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(Joint post from Linda Raftree, MERL Tech and Megan Colnar, Open Society Foundations)

The American Evaluation Association Conference happens once a year, and offers literally hundreds of sessions. It can take a while to sort though all of them. Because there are so many sessions, it’s easy to feel a bit lost in the crowds of people and content.

So, Megan Colnar (Open Society Foundations) and I thought we’d share some of the sessions that caught our eye.

I’m on the look-out for innovative tech applications, responsible and gender-sensitive data collection practices, and virtual or online/social media-focused evaluation techniques and methods. Megan plans to tune into sessions on policy change, complexity-aware techniques, and better MEL practices for funders. 

We both can’t wait to learn about evaluation in the post-truth and fake news era. Full disclosure, our sessions are also featured below.

Hope we see you there!

Wednesday, November 8th

3.15-4.15

4.30-6.00

We also think a lot of the ignite talks during this session in the Thurgood Salon South look interesting, like:

6.15-7.15

7.00-8.30

Tour of a few poster sessions before dinner. Highlights might include:

  • M&E for Journalism (51)
  • Measuring Advocacy (3)
  • Survey measures of corruption (53)
  • Theory of change in practice (186)
  • Using social networks as a decision-making tool (225)

 

Thursday, Nov 9th

8.00-9.00 – early risers are rewarded with some interesting options

9.15-10.15

10.30-11.15

12.15-1.15

1.15-2.00

2.15-3.00

3.15-4.15

4.30-5.15

 

Friday, Nov 10th

8.00-9.30early risers rewarded again!

11.00-11.45

1.45-3.15

3.30-4.15

4.30-5.15

5.30-6.15– if you can hold out for one more on a Friday evening

6.30-7.15

 

Saturday, Nov 11th–you’re on your own! Let us know what treasures you discover

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For our Tuesday, July 27th Salon, we discussed partnerships and interoperability in global health systems. The room housed a wide range of perspectives, from small to large non-governmental organizations to donors and funders to software developers to designers to healthcare professionals to students. Our lead discussants were Josh Nesbit, CEO at Medic Mobile; Jonathan McKay, Global Head of Partnerships and Director of the US Office of Praekelt.org; and Tiffany Lentz, Managing Director, Office of Social Change Initiatives at ThoughtWorks

We started by hearing from our discussants on why they had decided to tackle issues in the area of health. Reasons were primarily because health systems were excluding people from care and organizations wanted to find a way to make healthcare inclusive. As one discussant put it, “utilitarianism has infected global health. A lack of moral imagination is the top problem we’re facing.”

Other challenges include requests for small scale pilots and customization/ bespoke applications, lack of funding and extensive requirements for grant applications, and a disconnect between what is needed on the ground and what donors want to fund. “The amount of documentation to get a grant is ridiculous, and then the system that is requested to be built is not even the system that needs to be made,” commented one person. Another challenge is that everyone is under constant pressure to demonstrate that they are being innovative. [Sidenote: I’m reminded of this post from 2010….] “They want things that are not necessarily in the best interest of the project, but that are seen to be innovations. Funders are often dragged along by that,” noted another person.

The conversation most often touched on the unfulfilled potential of having a working ecosystem and a common infrastructure for health data as well as the problems and challenges that will most probably arise when trying to develop these.

“There are so many uncoordinated pilot projects in different districts, all doing different things,” said one person. “Governments are doing what they can, but they don’t have the funds,” added another, “and that’s why there are so many small pilots happening everywhere.” One company noted that it had started developing a platform for SMS but abandoned it in favor of working with an existing platform instead. “Can we create standards and protocols to tie some of this work together? There isn’t a common infrastructure that we can build on,” was the complaint. “We seem to always start from scratch. I hope donors and organizations get smart about applying pressure in the right areas. We need an infrastructure that allows us to build on it and do the work!” On the other hand, someone warned of the risks of pushing everyone to “jump on a mediocre software or platform just because we are told to by a large agency or donor.”

The benefits of collaboration and partnership are apparent: increased access to important information, more cooperation, less duplication, the ability to build on existing knowledge, and so on. However, though desirable, partnerships and interoperability is not easy to establish. “Is it too early for meaningful partnerships in mobile health? I was wondering if I could say that…” said one person. “I’m not even sure I’m actually comfortable saying it…. But if you’re providing essential basic services, collecting sensitive medical data from patients, there should be some kind of infrastructure apart from private sector services, shouldn’t there?” The question is who should own this type of a mediator platform: governments? MNOs?

Beyond this, there are several issues related to control and ownership. Who would own the data? Is there a way to get to a point where the data would be owned by the patients and demonetized? If the common system is run by the private sector, there should be protections surrounding the patients’ sensitive information. Perhaps this should be a government-run system. Should it be open source?

Open source has its own challenges. “Well… yes. We’ve practiced ‘hopensource’,” said one person (to widespread chuckles).

Another explained that the way we’ve designed information systems has held back shifts in health systems. “When we’re comparing notes and how we are designing products, we need to be out ahead of the health systems and financing shifts. We need to focus on people-centered care. We need to gather information about a person over time and place. About the teams who are caring for them. Many governments we’re working with are powerless and moneyless. But even small organizations can do something. When we show up and treat a government as a systems owner that is responsible to deliver health care to their citizens, then we start to think about them as a partner, and they begin to think about how they could support their health systems.”

One potential model is to design a platform or system such that it can eventually be handed off to a government. This, of course, isn’t a simple idea in execution. Governments can be limited by their internal expertise. The personnel that a government has at the time of the handoff won’t necessarily be there years or months later. So while the handoff itself may be successful in the short term, there’s no firm guarantee that the system will be continually operational in the future. Additionally, governments may not be equipped with the knowledge to make the best decisions about software systems they purchase. Governments’ negotiating capacity must be expanded if they are to successfully run an interoperable system. “But if we can bring in a snazzy system that’s already interoperable, it may be more successful,” said one person.

Having a common data infrastructure is crucial. However, we must also spend some time thinking about what the data itself should look like. Can it be standardized? How can we ensure that it is legible to anyone with access to it?

These are only some of the relevant political issues, and at a more material level, one cannot ignore the technical challenges of maintaining a national scale system. For example, “just getting a successful outbound dialing rate is hard!” said one person. “If you are running servers in Nigeria it just won’t always be up! I think human centered design is important. But there is also a huge problem simply with making these things work at scale. The hardcore technical challenges are real. We can help governments to filter through some of the potential options. Like, can a system demonstrate that it can really operate at massive scale?” Another person highlighted that “it’s often non-profits who are helping to strengthen the capacity of governments to make better decisions. They don’t have money for large-scale systems and often don’t know how to judge what’s good or to be a strong negotiator. They are really in a bind.”

This is not to mention that “the computers have plastic over them half the time. Electricity, computers, literacy, there are all these issues. And the TelCo infrastructure! We have layers of capacity gaps to address,” said one person.

There are also donors to consider. They may come into a project with unrealistic expectations of what is normal and what can be accomplished. There is a delicate balance to be struck between inspiring the donors to take up the project and managing expectations so that they are not disappointed.” One strategy is to “start hopeful and steadily temper expectations.” This is true also with other kinds of partnerships. “Building trust with organizations so that when things do go bad, you can try to manage it is crucial. Often it seems like you don’t want to be too real in the first conversation. I think, ‘if I lay this on them at the start it can be too real and feel overwhelming.…'” Others recommended setting expectations about how everyone together is performing. “It’s more like, ‘together we are going to be looking at this, and we’ll be seeing together how we are going to work and perform together.”

Creating an interoperable data system is costly and time-consuming, oftentimes more so than donors and other stakeholders imagine, but there are real benefits. Any step in the direction of interoperability must deal with challenges like those considered in this discussion. Problems abound. Solutions will be harder to come by, but not impossible.

So, what would practitioners like to see? “I would like to see one country that provides an incredible case study showing what good partnership and collaboration looks like with different partners working at different levels and having a massive impact and improved outcomes. Maybe in Uganda,” said one person. “I hope we see more of us rally around supporting and helping governments to be the system owners. We could focus on a metric or shared cause – I hope in the near future we have a view into the equity measure and not just the vast numbers. I’d love to see us use health equity as the rallying point,” added another. From a different angle, one person felt that “from a for-profit, we could see it differently. We could take on a country, a clinic or something as our own project. What if we could sponsor a government’s health care system?”

A participant summed the Salon up nicely: “I’d like to make a flip-side comment. I want to express gratitude to all the folks here as discussants. This is one of the most unforgiving and difficult environments to work in. It’ SO difficult. You have to be an organization super hero. We’re among peers and feel it as normal to talk about challenges, but you’re really all contributing so much!”

Salons are run under Chatham House Rule so not attribution has been made in this post. If you’d like to attend a future Salon discussion, join the list at Technology Salon.

 

(I’ve been blogging a little bit over at MERLTech.org. Here’s a repost.)

It can be overwhelming to get your head around all the different kinds of data and the various approaches to collecting or finding data for development and humanitarian monitoring, evaluation, research and learning (MERL).

Though there are many ways of categorizing data, lately I find myself conceptually organizing data streams into four general buckets when thinking about MERL in the aid and development space:

  1. ‘Traditional’ data. How we’ve been doing things for(pretty much)ever. Researchers, evaluators and/or enumerators are in relative control of the process. They design a specific questionnaire or a data gathering process and go out and collect qualitative or quantitative data; they send out a survey and request feedback; they do focus group discussions or interviews; or they collect data on paper and eventually digitize the data for analysis and decision-making. Increasingly, we’re using digital tools for all of these processes, but they are still quite traditional approaches (and there is nothing wrong with traditional!).
  2. ‘Found’ data.  The Internet, digital data and open data have made it lots easier to find, share, and re-use datasets collected by others, whether this is internally in our own organizations, with partners or just in general. These tend to be datasets collected in traditional ways, such as government or agency data sets. In cases where the datasets are digitized and have proper descriptions, clear provenance, consent has been obtained for use/re-use, and care has been taken to de-identify them, they can eliminate the need to collect the same data over again. Data hubs are springing up that aim to collect and organize these data sets to make them easier to find and use.
  3. ‘Seamless’ data. Development and humanitarian agencies are increasingly using digital applications and platforms in their work — whether bespoke or commercially available ones. Data generated by users of these platforms can provide insights that help answer specific questions about their behaviors, and the data is not limited to quantitative data. This data is normally used to improve applications and platform experiences, interfaces, content, etc. but it can also provide clues into a host of other online and offline behaviors, including knowledge, attitudes, and practices. One cautionary note is that because this data is collected seamlessly, users of these tools and platforms may not realize that they are generating data or understand the degree to which their behaviors are being tracked and used for MERL purposes (even if they’ve checked “I agree” to the terms and conditions). This has big implications for privacy that organizations should think about, especially as new regulations are being developed such a the EU’s General Data Protection Regulations (GDPR). The commercial sector is great at this type of data analysis, but the development set are only just starting to get more sophisticated at it.
  4. ‘Big’ data. In addition to data generated ‘seamlessly’ by platforms and applications, there are also ‘big data’ and data that exists on the Internet that can be ‘harvested’ if one only knows how. The term ‘Big data’ describes the application of analytical techniques to search, aggregate, and cross-reference large data sets in order to develop intelligence and insights. (See this post for a good overview of big data and some of the associated challenges and concerns). Data harvesting is a term used for the process of finding and turning ‘unstructured’ content (message boards, a webpage, a PDF file, Tweets, videos, comments), into ‘semi-structured’ data so that it can then be analyzed. (Estimates are that 90 percent of the data on the Internet exists as unstructured content). Currently, big data seems to be more apt for predictive modeling than for looking backward at how well a program performed or what impact it had. Development and humanitarian organizations (self included) are only just starting to better understand concepts around big data how it might be used for MERL. (This is a useful primer).

Thinking about these four buckets of data can help MERL practitioners to identify data sources and how they might complement one another in a MERL plan. Categorizing them as such can also help to map out how the different kinds of data will be responsibly collected/found/harvested, stored, shared, used, and maintained/ retained/ destroyed. Each type of data also has certain implications in terms of privacy, consent and use/re-use and how it is stored and protected. Planning for the use of different data sources and types can also help organizations choose the data management systems needed and identify the resources, capacities and skill sets required (or needing to be acquired) for modern MERL.

Organizations and evaluators are increasingly comfortable using mobile and/or tablets to do traditional data gathering, but they often are not using ‘found’ datasets. This may be because these datasets are not very ‘find-able,’ because organizations are not creating them, re-using data is not a common practice for them, the data are of questionable quality/integrity, there are no descriptors, or a variety of other reasons.

The use of ‘seamless’ data is something that development and humanitarian agencies might want to get better at. Even though large swaths of the populations that we work with are not yet online, this is changing. And if we are using digital tools and applications in our work, we shouldn’t let that data go to waste if it can help us improve our services or better understand the impact and value of the programs we are implementing. (At the very least, we had better understand what seamless data the tools, applications and platforms we’re using are collecting so that we can manage data privacy and security of our users and ensure they are not being violated by third parties!)

Big data is also new to the development sector, and there may be good reason it is not yet widely used. Many of the populations we are working with are not producing much data — though this is also changing as digital financial services and mobile phone use has become almost universal and the use of smart phones is on the rise. Normally organizations require new knowledge, skills, partnerships and tools to access and use existing big data sets or to do any data harvesting. Some say that big data along with ‘seamless’ data will one day replace our current form of MERL. As artificial intelligence and machine learning advance, who knows… (and it’s not only MERL practitioners who will be out of a job –but that’s a conversation for another time!)

Not every organization needs to be using all four of these kinds of data, but we should at least be aware that they are out there and consider whether they are of use to our MERL efforts, depending on what our programs look like, who we are working with, and what kind of MERL we are tasked with.

I’m curious how other people conceptualize their buckets of data, and where I’ve missed something or defined these buckets erroneously…. Thoughts?

Our latest Technology Salon, at the African Evaluation Association (AfrEA) Conference in Uganda on March 29th, focused on how mobile and social media platforms are being used in monitoring and evaluation processes. Our lead discussants were Jamie Arkin from Human Network International (soon to be merging with VotoMobile) who spoke about interactive voice response (IVR); John Njovu, an independent consultant working with the Ministry of National Development Planning of the Zambian government, who shared experiences with technology tools for citizen feedback to monitor budgets and support transparency and accountability; and Noel Verrinder from Genesis who talked about using WhatsApp in a youth financial education program.

Using IVR for surveys

Jamie shared how HNI deploys IVR surveys to obtain information about different initiatives or interventions from a wide public or to understand the public’s beliefs about a particular topic. These surveys come in three formats: random dialing of telephone numbers until someone picks up; asking people to call in, for example, on a radio show; or using an existing list of phone numbers. “If there is an 80% phone penetration or higher, it is equal to a normal household level survey,” she said. The organization has list of thousands of phone numbers and can segment these to create a sample. “IVR really amplifies people’s voices. We record in local language. We can ask whether the respondent is a man or a woman. People use their keypads to reply or we can record their voices providing an open response to the question.” The voice responses are later digitized into text for analysis. In order to avoid too many free voice responses, the HNI system can cut the recording off after 30 seconds or limit voice responses to the first 100 calls. Often keypad responses are most effective as people are not used to leaving voice mails.

IVR is useful in areas where there is low literacy. “In Rwanda, 80% of women cannot read a full sentence, so SMS is not a silver bullet,” Jamie noted. “Smartphones are coming, and people want them, but 95% of people in Uganda have a simple feature phone, so we cannot reach them by Facebook or WhatsApp. If you are going with those tools, you will only reach the wealthiest 5% of the population.”

In order to reduce response bias, the survey question order can be randomized. Response rates tend to be ten times higher on IVR than on SMS surveys, Jamie said, in part, because IVR is cheaper for respondents. The HNI system can provide auto-analysis for certain categories such as most popular response. CSV files can also be exported for further analysis. Additionally, the system tracks length of session, language, time of day and other meta data about the survey exercise.

Regulatory and privacy implications in most countries are unclear about IVR, and currently there are few legal restrictions against calling people for surveys. “There are opt-outs for SMS but not for IVRs, if you don’t want to participate you just hang up.” In some case, however, like Rwanda, there are certain numbers that are on “do not disturb” lists and these need to be avoided, she said.

Citizen-led budget monitoring through Facebook

John shared results of a program where citizens were encouraged to visit government infrastructure projects to track whether budget allocations had been properly done. Citizens would visit a health center or a school to inquire about these projects and then fill out a form on Facebook to share their findings. A first issue with the project was that voters were interested in availability and quality of service delivery, not in budget spending. “”I might ask what money you got, did you buy what you said, was it delivered and is it here. Yes. Fine. But the bigger question is: Are you using it? The clinic is supposed to have 1 doctor, 3 nurses and 3 lab technicians. Are they all there? Yes. But are they doing their jobs? How are they treating patients?”

Quantity and budget spend were being captured but quality of service was not addressed, which was problematic. Another challenge with the program was that people did not have a good sense of what the dollar can buy, thus it was difficult for them to assess whether budget had been spent. Additionally, in Zambia, it is not customary for citizens to question elected officials. The idea that the government owes the people something, or that citizens can walk into a government office to ask questions about budget is not a traditional one. “So people were not confident in asking question or pushing government for a response.”

The addition of technology to the program did not resolve any of these underlying issues, and on top of this, there was an apparent mismatch with the idea of using mobile phones to conduct feedback. “In Zambia it was said that everyone has a phone, so that’s why we thought we’d put in mobiles. But the thing is that the number of SIMs doesn’t equal the number of phone owners. The modern woman may have a good phone or two, but as you go down to people in the compound they don’t have even basic types of phones. In rural areas it’s even worse,” said John, “so this assumption was incorrect.” When the program began running in Zambia, there was surprise that no one was reporting. It was then realized that the actual mobile ownership statistics were not so clear.

Additionally, in Zambia only 11% of women can read a full sentence, and so there are massive literacy issues. And language is also an issue. In this case, it was assumed that Zambians all speak English, but often English is quite limited among rural populations. “You have accountability language that is related to budget tracking and people don’t understand it. Unless you are really out there working directly with people you will miss all of this.”

As a result of the evaluation of the program, the Government of Zambia is rethinking ways to assess the quality of services rather than the quantity of items delivered according to budget.

Gathering qualitative input through WhatsApp 

Genesis’ approach to incorporating WhatsApp into their monitoring and evaluation was more emergent. “We didn’t plan for it, it just happened,” said Noel Verrinder. Genesis was running a program to support technical and vocational training colleges in peri-urban and rural areas in the Northwest part of South Africa. The young people in the program are “impoverished in our context, but they have smartphones, WhatsApp and Facebook.”

Genesis had set up a WhatsApp account to communicate about program logistics, but it morphed into a space for the trainers to provide other kinds of information and respond to questions. “We started to see patterns and we could track how engaged the different youth were based on how often they engaged on WhatsApp.” In addition to the content, it was possible to gain insights into which of the participants were more engage based on their time and responses on WhatsApp.

Genesis had asked the youth to create diaries about their experiences, and eventually asked them to photograph their diaries and submit them by WhatsApp, given that it made for much easier logistics as compared to driving around to various neighborhoods to track down the diaries. “We could just ask them to provide us with all of their feedback by WhatsApp, actually, and dispense with the diaries at some point,” noted Noel.

In future, Genesis plans to incorporate WhatsApp into its monitoring efforts in a more formal way and to consider some of the privacy and consent aspects of using the application for M&E. One challenge with using WhatsApp is that the type of language used in texting is short and less expressive, so the organization will have to figure out how to understand emoticons. Additionally, it will need to ask for consent from program participants so that WhatsApp engagement can be ethically used for M&E purposes.

Our Tech Salon on Thursday March 9th focused on the potential of Microwork to support youth economic empowerment. Joining us as lead discussants were Lis Meyers, Banyan Global; Saul Miller, Samasource; and Elena Matsui, The Rockefeller Foundation. Banyan Global recently completed a report on “The Nexus of Microwork and Impact Sourcing: Implications for Youth Employment,” supported by the Global Center for Youth Employment and RTI, who also sponsored this Salon. (Disclosure: I worked on the report with the team at Banyan)

Definitions: To frame the discussion, we provided some core definitions and an explanation of the premise of microwork and its role within Impact sourcing.

  • Business Process Outsourcing (BPO): the practice of reducing business costs by transferring portions of work to outside suppliers rather than completing it internally.
  • Online Outsourcing: Contracting a third-party provider (often in a different country) to supply products or services that are delivered and paid for via the Internet. The third party is normally an individual (e-lancing), an online community(crowdsourcing) or a firm.
  • Microwork: a segment of online outsourcing where projects or complex tasks are broken into simple tasks that can be completed in seconds or minutes. Workers require numeracy and understanding of internet and computer technology, and advanced literacy, and are usually paid small amounts of money for each completed task.
  • Impact sourcing: (also known as socially responsible outsourcing), is a business practice in which companies outsource to suppliers that employ individuals from the lowest economic segments of the population.

The premise: It is believed that if microwork is done within an impact sourcing framework, it has the potential to create jobs for disadvantaged youth and disconnected, vulnerable populations and to provide them with income opportunities to support themselves and their families. Proponents of microwork believe it can equip workers with skills and experience that can enable them to enhance their employability regardless of gender, age, socio-economic status, previous levels of employment, or physical ability. Microwork is not always intentionally aimed at vulnerable populations, however. It is only when impact sourcing is adopted as the business strategy that microwork directly benefits the most disadvantaged.

The ecosystem: The microwork industry includes a variety of stakeholders, including: clients (looking to outsource work), service providers (who facilitate the outsourcing by liaising with these clients, breaking tasks down into micro tasks, employing and managing micro workers, and providing overall management and quality control), workers (individual freelancers, groups of people, direct employees, or contractors working through a service provider on assigned micro tasks), donors/investors, government, and communities.

Models of Microwork: The report identifies three main models for microwork (as shown below); micro-distribution (e.g., Amazon Mechanical Turk or CrowdFlower); the direct model (e.g., Digital Divide Data or iMerit) and the indirect model (e.g., Samasource or Rural Shores).

 

Implementer Case Study. With the framework settled, we moved over to hearing from our first discussant, from Samasource, who provided the “implementer” point of view. Samasource has been operating since 2008. Their goal is to connect marginalized women and/or youth with dignified work through the Internet. The organization sees itself as an intermediary or a bridge and believes that work offers the best solution to the complex problem of poverty. The organization works through 3 key programs: SamaSchools, Microwork and SamaHub. At the Samaschool, potential micro workers are trained on the end-to-end process.

The organization puts potential micro workers through an assessment process (former employment history, level of education, context) to predict and select which of the potential workers will offer the highest impact. Most of Samasources’ workers were underemployed or unemployed before coming to Samasource. At Samaschool they learn digital literacy, soft skills, and the technical skills that will enable them to succeed on the job and build their resumes. Research indicates that after 4 years with Samasource, these workers show a 4-fold increase in income.

The organization has evolved over the past couple of years to opening its own delivery center in Nairobi with 650 agents (micro workers). They will also launch in Mumbai, as they’ve learned that hands-on delivery center. Samasource considers that their model (as opposed to the micro-distribution model) offers more control over recruitment and training, quality control, worker preparation, and feedback loops to help workers improve their own performance. This model also offers workers wrap-around programs and benefits like full-time employment with financial literacy training, mentorship, pensions and healthcare.

In closing, it was highlighted that Impact measurement has been a top priority for Samaource. The organization was recently audited with 8 out of 9 stars in terms of quality of impact, evidence and M&E systems. Pending is an RCT that will aim to address the counterfactual (what would happen if Samasource was not operating here?). The organization is experiencing substantial growth, doubling its revenue last year and projecting to grow another 50%. The organization achieved financial sustainability for the first time in the last quarter of 2016. Growth in the industries that require data processing and cleaning and the expansion of AI has driven this growth.

Questions on sustainability. One participant asked why the organization took 8 years to become sustainable. Samasource explained that they had been heavily subsidized by donors, and part of the journey has been to reduce subsidies and increase paid clients. A challenge is keeping costs down and competing with other service providers while still offering workers dignified work. As one of our other discussants noted, this is a point of contention with some local service providers who are less well-known to donors. Because they are not heavily subsidized, they have not been able to focus as much on the “impact” part.

For Digital Divide Data (DDD), who was also present at the Salon, the goal was not quickly getting to profit. Rather the initial objective was social. Now that the organization is maturing it has begun thinking more about profitability and sustainability. It remains a non-profit organization however.

Retention and scale. Both Samasource and DDD noted that workers are staying with them for longer periods of time (up to 4 years). This works well for individual employees (who then have stable work with benefits). It also works well for clients, because employees learn the work, meaning it will be of higher quality – and because the BPO industry has a lot of turnover, and if micro workers are stable it benefits the BPO. This, however, is less useful for achieving scale, because workers don’t move through the program quickly, opening up space for new recruits. For Samasource, the goal would be for workers to move on within 2 years. At DDD, workers complete university while working for DDD, so 4 years is the norm. Some stay for 6 years, which also impacts scaling potential. DDD is looking at a new option for workers to be credentialed and certified, potentially through a 6 month or 1-year program.

The client perspective. One perspective highlighted in the Banyan report is the client perspective. Some loved microwork and impact sourcing. Others said it was challenging. Many are interested in partnering with microwork service providers like iMerit and Daiprom because it offers more data security (you can sign an NDA with service provider, whereas you can’t with individual workers who are coming in through micro-distribution and crowdsourcing). Working with a service provider also means that you have an entity that is responsible for quality control. Experiences with service providers have varied, however, and some companies had signed on to jobs that they were unprepared to train workers on and this resulted in missed deadlines and poor quality work. Clients were clear that their top priority was business – they cared first about quality, cost, and timeliness. “Impact was the cherry on top,” as one discussant noted.

The worker perspective. An aspect missing from the study and the research is that of worker experiences. (As Banyan noted, this would require additional resources for a proper in-depth study). Do workers really seek career growth? Or are they simply looking for something flexible that can help them generate some income in a pinch or supplement their incomes during hard times. In Venezuela, for example, the number of micro workers on CrowdFlower has jumped astronomically during the current political and economic crisis, demonstrating that these type of platforms may serve as supplemental income for those in the most desperate situations. What is the difference in what different workers need?

One small study of micro workers in Kenya noted that when trying to work on their own through the micro-distribution model, they had major challenges: they were not able to collect electronic payments; they got shut out of the system because there were several youth using the same IP address and it was flagged as fraud; language and time zones affected the work was available to them; some companies only wanted workers from certain countries whom they trusted or felt could align culturally; and young women were wary of scams and sexual harassment if accessing work online, as this was their experience with work offline. Some participants wondered what the career path was for a micro worker. Did they go back to school? Did they move ahead to a higher level, higher paying job? Samasource and DDD have some evidence that micro workers in their programs do go on to more dignified, higher paying, more formal jobs, however much of this is due to the wraparound programming that they offer.

The role of government was questioned by Salon participants. Is there a perfect blend of private sector, government and an impact sourcing intermediary? Should government be using micro workers and purposefully thinking about impact sourcing? Could government help to scale microwork and impact sourcing? To date the role of government has been small, noted one discussant. Others wondered if there would be touch points through existing government employment or vocational programs, but it was pointed out that most of the current micro workers are those that have already fallen through the cracks on education and vocational training programming.

A participant outlined her previous experiences with a local municipality in India that wanted to create local employment. The contracting process excluded impact sourcing providers for inexplicable reasons. There were restrictions such as having been in operation for at least 3 years, having a certain minimal level of turnover, number of employees in the system, etc. “So while the government talked about work that needed to be digitized and wanted rural employees, and we went on a three year journey with them to make it inclusive of impact sourcers, it didn’t really work.”

What about social safeguards? One Salon participant raised concerns about the social services and legal protections in place for micro workers. In the absence of regulations, are these issues being swept under the carpet, she wondered. Another noted that minimum standards would be a positive development, but that this will be a long process, as currently there is not even a standard definition of impact sourcing, and it’s unclear what is meant by ‘impact’ and how it’s measured.

This is one area where government could and should play a role. In the past, for example, government has pushed procurement from women-owned or minority owned businesses. Something similar could happen with impact sourcing, but we need standards in order for it to happen. Not all clients who use micro workers are doing it within a framework of impact sourcing and social impact goals. For example, some clients said they were doing “impact sourcing” simply because they were sourcing work from a developing country. In reality, they were simply working with a normal BPO, and so the risk of “impact washing” is real.

Perhaps, noted another participant, the focus should be on drumming up quality clients who actually want to have an impact. “A mandated standard will mean that you lose the private sector.” Some suggested that there would be some type of a ‘certified organic’ or ‘good housekeeping’ seal of approval from a respected entity. Some felt that business were not interested and government would never move something like this. Others disagreed, saying that some large corporation really wanted to be perceived as ethical players.

Definitions proved a major challenge – for example at what point does an ‘impact worker’ cease being an impact worker and how do you count them? Should someone be labeled for life as an impact worker? There was disagreement in the room on this point.

A race to the bottom? Some wondered if microwork was just the same re-hashing of the ‘gig economy’ debate. Would it drive down prices and create extremely unstable work for the most disadvantaged populations? Were there ways that workers could organize if they were working via the micro-distribution model and didn’t even know where to find each other, and if the system was set up to make them bid against each other. It was noted that there was one platform that had been identified that aimed to support workers on Amazon Mechanical Turk, that workers there helped each other with tips on how to get contracts. However as with Uber and other gig economy players, it appeared that all the costs for learning and training were then being pawned off onto the workers themselves.

Working through the direct or indirect models can help to protect individual workers in this aspect, as Samasource, for example, does offer workers contracts and benefits and has a termination policy. The organization is also in a position to negotiate contracts that may be more beneficial to workers, such as extending a 3-week contract with lots of workers over a longer period of time with fewer workers so that income is steadier. Additionally, evaluations have shown that these jobs are pulling in workers who have never had formal jobs before, and that there is an increase in income over time for Samasource workers.

What can donors do? Our third discussant noted that the research is mixed in terms of how different kinds of microwork without any intermediary or wraparound services can actually build a career pathway. Some who are active in the space are still working hard to identify the right partnerships and build support for impact sourcing. It has been difficult to find a “best of breed” or a “gold standard” to date as the work is still evolving. “We’re interested in learning from others what partners need from donors to help scale the work that is effective.” It’s been difficult to evaluate, as she noted, because there has been quite a lot of secrecy involved, as often people do not want to share what is working for fear of losing the competitive edge.

What does the future hold? One Salon participant felt that something very bold was required, given how rapidly economies and technologies are changing. Some of the current microwork will be automated in the near future, he said. The window is closing quickly. Others disagreed, saying that the change in technology was opening up new growth in the sector and that some major players were even delaying their projections because of these rapid shifts and changes in robotics and automation. The BPO sector is fickle and moves quickly – for example voice has shifted rapidly from India to The Philippines. Samasource felt that human components were still required to supplement and train AI and DDD noted that their workers are actually training machines to take over their current jobs. It was also noted that most of the current micro workers are digital natives and a career in data entry is not highly enticing. “We need to find something that helps them feel connected to the global economy. We need to keep focused on relevant skills. The data stuff has a timestamp and it’s on its way out.” DDD is working with universities to bring in courses that are focused on some of the new and emerging skills sets that will be needed.

Conclusions. In short, there are plenty of critical questions remaining in the area of microwork, impact sourcing and around the broader question of the future of youth employment at the global level. How to stay abreast of the rapid changes in economy, business, and technology? What skill sets are needed? A recent article in India’s Business Standard notes constant efforts at re-skilling IT workers. These question are facing not only ‘developing countries’ but the US is also in a similar crisis. Will online work with no wraparound services be a stopgap solution? Will holistic models be pushed so that young people develop additional life skills that will help them in the longer term? Will we learn how to measure and understand the ‘impact’ in ‘impact sourcing?’ Much remains to explore and test!

Thanks to the Global Center for Youth Employment and RTI for supporting this Salon, to our lead discussants and participants, and to ThoughtWorks for hosting us! If you’d like to join us for a future Technology Salon, sign up here!

 

 

 

 

 

Development, humanitarian and human rights organizations increasingly collect and use digital data at the various stages of their programming. This type of data has the potential to yield great benefit, but it can also increase individual and community exposure to harm and privacy risks. How can we as a sector better balance data collection and open data sharing with privacy and security, especially when it involves the most vulnerable?

A number of donors, humanitarian and development organizations (including Oxfam, CRS, UN bodies and others) have developed or are in the process of developing guidelines to help them to be more responsible about collection, use, sharing and retention of data from those who participate in their programs.

I’m part of a team (including mStar, Sonjara, Georgetown University, the USAID Global Development Lab, and an advisory committee that includes several shining stars from the ‘responsible data’ movement) that is conducting research on existing practices, policies, systems, and legal frameworks through which international development data is collected, used, shared, and released. Based on this research, we’ll develop ‘responsible data’ practice guidelines for USAID that aim to help:

  • Mitigate privacy and security risks for beneficiaries and others
  • Improve performance and development outcomes through use of data
  • Promote transparency, accountability and public good through open data

The plan is to develop draft guidelines and then to test their application on real programs.

We are looking for digital development projects to assess how our draft guidelines would work in real world settings. Once the projects are selected, members of the research team will visit them to better understand “on-the-ground” contexts and project needs. We’ll apply draft practice guidelines to each case with the goal of identifying what parts of the guidelines are useful/ applicable, and where the gaps are in the guidelines. We’ll also capture feedback from the project management team and partners on implications for project costs and timelines, and we’ll document existing digital data-related good practices and lessons. These findings will further refine USAID’s Responsible Data Practice guidelines.

What types of projects are we looking for?

  • Ongoing or recently concluded projects that are using digital technologies to collect, store, analyze, manage, use and share individuals’ data.
  • Cases where data collected is sensitive or may put project participants at risk.
  • The project should have informal or formal processes for privacy/security risk assessment and mitigation especially with respect to field implementation of digital technologies (listed above) as part of their program. These may be implicit or explicit (i.e. documented or written). They potentially include formal review processes conducted by ethics review boards or institutional review boards (IRBs) for projects.
  • All sectors of international development and all geographies are welcome to submit case studies. We are looking for diversity in context and programming.
  • We prefer case studies from USAID-funded projects but are open to receiving case studies from other donor-supported projects.

If you have a project or an activity that falls into the above criteria, please let us know here. We welcome multiple submissions from one organization; just reuse the form for each proposed case study.

Please submit your projects by February 15, 2017.

And please share this call with others who may be interested in contributing case studies.

Click here to submit your case study.

Also feel free to get in touch with me if you have questions about the project or the call!

 

At the 2016 American Evaluation Association conference, I chaired a session on benefits and challenges with ICTs in Equity-Focused Evaluation. The session frame came from a 2016 paper on the same topic. Panelists Kecia Bertermann from Girl Effect, and Herschel Sanders from RTI added fascinating insights on the methodological challenges to consider when using ICTs for evaluation purposes and discussant Michael Bamberger closed out with critical points based on his 50+ years doing evaluations.

ICTs include a host of technology-based tools, applications, services, and platforms that are overtaking the world. We can think of them in three key areas: technological devices, social media/internet platforms and digital data.

An equity focus evaluation implies ensuring space for the voices of excluded groups and avoiding the traditional top-down approach. It requires:

  • Identifying vulnerable groups
  • Opening up space for them to make their voices heard through channels that are culturally responsive, accessible and safe
  • Ensuring their views are communicated to decision makers

It is believed that ICTs, especially mobile phones, can help with inclusion in the implementation of development and humanitarian programming. Mobile phones are also held up as devices that can allow evaluators to reach isolated or marginalized groups and individuals who are not usually engaged in research and evaluation. Often, however, mobiles only overcome geographic inclusion. Evaluators need to think harder when it comes to other types of exclusion – such as that related to disability, gender, age, political status or views, ethnicity, literacy, or economic status – and we need to consider how these various types of exclusions can combine to exacerbate marginalization (e.g., “intersectionality”).

We are seeing increasing use of ICTs in evaluation of programs aimed at improving equity. Yet these tools also create new challenges. The way we design evaluations and how we apply ICT tools can make all the difference between including new voices and feedback loops or reinforcing existing exclusions or even creating new gaps and exclusions.

Some of the concerns with the use of ICTs in equity- based evaluation include:

Methodological aspects:

  • Are we falling victim to ‘elite capture’ — only hearing from higher educated, comparatively wealthy men, for example? How does that bias our information? How can we offset that bias or triangulate with other data and multi-methods rather than depending only on one tool-based method?
  • Are we relying too heavily on things that we can count or multiple-choice responses because that’s what most of these new ICT tools allow?
  • Are we spending all of our time on a device rather than in communities engaging with people and seeking to understand what’s happening there in person?
  • Is reliance on mobile devices or self-reporting through mobile surveys causing us to miss contextual clues that might help us better interpret the data?
  • Are we falling into the trap of fallacy in numbers – in other words, imagining that because lots of people are saying something, that it’s true for everyone, everywhere?

Organizational aspects:

  • Do digital tools require a costly, up-front investment that some organizations are not able to make?
  • How do fear and resistance to using digital tools impact on data gathering?
  • What kinds of organizational change processes are needed amongst staff or community members to address this?
  • What new skills and capacities are needed?

Ethical aspects:

  • How are researchers and evaluators managing informed consent considering the new challenges to privacy that come with digital data? (Also see: Rethinking Consent in the Digital Age)?
  • Are evaluators and non-profit organizations equipped to keep data safe?
  • Is it possible to anonymize data in the era of big data given the capacity to cross data sets and re-identify people?
  • What new risks might we be creating for community members? To local enumerators? To ourselves as evaluators? (See: Developing and Operationalizing Responsible Data Policies)

Evaluation of Girl Effect’s online platform for girls

Kecia walked us through how Girl Effect has designed an evaluation of an online platform and applications for girls. She spoke of how the online platform itself brings constraints because it only works on feature phones and smart phones, and for this reason it was decided to work with 14-16 year old urban girls in megacities who have access to these types of devices yet still experience multiple vulnerabilities such as gender-based violence and sexual violence, early pregnancy, low levels of school completion, poor health services and lack of reliable health information, and/or low self-esteem and self-confidence.

The big questions for this program include:

  • Is the content reaching the girls that Girl Effect set out to reach?
  • Is the content on the platform contributing to change?

Because the girl users are on the platform, Girl Effect can use features such as polls and surveys for self-reported change. However, because the girls are under 18, there are privacy and security concerns that sometimes limit the extent to which the organization feels comfortable tracking user behavior. In addition, the type of phones that the girls are using and the fact that they may be borrowing others’ phones to access the site adds another level of challenges. This means that Girl Effect must think very carefully about the kind of data that can be gleaned from the site itself, and how valid it is.

The organization is using a knowledge, attitudes and practices (KAP) framework and exploring ways that KAP can be measured through some of the exciting data capture options that come with an online platform. However it’s hard to know if offline behavior is actually shifting, making it important to also gather information that helps read into the self-reported behavior data.

Girl Effect is complementing traditional KAP indicators with web analytics (unique users, repeat visitors, dwell times, bounce rates, ways that users arrive to the site) with push-surveys that go out to users and polls that appear after an article (“Was this information helpful? Was it new to you? Did it change your perceptions? Are you planning to do something different based on this information?”) Proxy indicators are also being developed to help interpret the data. For example, does an increase in frequency of commenting on the site by a particular user have a link with greater self-esteem or self-efficacy?

However, there is only so much that can be gleaned from an online platform when it comes to behavior change, so the organization is complementing the online information with traditional, in-person, qualitative data gathering. The site is helpful there, however, for recruiting users for focus groups and in-depth interviews. Girl Effect wants to explore KAP and online platforms, yet also wants to be careful about making assumptions and using proxy indicators, so the traditional methods are incorporated into the evaluation as a way of triangulating the data. The evaluation approach is a careful balance of security considerations, attention to proxy indicators, digital data and traditional offline methods.

Using SMS surveys for evaluation: Who do they reach?

Herschel took us through a study conducted by RTI (Sanders, Lau, Lombaard, Baker, Eyerman, Thalji) in partnership with TNS about the use of SMS surveys for evaluation. She noted that the rapid growth of mobile phones, particularly in African countries, opens up new possibilities for data collection. There has been an explosion of SMS surveys for national, population-based surveys.

Like most ICT-enabled MERL methods, use of SMS for general population surveys brings both promise:

  • High mobile penetration in many African countries means we can theoretically reach a large segment of the population.
  • These surveys are much faster and less expensive than traditional face-to- face surveys.
  • SMS surveys work on virtually any GSM phone.
  • SMS offers the promise of reach. We can reach a large and geographically dispersed population, including some areas that are excluded from FTF surveys because of security concerns.

And challenges:

  • Coverage: We cannot include illiterate people or those without access to a mobile phone. Also, some sample frames may not include the entire population with mobile phones.
  • Non-response: Response rates are expected to be low for a variety of reasons, including limited network connectivity or electricity; if two or people share a phone, we may not reach all people associated with that phone; people may feel a lack of confidence with technology. These factors might affect certain sub-groups differently, so we might underrepresent the poor, rural areas, or women.
  • Quality of measurement. We only have 160 CHARACTERS for both the question AND THE RESPONSE OPTIONS. Further, an interviewer is not present to clarify any questions.

RTI’s research aimed to answer the question: How representative are general population SMS surveys and are there ways to improve representativeness?

Three core questions were explored via SMS invitations sent in Kenya, Ghana, Nigeria and Uganda:

  • Does the sample frame match the target population?
  • Does non-response have an impact on representativeness?
  • Can we improve quality of data by optimizing SMS designs?

One striking finding was the extent to which response rates may vary by country, Hershel said. In some cases this was affected by agreements in place in each country. Some required a stronger opt-in process. In Kenya and Uganda, where a higher percentage of users had already gone through an opt-in process and had already participated in SMS-based surveys, there was a higher rate of response.

screen-shot-2016-11-03-at-2-23-26-pm

These response rates, especially in Ghana and Nigeria, are noticeably low, and the impact of the low response rates in Nigeria and Ghana is evident in the data. In Nigeria, where researchers compared the SMS survey results against the face-to-face data, there was a clear skew away from older females, towards those with a higher level of education and who are full-time employed.

Additionally, 14% of the face-to-face sample, filtered on mobile users, had a post-secondary education, whereas in the SMS data this figure is 60%.

Additionally, Compared to face-to-face data, SMS respondents were:

  • More likely to have more than 1 SIM card
  • Less likely to share a SIM card
  • More likely to be aware of and use the Internet.

This sketches a portrait of a more technological savvy respondent in the SMS surveys, said Herschel.

screen-shot-2016-11-03-at-2-24-18-pm

The team also explored incentives and found that a higher incentive had no meaningful impact, but adding reminders to the design of the SMS survey process helped achieve a wider slice of the sample and a more diverse profile.

Response order effects were explored along with issues related to questionnaire designers trying to pack as much as possible onto the screen rather than asking yes/no questions. Hershel highlighted that that when multiple-choice options were given, 76% of SMS survey respondents only gave 1 response compared to 12% for the face-to-face data.

screen-shot-2016-11-03-at-2-23-53-pmLastly, the research found no meaningful difference in response rate between a survey with 8 questions and one with 16 questions, she said. This may go against common convention which dictates that “the shorter, the better” for an SMS survey. There was no observable break off rate based on survey length, giving confidence that longer surveys may be possible via SMS than initially thought.

Hershel noted that some conclusions can be drawn:

  • SMS excels for rapid response (e.g., Ebola)
  • SMS surveys have substantial non-response errors
  • SMS surveys overrepresent

These errors mean SMS cannot replace face-to-face surveys … yet. However, we can optimize SMS survey design now by:

  • Using reminders during data collection
  • Be aware of response order effects. So we need to randomize substantive response options to avoid bias.
  • Not using “select all that apply” questions. It’s ok to have longer surveys.

However, she also noted that the landscape is rapidly changing and so future research may shed light on changing reactions as familiarity with SMS and greater access grow.

Summarizing the opportunities and challenges with ICTs in Equity-Focused Evaluation

Finally we heard some considerations from Michael, who said that people often get so excited about possibilities for ICT in monitoring, evaluation, research and learning that they neglect to address the challenges. He applauded Girl Effect and RTI for their careful thinking about the strengths and weaknesses in the methods they are using. “It’s very unusual to see the type of rigor shown in these two examples,” he said.

Michael commented that a clear message from both presenters and from other literature and experiences is the need for mixed methods. Some things can be done on a phone, but not all things. “When the data collection is remote, you can’t observe the context. For example, if it’s a teenage girl answering the voice or SMS survey, is the mother-in-law sitting there listening or watching? What are the contextual clues you are missing out on? In a face-to-face context an evaluator can see if someone is telling the girl how to respond.”

Additionally,“no survey framework will cover everyone,” he said. “There may be children who are not registered on the school attendance list that is being used to identify survey respondents. What about immigrants who are hiding from sight out of fear and not registered by the government?” He cautioned evaluators to not forget about folks in the community who are totally missed out and skipped over, and how the use of new technology could make that problem even greater.

Another point Michael raised is that communicating through technology channels creates a different behavior dynamic. One is not better than the other, but evaluators need to be aware that they are different. “Everyone with teenagers knows that the kind of things we communicate online are very different than what we communicate in a face-to-face situation,” he said. “There is a style of how we communicate. You might be more frank and honest on an online platform. Or you may see other differences in just your own behavior dynamics on how you communicate via different kinds of tools,” he said.

He noted that a range of issues has been raised in connection to ICTs in evaluation, but that it’s been rare to see priority given to evaluation rigor. The study Herschel presented was one example of a focus on rigor and issues of bias, but people often get so excited that they forget to think about this. “Who has access.? Are people sharing phones? What are the gender dynamics? Is a husband restricting what a woman is doing on the phone? There’s a range of selection bias issues that are ignored,” he said.

Quantitative bias and mono-methods are another issue in ICT-focused evaluation. The tool choice will determine what an evaluator can ask and that in turn affects the quality of responses. This leads to issues with construct validity. If you are trying to measure complex ideas like girls’ empowerment and you reduce this to a proxy, there can often be a large jump in interpretation. This doesn’t happen only when using mobile phones for evaluation data collection purposes but there are certain areas that may be exacerbated when the phone is the tool. So evaluators need to better understand behavior dynamics and how they related to the technical constraints of a particular digital or mobile platform.

The aspect of information dissemination is another one worth raising, said Michael. “What are the dynamics? When we incorporate new tools, we tend to assume there is just one-step between the information sharer and receiver, yet there is plenty of literature that shows this is normally at least 2 steps. Often people don’t get information directly, but rather they share and talk with someone else who helps them verify and interpret the information they get on a mobile phone. There are gatekeepers who control or interpret, and evaluators need to better understand those dynamics. Social network analysis can help with that sometimes – looking at who communicates with whom? Who is part of the main infuencer hub? Who is marginalized? This could be exciting to explore more.”

Lastly, Michael reiterated the importance of mixed methods and needing to combine online information and communications with face-to-face methods and to be very aware of invisible groups. “Before you do an SMS survey, you may need to go out to the community to explain that this survey will be coming,” he said. “This might be necessary to encourage people to even receive the survey, to pay attention or to answer it.” The case studies in the paper “The Role of New ICTs in Equity-Focused Evaluation: Opportunities and Challenges” explore some of these aspects in good detail.