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

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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:

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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.

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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.

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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’!

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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.

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

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.

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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.