MALAWI COVID-19 IMPACT MONITORING REPORT HIGH-FREQUENCY PHONE SURVEY SURVEY REPORT FOR THE PERIOD MAY TO SEPTEMBER 2020 January 2021 1 Contents 1.0 BACKGROUND ................................................................................................................................... 8 2.0 METADATA ......................................................................................................................................... 10 Sample Composition ..........................................................................................................10 Sample Composition ..........................................................................................................11 Respondents characteristics ..............................................................................................11 Respondent relationship to head of household ...............................................................12 Respondent education ......................................................................................................13 Respondents over time .......................................................................................................14 Dependency Ratio .............................................................................................................15 3.0 KNOWLEDGE OF COVID-19 TRANSMISSION ............................................................................. 16 3.1 Knowledge about COVID-19 Transmission ..................................................................16 3.2 Government’s action to curb further spread of COVID-19 .......................................17 3.3 Satisfaction with Government action ..........................................................................17 3.4 Prevalence of safe practices .......................................................................................18 3.5 Degree of worry about self or immediate family member becoming ill of COVID-19 ..............................................................................................................................................19 3.6 Perceived threat to household’s finances ..................................................................20 3.7 Reported symptoms of COVID-19................................................................................21 3.8 Use of Government-provided Toll-Free Numbers .......................................................23 3.9 Safe Practices – Use of Face Masks .............................................................................23 4.0 ACCESS TO SERVICES ..................................................................................................................... 26 4.1 Basic needs ....................................................................................................................26 4.2 Access to services since date of outbreak .................................................................27 4.4 Reasons households could not access pre-natal or post-natal services ..................28 4.5 Reasons households could not access check-up or Preventative care visit ...........29 4.5 Access to water.............................................................................................................30 4.6 COVID-19 guidelines - effects on education ..............................................................33 4.7 Access to Credit ............................................................................................................36 5.0 EMPLOYMENT.................................................................................................................................... 39 5.1 Employment status last week .......................................................................................39 5.2 Main industry of those respondents working ...............................................................39 5.3 Job Stability ....................................................................................................................40 2 5.4 Family businesses ...........................................................................................................44 6.0 Agricultural Activities ...................................................................................................................... 47 7.0 Shocks and Safety Nets ................................................................................................................. 50 7.1 Shocks ............................................................................................................................50 7.2 Coping mechanisms .....................................................................................................52 7.3 Safety Nets .....................................................................................................................52 3 List of Tables Table 0-1 : Topics Covered during each survey round ................................................................. 9 Table 2-0-1 Sample Composition ...................................................................................................... 10 Table 2-0-2: Sample composition ...................................................................................................... 11 Table 2-0-3 Respondent relationship to head ............................................................................... 12 Table 2-0-4 Respondent education .................................................................................................. 13 Table 3- 1 COVID-19 outbreak - awareness & government action ........................................ 18 Table 3- 2 Prevalence of safe practices over time ...................................................................... 18 Table 3- 3 Degree of perception of threat to household's finance caused by COVID-19 .................................................................................................................................................................... 20 Table 3- 5 Prevalence of Safe Practices (Wearing a Mask when in Public), Last 7 days .. 23 Table 3- 7 Reasons Households Could Not Access pre-natal or post-natal care (% of HHs that could not access) ......................................................................................................................... 29 Table 4 - 1 Access to basic needs, past 7 days ............................................................................ 26 Table 4 - 2 Reasons households could not access check-up or preventive care visit (% of HHs that could not access)................................................................................................................. 30 Table 4 - 3. Reasons Households Could Not Access Water to Wash their Hands (% of HHs that could not access) ......................................................................................................................... 32 Table 4 - 4 Reasons Households Could Not Access to Water to Drink (% of HHs that could not access) .............................................................................................................................................. 32 Table 4 - 5 Sources of Credit Since the August (% of HHs that got credit) ............................ 36 Table 4 - 6 Reasons for obtaining a loan......................................................................................... 37 Table 4 - 7 Expected Repayment Period of Loans Taken since August ................................. 38 Table 5 - 1 Respondents working status last week (any work for pay or any income generating activities)............................................................................................................................ 39 Table 5 - 2 Percentage of respondents that stopped working and relation to COVID-19 outbreak. .................................................................................................................................................. 40 Table 5 - 3 Type of work for those working...................................................................................... 41 Table 5 - 4 Changes in working condition in wage work ........................................................... 42 Table 5 - 5 Wage Workers that worked last week, RESPONDENTS ONLY ............................... 42 Table 5 - 6 Family businesses by sector ............................................................................................ 44 Table 5 - 7 Challenges NFE has faced due to COVID-19 ........................................................... 45 Table 5 - 8 Types of changes doing/planned for the Non-Farm Enterprise during 3rd Round (August) ...................................................................................................................................... 45 Table 7 - 1 Types of shocks, since last call (R2) & (R3) 51 Table 7 - 2 Safety Nets since mid-Match(R1) last call (R2) & (R3) ........................................... 53 Table 7 - 3 Source of Food Assistance since mid-March(R1) last call (R2) & (R3) ............... 54 4 List of figures Figure 2 - 1: Dependency ratio by IHPS PCA Index based Wealth Quintiles ........................ 15 Figure 3 - 1 Knowledge of measures that minimize the risk of contracting COVID-19 (% of HH)......................................................................................................................................................... 16 Figure 3 - 2 Knowledge of government actions to curb the spread of COVID-19 (% of HH).............................................................................................................................................................. 17 Figure 3 - 3 Degree of worry about self/immediate family becoming seriously ill from COVID-19 (% of respondents) ............................................................................................................ 20 Figure 3 - 4: Incidence of households with at least a member that experienced COVID- 19 Symptoms ........................................................................................................................................... 22 Figure 3 - 5 Use of Toll-Free Lines After Experiencing COVID-19 Symptoms (% of Respondents who experienced Symptoms) .................................................................................. 23 Figure 4 - 1 Access to pre-natal or post-natal care since date of outbreak (round 3) and since last call (round 4) ........................................................................................................................ 27 Figure 4 - 2 Access to check-up or preventive care visit since last call ................................. 28 Figure 4 - 3 Access to water for washing hands (% of households) ........................................ 31 Figure 4 - 4 Access to sufficient drinking water ............................................................................. 31 Figure 4 - 5 Households with children aged 6-18 and those attending school pre closure .................................................................................................................................................................... 34 Figure 4 - 6 Children to return to school in September .............................................................. 34 Figure 4 - 7 Children returning to school by phase, % of households .................................... 35 Figure 5 - 1 Main industry of those respondents working, (% of respondents for selected sectors)...................................................................................................................................................... 40 Figure 5 - 2 Changes in job ................................................................................................................. 40 Figure 5 - 3 Change in hours worked last week ............................................................................ 43 Figure 5 - 4 Family businesses, main reason for closure............................................................... 44 Figure 6. 1 Prevalence of Livestock and Dry/Dimba Season Crop Farming Households, by region .................................................................................................................................................. 48 Figure 6. 2 Share of Livestock keeping households affected by COVID 19 in May/June and September by region and location......................................................................................... 48 Figure 6. 3 Distribution of How COVID-19 affected Livestock keeping households in May/June and September by region and location .................................................................... 49 Figure 7 - 1a: Number of shocks per household since mid-March ......................................... 50 Figure 7 - 2b: Number of Shocks since July (Round 2) ................................................................. 50 Figure 7 - 3 Coping mechanisms for shocks ................................................................................... 52 5 NUMERICAL HIGHLIGHTS Knowledge and behavior ✓ 99% of respondents consider washing hands with soap as a measure to minimize risk of contracting COVID-19. ✓ 100% of respondents have heard of COVID-19. ✓ 57% of respondents are aware of government’s advice to avoid gatherings. ✓ 79% of respondents were satisfied with government’s action on COVID-19. ✓ 88% is worried about themselves or any immediate household member becoming ill of COVID-19 but in September, this share declined to 73%. ✓ 90% consider COVID-19 as a substantial threat to their household’s finances. ✓ 16% experienced coughing in September, a decline from 21% experienced in August, and a decline from 29% experienced in July. ✓ 55% of respondents did not experience any COVID-19 related symptoms in July, 68% in August, and 74% in September. ✓ 2% of respondents who reportedly experienced some COVID-19 related symptoms in August (third round) called the toll-free number. ✓ 19% used face makes all of the time in public in July, 56% in August, 47% in September. Access to services ✓ 55% of households needed to buy maize in May/June, of these households, 23% could not buy the maize. ✓ 46% of households needed to buy maize in July, of these households 30% could not buy the maize. ✓ 36% of households with child-bearing age women needed to access pre- natal or post-natal care in August and 26% in September. ✓ 7% of women who needed pre-natal or post-natal care could not access in August while 4% could not access in September. ✓ 40% of women who could not access pre-natal or post-natal care could not do so because of unavailable medical personnel. ✓ 2% of households did not have access to sufficient drinking water in August and 4% in September. ✓ 96% of households with children ages 6-18 were attending school pre- closure/pre-COVID-19. ✓ 9% of households with children attending school pre-closure would not send them back to school in September. ✓ 47% of households took a loan to buy food. Employment 6 ✓ 50% of respondents are working in Agriculture sector as of September. ✓ 16% of respondents changed jobs in July, 23% in August and 20% in September. ✓ 56% of respondents stopped working in May/June potentially on issues related to COVID-19, 12% in July and August, and 26% in September. ✓ 36% of wage workers worked less in July, 38% in August, and 28% in September. ✓ 20% of non-farm enterprises have changed or plan to change how business is conducted. Agricultural activities ✓ 31% of households practice both crop and livestock farming. Shocks and Coping Strategies ✓ 83% of households experienced at least a shock between mid-March and May/June while 76% experienced the same between May/June and July. ✓ 29% of households that received food assistance in May/June received from NGOs, 46% in July, and 18% in August. 7 1.0 BACKGROUND Although Malawi had not yet registered any COVID-19 case, the country was declared a state of disaster on March 20, 2020. All schools were closed on March 23, government offices were restricted to essential duties only, public gatherings were restricted to 50 people, and although not very effective, local councils also announced measures such as closure of bars, banning weddings and other public gatherings. On April 2, the first COVID-19 case was registered in Malawi. On April 14, the government announced a 21-day lockdown to prevent further spread of the virus effective from April 18 till May 9th. On April 16 market vendors took to the street to protest the lockdown vowing to disregard it as the consequences of the lockdown would be devastating on their livelihood. Meanwhile, the Human Rights Defenders Coalition (HRDC) and other concerned citizens also challenged the lockdown at the High Court. On April 17, the High Court judge blocked the government from implementing the proposed nationwide lockdown for at least seven days. Following fresh presidential elections on June 23 that ushered in a new government, on July 10, another set of new COVID-19 guidelines was issued through the Ministry of Health and as previous guidelines. As of October 22, Malawi had a total of 5874 confirmed cases and 183 deaths. Since August, the rate of confirmed cases has dropped prompting government to relax some of its guidelines. A phased reopening of schools was proposed effective September 7 for those sitting for final examinations and final year college students. Furthermore, the government has increased the number of people in public gatherings from 50 to 100 which include churches, mosques and workplaces. Overtime, there is a need to understand the socio-economic impact of the pandemic on the people of Malawi. Since government-imposed Covid-19 guidelines which among others include social distancing are increasingly becoming common to fight the spread of the virus, these measures limit the use of traditional face-to-face interviews in population-based surveys to address data needs. Phone surveys, on the other hand, do not require face-to-face interactions and could elicit information from individuals and households rapidly and at low cost. It is against this background that in May 2020, the National Statistics Office (NSO), with support from the World Bank, launched the High-Frequency Phone Survey on COVID-19 (HFPS COVID-19), which tracks the socio-economic impacts of the pandemic on a monthly basis for a period of 12 months. The approach to this survey offers flexibility to alter questionnaire design in response to evolving information needs over the twelve-month period. The survey aimed to recontact the entire sample of households that had been interviewed during the Integrated Household Panel Survey (IHPS) 2019 and that had a phone number for at least 8 one household member or a reference individual. This report presents results from the first four rounds of the survey. The first round of the survey was conducted during the period of May 26-June 14. The second round was conducted over the period July 1 to 22, the third was from August 12 to 28 while the fourth round was from September 14 to October 2. New questions were added or dropped in each round depending on needs and other developments in the country. Table 0-1 : Topics Covered during each survey round May/June July August September Topic (Round 1) (Round 2) (Round 3) (Round 4) Household Composition X X X X Knowledge and False Beliefs Re: X X COVID-19 Concerns Re: COVID-19 Impacts X X* X X COVID-19 Symptoms + Lab Diagnosis X X X X Anti-COVID-19 Behavior and Social X – basic X – basic X – basic set X – basic set + Distancing set set + mask mask Anti-COVID-19 Behavior and Social Distancing – Intention to Comply X w/Gov Regulations Perceptions Re: Efficacy of X X Government Actions Access to Financial Services X X - washing X –washing X– X –washing hands, hands, Access to Water washing hands, drinking drinking drinking hands only water water water Access to Soap/Cleaning Supplies X X Access to Staple Foods X X Health - Access to Medicine and X X X Treatment Health – Women’s Pre/post-natal X X Care Health - Vaccination/ Immunization X children 0-5 X – asked if X– X - intention children return, detailed on Education to return to any safety pre/post school measures, and outbreak satisfaction 9 Employment of All Respondents X X X X Non-Farm Enterprises X X X Agriculture – Detailed Post Harvest X Agriculture – Dry Season Incidence X Agriculture – Crop Sales X Other Income X X Income Losses X X X X Credit X X Shocks and Coping Strategies X X X Food Security X X X Safety Nets X X X Livestock Detailed Module X Livestock Sales X Livestock Products X 2.0 METADATA Sample Composition In May/June during round 1 of the survey, 2,337 households were targeted for interviews. Of these, 1,729 were successfully interviewed (74%). In July during the second round, the target households were all those who were successfully interviewed in round one and of these, 1,646 were successfully interviewed representing 95% response rate. In August during the third round, the target number of households was 1,722 of which 1,624 were successfully interviewed (94%) while in the fourth round in September, 1,709 households were targeted of which 1,617 households were successfully interviewed (95%). Table 2-0-1 Sample Composition (# of Households) Sector Design Strata Sample Size Total Urban Urban Urban Rural Rural Rural Urban Rural Response North Center South North Center South rate May/June (Round 1 ) 2,337 779 1,558 123 390 266 202 658 698 74% HHs fully interviewed 1,729 617 1,112 107 292 218 149 443 520 July (Round 2) 1,729 637 1,092 108 304 225 148 431 513 95% HHs fully interviewed 1,646 613 1,033 103 293 217 141 403 489 August (Round 3) 1,722 633 1,089 107 301 225 148 431 510 94% HHs fully interviewed 1,624 596 1,028 103 279 214 142 403 483 September (Round 4) 1,709 624 1,085 105 297 222 148 428 509 95% HHs fully interviewed 1,617 597 1,020 102 284 211 141 400 479 10 Sample Composition Of the interviewed households, the weighted average household size is about 5. In terms of sex of household head, about 30% of the interviewed households are female headed. The average age of the household head is 46 years while about 77% of the household heads can read and write in any language. Based on pre-COVID-19 figures, about 80% of the weighted interviewed households reported to own a mobile phone, about 16% own television, about 8% own a refrigerator, 3% own a car while only 1% reported to own a generator. This composition was also factored in the computation of wealth quintiles. The weighted distribution of households from the Integrated Household Panel Survey Index Based Wealth Quintiles shows that about 15% of the households are in the lowest quintile (5th), the second quintile has about 18%, the third has about 25% and probably the highest. The fourth quintile has about 23% of the households while nearly one in every five households belong to the highest quintile (5th). Table 2-0-2: Sample composition May/June July August September (Round 1) (Round 2) (Round 3) (Round 4) Characteristics Un Un Un Un weighted Weighted weighted Weighted weighted Weighted weighted Weighted IHPS PCA Index Based Wealth Quintiles Q1 7 13 8 15 8 15 8 15 Q2 13 18 13 18 13 18 13 19 Q3 21 26 21 25 21 25 21 25 Q4 27 23 27 23 27 23 27 23 Q5 32 20 31 19 31 18 32 19 Respondents characteristics In this survey, the median age of respondents is 37 and has remained so across the four rounds. Male respondents are slightly older (38 years) compared to their female counterparts (36 years). In May/June, almost 46% of the respondents were of the age group 25-39 years followed by those in the age group 40-49 years 20%. In July, there was a slight decline in respondents of the age group 25-39 years from 46% to 40% and this went further down in August to 39% and back to 40% in September. In terms of share of respondents by sex, about 40% of respondents are female across all the months. In August, the share was slightly higher at 43%. 11 Respondent relationship to head of household Across all the four rounds from May to September, almost 78% of the respondents are household heads. Spouses have been respondents in 15 to 17% of the interviews while children have responded to about 4 to 5% of all the interviews. Table 2-0-3 Respondent relationship to head Number of respondents Distribution of respondents Relationship to HH Head: Total Male Female Total Male Female Head 1353 1039 314 78 96 49 Spouse 290 8 282 17 1 44 May/June Child (own/step/adopted) 60 23 37 3 2 6 (Round 1) Other relative 25 11 14 1 1 2 Not related 1 1 0 0 Head 1273 975 298 78 94 56 Spouse 296 6 290 16 1 37 July Child (own/step/adopted) 48 17 31 4 3 5 (Round 2 Other relative 28 14 14 2 2 2 Not related 1 1 0 0 Head 1207 907 300 76 94 54 Spouse 335 7 328 17 1 39 August Child (own/step/adopted) 59 21 38 5 4 5 (Round 3) Other relative 22 10 12 2 2 2 Not related 1 1 0 0 Head 1221 934 287 78 94 54 Spouse 308 8 300 15 1 36 September Child (own/step/adopted) 63 24 39 5 3 7 (Round 4) Other relative 23 9 14 2 2 3 Not related 1 1 0 0 In this survey, male respondents are generally the household heads (96%) with a very small share being male child about 2 to 3%. Amongst female respondents, there has been some variation across rounds. During round 1 in May/June, almost half (49%) of the female respondents were household heads while in July, this rose to 56%. In August and September, 54% of the female respondents were household heads. Amongst female respondents, spouses were respondents in 44% of the interviews in May/June, and the share dropped to 37% in July, and rose again to 39% in August, and dropped again to 36% in round 4 in September. Compared to their male counterparts, more female children responded to the interviews across all the four survey rounds. 12 Respondent education Of the 1,729 successful interviews in May/June, 1,516 respondents are literate representing 88%. In July, the share of literate respondents rose to 92% but dropped to 83% in August and dropped further to 81% in September. Across all survey rounds, literacy is higher amongst male respondents than their female counterparts. Table 2-0-4 Respondent education Distribution of Education Number of respondents respondents Total Male Female Total Male Female Literate (in any language) 1516 987 529 88 91 82 Level No school 94 38 56 6 4 9 May/June Primary - partial 841 492 349 50 46 55 (Round 1) Primary - completed 607 417 190 36 39 30 Tertiary - partial & completed 155 120 35 9 11 6 Literate (in any language) 1583 995 588 92 97 85 Level No school 62 17 45 8 3 15 July Primary - partial 526 308 218 44 43 47 (Round 2) Primary - completed 290 169 121 15 15 15 Secondary - partial 606 400 206 28 33 21 Tertiary - partial & completed 161 118 43 4 6 2 Literate (in any language) 1461 895 566 83 92 72 Level No school 73 20 53 10 5 16 August Primary - partial 520 289 231 43 41 46 (Round 3) Primary - completed 288 161 127 15 15 15 Secondary - partial 590 370 220 28 33 21 Tertiary - partial & completed 152 106 46 4 6 2 Literate (in any language) 1403 875 528 81 86 73 Level No school 64 20 44 9 4 15 September Primary - partial 520 310 210 44 45 43 (Round 4) Primary - completed 271 156 115 15 14 16 Secondary - partial 602 383 219 28 31 23 Tertiary - partial & completed 153 103 50 4 5 3 13 In terms of level of education completed, most of the respondents have attended some primary school education but did not complete. In May/June, half of the respondents were in this category, and about 44% in the other three rounds July, August and September. Across sex of respondent, there are more female respondents with no education than male respondents. In May/June, 9% of female respondents compared to 4% of male respondents have no education. In the other three rounds, there is over 10- percentage point gap between male and female respondents with no education. The situation is reversed for those with tertiary education. There are more male respondents with partial or completed tertiary education compared to female respondents. Respondents over time This survey aims to interview the same household once in a month for a period of twelve- months (twelve surveys rounds). About 90% of the targeted households have been interviewed in all the four survey rounds. Less than 1% of the households were interviewed in round 4 (September) and round 1 (May/June). About 2% were interviewed in rounds 1 (May/June), 3 (August) and 4 (September) but not in round 2 (July). About 2% of households were interviewed in rounds 1 (May/June), 2 (July) and 4 (September) but not in round 3 (August). About 2% were only interviewed in round 1 (May/June) of the survey and not in the other three survey rounds. 2% have been interviewed in the first three survey rounds (May-August) but not in the fourth round (September). Less than 1% of households have been interviewed in rounds 1 (May/June) and 3 (August) but not in rounds 2 (July) and 4 (September). About 2% of the households have been interviewed in the first 2 rounds (May-July) but not in the last two rounds (August-September). Table 2 - 1 Proportion of Households Repeatedly Interviews Across Survey Rounds Survey rounds # of Households % of Households R4+ R3+ R2+ R1+ 1550 90 R4+ R3- R2- R1+ 4 0 R4+ R3+ R2- R1+ 33 2 R4+ R3- R2+ R1+ 31 2 R4- R3- R2- R1+ 41 2 R4- R3+ R2+ R1+ 36 2 R4- R3+ R2- R1+ 5 0 R4- R3- R2+ R1+ 29 2 Malawi 1729 100 14 Figure 2 - 1 Respondents Across All the Four Survey Rounds Respondent Across Survey Rounds (%) 30 70 Same Respondent Across Survey Rounds Not Same Respondents Across Survey Rounds Of the households that have been interviewed in all the four survey rounds, about 70% of the interviews were responded to by the same respondent. The remaining 30% had different respondents across survey rounds. Dependency Ratio Figure 2 - 2: Dependency ratio by IHPS PCA Index based Wealth Quintiles DEPENDENCY RATIO Q1 Q2 Q3 Q4 Q5 1.3 1.3 1.2 1.2 1.2 1.1 1.1 1.1 1.1 1.1 1.1 1.0 0.9 0.9 0.9 0.8 0.8 0.8 0.7 0.7 MAY/JUNE (ROUND 1) JULY (ROUND 2) AUGUST (ROUND 3) SEPTEMBER (ROUND 4) Across IHPS PCA Index Based Wealth Quintiles, dependency ratio is higher the lower the quintile. This is true across all the four survey rounds. 15 3.0 KNOWLEDGE OF COVID-19 TRANSMISSION 3.1 Knowledge about COVID-19 Transmission During the first round of the survey (May 26 to Jun 14), respondents were asked of their knowledge regarding COVID-19 transmission and other actions the government is taking to prevent further spread of the pandemic. Figure 3 - 1 Knowledge of measures that minimize the risk of contracting COVID- 19 (% of HH) 99 Handwashing with soap 97 99 75 Maintain enough distance of at least 1 meter 79 76 61 Avoid crowded places or gatherings with many people 64 61 53 No Handshake / physical greetings 49 52 47 Cough Etiquette 48 48 46 Avoiding touching your face 50 47 35 Use of mask 50 38 35 Staying at home and avoid going out unless necessary 39 36 29 Avoid travel 31 30 21 Use of gloves 27 23 19 Use of sanitizer 37 22 Rural Urban Malawi Handwashing with soap as a measure of reducing the risk of contracting COVID- 19 is widely known as reported by 99% of the respondents. Maintaining enough distance of at least 1 meter is the second most reported measure by 75% of respondents. The third most reported measure is that of avoiding crowded places or gatherings with many people which has been reported by 61% of the respondents. There are some measures which are more pronounced in urban than in rural areas and these include use of face masks (50% urban against 35% rural), use of gloves (28% in urban areas against 22% in rural areas), and use of sanitizers (37% in urban areas against 19% in rural areas). Almost all the respondents reported one or more measure of reducing the risk of contracting COVID-19. 16 3.2 Government’s action to curb further spread of COVID-19 In order to prevent further spread of COVID-19, the government has put in place several measures. Respondents were asked to mention these measures on their own. Overall, 87% of respondents were able to mention at least one government action to curb the spread COVID-19. Figure 3 - 2 Knowledge of government actions to curb the spread of COVID-19 (% of HH) Advised to avoid gatherings 57 Sensitization/ Public Awareness 41 Closure of schools and universities 29 Advised citizens to stay at home 21 Disinfection of public places 10 Restricted international travel 5 Restricted travel within country/area 4 Closure of non essential businesses 3 Established isolation centers 2 Curfew/ lockdown 1 Other 4 0 10 20 30 40 50 60 Malawi The most reported government action is that people were advised to avoid gatherings. This was reported by 57% of the respondents followed by sensitization or public awareness (41%). Lockdown was least reported government action. 3.3 Satisfaction with Government action Overall, 79% of respondents were satisfied with government response to the COVID-19 crisis. Of those not satisfied, shortage of medical materials was the most reported (26%) followed by no food assistance from the government (24%). More urban respondents (31%) were not satisfied with government action compared to rural respondents (18%). All respondents have heard COVID-19 and are aware of any government action that may have been taken by the government to curb spread of COVID- 19. 17 Table 3- 1 COVID-19 outbreak - awareness & government action Overall % of respondents by IHPS Wealth % of respondents (% of Quintile by residence responde Q1 Q2 Q3 Q4 Q5 Urban Rural nts) Respondents - have heard of COVID-19 100 100 100 100 100 100 100 100 Respondents - aware of any government action* 100 99 100 100 100 100 100 100 Respondents - satisfied with government action 79 87 89 81 79 59 69 82 * The respondent is aware of at least one action that may have been taken by the Government according to the respondent Across wealth quintiles, the share of respondents satisfied with government action is higher amongst poor households as compared to richer households. Similarly, more respondents in the rural areas (82%) are satisfied with government action than in urban areas (70%). 3.4 Prevalence of safe practices Table 3- 2 Prevalence of safe practices over time % of % of respondents by respondents residence Urban Rural More frequent handwashing with soap 87 91 86 Avoid handshakes/physical greetings 68 68 68 Reduce Trips to Grocery Store 63 61 64 May/June Cancel Travel Plans 58 55 59 (Round 1) Stock up more food than normal, due to 27 29 27 restricted movement Avoid groups of more than 10 people 17 22 16 More frequent handwashing with soap 79 78 80 Avoid handshakes/physical greetings 71 65 73 Reduce Trips to Grocery Store 47 49 47 July Cancel Travel Plans 33 34 33 (Round 2) Avoid groups of more than 10 people 22 24 22 Stock up more food than normal, due to 15 16 15 restricted movement Avoid handshakes/physical greetings 75 74 76 August More frequent handwashing with soap 74 77 73 (Round 3) Avoid groups of more than 10 people 45 47 45 18 Avoid handshakes/physical greetings 79 74 80 September More frequent handwashing with soap 49 48 49 (Round 4) Avoid groups of more than 10 people 39 45 38 Over the four survey rounds, respondents were asked some questions relating to their behavior that can protect them from contracting COVID-19 and prevent its further spread. Across these survey rounds, there has been some variations in actions taken by individuals. During the first round of the survey (May 26 to June 14), hand washing with soap was the most prevalent action as reported by 87% of respondents and this trend continued into the second round of the survey although there was an eight percentage point decline to 79%. In the third and fourth rounds the behaviors changed. The most reported behavior is that of avoiding handshakes/physical greetings as reported by 75% of respondents in round three and 79% of respondents in round four. In urban areas, during the first round, nearly 90% of the respondents reported more frequent handwashing with soap while in the rural areas 86% reported the same. 68% of urban respondents and a similar share of rural respondents reported avoiding handshakes/physical greetings in round one of the survey. Other actions that were less reported during the second round of the survey include stock up more food than normal, restricted movement reduced trips to grocery store and cancellation of travel plans. However, avoiding groups of more than 10 people has been increasing from survey round one (17%) to 23% in round two and 45% in round three but declined slightly to 39% in the fourth round. 3.5 Degree of worry about self or immediate family member becoming ill of COVID-19 Respondents were asked how worried they were about themselves or any of their immediate family member becoming ill of COVID-19. Across the four survey rounds, respondents have become less worried in the fourth round. The share of respondents that reported being very worried declined from around 88% in the first three rounds to 73% in the fourth-round while those that were somewhat worried rose from around 5% in the first three rounds to 11% in the fourth round. Those that reported not to be worried at all also increased from 5% in the first round to 8% in the fourth round. 19 Figure 3 - 3 Degree of worry about self/immediate family becoming seriously ill from COVID-19 (% of respondents) Very worried Somewhat worried Not too worried Not worried at all SEPTEMBER (ROUND 4) 73 11 7 8 AUGUST (ROUND 3) 88 5 3 5 JULY (ROUND 2) 90 3 3 5 MAY/JUNE (ROUND 1) 89 5 2 4 3.6 Perceived threat to household’s finances Apart from being asked about their worry about self or immediate family member being sick of COVID-19, respondents were also asked the degree of perception of threat to their household’s finance caused by the corona virus. Table 3- 3 Degree of perception of threat to household's finance caused by COVID-19 % of % of respondents by IHPS Wealth % of respondents by Quintile respondents residence Q1 Q2 Q3 Q4 Q5 Urban Rural A substantial threat 90 93 93 91 88 88 90 90 A moderate threat 5 2 5 5 5 6 6 4 May/June Not much of a threat 2 2 0 2 4 2 1 2 (Round 1) Not a threat at all 3 3 2 2 3 4 4 3 A substantial threat 89 91 92 88 90 86 83 91 A moderate threat 6 5 4 7 5 9 10 5 July Not much of a threat 2 3 2 2 2 3 4 2 (Round 2) Not a threat at all 2 1 2 3 3 2 3 2 A substantial threat 89 93 96 88 88 83 85 90 August A moderate threat 7 1 3 8 8 12 11 6 (Round 3) Not much of a threat 2 4 0 2 1 2 0 2 20 Not a threat at all 2 3 1 3 3 4 3 2 A substantial threat 76 79 79 77 73 72 70 77 A moderate threat 19 16 18 20 20 19 23 18 September Not much of a threat 3 2 2 1 5 3 1 3 (Round 4) Not a threat at all 3 3 2 2 2 6 6 2 There is a decline in the perceived threat of the virus on household’s finance between the first three months May/June and August to the September (fourth round). In the first three months of the survey (, about 90% perceived the virus as substantial threat to their household’s finances while this share declined in September (fourth round) to 76%. Across the survey rounds, there has been a shift from perceiving the virus as substantial threat to moderate threat. In survey May/June, about 5% of the respondents perceived the virus as moderate threat to their household’s finances, and this slightly rose to 6% in July and rose further to about 7% in August, and substantially rose to 19% in September. Across wealth quintiles, more respondents in the lowest wealth quintile perceive COVID-19 as a substantial threat to their household’s finances as reported by 93% in May/June, 91% in July, 93% in August and 79% in September. This perceived substantial threat is declining amongst richer households reported by 88% in May/June, 86% in July, 83% in August and 72% in September. By place of residence, the share of respondents that perceived COVID-19 as substantial threat to their finances was higher in rural areas than in urban areas. For instance, about 91% of the respondents in rural areas reported that COVID-19 was substantial threat to their finances in May/June compared 90% in urban areas in the same period. In September, 77% of rural respondents perceived COVID-19 a substantial threat compared to 70% in urban areas. 3.7 Reported symptoms of COVID-19 The respondents were asked if they experienced any of the COVID-19 related symptoms since last week from the July to the September. 21 Figure 3 - 4: Incidence of households with at least a member that experienced COVID-19 Symptoms Round 2 Round 3 55 59 54 68 73 66 19 22 18 16 14 16 10 7 11 12 11 12 14 15 7 6 7 9 No Symptom One Two More than 2 No Symptom at One Symptom Two Symptoms More than 2 at all Symptom Symptoms Symptoms all Symptoms Malawi Urban Rural Malawi Urban Rural Round 4 74 72 75 12 13 12 7 9 7 7 7 5 No Symptom One Symptom Two Symptoms More than 2 at all Symptoms Malawi Urban Rural The share of respondents that did not experience and COVID-10 related symptoms increased over the reporting period. Overall, 55% of respondents did not experience any symptom in July. This share increased from 55% July to 68% in August and to 74% in September. The share of those who experienced one or more symptoms has declined over the reporting period. Overall, the share of those that experienced more than two symptoms declined from 14% in July to 10% in August to 7% in September. Across place of residence, the share of rural residents that reported experiencing more than 2 symptoms is higher than in urban areas over the entire reporting period. 22 3.8 Use of Government-provided Toll-Free Numbers1 Figure 3 - 5 Use of Toll-Free Lines After Experiencing COVID-19 Symptoms (% of Respondents who experienced Symptoms) 9 6 6 2 2 2 August (Round 3) September (Round 4) Overall Urban Rural Government has provided toll-free numbers to help those in need of help relating to COVID-19. During the third and fourth survey rounds, the survey asked those who experienced some symptoms if they called any of the government provided toll-free numbers. Around 2% of respondents who reportedly experienced some symptoms in the third round called the toll-free number. This share almost tripled in the fourth round to 6%. Urban areas the proportion of respondents that called the toll-free number increased from 2% in round 3 to 9% in round four. 3.9 Safe Practices – Use of Face Masks Respondents were asked if they were wearing a face mask or cover when in public places over the last 7 days. from the second round of the survey through the fourth round, Table 3- 4 Prevalence of Safe Practices (Wearing a Mask when in Public), Last 7 days % of respondents by IHPS Wealth Quintile by residence Overall Q1 Q2 Q3 Q4 Q5 Urban Rural All of the time 19 11 22 19 15 29 26 18 Most of the time 6 3 5 4 10 10 7 6 July About half of the time 1 1 3 1 1 2 2 1 (Round Some of the time 7 12 5 3 7 12 12 6 2) None of the time 61 66 62 69 64 42 48 64 1 Toll-free numbers include: Airtel 54747 or *929# or 321; for tnm 929 or *929# or whatsap 0990 800 000 23 I have not been in public during the last 7 days 5 7 4 4 4 5 6 4 All of the time 56 36 51 58 58 71 68 53 Most of the time 16 16 16 15 19 13 12 17 About half of the time 3 3 2 4 4 2 4 3 August Some of the time 8 6 8 7 8 8 8 7 (Round None of the time 15 32 23 12 10 3 6 17 3) I have not been in public during the last 7 days 3 6 0 3 2 2 2 3 All of the time 47 42 51 47 45 50 53 46 Most of the time 23 19 22 21 25 24 23 23 About half of the time 4 3 5 3 6 4 3 5 Septemb Some of the time 13 14 11 12 14 13 14 13 er None of the time 11 19 8 15 8 8 6 12 (Round I have not been in 4) public during the last 7 days 1 3 2 1 1 0 1 1 In July, most respondents (61%) reported not wearing a face mask when in public in the last 7 days. In August and September, the share of those reporting not wearing a face mask in public dropped to 15% and 11% respectively. Over the same period, those who reported to wear a mask all of the time in public has increased from 19% in July to 56% in August and 47% in September. Across wealth quintiles, those who did not wear face mask when in public were highest among the poorest quintile and lowest among the richest wealth quintile. In July, 61% of respondents in the lowest quintile did not wear masks when in public compared to 42% in the highest quintile. In the following month in August, the share is 32% for the lowest quintile compared to 3% for the highest quintile while in September, the share is 22% in the lowest quintile compare to 7% in the highest quintile. Looking at those wearing a face mask all of the time when in public across wealth quintiles, the highest share is among the richest households while the lowest share is amongst the poorest households and this is true over the entire reporting period. In July, 11% of respondents in the lowest quintile wore face masks all of the time when in public compared to 29% of respondents in the highest quintile. 36% of respondents in the lowest quintile compared to 29% in the highest quintile was the case August while in September, 43% of respondents in the lowest quintile compared to 50% in the highest quintile wore face masks all the time 24 when in public. Across all the survey rounds, there is an increase in the share of respondents wearing face masks all the time when in public amongst poorest and richest respondents. By place of residence and over time, the share of respondents that reported wearing face masks none of the time when in the public is higher in the rural areas than in urban areas and the opposite is true for those wearing face masks all of the time when in public. In July, 64% of rural respondents compared to 48% of urban respondents did not wear face masks when in public. In August, the share was 17% of rural respondents compared to 6% of urban residents while in September the share was 14% of rural residents compared to 7% of urban residents. In July, 18% of rural respondents wore face masks all of the time in public compared to 27% of urban respondents. In August, 53% of rural respondents compared to 68% and in September, 45% of rural residents compared to 54% of urban respondents wore face masks all of the time when in public. 25 4.0 ACCESS TO SERVICES 4.1 Basic needs During the first two rounds of the survey, respondents were asked if they needed to buy a selected list of items including their staple food. Of those who needed to buy the items, they were further asked if they were able to buy the required items. Table 4 - 1 Access to basic needs, past 7 days Could not buy (% HH that needed to buy) Neede % of d to IHPS PCA Index Based Wealth respondents buy (% Overal Quintiles by residence of HHs) l Urba Rura Q1 Q2 Q3 Q4 Q5 n l Soap 94 7 7 8 8 7 4 7 7 Medicine 64 12 8 13 13 14 9 10 12 Medical May/Jun 61 16 13 15 15 17 17 20 14 e Services (Round Cleaning 1) 60 34 39 35 41 37 28 30 37 supplies Maize 55 23 36 21 27 24 18 18 27 Maize 46 30 33 31 27 31 27 21 32 Medical July 46 16 19 7 16 18 18 26 14 (Round Services 2) Medicine 45 14 13 2 19 16 18 13 14 In terms of basic items, in May/June, most people needed to buy soap (94%), medicine (64%), medical services (61%) and cleaning supplies (61%). Of the households that needed to buy cleaning supplies, about 35% could not buy. Those that needed medical services 16% could not access while 12% of those that needed to buy medicine could not. A small share of those who needed to buy soap was not able to (7%). Access to staple food was asked in May/June and July. In May/June, about 55% of respondents needed to buy maize but this share dropped to 44% in July. The share of households that needed to buy maize but could not do so, rose from 23% in the May/June to 29% in July. Across wealth quintiles, more respondents from the poorest households who needed to buy some selected items were not able to buy compared to the richest households. In May/June, 7% of respondents who wanted to buy soap in 26 the lowest wealth quintile could not do so compared to 4% in the highest quintile. 39% of the lowest quintile respondents could not buy cleaning supplies compared to 28% in the highest quintile. The same is true for staple food. In May/June, 36% of respondents who needed to buy maize in the lowest wealth quintile could not do so compared to 18% in the highest wealth quintile. In July, 41% compared to 29% of rural and urban respondents respectively could not buy maize. By place of residence, the share of households that could not buy items or services they needed is higher in the rural areas than in urban areas Over the reporting period except for medical services. 37% of rural respondents who needed to buy cleaning supplies could not do so compared to 30% of urban respondents in May/June.12% of rural residents who needed to buy medicine could not do so compared to 10% of urban residents. More of those rural areas respondents who needed to buy staple food, could not buy compared to their urban counterparts. In May/June, 27% of rural respondents could not buy maize compared to 18% of urban respondents. In July, 29% of rural respondents could not buy maize compared to 28% of rural respondents. Overall, there was a rise in the share of respondents who needed to buy maize but could not do so in both rural and urban areas between May/June and July. 4.2 Access to services since date of outbreak Households with women of child-bearing age were asked if any of the women in the household needed to access pre-natal or post-natal care August and/or September. In August, about 36% of households with women of child-bearing age needed access to pre-natal or post-natal care but this share dropped to 26% in the September. Figure 4 - 1 Access to pre-natal or post-natal care since date of outbreak (round 3) and since last call (round 4) Access to Pre-natal or Post-natal care 36 26 6 8 6 4 2 4 Overall Urban Rural Needed to access Could not access (% HH that needed to Access) August (Round 3) September (Round 4) 27 Overall, of those who needed to access the pre/post-natal services, 6% could not access the service in August and the share dropped to 4% in September. By place of residence, both urban and rural areas experienced a decline in the share of households that could not access the services between August and September, but urban areas had a larger decline of 6 percentage points compared to rural areas with a decline of about 2 percentage points. 4.3 Access to check-up or preventative care visit The share of households that needed to access check-up or preventive care visit is generally very low at about 1% in both August and September. Of these households, the share of households that could not access these services is also very low at 1% over the same period. Figure 4 - 2 Access to check-up or preventive care visit since last call Check-up or Preventative care visit 1 1 1 1 1 1 1 0 Overall Urban Rural Needed to access Could not access (% HH that needed to Access) Round 3 Round 4 4.4 Reasons households could not access pre-natal or post-natal services Unavailable medical personnel was the most reported reason (40%) households could not access pre-natal or post-natal services in August. The second most reported reason is that those seeking services were turned away because facility was full (24%). Restriction to go out was reported by 16% of the households as the reason they could not access pre-natal or post-natal care in August. In September, the most reported reason households could not access pre-natal or post-natal care was that they were turned away because facility was full (35%) while lack of funds was the second most reported reason (24%). Unavailable 28 medical personnel was the third most reported reason down to 21% from 39% in August. By place of residence, 46% of rural households that needed pre-natal or post- natal care in August could not access the care due to unavailable medical personnel compared to 12% of urban households. In urban areas however, 58% of households were turned away because facility was full versus 17% in rural areas. In September more respondents in the rural areas (36%) were turned away because facility was full compared to the urban counterparts at 19%. Table 3- 5 Reasons Households Could Not Access pre-natal or post-natal care (% of HHs that could not access) Pre-natal or Post-natal care Frequency of reasons given across all times Overall Urban Rural Unavailable Medical Personnel 40 12 46 Turned away because facility was full 24 58 17 Restriction to go outside 16 2 19 August Refused Treatment by the Facility 14 0 18 (Round 3) Suspicion of Being positive for COVID-19 5 27 0 Lack of Funds 3 13 1 Other Specify 2 0 3 Turned away because facility was full 35 19 36 Lack of Funds 24 12 24 Unavailable Medical Personnel 21 0 22 September Restriction to go outside 11 0 12 (Round 4) Other Specify 9 51 6 Suspicion of Being positive for COVID-19 1 18 0 Refused Treatment by the Facility 0 0 0 4.5 Reasons households could not access check-up or Preventative care visit In August, households that wanted to access check-up or preventive care, 37% could not access due to unavailable medical personnel. Lack of funds and distance to the facility are other reasons that prevented households to access check-up or preventive care visit in August (17%). However, in the following month in September, the most reported reason for not accessing check-up or preventing care visit is lack of funds (32%) and fear of contracting COVID-19 (15%). About 43% of rural households that wanted to access check-up or preventive care could not access due to unavailable medical personnel reported during the third round of the survey as compared to 8% of urban households. Distance to the 29 facility (21%) and lack of funds (19%) are also some of the most reported reasons by rural households that prevented them from accessing check-up or preventive care visit. In the same round of the survey, urban areas reported turned away because facility was full (37%) and fear of contracting the virus (31%) as the reasons they could not access the services. In the fourth round, lack of funds was most reported in rural areas (33%) followed by fear of contracting the virus (15%). Table 4 - 2 Reasons households could not access check-up or preventive care visit (% of HHs that could not access) Check-up or Preventative care Frequency of reasons given across all times visit Overall Urban Rural Unavailable Medical Personnel 37 8 43 Lack of Funds 17 11 19 Distance to Facility 17 0 21 Restriction to go outside 7 0 9 August Fear of Contracting COVID-19 7 31 2 (Round 3) Turned away because facility was full 7 37 0 Refused Treatment by the Facility 5 0 6 Suspicion of Being positive for COVID-19 2 14 0 Lack of Funds 32 0 33 Fear of Contracting COVID-19 15 0 15 Unavailable Medical Personnel 7 94 4 Refused Treatment by the Facility 1 0 1 September Turned away because facility was full 0 0 0 (Round 4) Restriction to go outside 0 0 0 Suspicion of Being positive for COVID-19 0 0 0 Distance to Facility 0 0 0 Other Specify 49 6 51 4.5 Access to water In August and September, respondents were asked if household was unable to access water for washing hands since last week from the interview date. In August, 3% of households did not access water for washing hands. This share increased by 1 percentage point to 4% in the following month. 30 Figure 4 - 3 Access to water for washing hands (% of households) Water For Washing Hands 7 6 5 4 4 4 4 4 4 4 3 3 3 2 2 1 Q1 Q2 Q3 Q4 Q5 Urban Rural Did not have Wealth quntile Place of residence access (% of HHs) August (Round 3) September (Round 4) By wealth quintiles, in August, 1% of poorest households did not have access to adequate water for washing hands compared to 4% of richest households. In September, 7% of the poorest households did not access adequate water for washing hands compared to 6% of the richest households. By place of residence, 4% of urban households could not access adequate water for washing hands in August compared to 3% of rural households. In September, 5% of urban households could not access adequate water for washing hands compared to 4% of rural households. Overall, both urban and rural households experienced a rise in the share of households that could not access water for washing hands in between the two months. Figure 4 - 4 Access to sufficient drinking water Sufficient drinking water 5 4 4 4 4 4 4 3 4 2 2 2 2 1 1 0 Q1 Q2 Q3 Q4 Q5 Urban Rural Did not have Wealth quntile Place of residence access (% of HHs) August (Round 3) September (Round 4) There was a slight increase in the share of households that did not access sufficient drinking water from 2% in August to 4% in September. Across wealth quintiles, there were no households that did not have access to sufficient drinking water in the lowest wealth quintile in August, but the share rose to 5% in September. 2% of 31 households in the richest quintile did not have access to sufficient drinking water in August compared to 4% in September. Share of urban residents that did not have access to sufficient drinking water dropped over the two months from 4% to 3% but the opposite is true for rural residents as the share increased from 2% to 4% over the months August to September. Table 4 - 3. Reasons Households Could Not Access Water to Wash their Hands (% of HHs that could not access) % of respondents by Frequency of reasons given across all times residence Overall Urban Rural Water Source Too Far 31 7 40 Too Many People at the Water Source 25 1 34 August No Money 22 66 6 (Round 3) Other Specify 22 26 21 Water Source Too Far 36 32 38 September Too Many People at the Water Source 14 2 18 (Round 4) Other Specify 20 27 18 In August, of the households that could not access water to wash their hands, 31% percent cited water source being too far, 25% indicated too many people at the water source, while 22% indicated lack of money. In September, during the fourth round, 36% cited water source being too far and 14% cited too many people at the water source as the reasons they could not access water to wash their hands. In August, 40% of rural households cited water source being too far compared to urban households 7%. Too many people at the water source was also reported more by rural respondents at 34% compared to urban respondents reported at 1%. The situation was almost the same in September as 32% or urban households reported that water source was too far compared to 38% of rural households. There were more households that could not access water for washing hands in rural areas due to too many people at the water source, reported by 18%, compared to urban households, reported by 2%. Table 4 - 4 Reasons Households Could Not Access to Water to Drink (% of HHs that could not access) % of respondents by Frequency of reasons given across all times residence Overall Urban Rural Water Supply Reduced 32 27 36 August Other Specify 30 46 20 (Round 3) Unable to access communal sources 29 23 34 32 Water Supply No Longer Available 6 1 9 Unable to afford Water 3 4 2 Unable to access communal sources 30 0 37 Water Supply Reduced 28 42 25 September Other Specify 19 10 21 (Round 4) Water Supply No Longer Available 17 20 16 Unable to afford Water 5 28 0 Households that did not access sufficient water for drinking were further asked to cite the main reason as to why they were not able to access sufficient water for drinking. In August during the third round, 32% of households reported water supply reduced as the main reason. This proportion dropped to 28% in September. Unable to access communal sources was the second most reported reason in August reported by 29% of the respondents and slightly rose to 30% in September. In August, about 36% of households in rural areas reported water supply reduced as the main reason they could not access sufficient drinking water. This is followed by unable to access communal sources reported by 34% of the households. In September, 37% of the households reported unable to access communal sources as the main reason for not accessing sufficient drinking water which is an increase from 34% reported in the previous month. Water supply reduced was reported by 25% of respondents in September down from 36% reported in August. Water supply reduced is the most reported reason in urban areas reported by 27% and 42% of respondents in August and September respectively. 4.6 COVID-19 guidelines - effects on education Following government’s closure of schools and the proposed phased opening in September and October, households with school-going age children (6-18 years) were asked whether the children were attending school prior to COVID-19 outbreak in March. 33 Figure 4 - 5 Households with children aged 6-18 and those attending school pre closure Households with school-age children HHs with children ages 6 - 18 HHs with children attending school, pre-closures 96 92 94 97 96 98 97 96 80 84 82 83 83 85 83 66 Q1 Q2 Q3 Q4 Q5 Urban Rural % all HHs Wealth Quintile Place of residence Overall, 81% of households interviewed in August had children aged 6-18. Of these households, 96% had children that were attending school pre-closure in March 2020. The lowest quintile reported the lowest share of households with children aged 6-18 at 66% while the second quintile had the highest at 84%. Households with the most children attending school were in the highest quintile (98%) and lowest in the poorest quintile (92%). Urban areas reported slightly more households with children aged 6-18 at 85% compared to rural areas at 83%. There is only one percentage point gap between urban and rural areas in terms of children attending school pre-closure with the urban areas registering 97% and rural areas slightly lower at 96%. Figure 4 - 6 Children to return to school in September Children to return to school in September residen Rural 86 8 6 Place ce of Urban 79 15 6 Q5 77 15 7 s Wealth Quintile Q4 86 10 4 Q3 85 7 8 Q2 88 3 8 Q1 87 13 1 HH all 85 9 6 % Yes children will return to School No, children will not return to School Not Sure if Children will return to School 34 Households with children attending school pre-closure were asked if the children would return to school when schools re-opens. 85% indicated that their children will return to school while 9% reported that their children will not return to school. About 6% were not sure if children will return to school. Across wealth quintiles, the second quintile reported the highest share of households whose children will return to school at 89% and the fourth quintile at 86%. The richest quintile has the lowest share at 78%. Likewise, the richest quintile has the highest share of households whose children will not return to school at 15% followed by the lowest quintile at 13%. In urban areas, 79% of households with school-age children will return to school compared to 86% in rural areas. In September, households with school-age children were further asked if their children have returned to school for the phase one re-opening or would return for the second phase reopening. Figure 4 - 7 Children returning to school by phase, % of households Children in school for phase 1 re-opening reside Rural 37 50 Place s Wealth Quintile nce of Urban 40 46 Q5 44 45 Q4 41 49 Q3 34 55 Q2 39 48 Q1 30 46 HH all 38 49 % Yes children returned No, children did not return (will return in next phase) No, children will not return to School Not Sure if Children will return to School No School Going Children* In September, 38% of households had their children returned to school and 49% were expected to return in the next phase in October. The share of households whose children returned to school is increasing from the lowest quintile to the highest quintile. 40% of urban households had their children back in school compared to 37% of rural households. Inversely, 50% of rural households compared to 46% of urban households have children who will return in the next phase in October. For households that reported their children will not return to school, the main reason cited is that the schools are not yet safe from COVID-19. The proportion was much high in August at 86% and declined to 61% in September. The proportion of households that 35 reported financial challenges due to unavailability of jobs as the main reason for not sending children to school rose from 4% in August to 37% in September. 4.7 Access to Credit In September, respondents were asked if anyone in their household successfully obtained a loan from sources such as banks, cooperative societies, savings associations, micro- finance institutions, money lenders, family, friends, etc. About 16% of households took a new loan in August. More urban households (20%) took a loan in August than rural households (16%). About 22% of households that took a loan in August have also outstanding loan that was taken pre-COVID-19. Additionally, more urban households (31%) have outstanding loan taken pre-COVID-19 than rural households (20%). Table 4 - 5 Sources of Credit Since the August (% of HHs that got credit) % of IHPS PCA Index Based Wealth Quintiles respondents Overall by residence Frequency of reasons Urban Rural given across all times Q1 Q2 Q3 Q4 Q5 Micro Finance 48 32 69 54 40 47 28 52 Friends & Relatives 27 30 31 18 32 30 45 23 Savings Association 22 39 0 23 19 17 22 22 Money Lenders 2 0 0 4 7 0 3 2 Bank 1 0 0 0 0 6 1 1 The most reported source of loan is micro finance (48%) followed by friends and relatives at 27% and savings association at 22%. In the lowest quintile, most respondents obtained their loan from savings (39%) then micro finance (32%) and from friends and relatives (30%). About 47% of households in the richest quintile took their loan from micro finance while 30% from friends and relatives and 17% from savings association. By place of residence, 45% of those who took a loan in urban areas got the loan from friends and relatives. About 28% got the loan from micro finance and 22% from savings association. For rural households, of those who took a loan, slightly above half (52%) got their loan from micro finance, 23% from friends and relatives and 22% from savings association. Respondents that obtained a loan or attempted to obtain a loan were asked the main purpose for borrowing/attempting to borrow Money. 47% of respondents 36 wanted to buy food, 37% wanted to purchase inputs/working capital for non-farm enterprise and 8% to buy farm inputs either seeds or fertilizer. By wealth quintile, 45% of respondents in the poorest quintile compared to 33% in the richest quintile obtained a loan to buy food stuff. 48% in the poorest quintile compared to 55% in the richest quintile obtained or attempted to obtain a loan in order to purchase inputs /working capital for non-farm enterprise. 63% of urban respondents compared to 42% or rural respondents obtained a loan or attempted to obtain a loan to buy food stuff. 36% of urban respondents compared to 37% of rural respondents obtained or attempted to obtain a loan to purchase input/ working capital of non-farm enterprise. Table 4 - 6 Reasons for obtaining a loan Malaw Urba Rura i Q1 Q2 Q3 Q4 Q5 n l Buy food stuff 47 45 68 46 40 33 63 42 Purchase of inputs/ working capital for non-farm enterprises 37 48 25 29 36 55 35 37 Buy farm inputs (seeds, fertilizer) 8 4 12 2 9 14 10 7 Buy other non-food consumption goods/services 5 0 4 2 7 10 6 5 House construction or purchase 5 0 1 12 1 5 3 5 Pay for health expenses 3 3 0 5 5 1 4 3 Pay for education expenses 3 0 1 4 1 8 0 4 Buy farm tools/implements 2 0 0 0 7 2 0 3 Other 2 0 0 2 3 1 4 1 Pay for ceremonies expenses 1 0 2 3 0 0 4 1 Buy livestock 1 0 0 2 0 2 0 1 About 11% of those who took a loan in August already repaid while 7% had an overdue loan. About 50% had their loan due within one month while 23% had their loan due within the next 2-3 months. Of the respondents that had not yet repaid their loan, they were asked of the degree of worry about not paying back the loan within loan repayment period. About 62% is very worried while 17% is somewhat worried and 13% is not worried at all. 37 Table 4 - 7 Expected Repayment Period of Loans Taken since August Overall Status of loan repayment Loan Already Due 7 Within One Month 50 Within the Next 2 - 3 Months 23 Within the Next 4 - 6 Months 5 Within the Next 7 - 12 Months 1 More than 12 Months 3 Loan Already Paid 11 Households with outstanding loan were asked of the degree of worry about not Paying back loan within Loan Repayment Period. The majority (62%) is very worried with 17% somewhat worried. Only 13% is not worried at all. Comparing rural and urban respondents, 11% of urban households responded that they were not worried at all about not paying back the loan within the stipulated tie frame compared to 13% of rural areas. 38 5.0 EMPLOYMENT 5.1 Employment status last week Respondents were asked if during the week before the interview, they did any work for pay, any kind of business, farming or other activity to generate income, even if only for an hour. Table 5 - 1 Respondents working status last week (any work for pay or any income generating activities) Table 4.1 Respondents working status last week (any work for pay or any income generating activities) Round 1 Round 3 Round 4+ Round 2 (July) Status of work (May/June) (August) (September) Respondent WORKING (%) 69 68 73 80 Also was working in previous round 81 80 85 Returned to work since previous round 19 20 15 Respondent NOT WORKING (%) 31 32 27 20 Also not working in previous round* 68 56 56 65 Stopped working since previous round* 32 44 44 35 This table includes only 1209 observations that represent HHs with information for all rounds and that they did not change respondents along the way for Round Impacts in employment do not seem to be significant; since May, around 70% of the population has been working despite COVID-19. In May/June, during the first round of the survey, 69% of respondents was working. The share dropped slightly to 68% in July but rose again to 73% in August and rose further to 79% in September. Of those interviewed in July, 81% was also working May/June while of those interviewed in August, 80% was working in July while 85% of those interviewed September was also working in August during the third round. 5.2 Main industry of those respondents working The respondents that reported to be working were asked the main activity of the business or organization in which they are working in their main job. As expected, most of the employees work in Agriculture. About half of respondents work in agriculture with seasonal variations. Buying and selling sector is the next big employer employing around 20% of the respondents, following the opposite seasonal effects than agriculture. 39 Figure 5 - 1 Main industry of those respondents working, (% of respondents for selected sectors) 50 46 47 44 22 18 19 18 11 12 13 10 9 7 6 6 3 4 3 1 Agriculture Commerce Industry, Personal Services Other Manufacturing and Food Processing May/June (Round 1) July (Round 2) August (Round 3) September (Round 4) 5.3 Job Stability Figure 5 - 2 Changes in job Round 4 (September) 81 19 Round 3 (August) 77 23 Round 2 (July) 84 16 Same job as before Changed jobs Most of the respondents (84%) have maintained their jobs between May/June and July. However, this share dropped to 77% for those still working between July and August but rose again to 81% between August and September. Table 5 - 2 Percentage of respondents that stopped working and relation to COVID-19 outbreak. Relation to COVID- Percentage of respondents that stopped working 19 June/May (Round September July (Round 2) August (Round 3) 1) (Round 4) 56 12 12 26 Potentially related 44 88 88 74 Potentially unrelated 40 Respondents that stopped working were asked reasons that lead them to stop working. Reasons such as business/office closed - COVID-19 legal guidelines; ill/quarantined; need to care for ill relative; not able to go to farm - movement restrictions; laid off while business continues; furlough (temporarily laid off); and not able to farm due to lack of inputs are assumed to be potentially related to COVID-19 outbreak. While reasons such as business/office closed for another reason; not farming season; seasonal worker/or farming season; retired; vacation; and rotation of personnel are assumed to be potentially unrelated to COVID-19. In May/June there were more signs of potential impacts of COVID to the labor market. 56% of those who stopped working had reasons that were potentially related to COVID-19. However, the potential effects drop significantly in subsequent months of the survey recording 12% in July and August but rose again to 26% in September. Table 5 - 3 Type of work for those working % of all respondents working May/June July August September (Round 1) (Round 2) (Round 3) (Round 4) Percentage of respondents working 69 68 73 72 Family farming (or livestock or fishing) 41 38 39 44 Own business 30 33 30 29 As an employee for someone else 27 28 29 25 Business of HH or family member 2 1 2 2 As an apprentice, trainee, intern 1 0 0 0 Around 40% of the working respondents work in family farming. During the first round of the survey in May/June, of the 69% respondents working, 41% are in family farming or livestock or fishing. 30% are in own business while 27% are working as an employee for someone else. A very small share (1%) is working as an apprentice, trainee or intern. This trend is almost similar across all the four survey rounds. 41 Table 5 - 4 Changes in working condition in wage work R3 R4 R2 (July) - (August) - (September) - Percent Percent Percent Respondent working less* (% of respondents with wage- work) 7 7 4 Other adults working less* (% of HHs) 10 4 1 Average number of HH members working less, HH w respondent wage worker* 0.17 0.11 0.05 Average number of HH members working less, all HHs* 0.08 0.06 0.04 * NOT ABLE to work as usual in their WAGE JOB (at place of work or from home) last week. Respondents with wage work were asked if there are changes in the number of hours of work. During July and August, there were slightly more wage workers who worked less hours than during the fourth round of the survey in September. If other household adults are considered, there were more other adults working less hours in July than in August and much less in September than in August. The average number of household members working less among the households with a wage worker was slightly higher in July, then dropped slightly in August and dropped further in September and the same is true if all households are considered. Respondents with wage employment were asked if they were able to work as usual. 81% of respondents working in wage employment was able to work as usual in May/June during round one. The share remained at 80% in July and August but rose to 91% in the fourth round in September. Table 5 - 5 Wage Workers that worked last week, RESPONDENTS ONLY Average # of hours worked last week July August September (Round 2) (Round 3) (Round 4) All 30 32 32 Tourism 51 64 31 Food Processing 51 59 57 Manufacturing 44 55 48 Professional/Scientific/Technical Activities 38 36 40 Health 36 34 38 Construction 34 31 38 Transportation 32 46 38 Personal Services 31 24 29 Financial/Insurance/Real Estate Services 31 35 42 Mining 29 15 42 Agriculture 27 30 24 42 Buying and Selling 26 28 36 Public Administration 24 37 49 Utilities 20 25 16 Education 4 20 26 Among respondents with wage work, the average number of hours worked last week is 30 in July and slightly higher to 32 in August and September. By sector, tourism, food processing and manufacturing recorded the highest average number of hours worked while education registered the lowest in July averaging 3.8 hours, but the hours increased in August and September. Figure 5 - 3 Change in hours worked last week % of wage workers, RESPONDENTS ONLY 28 36 38 52 65 57 11 5 7 July (Round 2) August (Round 3) September(Round 4) Worked more hours Same amount Less hours The share of respondents working more hours in July was about 11% but dropped almost by half to 5% in August and slightly rose to 7% in September. Those that worked less hours in July and August were about 37% of the respondents but in September, this share dropped to 28%. 43 5.4 Family businesses Figure 5 - 4 Family businesses, main reason for closure Family businesses, main reason for closure 32 28 33 38 38 37 68 72 67 63 62 63 Temporary closed Permanently Temporary closed Permanently Temporary closed Permanently closed closed closed July (Round 2) August (Round 3) September (Round 4) Potentiall related to COVID-19 Factors Potentially unrelated to COVID-19 Factors Family businesses that have closed were asked whether the closure was temporary or permanent. Whether temporary or permanent, the family businesses were also asked the reasons for closure. Reasons such as usual place of business closed due to COVID-19 legal guidelines; no customers/ fewer customers; can't get inputs; can't travel/ transport goods for trade; and ill/ quarantined due to COVID-19 were all considered to be potentially related to corona virus. Conversely, reasons such as usual place of business closed, other reasons; ill, other reason/disease; need to take care of a family member; seasonal closure; and vacation were considered potentially unrelated to corona virus. Generally, the share of family businesses closed for reasons potentially related to corona virus are higher ranging from 63% in July to 72% in September among those temporary closed and from 62% in July to 67% in September among those permanently closed. Table 5 - 6 Family businesses by sector May/June July August (September) (Round 1) (Round 2) (Round 3) (Round 4) Buying and Selling 60 63 53 59 Food Processing 14 11 17 0 Construction 6 5 4 4 Agriculture 5 3 2 0 Personal Services 5 5 10 10 Professional/Scientific/Technical Activities 3 4 3 4 Manufacturing 3 2 4 4 44 Transportation 3 3 4 4 Others 1 1 0 0 Buying and selling is the dominant sector among family businesses. Across all the survey rounds, over half of the family businesses are in this sector. Some seasonality was observed that it is likely correlated with the agricultural cycle where workers support the harvesting period and go back to no-farm related work in September. Food processing is the next dominant sector across the first three months of the survey rounds and dropped in the fourth month. Table 5 - 7 Challenges NFE has faced due to COVID-19 Percent of respondents with NFE, Round 3 (August) All Urban Rural Have changed or plan to change how business is conducted 19 15 21 Difficulty raising money for the business 67 65 68 Difficulty selling goods or services to customers 44 39 46 Difficulty buying and receiving supplies and inputs to run my business 29 30 29 Difficulty repaying loans or other debt obligations 24 16 26 Difficulty paying rent for business location 8 9 8 Difficulty paying workers 4 4 4 Households that had non-farm enterprise were asked if they changed or plan to change how business is conducted. About 20% confirmed changing the way their business is conducted. More households in rural areas (21%) than in urban changed the conduct of their business. The most reported challenge is difficulty to raise money for the business (67%) followed by difficulty to sell goods or services to customers (44%). Table 5 - 8 Types of changes doing/planned for the Non-Farm Enterprise during 3rd Round (August) Percent of respondents that reported doing/plan to change Types of changes doing/planned (multiselect possible) how business is conducted in August (Round 3) All Urban Rural Require customers to wear masks 61 82 55 Maintain distance between customers 61 85 53 Reduce number of customers at a time 25 19 26 Switched to delivery only 3 2 4 Market products/services by phone/social media 2 8 0 Switched product/service offering 0 0 0 45 Other 29 16 32 As a cost-effective coping strategy to the COVID-19 related challenges facing the non-farm enterprises the most reported change is to require customers wear masks (61%) and maintain a distance between customers (61%). Nearly a quarter has resorted to reduce number of customers at a time. 46 6.0 Agricultural Activities Malawi has one main cropping seasons starting November to March /April and the dry farming or Dimba season starting May to October. The agricultural modules (crop and livestock) were include in round 1 in May/June and round 4 in September. In September, the interviewed households were asked if they practiced any agricultural activity during the 2020 Dimba season. To assess the COVID-19 impacts the data on participation in agriculture, data from September (2020 Dimba season) was compared with 2019 IHS Panel survey data. The results reveal that the share of households participating in agriculture during the Dimba season significantly increased in 2020 across all regions in Malawi as shown in Figure 6.1 below. The highest increase in agricultural activity was in the Southern Malawi followed by central and the least increase being in the Northern. Also, the share of households that did not participate in the farming activities declined by 10% and highest decline was Southern Malawi (12%), followed by central (8%) and last was Northern Malawi (5%). The result suggests that COVID-19 increased households’ participation in agriculture. However, the share of households keeping livestock declined by 13% between 2019 and 2020 Dimba seasons. The greatest decline in livestock activity occurred in Northern Malawi (17%) followed by Central Malawi (16%) and lastly southern Malawi (9%). Again, suggesting that COVID-19 had negative effect on livestock farming. Overall, the results suggest that COVID-19 had positive impact on crop farming and negative impact on livestock farming. Households that grew dry season crops (Dimba farming) were asked the type of crops that they had grown and the results reveal that the most grown crops to be vegetables (26%) followed by maize (13%),beans (4%, Irish Potatoes (3%) and sweet potatoes (2%). 47 Figure 6. 1 Prevalence of Livestock and Dry/Dimba Season Crop Farming Households, by region 45 2019 No Farming Activities 39 41 43 2020 No Farming Activities 33 33 34 33 11 2019 Mixed farming 66 8 2020 Mixed farming 19 23 22 24 2019 Livestock farming only 38 51 53 45 2020 Livestock farming only 28 35 37 32 2019 Crop farming only 2 6 1 4 2020 crop farming only 5 13 15 13 0.0 10.0 20.0 30.0 40.0 50.0 60.0 % of respondents by Region South % of respondents by Region Central % of respondents by Region North AG Practising (% of HHs) In May/June 2020 (Round 1) and in September 2020 (Round 4) Households that practiced livestock farming were asked if activities were affected by COVID-19. The results show that the share of livestock keeping households affected by COVID-19 declined from 5% to 1% probably because of the relaxation of the COVID-19 restrictions and declining cases over the same period. The results further show that the most affected livestock keeping households in both months were from the central followed by southern and last being Northern Malawi as shown figure 6.2. Similarly, the results also show that the most livestock keeping households in both months were those from urban areas compared to the rural areas. Figure 6. 2 Share of Livestock keeping households affected by COVID 19 in May/June and September by region and location Rural 1 5 Urban 2 5 South 1 5 Central 2 5 North 0 3 Overal 1 5 Yes, COVID 19 Affected Livestock Activities in My Farm (September -2020) Yes, COVID 19 Affected Livestock Activities in My Farm (May/June-2020) The livestock keeping households were also asked how COVID-19 affected their livestock farming activities the result revealed that the main effects of COVID-19 48 to livestock keeping households is reduced access to animal feeds and veterinary services. Overall, the share of livestock farming households reporting reduced access to animal feeds decline between May/June and September 2020. The decline in the share of households reporting reduced access to animal feeds was higher in central, north and least in the South. In the case of access to veterinary services. the difference in the share of the livestock keeping households reporting reduced access to veterinary services was small (insignificant) between the two months as shown in figure 6.3. Figure 6. 3 Distribution of How COVID-19 affected Livestock keeping households in May/June and September by region and location 100 35 86 78 40 43 41 36 31 24 21 24 4 6 0 0 % % % Overall Reduced access to animal feeds May/June Reduced access to animal feeds September Reduced access to veterinary services May/June Reduced access to veterinary services September The households were further asked if they needed to sell their livestock and 30% confirmed they wanted to sell. Of these households, 61% were not able to sell their livestock. In the northern region, up to 82% of households were not able to sell their livestock compared to 59% in the south and 52% in the center. Households that sold their livestock were asked to compare what they normally sell to the revenues from livestock sales since mid-March 2020. About half of the respondents reported the revenue is not good and less than normal. 20% reported revenue is good and better than normal while 17% believe the revenue is average. 49 7.0 Shocks and Safety Nets 7.1 Shocks Households were asked if they have been affected by any of the following shocks: - job loss; nonfarm business closure; theft/looting of cash and other property; disruption of farming, livestock, fishing activities; increase in price of farming/business inputs; fall in the price of farming/business output; lack of availability of farming/business inputs; increase in price of major food items consumed; and illness, injury, or death of income earning member of household. Figure 7 - 1a: Number of shocks per household since mid-March Round 2 (July) 1 1 0 0 1 1 12 15 10 10 13 11 36 31 34 30 45 35 46 30 35 38 32 32 17 16 18 25 13 13 Q1 Q2 Q3 Q4 Q5 % of all HHs Percent HHs, by (IHPS) wealth quintiles No shocks 1 shock 2 - 3 shocks 4 - 5 shocks 6 - 9 shocks Figure 7 - 2b: Number of Shocks since July (Round 2) Round 3 (August) 0 5 0 4 1 6 0 5 0 6 0 2 33 32 32 33 36 33 37 46 41 35 31 38 24 18 20 27 27 26 Q1 Q2 Q3 Q4 Q5 % of all HHs Percent HHs, by (IHPS) wealth quintiles No shocks 1 shock 2 - 3 shocks 4 - 5 shocks 6 - 9 shocks 50 During the July interviews (round 2), about 83% of households reportedly experienced a shock between mid-March and July. In August, during the third round of the survey, 76% of the households reportedly experienced a shock between the July interviews and the August interviews. 35% of households experienced one shock during July interviews while 37% experienced the same during the August interviews. Across wealth quintiles, the share of households that did not experience any shock is higher among richer households which rely less in agricultural self- production. Most of the households experienced one to three shocks and this is true across all the wealth quintiles. Table 7 - 1 Types of shocks, since last call (R2) & (R3) % of HHs, by (IHPS) wealth % of quintiles all HHs Q1 Q2 Q3 Q4 Q5 Fall in the price of farming/business output 66 72 67 68 65 58 Increase in price of farming/business inputs 30 35 27 36 31 21 Disruption of farming, livestock, fishing activities 29 26 27 32 30 28 Theft/looting of cash and other property 16 15 17 15 18 14 July Job loss 14 7 17 14 10 19 (Round 2) Increase in price of major food items consumed 10 14 7 8 14 9 Nonfarm business closure 7 7 3 9 8 6 Illness, injury, or death of income earning HH member 4 1 1 4 4 7 Other (specify) 0 0 0 0 0 0 Fall in the price of farming/business output 56 60 60 58 53 52 Disruption of farming, livestock, fishing activities 22 27 28 20 20 18 Increase in price of farming/business inputs 18 16 23 15 23 11 Theft/looting of cash and other property 12 9 10 11 17 9 August Job loss 10 6 14 11 7 13 (Round 3) Increase in price of major food items consumed 10 9 10 12 11 6 Nonfarm business closure 6 7 2 5 4 10 Illness, injury, or death of income earning HH member 2 0 3 1 2 2 Other (specify) 1 2 0 0 0 0 The most reported shock by households is fall in the price of farming/business output. This is reported by over half of households both in July (66%) and in August (56%). Increase in price of farming/business inputs and disruption of farming, 51 livestock, fishing activities became second and third respectively July but swapped positions in August. More poor than rich households reported fall in the price of farming/business output 72% versus 58% in July and 60% versus 52% in August; and increase in price of farming/business inputs 35% versus 21% in July and 16% versus 11% in August. 7.2 Coping mechanisms Figure 7 - 3 Coping mechanisms for shocks % of HH with Shock Did Nothing 74 78 Relied on savings 18 20 Reduced food consumption 5 10 Engaged in additional income-generating activity 7 6 Received assistance from friends & family 5 5 Other Specify 7 5 Sale of (agriculture/non-agric) assets 3 5 Borrowed from friends & family 5 4 reduced non-food consumption 2 3 Received assistance from government 2 2 August (Round 3) July (Round 2) Most of the households that experienced a shock did nothing as a coping mechanism against the shock. This is the case in both July and August. However, close to one in every five households relied on savings while 10% of households in July reduced food consumption but only 5% reported the same during the August. 6% of households reported that they engaged in additional income-generating activities in July and almost similar share (7%) in August. 7.3 Safety Nets The first three rounds of the survey collected information on safety nets. During the first round in May/June, the reference period was mid-March while for July and August interviews, the reference period was last call. Respondents were therefore asked if any member of the household received any assistance from any 52 institution such as the government, international organization, religious bodies, excluding assistance from family and friends. Table 7 - 2 Safety Nets since mid-Match(R1) last call (R2) & (R3) % of Types of assistance, any respondents by % of HHs institution % of HHs, by (IHPS) wealth quintiles residence Q1 Q2 Q3 Q4 Q5 Urban Rural Food 2 0 3 2 2 3 1 2 Direct cash transfers 1 4 3 0 1 0 0 2 May/June Average amount of (Round cash transfer (in Kwacha) 17,861 8,489 27,313 9,826 22,519 11,887 18,309 1) Other in-kind (not food) transfers 6 8 7 7 5 5 5 7 Food 1 1 1 1 3 1 3 1 Social Cash Transfer, SCT (Mtukula Pakhoma) 0 0 0 0 1 1 0 0 Average amount of SCT (in Kwacha) 8,695 16,052 24,000 9,014 4,052 500 8,870 COVID-19 Urban Cash Intervention, CUCI (Mzati July (Round Pa Covid) 1 0 2 1 2 1 1 1 2) Average amount of Mzati Pa COVID-19 (in Kwacha) Other cash transfers 1 0 1 0 2 1 0 1 Other in-kind transfers (excluding food) 4 7 3 5 2 3 1 5 Food 1 0 0 1 1 2 2 1 Social Cash Transfer, SCT (Mtukula Pakhoma) 1 1 3 1 1 1 0 2 Average amount of SCT (in Kwacha) 19,172 14,000 24,808 15,755 13,625 19,916 35,091 18,259 COVID-19 Urban Cash Intervention, CUCI (Mzati August (Round Pa Covid) 0 0 0 0 0 1 1 0 3) Average amount of Mzati Pa Covid (in Kwacha) 31,683 36,000 30,214 31,683 Other cash transfers 1 1 1 0 1 0 0 1 Other in-kind transfers (excluding food) 2 1 3 2 2 1 1 2 53 Social assistance is not abundant in Malawi. In May/June during the first round of the survey, about 2% of the households received food between mid-March and the interview date. In July (second round) and in August, the share of households that received food dropped to 1% in both survey rounds. In May/June, households were also asked if they received direct cash transfer of which 1% of households confirmed receiving. During the second and third rounds, direct cash transfer was split into Social Cash Transfer, SCT (Mtukula Pakhoma) and COVID-19 Urban Cash Intervention, CUCI (Mzati Pa COVID-19) to capture specific interventions. A very small share (0.4%) reportedly received Social Cash Transfer between May/June (R1) and July (R2). However, there was an increase in the share of households that received the same between July and August from 0.4% to 1.4%. COVID-19 Urban Cash Transfer was received by 1% of households between mid-March and May/June. But this share dropped to 0.2% the period between May/June and July. The average amount of money received through direct cash transfer between mid-March and May/June is MK17,861. Between May/June and July, an average of MK8,695 was received through Social Cash Transfer and this average nearly doubled to MK19,172 between July and August. Although the amount of COVID- 19 Urban Cash transfer was only collected in the third round (August) of the survey, the average amount is above MK30,000. By place of residence, a slightly higher share of households in rural areas received food (2%) between mid-March and May/June than their urban counterparts (1%). However, the situation reversed between May/June and July as the share of urban households that received food was higher (3%) than rural households (1%) and for the period July and August recording 2% for urban and 1% for rural households. Table 7 - 3 Source of Food Assistance since mid-March(R1) last call (R2) & (R3) % of HH that Main source of food received assistance food assistance % of respondents by residence Urban Rural NGO 29 67 23 Religious bodies 26 11 28 May/June Government 23 8 25 (Round 1) International Organization 8 3 9 Community Organization 8 5 8 54 Cooperative Companies 5 6 5 Other 1 0 2 NGO 46 56 44 Government 27 16 29 Community Organization 10 0 12 July Cooperative Companies 8 10 7 (Round 2) Religious bodies 7 18 5 International Organization 2 0 3 Other Government 54 49 56 NGO 19 39 14 Community Organization 14 1 17 August International Organization 7 8 6 (Round 3) Religious bodies 5 2 6 Other 1 1 1 Of the households that received food, the most reported source in May/June was Non-Governmental Organization (NGO) (29%) followed by religious bodies (26%) and government at 23%. During the second round, in July, NGOs remained the most reported source of food assistance (46%) and then the government come second (27%). In the third round, in August, government became the highest provider of food assistance (54%) followed by NGOs (19%). In terms of rural-urban comparison, the share of respondents that received food assistance from NGOs is higher in urban areas than in rural areas across all the three survey rounds from May/June to August. Conversely, the share of households that received food assistance from the government is consistently higher in rural areas than urban areas across all the three survey rounds. 55