A new mVAM baby in Mali, weight: 7800 respondents!

WFP/Sebastien Rieussec

WFP/Sebastien Rieussec

This week we’re reporting on our latest news from mVAM in Mali. In this landlocked country in the Sahel chronic food insecurity and malnutrition is widespread – WFP has been present in Mali since 1964. In the last few years Mali has been coping with numerous shocks – such as droughts, floods and a military coup – that led to a political and security crisis and increased food insecurity in the country: by 2016 around 3.1 million people in Mali were food insecure. Households are particularly affected during the lean season, between June and September; and this year WFP estimated 3.8 million people affected by food insecurity, of which 601,00 people in urgent need of food assistance.

To monitor the food security situation, the Government of Mali, with WFP support, does two nationwide face-to-face surveys, in February and September each year. However, in between these times and especially during the lean season that takes place during the summer in Mali there was no data collection – so mVAM was there to fill the ‘data gap.’ We’ve previously blogged about the Mali mode experiment we did comparing data collected by live calls and face-to-face data. As the results showed that there was little difference between the modes, in August the Country Office rolled out mVAM nationwide so that they could get food security information from households affected by this particularly difficult period of the year. During the previous face-to-face survey phone numbers were collected…out of the 13,400 numbers we collected we reached over 7,800 households – mVAM’s largest-ever survey!

With each survey comes different country-specific ‘problems’. There are many different reasons why people might not want to take part in a phone survey – but in Mali, we found one of the biggest was mistrust. People are not used to doing surveys via mobile phones and are sure that there is some form of trick behind them. Many reported that they know that there are lots of mobile phone scams and worry that the call from an unknown number purporting to be from WFP is just another one of these. One of the reasons why they were suspicious  was due to the fact that there was a long time gap between the number collection and the phone survey. This was actually a deliberate choice by the Country Office to ensure that the survey was not just a ‘follow up’ survey to face-to-face data collection like our mode experiment and was getting new information during this specific time period. What wasn’t foreseen was that this meant people forgot that they had given WFP their number and may have not fully understood why they did so in the first place.

Mali blog Edith 2

WFP/Nanthilde Kamara

To get around this issue, the Country Office is planning to use several tactics. As well as using SMS and national radio to advertise the survey, the next time that phone numbers are collected, there will be more time spent on explaining exactly what the purpose of the survey is. The annual September face-to-face food security survey is currently ongoing, so enumerators are now explaining that they might be called by WFP later on this year. The call centre that supports mVAM in Mali calls everyone with the same unique number, this number will be shared with community leaders just before the survey so that they can inform people that they will be rung by this specific number and that it’s an official call from WFP. Respondents will then be able to save the number in their phone so they know when they get the call exactly who it is and it won’t be just an unknown number.

The analysis is still ongoing: We’re looking forward to the results!

 

New places, new tools: what’s up next for mVAM?

KOICA pic 2

We’ve just got back from Rwanda where we were holding a workshop on using mVAM to expand real-time food security and nutrition monitoring with Internally Displaced Persons (IDPs) and refugee populations. The project, which is made possible by the support of the Korean International Cooperation Agency (KOICA), will be implemented in ten countries in sub-Saharan Africa where WFP works.

What’s the project?

The KOICA project has two aims. First, it aims to empower information exchange with marginalized populations, specifically IDPs and Refugees. Secondly, it supports the collection of food security and nutrition data using the latest mobile and satellite technologies. This will happen in ten countries in Sub-Saharan Africa: the Central African Republic (CAR),The Democratic Republic of Congo (DRC), Kenya, Malawi, Niger, Nigeria, Rwanda, Somalia, South Sudan and Uganda.

How are we going to do this?

As you know, two-way communication systems are an important part of our work. As well as getting information that we can use to inform WFP programmes, we want to ensure that the line is open so that people in the communities we serve can contact us and access information that is useful to them. We’ve already been using Interactive Voice Response and live calls to share information with affected populations, and are now expanding our toolbox to include new technologies: Free Basics and a chatbot.

Remote data collection isn’t just done by mobile phones – VAM already uses other sources, such as  satellite imagery analysis – to understand the food security situation on the ground.  Under this project, we’ll also help countries incorporate similar analysis which will complement two-way communication systems to provide a fuller picture of the food security situation.

Finally, we’re going to harness our knowledge of Call Detail Records analysis: de-identified metadata collected via cell phone towers about the number of calls or messages people are sending and which towers they are using. We have already used this technique in Haiti to track displacement after Hurricane Matthew, and we’re really excited to transfer these ideas to another context to ensure we get up-to-date information on where affected communities are so we can better target food assistance in the right locations.

What happened at the workshop?

Representatives from all 10 country offices, three regional bureaus and staff from HQ came together to discuss the three main project components. During the workshop, the different country offices had the chance to learn more from members of the mVAM team about the specific tools they can harness and ensure their collected data is high quality, standardised and communicated effectively. However, the best part about bringing everyone together was that country teams could share their experiences about how they are already using mVAM tools. We heard from the Malawi country office about their Free Basics pilot, and Niger and Nigeria explained how they’re implementing IVR so affected communities can easily contact WFP, even after work hours. Sharing their different experiences and learning about how different tools have worked in each context not only gave everyone an overview of what mVAM is doing so far, it also helped everyone understand the implementation challenges and how to overcome them.

What’s next for the KOICA project?

We’re really excited for the next stage of the project. Each country office has now planned what tools they’re going to use to increase their communications with affected communities and how they will improve their existing data collection systems. It’s going to be great to see the impact these tools will have not only on WFP’s response, but also how they will empower the communities we’re serving. 

Mind the mode:

Who's texting & who's talking in Malawi?

Malawi mVAM respondent WFP/Alice Clough

Malawi mVAM respondent
WFP/Alice Clough

It’s time for another installment of our Mind the Mode series. For those of you who follow this blog regularly, you know that the mVAM team is continually evaluating the quality of the data we collect. Past Mind the Mode blogs have discussed our work in Mali looking at face-to-face versus voice calls, our comparison of SMS and IVR in Zimbabwe and the differences in the Food Consumption Score (FCS) for face-to-face versus Computer-Assisted Telephone Interviews (CATI) interviews in South Sudan.

This month, we turn our attention to Malawi, where we recently completed a study analyzing the differences in the reduced Coping Strategies Index (rCSI) when it’s collected via CATI and SMS. This indicator helps measure a household’s food security by telling us what actions they might be taking to cope with any stresses such as reducing the number of meals a day or borrowing food or money from friends or family. From February to April 2017, around 2,000 respondents were randomly-selected for an SMS survey and 1,300 respondents were contacted on their mobile phones by an external call centre to complete a CATI survey.

People Profiling: who’s Texting and who’s Talking? 

Across all three rounds, a greater proportion of respondents in both modalities were men who lived in the South and Central Regions of the country and came from male-headed households. However, the respondents taking the SMS survey were much younger (average age 29) than those who took the CATI survey (average age 40). This probably isn’t surprising when you consider that young people across the world tend to be much more interested in new technologies and in Malawi are more likely to be literate.

The results from our mode experiment in Zimbabwe showed that IVR and SMS surveys reached different demographic groups so we figured we might see the same results in Malawi. However, this was surprisingly not the case: both CATI and SMS participants seemed to come from better-off households. In our surveys we determine this by asking them what material the walls of their home are made from (cement, baked bricks, mud, or unbaked bricks).

better off-worse off wall type malawi

More respondents (60%) said they have cement or baked brick walls as opposed to mud or unbaked brick walls, an indicator of being richer.

Digging into the rCSI

So what about the results observed for the rCSI between the two modes? The CATI rCSI distribution shows a peak at zero (meaning that respondents are not employing any negative coping strategies) and is similar to the typical pattern expected of the rCSI in face-to-face surveys (as you can see in the two graphs below).

Density plot for CATI Feb-April 2017

 

SMS rCSI

The SMS results, on the other hand, tend to have a slightly higher rCSI score than in CATI, meaning that respondents to the SMS survey are employing more negative coping strategies than households surveyed via CATI. This is counter-intuitive to what we might expect, especially since the data illustrates that these households are not more vulnerable than CATI respondents. Presumably, they would actually be better educated (read: literate!) to be able to respond to SMS surveys. We’re therefore looking forward to doing some more research in to why this is the case.

Box plot cati rcsi

It’s All in the Numbers

Some interesting patterns in terms of responses were also observed via both modalities. SMS respondents were more likely to respond to all five rCSI questions by entering the same value for each question (think: 00000, 22222…you get the idea!). At the beginning of the survey, SMS respondents were told that they would earn a small airtime credit upon completion of the questionnaire. We conjecture that some respondents may have just entered numbers randomly to complete the questionnaire as quickly as possible and receive their credit. Keep in mind that entering the same value for all five rCSI questions via CATI is a lot more difficult, as the operator is able to ask additional questions to ensure that the respondent clearly understands the question prior to entering the response.  For SMS, there’s no check prohibiting the respondent from dashing through the questionnaire and entering the same response each time.

We also saw that the percentage of respondents stating that they were employing between zero and four strategies was much lower among SMS respondents than CATI respondents across all three months of data collection. Conversely, more respondents (three out of five) in the SMS survey reported that they were using all five negative coping strategies than in the CATI survey. Again, this is counter-intuitive to what we would expect.  It might mean that SMS respondents didn’t always correctly understand the questionnaire or that they didn’t take the time to reflect on each question, completing questions as rapidly as possible to get their credit; or simply entered random numbers in the absence of an operator to validate their responses.  The graphs below illustrate the differences in rCSI responses between CATI and SMS.

Figure 3: Distribution of the number of coping strategies reported by SMS and CATI respondents by months

Figure 3: Distribution of the number of coping strategies reported by SMS and CATI respondents by months

From these results, you can see that we still have a lot to learn on how survey modality affects the results. This is just the start of our research; so expect more to come as the team digs deeper to better understand these important differences.

Mind the mode …. and the non-response

How voice and face-to-face survey data compares in Mali

This is the third entry in our ‘Mind the Mode’ series on the mVAM blog. We are constantly assessing our data collection modalities to better understand what produces the most-accurate results and what biases may be present. One of our recent experiments took us to Mali, where we were comparing the food consumption score between face-to-face (F2F) interviews versus mVAM live calls.

It’s all in the details
To do this, in February and March, the WFP team first conducted a baseline assessment in four regions of the country. As part of the baseline, we collected phone numbers from participants. Approximately 7-10 days later, we then re-contacted those households who had phones, reaching roughly half of those encountered during the face-to-face survey. We weren’t able to contact the other households. To ensure the validity of the results, we made sure the questionnaire was the exact same between the F2F and telephone interviews. Any differences in wording or changes in the way in which the questions were asked could adversely affect our analysis.

The findings from our analysis were quite interesting. We found that food consumption scores (FCS) collected via the mVAM survey tended to be slightly higher than those collected via the face-to-face survey. The graph below illustrates this shift to higher scores between the two rounds. Higher FCS via mVAM versus F2F surveys is not atypical to Mali. We’ve observed similar outcomes in South Sudan and other countries where mVAM studies have taken place.

mali dist

 

Why could this be? There are two main reasons that could explain this difference. Either it might be due to the data collection modality (i.e., people report higher food consumption scores on the phone)? Or, a perhaps a selection bias is occurring? Remember that we were only able to contact roughly half of the participants from the F2F survey during the telephone calls. So, it’s possible that people who responded to the phone calls are less food insecure, which could make sense, since we often see that the poorest of the poor either don’t own a phone or have limited economic means to charge their phone or purchase phone credit.

To test these hypotheses, we dug a bit deeper.

Same same…
Are people telling the same story on the phone versus face-to-face? Based on our results, the answer is yes! If we compare the same pool of respondents who participated in both the F2F and telephone survey rounds, their food security indicators are more or less the same. For example, the mean mVAM FCS was 56.21 while the mean F2F FCS was 55.65, with no statistically significant difference between the two.

But different…
So what about selection bias? In the F2F round, there are essentially three groups of people: 1) those who own phones and participated in both the F2F and mVAM survey; 2) people who own phones but didn’t participate in the mVAM survey, because they either didn’t answer the calls or their phone was off; and 3) people who do not own a phone and thus couldn’t participate in the mVAM survey.

People who replied to the mVAM survey have overall higher FCS than those that we were unable to contact. What we learned from this experiment is that bias does not only come from the households that do not own a phone but also from non-respondents (those households who shared their phone number and gave consent but then were not reachable later on for the phone interview). Possible reasons why they were not reachable could be that they have less access to electricity to charge their phone or that they live in areas with bad network coverage. The graph below illustrates the distribution by respondent type and their respective FCS.

mali boxp

When you compare the demographics of people in these three groups based on the data collected in the baseline, you can see that there are significant differences, as per the example below. Notice that the education levels of respondents varies amongst the three groups—those without a phone tend to be less educated than those who own a phone and participated in the mVAM survey.

mali profile

This study taught us a valuable lesson. While we are confident that there is no statistically significant difference between face-to-face and phone responses within the Mali context, there is a selection bias in mVAM-collected data. By not including those without phones as well as those who did not respond, we are missing an important (and likely poorer) subset of the population, meaning that the reported FCS is likely higher than it may be if these groups were included. One way to account for this bias is to ensure that telephone operators attempt to contact the households numerous times, over the course of several days. It’s important that they really try to reach them. The team is also studying how to account for this bias in our data analyses.

mVAM for nutrition: findings from Kenya

2WFP-Kusum_Hachhethu

Photo: WFP/Kusum Hachhethu

We’ve used mVAM to collect data on a range of things that impact food security – so what about information on nutrition? Back in October, we went to Kenya to conduct a study on whether we could use remote mobile data collection to gather information on women and children’s nutrition.

The summary of our findings from the case study are now available in a new report from mVAM and our partners in the study, WFP’s Nutrition Division and the World Agroforestry Centre (ICRAF).

Read more:

kenya-report

Myanmar: assessing emergency needs without access

Photo: WFP/Myanmar

Photo: WFP/Myanmar

 

Late last year, an attack by an armed group on border police posts in Myanmar led to a government security sweep in Rakhine State and recurrent clashes and violence in many villages. As a result, access to a large part of the north of the state was closed off to humanitarian organizations, leaving the already highly vulnerable inhabitants of the townships to fend for themselves.

Unable to access the area since 9 October, WFP decided to use mobile surveys to conduct remote emergency assessments. While not as thorough as face-to-face assessments, mobile surveys could still provide a good snapshot of how people were coping in the areas that were closed off. Furthermore, mobile surveys serve as a means to address a critical information gap where there is little to no information about the needs of the most vulnerable and food insecure, as we have seen in complex emergency settings elsewhere such as during the Ebola crisis and Yemen. But let’s come back to Myanmar and rewind just a few years: hearing from people in these areas would have been impossible – essentially no one had mobile phones.

Myanmar’s mobile transformation

Myanmar’s telecommunication market has come a long way. Not so long ago, Myanmar was one of the “leastconnected countries in the world” – just seven years ago, SIM cards cost up to $1,500, and few people had them. In 2013, after the government awarded contracts to two foreign mobile operators, the price of a SIM card fell to $1.50 and network coverage began to roll out across the country. Once the mobile revolution began, things moved fast. Soon, mobile penetration exceeded even that of much better-off neighboring countries, such as Thailand[1]. By 2015, 96 percent of wards and 87 percent of villages in Myanmar had a mobile signal, and nearly 60 percent of households owned a mobile phone[2].

A case for mobile surveys in Myanmar

WFPs first mobile assessment in Myanmar took place in November 2016, with 32 key informants from 12 villages in Maungdaw and Buthidaung north, complementing face-to-face interviews of 48 WFP beneficiaries at 8 food distribution points in Buthidaung south. This was at the end of the lean season (the period between harvests when households’ food stocks tend to be the lowest), and respondents told us that due to the deteriorated security situation, people faced serious difficulties in reaching markets, were not able to go to work, nor access agricultural land and fishing areas and. Resulting crop losses could result in mid to long-term impact on food security while households’ terms of trade had decreased and posed a serious concern regarding their ability to purchase sufficient food.

Though low mobile penetration in rural areas of the country posed a challenge for phone surveys, people were nonetheless eager to participate in the survey and share their stories. In order to participate, some people even arranged to borrow phones from neighbors if they did not own one themselves.

A second phone survey in December allowed for a greater sample size and therefore a better understanding of the living conditions in the surveyed areas. WFP spoke to 116 respondents in 70 villages in Maungdaw Township. By this time, the people we spoke with mentioned that there was widespread food insecurity throughout the township. The situation was particularly problematic in the north, where markets were not functioning and access to agricultural land or fishing grounds was restricted. Livelihood opportunities were scarce and the lower demand for daily labour had had an immediate impact on the most vulnerable.

Photo: WFP/Myanmar

Photo: WFP/Myanmar

What’s next?

The data collected through the phone surveys helped WFP to get some understanding of the needs in the no-access areas, and to use this information for advocacy with the Government and humanitarian stakeholders. On 9 January 2017, after three months, WFP was finally granted access to all areas where it had operations prior to 9 October, and was able to distribute food to 35,000 people in the villages of Maungdaw north. With the area open again, WFP and its partners are now preparing for thorough assessments on the ground, which will give a fuller picture of the food security situation and also allow us to validate the findings of the phone surveys.


[1]http://lirneasia.net/wp-content/uploads/2015/07/LIRNEasia_MyanmarBaselineSurvey_DescriptiveStats_V1.pdf

[2]http://www.gsma.com/mobilefordevelopment/wp-content/uploads/2016/02/Mobile-phones-internet-and-gender-in-Myanmar.pdf

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

SONY DSC

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.

Going mobile in Afghanistan

mudasair

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.

kabul-429

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.

 

 

Voice calls in Niger: when basic works best

Niger 4

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.  

niger-blog

WFP/VAM

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.

Niger 5

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?

Niger 1edit

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
mobile
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.

The El Niño Aftermath: Tracking Hunger in the Millions in Southern Africa

We’ve been writing a lot about how mVAM can help in conflict situations where whole areas are cut off because of violence or an epidemic (see our blogs on Yemen, Somalia, Iraq and article on Ebola). But over the past year, the world was disrupted by another type of event- a climatic one: El Niño. The El Niño weather pattern results from a warming of sea temperatures in the Pacific roughly every three to seven years. This El Niño was one of the strongest on record.  The reason why El Niño was so concerning is its global reach, it didn’t just affect the Pacific; places as far away as Guatemala, Pakistan, Indonesia and Ethiopia were all at risk of floods and/or droughts. While the El Niño itself has abated, it has left millions hungry in its wake (current estimates are that 60 million people are food insecure globally). And a La Niña year is looming.

One area that has been particularly affected is Southern Africa. Across the region, this year’s rainfall season was the driest in the last 35 years. Most farmers are facing significantly reduced and delayed harvests.

El Niño hit when Southern Africa was already vulnerable to food insecurity. The region had already experienced a poor 2014-15 harvest season, meaning that food stocks were already depleted. Now, after El Niño, roughly 41 million people are classified as food insecure. On 13 June 2016 WFP categorized the region as an L3 emergency – a situation requiring the highest level of humanitarian support. We’re therefore dramatically expanding our national food security monitoring in the region so WFP can quickly provide as much relevant food security information as possible to effectively respond to the crisis.

Predictions that this El Niño would have a big effect had already started coming in 2015 so we began setting up mobile monitoring in countries that were particularly vulnerable to El Niño. We started in Malawi which had very disruptive weather patterns looming (potentially too much rain in the north and huge rainfall deficits in the south). We lacked current household data to track the impact on food security across the country.

To get information quickly and cheaply, we started a monthly SMS survey with GeoPoll in December 2015. And Malawians sure were quick to respond! In 24 hours, we had 1,000 questionnaires completed.  When analyzing the results, we wanted to make sure people were understanding our texts. The adult literacy rate in Malawi is only 61.3% so we kept the questionnaire short and as simple as possible. We included questions for one food security indicator- the reduced coping strategy index (rCSI) which asks people about the coping strategies they are using when they don’t have enough to eat. We also checked that the data made sense, and in general, the rCSI behaved as we would suspect. It was correlated with people’s messages about their community’s food security situation and their wealth status. As with all of our surveys, we are continually improving them. In this case, we increased our sample size and district quotas to capture more people in rural areas.

Monitoring Maize Prices

IMG_0095Market prices, especially maize prices, are key to Malawians’ food security. Maize is the staple food, used to make nsima which is consumed daily. So to monitor market prices in 17 hotspot districts, we collected phone numbers from over 100 traders in 51 markets throughout Malawi. We first tried asking them prices by text message, but we didn’t receive many responses.  It seems like sending back a series of texts is a bit too much to ask of traders who volunteered out of their own good will to participate in our market survey. We therefore set up a small call center in WFP’s country office. We trained two operators, and they were quickly placing calls to traders every week. When they could just answer a quick phone call instead of having to type in answers, traders willingly reported current commodity prices.

Our latest report from June 2016 shows that maize prices are now between 50 and 100 percent higher than this time last year. This is having a big effect on Malawians. As you can see from our word cloud, alarmingly ‘not-enough’ featured prominently in our open ended question about maize.

word cloud_cropped

Nutrition Surveillance for the first time

In most countries, we have been concentrating on household level indicators like food consumption. But health centers treating malnutrition could potentially give us important indications of the nutrition situation of different parts of the country. In Malawi, WFP works with health centers to address moderate acute malnutrition (MAM) in Malawi by providing fortified blended foods. So to make the most of our call center, we decided to call these health centers every two weeks and track malnutrition admission data for children (aged 6-59 months) and for adults with HIV/AIDS or tuberculosis. In the first six weeks of monitoring, we saw a big increase in the number of moderate acute malnutrition admissions for children increased greatly where severe acute malnutrition rates did not show a clear pattern. We dug further, and the Ministry of Health had initiated mass screenings to enroll malnourished children in nutrition programmes which generally pick up moderately malnourished children. With health center admission data, it’s important to check what else is going on in the country. We’re hoping to soon pilot contacting mothers of malnourished children about their children’s progress to gain additional insight into the nutrition status of vulnerable populations in Malawi.

Now that we have Malawi firmly established, we’ve started reporting on Madagascar and our data collection is ongoing in Zambia, Lesotho and Mozambique. So watch this space for more news about how we get on in these next few months.