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Malawi Policy Learning Event - Combined Summary Presentation - April 28, 2021

This presentation was delivered by Dr. Greenwell Matchaya, ReSAKSS-SA coordinator

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Malawi Policy Learning Event - Combined Summary Presentation - April 28, 2021

  1. 1. Effects of Covid 19 on Agricultural Production, Food Markets, Growth and Poverty in Malawi Presented at the Malawi Policy Learning Event by Greenwell Matchaya, PhD g.matchaya@cgiar.org Virtual - April 28, 2021
  2. 2. PRESENTATION STRUCTURE • Introduction • COVID-19-related market disruptions and maize price behavior- Malawi • Spatial community vulnerability to Covid-19 impacts in Malawi • Food production systems disruptions under Covid-19 • Macroeconomic effects of global trade disruptions • Conclusions and recommendations
  3. 3. PRICE EFFECTS OF COVID-19 IN FOOD SURPLUS AND DEFICIT AREAS OF MALAWI. • COVID-19 introduced in Southern Africa- end Feb 2020 • Control measures to curb the spread of the pandemic : restrictions of the movement of people, Market bans, border crossing restrictions, curfews, etc. • Inadvertently led to market disruptions • We focus on maize markets in Malawi, and the SADC
  4. 4. Key Messages • We focus on local staples and community-level markets which are often overlooked in international data collection and analysis efforts. • In most of the countries examined, price effects of the Covid crisis propagated very quickly across space. • The magnitude of price deviations also increased very quickly. • In some countries, the shock caused prices to rise in deficit areas and decline in surplus areas as transport restrictions took hold. • But in countries that export staples regionally, trade restrictions overrode those effects and prices fell both in surplus and deficit areas. 4
  5. 5. Key Messages (continued) • In responses to future shocks, care should be taken to minimize disruptions in local market operations. • Effects of local market disruptions on incomes, access to food, etc. can be devastating. • Measures should be coordinated across countries to respond to crises while minimizing interference in cross-border trade. 5
  6. 6. METHODOLOGY • To understand the price impacts of COVID-19 • We use data collected by WFP in Malawi • Employ Seasonal Auto Regressive Integrated Moving Average (SARIMA) modeling • to calculate model parameters with 2015-2019 sample • Then use the model parameters to predict prices for the first half of 2020 • We also cross-check results with Difference-in-Differences methodology
  7. 7. • Prices showed a general downward trend from end of February in both deficit and surplus areas in Malawi • Although the trends are not homogenous across cities, there was a general decline after February COVID-19-related market disruptions and maize price behavior-Malawi 0 50 100 150 200 250 300 350 400 Jan-19 Fév-19 Mar-19 Avr-19 Mai-19 Juin-19 Juil-19 Aou-19 Sep-19 Oct-19 Nov-19 Déc-19 Jan-20 Fév-20 Mar-20 Avr-20 Mai-20 Malawi Kwacha/kg Maize prices Blantyre Lilongwe Mzimba
  8. 8. RAPID INCREASE IN THE SHARE OF MARKETS WITH LOWER-THAN-PREDICTED PRICES FOR MALAWI 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Jan Feb Mar Apr May Rural primary markets Rising prices Declining prices 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Jan Feb Mar Apr May Urban markets Rising prices Declining prices COVID-19-related market disruptions and maize price behavior-Malawi
  9. 9. SUMMARY AND RECOMMENDATIONS  COVID-19 pandemic has led to governments measures to control the spread of the COVID-19 pandemic  Some of these measures have impeded arbitrage between markets and further led to rising supplies relative to demand  The reduced demand, together with an increase in supplies from the harvest season have led to rapidly falling prices in local markets
  10. 10. SUMMARY AND RECOMMENDATIONS (CONT’D)  The potential negative impact from the observed decline in prices shows the critical importance of trans-border trade for smallholder farmers and small businesses. • To avoid enormous negative effects on demand, future restrictions and interventions should be sequenced such that the impact on domestic markets and cross-border operations are minimized
  11. 11. • In the world over, disease and disaster mitigation have gained traction because of the COVID-19 Pandemic. • This has heightened the urgency for public policy intervention. • COVID-19 pandemic has spread worldwide and affected individuals, communities, & national and global economy • Effective and efficient response to this pandemic requires resources be directed to the most vulnerable sections and areas • To be effective and efficient such response must be informed by data and evidence Assessing Community Vulnerability to Covid-19 in Malawi
  12. 12. Assessing CommunityVulnerability  Due to limited resources - prioritize the most vulnerable communities where the effects of the pandemic are likely to be proportionately more devastating  Vulnerability is defined as the propensity of an area to be exposed to the spread of Covid-19 combined with limited capacity to control the it and care for infected people, as well as high exposure to negative food security impacts  Vulnerability is not geographically uniform  This study focused on the differentiated spatial vulnerability using an overlay of indicators: - • Food and nutrition security, • Disease burden, • Health infrastructure and outcomes, • Population density
  13. 13. Key Messages • The impacts of crises are especially severe in areas where chronic vulnerability is high. • Understanding patterns of vulnerability is thus of major importance to guide strategies to prepare for and respond to crises. • Our goal is to identify areas which, if affected by Covid, would have the least capacity to absorb shocks and would face the most serious consequences. 14
  14. 14. Key Messages (continued) • We construct an indicator to measure the level of vulnerability to serious food security consequences of Covid-19. • Three components: Susceptibility to the spread of the disease, inability to care for infected people, and existing food and nutrition insecurity. • We identify areas of particular concern where governments should focus crisis monitoring and mitigation efforts. 15
  15. 15. • We use data from various sources in Malawi including DHS and LSMS • We construct 9 sub-indices of vulnerability using data on prevalence of blood pressure, diabetes, stunting, access to hospitals, food expenditure, and population density • We then combine those sub-indices to construct an overall community level vulnerability index. Methodology
  16. 16. • An overwhelming 35 % of districts are much more vulnerable (Dedza, Mchinji, Nkhotakota, Ntcheu, Salima, Neno, Zomba, Blantyre, Chikwawa, Thyolo and Nsanje). • Further, another overwhelming 39% of districts are also much vulnerable (Mwanza, Mulanje, Chiradzulu, Phalombe, Machinga, Balaka, Mangochi, Lilongwe, Dowa, Ntchisi, Mzimba and Nkhatabay) Nkhatabay).. • The factors contributing to spatial variation include, density, diets, food security, health access services. Key Findings
  17. 17. • The vulnerability indices revealed widespread inequalities across the three administrative regions of Malawi • The most vulnerable locations are likely to be found in Chitipa, Thyolo, Blantyre Rural, Chikwawa, Dedza, Mchinji, Nsanje, Neno, Nkhota Kota, Ntcheu, Salima, Zomba Rural, Balaka, Lilongwe Rural, Mangochi and Mulanje. • These areas need to be prioritised in the design of intervention efforts to prevent and mitigate the effects of the pandemic and future crises. Recommendations
  18. 18. • Malawi Government and her other stakeholders; • Information useful to guide and inform their interventions spatially in the short term • There is a need to reduce inequalities in the longer term, beyond the current COVID-19 pandemic • Preparation for future pandemics and other health shocks that are inevitable. • Implement policies that reduce vulnerability to such shocks in the future. Recommendations
  19. 19. Production Systems Disruption • The challenge of COVID-19 on food production systems is not only the likely extent and complexity of the disruptions but also the difficulty to identify and track them in real time. • The propagation of the disease can be tracked through testing and tracing, while it is impossible, even in normal times, to have accurate information on cropping activities. • The lack of information about growing conditions can be overcome by using today’s digital technologies e.g., remote sensing data and machine learning techniques. • The many weaknesses hampering the access to good quality agricultural statistics can be overcome using the same digital technologies. 21
  20. 20. 2. Remotely Sensed Data • Remotely sensed data through sat. images provide a wealth of information about features on earth. • Several advantages of using multispectral satellite images • Vegetation, including crops, have a specific way to respond to light Figure 1. (left) False RGB color scene of the North of Senegal with agricultural lands, bare soil, and water. (Right) The same scene after an unsupervised classification with seven clusters using K-means and Landsat 8 spectral bands. Key messages 1. Features on earth react differently to the electromagnetic spectrum. 2. Features on earth can be identified from satellite images based on their reflectance signature. 22
  21. 21. Key Messages • Access to adequate data for development planning and crisis response is always a challenge, even more so during crises. • It is important to invest in ways to access data faster and more efficiently to guide crisis interventions. • Remote sensing data and machine learning techniques offer novel ways to access and learn from data to improve the quality of interventions. • We track crop production systems as they evolve during growing seasons and forecast harvests and yields. • Our methodology allows us to track developments in near-real time to inform crisis monitoring and management. 23
  22. 22. Methodology • We utilize remote sensing and Machine Learning techniques to understand production changes over time. • We use these techniques to extract satellite data and train computer machines to explore the complex relationship between inputs and their corresponding outputs. 24
  23. 23. Results (Cont’d) • The map shows the ratio between the 2020 (predicted) and 2017 maize production quantities in Malawi. • When the ratio is below unity, the 2020 production is expected to be less than the 2017 production. • The central and southern areas are expected to have more areas with a decline in production compared to the north. 25
  24. 24. 6. Conclusions • The COVID-19 suggests the need to build a more resilient food system and to increase countries’ level of preparedness and capacity to respond to shocks. • Such requires the availability of quality data and analytics to support policymaking and more efficient interventions. • Emerging technologies – remote sensing and machine learning – can help to bring those efficiencies in decision-making processes. 26
  25. 25. 6. Conclusions • Capacity Building in emerging technologies must be institutionalized; The use of such technologies into the agricultural sector needs to be incentivized. • A robust and efficient ICT infrastructure must be built and maintained to facilitate data gathering on the ground and analytics. > Internet connectivity in rural areas, cloud storage and computing. • To fully take advantage of emerging technologies for analytics, metadata are as important as primary data for contextualization. > Collecting crop type data, farm GPS coordinates, seeds and fertilizers types, among others. 27
  26. 26. Effects of Global PRIMARY Commodity Market DISRUPTION on Growth and Poverty in MALAWI Why assessing the effects of global primary commodity market disruption? • The COVD-19 pandemic affects African economies through several channels including,  Global market disruption and the change in the international price of primary commodities. • The COVID-19 pandemic has driven most primary commodity prices down in 2020. • Heavy contribution of primary commodities in the export basket of Malawi. 80% total exports
  27. 27. Key Messages • With African countries closely linked to global markets, disruptions in global trade have important effects on African economies. • Exposure to global trade shocks is a function of a country’s import and export basket as well as the capacity of the economy to respond. • The same disruption could have positive or negative impacts depending on how well the economy absorbs new costs or pursues new opportunities. • Responses to Covid-19 or other crises should not undermine the economy’s ability to respond to changes in the global economy. 30
  28. 28. • Use Computable General Equilibrium Models • Micro-simulations. Methodology
  29. 29. IV. Findings Slight Decline in GDP Growth in Malawi Percentage point change in GDP growth, COVID vs. BaU, Price and Trade Shocks -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 Angola Zimbabwe Zambia Mozambique South Africa Malawi Namibia Lesotho Eswatini Botswana A A A A B B B B C C
  30. 30. Change in Poverty Headcount Ratio, COVID vs. Baseline, Price and Trade Shocks (pp) IV. Findings Slight Increase in Income Poverty in Malawi 8,0 0,3 3,1 -0,3 0,7 -0,5 0,0 0,2 Angola Zambia Mozambique South Africa Malawi Namibia Lesotho Eswatini
  31. 31. IV. Findings Increased Urban Poverty More Than Rural Poverty in Malawi Changes in urban and rural headcount poverty rates, COVID vs. Baseline, Price and Trade Shock (pp) -2 0 2 4 6 8 10 12 Angola Zambia Mozambique Malawi Namibia Lesotho Eswatini Rural Urbain
  32. 32. • Decline in the international price of primary commodities traded by Malawi. • Negative effects on economic growth and (income) poverty in Malawi. • Increase in urban poverty more than rural poverty in Malawi. IV. Conclusion: Key findings
  33. 33. • Protect vulnerable populations • Diversify the external trade Increase the contribution of agricultural and food commodities and other high value commodities in the export basket IV. Conclusion: Policy Recommendations
  34. 34. FURTHER INFORMATION https://www.akademiya2063.org/events.php?lang=en www.resakss.org https://www.iwmi.cgiar.org/
  35. 35. Thank You

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