Collecting Mobile Data Responsibly: webinar recording & takeaway messages

Thank you to everyone who tuned in to our live webinar on Responsible Mobile Data Collection last week! Five panelists on three continents discussed the challenges of remote data collection projects and shared best practices, tools, and tips for adhering to privacy and protection guidelines – from the field level to the WFP context and across the broader humanitarian and development sphere.

But don’t worry if you missed the event – a recording is available above and here, and we have summarised the key takeaways messages for a quicker read below. We’re also sharing at the end of this page additional resources and answers to the questions that we were not able to answer during the webinar due to time constraints.

Thank you very much to our five great panelists:

  • Asif Niazi and Raul Cumba, Vulnerability Analysis and Mapping Officers, WFP Iraq Country Office
  • Angie Lee, Food Security Analyst, WFP mVAM
  • Jos Berens, Data Policy Officer, UN Office for the Coordination of Humanitarian Affairs (UN OCHA)
  • Michela Bonsignorio, Global Advisor on Protection and Accountability to Affected Populations, WFP
  • And moderator Maribeth Black, Food Security Analyst, WFP mVAM

What you should know about Responsible Mobile Data Collection:

Greater risks and challenges: In a pre-digital era, there was more direct control over data. Now, as data collection for humanitarian action relies increasingly on digital tools and automated processes, there is a real need to raise awareness of the risks and harms that can occur at every stage of the humanitarian life cycle as well as  the methods for reducing these risks. Challenges include: data falling into the wrong hands, risks related to the IT infrastructure and outsourcing, bias and discrimination, and risks to the rights of data subjects.The example from Iraq highlights some of the challenges with remote data collection: In ISIL-controlled Mosul, people were afraid to answer calls, as it was illegal for the public to use mobile phones. When people responded, the length of the questionnaire and the short time available to ask the questions affected the data quality and response rate.

Step One: an even better understanding of the potential risks. Despite the recognition of the data protection risks and the development of ways to mitigate these, we, humanitarian actors, need to develop a better understanding of where these dangers lie. Sometimes we just don’t know how sensitive a dataset can be; in these cases, it is better to err on the side of caution. UN OCHA’s Centre for Humanitarian Data works on data policy with the aim of supporting a responsible growth of the use and impact of data in the humanitarian sector.

Step Two: mitigating the risks. Responsible mobile data collection has humanitarian principles at its core, such as “do no harm.” At every stage of the data collection and analysis lifecycle, these principles must be adhered to. In a first instance, understanding the local context and engaging in sensitisation campaigns and digital literacy trainings are important. The survey design should strive to minimise bias and ensure that no information other than the information that is really needed is collected. Just as important is the the transmission and storage of data using state-of-the-art security means. Finally, the publication of results ought to be anonymised and protection-sensitive, and there must be functioning and safe mechanisms for participants and others to report problems.In the Iraq case, the survey questionnaire was shortened, and the food security and market components were put together so as to minimize the time respondents had to use their phones.The WFP Data Responsibility Field Book offers both guidelines for the daily work of staff involved in mobile data collection and forms a basis for WFP’s internal dialogue on data responsibility.WFP’s corporate data privacy and security policies are contained within the Guide to Privacy and Personal Data Protection.

Several international collaborations already exist to address the issue of data privacy in humanitarian response. Examples are the Harvard Humanitarian Initiative; the framework developed by UNHCR and the Danish Refugee Council for data-sharing in practice, introducing a common language among humanitarian actors as well as a set of principles and shared processes; the ICRC’s work in collaboration with a Brussels privacy hub; and the International Data Responsibility Group, constituted of research institutes, think-tanks, and the international public sector.


Q & A

1. How do you ensure the authenticity of the interviewee, do you monitor the location of the mobile phones?

Firstly, the interviewer has the name and phone number of the respondent and will check with the person answering the phone whether they are talking to the same person.

Secondly, the service provider has series of towers and knows where a particular mobile phone is calling/answering from. That way, service providers can programme their computers to only call people from particular areas and we can ensure that interviewees are actually coming from a particular location.

Thirdly, we closely scrutinise the output of our data and analyses and make sure that, where the data does not seem to make sense, we investigate all possible sources of bias and error.

More generally, mVAM identifies respondents in three ways:

  • by asking respondents of traditional face-to-face surveys to agree to a follow-up phone survey;
  • by randomly calling people through mobile phone user rolls who have volunteered to take phone surveys. Telecom companies maintain a list of phone numbers of subscribers who agree to participate in surveys. Randomly selected mobile phone users in the areas of interest to WFP are then contacted, as per our sampling instructions.
  • by calling numbers generated through random digit dialing. Respondents are always given the choice to opt in to the survey or decline. Whether it’s WFP or third party providers that conduct phone surveys, the list of contacts (names, phone numbers, locations) are stored and managed in a safe and secure environment; only processed and aggregate data are shared for monitoring purposes – no individual’s statistics or geographic coordinates are released.

2. How do you ensure accuracy and validity of the information through phone call interviews?

In order to ensure the reliability of data, mVAM phone surveys are designed on the basis of representative sampling and using stratification techniques where possible. Results are reported by drawing inferences from large enough samples, complemented by thorough identification of key informants. The quality of data collected through phone surveys is also evaluated with reference to data from concurrent face-to-face surveys and/or secondary baseline data whenever feasible. For more information on representativity and how to account for bias, please refer to the methodology section of the mVAM blog:

3. What mechanisms are put in place to ensure the reliability of crowd-sourced data?

Data is triangulated with existing secondary sources of information including face-to-face assessments, field monitoring and key informant reports.

4. How can we integrate information security considerations during the early phases of a survey (especially during survey design and data collection)?   

The advice given in the Data Responsibility Field Book is to:

Understand and engage with local context – Engage with the community about major risks related to the proposed data collection. This can be done by interviewing members of the community and through a quick literature review on the mobile phone landscape (e.g. mobile phone ownership and usage rate, social and gender norms) in the country. Work with a community-based organization (CBO) or NGO in the community that can sensitize people about the activity. It is vital to engage with the community before collecting data. If there are protection risks, these need to be communicated. Explore opportunities with ‘self-organizing’ groups, whereby respondents set up management committees themselves.

Choose the right provider – When outsourcing phone surveys to commercial centers, ensure providers are scrutinized and vetted. Undertake due diligence on candidate companies and assess their compliance with best practices in terms of data security and privacy.

Conduct a Privacy Impact Assessment – as outlined above. It is important that, prior to any intervention, WFP conducts a Privacy Impact Assessment (PIA). The purpose of a Privacy Impact Assessment is to identify, evaluate and address the risks arising from the processing of personal data within an activity, project, programme or other initiative. It is important to note that such risks are not only related to IT aspects; they necessarily span across social, political, protection and legal considerations. A PIA framework is available to guide country offices in conducting a PIA. Please contact for further assistance.

Data minimization: collect data on a need-to-know basis only – Collected data must be limited to the minimum necessary to achieve the objective in order to avoid unnecessary and potentially harmful intrusion into people’s private lives. In particular, information about people’s ethnicity, political opinions, religious beliefs or health or sexual orientation/choices should be strictly avoided unless absolutely necessary to the purpose of the survey. This information is not usually collected in WFP’s food security surveys.

Ensure your data collection has a specified purpose – Given the sensitivities and risks of collecting, storing and sharing data, personal and demographic data should never be collected indiscriminately. The purpose of data collection and processing must be clear and unambiguous and must be defined prior to data collection.

Review existing domestic legislation – Local legislation may pose challenges when collecting sensitive data. For example, applicable domestic laws may contain provisions that could force your local partners to disclose personal data in their possession to the government. Under such circumstances, you should only collect data if it is comfortable with the data being shared with the government.

Furthermore, it is advisable to conduct an assessment of the data landscape (including a check of whether the desired data is already (being) collected by other organizations, and whether it would be possible to gain access to, or use that data?).

5. With the rapid increase in datasets shared through the HDX platform, is there any mechanism established to check the data quality and authenticity of these datasets?

Organizations joining the HDX platform are vetted by our team. Every dataset uploaded to the platform is subjected to a quality assurance process, including a data-sensitivity check. The HDX team is not in a position to verify the authenticity of all datasets –this is the responsibility of the contributing organizations.

6. What about the level of dropout of respondents in mobile surveys?

Non-response and attrition rates vary across countries and can be attributable to different reasons (e.g. insecurity, displacement, survey fatigue). Since the inception of the project, mVAM has been following the best practice of providing a modest amount of airtime credits to survey respondents as an incentive for continued engagement. However, more than material incentives, we found that altruism is the biggest driver of response. The respondents must, however, feel that their identity will be protected and they have no need to worry about any negative repercussions. Additionally, we are exploring ways to leverage mobile technology to empower vulnerable communities by increasing their access to information on food prices, nutrition and feedback mechanisms.

7. Is the information from the service provider input into your organization’s database?

Yes. Raw data is sent to WFP in a CSV file at the end of each data collection round which is then stored in a dedicated database for cleaning and processing prior to analysis. Phone numbers or any personally identifiable information are anonymized to ensure sensitive data remains confidential.

8. Thanks to all panellists! Michela mentioned the best practices of having mechanisms for research participants or survey respondents to access findings and/or have a say in how their data is used. Do you have any examples of good mechanisms that have been established for that, especially in areas where access is an issue or with hard to reach populations?

Consultations with the affected population prior to designing an intervention is commonly considered a good practice. Where the population is accessible, it is recommended to hold focus groups and interviews with key informants to gather a representative picture of the reality on the ground. This can also be part of a PIA (see above). In the case of mVAM, such consultations are aimed at understanding issues like effective access to mobile phones and technology, digital literacy among the population, possible social and cultural obstacles affecting individuals’ free participation in surveys, perception issues, security threats. The mVAM team is particularly committed to engaging with the local population at all stages of its interventions. Feedback from the people is regularly gathered by field monitors and through ad hoc field missions.

It would be equally important to ensure that people participating in mVAM two-way communications can contact us at any time to request clarifications and/or express concerns about the utilization of their personal data. This might be built as an ad hoc option into the same mVAM communication system or may be achieved through the establishment of dedicated communication channels (e.g. hotline, email, etc.). Existing complaint and feedback mechanisms previously set up for programmatic purposes can also be used to that end. For example, in Lebanon efforts are underway to set up an interagency common call center as a mechanism to address queries specifically related to the assistance channeled through a Common Card. The call center will be also used as a receptor of concerns and requests related to personal data protection.

When the population resides in a hard-to-reach area, WFP should still conduct proxy consultations with humanitarian partners who have access to the population, if possible. A soft preliminary survey could be also launched via mobile phones, aimed at reaching people and understanding any possible risk affecting the roll out of the mVAM initiative. The survey itself may be sensitive and potentially dangerous, so it is recommended to avoid highly sensitive topics and utilize neutral, soft scripts. The assistance of a protection officers/advisors to that end is recommended.

Resources referenced during the webinar:

Can we reach rural women via mobile phone? Kenya case study


WFP/Kusum Hachhethu


A few months ago, we published a blog post on our plans to test collecting nutrition data through SMS in Malawi and through live voice calls in Kenya. We just got back from Kenya where we conducted a large-scale mode experiment with ICRAF to compare nutrition data collected face-to-face with data collected through phone calls placed by operators at a call center. But before we started our experiment, we did a qualitative formative study to understand rural women’s phone access and use.

We traveled to 16 villages in Baringo and Kitui counties in Kenya, where we conducted focus group discussions and in-depth interviews with women. We also conducted key informant interviews with mobile phone vendors, local nutritionists, and local government leaders.

So in Kenya, can rural women be reached via mobile phone?

Here are the preliminary findings from our qualitative study:

  1. Ownership: Women’s phone ownership is high in both counties. However, ownership was higher in Kitui than Baringo, which is more pastoralist. From our focus group discussions and interviews, we estimate that 80-90% of women own phones in Kitui and 60-70% own phones in Baringo.
  1. Access: The majority of women had access to phones through inter- and intra-household sharing even if they didn’t own one themselves. This suggests that even women who don’t own a phone personally have access to phones that they may be able to use to participate in phone surveys.
  1. Usage: Women mostly use phones to make and receive calls, not send SMS. This supports our hypothesis that voice calls, not SMS, would be the optimal modality to reach women through mobile surveys.
  1. Willingness: Women were enthusiastic about participating in phone surveys during our focus group discussions and in-depth interviews, implying that they are interested in phone surveys and willing to take part.
  1. Trust: Unknown numbers create trust issues, but they are not insurmountable. Women voiced concerns about receiving phone calls from unknown numbers. Despite these trust issues, we were eventually able to successfully conduct our phone surveys after sensitizing the community, using existing community and government administration structures.
  1. Network: Poor network coverage, not gender norms or access, is the biggest barrier to phone surveys in the two counties. Women identified network coverage as the biggest barrier for communication. Some parts of the counties had poor to no network coverage. However, we found that phone ownership was high even in these areas, and women would travel to find network hotspots to make or receive phone calls.

So in conclusion, yes, in Kenya it is possible to reach rural women by phone.
Our findings from Kitui and Baringo counties show that we can reach women in similar contexts with mobile methodologies to collect information on their diet as well as their child’s diet.

We are also analysing the quantitative data from our mode experiment to find out whether data on women and children’s diet collected via live phone operators gives the same results as data collected via traditional face-to-face interviews.

Our 5 mVAM Highs from 2016


1. Awards for Remote Mobile Data Collection Work

At the Humanitarian Technology 2016 conference, our paper Knowing Just in Time Knowing Just in Time’ won Best Paper for Outstanding Impact. In the paper, we assessed mVAM’s contribution to decision-making by looking at use cases for mVAM in camps, conflict settings and vulnerable geographies. Check out our blog Tech for Humanity for more on it and our other conference paper  mVAM: a New Contribution to the Information Ecology of Humanitarian Work

To close the year, we had a nice surprise from Nominet Trust, the UK’s leading tech for good funder. We made their 100 most inspiring social innovations using digital technology to drive social change around the world.  

2. New Tech

In this day and age there’s a lot of buzz around data visualization. We’ve been honing our skills with Tableau. Check out the data visualizations we did for Yemen and Haiti.

We’re also in the era of Big Data. We partnered with Flowminder, experts in analyzing call detail records, to track displacement in Haiti after Hurricane Matthew.  Find out more in ‘After the storm: using big data to track displacement in Haiti

We’re also super excited about the chatbot we started developing for messaging apps and our roll out of Free Basics in Malawi which is allowing us to share the food prices we collect in mVAM surveys with people in Malawi With mVAM, our main focus has been reaching people on their simple feature phones. But we know that smartphone ownership is only going to increase. Contacting people through internet-enabled phones opens up loads of new forms of communication and data collection. is still reaching people on their -free basics

3. Expansion!

mVAM expanded to 16 new countries facing a wide set of challenges: conflict, El Nino drought, hurricanes, extremely remote geographies. We’ve been tracking and learning about what remote mobile data collection can add to food security monitoring systems and what its limits are in different contexts. For some of the highlights, check out our blogs on Afghanistan, Democratic Republic of Congo, Haiti, Nigeria, Papua New Guinea, and  El Nino in Southern Africa,

4. Dynamic Partnerships

To have a lasting impact, we need to work with governments. We are really proud of our partnership with CAID, the Cellule d’Analyses des Indicateurs du Développement  under the Prime Minister’s Office in the Democratic Republic of Congo. We collaborated on setting up a national market monitoring system- mKengela that they are now running. We’ve had intensive technical sessions with the CAID team in Rome and Kinshasa to work on solutions that will fit their data management and analysis needs. The CAID team even traveled to Johannesburg to share their remote mobile data experience with other African countries and help other governments use this technology.

We’re also working with Leiden University. Bouncing ideas off of their team at the Centre for Innovation helps us move forward on tricky challenges. We’re also collaborating with them to develop an online course where we’re going to share our methodologies and how to use remote technology to monitor food security. Check out Welcome to Vamistan for more.

We are in the field of tech. So we can’t do our job well without partnering with the private sector. It’s definitely a dynamic area, and also one where we at mVAM are learning what works best in melding our humanitarian goals with the exciting private tech potential out there. Check out our blog From the Rift Valley to Silicon Valley and our hackathon with Data Mission for more.

5. Learning- the neverending process

In addition to trying out new technology, we’ve been trying to answer some important questions about the live calls, SMS, and IVR surveys which make up the bulk of mVAM data collection.  We’re also doing mode experiments to understand how people answer differently based on which mode we use to contact them. Check out our first Mind the Mode article with more coming in 2017. In Kenya, we are looking into whether we can ask nutrition indicators through mVAM methods. A major challenge is reaching women through phone surveys so we organized a gender webinar with partners to learn from what they are doing- check out our key gender takeaways. These are key questions and they can’t be resolved overnight. But we’re making steady progress in understanding them, and we’re excited for what more we’ll find out in 2017.

Thanks to everyone who has supported our work this year and kept up with our blog!

Our key takeaways from the gender webinar


As you know we’ve recently held our first ever #data4food webinar, thanks to everyone who managed to join! But don’t worry if you didn’t get the chance – a recording is now available on our bigmarker account.  We thought it would be good to share our takeaways. 

We were lucky enough to have 4 great panelists who spoke to us about their experiences with data collection and gender:

  • Joyce Luma, Country Director of WFP South Sudan
  • Sangita Vyas Managing Director of r.i.c.e. (Research Institute for Compassionate Economics)
  • Micah Boyer, University of South Florida
  • Kusum Hachhethu, mVAM Team and Nutritionist

Thanks for joining our #data4food webinar! Our key takeaways are:

  1. To get to Zero Hunger, it is critical to understand women’s experience. As Joyce explained, women are the best placed to describe issues that matter – including child nutrition, feeding practices and household food consumption. Without having women’s perspectives, it’s not possible to have programmes that are well designed. If mobile surveys are to play the role in delivering information to design relevant hunger alleviating programs, we need to reach women.  
  2. Understand your context. Using mobile technology to reach women is easier in some communities than others. Micah explained  that there are important barriers to women’s access to and use of mobile phones in many places in West Africa. In Kenya on the other hand, Kusum found that many women either owned or had access to phones. A good practice is to conduct formative research that helps understand women’s access to mobile before launching your survey. You can then plan your questionnaire design and project around this information.
  3. Yes, it is possible to reach out to women who do not own phones. Sangita explained how  r.i.c.e asks mobile survey respondents to identify harder to reach demographics, including women from deprived backgrounds. Asking to speak to women members of the household even if a man answers or going through shared phones are ways to reach women. Similarly Joyce pointed out that in these contexts you should simplify the questionnaire – making sure that you use voice rather than SMS to ensure that you don’t have any problems with literacy.
  4. Don’t push it. Does it really make sense to reweigh a sample that is 95% male and 5% female? While mobile data collection is cheap and quick, in some cases, like when the biases are too large, we are better off collecting data face to face.
  5. Consider alternatives to representative statistics.  More use of qualitative approaches would help. Joyce said that in South Sudan, mobile phone ownership is too low to carry out representative surveys. WFP South Sudan therefore uses key informants to obtain food security information. One could obtain information about women’s health and nutrition from health workers.
  6. Continue investing in methodology. The potential of remote data collection to provide food security information in contexts like conflicts means that it’s important to continue investing in methodology to ensure that this information is as good as possible.  Sangita pointed to the importance of thoroughly training enumerators to achieve quality results.  

You can also track our conversation on Twitter by following the hashtag #data4food. Stay tuned for our next webinar!

#data4food: Join us for the first mVAM Webinar!


Join us on Monday for the first of our webinar series! We’ll be hosting a discussion with experts in the fields of mobile data collection, gender, and data analysis:

Addressing Gender-related Challenges in Remote Mobile Data Collection

12 December 2016
9am EST/2pm Dakar/3pm Rome/5pm Nairobi 


The discussion will explore some key issues that arise in remote mobile data collection, such as:

  1. Women’s Participation: How can we engage more women when conducting surveys via mobile phone? How can qualitative research help improve female participation rates?
  1. Analyzing Data for Zero Hunger: How do low female participation rates bias our data and thus our ability to design effective, evidence-based programmes? Given the barriers to women’s participation, what can we do right now to analyze our data in a way that better represents women’s experiences? Are we even asking the right questions?
  1. Mobile’s Potential: What are the untapped possibilities for using remote mobile data collection to collect information on both men’s and women’s experiences (e.g. protection issues like anonymous reporting of gender-based violence)? What are the limitations?


  • Joyce Luma, Country Director, WFP South Sudan (former head of WFP Trends and Analysis Service): Gender, mobile phone surveys, and data analysis for Zero Hunger
  • Sangita Vyas, Managing Director, r.i.c.e. (Research Institute for Compassionate Economics): Methodologies for capturing women’s experiences in mobile phone surveys in India
  • Micah Boyer, University of South Florida: Women, markets, and mobile phones in the Sahel
  • Kusum Hachhethu, WFP mVAM Team and Nutritionist, Qualitative research for using mVAM to reach rural Kenyan women


To join the webinar, connect via this link:


Are you on Twitter? Participate in the discussion on Monday with the hashtag #data4food


Is the road to hell paved with donated smartphones?



From time to time, people interested in mVAM will suggest we distribute cell phones to people who live in the vulnerable and food insecure communities where we work. It is usually a well-meaning idea, originating from specialists as they scope out a mobile-based data collection activity or a donor trying to help. After all, while phone
ownership has expanded exponentially, many poor people still do not have phones. It’s tempting to distribute devices to them as a solution to this disparity. However, our experience at mVAM and elsewhere has shown that we’re often better off going with the phones that people already have. This blog post explains why.

A checklist from UNICEF innovation

Others working with technology in developing countries have faced the same debates. We found that UNICEF innovation has come up with a useful list of questions to think about before distributing handsets or other hardware.

  1. Do you really need to distribute these devices?
  2. Who are they going to?
  3. How many of those people already have a smartphone? How about a dumb phone?
  4. How will you account for any loss of devices? Is that planned for?
  5. How will they be fixed when they break?
  6. How will you make sure devices are charged?
  7. What infrastructure do they connect to for information flow? Internet?
  8. Who trains people in how to use them?
  9. How does this affect the local economy? What is the market distortion on local hardware sellers? How many vendors might get put out of business?
  10. How do you make sure you don’t create a false incentive for the future?

An addendum from mVAM

On the basis of our experience, we’d like to add a few additional considerations to this list. mVAM’s own experience with cell phone distribution has been mixed. In 2012, we provided 400 cell phones to IDPs in eastern DR Congo, an idea we picked up from the literature (e.g. Listening to Dar). We tried to do the right thing by consulting with the community first. We offered low-end feature phones. We set up a solar charging station in the camp where people could recharge their phones for free. We also ensured that people received training in using the new devices.

On the one hand, we were pleased to see that we obtained a good response rate to our surveys from the camp and that providing access to cell phone technology empowered people (because the cell phones we provided allowed displaced people to call home, use mobile money for remittances, and obtain information). On the other hand, there unfortunately was also theft (42 phones were reported stolen!) and even cases of people being attacked for their devices. These findings are captured in the independent review of the mVAM activity that was published in 2015. Due to these concerns, we have not provided cell phones to people in other settings as mVAM has expanded.

So, we want to add another set of considerations to the UNICEF list :

  1. Are we putting people at risk ? mVAM surveys are sometimes carried out in very vulnerable conflict-affected communities. By receiving phones, people can be put at risk.
  2. Are there specific risks to women, the elderly, and the disabled? These groups are at specific risk of physical abuse.
  3. How would you replace a lost or stolen phone?
  4. Is providing phones sustainable? Providing and replacing phones in the long term can quickly become a financial burden, especially if people are in remote areas. When the population is concentrated (e.g. a camp or city), costs are lower.
  5. How could local regulations on cell phones affect your project? SIM cards are becoming highly regulated with requirements to provide an ID to authorities. This can be a barrier for some groups such as refugees. Some SIM cards will be cancelled if they are not ‘registered’ after a few weeks.
  6. What are the alternatives to providing phones? In some communities, poor and vulnerable households already have phones or have access to shared phones. It’s also important to remember that mobile data collection is not appropriate in all settings: sometimes, conventional tools such as face to face surveys are a better choice.

As we have gained more experience working in different contexts, we have concluded that we’re better off working with the phones people already have. Sometimes, that means designing our data collection strategy around the information that can be credibly collected when phone ownership or network coverage is limited. For instance, in places where cell phone penetration is low (~20%), we have not attempted to run a representative survey of households but have focused instead on obtaining information from a set of key informants, as we did in Afghanistan or the Central African Republic.

Going mobile in Afghanistan


WFP food security analyst Mudasir Nazar talking to internally displaced people (IDPs) in a camp near Kabul, during an mVAM scoping mission in October 2016. (Photo: WFP/Jean-Martin Bauer)

More than three decades of war, unrest and natural disasters has left Afghanistan with poor infrastructure and millions in severe poverty and facing enormous recovery needs. This insecurity pushed many Afghans to flee to surrounding countries like Iran or further afield to western Europe. It’s estimated there are 2.5 million Afghan refugees currently living in Pakistan many of whom arrived in the country in the late ‘70s during the war with the Soviet Union. In fact, in Pakistan, most Afghan refugees are second or third generation. Because of renewed political tensions, thousands are now starting to return to Afghanistan from Pakistan and it’s expected that there will be 600,000 arrivals by the end of the year. These returnees will require temporary assistance as they reestablish their livelihoods. Along with other humanitarian agencies, WFP is ramping up its work to prepare for this influx of people.

Mobile population, mobile monitoring

For humanitarian agencies like WFP, moving around Afghanistan is often difficult due to security restrictions and remoteness. This means we often have trouble directly contacting the returnees and IDPs we are helping, and getting information on the security or market situation in areas where they are settling.

But this is changing: mobile technologies now allow us to collect information remotely, not only from beneficiaries themselves, but also from members of the community such as tribal elders or shopkeepers. We are now preparing to use mVAM to reach people throughout Afghanistan – an approach that WFP already uses in nearly 30 countries.

Mudasir Nazar is a food security and market analyst with WFP Afghanistan, and is leading the set-up of mVAM here. After completing a Master’s degree in  Humanitarian Assistance at Tufts University (US), Mudasir is now back in Afghanistan with WFP. Like many of the returnees WFP is now helping, Mudasir grew up as an Afghan refugee in Peshawar, Pakistan. He came back to Afghanistan with his family years ago, settling in Kabul, but still relates very personally to what returnee families are going through at the moment: ‘A few years ago, I was in their shoes,’ he says.

Through mVAM, we will be asking questions about market food prices and food availability in areas where people are settling; what humanitarian assistance people need and what they are already receiving; and what livelihoods and coping strategies they are using to survive in their new (often temporary) homes. This data will allow us to understand the context into which people are resettling, and help WFP and others to provide the right type of assistance, to the right people.

Using mobile monitoring makes sense: the Afghan cell phone market has grown tremendously in past years. There are an estimated 20 million cell phone subscriptions in the country, out of a total population of 30 million people.  A recent study by USAID shows that while only 25% of women are literate, 80% have access to a mobile phone – either their own or shared within their household. When we visited an IDP camp recently and asked who owned at least one mobile phone in their household, everyone raised their hands.


Mudasir holds a power bank which is typically used to charge phones. (Photo: WFP/Jean-Martin Bauer)

We have found that most of the people we meet tend to utilize only the basic features of their phones, and rarely use SMS or other messaging services. IDPs and returnees also often have trouble keeping their phones charged, since many are living in informal settlements with no electricity. Though some own small portable ‘power banks’, many have to pay to charge their phones elsewhere. People also often don’t have any airtime balance on their phone. They typically top up once a month with a credit of 50 Afghanis (roughly US$1), which runs out quite fast.

So what does this mean for mVAM in Afghanistan?

Firstly, we will be calling people through live operators – rather than using more sophisticated tools such as SMS or robocalls, as WFP did in other countries. Secondly, we will need to provide a modest airtime credit incentive to encourage people to answer, and to help offset any battery charging costs.

We  will also make sure that our call center is staffed by all-female operators, to make sure we reach women, some of whom might otherwise be reluctant to speak to a male stranger over the phone.



Can mobiles be used to monitor nutrition?

WFP/Trust Mlambo

We told you in a recent blog post that we will be adding nutrition indicators to the existing data that we collect using our mobile modalities. We are thrilled to announce that this is finally happening!

Monitoring nutrition: why it’s important

Undernutrition is a huge global problem. Worldwide, 800 million people are calorie deficient and about two billion suffer from micronutrient malnutrition – not having the essential vitamins and minerals. Women and young children are at the greatest risk – nearly half of all deaths in children under five, or 3.1 million child deaths annually, are linked to undernutrition. Malnutrition in the first 1,000 days (from conception to child’s 2nd birthday) can cause irreversible damage to children’s brains and growth.

We have recently seen a lot of high-level political commitments to address undernutrition. However, one of the biggest challenges to turn the commitments into action has been the lack of timely data for effective programming. This is where our mVAM modalities could help: voice calls, SMS or IVR could be used to collect data for nutrition surveillance (especially in hard-to-reach areas). Potentially, mVAM tools could help provide real-time information to help manage nutrition programs. Over the coming months, we’ll try testing this approach.

Mobile data has worked for food security indicators. Will it work for children’s nutrition?

WFP/Nancy Aburto

In the past, we have tested various mobile methodologies to demonstrate that it can be used to gather credible data on food security. In the last few years whilst expanding to 26 countries we’ve learnt that remote data collection is fast, cost-effective and the most efficient way to collect information, especially in hard-to-reach areas. The results of our experiments show that live voice calls and SMS are complementary and can be useful in different contexts. We are testing both how both mobile methodologies could collect data on nutrition indicators. In Southern Africa, we are going to be trying a nutrition survey using SMS and in Eastern Africa, we will be comparing the results on nutrition indicators from face-to-face and live phone call interviews.

The challenge of monitoring nutrition by mobile

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WFP/Mica Jenkins

To date, mVAM has collected information about household food consumption and coping strategies. This usually involves calling randomly selected people. We also call trusted key informants that tell us about food security in their community. Nutrition is different because we’re looking for information about women of reproductive age and children under five. Mothers of children of that age are also a relatively small group, and the challenge will be reaching such a small demographic and ensuring  their participation.  

How do you actually monitor nutrition?

You might be wondering how we go about monitoring nutritional status. Undernutrition results from a combination of immediate, underlying and basic determinants – diseases and inadequate dietary intake are the two immediate determinants of undernutrition, and food security is one the three underlying determinants of undernutrition. While there are many underlying causes of undernutrition, dietary quality is a very important determinant of nutritional adequacy; therefore our efforts will be focused on monitoring dietary quality of women and young children.

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WFP/Mica Jenkins

In the first phase, in line with the 1000 days initiative,  we will be testing two internationally validated indicators. The first indicator we have decided to collect data on is Minimum Acceptable Diet (MAD) (MAD).  This is one of the globally validated indicators to assess Infant and Young Child Feeding (IYCF). It collects information on both the minimum feeding frequency and the appropriate minimum dietary diversity for various age groups. The other indicator we are going to collect is the Minimum Dietary Diversity-Women (MDD-W) that collects information about whether or not women 15-49 years of age have consumed at least five out of ten defined food groups the previous day or night. This will allow us to assess the diversity of women’s diets, an important dimension of their diet quality. This information is crucial, not just because inadequate dietary intake is an immediate cause of undernutrition, but also because dietary diversity is correlated with many other aspects of food insecurity. Eventually, we will also explore using other indicators of maternal and child undernutrition, as well as other mobile methodologies.

We’re aware that others have tested mobile to collect nutrition data (see an interesting paper in the mHealth series about testing SMS for IYCF indicators in China, published in 2013). We look forward to building on these lessons. We are very excited to collaborate with our internal and external partners to test the indicators. Stay tuned to know more about how we are bringing an innovation in nutrition and food security monitoring!

Voice calls in Niger: when basic works best

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WFP/ Cecilia Signorini

As many of you know, mVAM has expanded considerably in the last few years and we are now present in 26 countries. You may be familiar with high profile places we work in like Syria, or those where we are testing out new technologies like Haiti. We therefore wanted to write an update on one country that we haven’t spoken about in a while: Niger.

What is WFP doing in Niger?

To give you a reminder of why we are working there here’s a quick rundown. Niger is a landlocked low-income country in the Sahara-Sahel belt with a population of over 16 million people. Every year the United Nations Development Programme (UNDP) does a Human Development Index based on indicators of income, health and education indicators and Niger has ranked 188 out of 188 for the last few years. WFP estimates that 2.5 million people in Niger are chronically food-insecure. Increasing regional instability has only worsened the situation. Niger is currently responding to two emergencies: the recent Malian civil war in the north and Boko Haram in the south east whose insurgency and systemic violence has forced even more people to move, destroying community assets and food reserves. The volatility of this situation means that getting accurate food security information is both incredibly important and unfortunately very difficult. To get some more information from the ground we spoke to Moustapha Touré, who works on VAM and Monitoring and Evaluation (M&E) in WFP’s Niger country office. For the full interview (in French) watch out for an upcoming episode of our VAM Talks series, our podcast about how WFP sources its food security data.

Why mVAM?

The country office in Niger was keen to add extra dimensions to their food security analysis, and with insecurity rife in and around Diffa, it made sense to try remote monitoring.  Moustapha was the VAM officer in Goma where mVAM started in 2013, and when he arrived in Niger, his ideas and experience helped him to establish mVAM in the country, which quickly flourished. In our blog last September we wrote about how the team in Niger scaled up from their pilot surveys in the refugee camps working with a Niamey-based call center, iTelCom. This call center is pretty special – they are actually based in Niger’s first and only start-up incubator. By working with them, we are also contributing to the emergence of a local start-up specializing in digital engagement in vulnerable communities.  



The call center had begun placing calls to refugees from Mali in the camps of Abala and Mangaize in early 2015. One of the advantages of mVAM is ‘no boots on the ground’: we can conduct food security surveys without having to put anyone in the line of fire. When increasing attacks from Boko Haram meant it became urgent to get data from Diffa, the corner of Niger on the Lake Chad basin, our partner was able to ‘shift’ to this new area with relative ease, thanks to their prior experience.

mVAM and displacement

The complexity and longevity of the insecurity affecting Niger means that ‘displacement’ has many different meanings. Populations have been moving in and out of the country for so long that it’s sometimes almost impossible to define them as a ‘refugee’ a ‘returnee’ or an ‘internally displaced person’ (IDP). Many of the Malian refugees in the north are pastoralists whose livelihoods depend on moving around with their livestock so living in an enclosed refugee camp is even more of an issue. To try and solve this problem new areas have been designated as ‘Zones d’accueil des réfugiés’ (ZAR) or refugee hosting areas. Unlike a standard camp setting, they are open spaces where displaced people have room to graze animals, allowing them to continue their traditional lifestyle. In Diffa, recent Boko Haram attacks have caused a new wave of displacement in June raising the total to more than 240,00 displaced people in the region. All of this means that the questions we have in our food security surveys about a household’s displacement status is not nuanced enough to provide us with any useful answers. So we were wondering how the team in Niger is dealing with this complex landscape in terms of their implementation of mVAM.

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WFP/Cecilia Signorini

Moustapha said one way of coping with this was making sure to “conduct face-to-face surveys before mVAM” to get some prior information about the households. This information serves as a base which they can use to monitor the movements of the populations. They also change their terminology, making sure they only refer to “forced displacements” to specify that they only want to know about the movements because of a specific shock rather than seasonal movements. The reason this works in Niger is because the only mVAM modality used is our live calls so more time can be taken explaining this terminology than with SMS. Sometimes basic really works best!

In fact, this baseline information has already come into use. One area that has suffered from a recent attack is Bosso, resulting in a large amount of displaced households. As Moustapha pointed out, via mobile phones we can maintain a direct line to the affected populations, wherever they happen to be. Based on their responses, “we can see when they moved, whether they moved just once or if they are constantly moving or returned”. This might sound a bit CIA but the information is actually really useful for WFP operations. With a better understanding of affected populations we can make sure distributions are in the best places.

Challenges ahead?

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WFP/Cecilia Signorini

Whilst the system is working quite well there are always areas that can be improved. We spoke before about the challenges of mobile coverage in this largely rural context, so they are talking to the phone companies to try and get better coverage. There are also issues in terms of female responses. Less than 5 percent of women own their own
and some women don’t have the right to use a phone to receive a call without their husband’s permission, otherwise they could invite accusations of adultery or subversion of their husband’s authority. WFP’s West Africa Regional Bureau is working hard to try and solve some of these issues, exploring the possibilities of using female operators or using face-to-face recruitment.

We don’t think we will be using our fancy IVR and chatbots in Niger anytime soon, but it does look like mVAM is set to stay. As well as continuing the regular data collection in Diffa, Moustapha and the team plan to expand countrywide.

Our 5 hacks for mobile surveys for 2015


An mVAM respondent in Mugunga III camp, DRC.

  1. Gender matters. Design and run your survey in a way that promotes women’s participation. With mobile surveys, it’s hard to get as many women to respond as men. Make sure you’re calling at the right time and that you provide incentives. We also recommend having women operators. For more of our thinking on gender in mobile surveys, check out our blog entry on gender issues in West Africa.
  1. Validate mobile data against face-to-face data. Your mobile survey results may differ significantly. In many contexts, cell phone penetration has not reached the most vulnerable groups. In DRC, we had to provide phones to Internally Displaced Persons (IDPs) and access to electricity- to learn more check out our video and our blog entry. But it’s not always possible to distribute phones so it’s important to check your results against other data sources. Also, people get tired of answering their phones all the time so attrition and low response rates will affect your results.
  1. Mind the mode!  Your results will differ according to whether the survey is done through SMS, IVR, or live calls by an operator. Live calls have the highest response rates, but you have to be ready to pay. For simpler data, we have found that SMS is effective and cheap. Just remember- the context matters. SMS is working well with nationwide surveys, even in countries where literacy rates are not that high- check out our recent results in Malawi. However, SMS can be a problem in communities where literacy rates are very low or familiarity with technology is low as we found in DRC IDP camps. For Interactive Voice Response (IVR) that use voice-recorded questions, the jury is still out on its usefulness as a survey tool.  IVR didn’t work as well as SMS in Liberia, Sierra Leone, and Guinea during the Ebola crisis (HPN June 2015). But IVR has potential as a communication tool to push out information to people. Check out our entry on our two-way communication system where we use IVR to send distribution and market price information to IDPs in DRC.
  1. Keep the survey user friendly and brief. Always keep your survey short and simple. Stay below 10 minutes for voice calls, or people will hang up. If you are texting people, we don’t recommend much longer than 10 questions. Go back to the drawing board if respondents have trouble with some of your questions. With mobile surveys, you don’t have the luxury of explaining everything as with in person interviews. It might take a few rounds to get it right. When we want food prices, we’ve found we need to tweak food items and units of measurement in Kenya and DRC to best capture what people buy in local markets. Again, short and sweet should be the mobile survey mantra.
  1. Upgrade your information management systems. There is nothing as frustrating as collecting a lot of great data – without being able to manage it all! Standardize, standardize, standardize! Standardize questions, answer choices, variable names, and encoding throughout questionnaires. Automate data processing wherever possible. Also, you’ll be collecting phone numbers. This is sensitive information so make sure you have the correct confidentiality measures in place. Check out our Do’s and Don’ts of Phone Number Collection and Storage and our script for anonymizing phone numbers. Finally, share your data so others can use it! We’re posting our data in an online databank.