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!

 

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.

Postcard from Bangui

Good to be OKING:It may not be new and super large, but the owner claims this phone has a week-long battery life! WFP/Dominique Ferretti

It may not be new and super large, but the owner claims this phone has a week-long battery life!
WFP/Dominique Ferretti

Greetings from the Central African Republic (CAR)! Our team recently visited Bangui and Kaga-Bandoro to help the Country Office team assess how to enhance the current mVAM system and see what other mVAM technologies we might be able to deploy. CAR is a very unique context, because there’s little-to-no cell phone reception outside of main towns. Only 26% of the population own a phone, one of the lowest rates in the world according to the World Bank.  This means that collecting data remotely takes some creativity. The CAR team uses a key informant system, where they contact approximately 200 people around the country each month to collect information on basic commodity prices, market access, population movements, and security issues. The collected information is then shared with the humanitarian community, who appreciate the data, as it’s the only national-level food security data that’s currently collected regularly!

A local woman in Kaga-Bandoro selling a great source of protein and a central African delicacy—caterpillars! WFP/Dominique Ferretti

A local woman in Kaga-Bandoro selling a great source of protein and a central African delicacy—caterpillars!
WFP/Dominique Ferretti

The only downfall to the key informant system is that it doesn’t give us household-level food security information. The CAR team has therefore decided to try a small pilot using household questionnaires in the city of Kaga-Bandoro. Courtesy of UNHAS, we visited the city (more like a very small town!) and the 2 IDP camps it hosts during our day trip. While not that many people had cell phones, enough community members and displaced persons had phones that we’ll be able to get some idea of the food security situation.

Stay tuned for more as the pilot unfolds…!

Chatting with community members as they collect water WFP/Dominique Ferretti

Chatting with community members as they collect water
WFP/Dominique Ferretti

If you’re not human then who are you?

Experimenting with chatbots in Nigeria and Haiti

WFP/Lucia Casarin

Testing the bot in Haiti – WFP/Lucia Casarin

Readers of this blog know that the team has been experimenting with chatbots to communicate with disaster-affected communities – read our previous posts about our prototype and the Nielsen Hackathon.

As part of this effort, during recent missions to Haiti and Nigeria, our team went out to talk to communities to find out whether a chatbot would be right for them.

Would a chatbot be a stretch in these communities?

Well it’s not that much of a stretch.

In North East Nigeria, most displaced people live in Maiduguri, a city of over 1 million people. In this ‘urban’ setting connectivity is good, most people own cell phones and many young people use social media and messaging apps. Mobile operators have been offering services that allow people to access the internet by selling ‘social bundles’ (unlimited social media access sold in very small increments) and offer some services for free, including Facebook Light and Facebook Messenger.

In Haiti, three-quarters of the population live in the capital, Port-au-Prince, where 3G connectivity is good and most people use messaging apps to communicate with friends and family. Even in rural and difficult-to-reach communities, leaders and young people own smartphones and connect to the internet. There is a lot of competition between mobile operators so the prices for mobile data are very low. This means that most people can afford to access the internet either via their own smartphone or from shared smartphones.

A

Mobile phones charging station on the road from Léogane Peri to Port-au-Prince WFP/Lucia Casarin

A bare-bones demo

In both countries we tested a simple chatbot that asks people about food prices and what the food security is like in their community. The survey we used was much more basic than our usual mobile questionnaires as we felt it was important to keep things simple at this stage.

For Nigeria, the bot demo was initially in English but we soon translated it into Hausa, the primary language spoken by displaced persons in Maiduguri. In Haiti we made it available both in Creole and French. The chatbot was very responsive on 3G and it even worked with slower 2G connections so the technology works in these contexts. But this was only the starting point, what we really wanted to know was what ‘real’ people thought about the bot.

We organized focus group discussions with displaced people in Maiduguri and with community representatives in Haiti. We helped people access the WFP bot via their Facebook accounts, and they began chatting away.

Sounds cool, but what are the limitations?

Here’s what people said:

First of all, people thought the bot is a convenient, quick, and easy way to get in touch directly with WFP and they really liked that the bot allows them to speak to WFP without intermediaries. They had lot to tell us particularly through the open-ended question where they typed out detailed responses.

In Nigeria, they did tell us that our (somewhat wordy) English-language demo should be translated into Hausa because it would make it easier for everyone to use. Our first group of testers were young people who were already Facebook users and so were familiar with Messenger. It was therefore no surprise that they were interacting smoothly with the bot and able to go through our questionnaire in minutes.

WFP/Jean-Martin Bauer

Testing the bot in Nigeria – WFP/Jean-Martin Bauer

In Haiti, people started interacting with the bot as if it was a human rather than an automated questionnaire so they got stuck pretty fast when it wasn’t as naturally responsive as they’d expected. This means that either we give clearer instructions to people or we add Natural Language Processing capabilities to our bot.

There are of course other barriers. In both countries women appeared to be less likely to own a smartphone. This means that bot users will likely be overwhelmingly young, male and better educated than other people – hardly ‘representative’ of WFP’s target affected population. The free version of the bot is also not always available: in Nigeria only Airtel subscribers can access it, while in Haiti the free service doesn’t exist yet.

This means that the bot would need to be a complement to the other tools we have. We might use data from the bot to obtain a quick situation update, but we will continue relying on other sources for more representative data.

What we found at the market: using Free Basics in Malawi

FreeBasicsAd_Chichewa

We wrote to you back in November about one of our new innovations – our Free Basics website ‘Za Pamsika’ where we’re posting commodity prices using the weekly price data we’re collecting through our mVAM operators on a free website. We said that the project had the potential to reach millions of Malawians – well, a lot has happened since then.

Rather than continuing to willfully upload prices while watching our user statistics go up and down, we went to Malawi to carry out a short ground truth study and get some first hand user feedback.  The aim of the mission was to investigate the best way of using the website and interrogate the assumptions we’d made when designing it.

With this in mind, we tried to answer two big questions:

  1. Who can access our website – what are the potential barriers and how can we work around them?
  2. Do Malawians really want a website where they can find out maize and beans prices?

So we went to rural and urban markets in the Central and Southern regions to speak to the mVAM traders and the consumers in their markets about their mobile phone usage and market activity and to get their feedback on the website.

What kind of answers did we get?

First – access issues. While you don’t need a smartphone to access the website we knew that mobile penetration in Malawi is low. So we were most worried about the prevalence of internet-enabled phones and network coverage. From our study we found out that while we aren’t going to be able to reach everyone in Malawi via a website, we can still communicate with people. Network coverage was a problem in some areas. However, overall we found that most of the traders had internet enabled phones or wanted to buy one. We also found that Malawi’s MNOs have been recently trying to push out better network coverage. All good news for future reach of the website.

Actually the biggest barrier was language and literacy. While English is the national language of Malawi, most of the literate people we spoke to were much more comfortable reading and writing in Chichewa because that’s what they were taught in. While they were very enthusiastic about the website content when it was explained to them, they found the initial design (all in English and text heavy) confusing and difficult to use. Luckily this is an easy change to make so we did a quick redesign of the website and translated it into Chichewa:

malawiblog1

With our new design we headed back into the markets and got much better feedback. Rather than just saying that they liked the website content they could really interact with it and were making comments on the different maize and beans prices.

The second barrier we found was digital literacy. Many of the people we spoke to had internet-enabled phones but either didn’t know how to use them or didn’t even realise that they had the internet on them! Unlike the language change this is not a quick fix. This was particularly prominent amongst the women we spoke to, none of whom were comfortable with mobile internet. We’re therefore going to partner with civil society organisations promoting digital literacy. WFP has a network of partners and farmers on the ground who they reach out to with climate information so we’re going to try and use these focal points to communicate our prices with vulnerable populations and communities who have limited access to information.

IMG_1205

But do Malawians really want a ‘Za Pamsika’ website?

It turned out that maize and beans prices really are something that people want to see on the website. The recent drought was on everyone’s minds and they were really emphasising how much of a difference getting a good price could make. People were also already using their phones to get prices – by calling their friends or other traders in different areas and were quite enthusiastic about the possibility of getting this information for free.

With these learnings in mind and feeling confident with our website redesign and excited to be working closely with the country office, we embarked on our next steps. We now have a new focal point in the Lilongwe office who’s looking after the project and in a much better placed position than us in Rome to reach out to millions of Malawians. By this point over 25,000 people had already visited the Za Pamsika website but we knew our reach could be much further. We therefore started experimenting with ways of advertising the website.

malawiblog2

First – we decided to take out a Facebook ad to try and raise the site profile so we created our own ‘Za Pamsika’ page on Facebook and put out some ads in English and Chichewa. We were pretty excited when they started showing up on Malawian colleagues’ Facebook newsfeeds and within 10 days we’d reached more than 130,000 people and got 650 likes to our Facebook page.

What we didn’t expect was the organic reaction we’d get to our page. Within 3 days we’d not only reached more than 80,000 people with our post, we’d also seen that people started having conversations about maize prices on our advert.  People have also started messaging us about whether we can add their market to our website. We’re also getting comments about what other commodities we should add, for instance more seasonal foods such as groundnuts or soya. Most excitingly we even had someone knock on the door of the sub-office to inquire about the website after seeing our advert!

On a second mission in April we went out to the markets in Lilongwe again armed with our new ‘Za Pamsika’ posters. We were putting them up in the trader’s shops and were pretty quickly swamped with people excited about the website and how it could save them money. But again – everyone was asking us to add more food prices to the site – it seems like Malawians just keep wanting to know more about ‘things you find in the market’!

IMG_1201

So what’s next for Za Pamsika?

We’ve got our new focal point Khataza on board who’s taking charge of the website. First up, taking requests into account, we will be adding other seasonal commodities to the website. We’re going to continue experimenting with our Facebook ads and start using our Facebook page to reach out and engage with people about what they’d like on the page. We’ve also got some new partnerships coming up with civil society organisations who are keen to spread the word about ‘Za Pamsika’ and who we can work with to break down access barriers to this information.

Are millions in Malawi being reached? Not yet – but we’re getting there.

Hearing from those who are #FacingFamine

Photo: WFP/Amadou Baraze

Photo: WFP/Amadou Baraze

 

In early March, Stephen O’Brien, the United Nations’ Emergency Relief Coordinator, reported that 20 million people across four countries face starvation and famine.  The famines looming in Yemen, South Sudan, Somalia and Nigeria represent the largest humanitarian crisis since the UN’s creation. “Without collective and coordinated global efforts,” O’Brien said, “People will simply starve to death, and many more will suffer and die from disease.”

One of the components that complicates these particular emergencies is access to the areas in crisis. Without safe and unimpeded access for humanitarian aid workers, it’s difficult to get a picture of what’s going on in the affected areas, which adds another dimension to an already challenging response. In Northeast Nigeria, the threat of violence made it difficult for WFP’s food security analysts to visit vendors in local markets or speak with people in their homes – all part of their usual food security monitoring routine.

In order to continue gathering information needed to understand the situation in the affected areas, WFP used remote mobile data collection to get a picture of what was happening in the communities they could no longer speak to in person. With an overwhelming amount of responses, we turned to Tableau , who had already helped us create data visualizations for other countries which use mVAM, to help us visualize the results in a way that could be easily understood by everyone.

mVAM hears directly from people in affected communities in the northeast of Nigeria

mVAM hears directly from people in affected communities in the northeast of Nigeria

 

Our latest interactive data visualization of the food security situation in Northeast Nigeria is now online, and the story of how it came to be can be found on Tableau’s blog. Make sure to check out the free response section, where you can hear from 5,500 households on what should be done to improve the food security in their community.

 

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

How many pizzas does it take to build a chatbot?

Hackers are hungry Photo: WFP/Pia Facultad

Hackers are hungry
Photo: WFP/Pia Facultad

This week, government, business, academia and civil society leaders will gather at Davos to discuss solutions to the world’s biggest challenges – including how new technologies can be leveraged to solve some of the most serious problems we face. At mVAM, we continue to explore how some of these technologies could be used to help eliminate chronic hunger, malnutrition and food insecurity – most recently looking at how chatbots could help collect important information during a humanitarian response.

Last week, our collaborators at Nielsen – one of the early supporters of mVAM – organized a 24-hour hackathon at the Nielsen Tech Hub in New York City. As part of ongoing efforts through Nielsen Cares, the hackathon aimed to develop an open-source humanitarian chatbot that can collect real-time information about food security. This came at the right time for WFP – we’d developed and tested a prototype of the chatbot with InSTEDD, and Nielsen’s technology and development input helped bring in important new capabilities. Ultimately, our goal is to field-test a chatbot in Haiti in the next few months to help us track food security conditions as people recover from the impacts of Hurricane Matthew.

The event was open to the public. A diverse group of students, volunteer hackers, and Nielsen staff showed up to take on the challenge, despite the wintry weather. InSTEDD’s Director of Platform Engineering, Nicolás di Tada also participated.

Much more than a chatbot

What the hackers built is much more that a chatbot: it is a bona-fide chat-based data collection and reporting system. Rather than attempt to outdo each other (as is the case in most hackathons), the teams split up to build the different components of the system. The different teams, made up of perfect strangers, communicated during the hackathon through Slack. After 24 hours, most components were fully coded up, but there were still bugs with the orchestrator and the gateway that additional post-hackathon work will resolve.

The architecture of the system, as defined by Nielsen, includes:

  • a management interface that allows an analyst to set up a questionnaire, including and skip logic, and validation rules that prompt the user when they enter a wrong answer. The interface was built using the Angular 2 JavaScript framework;
  • a gateway that is able to interact with respondents through Facebook Messenger and potentially other chat applications. The Facebook gateway was built on top of the AWS Lambda service;
  • a natural language processing engine that analyzes text on the fly. It allows the chatbot to ‘interpret’ a user’s answers. For now, the NLP engine processes English language text, although the engine includes a translation service and, by default, translates all languages to English for more advanced NLP tasks. The engine was built using the AWS Lambda service and leverages IBM Watson’s AlchemyLanguage service for text processing.;
  • a set of ‘backend APIs’ that manage respondent and survey data, route respondents from each response to the next question, and provide data to user interfaces .  The APIs were built using the Django framework for python and deploys on the AWS Elastic Beanstalk service;
  • an ‘orchestration layer’ that maintains survey status and routes messages between the end user and the various backend services. The orchestration service is built on top of the AWS Lambda service; and
  • a “reporting and data visualization engine”. Data vizzes were built using Highcharts, a JavaScript-based application. This allows an analyst to instantly see the results of the chatbot surveys.

 

chatbot

 

Leveraging cloud services from the Amazon Web Services product catalog, the teams were able to build a scalable, cost effective platform that can be deployed quickly to multiple locations globally.

Remember the humans

We also received tips from a chatbot specialist, Alec Lazarescu from Chatbots Magazine. He encouraged us to ‘onboard’ users with an initial message that gives people a clear idea of what the chatbot is for. He told us to avoid ‘dead ends’ and allow users to speak to a human being in case they get stuck.

We’re very grateful to Nielsen for their support and to all the participants for their energy and creativity. The next steps involve WFP and InSTEDD accessing the code and work on ironing out the kinks. We expect challenges with the natural language processing in Haitian Creole, a language that is probably under-researched. Making the different parts of the chatbot work together seamlessly also appears to be an area we will still have to work on.  And, of course, the final test will be to see whether our target group – people living in Haiti – find the chatbot engaging.

Mind the Mode

IVR vs SMS in Zimbabwe

img_0046It’s all in the mode. Or is it? Would your response over the phone be different than when you had a person in front of you asking a question?  When answering a question over the phone would you respond differently if you were speaking to a friendly operator or a recorded voice or were replying by SMS? These are pretty key considerations when you are in the business of asking people questions from afar, and we get asked about it a lot.

So, welcome to our first edition of our ‘Mind the Mode‘ series. We have been conducting some mode experiments to find out whether people respond differently to different survey modes: live calls, IVR (Interactive Voice Response- that recorded voice asking you to press 1 for English or 2 for Spanish), SMS, or face-to-face. In this first edition, we look at IVR and SMS in Zimbabwe.

You might never have thought about it before, but it turns out that IVR and SMS compete. In the automated data collection space, there are two schools of thought: one favors data collection via SMS, the other IVR. The SMS advocates argue that a respondent can take the survey at the time of their choice and at their pace. Proponents of IVR point to the fact that voice recordings are easier to understand than a text message because you don’t need to be literate to take the survey.  It’s therefore the more ‘democratic’ tool.

At mVAM, we’ve mostly been using SMS but in Zimbabwe, we had the opportunity to compare these two modes. Food security data was collected by both SMS and IVR in August 2016. IVR responses were received from 1760 randomly selected respondents throughout Zimbabwe and 2450 SMS responses were received from a different set of random respondents stratified by province. Most responses came from Manicaland, Harare, Masvingo and Midlands for both types of surveys due to higher population densities, better network coverage and higher phone ownership in these areas.

Respondents were asked pretty similar questions in both surveys. Both surveys asked:

  • demographic and location questions such as the age and gender of the respondent, the gender of the head of household, and the province and district that they lived in
  • type of toilet in their house (to gain a rough estimate of socio-economic status);
  • daily manual labour wage and
  • whether they used any of the five coping strategies (a proxy for food insecurity
    1.  Rely on less preferred or less expensive food due to lack of food or money to buy food?
    2. Borrow food, or rely on help from a friend or relative due to lack of food or money to buy food?
    3. Reduce the number of meals eaten in a day due to lack of food or money to buy food?
    4. Limit portion sizes at mealtime due to lack of food or money to buy food?
    5. Restrict consumption by adults so children could eat

However, there were a few aspects where the surveys were slightly different. The SMS survey gave an incentive of USD 0.50 airtime credit to respondents who completed the survey whilst there was no incentive to do the IVR one. In the IVR survey, respondents could choose between English or Shona (most respondents chose to take it in Shona) whereas the SMS survey was only conducted in English.

So, what have we learned?

IVR and SMS reach different demographics.

Our IVR and SMS surveys reached different demographics. A higher proportion of IVR responses came from the worse-off households, i.e. those with no toilets or with pit latrines compared to SMS responses. Similarly, a higher proportion of households headed by women participated in the IVR survey than the SMS survey. WFP generally finds that households headed by women usually are more food insecure. So IVR surveys appear have greater reach to worse-off households. This may be because they do not require literacy or knowledge of Englishas with SMS surveys.

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Fig. 1a: IVR respondents by toilet type

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Fig. 1b: SMS respondents by toilet type

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Fig. 1c: IVR respondents by head of household sex

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Fig. 1d: SMS respondents by head of household sex

 

 

 

 

 

 

 

 

 

 

 

 

 

IVR surveys give higher food insecurity estimates than SMS. Spoiler: The reason is unclear.

In general, we found that IVR responses showed higher coping levels than SMS responses. The mean reduced coping strategy index (rCSI) is used as a proxy for food insecurity. A higher rCSI means people have to cope more in response to lack of food or money to buy food, meaning they are more food insecure. In Zimbabwe, mean rCSI captured through IVR (21.9) was higher than that captured through SMS (18.3) for the entire country. This difference in mean rCSI was consistent across cross-sections by the sex of the household head and by province (Figs. 2 and 3).

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Fig. 2: rCSI by sex of household head

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Fig. 3: Mean rCSI by province

However, when the data was analysed by toilet type, which was used as the proxy indicator for wealth, we saw a slightly different pattern. Flush toilets are considered as a proxy for the best-off, followed by Blair pit latrine (a ventilated pit latrine), then pit latrine and then no toilets. We also asked about composting toilets but too few households had them to make any meaningful comparisons. The mean rCSI was only significantly different for households with flush toilets and with pit latrines (in both cases IVR responses had higher rCSI). The mean rCSI results for the other two toilet categories (Blair pit latrine and no toilet) were not significantly different in the two types of surveys. Therefore, the commonly observed difference between IVR and SMS responses is not observed across all wealth groups (Fig. 4).

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Fig. 4: rCSI by toilet type

This suggests that the higher overall mean rCSI in IVR respondents compared to SMS respondents is not be coming from the fact that IVR reached more worse off households. However, we say this with a big caveat. Toilet type as we said above is a rough indicator and it might not be an accurate indication of which households are worse off.  It’s possible that we would have seen different results if we had used a different type of proxy indicator for wealth groups.

When we examine this a bit further and break down the rCSI into the individual coping strategies in Figure 5, we see that IVR respondents use more coping strategies more frequently than SMS respondents. This make sense because the individual coping strategies are what are used to calculate the rCSI and we already observed higher mean rCSI in IVR respondents.

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Fig. 5: Percentage of households using different coping strategies

However, we also noticed something else when looking at responses to each coping strategy.  There is a much higher variation in coping strategy use within SMS respondents compared to IVR respondents (see Figure 5). This suggests that respondents may be ‘straightlining’, i.e. providing the same response to every question. Straightlining suggests that people just don’t respond well to a recorded voice over the phone. While SMS is not good for literacy reasons, it does give the respondent more control over the pace of the survey. With SMS, respondents have as much time as they want to read (or re-read) the whole text and respond. With IVR, people have to go at the speed of the questions. They could get impatient waiting to hear all the answers to a question or they might not have enough time to understand the question. In both cases, they might just start pressing the same answer to get to the next question. Thus IVR might not give quality results.

Interestingly, we saw a similar pattern in Liberia during the Ebola epidemic. We used both SMS and IVR to collect information during the emergency. IVR results showed very high rCSI with limited variation. SMS data consistently produced lower (and more credible) rCSI estimates, and the variation in the data was greater (perhaps a sign of greater data quality).

Different demographics or differences in user experiences (i.e. straightlining) could be contributing to different food security estimates in IVR and SMS.

The upshot is that different survey tools lead to different results, and we need to understand these differences as the use of automated mobile data collection expands. We are not sure whether the different demographics among IVR and SMS respondents are the cause of higher food insecurity estimates for IVR or whether the different user experiences are in play, especially that IVR respondents may be straightlining their answers and not accurately reflecting their coping levels. We suspect that a bit of both might be in play.

Stay tuned for the next editions of our ‘Mind the Mode’ series as we continue to document our learning on the mode experiments

After the storm: using big data to track displacement in Haiti

Photo: Igor Rugwiza – UN/MINUSTAH


This week’s blog is a guest entry by Gabriela Alvarado, the WFP Regional IT Officer for Latin America and the Caribbean. In the aftermath of Hurricane Matthew, Gaby lead the IT Working Group in Haiti, which provided support to the humanitarian response through the provision of
ETC Connectivity Services. The team from the Regional Bureau worked with mVAM and Flowminder to supply valuable time-bound information to the operation.

 

Supporting Emergencies through Technology & Joint Efforts

It’s now been just over a month since Hurricane Matthew made landfall in Haiti, devastating the western side of the country. The hurricane has affected an estimated 2.1 million people, leaving 1.4 million in need of humanitarian assistance.

In the days following the hurricane, a rapid food security assessment was carried out to determine the impact of the hurricane on the food security of households and communities in the affected areas.  In the most-affected areas, the départements of Grande-Anse and Sud, people reported that crops and livestock, as well as agricultural and fishing equipment, were almost entirely destroyed.

 

Credit: WFP

Credit: WFP


We all know the challenges we face at WFP when looking to collect information, in order to determine what would be the best response under the circumstances on the ground.  In the aftermath of the hurricane, which had destroyed infrastructure, caused flooding, and temporarily knocked out telecommunications, gathering information from affected areas was especially difficult. So, WFP’s Information Technology team in the Regional Bureau for Latin America and the Caribbean reached out to Flowminder, a non-profit organization that uses big data analysis to answer questions that would be operationally relevant for government and aid agencies trying to respond to emergencies. Thanks to an existing agreement between WFP and Flowminder, WFP was able to quickly establish a working group and start data collection one day after the hurricane struck Haiti.

 

An aerial view of Jérémie following the passage of Hurricane Matthew (photo: Logan Abassi - UN/MINUSTAH)

An aerial view of Jérémie following the passage of Hurricane Matthew
(photo: Logan Abassi – UN/MINUSTAH)

Flowminder aggregates, integrates and analyses anonymous mobile operator data (call detail records), satellite and household survey data, which helps to estimate population displacements following a crisis. Displaced people are some of the most vulnerable following a hurricane, and knowing where people have gone helps to provide more effective assistance.

By 24 October 2016, Flowminder estimated that 260,500 people had been displaced within the Grande Anse, Sud, and Nippes départements. In Les Cayes, the major city in Sud, the population grew by an estimated 42% in the aftermath of Hurricane Matthew according to Flowminder analysis. In fact, Flowminder analyses suggest that many people moved toward cities, even Jérémie and Les Cayes, which were severely damaged by the hurricane.

 

Flowminder.org

Flowminder.org

So how exactly did Flowminder make these estimates with so many areas barely accessible? By analysing anonymized call detail records from Digicel, one of Haiti’s major cell phone network providers, and comparing where people placed calls before and after the hurricane, Flowminder was able provide an estimate of the number of displaced people. Flowminder uses algorithms that look at where the last “transaction” (phone call or sms) took place each day in order to identify the place where people were living before the hurricane and then subsequently moved afterwards. . It makes sense – the last few calls or texts you make at night are often from your home. While Flowminder does not get exact locations from the call data records, they can identify a general home location using the closest cell phone tower. After identifying the home location, Flowminder needs to determine how many people each phone represents. In poorer areas, not everyone may own a phone, or many people may not be able to charge and use their phones after a natural disaster like a hurricane. Flowminder uses formulas which takes these factors into account, and translates the number of phones into an estimate of the number of people who are displaced.

How will this further help?

With the information provided by Flowminder, WFP is able to estimate:

  • possible gaps in assistance in areas of the country which were not damaged by Hurricane Matthew, but which are experiencing an influx of people in need of food assistance following the hurricane;
  • use and community ‘acceptance’ of the use of mobile money (one aspect is the availability of the service, while the other aspect is if it is being used in that area);
  • the prevalence and spread of diseases (including Cholera, which continues to pose a risk in the aftermath of the hurricane).

It has been a very challenging yet incredible opportunity to see where and how technology can be used to further support an emergency response under difficult conditions and to ensure that WFP can reach the most vulnerable after a disaster.