Isabel Lambrecht, Derek Headey POLICY SEMINAR COVID-19 Impact Assessments for Myanmar NOV 11, 2020 - 02:00 PM TO 03:30 PM MMT

1. Assessments of the impact of COVID-19
on Myanmar’s food security and welfare
November 11, 2020
2:00 – 3:30 PM MMT
MODERATOR
Katy Webley
Fund Director, Livelihoods and Food Security Fund
SPEAKERS
Isabel Lambrecht
Research Fellow, International Food Policy
Research Institute
Derek Headey
Senior Research Fellow, International Food Policy
Research Institute
Emilie Perge
Senior Economist, World Bank
OPENING REMARKS
U Min Ye Paing Hein
Deputy Minister, Ministry of Planning, Finance and
Industry
CLOSING REMARKS
U Kyaw Swe Lin
Director General, Department of Planning, Ministry of
Agriculture, Livestock and Irrigation
V I R T U AL P O L I C Y S E M I N AR
PANELIST
Daw Ei Ei Phyo
Deputy Director, Department of Social Welfare,
Ministry of Social Welfare, Relief, and Resettlement

2. Impacts of COVID-19 on
rural men and women: the
case of Central Myanmar
Isabel Lambrecht and Catherine Ragasa
Kristi Mahrt, Zin Wai Aung, Hnin Ei Win, A Myintzu, Michael Wang
Development Strategy and Governance Division
International Food Policy Research Institute
November 11, 2020

3. Study site and data
Interviews with rural households (male and female respondents)
from two irrigation schemes in Central Dry Zone, Myanmar
Phone surveys focus on impacts of C19 on rural livelihoods and
women’s empowerment
Recall period Implementation # households
Baseline, in-
person
Jan 2019 –2020 January 2020 998
Phone Survey
round 1
Feb-May 2020 June 2020 606
Phone Survey
round 2
June-July 2020 August 2020 535
Phone Survey
round 3
Aug-Sept 2020 October 2020 503
Phone Survey
round 4
TBD

4. Lower household incomes among both landed and landless
households
▪ 72% of households report lower
income than usual
▪ Landless households are more
likely to have lower incomes
than landed households
▪ “Sticky effect” after 1st wave
(June-July)
▪ Even more households report
decreased incomes at the onset
of the 2nd wave
0
10
20
30
40
50
60
70
80
90
100
All Landed Landless
Percent of households with lower
income
Feb-May June-July Aug-Sep

5. All sources of income are affected, though non-farm income
to a higher extent
0 20 40 60 80 100
Remittances: Aug-Sept
Remittances: June-July
Business: Aug-Sept
Business: June-July
Wage: Aug-Sept
Wage: June-July
Farm: Aug-Sept
Farm: June-July
% of households with lower incomes, by income source
Somewhat lower (1-20%) Much lower (>20%)

6. Farmers face additional challenges to crop production
▪ Crop production
o Challenges in accessing agricultural
inputs (16% - 13% - 22%)
o Farmers invested less than usual in
inputs because of financial constraints:
16% in June-July; 28% in August-
September
o Natural shocks: prolonged drought,
lack of irrigation water
▪ MADB loans:
o 58% of farmers obtained an MADB
loan in June or July, half of them also
obtained the special COVID-19 loan.

7. But even more challenges to sell their crops
Feb-May
% of farmers
June-July
% of farmers
Aug-Sept
% of farmers
Any difficulties in selling your harvest? 68 33 24
If yes, which difficulties:
Lower prices 63 85 66
Poor demand/no buyers 32 16 20
Markets closure 28 17 10
Movement restrictions 27 9 38
No means of transportation to markets 25 0 11
Do you anticipate any difficulties in selling your harvest? 36 47 42
If yes, which difficulties:
Lower prices 75 55 40
Poor demand/no buyers 45 59 50
Markets closure 8 5 9
Movement restrictions 15 15 71
No means of transportation to markets 22 14 20

8. Coping mechanisms are using savings, selling assets,
reducing food expenditures, and
0 20 40 60 80 100
Received government transfer
Received NGO or private
transfer
Used savings
Sold assets
Borrowed
Reduced food expenditures
% of all households
Feb-May: All
June-July
Aug-Sept
• Increasingly more households
apply coping mechanisms
By August-September:
• 56% was using savings
• 44% reduced food
expenditures
• 26% sold assets: gold and
jewelry, livestock, agricultural
parcels and implements
• Nearly all receive government
transfers by August-September,
but men are typically considered
the recipients of the transfers
(71%)

9. Nutrition impacts – less meat and fish consumption, but dietary
diversity is maintained
Feb-May June-July Aug-Sept
Meat – did you eat meat less often than normal? 39 24 39
Did you eat a smaller quantity of meat? 37 24 27
[For those that reported doing so] Why?
Reduced income 78 83 88
Not available 13 2 16
Higher price 16 19 6
Afraid of COVID 12 10 10
Fish – did you eat fish less often than normal? 29 18 30
Did you eat a smaller quantity of fish? 26 17 23
[For those that reported doing so] Why?
Reduced income 67 65 81
Not available 12 14 22
Higher price 13 12 4
Afraid of COVID 10 7 8
Diet Diversity (MDD-W) 6.4 6.4 6.8

10. More household disagreements or tension than usual
0
10
20
30
40
50
60
All respondents Respondents in
households with
normal income
Respondents in
households with
income loss
Experience of unusual tension or conflict in the
household
Feb-May June-July Aug-Sept
• More tension and conflict at
home due to COVID-19
• Significantly more tension in
households who reported
having lower incomes than
usual
• Tension remained in June-July,
and increased further in
August-September.

11. Summary
▪ Rural livelihoods strongly affected:
o Income loss more frequent among landless households compared to landed
households, off-farm wage employment and non-farm enterprises more strongly
affected than farm work
o Farmers face additional (non-COVID) challenges: drought, shortage of irrigation water
…
o “Sticky” and worsening impacts over time
▪ Coping mechanisms potentially jeopardize future income and health:
o Drawing down savings, selling assets, borrowing
o Reducing consumption of expensive (but nutritious) foods
▪ Stress also affects relations within households:
o More disagreements or tension at home

12. Recommendations
▪ Maintain focus on keeping the agri-food sector active and viable: both
input and output markets, including farmer credit
▪ Rural non-farm enterprises and wage employment are key to income
generation. Also ensure rural enterprises and workers are supported and
benefit from cash-for-work schemes, etc.
▪ Nutrition messages are reaching respondents, but could be further used
to falsify myths on fish and meat consumption and bring messages on
dealing with intra-household tension.
▪ Consider the option of targeting transfers to women in the household

13. Thank you!
i.lambrecht@cgiar.org
c.ragasa@cgiar.org
ADSP – MOALI;
Myanmar Survey Research;
Community leaders and
respondents
Japanese SUN Trust Fund;
PIM; USAID; LIFT

14. Poverty, food insecurity, and social
protection during COVID-19:
Combined evidence from household phone
surveys and micro-simulations
1
Dr. Derek Headey, Senior Research Fellow, IFPRI

15. Research & survey teams
Research team
Study team: Derek Headey, Than Zaw Oo, Kristi Mahrt, Xinshen Diao, Sophie
Goudet, and Isabel Lambrecht
Other collaborators:
• LEGACY Evaluation Team: Elisa Maria Maffioli (U. Michigan), Erica Field (Duke
University)
• Maymay Evaluation Team: Russell Toth, Andrea Di Miceli
• IPA survey implementation team: Thein Zaw Oo, Afke Jager, Ricardo Morel
2

16. Introduction
• C19 international shocks & prevention measures contributed to rising poverty in
1st COVID wave between January and June
• IFPRI simulation studies predict income would fall by 1/3rd (Diao & Mahrt, 2020)
• Similar results from Rd 1 of C19 Rural-Urban Food Security Survey (C19-RUFSS)
• Second wave of C19 in mid August poses a much greater challenge
1. Much larger numbers of cases: larger negative impact on consumer demand
2. Strict, widespread & prolonged lockdown measures
3. More disruption to internal trade and marketing (See previous webinar)
4. Households may have exhausted viable coping mechanisms in 1st wave
3

17. Introduction
We try to answer two retrospective questions on the second C19 wave using four
rounds of the C19-RUFSS from June-September:
1. What impact has this second C19 wave had on poverty and food insecurity?
2. How have households coped with income losses, including access to assistance?
And two prospective questions on impacts of 2nd C19 wave in 2020-2021 using
microsimulations from nationally representative household survey data (MPLCS):
1. What are likely national poverty impacts in 2019-2020 of different economic
disruptions scenarios and different cash transfer quantities to the poor?
2. What would it cost to implement larger-scale social protection measures to reduce
extreme poverty?
4

18. Methods
C19-RUFSS (Phone Survey)
• ~2000 women in urban Yangon & rural dry zone: June, July, August, September
• Monthly incomes & $1.90/day poverty, food insecurity, dietary diversity
• Coping mechanisms, external assistance/social protection
POVERTY SIMULATION
• Income, expenditure and livelihood data from 2015 MPLCS (national survey)
• Measure extreme poverty based on food poverty line of 1213 kyat/person
• Use C19 economic disruption scenarios from Diao & Mahrt (2020) to
negatively shock 19 different sources of household income in 2020-21
• Additionally, we positively shock household income with monthly cash
transfer increments, and measure net poverty impacts and fiscal costs
5

19. Phone survey results from C19-RUFSS
6

20. Income-based poverty has risen rapidly, climbing from
16% in January to 63% in September
7
16%
7%
25%
42%
32%
52%
33%
21%
44%
41%
29%
52%
63%
59%
66%
0%
25%
50%
75%
100%
Total Urban Rural
Income-basedpoverty(%)
January June July August September
35% reported zero
income in Sept.

21. C19 leading to income losses for a wide range of livelihoods,
while weather shocks are hurting farm households
8
Total Farms
Skilled
labor
Unskilled
labor
Salary Trade Other
N=1,761 N=260 N=236 N=489 N=172 N=93 N=511
Loss of employment 40% 17% 25% 40% 30% 11% 66%
No work - movement restrictions 35% 22% 50% 31% 20% 39% 41%
Daily labor opportunities reduced 28% 18% 47% 45% 28% 15% 8%
Less customers/clients 11% 9% 11% 16% 2% 56% 3%
Lean season 9% 48% 1% 1% 2% 4% 2%
Weather/climate problems 8% 30% 1% 5% 2% 1% 5%
Reduced salary/wage 7% 3% 8% 3% 36% 3% 4%
Reasons for changes in income by livelihood among households reporting income losses

22. Food insecurity is rising again in Yangon, and the share of
urban mothers with poor diet diversity is rising sharply
9
23%
28%
11% 11%
30%
13%
19%
5% 4%
39%
11%
18%
3% 4%
50%
17%
25%
6% 8%
53%
0%
10%
20%
30%
40%
50%
60%
Ate less quantity Not enough healthy
food
Ran out of food Skipped meal Poor maternal dietary
diversity
Food insecurity experiences in past 30 days Past 24 hrs
June/July July/Aug Aug/Sep Sep/Oct
Food insecurity about half as
common in rural sample
In adequate diets also rising
in rural sample: 15 to 24%
Trends in food insecurity experiences & poor maternal dietary diversity in Yangon

23. Over half of households received cash transfers by
September, but targeting is far from perfect
10
Asset-based poverty Income-based poverty
All
households
Asset
poor
Asset
non-poor
Income
poor
Income
non-poor
Both samples 44.6% 47.6% 32.8% 49.6% 36.1%
Rural sample 48.2% 50.3% 32.5% 53.2% 38.7%
Urban sample 41.0% 44.2% 32.9% 45.7% 34.1%
Receipts of cash-based assistance by asset-based and income-based poverty status
And very few qualified mothers received one-off MCCTs
Total Urban Rural
Monthly cash payment 0.4% 0.8% 0.0%
One-off payment 2.5% 4.2% 0.8%
Qualified mothers who reported receiving a one-time MCCT in September 2020

24. Extreme poverty & cash transfer results
from national-level microsimulations
11

25. National-level simulations show extreme poverty triples
in lockdowns, but can be reduced by larger cash transfers
12
monthly transfers to poor households (Myanmar Kyat)
0 10,000 20,000 30,000 40,000 50,000 60,000
NATIONAL poverty prior to C19 disruptions 9.8
Strict lockdown + external disruptionsa 31.6 28.7 25.9 23.5 21.1 19.0 16.8
Lockdown easing + external disruptionsb 21.7 18.7 16.1 14.0 12.2 10.2 8.5
Modest restrictions + some external recoveryc 15.8 13.5 11.3 9.2 7.7 6.3 5.1
Modest restrictions + further external recoveryd 12.9 11.1 8.9 7.3 6.0 4.9 4.1
• Poverty rises from 9.8% pre-C19 to 31.6% in lockdown, and falls in recovery phases
• Lockdowns badly affect urban poor: Urban share of poor is 6.7% pre-C19, but 22.6% in lockdowns
• Perfectly targeted 20,000 kyat transfers reduce poverty by 4.7 points in lockdown
• Perfectly targeted 40,000 kyat transfers reduce poverty by 10.5 points in lockdown
Predicted impacts of C19 economic disruptions under different cash transfer quantities

26. Monthly cash transfers for the extreme-poor are
expensive during lockdowns, but sorely needed
• With perfect targeting Myanmar needs to spend 61.1 billion kyat/month to give
20,000 kyat to 3.05 million households expected to be poor during lockdowns
• In recovery scenarios there are many fewer extremely poor, so costs fall substantially
13
Magnitudes for monthly transfers to poor households (Myanmar Kyat)
10,000 20,000 30,000 40,000 50,000 60,000
Scenario 1: Strict lockdown + external disruptions 30.5 61.1 91.6 122.2 152.7 183.3
Scenario 2: Lockdown easing + external disruptions 19.3 38.7 58.0 77.4 96.7 116.1
Scenario 3: Modest restrictions + slow recovery 13.4 26.8 40.2 53.6 67.0 80.4
Scenario 4: Modest restrictions + faster recovery 10.9 21.7 32.6 43.4 54.3 65.2
Monthly costs of perfect targeting of extremely poor households (billion Myanmar Kyat)

27. Recommendations summary
• Rising poverty requires more social protection outlay and better targeting (IGC report):
1. Estimate required funding at community level via asset-based poverty measures
2. Identify beneficiary households within each community via local leaders
3. Establish clear criteria & budget ceiling for transfer allocations in communities
• Our results suggest some principles for more strategic social protection during C19:
1. Flexibly increase cash transfers during lockdowns to combat extreme poverty
• May also improve compliance of stay-at-home orders
2. Expect severe impacts in urban areas (farming more robust to C19)
3. Strategically relax lockdown measures to reduce poverty, but find ways to
target households not able to recover livelihoods quickly (e.g. cash for work)
4. Invest in M&E of C19 social protection measures to track targeting & impacts
• Larger-scale national phone survey as a platform for more longer term M&E system
14

28. Thank you
• Project note for this study is available online:
https://www.ifpri.org/publication/poverty-food-insecurity-and-social-
protection-during-covid-19-myanmar-combined-evidence
Or google “IFPRI poverty social protection Myanmar”
15

29. V I R T U AL P O L I C Y S E M I N AR
က ျေးဇျေးတင်ပါတယ်
Thank you
IFPRI-Myanmar website: https://myanmar.ifpri.info