For any entrepreneur – especially social entrepreneurs – it is critical to collect feedback from the people you are serving.
According to a new World Bank report, 114 million people worldwide moved out of extreme poverty in 2013, accelerating an overall positive trend that researchers have observed over the past two decades. There is much to celebrate: as stated in the report, “the world had almost 1.1 billion fewer poor in 2013 than in 1990, a period in which the world population grew by almost 1.9 billion people.”
India in recent years has made remarkable strides, and is a significant contributor to this trend. Just five years ago, around the time Upaya was getting off the ground, we lamented the fact that experts a few years earlier reported over 400 million people living in abject poverty in India. This put the country on par with — or even exceeding — the same numbers reported for all of the countries in sub-Saharan Africa combined.
Today, India as a country still houses the largest number of people living in extreme poverty, but this figure is now estimated to be 224 million. Economic growth, increased employment, and rising incomes have all contributed to this progress. We at Upaya are proud to play our role in encouraging inclusive growth, and nurturing the development of investable businesses that create lasting, dignified employment for the most marginalized communities.
The World Bank deems a household living under $1.90/day as living in extreme poverty. Upaya ensures that the jobs it helps create pay incomes in excess of this amount. But just as important as the increase in income is ensuring the regularity and stability of that income stream. Households that remain mired in abject poverty are often reliant on cobbling together “odd jobs” in the informal economy – manual labor, trash collection, and even begging when nothing productive materializes – to make ends meet. On a good day, there is work to be had and a wage earned. On a bad day, there is no work, and hence no income. An entire family goes to bed hungry, anxiously hoping that its luck will improve tomorrow. This erratic and uneven and unpredictable existence does not allow a household to build a firm economic foundation and move out of poverty.
In keeping with our mission to create dignified jobs for the poorest of the poor, Upaya from day one has been committed to not only track how many jobs have been created, but also monitor how incomes have improved, and how these incomes have helped previously destitute families make improvements to their quality of life. We refer to this practice of collecting, assessing, and reporting data as social performance management (SPM), and this activity over the years has yielded invaluable insights for our entrepreneurs and other stakeholders. Are households making progress out of poverty in the ways we expect? If not, are there refinements we can make to our interventions to effect better outcomes?
This week, we are releasing a report for Maitri Livelihood Services, one of our partner businesses working in the Northeast states of West Bengal and Assam. Maitri provides skill development, training, and job placement opportunities in the domestic work sector for women from economically disadvantaged backgrounds. Traditionally, women working as housekeepers, cooks, nannies, and at-home nurses have dealt with highly informal work arrangements. It was not uncommon for employers to delay compensation to workers, negotiate below-market wages, and deny workers basic rights in terms of number of hours or working conditions. Maitri is bringing much needed structure and formality to this sector: women who are trained and placed in affluent households are guaranteed a steady and reliable income, an assurance of their rights and safety, and proper recourse in case of any conflict.
Our report demonstrates that Maitri jobholders do indeed benefit from increased, reliable income. It also points to improvements these households are able to make to their quality of life as a result -- such as being able to afford formal electricity and gas connections. The household is less likely to live in extreme poverty the longer a jobholder maintains her relationship with Maitri. We fully expect to see positive improvements in other indicators as time passes, such as housing quality, asset purchases and savings patterns.
In the coming weeks, we plan to release reports for two other partner businesses, Saahas and ElRhino, as well as a year-end portfolio wide assessment. Preliminary findings reveal significant improvements to household income, job satisfaction, and overall well-being. We view these developments as critically important components in our continued fight against extreme poverty. Knowledge is indeed power … studying the exciting progress our jobholders make over time, and listening closely to their feedback and ideas, allows us to fully engage these hard-working, ambitious women and men and empower them to build pathways to a better life on their own terms.
At Upaya, the process of collecting and assessing beneficiary data to track outcomes and measure impact is fundamental to our work. After all, four years ago the organization was founded with a hypothesis that providing jobs -- and not handouts -- is the most efficient and effective way for the “ultra poor” to progress out of poverty. To date, we have sought to prove or disprove this hypothesis through social performance measurement (what we internally refer to as “SPM”), the systematic collection and analysis of beneficiary level information.
I recently had the wonderful opportunity of attending the “Impact Measurement (IM) and Performance Management Training” in Bangalore organized by the Global Impact Investing Network (GIIN) in collaboration with international consultancy Steward Redqueen and Social Value International, to reflect on Upaya’s methodology and revisit some of these ideas. The purpose of the training was to introduce practitioners to different IM frameworks and ways in which these frameworks could be used to suit the context of an individual organization and its mission.
Some of my key takeaways from the training were:
- Don’t overstress the rigour; it should be “good enough”
- Data is not just for reporting but also for decision making
- Consider establishing a counterfactual in the setting
Financial and business decisions are made everyday on the basis of imperfect information provided by financial accounting frameworks. Yet when it comes to evaluating impact, we tend to hold ourselves to a higher standard or rigour --where randomized control trials are considered to be the gold standard. However, sometimes the fear of publishing imperfect impact data gets in the way of doing any IM activity. If the data is ”good enough” to spot trends, aid in decision-making and undertake some course correction, and not overly tax human or financial resources, then it is worth undertaking.
Secondly, these days it appears that impact data is mostly used for reporting outwardly to stakeholders, such as philanthropists and investors. In doing this, we overlook the critical role data can play in helping an entrepreneur and/or management team make decisions about the business itself. The challenge most often cited among practitioners is one of constrained resources - the buy-in needed to undertake the exercise of data collection and the need for doing it all. For an early stage enterprise that is working to expand the business, build the team, formulate marketing strategy, and raise funding, IM is a weighty commitment.
Data collected through impact measurement, however, should not be seen separate and distinct from business data. Instead, if an IM framework were designed to provide useful insights for the business about its beneficiaries, that could then inform refinements to the core product or service, then there exists a higher probability of entrepreneur’s buy-in and also better quality data.
Last, but certainly not least, is the need to accommodate the counterfactual in the actual impact assessment. A counterfactual simply put means “what would have happened anyways, without the intervention in question.” A counterfactual could be in the form of state and national averages based on government data. Or it could be a more statistically rigorous “control group” or “non- intervention group.” The counterfactual, when compared with data that depicts the outcomes of the intervention, can give us a clearer indication of impact, or what positive effects can be attributed to the intervention. In essence, it can provide the necessary context and help us paint a picture of the impact that has occurred.
In the coming months, we will integrate some of these learnings into our SPM framework. We hope to make our system of impact assessment more robust so as to provide high quality information on the progress made out of poverty by our beneficiaries. Our goal is to simultaneously provide valuable insights and learnings to our entrepreneurs, to support the continued growth of their businesses and the creation of hundreds more jobs.
Authored by Upaya's own Jyotsna Taparia, this article was submitted in June to the Devex/ USAID "Frontiers in Development" Essay Competition. The competition prompted writes to submit their thoughts on a variety of questions under the heading How would you Eradicate Extreme Poverty by 2030?
Q: Aside from income, how might we define and measure other dimensions of extreme poverty?
“Development requires the removal of major sources of unfreedom: poverty as well as tyranny, poor economic opportunities as well as systematic social deprivation, neglect of public facilities as well as intolerance or overactivity of repressive states.”
-Amartya Sen (1999), Development as Freedom
For far too long the discourse on poverty has been limited to income or lack of it thereof. The discourse on extreme poverty or absolute poverty has been taking its shape and form since early 80’s. In 1990, the World Bank proposed that global poverty should be measured through the standards of the poorest countries and arrived at a $1 a day poverty threshold, a figure that was last updated to $1.25 a day (based on 2005 PPP). This definition also became the basis for Millennium Development Goal #1: reduce by half the proportion of extreme poor (those living under $1.25 a day) by 2015.
However, income measures can only go so far as to capture the consumption capacity of an individual, calculated either in monetary terms or nutritional count. They are grossly insufficient in capturing extreme poverty as they do not exhibit any sensitivity towards the depth, duration and direction of poverty.
In his seminal work Development as Freedom (1999), Nobel laureate Amartya Sen outlines how the development debate should be structured. Sen postulates that development is closely linked to three sets of freedom: economic, social and political. Poverty in this framework is described as absence of at least one freedom. According to Joseph Wrensinski, a lifelong activist and founder of the ATD Fourth World, extreme poverty is a “... lack of basic security [that] simultaneously affects several aspects of people’s lives, when it is prolonged and when it severely compromises people’s chances of regaining their rights and of reassuming their responsibilities in the foreseeable future.” The underpinnings of this approach are largely similar to what Sen proposes - poverty is deeper than just the state of material deprivation and is not static in time.
From the postulations of Sen and Wrensinski, it’s clear that extreme poverty is a result of three crucial factors:
a. Availability and efficiency of human, financial and physical assets
b. Inequality in the availability of opportunities and ability to exercise agency
c. Interaction with measurable deprivations that reinforce the impact of others
Despite the longstanding focus on income as the sole indication, a significant body of work has emerged establishing the multidimensional nature of poverty both at the household and at the community level. However, identification of the extremely poor based on this multidimensionality poses its own set of unique challenges. For example, if the indicators being used are income, school attendance, nutritional status and health status, then there are some scholars who argue that a household falling below the minimum threshold on any one of the indicators should be considered poor. There are still others who contend that households should score low on all indicators in order to qualify as poor. With these conflicting approaches to poverty identification, one runs the risk of erroneously including or excluding a fraction of the poor population when developing programme interventions (also known as an error of commission or omission.)
Extreme poverty measurement is much more complex than a simple error of omission or commission. It has been observed that deprivation of one indicator actually has negative impact on other indicators, resulting in a self-perpetuating cycle of poverty that is often referred to as the “vicious cycle of poverty.” Thus any discussion of alternative measures must look at these trailing indicators as well as leading ones. These additional indicators can not only capture the effect of extreme poverty, but also show us the progress being made by a household. For example, a household that cannot afford to send its children to school will see them working either with their parents or elsewhere. The lack of formal education and skills won’t allow them to compete in the more remunerative skilled job market and will often result in a lower household income.
Following this logic, it is useful to look deeper at some of the trailing indicators of extreme poverty that are common to all contexts and benchmark them against established trends, such as:
● Households that typically spend more than 50% of their income on food expenditure are more sensitive to income shocks and less likely to avail of services like health and education. Deprivation for these households would be on multiple counts - lack of food, education, quality health care and other services. Therefore an increase in income should result in a decline in the food expenditure to income ratio but also a concurrent increase in the uptake of the other services.
● Households relying on manual labour (informal and unorganized) as their primary source of income are more likely to be in the extreme poor category as the availability of work is not only infrequent and erratic in nature but also low paying. Therefore, tracking changes in the nature of the work that generates income for the family can provide valuable insights.
● The presence or absence of certain classes of assets is also an indicator of the extent of poverty in the household. A low percentage ownership of productive assets (land, livestock, simple machinery and tools etc.) is a likely trailing indicator of extreme poverty.
● Households rate of electrification against local and regional statistics. Electrification has a direct impact on trailing indicators - household assets, cooking and refrigeration, educational success - and therefore is often prioritized by households as income stabilizes or increases. Admittedly, the legality of such connections is often murky at best, but it is nonetheless an indicator of a household’s day-to-day income situation.
Because extreme poverty is relative, we must look at each case in the context of a larger community. Geography and surroundings play an important role in determining the common minimum threshold for a poverty measure or even if the measure will prove to be valuable in providing insights into the extent of deprivation. Therefore, the goal of poverty measurement should not be to create a one-size-fits-all multidimensional index but rather a set of robust indicators that is most relevant to the local context. While this route may not allow for seamless cross comparison, it is successful in achieving a high degree of universality.
Ravillion et. al (2008). Dollar a Day revisited, http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2008/09/02/000158349_20080902095754/Rendered/PDF/wps4620.pdf
Depth of poverty is related to the extent by which a household falls below the poverty line threshold.
Households also show movement out of poverty to fall back again due to external shocks (for example, detection of an ailment with prolonged treatment, natural calamities etc.)
For a richer discussion on this refer to http://www.ers.usda.gov/publications/err-economic-research-report/err89.aspx