplinary experts that require a combination of technical, facilitation, change and business skills. The number of people that can support such a process are few and far in between.
  • The public sector: To overcome constraints created by complexity requires that dissent be nurtured and premature alignment be avoided. This is also risky for the public sector as things may not be so neat nor supportive of past policies and decisions. Furthermore, when more options are created it is not certain which firms will really take up the solutions – meaning that in a country like South Africa with strong benefit bias this is too risky, as preferred candidates might not be the beneficiaries of public support.
  • Donors and development organizations: Simple cause and effect interventions that depends on controlling certain inputs in order to benefit specific target groups still dominate the logic of donors. Therefore a process that is not specific, and that explores different alternatives may not be appealing to donors. Furthermore, donors are expected to be able to very precisely report not only in inputs, but also on impact. A process that has multiple shifting goal posts makes planning and resource management very difficult. However, many examples exist of donor supported projects that are very open to this approach, but this is mainly the prerogative of the programme managers deployed into the field – it is not systemic.
  • The private sector: Yes, even firms may resist an open ended and exploratory approach. One reason is that firms try to push the problems experienced in the private sector back onto the public sector (blame and responsibility shifting). An exploratory approach puts much more onus on the private sector to not only contribute, but to be open for alternatives and to then actively pursue opportunities that arise. Secondly, the incumbents in the private sector sometimes profits from a disorderly system. Many existing firms will resist newcomers trying different things and trying to create new markets, as this disrupts the way things are done at the moment. In a complexity sensitive approach we have to on purpose introduce novelty into the existing structures, and this means challenging some of the dominant views and agreements about what is going on, what must be done and why nothing has changed. This is very unsettling for the existing actors.
  • Top management in an organization: Management science in itself assumes many casual relations. For instance, strategy development typically starts with defining a vision and objectives, and then making sure that everyone is aligned and committed to these goals. As one of my favorite strategy David Maister argued  “strategy means saying no”. This means that resources are dedicated to a few specific areas in the belief that addressing these would have predictable and desirable effects.

Now I must state that in more ordered domains, where there is less complexity, many of the arguments outlined above are valid. In a small organization with limited resources priorities must be set. Governments cannot help everyone, so somehow a selection must be made. However, I believe that industry development is in many cases complex also because it is so hard to see how unpredictable effects will affect an industry.

I am grateful that I work with organizations that are willing to embark on industry development or institutional development processes that are more complexity sensitive. I believe that such an approach is particularly important for innovation systems promotion and for industrial policy. I am surprised at how many manufacturers and universities have agreed to embrace a more complexity sensitive approach to development, strategy formation and developing new services/products. All involved have been amazed at the early results this far, as these processes typically unleash a lot of energy and creativity by different stakeholders that in the past were more than willing to just observe from a distance what was going on.

Recognizing competing hypothesis as complex

In order to improve the economic performance of an industry or a territory, it is important to recognize the current Status Quo of the economy. This is basically to understand “what is?”, but to also understand “what is possible next?”. You may think that local stakeholders, firms and public officials will know the answer to “what is going on now?”, but every time I have done such an assessment I have discovered new suppliers, new innovations, new demands and many new connections between different actors.

The benefit of being a facilitator, process consultant or development expert, is that we can move between different actors, observe certain trends, recognize gaps and form an overall picture of what we think is going on. It is very difficult for enterprises to form such a picture as they can only observe other firms from a distance.

The main challenge is about figuring out what can be done to improve certain gaps or to change the patterns that we observe. These are answers to “What is possible next?” questions . As Mesopartner, we always insist that any process to diagnose an industry or a region starts with the formulation of various hypothesis. This hypothesis formulation before we commence is not only about revealing our bias, nor only about figuring out what exactly we want to find out. It also helps us to figure out what kind of process is needed, the scope of the analysis and what different actors expect from the process.

Unlike in academic or scientific research, hypothesis formulation does not only happen in the early stages of a diagnostic or improvement process, it should be constantly reflected upon and expanded as we go on during the process of meeting stakeholders and analyzing data. This is where the importance of recognizing competing hypothesis within our team and between different stakeholders are important.This process is not about convergence, but about revealing what different actors and the investigator believes is going on.

Economic development practice is full of competing hypothesis that all seem to be very plausible. In a recent training event with Dave Snowden the consequences of not recognizing or revealing these competing hypothesis struck me. According to Dave, competing hypothesis that plausibly explains the same phenomena indicates that we are most likely dealing with a complex issue. For instance, in South Africa we have competing hypothesis about the role of small firms in the economy. One hypothesis is that small firms are engines of growth and innovation, therefore they deserve support. A competing hypothesis is that large firms invest more in innovation and growth, and that they are better drivers of economic growth. Both hypotheses are plausible – the issue is complex. Recognizing this complexity is very important, as the cause and effect relations are not easy to identify and they might even be changing – the situation is non-linear. (Marcus Jenal and I wrote a working paper on complexity in development). This simply means that to get a specific outcome, the path will most likely be indirect or oblique – cause and effect is not linear.

Why is it important to recognize competing hypothesis, or to know when some patterns in the economy or complex? The answer is that it is almost impossible to analyze a complex issue with normal diagnostic instruments. Complex patterns can only be understood by engagement, that is, through experimentation. Again, according to Dave Snowden, you have to probe a complex issue by trying several different possible fixes simultaneously, then observe (sense) what seems to work best under the current circumstances. The bottom line is that you analyze a complex issue by experimenting with it, not by observing or analyzing it.

The implication of this insight in my own work has been huge. By recognizing that many issues that I am dealing with are complex (due to competing hypothesis that are very plausible) and can only be addressed through direct engagement has saved me and my customers a lot of resources that was previously spent on seemingly circular analysis. I now use the hypothesis formation with my clients to try and see if we have competing hypothesis of “what is” and “what must be done”. Where the hypothesis seems to be straight forward, we can define a research process to reveal what is going on and what can be done to improve the situation. But when we have different competing hypothesis of what is going on, we have to immediately devise several simultaneous experiments to try and find an upgrading path. I thought my customers would not like the idea of experiments, but I was wrong.

The conditions are that you must take steps to ensure that there are many different experiments that are all very small, and that by design take different approaches to try and solve the same problem. This takes learning by doing to a new level – because now failure is as important as success as it helps us to find the paths to better performance by reducing alternatives and finding the factors in the context that makes progress possible. The biggest surprise for me is that this process of purposeful small experiments to see what is possible under current conditions (context) has unlocked my own and my customers creativity.

Perhaps a topic for a separate blog is that to really uncover these competing hypothesis we have to make sure that we do not converge too soon about what we think is going on. Maintaining divergence and variety is key – this is another challenge for me as a facilitator that is used to helping minds meet!

Linking: Gregory Mankiw article “when the scientist is also a philosopher”

In the last two years we (Mesopartner) have been exploring how complexity science affects development practice. Well, we were quite shocked to realize how much of development is based on preference and bias, and how little is actually based on proper scientific research. Frequently practitioners takes little bits and pieces of different theoretical bases to create a construct of an approach that is suitable to them because it meets their own hypotheses of how the world works. It is important also to not confuse evidence based monitoring and evaluation with scientific evidence.

The famous economic thinker, Gregory Mankiw, has published an article in the New York Times where he goes into this topic with his usual easy to understand arguments. The title of his article is “when the scientist is also a philosopher”. He argues that a danger of economics (I would argue of all economic development) is that we are not aware of our bias, and we do not depend on proper scientific methods. He recommends that we offer our advice with a healthy dose of humility, as we are often not aware of how complex the economy is and how our advice will affect other systems, or whether our advice will work at all.

Gregory Mankiw was a great inspiration for me during my PhD research and I am grateful to have stumbled across this article. He is currently an economics professor at Harvard.

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