Tag Archives: evaluation

Early Concepts for Designing and Evaluating Blockchain Interventions for Behavior Change

Guest post by Michael Cooper, a former DoS, MCC Associate Director for Policy and Evaluation who now runs Emergence.  Mike advises numerous donors, private clients and foundations on program design, MEL, adaptive management and other analytical functions.

International development projects using the blockchain in some way are increasing at a rapid rate and our window for developing evidence around what does and does not work (and more importantly why) is narrow before we run into un-intended consequences.  Given that blockchain is a highly disruptive technology, these un-intended consequences could be significant, creating a higher urgency to generate the evidence to guide how we design and evaluate blockchain applications. 

Our window for developing evidence around what does and does not work (and more importantly why) is narrow before we run into un-intended consequences.

To inform this discussion, Emergence has put out a working paper that outlines 1.) what the blockchain is, 2.) how it can be used to leverage behavior change outcomes in international development projects and 3.) the implications for how we could design and evaluate blockchain based interventions.  The paper utilizes systems and behaviorism principles in comparing how we currently design behavior change interventions to how we could design/evaluate the same interventions using the blockchain.  This article summarizes the main points of the paper and its conclusions to generate discussion around how to best produce the evidence we need to fully realize the potential of blockchain interventions for social impact.

Given the scope of possibilities surrounding the blockchain, both in how it could be used and in the impact it could leverage, the implications for how MEL is conducted are significant.  The time is long gone where value adding MEL practitioners are not involved in intervention design.  Blockchain based interventions will require additional integration of MEL skill sets in the early design phases since so much will need to be “tested” to determine what is and is not working.  While rigid statistical evaluations will needed for some of these blockchain based interventions, the level of complexity involved and the lack of an evidence base indicate that more flexible, adaptive and more formative MEL approaches will be needed.  The more these approaches are proactive and involved in intervention design, the more frequent and informative the feedback loops will be into our evidence base. 

The Blockchain as a Decentralizing Technology

At its core, the blockchain is just a ledger but the importance of ledgers in how society functions cannot be understated.  Ledgers, and the control of them, are crucial in how supply chains are managed, financial transactions are conducted, how data is shared, etc.  Control of ledgers is a primary factor in limiting access to life changing goods and services, especially for the worlds’ poor. In part, the discussion over decentralization is essentially a discussion over who owns and how ledgers are managed. 

Decentralization has been a prominent theme in international development and there is strong evidence of its positive impact across various sectors, especially regarding local service delivery.  One of the primary value adds of decentralization is empowering those further from traditional concentrations of power to have more authority over the problems that impact them.  As a decentralizing technology, the blockchain holds a lot of potential in reaching these same impacts from decentralization (empowerment, etc.) in a more efficient and effective manner partly due to its ability to better align interests around common problems.  With better aligned interests, less resources (inputs) are needed to try and facilitate a desired behavior change. 

Up until now, efforts of international development actors have focused on “nudging” behavior change amongst stakeholders and in very rare cases, such as in results based financing, give loosely defined parameters to implementers with less emphasis on the manner in which outcomes are achieved.  Both of these approaches are relevant in the design and testing of blockchain based interventions but they will be integrated in unique new ways that will require new thinking and skills sets amongst practitioners. 

Current Designing and Evaluating for Behavior Change

MEL usually starts with the relevant theory of change, namely what mechanisms bring about targeted behavior change and how.  Recent years have seen a focus on how behavior change is achieved through an understanding of mindsets and how they can be nudged to achieve a social outcome.  However the international development space has recognized the limitations of designing interventions that attempt to nudge behavior change.  These limitations center around the level of complexity involved, the inability to recognize and manage this complexity and lack of awareness about the root causes of problems.  Hence the rise in things like results based financing where the type of prescribed top-down causal pathway (usually laid out in a theory of change) is not as heavily emphasized as in more traditional interventions.  Donors using this approach can still mandate certain principles of implementation (such as the inclusion of vulnerable populations, environmental safeguards, timelines, etc.) but there is much more flexibility to create a causal pathway to achieve the outcome. 

Or, for example, take the popular PDIA approach where the focus is on iteratively identifying and solving problems encountered on the pathway to reform.  These efforts do not start with a mandated theory of change, but instead start with generally described targeted outcomes and then the pathway to those outcomes is iteratively created, similar to what Lant Pritchett has called “crawling the design space”.  Such an approach has large overlaps with adaptive management practices and other more integrative MEL frameworks and could lend themselves to how blockchain based interventions are designed, implemented and evaluated. 

How the Blockchain Could Achieve Outcomes and Implications for MEL

Because of its decentralizing effects, any theory of change for a blockchain based intervention could include some possible common attributes that influence how outcomes are achieved:

  • Empowerment of those closest to problems to inform the relevant solutions
  • Alignment of interests around these solutions
  • Alleviation of traditional intermediary services and relevant third party actors

Assessing these three attributes, and how they influence outcomes, could be the foundation of any appropriate MEL strategy for a blockchain-based intervention.  This is because these attributes are the “value add” of a blockchain-based intervention.  For example, traditional financial inclusion interventions may seek to extend financial services of a bank to rural areas through digital money, extension agents, etc.  A blockchain-based solution, however, may cut out the bank entirely and empower local communities to receive financial services from completely new providers from anywhere in the world on much more affordable terms in and in a much more convenient manner.  Such a solution could see an alignment of interests amongst producers and consumers of these services since the new relationships are mutually serving.  Because of this alignment there is a less of a need, or even less of a benefit, of having donors script out the causal pathway for the outcomes to be achieved.  Because of this alignment of interests, those closest to the problem(s) and solutions can work it out because it is in their interest to do so. 

Hence while a MEL framework for such a project could still use more standardized measures around outcomes like increased access to financial services and could even use statistical methods to evaluate questions around attributable changes in poverty status; there will need to be adaptive and formative MEL that assess the dynamics of these attributes given their criticality to whether and how outcomes could be achieved.  The dynamics between these attributes and the surrounding social eco-system have the potential to be very fluid (going back to the disruptive nature of blockchain technology), hence flexible MEL will be required to respond to new trends as they emerge. 

Table: Blockchain Intervention Attributes and the Skill Sets to Assess Them

Blockchain Attributes Possible MEL Approaches
Empowerment of those closest to problems to inform the relevant solutions   Problem driven design and MEL approach, stakeholder mapping (to identify relevant actors) Decentralization focused MEL (MEL that focuses on outcomes associated with decentralization)
Alignment of interests Political economy analysis to identify incentives and interests Adaptive MEL to assess shifting alignment of interest between various actors
Alleviation of traditional intermediary services Political economy analysis to inform risk mitigation strategy for potential spoilers and relevant MEL

While there will need to be standard accountability and other uses, feedback from an appropriate MEL strategy could have two primary end uses in a blockchain based intervention: governance and trust.

The Role of Governance and Trust

Blockchain governance sets outs the rules for how consensus (ie. agreement) is achieved for deciding what transactions are valid on a blockchain.  While this may sound mundane it is critical for achieving outcomes since how the blockchain is governed decides how well those closest to the problems are empowered to identify and achieve solutions and aligned interests. Hence the governance framework for the blockchain will need to be informed by an appropriate MEL strategy.  A giant learning gap we currently have is how to iteratively adapt blockchain governance structures, using MEL feedback, into increasingly more efficient versions.  Closing this gap will be critical to assessing the cost effectiveness of blockchain based solutions over other solutions (ie. alternatives/cost benefit analysis tools) as well as maximizing impact. 

A giant learning gap we currently have is how to iteratively adapt blockchain governance structures, using MEL feedback, into increasingly more efficient versions. 

Another focus of an appropriate MEL strategy would be to facilitate trust in the blockchain-based solution amongst users much the same as other technology-led solutions like mobile money or pay as you go metering for service delivery.  This includes not only the digital interface between the user and the technology (a phone app, SMS or other interface) but other dimensions of “trust” that would facilitate uptake of the technology.  These dimensions of trust would be informed by an analysis of the barriers to uptake of the technology amongst intended users, given it could be an entirely new service for beneficiaries or an old service delivered in a new fashion.  There is already a good evidence base around what works in this area (ie. marketing and communication tools for digital financial services, assistance in completing registration paperwork for pay as you go metering, etc.). 

The Road Ahead

There is A LOT we need to learn and a short time to do it in before we feel the negative effects from a lack of preparedness.  This risk is heightened when you consider that the international development industry has a poor track record of designing and evaluating technology-led solutions (primarily due to the fact that these projects usually neglect uptake of the technology and operate on the assumption that the technology will drive outcomes instead of users using the technology as a tool to drive the outcomes). 

The lessons from MEL in results based financing could be especially informative to the future of evaluating blockchain-based solutions given their similarities in letting solutions work themselves out and the role of the “validator” in ensuring outcomes are achieved.  In fact the blockchain has already been used in this role in some simple output based programming. 

As alluded to, pre-existing MEL skill sets can add a lot of value to building an evidence base but MEL practitioners will need to develop a greater understanding of the attributes of blockchain technology, otherwise our MEL strategies will not be suited to blockchain based programming.

We Wrote the Book on Evaluation Failures. Literally.

by Isaac D. Castillo, Director of Outcomes, Assessment, and Learning at Venture Philanthropy Partners.

Evaluators don’t make mistakes.

Or do they?

Well, actually, they do. In fact, I’ve got a number of fantastic failures under my belt that turned into important learning opportunities. So, when I was asked to share my experience at the MERL Tech DC 2018 session on failure, I jumped at the chance.

Part of the Problem

As someone of Mexican descent, I am keenly aware of the problems that can arise when culturally and linguistically inappropriate evaluation practices are used. However, as a young evaluator, I was often part of the problem.

Early in my evaluation career, I was tasked with collecting data to determine why teenage youth became involved in gangs. In addition to developing the interview guides, I was also responsible for leading all of the on-site interviews in cities with large Latinx populations. Since I am Latinx, I had a sufficient grasp of Spanish to prepare the interview guides and conduct the interviews. I felt confident that I would be sensitive to all of the cultural and linguistic challenges to ensure an effective data collection process. Unfortunately, I had forgotten an important tenet of effective culturally competent evaluation: cultures and languages are not monolithic. Differences in regional cultures or dialects can lead even experienced evaluators into embarrassment, scorn, or the worst outcome of all: inaccurate data.

Sentate, Por Favor

For example, when first interacting with the gang members, I introduced myself and asked them to “Please sit down,” to start the interview by saying “Siéntate, por favor.” What I did not know at the time is that a large portion of the gang members I was interviewing were born in El Salvador or were of Salvadoran descent, and the accurate way to say it using Salvadoran Spanish would have been, “Sentate, por favor.”

Does one word make that much difference? In most cases it did not matter, but it caused several gang members to openly question my Spanish from the outset, which created an uncomfortable beginning to interviews about potentially sensitive subjects.

Amigo or Chero?

I next asked the gang members to think of their “friends.” In most dialects of Spanish, using amigos to ask about friends is accurate and proper. However, in the context of street slang, some gang members prefer the term chero, especially in informal contexts.

Again, was this a huge mistake? No. But it did lead to enough quizzical looks and requests for clarification that started to doubt if I was getting completely honest or accurate answers from some of the respondents. Unfortunately, this error did not arise until I had conducted nearly 30 interviews. I had not thought to test the wordings of the questions in multiple Spanish-speaking communities across several states.

Would You Like a Concha?

Perhaps my most memorable mistake during this evaluation occurred after I had completed an interview with a gang leader outside of a bakery. After we were done, the gang leader called over the rest of his gang to meet me. As I was meeting everyone, I glanced inside the bakery and noticed a type of Mexican pastry that I enjoyed as a child. I asked the gang leader if he would like to go inside and join me for a concha, a round pastry that looks like a shell. Everyone (except me) began to laugh hysterically. The gang leader then let me in on the joke. He understood that I was asking about the pan dulce (sweet bread), but he informed me that in his dialect, concha was used as a vulgar reference to female genitalia. This taught me a valuable lesson about how even casual references or language choices can be interpreted in many different ways.

What did I learn from this?

While I can look back on these mistakes and laugh, I am also reminded of the important lessons learned that I carry with me to this day.

  • Translate with the local context in mind. When translating materials
    or preparing for field work, get a detailed sense of who you will be collecting data from, including what cultures and subgroups people represent and whether or not there are specific topics or words that should be avoided.
  • Translate with the local population in mind. When developing data collection tools (in any language, even if you are fluent in it), take the time to pre-test the language in the tools.

Be okay with your inevitable mistakes. Recognize that no matter how much preparation you do, you will make mistakes in your data collection related to culture and language issues. Remember it is how you respond in those situations that is most important.

As far as failures like this go, it turns out I’m in good company. My story is one of 22 candid, real-life examples from seasoned evaluators that are included in Kylie Hutchinson’s new book, Evaluation Failures: 22 Tales of Mistakes Made and Lessons Learned. Entertaining and informative, I guarantee it will give you plenty of opportunities to reflect and learn.

MERL and the 4th Industrial Revolution: Submit your AfrEA abstract now!

by Dhashni Naidoo, Genesis Analytics

Digitization is everywhere! Digital technologies and data have changed the way we engage with each other and how we work. We cannot escape the effects of digitization. Whether in our personal capacity — how our own data is being used — or in our professional capacity, in terms of understanding how to use data and technology. These changes are exciting! But we also need to consider the challenges they present to the MERL community and their impact on development.

The advent and proliferation of big data has the potential to change how evaluations are conducted. New skills are needed to process and analyse big data. Mathematics, statistics and analytical skills will be ever more important. As evaluators, we need to be discerning about the data we use. In a world of copious amounts of data, we need to ensure we have the ability to select the right data to answer our evaluation questions.

We also have an ethical and moral duty to manage data responsibly. We need new strategies and tools to guide the ways in which we collect, store, use and report data. Evaluators need to improve our skills as related to processing and analysing data. Evaluative thinking in the digital age is evolving and we need to consider the technical and soft skills required to maintain integrity of the data and interpretation thereof.

Though technology can make data collection faster and cheaper, two important considerations are access to technology by vulnerable groups and data integrity. Women, girls and people in rural areas normally do not have the same levels of access to technology as men and boys This impacts on our ability to rely solely on technology to collect data from these population groups, because we need to be aware of inclusion, bias and representativity. Equally we need to consider how to maintain the quality of data being collected through new technologies such as mobile phones and to understand how the use of new devices might change or alter how people respond.

In a rapidly changing world where technologies such as AI, Blockchain, Internet of Things, drones and machine learning are on the horizon, evaluators need to be robust and agile in how we change and adapt.

For this reason, a new strand has been introduced at the African Evaluation Association (AfrEA) conference, taking place from 11 – 15 March 2019 in Abidjan, Cote d’Ivoire. This stream, The Fourth Industrial Revolution and its Impact on Development: Implications for Evaluation, will focus on five sub-themes:

  • Guide to Industry 4.0 and Next Generation Tech
  • Talent and Skills in Industry 4.0
  • Changing World of Work
  • Evaluating youth programmes in Industry 4.0
  • MERLTech

Genesis Analytics will be curating this strand.  We are excited to invite experts working in digital development and practitioners at the forefront of technological innovation for development and evaluation to submit abstracts for this strand.

The deadline for abstract submissions is 16 November 2018. For more information please visit the AfrEA Conference site!

How I Learned to Stop Worrying and Love Big Data

by Zach Tilton, a Peacebuilding Evaluation Consultant and a Doctoral Research Associate at the Interdisciplinary PhD in Evaluation program at Western Michigan University. 
 
In 2013 Dan Airley quipped “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it….” In 2015 the metaphor was imported to the international development sector by Ben Ramalingam, in 2016 it became a MERL Tech DC lightning talk, and has been ringing in our ears ever since. So, what about 2018? Well, unlike US national trends in teenage sex, there are some signals that big or at least ‘bigger’ data is continuing to make its way not only into the realm of digital development, but also evaluation. I recently attended the 2018 MERL Tech DC pre-conference workshop Big Data and Evaluation where participants were introduced to real ways practitioners are putting this trope to bed (sorry, not sorry). In this blog post I share some key conversations from the workshop framed against the ethics of using this new technology, but to do that let me first provide some background.
 
I entered the workshop on my heels. Given the recent spate of security breaches and revelations about micro-targeting, ‘Big Data’ has been somewhat of a boogie-man for myself and others. I have taken some pains to limit my digital data-footprint, have written passionately about big data and surveillance capitalism, and have long been skeptical of big data applications for serving marginalized populations in digital development and peacebuilding. As I found my seat before the workshop started I thought, “Is it appropriate or ethical to use big data for development evaluation?” My mind caught hold of a 2008 Evaluation Café debate between evaluation giants Michael Scriven and Tom Cook on causal inference in evaluation and the ethics of Randomized Control Trials. After hearing Scriven’s concerns about the ethics of withholding interventions from control groups, Cook asks, “But what about the ethics of not doing randomized experiments?” He continues, “What about the ethics of having causal information that is in fact based on weaker evidence and is wrong? When this happens, you carry on for years and years with practices that don’t work whose warrant lies in studies that are logically weaker than experiments provide.”
 
While I sided with Scriven for most of that debate, this question haunted me. It reminded me of an explanation of structural violence by peace researcher Johan Galtung who writes, “If a person died from tuberculosis in the eighteenth century it would be hard to conceive of this as violence since it might have been quite unavoidable, but if he dies from it today, despite all the medical resources in the world, then violence is present according to our definition.” Galtung’s intellectual work on violence deals with the difference between potential and the actual realizations and what increases that difference. While there are real issues with data responsibility, algorithmic biases, and automated discrimination that need to be addressed, if there are actually existing technologies and resources not being used to address social and material inequities in the world today, is this unethical, even violent? “What about the ethics of not using big data?” I asked myself back. The following are highlights of the actually existing resources for using big data in the evaluation of social amelioration.
 

Actually Existing Data

 
During the workshop, Kerry Bruce from Social Impact shared with participants her personal mantra, “We need to do a better job of secondary data analysis before we collect any more primary data.” She challenged us to consider how to make use of the secondary data available to our organizations. She gave examples of potential big data sources such as satellite images, remote sensors, GPS location data, social media, internet searches, call-in radio programs, biometrics, administrative data and integrated data platforms that merge many secondary data files such as public records and social service agency and client files. The key here is there are a ton of actually existing data, many of which are collected passively, digitally, and longitudinally. Despite noting real limitations to accessing existing secondary data, including donor reluctance to fund such work, limited training in appropriate methodologies in research teams, and differences in data availability between contexts, to underscore the potential of using secondary data, she shared a case study where she lead a team to use large amounts of secondary indirect data to identify ecosystems of modern day slavery at a significantly reduced cost than collecting the data first-hand. The outputs of this work will help pinpoint interventions and guide further research into the factors that may lead to predicting and prescribing what works well for stopping people from becoming victims of slavery.
 

Actually Existing Tech (and math)

 
Peter York from BCT Partners provided a primer on big data and data science including the reality-check that most of the work is the unsexy “ETL,” or the extraction, transformation, and loading of data. He contextualized the potential of the so-called big data revolution by reminding participants that the V’s of big data, Velocity, Volume, and Variety, are made possible by the technological and social infrastructure of increasingly networked populations and how these digital connections enable the monitoring, capturing, and tracking of ever increasing aspects of our lives in an unprecedented way. He shared, “A lot of what we’ve done in research were hacks because we couldn’t reach entire populations.” With advances in the tech stacks and infrastructure that connect people and their internet-connected devices with each other and the cloud, the utility of inferential statistics and experimental design lessens when entire populations of users are producing observational behavior data. When this occurs, evaluators can apply machine learning to discover the naturally occurring experiments in big data sets, what Peter terms ‘Data-driven Quasi-Experimental Design.’ This is exactly what Peter does when he builds causal models to predict and prescribe better programs for child welfare and juvenile justice to automate outcome evaluation, taking cues from precision medicine.
 
One example of a naturally occurring experiment was the 1854 Broad Street cholera outbreak in which physician John Snow used a dot map to identify a pattern that revealed the source of the outbreak, the Broad Street water pump. By finding patterns in the data John Snow was able to lay the groundwork for rejecting the false Miasma Theory and replace it with a proto-typical Germ Theory. And although he was already skeptical of miasma theory, by using the data to inform his theory-building he was also practicing a form of proto-typical Grounded Theory. Grounded theory is simply building theory inductively, after data collection and analysis, not before, resulting in theory that is grounded in data. Peter explained, “Machine learning is Grounded Theory on steroids. Once we’ve built the theory, found the pattern by machine learning, we can go back and let the machine learning test the theory.” In effect, machine learning is like having a million John Snows to pour over data to find the naturally occurring experiments or patterns in the maps of reality that are big data.
 
A key aspect of the value of applying machine learning in big data is that patterns more readily present themselves in datasets that are ‘wide’ as opposed to ‘tall.’ Peter continued, “If you are used to datasets you are thinking in rows. However, traditional statistical models break down with more features, or more columns.” So, Peter and evaluators like him that are applying data science to their evaluative practice are evolving from traditional Frequentist to Bayesian statistical approaches. While there is more to the distinction here, the latter uses prior knowledge, or degrees of belief, to determine the probability of success, where the former does not. This distinction is significant for evaluators who are wanting to move beyond predictive correlation to prescriptive evaluation. Peter expounded, Prescriptive analytics is figuring out what will best work for each case or situation.” For example, with prediction, we can make statements that a foster child with certain attributes is 70% not likely to find a home. Using the same data points with prescriptive analytics we can find 30 children that are similar to that foster child and find out what they did to find a permanent home. In a way, only using predictive analytics can cause us to surrender while including prescriptive analytics can cause us to endeavor.
 

Existing Capacity

The last category of existing resources for applying big data for evaluation was mostly captured by the comments of independent evaluation consultant, Michael Bamberger. He spoke of the latent capacity that existed in evaluation professionals and teams, but that we’re not taking full advantage of big data: “Big data is being used by development agencies, but less by evaluators in these agencies. Evaluators don’t use big data, so there is a big gap.”

He outlined two scenarios for the future of evaluation in this new wave of data analytics: a state of divergence where evaluators are replaced by big data analysts and a state of convergence where evaluators develop a literacy with the principles of big data for their evaluative practice. One problematic consideration with this hypothetical is that many data scientists are not interested in causation, as Peter York noted. To move toward the future of convergence, he shared how big data can enhance the evaluation cycle from appraisal and planning through monitoring, reporting and evaluating sustainability. Michael went on to share a series of caveats emptor that include issues with extractive versus inclusive uses of big data, the fallacy of large numbers, data quality control, and different perspectives on theory, all of which could warrant their own blog posts for development evaluation.

While I deepened my basic understandings of data analytics including the tools and techniques, benefits and challenges, and guidelines for big data and evaluation, my biggest take away is reconsidering big data for social good by considering the ethical dilemma of not using existing data, tech, and capacity to improve development programs, possibly even prescribing specific interventions by identifying their probable efficacy through predictive models before they are deployed.

(Slides from the Big Data and Evaluation workshop are available here).

Do you use or have strong feelings about big data for evaluation? Please continue the conversation below.

 

 

Report back on MERL Tech DC

Day 1, MERL Tech DC 2018. Photo by Christopher Neu.

The MERL Tech Conference explores the intersection of Monitoring, Evaluation, Research and Learning (MERL) and technology. The main goals of “MERL Tech” as an initiative are to:

  • Transform and modernize MERL in an intentionally responsible and inclusive way
  • Promote ethical and appropriate use of tech (for MERL and more broadly)
  • Encourage diversity & inclusion in the sector & its approaches
  • Improve development, tech, data & MERL literacy
  • Build/strengthen community, convene, help people talk to each other
  • Help people find and use evidence & good practices
  • Provide a platform for hard and honest talks about MERL and tech and the wider sector
  • Spot trends and future-scope for the sector

Our fifth MERL Tech DC conference took place on September 6-7, 2018, with a day of pre-workshops on September 5th. Some 300 people from 160 organizations joined us for the 2-days, and another 70 people attended the pre-workshops.

Attendees came from a wide diversity of professions and disciplines:

What professional backgrounds did we see at MERL Tech DC in 2018?

An unofficial estimate on speaker racial and gender diversity is here.

Gender balance on panels

At this year’s conference, we focused on 5 themes (See the full agenda here):

  1. Building bridges, connections, community, and capacity
  2. Sharing experiences, examples, challenges, and good practice
  3. Strengthening the evidence base on MERL Tech and ICT4D approaches
  4. Facing our challenges and shortcomings
  5. Exploring the future of MERL

As always, sessions were related to: technology for MERL, MERL of ICT4D and Digital Development programs, MERL of MERL Tech, digital data for adaptive decisions/management, ethical and responsible data approaches and cross-disciplinary community building.

Big Data and Evaluation Session. Photo by Christopher Neu.

Sessions included plenaries, lightning talks and breakout sessions. You can find a list of sessions here, including any presentations that have been shared by speakers and session leads. (Go to the agenda and click on the session of interest. If we have received a copy of the presentation, there will be a link to it in the session description).

One topic that we explored more in-depth over the two days was the need to get better at measuring ourselves and understanding both the impact of technology on MERL (the MERL of MERL Tech) and the impact of technology overall on development and societies.

As Anahi Ayala Iacucci said in her opening talk — “let’s think less about what technology can do for development, and more about what technology does to development.” As another person put it, “We assume that access to tech is a good thing and immediately helps development outcomes — but do we have evidence of that?”

Feedback from participants

Some 17.5% of participants filled out our post-conference feedback survey, and 70% of them rated their experience either “awesome” or “good”. Another 7% of participants rated individual sessions through the “Sched” app, with an average session satisfaction rating of 8.8 out of 10.

Topics that survey respondents suggested for next time include: more basic tracks and more advanced tracks, more sessions relating to ethics and responsible data and a greater focus on accountability in the sector.  Read the full Feedback Report here!

What’s next? State of the Field Research!

In order to arrive at an updated sense of where the field of technology-enabled MERL is, a small team of us is planning to conduct some research over the next year. At our opening session, we did a little crowdsourcing to gather input and ideas about what the most pressing questions are for the “MERL Tech” sector.

We’ll be keeping you informed here on the blog about this research and welcome any further input or support! We’ll also be sharing more about individual sessions here.

Integrating big data into program evaluation: An invitation to participate in a short survey

As we all know, big data and data science are becoming increasingly important in all aspects of our lives. There is a similar rapid growth in the applications of big data in the design and implementation of development programs. Examples range from the use of satellite images and remote sensors in emergency relief and the identification of poverty hotspots, through the use of mobile phones to track migration and to estimate changes in income (by tracking airtime purchases), social media analysis to track sentiments and predict increases in ethnic tension, and using smart phones on Internet of Things (IOT) to monitor health through biometric indicators.

Despite the rapidly increasing role of big data in development programs, there is speculation that evaluators have been slower to adopt big data than have colleagues working in other areas of development programs. Some of the evidence for the slow take-up of big data by evaluators is summarized in “The future of development evaluation in the age of big data”.  However, there is currently very limited empirical evidence to test these concerns.

To try to fill this gap, my colleagues Rick Davies and Linda Raftree and I would like to invite those of you who are interested in big data and/or the future of evaluation to complete the attached survey. This survey, which takes about 10 minutes to complete asks evaluators to report on the data collection and data analysis techniques that you use in the evaluations you design, manage or analyze; while at the same time asking data scientists how familiar they are with evaluation tools and techniques.

The survey was originally designed to obtain feedback from participants in the MERL Tech conferences on “Exploring the Role of Technology in Monitoring, Evaluation, Research and Learning in Development” that are held annually in London and Washington, DC, but we would now like to broaden the focus to include a wider range of evaluators and data scientists.

One of the ways in which the findings will be used is to help build bridges between evaluators and data scientists by designing integrated training programs for both professions that introduce the tools and techniques of both conventional evaluation practice and data science, and show how they can be combined to strengthen both evaluations and data science research. “Building bridges between evaluators and big data analysts” summarizes some of the elements of a strategy to bring the two fields closer together.

The findings of the survey will be shared through this and other sites, and we hope this will stimulate a follow-up discussion. Thank you for your cooperation and we hope that the survey and the follow-up discussions will provide you with new ways of thinking about the present and potential role of big data and data science in program evaluation.

Here’s the link to the survey – please take a few minute to fill it out!

You can also join me, Kerry Bruce and Pete York on September 5th for a full day workshop on Big Data and Evaluation in Washington DC.

September 5th: MERL Tech DC pre-workshops

This year at MERL Tech DC, in addition to the regular conference on September 6th and 7th, we’re offering two full-day, in-depth workshops on September 5th. Join us for a deeper look into the possibilities and pitfalls of Blockchain for MERL and Big Data for Evaluation!

What can Blockchain offer MERL? with Shailee Adinolfi, Michael Cooper, and Val Gandhi, co-hosted by Chemonics International, 1717 H St. NW, Washington, DC 20016. 

Tired of the blockchain hype, but still curious on how it will impact MERL? Join us for a full day workshop with development practitioners who have implemented blockchain solutions with social impact goals in various countries. Gain knowledge of the technical promises and drawbacks of blockchain technology as it stands today and brainstorm how it may be able to solve for some of the challenges in MERL in the future. Learn about ethical design principles for blockchain and how to engage with blockchain service providers to ensure that your ideas and programs are realistic and avoid harm. See the agenda here.

Register now to claim a spot at the blockchain and MERL pre-workshop!

Big Data and Evaluation with Michael Bamberger, Kerry Bruce and Peter York, co-hosted by the Independent Evaluation Group at the World Bank – “I” Building, Room: I-1-200, 1850 I St NW, Washington, DC 20006

Join us for a one-day, in-depth workshop on big data and evaluation where you’ll get an introduction to Big Data for Evaluators. We’ll provide an overview of applications of big data in international development evaluation, discuss ways that evaluators are (or could be) using big data and big data analytics in their work. You’ll also learn about the various tools of data science and potential applications, as well as run through specific cases where evaluators have employed big data as one of their methods. We will also address the important question as to why many evaluators have been slower and more reluctant to incorporate big data into their work than have their colleagues in research, program planning, management and other areas such as emergency relief programs. Lastly, we’ll discuss the ethics of using big data in our work. See the agenda here!

Register now to claim a spot at the Big Data and Ealuation pre-workshop!

You can also register here for the main conference on September 6-7, 2018!

 

How MERL Tech Jozi helped me bridge my own data gap

Guest post from Praekelt.org. The original post appeared on August 15 here.

Our team had the opportunity to enjoy a range of talks at the first ever MERL Tech in Johannesburg. Here are some of their key learnings:

During “Designing the Next Generation of MERL Tech Software” by Mobenzi’s CEO Andi Friedman, we were challenged to apply design thinking techniques to critique both our own as well as our partners’ current projects. I have previously worked on an educational tool that is aimed to improve the quality of learning of students who are based in a disadvantaged community in the Eastern Cape, South Africa. I learned that language barriers are a serious concern when it comes to effectively implementing a new tool.

We mapped out a visual representation of solving a communication issue that one of the partners had for an educational programme implemented in rural Eastern Cape, which included drawing various shapes on paper. What we came up with was to replace the posters that had instructions in words with clear visuals that the students were familiar with. This was inspired by the idea that visuals resonate with people more than words.

-Perez Mnkile, Project Manager

Amy Green Presenting on Video Metrics

I really enjoyed the presentation on video metrics from Girl Effect’s Amy Green. She spoke to us about video engagement on Hara Huru Dara, a vlog series featuring social media influencers. What I found really interesting is how hard it is to measure impact or engagement. Different platforms (YouTube vs Facebook) have different definitions for various measurements (e.g. views) and also use a range of algorithms to reach these measurements. Her talk really helped me understand just how hard MERL can be in a digital age! As our projects expand into new technologies, I’ll definitely be more aware of how complicated seemingly simple metrics (for example, views on a video) may be.

-Jessica Manim, Project Manager

Get it right by getting it wrong: embracing failure as a tool for learning and improvement was a theme visible throughout the two day MERL Tech conference and one session highlighting this theme was conducted by Annie Martin a Research Associate at Akros, who explored challenges in Offline Data Capture.

She referenced a project that took place in Zambia to track participants of an HIV prevention program, highlighting some of the technical challenges the project faced along the way. The project involved equipping field workers with an Android tablet and an Application developed for capturing offline data and synching data, when data connectivity was available. A number of bugs due to insufficient system user testing along with server hosting issues resulted in field workers often not successfully being able to send data or create user IDs.

The lesson, which I believe we strive to include in our developmental processes, is to focus on iterative piloting, testing and learning before deployment. This doesn’t necessarily mean that a bug-free system or service is guaranteed but it does encourage us to focus our attention on the end-users and stakeholders needs, expectations and requirements.

-Neville Tietz, Service Designer

Slide from Panel on WhatsApp and engagement

Sometimes, we don’t fully understand the problems that we are trying to solve. Siziwe Ngcwabe from the African Evidence Network gave the opening talk on evidence-based work. It showed me the importance of fully understanding the problem we are solving and identifying the markers of success or failure before we start rolling out solutions. Once we have established all this, we can then create effective solutions. Rachel Sibande from DIAL, gave a talk on how their organisation is now using data from mobile network providers to anticipate how a disease outbreak will spread, based on the movement patterns of the network’s subscribers. Using this data they can advise ministries to run campaigns in certain areas and increase medical supplies in another. The talk by Siziwe showed me the importance of fully understanding the problem you are trying to solve and how to effectively measure progress. Rachel’s talk really showed me how easy it is to create an effective solution, once you fully understand the problem.

-Katlego Maakane, Project Manager

I really enjoyed the panel discussion on Datafication Discrimination with William Bird, Director of Media Monitoring Africa, Richard Gevers, Director of Open Data Durban, Koketso Moeti, Executive Director of amandla.mobi that was moderated by Siphokazi Mthathi, Executive Director of Oxfam South Africa. The impact that the mass collection of data can have on communities can potentially be used to further discriminate against them, especially when they are not aware on what their data will be used for. For example, information around sexuality can be used to target individuals during a time when there is rapid reversing of anti-discrimination laws in many countries.

I also thought it was interesting how projection models for population movement and the planning of new areas for residential development and public infrastructure in cities in South Africa are flawed, since the development of these models are outsourced by government to the private sector and different government departments often use different forecasts. Essentially the various government departments are all planning cities with different projections further preventing the poorest people from accessing quality services and infrastructure.

For me this session really highlighted the responsibility we have when collecting data in our projects from vulnerable individuals and that we have to ensure that we interrogate what we intend to use this data for. As part of our process, we must investigate how the data could potentially be exploited. We need to empower people to take control of the information they share and be able to make decisions in their best interest.

-Benjamin Vermeulen, Project Manager

Evaluating for Trust in Blockchain Applications

by Mike Cooper

This is the fourth in a series of blogs aimed at discussing and soliciting feedback on how the blockchain can benefit MEL practitioners in their work.  The series includes: What does Blockchain Offer to MERL,  Blockchain as an M&E Tool, How Can MERL Inform Maturation of the Blockchain, this post, and future posts on integrating  blockchain into MEL practices. The series leads into a MERL Tech Pre-Workshop on September 5th, 2018 in Washington D.C.  that will go into depth on possibilities and examples of MEL blockchain applications. Register here!

Enabling trust in an efficient manner is the primary innovation that the blockchain delivers through the use of cryptology and consensus algorithms.  Trust is usually a painstaking relationship building effort that requires iterative interactions to build.  The blockchain alleviates the need for much of the resources required to build this trust, but that does not mean that stakeholders will automatically trust the blockchain application.  There will still need to be trust building mechanisms with any blockchain application and MEL practitioners are uniquely situated to inform how these trust relationships can mature.

Function of trust in the blockchain

Trust is expensive.  You pay fees to banks who provide confidence to sellers who take your debit card as payment and trust that they will receive funds for the transaction.  Agriculture buyers pay fees to third parties (who can certify that the produce is organic, etc.) to validate quality control on products coming through the value chain  Often sellers do not see the money from debit card transaction in their accounts automatically and agriculture actors perpetually face the pressures resulting from being paid for goods and/or services they provided weeks previously. The blockchain could alleviate much of these harmful effects by substituting trust in humans by trust in math.

We pay these third parties because they are trusted agents, and these trusted agents can be destructive rent seekers at times; creating profits that do not add value to the goods and services they work with. End users in these transactions are used to using standard payment services for utility bills, school fees, etc.  This history of iterative transactions has resulted in a level of trust in these processes. It may not be equitable but it is what many are used to and introducing an innovation like blockchain will require an understanding of how these processes are influencing stakeholders, their needs and how they might be nudged to trust something different like a blockchain application.  

How MEL can help understand and build trust

Just as microfinance introduced new methods of sending/receiving money and access to new financial services that required piloting different possible solutions to build this understanding, so will blockchain applications. This is an area where MEL can add value to achieving mass impact, by designing the methods to iteratively build this understanding and test solutions.  

MEL has done this before.  Any project that requires relationship building should be based on understanding the mindset and incentives for relevant actions (behavior) amongst stakeholders to inform the design of the “nudge” (the treatment) intended to shift behavior.

Many of the programs we work on as MEL practitioners involve various forms and levels of relationship building, which is essentially “trust”.  There have been many evaluations of relationship building whether it be in microfinance, agriculture value chains or policy reform.  In each case, “trust” must be defined as a behavior change outcome that is “nudged” based on the framing (mindset) of the stakeholder.  Meaning that each stakeholder, depending on their mindset and the required behavior to facilitate blockchain uptake, will require a customized nudge.  

The role of trust in project selection and design: What does that mean for MEL

Defining “trust” should begin during project selection/design.  Project selection and design criteria/due diligence are invaluable for MEL.  Many of the dimensions of evaluability assessments refer back to the work that is done in the project selection/design phrase (which is why some argue evaluability assessments are essentially project design tools).  When it comes to blockchain, the USAID Blockchain Primer provides some of the earliest thinking for how to select and design blockchain projects, hence it is a valuable resources for MEL practitioners who want to start thinking about how they will evaluate blockchain applications.  

What should we be thinking about?

Relationship building and trust are behaviors, hence blockchain theories of change should have outcomes stated as behavior changes by specific stakeholders (hence the value add of tools like stakeholder analysis and outcome mapping).  However, these Theories of Change (TOC) are only as good as what informs them, hence building a knowledge base of blockchain applications as well as previous lessons learned from evidence on relationship building/trust will be critical to developing a MEL Strategy for blockchain applications.  

If you’d like to discuss this and related aspects, join us on September 5th in Washington, DC, for a one-day workshop on “What can the blockchain offer MERL?”

Michael Cooper is a former Associate Director at Millennium Challenge Corporation and the U.S. State Dept in Policy and Evaluation.  He now heads Emergence, a firm that specializes in MEL and Blockchain services. He can be reached at emergence.cooper@gmail.com or through the Emergence website.

How can MERL inform maturation of the blockchain?

by Mike Cooper

This is the third in a series of blogs aimed at discussing and soliciting feedback on how the blockchain can benefit MEL practitioners in their work.  The series includes: What does Blockchain Offer to MERL,  Blockchain as an M&E Tool, this post, and future posts on evaluating for trust in Blockchain applications, and integrating  blockchain into MEL practices. The series leads into a MERL Tech Pre-Workshop on September 5th, 2018 in Washington D.C.  that will go into depth on possibilities and examples of MEL blockchain applications. Register here!

Technology solutions in development contexts can be runaway trains of optimistic thinking.  Remember the play pump, a low technology solution meant to provide communities with clean water as children play?  Or the Soccket, the soccer ball that was going to help kids learn to read at night? I am not disparaging these good intentions, but the need to learn the evidence from past failure is widely recognized. When it comes to the blockchain, possibly the biggest technological innovation on the social horizon, the learning captured in guidance like the Principles for Digital Development or Blockchain Ethical Design Frameworks, needs to not only be integrated into the design of blockchain applications but also into how MEL practitioners will need to assess this integration and test solutions.   Data driven feedback from MEL will help inform the maturation of human centered blockchain solutions that mitigate endless/pointless pilots which exhaust the political will of good natured partners and creates barriers to sustainable impact.

The Blockchain is new but we have a head start in thinking about it

The blockchain is an innovation, and it should be evaluated as such. True the blockchain could be revolutionary in its impact.  And yes this potential could grease the wheels of the runaway train thinking referenced above, but this potential does not moot the evidence we have around evaluating innovations.

Keeping the risk of the runaway train at bay includes MERL practitioners working with stakeholders to ask : is blockchain the right approach for this at all?  Only after determining the competitive advantage of the blockchain solutions over other possible solutions should MEL practitioners work with stakeholders to finalize design of the initial piloting.  The USAID Blockchain Primer is the best early thinking about this process and the criteria involved.  

Michael Quinn Patton and others have developed an expanded toolkit for MERL practitioners to best unpack the complexity of a project and design a MERL framework that responds to the decision making requirements on the scale up pathway.  Because the blockchain is an innovation, which by definition means there is less evidence on its application but great potential, it will require MEL frameworks that iteratively test and modify applications to inform the scale up pathway.  

The Principles for Digital Development highlight the need for iterative learning in technology driven solutions.  The overlapping regulatory, organizational and technological spheres further assist in unpacking the complexity using tools like Problem Driven Iterative Adaptation (PDIA) or other adaptive management frameworks that are well suited to testing innovations in each sphere.  

How Blockchain is different: Intended Impacts and Potential Spoilers

There will be intended and unintended outcomes from blockchain applications that MEL should account for.  This includes general intended outcomes of increased access to services and overall costs savings while “un-intended” outcomes include the creation of winners and losers.  

The primary intended outcomes that could be expected from blockchain applications are an increase in cost savings (by cutting out intermediaries) which results in increased access to whatever service/product (assuming any cost savings are re-invested in expanding access).  Or a possible increase in access that results from creating a service where none existed before (for example creating access to banking services in rural populations). Hence methods for measuring the specific type of cost savings and increased access that are already used could be applied with modification.  

However, the blockchain will be disruptive and when I say “un-intended” (using quotation marks) I do so because the cost savings from blockchain applications are the result of alleviating the need for some intermediaries or middlemen. These middlemen are third parties who could be some form of rent-seeker in providing a validation, accreditation, certification  or other type of service meant to communicate trust. For example, with m-Pesa,  banking loan and other services from banks were expanded to new populations. With a financial inclusion blockchain project these same services could be accessed by the same population but without the need for a bank, hence incurring a cost savings. However, as is well known in many a policy reform intervention, creating efficiencies usually means creating losers and in our example the losers are those previously offering the services that the blockchain makes more efficient.  

The blockchain can facilitate efficiencies, not elimination of all intermediary functions. With the introduction of any innovation, the need for new functions will emerge as old functions are mooted.  For example mPesa experienced substantial barriers in its early development until they began working with kiosk owners who, after being trained up, could demonstrate and explain mPesa to customers.  Hence careful iterative assessment of the ecosystem (similar to value chain mapping) to identify mooted functions (losers) and new functions (winners) is critical.

MERL practitioners have a value add in mitigating the negative effects from the creation of losers, who could become spoilers.  MERL practitioners have many analytical tools/skills that can not only help in identifying the potential spoilers (perhaps through various outcome mapping and stakeholder analysis tools) but also in mitigating any negative effects (creating user personas of potential spoilers to better assess how to incentivize targeted behavior changes).  Hence MEL might be uniquely placed to build a broader understanding amongst stakeholders on what the blockchain is, what it can offer and how to create a learning framework that builds trust in the solution.

Trust, the real innovation of blockchain

MERL is all about behavior change, because no matter the technology or process innovation,  it requires uptake and uptake requires behavior. Trust is a behavior, you trust that when you put your money in a bank it will be available for when you want to use it.  Without this behavior, stemming from a belief, there are runs on banks which in turn fail which further erodes trust in the banking system. The same could be said for paying money to a water or power utility and expecting that they will provide service, The more use, the more a relationship matures into a trustful one. But it does not take much to erode this trust even after the relationship is established, again think about how easy it is to cause a run on a bank or stop using a service provider.  

The real innovation of the blockchain is that it replaces the need for trust in humans (whether it is an individual or system of organizations) with trust in math. Just as any entity needs to build a relationship of trust with its targeted patrons, so will the blockchain have to develop a  relationship of trust not only with end users but with those within the ecosystem that could influence the impact of the blockchain solution to include beneficiaries and potential loser/spoilers.  This brings us back to the importance of understanding who these stakeholders are, how they will interact with and influence the blockchain, and their perspectives, needs and capacities.

MERL practitioners who wish to use blockchain will need to pick up the latest thinking in behavioral sciences to understand this “trust” factor for each stakeholder and integrate it into an adaptive management framework.  The next blog in this series will go into further detail about the role of “trust” when evaluating a blockchain application.  

The Blockchain is different — don’t throw the baby out with the bath water

There will inevitably be mountains of pressure go to “full steam ahead” (part of me wants to add “and damn the consequences”) without sufficient data driven due diligence and ethical review, since blockchain is the next new shiny thing.  MERL practitioners should not only be aware of this unfortunate certainty, but they also need to pro-actively consider their own informed strategy on how they will respond to this pressure. MERL practitioners are uniquely positioned to advocate for data driven decision making and provide the data necessary to steer clear of misapplication of blockchain solutions.  There are already great resources for MEL practitioners on the ethical criteria and design implications for blockchain solutions.

The potential impact of blockchain is still unknown but if current thinking is to be believed, the impact could be paradigm shifting.  Given this potential, getting the initial testing right to maximize learning will be critical to cultivating the political will, the buy-in, and the knowledge base to kick start something much bigger.  

If you’d like to discuss this and related aspects, join us on September 5th in Washington, DC, for a one-day workshop on “What can the blockchain offer MERL?”

Michael Cooper is a former Associate Director at Millennium Challenge Corporation and the U.S. State Dept in Policy and Evaluation.  He now heads Emergence, a firm that specializes in MEL and Blockchain services. He can be reached at emergence.cooper@gmail.com or through the Emergence website.