AKADEMIYA2063-Ecowas Regional Learning event: Effects of COVID-19 on Staple Food Systems Dynamics in West Africa

1. Effects of Covid-19 on Staple
Food Systems Dynamics in
West Africa
Mbaye Yade, Maurice Taondyandé (ReSAKSS WA)
Anatole Goundan and Sunday Odjo (AKADEMIYA2063)

2. Outline
2
 Introduction
 Expected agricultural commodity prices behavior in
COVID-19 time
 Data
 Methods
 Results of case studies: Benin, Burkina Faso, Mali,
Nigeria, and Senegal
 Conclusions & recommendations

3. Introduction
3
• First cases of COVID 19 confirmed in Western Africa in March 2020
• Various measures to contain the spread of the very c contagious
pandemic: Bans on public gatherings; Market bans and other
restrictions; Schools, restaurants, and hotels closed; Travel bans;
curfews; etc.
• Expected market disruptions and revenue losses for most
vulnerable groups
• The objective of this work is to answer the question “Did
commodity price dynamics change due to COVID-19 pandemic and
related interventions? And if yes, how did they change and what
needs to be taken into account to mitigate the effects on food and
nutrition security”

4. Expected food price behavior due to covid-19
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• In surplus areas, one would expect that prices fall below
expected levels and recover once the confinement is lifted.
• In deficit areas, prices are expected to rise above predicted
level during confinement.
• However, the extent to which prices can increase in deficit
urban areas will depend, among others, on their access to
surplus areas and on changes in demand of particular groups
like schools, universities, restaurants and hotels
• Therefore, market connection and typology, and policy
options may affect the price behavior.

5. Data
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• Monthly data from the
national Market Information
Systems are used in this analysis
• 5 countries involved and 3
local cereals and 1 processed
item considered
Ecowas countries
Country Commodity Data coverage
Benin Maize Jan 2010/Aug 2020
Burkina Faso Maize Jan 2010/Jun 2020
Mali Maize Jan 2010/Oct 2020
Mali Rice Jan 2014/Oct 2020
Nigeria Gari May 2012/Aug 2020
Senegal Millet Jan 2010/Jun 2020

6. Methods
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• Seasonal ARIMA models to calculate the price trend in the
absence of a shock for forecast purpose.
• Data available up to December 2019 used to select and
identify the models with the best power for forecast.
• Forecasting prices for the year 2020.
• Analysis of the gap between the predicted and observed
prices  possible effect of market disruption due mainly to
the COVID-19 pandemic and the various related restrictive
policies taken by governments.

7. Results of case studies:
Benin, Burkina Faso, Mali,
Nigeria, and Senegal
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8. Millet price behavior in Senegal
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0%
20%
40%
60%
80%
100%
120%
Mar-20 Apr-20 May-20 Jun-20
Higher than predictions Lower than predictions
0%
20%
40%
60%
80%
100%
120%
Mar-20 Apr-20 May-20 Jun-20
Price trends in deficit areas
Less than -5% Between -5 and 5% Between 5 and 15% Greater than 15%
Proportion of retail markets with
higher-than-predicted millet prices

9. In April and May, proportion of markets with actual price higher than predicted
substantially higher in deficit areas than surplus areas
In surplus areas, the proportion steadily increased from 25 in March to 62 % in June
reflecting the price recovery that followed the lifting of the restrictions
The same proportion was more stable in deficit areas, where the proportion
declined, unlike the trend in surplus areas after the confinement.
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Maize price behavior in Burkina Faso
0%
20%
40%
60%
80%
100%
April 2020 May 2020 June 2020
Proportion
of
markets
Price trends surplus areas
Smaller than predictions Markets with prices
0%
20%
40%
60%
80%
100%
April 2020 May 2020 June 2020
Proportion
of
markets
Price trends deficit areas
Smaller than predictions Markets with prices

10. 10
Maize price behavior in Benin
Most markets recorded lower actual prices than predicted from Feb to Apr
Proportion of markets with actual prices higher than predicted increased
steadily from May, just after the lockdown.
The proportion of markets that recorded high positive price gap (>15%) has
increased quickly after the confinement, specially in July and August.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20 Aug-20
Higher than predictions Lower than predictions
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20 Aug-20
Greater than 15%
Between 5 and 15%
Between -5 and +5%
Lower than -5%
Proportion of markets with actual prices
higher than predicted
Distribution of price gap over time

11. 11
Maize price behavior in Mali
In production areas, actual prices were equal to predicted in Jan and Feb; the
proportion of markets with actual prices higher than predicted rose in March
and April (50%), decreased in May-Jun-Jul, before rising until Oct (100%)
Similar trend in deficit areas but with lower proportion of markets with actual
prices higher in October
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Jan-20 Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20 Aug-20 Sep-20 Oct-20
Price trends in collection markets located
in production areas
Higher than predictions Equal to predictions Lower than predictions
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Price trends in consumption markets
located in deficit areas
Higher than predictions Equal to predictions Lower than predictions

12. • The proportion of markets with higher observed prices than expected
remained stable before and during the lockdown period, even when the
number of markets with high price gaps( >15%) was relatively high (>=50%)
• From July, the proportion of markets with higher observed prices than
expected rose drastically as well as the number of markets with high
price gaps (>15%)
• Trends from August certainly, due to the political situation
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Rice price behavior in Mali
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Jan-20 Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20 Aug-20 Sep-20 Oct-20
Higher than predictions Lower than predictions
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Jan-20 Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20 Aug-20 Sep-20 Oct-20
Lower than -5% Between -5 and +5% Greater than 15%

13. 13
Gari price behavior in Nigeria
• Actual gari prices > the predicted prices, even before the pandemic.
• The proportion of markets that recorded higher prices than predicted prices
has increased during and after the confinement period.
• The proportion of markets that recorded high positive price gap (>15%) has
increased quickly during and after the confinement period.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20 Aug-20
Higher than predictions Lower than predictions
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20 Aug-20
Lower than -5% Between -5 and +5% Between 5 and 15% Greater than 15%
Price trends

14.  In general, results are heterogeneous across markets
and commodities and Price behavior is different
between markets in deficit areas compared to those in
surplus areas.
 In the region, prices were stable or declined during the
confinement period except in production areas for
maize in Burkina Faso and gari in Nigeria
 However, almost all markets showed a rise in prices over
the post-confinement period except in Senegal and
Burkina Faso, in particular in deficit areas
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Conclusion & recommendations

15. Conclusion & recommendations
 As expected, governments undertook measures to control the spread of
the COVID-19 pandemic, affecting the movement of goods and impacting
on the cost of food consumed by vulnerable segments of the population
  upward trend in prices, erosion of purchasing power and pressure to
adjust food staples demand and consumption often towards cheaper and
less nutritive food items
 To better manage future shocks, it is critical to ensure minimal disruption
in the flow of staple commodities from farms to market areas and trade
flows with neighboring countries, among others, by better targeting areas
for which to impose restrictions
 Also, food aid to vulnerable groups should be done in a way, minimizing
the negative effects on market, for example using vouchers wherever
possible instead of moving goods with huge logistical challenges
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16. Thank You
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