Using crowd-sourced data for real-time monitoring of food prices during the COVID-19 pandemic: Insights from a pilot project in northern Nigeria

Abstract

The COVID-19 pandemic and related lockdown measures have disrupted food supply chains globally and caused threats to food security, especially in Sub-Saharan Africa. Yet detailed, localized, and timely data on food security threats are rarely available to guide targeted policy interventions. Based on real-time evidence from a pilot project in northern Nigeria, where food insecurity is severe, we illustrate how a digital crowdsourcing platform can provide validated real-time, high frequency, and spatially rich information on the evolution of commodity prices. Daily georeferenced price data of major food commodities were submitted by active volunteer citizens through a mobile phone data collection app and filtered through a stepwise quality control algorithm. We analyzed a total of 23,961 spatially distributed datapoints, contributed by 236 active volunteers, on the price of four commodities (local rice, Thailand rice, white maize and yellow maize) to assess the magnitude of price change over eleven weeks (week 20 to week 30) during and after the first COVID-related lockdown (year 2020), relative to the preceding year (2019). Results show that the retail price of maize (yellow and white) and rice (local and Thailand rice) increased on average by respectively 26% and 44% during this COVID-related period, compared to prices reported in the same period in 2019. GPS-tracked data showed that mobility and market access of active volunteers were reduced, travel-distance to market being 54% less in 2020 compared to 2019, and illustrates potential limitations on consumers who often seek lower pricing by accessing broader markets. Combining the price data with a spatial richness index grid derived from UN-FAO, this study shows the viability of a contactless data crowdsourcing system, backed by an automated quality control process, as a decision-support tool for rapid assessment of price-induced food insecurity risks, and to target interventions (e.g. COVID relief support) at the right time and location(s).

Keywords: COVID-19; Commodity; Crowdsourcing; Food prices; Price increase; Real-time.

Graphical abstract

Fig. 1

Map of focal area covered by the FPCA (Food Price Crowdsourcing Africa) pilot project, in Katsina and Kano States (Nigeria), with volunteer locations at the time of registration in the mobile app, and population estimates within each local government area (LGA).

Fig. 2

Illustration of the web dashboard with interactive visualization of the data in [near]real time, here presented for retail prices of local rice (in ₦/Kg) in Kano state from September 2018 to July 2020. COVID-related measures and post-lockdown measures implemented by Kano state are indicated for the corresponding weeks in 2019. Source: FPCA web dashboard (https://datam.jrc.ec.europa.eu/datam/mashup/FP_NGA/index.html) and the Kano State Government (https://www.kanostate.gov.ng/).

Fig. 3

Average daily price of major grain commodities during weeks of COVID-related period in 2020, and preceding year (2019), as submitted by volunteers located across Kano and Katsina States in Nigeria.

Fig. 4

Price index illustrating the relative change relative to last year's price for the COVID-19 period in Kano and Katsina, based on FPCA 2019-20 data in Nigeria, contrasted with the price data reported by the National Bureau of Statistics (NBS) of Nigeria. For comparison, the light grey line (NBS, 2017-19) illustrates the average price index of change for the past two years (i.e. 2019 -2018 and 2018–2017), based on the NBS data. For the period 2019–20, the index for rice was relatively stable, however, it follows an upward trajectory for maize presumably due to government's restriction of maize importation which resulted in steep increase of maize prices.

Fig. 5

Spatial distribution of levels of crowdsourced prices of maize and rice in 2019 (top) and 2020 (bottom), relative to mean richness index (MRI) in Kano and Katsina, Nigeria. Colored dots indicate crowd-sourced local commodity prices, ranging from blue (low prices) to red (high prices). The color of each local government area (LGA) indicates the relative level of richness, going from poor (light red) to richer (light blue) than average. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6

a. Daily average of distance travelled by volunteers to acquire price data, based on actual GPS location data, during weeks of COVID-related restriction in 2020 and preceding year (2019). b. Daily share of data submissions for large packages (25 kg or more) of grains during weeks of COVID-related restriction in 2020 and preceding year (2019).