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Effects of Covid-19 on Staple
Food Systems Dynamics in
West Africa
Mbaye Yade, Maurice Taondyandé (ReSAKSS WA)
Anatole Gou...
Outline
2
 Introduction
 Expected agricultural commodity prices behavior in
COVID-19 time
 Data
 Methods
 Results of ...
Introduction
3
• First cases of COVID 19 confirmed in Western Africa in March 2020
• Various measures to contain the sprea...
Expected food price behavior due to covid-19
4
• In surplus areas, one would expect that prices fall below
expected levels...
Data
5
• Monthly data from the
national Market Information
Systems are used in this analysis
• 5 countries involved and 3
...
Methods
6
• Seasonal ARIMA models to calculate the price trend in the
absence of a shock for forecast purpose.
• Data avai...
Results of case studies:
Benin, Burkina Faso, Mali,
Nigeria, and Senegal
7
Millet price behavior in Senegal
8
0%
20%
40%
60%
80%
100%
120%
Mar-20 Apr-20 May-20 Jun-20
Higher than predictions Lower ...
In April and May, proportion of markets with actual price higher than predicted
substantially higher in deficit areas than...
10
Maize price behavior in Benin
Most markets recorded lower actual prices than predicted from Feb to Apr
Proportion of ma...
11
Maize price behavior in Mali
In production areas, actual prices were equal to predicted in Jan and Feb; the
proportion ...
• The proportion of markets with higher observed prices than expected
remained stable before and during the lockdown perio...
13
Gari price behavior in Nigeria
• Actual gari prices > the predicted prices, even before the pandemic.
• The proportion ...
 In general, results are heterogeneous across markets
and commodities and Price behavior is different
between markets in ...
Conclusion & recommendations
 As expected, governments undertook measures to control the spread of
the COVID-19 pandemic,...
Thank You
16
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AKADEMIYA2063-Ecowas Regional Learning event: Effects of COVID-19 on Staple Food Systems Dynamics in West Africa

A presentation based on analyses by ReSAKSS West Africa and AKADEMIYA2063 research

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

  1. 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. 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. 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. 4. Expected food price behavior due to covid-19 4 • 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. 5. Data 5 • 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. 6. Methods 6 • 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. 7. Results of case studies: Benin, Burkina Faso, Mali, Nigeria, and Senegal 7
  8. 8. Millet price behavior in Senegal 8 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. 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. 9 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. 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. 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. 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 12 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. 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. 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 14 Conclusion & recommendations
  15. 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 15
  16. 16. Thank You 16

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