The Face of Poverty

I am often amazed and inspired by how deeply social enterprises in developing countries reflect on the context in which they operate. Those that have created and significantly scaled lasting innovations have accumulated rich contextual knowledge—a keen understanding of how the environment surrounding them ticks. My own research strongly indicates that organizations must have a quality understanding of the contextual characteristics of the need or problem they are targeting to make productive decisions about innovation and scaling.

Unfortunately, even experienced social enterprises and funders rarely consider the implications of these contextual characteristics in a systematic way. This creates a prevalence of “technical” innovations, and diffusing and scaling “best practices.” But best practice solutions often have the characteristics of psychologist Abraham Maslow’s famous “hammer,” where it’s tempting to treat all poverty-related problems like “nails.” Here, I provide a diagnostic framework called “the face of poverty”—an appreciation of what poverty really looks like that can work as a starting point for productive decisions about intervention design involving innovation and scaling.

Organizations already using this framework report that it helps make differences in assumptions explicit, fuels richer discussions about interventions and communities that they target, and creates a shared language and understanding of important strategic challenges. This facilitates problem articulation, as well as systematic comparisons of interventions and organizations in similar and different contexts. All this facilitates designing interventions and strategic decisions—for example, whether an organization can best achieve change by investing in technical innovations or by investing in relational issues to build trust and legitimacy through direct, long-term engagement. It creates more realistic expectations about the possible pace of scaling, and whether or not an intervention can be replicated—all of which is helpful to both organizations and funders.

Four dimensions shape the face of poverty

Looking into “the face of poverty,” we see all kinds of barriers that sustain an undesirable, change-resistant status quo. I separate these barriers into four dimensions:

1. Economic: The Aravind Eye Care System hospital in India targets poor rural villagers that lack access to affordable cataract surgeries. Economic barriers thus determined the strategic priorities for Aravind’s health service model, and guided decisions about innovation and scaling. Today, Aravind is the most efficient eye hospital globally.
2. Cognitive: Pesticide-intensive cotton agriculture generated sustained negative environmental and health outcomes in Egypt in the 1970s and 80s. Sekem, a social enterprise, innovated around changing the reasoning of decision makers and communities. It’s organic cultivation method convinced skeptics and revolutionized Egypt’s cotton industry. This greatly benefited thousands of poor farmers.
3. Normative: Gram Vikas operates in rural India, where strong values define roles and behaviors for members of communities. It targets the restricted rights and opportunities of women and members of lower casts that sustain intolerable levels of inequality.
4. Power and politics: Abuse by public sector officials or powerful village leaders often exacerbate the problems of the poor. This is a particularly tricky dimension and a very slippery slope for social enterprises that require explicit and sophisticated engagement strategies.

Economic and cognitive barriers have closely related implications for innovation and scaling. I thus refer to them as technical problems or needs. Similarly, I group normative and power/politics barriers into relational problems or needs (see chart below). Often a mix of technical and relational barriers defines problems. An important decision, for example, is whether to target relational issues (which can often lead to a much slower pace of progress and strain organizational resources) or technical aspects of community problems first. This builds positive momentum, tangible outcomes, organizational resilience, and resources more quickly. Pragmatic organizations such as BRAC often use the latter strategy, and then tackle relational issues once it gains legitimacy and trust from positive technical interventions that scale much faster.

 

Above are the four types of barriers that characterize the problems and needs of poor people and communities.

What does this mean for innovation and scaling?

Technical and relational problems greatly differ in their level of resistance to change. For technical problems, organizations can quickly provide economic resources to remove barriers—at least in principle. However, the history of development projects is full of examples where the provision of economic resources has not generated much benefit. That means problems perceived as primarily economic also had strong cognitive and/or relational barriers that were not considered sufficiently. For example, Aravind’s extremely efficient model scaled down economic barriers but was insufficient. Poor patients were skeptical and did not even apply for free surgeries—a cognitive barrier. Aravind removed that barrier by directly demonstrating to patients the outcomes of surgeries that were visible within 24 hours and unambiguously positive. Technical problems thus generally facilitate faster scaling because organizations can scale down barriers rapidly, generate demand quickly, and improve economics of interventions through learning and scale effects. Because organizations can directly observe outcomes—both positive and negative—they can identify and correct errors in real time. This facilitates innovation, because organizations can rapidly test and evaluate new ideas. This also facilitates scaling because the effects of incremental changes and improvements from ongoing error correction or introduction of small new ideas are observable. Small changes accumulate over time and positively impact the economics and quality aspects of solutions, and thus their potential scale. Aravind owes much of its tremendous positive impact to focusing on technical problems. It is extremely hesitant to collaborate with the public or corporate sectors, both of which would introduce normative and power/politics barriers beyond its control.

Relational problems have very different implications for innovation and scaling, as Gram Vikas illustrates. The barriers to change for an issue such as inequality based on gender and social status are highly persistent. Values, norms, and traditions do not change overnight. In addition, powerful stakeholders who have an interest in maintaining the status quo do not perceive change as positive. Social enterprises need to engage with people and communities much more deeply, much more directly, and over a much longer time. Relational problems thus provide a stark contrast to technical problems. In general, innovation is much more difficult. Since positive outcomes may not be visible for a long time, real-time error correction is hardly possible. Furthermore, innovation errors or failures might damage the positive levels of trust and legitimacy essential for enacting change. This dramatically increases the risk of innovations. The slow feedback between action and outcomes—which limits explicit learning and knowledge accumulation—also means that scaling dynamics are generally much slower for relational problems. The difficulties to codify interventions based on in-depth, hands-on engagements and to observe how they work also limit their replicability. Because deep contextual knowledge and the accumulation of intangibles such as trust are essential, innovations may not work in other contexts, or they may require the same difficult approach to slow and careful learning about the particularities of a new context.

 

Above is a summary of some important implications of the “face of poverty,” and innovation and scaling.

Conclusions

Bad decisions about innovation and scaling often arise from the misperception of a relational problem as a technical problem or vice versa. Combining knowledge about the general implications of the “face of poverty” for innovation, and scaling and deep contextual knowledge should enable organizations and funders to make much better decisions about intervention design, expected outcomes, measures, and realistic timescales. These are just some basics to get organizations started. In collaboration with BRAC’s social innovation lab, we are currently developing this tool further to make it even more practical, and an expanded version of the framework will be available in a forthcoming book on my joint innovation research with Johanna Mair. There are many more important implications of this framework, and I look forward to your thoughts on these issues.

Read more stories by Christian Seelos.