Archive for the ‘Flexibility’ Category

Fundamental Elements of Informal Sector Commercial Activity

There are two key elements which underpin the dynamics of any business or commercial enterprise in the informal sector. These are Time and Money.

A generalized framework can be diagrammed, as shown above, where the dotted line denotes the degree of uncertainty and volatility of an individual’s cash flow patterns – whether from a variety of informal economic activities – such as for the farmer or trader; or from the salary received for a white collar job. The X axis – Time – denotes the increasing accuracy of estimating the Arrival date of a cash payment (from some revenue source), and the Y axis – Amount – denotes the increasing accuracy of estimating the Amount that will arrive. Their relative ability to estimate Arrival and Amount with any degree of accuracy is indicative of their ability to forecast and plan for expenditure.

Thus, at one end of the continuum, one can position an odd jobs labourer who may or may not get paid work on any given day, and is unable to predict with any degree of certainty what type of job he’ll get selected for, nor for how many days it will last. It could be as basic as loading a truck for half a day’s pay, which in turn might even be in kind, and not cash. And, at the other end of this continuum, one can position a the typical white collar salaried professional or civil servant who knows with certainty exactly on which day they will receive the salary and exactly how much will arrive.

 

Positioning and Location

Now, we can frame these two elements of the commercial operating environment in the form of a position map, as shown above, that maps the ability to plan expenditures against the stability of the cash flow. The red arrow is the continuum of certainty and stability of Timing and Amount of an income stream, anchored by the most vulnerable odd jobs labourer at one end and the relatively most secure salaried professional at the other.

Where it gets interesting is the relatively liminal space in the middle where the various economic actors in the informal economy constantly shift position as they seek to mitigate the volatility of their income streams, through a variety of mechanisms. Much of their decision making is related to their own perception of uncertainty and ability to forecast.

For the purpose of this explanatory diagram, I have selected 4 typical examples drawn from different sectors of the informal economy common in the developing country context. Each are at the more vulnerable end of their own segments i.e. a subsistence farmer, rather than one with an established cash crop; or a small roadside kiosk rather than an established general merchandise store in a market town; since they have not yet achieved the goal of their business development strategies to move their own entrepreneural ventures towards relative stability, and thus provide more insight on the relationship between cash flow patterns and investment and expenditure planning.

The hawker of goods at a traffic light or junction is in a comparatively more fragile situation than the kiosk owner with a fixed location who works to develop relationships with passing customers in order to convert them to regulars at her store. Unlike the kiosk, which might be located near a busy bus stop, or outside a densely populated gated community; the hawker cannot predict which cars will pause at the red light as he darts through traffic shouting his wares. However, compared to the odd jobs labourer, the hawker has comparatively more control over his income generation since his is not a passive function of waiting to be picked from the labour pool in a truckyard or construction site.

The smallholder farmer might actually be better off economically in many ways than his urban brethren involved in informal retail, being able to live off the land more cheaply than in the city. Experienced farmers, for the most part, are able to predict with reasonable accuracy, more or less the quantity of their crop, and the estimated timing of the harvest. However, his sense of uncertainty is often perceptually greater due to the unmitigatable impact of adverse weather conditions, or the sudden infestation of a pest or blight, any of which could at any time completely destroy his harvest, and thus, his expectations. This sense of insecurity in turn influences his decisions on expense commitments to far ahead in time, or too large a lumpsum at some point outside of his regional harvest season. The farmer’s income streams are relatively more out of his control than the disposable income in the pockets of the kiosk’s customer base.

The market woman with her display of fresh produce, at the entry level of inventory investment capacity, might only have one or two different varieties of vegetables or fruit to sell, and may not yet have established a permanent structure – a table, a kiosk – in the market. She might start off with only a tarpaulin on the ground with some tomatoes and onions for sale. Unlike the traffic intersection hawker, however, she is more likely to begin by assuming a regular placement and location as this establishes the foundation for her future business development, through the factors of discoverability and predictability among the customers in that locale.

That is, in addition to Timing and Amount of Income – the cash flow patterns and sources – we begin to see the role played by location – Place1, as a supporting element of the commercial activity in the informal economy. While farmers are least likely to have much control over the location of the land they may inherit, their risk mitigation strategies to minimize volatility of their income streams and maximize their ability to plan for the future and manage emergencies will be discussed in depth in the section2 on rural household financial management. These practices are the foundation of business development strategies commonly observed in the informal economy in developing countries which tend to be less urbanized, and as is often the case, more dependent on agriculture as a component of national GDP.

 

Appendix
1 People, Pesa, Place: A Multidisciplinary Lens on Innovating in Emerging Markets
2 Rural Household Financial Behaviour on Irregular Income Streams at the Base of the Pyramid

Financial Behaviour Patterns Observed Among Households in Rural Informal Economy in Asia

This is the original working paper of the research conducted on rural household financial management, in developing country conditions, pioneering the use of methods from human centered design for discovery, during Nov 2008 to March 2009, aka the Prepaid Economy Project. It was peer reviewed by Brett Hudson Matthews, and I have incorporated his comments into the PDF.

This research study was carried out with the aid of a grant from the iBoP Asia Project (http://www.ibop-asia.net), a partnership between the Ateneo School of Government and Canada’s International Development Research Centre (www.idrc.ca)

The abstract:


The challenge faced by Bottom of the Pyramid (BoP) ventures has been the lack of knowledge about their intended target audience from the point of view of business development whereas decades of consumer research and insights are available for conventional markets. What little is known about the BoP’s consumer behaviour, purchasing patterns and decision making tends to assume that there are no primary differences between mainstream consumers and the BoP except for the amount of their income – pegged most often between $2 to $5 a day.

In practice, the great majority at the BoP manage on incomes earned from a variety of sources rather than a predictable salary from a regular job and have little or no access to conventional financial tools such as credit cards, bank accounts, loans, mortgages. This is one of the biggest differentiators in the challenge of value creation faced by BoP ventures, particularly among rural populations (over 60% of the global BoP population lives in rural areas).

Exploratory research was conducted in the field among rural Indian and rural Filipino populations in order to understand how those on irregular incomes managed their household expenses. Empirical data collected by observations, interviews and extended immersion led us to identify patterns of behaviour among the rural BoP in their management of income and expenditure, ‘cash flow’ and ‘working capital’ and the significance of social capital and community networks as financial tools. Practices documented include ‘conversion to goods’, ‘stored wealth’, ‘cashless transactions’, and reliance on multiple sources of income that mature over different times.

This paper will share our observations from the field; identify some challenges these behaviours create for business and also explore some opportunities for value creation by seeking to articulate the elements that BoP ventures must address if they are to do business profitably with the rural ‘poor’ based on their own existing patterns of financial habits and norms.


The Conclusion:

In sum, it can be concluded that the challenges for value creation can be quite different for BoP ventures interested in addressing the rural markets. From the observations made in the field, we can highlight three key implications for business development. These are:

  • Seasonality – with the exception of the salaried, everyone else in the sample pool was able to identify times of abundance and scarcity over the course of natural year in their earnings. Identification of a particular region or market’s local pattern of seasonality would benefit the design of payment schedules, timing of entry or new product and service launch, for example.
  • Relative lack of liquidity – The majority of the rural households observed tended to ‘store wealth’ in the form of goods, livestock or natural resources, relying on a variety of cashless transactions within the community for a number of needs. Conventional business development strategies need to be reformulated to take this into account as these patterns of behaviour may reflect the household’s purchasing power or income level inaccurately.
  • Increasing the customer’s span of control over the timing, frequency and amount of cash required – Since the availability and amount of cash cannot be predicted on calendar time, this implication is best reflected by the success of the prepaid mobile phone subscriptions in these same markets. When some cash is available, it can be used to purchase airtime minutes for text or voice calls, when there is no money, the phone can still receive incoming calls. Models which impose an external schedule of periodicity, frequency and amount of cash required may not always be successful in matching the volatile cash flow particular to each household’s sources of income.

On the relationship between economics and design

This is an extract from the Introduction to John Heskett’s seminal paper, “Creating Economic Value by Design


The work of Herbert Simon, Nobel Laureate in Economics in 1978, is a rare exception of design being considered as a factor in economic theory. His starting point was acknowledging that the world we inhabit is increasingly artificial, created by human beings. For Simon (1981), design was not restricted to making material artefacts, but was a fundamental professional competence extending to policy-making and practices of many kinds and on many levels:

Everyone designs who devises courses of action aimed at changing existing situations into preferred ones. The intellectual activity that produces material artifacts is no different fundamentally from the one that prescribes remedies for a sick patient or the one that devises a new sales plan for a company or a social welfare policy for a state. Design, so construed, is the core of all professional training; it is the principal mark that distinguishes the professions from the sciences. (p.129)

Implicit in Simon’s reasoning is an emphasis on design as a thought-process underpinning all kinds of professional activities; yet the varied skills through which design is manifested are not discussed. He did indicate, however, why design is so rarely considered in economic theory. Economics, he stated, works on three levels, those of the individual; the market; and the entire economy (p. 31). The centre of interest in traditional economics, however, is markets and not individuals or businesses (p. 37). A serious problem is thereby raised at the outset: two important considerations relating to design—how goods and services are developed for the market place and how they are used—receive scant attention.


I was lucky enough to both work with him as a colleague as well as attend his classes in Design Policy and Design Planning & Market Forces as his student. I’ve been diving into my notes and his lectures of late as I wrestle with my theorizing on what I’ve been calling Biashara Economics, whose earliest avatar was the prepaid economy project of 2008/9.

A theoretical approach to Value for Money in aid & development: Optimizing research and design for ‘best fit’ iterative programming

Last year, I briefly touched upon this concept as an approach to cost effective programme design that was still flexible enough to provide room for iteration for best fit.

Today, I want to explore the concept further to evaluate its potential as a framework for incorporating the concurrent shift in development thinking towards Value for Money (DFID) principles, in addition to designing for best fit.

Value for Money as a Process Driver

Value for Money (VfM) is not the same as traditional monitoring and evaluation which seeks to measure impact of a project, and occurs usually after the fact. In many large scale projects, this may not happen until years after inception.

Instead, VfM is defined by the UK’s National Audit Office as ‘the optimal use of resources to achieve intended outcomes’, which in turn, the DFID document contextualizes for their aid programming investments as “We maximise the impact of each pound spent to improve poor people’s lives.”

If this applies to all investments in aid related programme development, then it follows that it must also apply to earliest stage of discovery and exploration that leads to problem framing i.e. the necessary groundwork to write a comprehensive and inclusive design brief for future programming.

Thus, the conceptual approach that I introduced at the beginning of this post, which is taken from the discipline of Operations Research, and seeks to solve the challenge framed so – what is the optimal solution that minimizes resources (inputs) for maximum outputs (value creation) – fits as a potential framework that can theoretically apply from the earliest stages of implementing development strategy, even before inception of any related projects, including early stage research and feasibility studies. After all, the function of Linear Programming is optimization.

Note: Here I will only consider the theoretical aspects from the point of view of programme design research and development, and not the mathematics. That will have to wait until I have gathered enough data for validation.

Design Research for Programme Design Purposes

In this context, the primary function of such an exploratory project is to identify the opportunity spaces for interventions that would together form an integrated programme designed to effect some sort of positive change in the ecosystem within which it would be implemented, and offer a wider (more inclusive) range of cross-cutting benefits.

In the language of product development, we are attempting to build a working prototype. We cannot build and test first prototypes to see if they work, directly, because our room for failure is much less spacious for experimenting with aid related programming, ethically speaking. This is not a laboratory environment but the real world with enough challenges and adversity already existent.

Programmes are not the same as consumer products, nor are they meant to be designed and tested in isolation before being launched for pilot testing in the market. Their very nature is such that innocent people are involved from the start, often with a history of skepticism regarding any number of well meant donor funded projects aimed at improving their lives. This changes the stringency of the early stage requirements for design planning.

At the same time, the nature of the task is such that no first prototype can be expected to be the final design. So, from the very beginning, what we must do is set the objective of the outcome as a Minimal Working Prototype (MWP) that meets all the criteria for an optimal solution, and NOT a Minimal Viable Product (which may or may not work wholly as intended until tested in the field for iteration.)

That is, the first implementation of the iterative programme design must fall within the bounds of the solution space – that which is represented by the shaded area in the diagram above.

The Optimal Solution is the Iterative Programme Design

Thus, what we must be able to do at the end of the discovery phase of research necessary to write the design brief, is tightly constrain the boundary conditions for the solution space within which the MWP can then be iterated. This minimizes the risk of utter failure, and maximizes the chances of discovering the best fit, and all of this within the definitions of Value for Money and it’s guidelines.

There are numerous ways to set the goals for optimization – one can minimize resources and maximize constraints, or minimize risk and maximize return on resources invested. These will guide our testing of this framework in field conditions to validate the robustness of this theoretical approach.

In this way, we can constrain our efforts to discover best fit within predefined limits of tolerance, while retaining the flexibility to adapt to changing real world circumstances and progressive transformation of operating conditions.

Best fit, then, becomes less a matter of experimentation without boundary conditions and more a discovery of which of the many right answers – if we take the entire shaded area as containing “right answers” to the problem at hand – help us meet the goals of intervention in the complex adaptive system in an optimal manner.

The point to note from this conceptual framework is that there is never any ONE right answer, so much as the answer will be that which we discover to the question “What is needed right now for us to meet our goals, given these changes since we last looked at the system?”

It is this aspect that loads the burden of a successful outcome on the front end of the entire research and development process, given that framing the problem correctly at the outset is what drives the research planning and steers the discovery process in the direction of relevant criteria, conditions, constraints, and user needs that will not only form the bounds of our solution space, but also act as waymarkers for monitoring change and evaluating its progression.

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.

Prepaid Mobile: The Business Model that Empowers

It feels like a long time since I last pondered the nuances of the prepaid business model, until I came across some words written by Indian social media researcher Swati Janu. She documented her observations on the infrastructure of insecurity from the tenements of New Delhi.  There’s value in reflecting on how our understanding only increases over time, and we can never say that we’ve stopped learning

This sentence caught my attention:

From a rural population that is fast going online to the resourceful teens in urban slums, the lower income demographics are choosing to buy internet, through small but recurrent amounts, which enable them to straddle the line between affordability and aspiration.

The small but recurrent amounts – the Rs 10 mobile recharge Janu writes about – are the lifeblood of the prepaid payment plan for voice, text, and data (airtime) for the now ubiquitous cellphone that has changed the landscape of the developing world.

To enable the lower income demographic’s ability to straddle the divide between their aspirations and their ability to afford them is empowering. One could say that:

Prepaid is a business model that empowers aspiration, through affordability, incrementally.

Instant gratification has never been within their purview.

Analysis of the mobile phone’s impact on cash flows and transactions in the informal sector

As we saw, Mrs Chimphamba needs to juggle time and money as part of her household financial management in order to ensure that expenses can be met by income. We also saw that the mobile phone was made viable and feasible by the availability of the prepaid business model that gave her full control over timing and the amount required to maintain it — how much airtime to purchase? when? how often? — all of these decisions were in her hands, within the limits of the operator’s business model. Now, we’ll take a closer look at the impact of the mobile on her domestic economy.

Readily available real time communication has helped Mrs C by speeding up the time taken for a decision on a purchase or a sale. That is, the transaction cycle has been shortened. As the speed of information exchange increases, it increases the speed of transactions — it shortens the duration of time taken to execute them from inception to completion. This, in turn, implies that more transactions can now take place in the same amount of time thereby increasing the frequency and the periodicity. When mobile money is present, one can see that as both quantity and frequency of transactions speed up, so does the cash flow. We’ll come back to this factor.

To explain using a real life example, Mrs Chimphamba does not need to sit at her homestead wondering if today someone will pass by to purchase a bottle of wine. Similarly, Mrs C’s customers do not need to go out of their way to pass by her homestead to see if the wine is distilled and ready for sale, or whether it will still take another day or two for the next batch to be ready. Further, the uncertainty of whether they’ll have cash on hand on that future day, or if they’ll return as promised are all elements that real time communication have minimized.

Now, Mrs C is able to let her regular customers know that she’s making a new batch for sale and do they want to reserve a bottle for purchase? It allows her customers to put aside cash for this purchase. She is even able to accept and execute larger orders for some future date, and even accept some cash advances if her operating environment includes the presence of a mobile money transfer system such as those more prevalent in East Africa. This in turn changes her purchasing patterns and decision making as the pattern of cash flows — timing and amount — changes. She isn’t making do anymore on an unknown and predictable sale based on sitting and waiting for someone to show up to buy her wine.

Real time communication has improved the decision making cycle for both buyer and seller in a transaction as it counteracts uncertainty and information asymmetry even while speeding up the time take for a decision.

As the quantity and frequency of transactions increase— first, in cash conducted face to face, and then later, remotely by mobile money, regardless of the size of each transaction — the change in cash flow patterns begins to smooth out the volatility (the uncertainty factor has changed completely) between incoming and outgoing, as well as the decisionmaking involved. That is, the gap between income and expense starts becoming less in terms of both timing and amount — there is the possibility of a steady stream in the pipeline. Calculus offers hints of how the curve can begin to smoothen out as frequency and periodicity of transactions begins to accelerate.

Size of transactions thus begin to matter less in that the incoming amount now does not need to be so large as to cover expenses for an unknown duration of time before the next incoming payment; nor do expenses have to be tightly controlled constantly due to the uncertainty of the duration of time before the next payment, and the types of expenses incurred during this unknown period of time.

So the boost in decision making — how long it takes to complete a transaction, how often can transactions be completed — enabled by the real time communication facilitated by the mobile phone; plus the attendant immediacy of receiving payment via the same platform is the root of the improvement in the hyperlocal economy and consumption patterns among the informal sector actors. This is why large established traders (with sufficient financial cushion) were heard to observe that both purchasing power and consumption patterns had changed in their market town (Busia, Kenya Jan 2016) in the past 10 years since first the mobile phone, and later, mPesa, were introduced into their operating environment.

Uncertainty and information asymmetry that have long characterized the fragile and volatile nature of the informal sector operating in inadequately provided environments with unreliable systems and scarce data. In the next chapter we’ll step back and take a broader look at communication, connectivity, and commerce in the informal economy starting with the description of the operating environment’s characteristics regardless of continent.

This is part of a newly launched Medium where I will write in detail on economic behaviour and its drivers in the informal economy. Much of it draws upon the original research in the field from 2008-2009 which was shared on the prepaid economy blog. I found that time had passed and increased my understanding and I wanted to explore those discoveries in writing. Much of this is the foundation for recent works on ‘Mama Biashara‘.

Systems design and the Monster who squats between the formal and the informal

system-monster

This framing of the real challenge to development and poverty alleviation comes from Ken Wong writing on his experience in Malawi:

We can only win the war on poverty and hunger in Malawi by targeting the real enemy – and that enemy is the system of how the world tries to help. Specifically:

The system that demands foreign aid be funneled through the government or large NGOs

The system that creates a hierarchy of aid and government workers whose job security and quality of life depends not on their wanting what is good on the ground, but pleasing whoever is above them in rank

The system that discriminates against on-the-ground, local initiatives because of a lack academic credentials, English-speaking skills, and the ability to complete unwieldy applications and fulfill misguided metric targets

If we are to win the war against poverty, we need to face the truth and admit that the system has not only not worked in Malawi, it has made the situation worse.

The system itself is the barrier to progress. The System Monster, as I dubbed it, is quite a nice fellow really, rather well meaning and all that, but he doesn’t see how he’s just stuck there inbetween, unable to adapt to the context on the ground.

Here’s is a 5 minute video where I introduce the concept, from the BankInter Foundation’s Future Trends Forum on Inequality and Technology held in Madrid in early June 2016.

Platforms that aggregate small businesses can integrate the informal with the formal economy

Continuing my thoughts on Nilekani’s vision introduced in the previous post, I want to use this post to focus on the key element of what captured my imagination from his article “The New Road to Nirvana“:

So manufacturing is squeezed on one side by Chinese overcapacity and on the other side by extreme automation. So the service sector is where the action is.

The era of large companies as we knew them is also over. It will be a world of platforms that aggregate small companies.

Amazon and Flipkart will aggregate goods made by lakhs of vendors and provide a platform to sell them. Similarly, Ola or Uber will aggregate millions of drivers who will work on the platform, Practo will aggregate doctors and patients and so on. Aggregation by platforms is the way that jobs creation will happen.

This platform aggregation will also lead to formalisation of the economy. India’s economy is largely informal. But once, say, a taxi driver becomes part of Ola, then in fact he becomes part of the formal economy.

He is able to use data, get a loan, buy a car, start paying taxes. So the formalisation of a few hundred millions of Indians will spur growth and that is where our focus should be.

My larger point is that it is now all about domestic not export, services not manufacturing and platform aggregation not big companies.

I will be writing further on this concept and exploring its implications for the African context, particularly East Africa.

 

Introducing the concept of Biashara Economics, underwritten by a value web of trusted relationships

vfm hidden

The true value of social network lies not in its actor’s activities but in their relationships to each other. When social networks attempt to monetize their users, they tend to identify them as discrete individuals rather than interconnected actors all acting in a wave at a concert. The ripple effect seen in biashara informed us of the presence of an underlying web of value exchange seen as form of social digital currency.

Social digital currency can be said to consist of the following component parts that symbiotically work holistically as an integrated whole.

  1. Social capital – Trust.
  2. Virtual capital – see research on the metafilter community
  3. Live capital – livestock, chickens, fish, rabbits et al
  4. Skills and information capital – experience of paperwork for instance
  5. Cash or easily liquid capital

Thus, in the informal sector we saw instances of extremely cooperative economic behaviour bordering on barter characteristics, with cash as one of the many instruments used.

How can we bank on this richly layered wealth in rural human capital?