COVID-19 has upended the lives of people around the world. With the number of COVID -19 positive cases increasing daily, Malawi is already beginning experience the adverse economic effects of the regional and international travel and trade restrictions on its economy, which will likely lead to localized food insecurity primarily due to access constraints and limited labour opportunities, particularly in urban and peri-urban areas.
While the country has enjoyed good crop production this year, realizing an 11.5% increase in maize compared to last season , COVID-19 is still likely to adversely impact food security in the coming months. It is in consideration of this that WFP has put in place remote household monitoring of food security to track changes in food security as influenced by COVID-19.
The months of May/June 2020 marked the first round of remote household survey data collection in response to COVID-19 monitoring and seasonal trends in food security. The survey for this report was conducted using live telephone calls from the 16th of May to the 14th of June 2020, collecting information from some 2,520 households in all districts and major cities.
Participating households were randomly selected from a national database of mobile subscribers.
The sample size was calculated based on the Integrated Food Security Phase Classification Technical Manual (Version 3.0) guideline of having at least 150 samples per strata. WFP increased the sample size per strata to 180 in order to include a safety buffer in case the call centre could not achieve the full sample in 30 days. Additional details on this methodology are available in Annex 1. The three regions of the country (ADM1) and the four cities (Mzuzu, Lilongwe, Blantyre and Zomba) were divided into 14 strata, with each stratum having an equal sample size of 180 households. Integrated stratification was conducted whereby each city (Lilongwe,
Blantyre, Mzuzu, and Zomba) was a stratum on its own to track the effects of COVID-19 in each city separately, as cities are likely to be most affected and the impact/severity of COVID-19 might differ from city to city.
Districts were stratified by clustering those with similar livelihood activities together while maintaining a maximum of four districts per stratum. Participants were randomly selected from a national database of mobile subscribers. Respondents opted in to the mobile call survey and were asked questions on socio-demographics, food consumption, coping behaviour, market access, health condition, and assistance received.
As of 2016, 54 percent of households in Malawi had a mobile phone (MDHS 2015-16).
As such, it is acknowledged that householdlevel mobile surveys contain a certain level of inherent bias. Due to biases, an attempt is made to capture patterns and trends. This first round of data collection provides the basis of a monitoring system that will track month-to-month changes. In terms of weights, the results are computed by applying a population weight at each respective district level (Admin 1) in order to debias the data.