Posts Tagged ‘Emerging Futures Lab’

Frame Insights: Going back to first principles in the Innovation Planning Process

After conducting research, we need to bring structure to what has been found and learned. We sort, cluster, and organize the data gathered and begin to find important patterns. We analyze contextual data and view patterns that point to untapped market opportunities or niches. Finding insights and patterns that repeatedly emerge from multiple analyses of data is beneficial. ~ Vijay Kumar, 101 Design Methods

“It’s what happens after the research that’s important” is something I found myself saying three times to three different people in three different contexts over the past couple of days. Anyone can go out and interview users and beneficiaries. What’s important is what happens during the Analysis phase.

To ponder this in detail, I wanted to go back to first principles, and drill down into the post research stage where we are expected to frame our insights.

Vijay’s slide pops out 5 key outcomes from this phase, and these are critical for solution development in the subsequent phase. These 5 outcomes from analysis of the data collected during the research phase are:

  1. Looking for patterns
  2. Exploring systems
  3. Identifying opportunities
  4. Developing guiding principles
  5. Constructing overviews

It is this stage that distinguishes the quality of the outcome. Now, in the case of our work in the informal economy operating environment, we have built up an overview of the landscape over the past several years, primarily through immersion and thick data collection using design ethnography methods.

Starting from the purchasing patterns and buyer behaviour of low income consumers, back in early 2008, all the way through to the development of guiding principles such as flexibility, we have explored and mapped the ecosystem from numerous vantage points.

Today, our synthesis of user research does not happen in isolation from the body of work – intellectual property – that has been developed over time, through experiential and practical knowledge.

This, then, is what underlay my conviction when I spoke about the importance of the quality of interpretation of the data, and the transmutation of these interpretations into implemented insights in the form of new product features, service design elements, or nuances of the payment plan in the business model.

Increasingly, the Frame Insights phase of our work has led to the evolution of our understanding of the commercial landscape in rural and informal markets where incomes tend to be irregular and volatile, and infrastructure is inadequate or missing. It is this that I’ve been attempting to capture under the category of Biashara Economics.

It’s not Africa specific. The patterns hold, give or take ~30% margin for historical/cultural/social differences, across continents. That is because these patterns are the natural response to the common characteristics of seasonality, volatility, uncertainty, and unpredictability. And this is why one can see the success of the prepaid business model around the world.

It strikes me here that this in fact validates the methodology and approach to exploration and discovery in unknown contexts, something I had framed as the starting point for the very first such project almost a decade ago. Over time, I discovered how much the methods, as delineated by Vijay in Chicago, had to be adapted for the context but that is a topic for another time.

Poverty is Dynamic and Flexible, Just like the Informal Economy: Evidence from India

…the concept of poverty today is fundamentally different from that of poverty three decades ago, and that safety nets need to be tailored to meet the needs of a society in transition.~ The Hindu, 2 Aug 2016

When quantitative data provided by the India Human Development Survey (the first large panel survey in India) provokes the academics involved to question their fundamental assumptions and premise of what poverty is, and what it might mean, its a noteworthy moment.

The survey, conducted by the University of Maryland and the National Council of Applied Economic Research (NCAER) for the same households at two points in time, viz. 2004-05 and 2011-12. Their analysis has led them to say:

Once we recognise that poverty is dynamic in nature, and that as per our conventional definition of poverty, poor households may move out of poverty and the non-poor may become poor over a period of time, we are forced to question the veracity of our fundamental assumptions about poverty. Perhaps poverty occurs not simply due to the accident of birth or as defined in terms of where and in which family people are born, but also due to the accident of life caused by the occurrence of disease, disability and unemployment. Achieving this recognition entails a complete transformation in our mindset.

I will leave them to their explorations from the perspectives of their disciplines, and explore the broader implications of their findings.

A few years ago, as part of my discoveries from more qualitative user research in the field on the informal sector’s financial context and operating environment, I had had my insight on the dynamic nature of poverty as it was conventionally defined.

It was when attempting to clearly distinguish between patterns of cash flow in the formal vs the informal economy, using the concept of the degree of control granted to the end user over the variables of time (duration, frequency, periodicity) and money (amount, cash or kind), that it struck me what kind of difference does control over timing mean for money.

That is, there is a complex value processing underneath each of the decisions on allocating available cash money, particularly in rural areas where cashless transactions can tend to be more common.

When one can control the timing of one’s payments – such as the advance purchase of airtime minutes to use a mobile phone – one’s income could be called dynamic. Within any particular set of calender based time eg a week or a month or a quarter; a vast majority of the lower income bracket cannot predict their total cash income nor feel confident enough to claim it. It can be affected by seasonality prevalent in their region, or it can be purely random volatility, one’s workshop burns down in an accidental fire.

Static income is that which is stuck, such as a fixed salary paid every calender period, regular in frequency, amount and periodicity.

As cash flows tend to be volatile, fluctuating with seasonal influences, chance, and the vagaries of daily life, those whose incomes are not as predictable as a periodic paycheck, are more often than not unable to clearly state (or even know) their monthly or weekly income.

That is, even as data gurus in development banks seek to segment people into neatly defined ranges such as $2 to $4 a day or whatever, it is neither a given that people will remain within this range over the course of the natural year, nor can it be a reliable and consistent indicator of their income level – Below Poverty Line (BPL) is the concept used in The Hindu’s article above.

Therefore, if the survey studied households in an agricultural region during its fallow season the first time, and then went back to study the same households during the post harvest season the second time, that simple little factor of calender time alone can create a difference of as much as 100% to the incomes being claimed during that period. If the study does not follow up the income question to ask if there was seasonality in their cash flows over the course of the natural year and if this question was being asked during the high season or the low season.

When I did the original fieldwork for the prepaid economy project on an IDRC grant, looking at the rural household financial management behaviour in rural India, Philippines, and Malawi, I found that depending on the local region’s primary cash crop harvest patterns over the natural year (say monsoon to monsoon, or Christmas to Christmas) the entire local economy felt the impact of the difference in cash flowing through their ecosystem during the high and the low season. Or, the wet and the dry season.

It was not the naming of the seasons that is important. It is the ability of the people to forecast known fluctuations in their income streams based on patterns recognized from experience and local wisdom. Within the context of an environment of uncertainty and volatility, it offered them some anchors for planning and financial management.

Given that the vast majority of the poor in the developing world, like in India and across Africa, are dependent on irregular, often unpredictable cash flows from a variety of sources, in an environment of higher risk and uncertainty, their incomes can confidently assumed to be dynamic, rather than a static salary.

And the dynamic nature of the informal sector precludes conventional classifications and categorizations of poverty, especially by any stated amount of money mapped against a particular duration of calender time. Time and money are themselves the uncertain elements requiring flexibility built into the systems if they are to work properly in this operating environment.

Thus, I can confidently state that what the Indian data is finally providing the evidence for are the findings from my qualitative research among the same segments of the population, using design ethnography methods. That is, we now have the quantitative data to support the insights derived from the qualitative research.

Full Stop.

Borderland Biashara: Mapping the Cross Border, National and Regional Trade in the East African Informal Economy

efl research team

Rinku Gajera & Michael Kimani, Malaba Border, Kenya, January 2016. Photo: Niti Bhan

And, we’re back! With apologies for the long delay in posting on the blog, we’d been busy wrapping up our groundbreaking design research for development programming project for Trade Mark East Africa this past month or so. As you can imagine, the last few weeks of any project suck all the bandwidth out and leave little for blogging or writing.

Let me be the first to say that this project could not have been executed or completed without a rockstar research team – Rinku Gajera, Research Lead, and Michael Kimani, Research Associate, together put in gruelling hours in the sun, and on Skype, to help increase our understanding of the informal economy in East Africa, particularly the informal trade sector – cross border, national, and regional. Emerging Futures Lab has been immersed in design and development of pioneering methodology for mapping the informal trade ecosystem – henceforward known as biashara, at the borderlands of the East African Community, since November 2015.

tmeaFor this opportunity, I must thank the CEO of Trade Mark East Africa, Frank Matsaert, who saw our passion and our belief in the worth and value of the informal sector, and recognized the need to understand the traders, their business practices, and their aspirations, as the first step necessary for the design of interventions that are not only people-centered, but cost effective and impactful.  We were granted creative license to colour outside the box of the terms of reference with our designer’s empathy and exploratory mindset, and frame this project as an exercise in developing the understanding necessary for the design of human centered methods, tools and frameworks for development programming. You can be sure that there will be more on this topic published soon on this blog, so grab the RSS feed now, or sign up for inboxed posts.

Download the Borderland Biashara Ecosystem Mapping project at the Kenya/Uganda border at Busia and Malaba.

Nov 2015Inception report Informal Economy, Kenya/East Africa/Uganda
Jan 2016Literature Review on Informal Cross Border Trade in the East African Community (EAC), the DRC and South Sudan
May 2016Final Report, General Public – Borderland Biashara, by Emerging Futures Lab