How MSNA language data can improve communication with crisis-affected people

08 October 2021

This post was written by Mia Marzotto and Ellie Kemp of Translators without Borders/CLEAR Global. Translators Without Borders is a cornerstone of CLEAR Global, an initiative launched in 2021 to expand our ambition to help people get vital information, and be heard, whatever language they speak.

 

Until now, humanitarians have not had access to up-to-date, easily verifiable and usable data about the languages of the people they aim to serve. But language data collected through the 2021 Multi-Sector Needs Assessments (MSNAs) conducted by REACH is about to improve how we communicate with people in humanitarian crises. 

In at least eight countries, a simple set of questions is driving this change. For the first time, MSNAs in Afghanistan, Burkina Faso, Central African Republic, Democratic Republic of Congo, Libya, Mali, Nigeria, and Somalia have included at least the primary question “What is the main language your household speaks at home?” Integrating this data in response plans and program decisions is a prerequisite for improving the effectiveness, reach, and accountability of humanitarian action across these countries.

Language data is as important as data on age and gender for meeting people’s needs

Without data, humanitarians often assume that communicating in official languages or what could be termed the “nearest colonial language” is good enough to engage with all crisis-affected people. It is not. 

A growing volume of evidence shows that if we stick to the official or dominant language in many countries, we won’t reach large sections of the population – especially women, older people, people with disabilities, and marginalized ethnic groups. Inclusive and accountable humanitarian action requires two-way communication strategies using the languages and formats that people understand. With language data, responders can plan for and resource that communication, and monitor the impact language has on program outcomes for participants. 

Language data helps track access and impact

Language data can help responders spot when language barriers are excluding intended program participants or reducing the impact of services. Using indicators and targets disaggregated by language to track program outcomes can show how people’s communication needs are being met. Donors can support this by asking funding applicants about their language planning, as FCDO (Foreign, Commonwealth & Development Office) and BHA (USAID Bureau for Humanitarian Assistance) do with their recently updated funding and reporting guidelines.

Language data informs communication and staffing decisions

Disaggregating needs assessment data by language can fill critical information gaps on the most relevant languages and communication needs across sectors. For example, when REACH collected language data in the 2019 MSNA, for the first time responders in northeast Nigeria had an evidence base for adapting communication to the needs of different groups in different areas. The Mine Action Sub-Sector among others was able to translate education materials on explosive ordnance into nine languages spoken by its audience of over 600,000 people. The International Organization for Migration further used this new data, complementing site-level language information from its own Displacement Tracking Matrix, to inform the set-up of multilingual audio community feedback mechanisms.

Language data can also help responders make local language testing part of recruitment processes and provide training and guidance to support bilingual staff in translation and interpreting roles. For example, training on accountability and language as part of the Common Service for Community Engagement and Accountability in Cox’s Bazar, Bangladesh, has allowed responders to communicate more effectively with Rohingya refugees and better listen to their needs. The training content based on language insights gathered through large-scale assessments and focused research have helped program teams enhance their Rohingya language skills as well as their confidence in communicating about sensitive issues.

A case in point: MSNA language data can support improved communication in Somalia

Preliminary analysis of the 2021 Joint MSNA data in Somalia highlights that 58% of households surveyed speak Somali as their main language; 20% speak Maay, and 18% speak Banaadir. Somali Sign Language is reported as the main language used at home for 256 households (3%). When asked about barriers in accessing humanitarian aid, respondents flagged a lack of information as one of the most common problems. The largest group who reported experiencing denial of or unequal access to humanitarian assistance speak Maay as their main language (40.45%). Almost all Somali Sign Language users interviewed reported not feeling able to influence site-level decisions (92%). 

To communicate effectively, humanitarians working in Somalia need to cater for the languages and communication needs of affected people in the area where they are working. Budgeting for increased language support across the response can ensure language barriers don’t stand in the way of people accessing the services and information they need.

Next up: getting language data into HNOs and HRPs

A careful analysis of language data should underpin humanitarian needs overviews (HNOs) and response plans (HRPs) in all countries for which it is available. That can ensure plans are informed by insights from crisis-affected people themselves on which groups might face language barriers to access services and information, and how to better target programs to leave no one behind. 

Over the next few months Translators without Borders will continue work with partners to incorporate language data analysis in strategic and planning documents in the eight countries that have collected it. But the work does not stop here. Making language data a standard component of response planning everywhere - alongside age, gender, and disability data - is key to meeting shared commitments to effective and accountable humanitarian action. Routinely asking the right language questions in MSNAs and other assessments is the first step to achieving that.

Further resources to close the language data gap

If this year’s MSNA didn’t include language questions for the context you’re interested in, you can advocate for their inclusion in the next assessment cycle. In the meantime, resources are available to provide general information on language and to help you collect language data through other assessments.

You can find a series of static and dynamic language maps and datasets on Humanitarian Data Exchange and Translators without Borders’ Language Data Initiative portal. These resources are based on existing data sources, mainly censuses and other government data. We have curated, cleaned, and reformatted the data to be more accessible for humanitarian purposes.

You can also download our recommended language questions, as well as language lists by country for pre-populated fields, from Translators without Borders’s Language Data Initiative portal. These lists are available in multiple languages and formatted to conform with the XLSForms standard, so compatible with OpenDataKit (ODK) tools, including Kobo Toolbox, ONA and SurveyCTO.

For more on managing language in data collection, see Translators without Borders and CartONG's guidance here.

 

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