Language and data-driven humanitarian action: 5 takeaways from a recent global discussion

In one word, what comes to mind when you think about language and data collection?

Challenging, expensive, necessary.

These are some of the answers we heard from attendees at a roundtable discussion TWB facilitated during the 2020 GeOnG Forum

Earlier this month, we were joined by panelists from IMPACT Initiatives, Mercy Corps, and the Internal Displacement Monitoring Centre (IDMC). We spoke about the role of language for data-driven humanitarian action and – crucially – how addressing language barriers can enable affected people to make their voices heard. The panelists shared their experiences and gave examples of why language is relevant at different stages of the data collection process. Here are five main conclusions from the discussion that are relevant for staff of any humanitarian organization that collects data:

1. Consult data on the languages people speak in the targeted area.

Some countries have sufficient data from government censuses to make an informed decision about the relevant language(s) targeted people speak and understand. However, this data isn’t always freely accessible, or easily verifiable. TWB is working with IMPACT Initiatives and other partners to make language data readily available to organizations that listen to and communicate with crisis-affected people. You can also collect this data during your survey to help fill the data gap. 

Image from IDMC.

2. Address language bias throughout the data collection process.

Language is usually only taken into account in the preparation phase when survey tools are translated into local language(s). This is often done hastily, without checking the translation quality. Mercy Corps highlighted the need to think carefully about language at each stage, from planning to data analysis and dissemination. This includes translating common questions and answers into as many languages as possible and with appropriate quality assurance procedures as a preparedness measure.

3. Support enumerators as needed and don’t make assumptions about their language skills.

Enumerators often take on many roles: administer a survey, but also act as interpreters, cultural mediators, program specialists and organizational representatives. Language support can take some of the burden off enumerators. Testing their literacy levels and comprehension of key terms can help screen enumerators and identify those that need additional training. Tools like glossaries can help them provide consistent and accurate translations of key terms in local languages and be confident that the person they are interviewing understands them.  

TWB research assistants interview Rohingya women in the Cox’s Bazar refugee camps, Bangladesh. Photo by Irene Scott/TWB (2018)

4. Identify ways to deal with unstructured data.

Asking open-ended questions or including “other” as an answer option can allow us to understand a situation in the words of affected people themselves. But this data can be particularly difficult to translate and understand. Regular debriefs with enumerators during data collection can help check the quality of any free text data. Translating open-format answers into a language the data analysis team understands as soon as possible after the data is collected was another lesson highlighted during the session. 

5. Use technology solutions appropriate to the context.

This could involve using a simple voice recorder as a quality assurance mechanism for multilingual surveys, as IDMC has piloted in northeast Nigeria. In other contexts, this might mean using Google Translate or other machine translation engines to translate information at speed. But this technology works best for major languages and machine translation needs to be approached with caution about anonymity and privacy. TWB and IMPACT Initiatives are developing machine translation and speech recognition tools adapted to humanitarian contexts and marginalized languages. Watch this space!  

Interested to find out more? Check out this infographic with more than 20 language tips for effective humanitarian data collection. Watch the video-recording of the session here. And find information about the other sessions of the GeOnG Forum here.

Written by Mia Marzotto, Senior Communication Officer for TWB

Countries with lower literacy levels need different COVID-19 communication strategies

People have a right to access the information they need during the COVID-19 pandemic. But the format and language of that information need to evolve as COVID-19 spreads to nations with lower literacy rates and more vulnerable groups of people.

covid-19 literacy rates communication strategies

The information should be easy for people to find, understand and use. It’s unwise to assume that written formats are always the most efficient way to convey information. As the disease rapidly expands into countries with lower rates of literacy, organizations involved in the response need to shift focus from written information to developing significantly more pictorial, audio, and video content. 

That is the best way to ensure that older people, women, and other vulnerable people in those countries have the best chance of understanding lifesaving information. 

It’s also a necessary adjustment where infection control limits in-person community engagement. Social media, SMS services, call centers, television, and radio will be essential communication channels. Formats need to diversify accordingly if the message is to get across.

Literacy dynamics are rapidly changing

COVID-19 is now rapidly spreading in countries with lower literacy rates. The average literacy rate in countries with confirmed COVID-19 cases on February 19 was 94%. One month later it was 89%.

The highest rates of change in new COVID-19 cases being recorded are predominantly in countries with lower literacy rates. Between March 16 and 22, the 15 countries with the highest percentage change of new COVID-19 cases had an average literacy rate of 85%. These include countries like Cambodia, Cameroon, Côte d’Ivoire, DRC, Nigeria, Tanzania, and Togo. All these countries saw increases in confirmed cases of at least 900% during this seven-day period.

Women’s literacy rates are often lower than men’s

In countries where UNESCO measures literacy, average literacy rates are 6% higher for men than for women. For example in Yemen, 73% of men and only 35% of women above the age of 15 can read or write a basic sentence about their life, a difference of 38%. The gender difference is also stark in Pakistan (25%), DRC (23%) and Mali (20%). 

The map below highlights the gender difference in adult literacy in individual countries. Orange shading indicates countries where male literacy rates are higher than female literacy rates. Blue shading indicates the few countries where female literacy rates are higher than male literacy rates. 

Older people often have lower literacy rates than people under 65 

In many countries, older people are less likely to be able to read than younger adults. This limits their ability to access written information on COVID-19.

The average elderly literacy rate in countries UNESCO reports literacy data for, is 65%. UNESCO defines elderly people as those aged 65 or older. In countries with documented literacy rates from the same year, people aged between 15 and 64 have an average literacy rate 19% higher than people 65 years or older. The difference is greatest in Libya (63%), Timor-Leste (53%), Cabo Verde (50%), and Iran (49%).

Use data to design more inclusive communication strategies

To design effective COVID-19 communication strategies, responders need reliable data about language and literacy. As part of our COVID-19 response, we are making the necessary data openly available.

This is part of a Translators without Borders initiative to help make targeted information strategies more data driven. Language and literacy maps and datasets exist for DRC, Guatemala, Malawi, Mozambique, Nigeria, Pakistan, the Philippines, Ukraine, and Zambia. 

Along with these existing maps and the interactive global literacy map above, we are also scaling up our efforts to release more subnational language and literacy data for countries affected by the COVID-19 pandemic. This week we released national and sub-national data for Thailand. We will release more datasets and data visualizations over the next few weeks and months, so stay tuned to our COVID-19 webpage or the Humanitarian Data Exchange for updates.

We derived most of those datasets from historical census data, typically available down to the Admin 2 (district or county) level. Such data is most useful when it is analyzed alongside up-to-date information on language and communication needs. To help us with our ongoing language data initiative, we urge organizations to include four simple language questions in needs assessments and surveys related to COVID-19.

Make content available in multiple formats

Organizations responding to the pandemic should use improved data to develop communication strategies that are geared to the needs of the target population. Preparedness is a critical component of this. Organizations should develop content in as many formats as possible, recognizing that pictorial, audio, and video content is easier to access and absorb for many people. Additionally, older people often benefit from content that is easier to read. This requires incorporating design considerations such as larger fonts and good contrast. Plain-language principles also offer a useful model for creating clear and concise written and verbal content. The WHO proposes several key principles for improving understanding of health content.

In the rapidly evolving context of the COVID-19 response, organizations should complement written information with other formats. This is vital to ensure information is both believed and understood. We need to do this early to ensure people living in places with lower literacy levels don’t receive information too late to make a difference. 

Written by Eric DeLuca, Monitoring, Evaluation, and Learning Manager, Translators without Borders

The project is funded by the H2H Fund, a funding mechanism for H2H Network members. The fund is a rapid funding vehicle for network members responding to humanitarian crises.

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