Resilience, Emerging Vulnerabilities, and Response Priorities
Evidence on livelihood and response realities in Bangladesh
during the Ongoing COVID-19 Crisis
Team Members
Atiya Rahman, Senior Research Associate Dr. Imran Matin, Executive Director
Dr. Narayan C Das, Senior Research Fellow Shakil Ahmed, Senior Research Associate Tanvir Ahmed Mozumdar, Research Associate Field Research Team
Dr. Hossain Zillur Rahman, Executive Chairman Professor Syed Hashemi, PPRC Trustee
Md. Abdul Wazed, Senior Fellow, PPRC & Former DG, BBS Umama Zillur, Research Associate
Sabrina Miti Gain, Research Associate Fatema Mohhamad, Research Assistant Field Research Team
BIGD PPRC
Contextual Realities
• Epidemiology: High infection-low mortality
• No extreme form of lockdown
• State response—school closure, mobility restriction through transport system and religious gathering control
• Micro-level adjustment through community and local government/ law enforcement
• Incremental and early resumption of economic activities
Crisis Timeline
March April May June July
25 Mar: Stimulus package announcement
for RMG
26 Mar 30 May
Lockdown 4 Apr 12 Apr
PPRC – BIGD Phase I Survey
20 Jun 2 Jul
PPRC – BIGD Phase II Survey 1 Jun: Lockdown relaxed from
here onwards
RMG workers returning to cities 25 Apr 30 Apr 13 April:
Stimulus package announcement
for SMEs
10 May:
Resumption of MFI activities at limited scale 8 Mar: First
COVID Case Identification
PPRC-BIGD Phase II Livelihoods Survey
7,638 HHs
of which
4424
(58%) were panel sample3121
(41%) are new sample93
(1.2%) are additional new sample from CHTStudy Period
20 June 2 July 43
56
1
Sample distribution by zone:
Urban: 4241 Rural: 3304
Rural Urban CHT
26
19
10 10 9 9 8
5 2 1
0 5 10 15 20 25 30
Unskilled labour
Micro and Small…
Skilled Labour
Transport worker
Salaried Job
Unemployed
Agriculture
Rickshaw puller
Factory Worker
Housemaid
Pre-COVID Occupational profile (% of HH Heads)
Complementary Surveys
To complement findings from our survey, we have utilized insights from other surveys
- BIGD-Monash University, Australia’s COVID Response Research on small enterprises and their workers
- BIGD-Universiti Malaya’s Research on the COVID impact on Education
How is COVID-19 Increasing
Overall Vulnerabilities?
Synopsis
Months of low
Income Inadequate
support
Savings Depletion and Debt dependence
Longer-term poverty
May cause
May cause
For many households
Accumulating non-food expenditure
Food Consumption
Reduction
Reverse Migration
Months of low income
Vulnerability: Income Loss
Rural Upper Poverty Line (BDT 88) Urban Upper Poverty Line (BDT 105)
COVID19 pushed majority of population below the upper poverty line
Sluggish rate of income
recovery in June, still below poverty line
April
June
Most did not get external support, for those who did, support was far from income loss
Vulnerability: Inadequate Support
39% households received any support between April and June 2020
For those who received support, it amounted to roughly 4% of their
lost income due to COVID-19
And, accumulating non-food expenditure
Vulnerability: Non-food Expenditure Burden
Burden of house rent
and utility expenditure is
an urban phenomenon
Lack of negotiation over house rent across poverty groups in urban slums
Vulnerability: Non-food Expenditure Burden
In June, Two-third of tenants were paying the same rent as the pre-COVID period
Extreme poor households have lowest negotiation power
58
76 76 84
0 20 40 60 80 100
Extreme-poor Moderate
Poor Vulnerable
Non-poor Non-poor
% of slum dwellers who live in rented house
June's Poverty Group
Months of Food Poverty
Dietary diversity remains low: No milk & meat since March for majority
Coping: Nutritional Deprivation
Per capita food expenditure went down by 21% in rural and 27% in urban slums
Did not improve from April
April
June
Food Consumption Reduction for coping may prove disastrous
Coping: Nutritional Deprivation
Reduced food consumption is one of the prominent coping mechanisms which
remains to be important
38
30
0 15 30 45
Phase I (April) Phase II (June)
% of HHs
Reduced Food Consumption for Coping
Early stage saving depletion, later debt-dependence
Coping: Savings and Debt
Majority using savings to meet food need
Dependency shifted from savings to loan and grocery credit, indicating possible savings depletion for many
April
June
Income loss and non-food
expenditure burden are the apparent drivers
15.64
8.35
1.26 0.62
Respondent moved from Dhaka to another district
Respondent moved from Chattogram to
another district
Respondent Moved from other districts
to Dhaka
Respondent Moved from other districts
to Chattogram
% of HHs
Reverse Migration: leaving cities in large numbers
Coping: Migration
Who are more vulnerable?
Female heads twice as much likely to become
unemployed; Half as much likely to find another job Female Labor Intensive Works (e.g. housemaids, beauty parlour, tailoring) more affected
Though factory workers got benefits of stimulus package as form of wage
77.14
6.97 15.89
64.59
3.91
31.49
Respondent
have same job Respondent shifted to another job
Unemployed in June
% of respondents
Male Female
Widening Gender Gap
Gender Gap
Occupational Shift, Workers’ income loss
Further marginalization in female labour market participation
Market Inefficiency
Efficient (profitable) businesses with lower capital are forced to leave business,
relatively inefficient businesses with higher capital are staying
Micro and Small businesses with lower capital
Small firms and their capital Government Support
Result?
Reduction in overall efficiency
Shifting to More Vulnerable Occupations
June Occupation February Occupation
Agriculture Formal Job Informal Job Unemployed
Agriculture 79.88 0.58
8.77
10.77Formal Job 0.88 76.46
7.13
15.53Informal Job 2.02 0.25 78.93
18.8
Informalisation
Urban Households are disproportionately affected
Urban Slum Dwellers
Occupations of urban poor more vulnerable
Almost three times higher burden of rent and utilities
Higher food price in cities
Who are migrating?
Extreme poor households less likely to migrate
Why?
No place to go, cost of migration, lower cost of living, etc.
Urban Slum Dwellers
10.36
14.58 13.94
16.65
0 5 10 15 20
Extreme poor Moderate
poor Vulnerable
non-poor Non-poor
Poverty Status in February
Ended up in casual/unskilled work after migration A quarter remained unemployed
Urban Slum Dwellers
Migration Outcome
5
8 7
21
3
10
15
26
5 0 1
5 10 15 20 25 30
Agriculture Transport
worker Skilled Labour Unskilled
labour Factory
Worker Salaried Job Micro and
Small Business Unemployed Rickshaw
puller Housemaid
% of Inter-district Migrant Respondents
June's Occupation
Synopsis
Months of low
Income Inadequate
support
Savings Depletion and Debt dependence
Longer-term poverty
May cause
May cause
For many households
Accumulating non-food expenditure
Food Consumption
Reduction
Reverse Migration
Resilience but risks of reversals and new traps
Reversal in reduction in poverty
Emerging New Poor; 22.8% in April and 21.7% in June Only 1.1 percentage point reduction between April and June
Long-term Impact
Nutritional reversal
For growing children, specially in extreme-poor and poor families, months of negative coping through reduction in consumption & diet
diversity can impact on stunting
Resilience but risks of reversals and new traps
Reversal in human capital
Significant and continuing learning loss is putting a generation of children at a new human capital disadvantage particularly from poorer and rural
families who have unequal access to technology
Long-term Impact
Reverse push-migration
In a historic reversal, there is urban to rural migration, more push
than pull, where those migrating are the non-poor and vulnerablenon-poor rather than the urban extreme poor
Resilience but risks of reversals and new traps
Health reversals
More significant than gaps in pandemic-care per se, disruptions in reproductive and child healthcare services and non-covid healthcare needs as well as continuing neglect of key reform priorities have deepened
health sector challenges
Long-term Impact
Labour market informalization
The resilience evident through early recovery appears to have come through further informalization towards lower-skill jobs creating the
risks of low-earnings trap and widening gender gap
Policy Recommendations
Social Protection
• Scale up urban social protection
• More effectively targeted cash support program for the ‘new poor’
• Address nutritional poverty of the extreme poor Economic
recovery
• New stimulus package for low capital but productive MSME through alternative delivery platform such as MFIs
• Address widening gender gap in the labour market through specific skill and financing support program for urban and rural women
• Alternative ‘stimulus’ of targeted policy support to critical economic export and domestic economy sectors
Policy Recommendations
Health • Specific push on urban primary healthcare
• Address disruptions in reproductive and child health services
• Strengthen preparedness on possible ‘second wave’ of the pandemic
Human capital
• National consultation on priority program to address learning loss recovery
• Leverage Covid-19 period innovations to revisit quality education agenda with particular attention to pedagogy and a new teaching mission
Annex
Female Labor Intensive Works are more affected
Occupation in June
Agriculture Transport worker Skilled
Labour Unskilled
labour Factory
Worker Salaried
Job Small
Business Unemploy
ed Rickshaw
puller Housemai d
February occupation
Agriculture 79.9% 0.6% 0.6% 5.8% 0.0% 0.0% 2.2% 10.8% 0.2% 0.0%
Transport worker 1.3% 77.4% 0.0% 2.6% 0.0% 0.2% 0.7% 16.7% 1.1% 0.0%
Skilled Labour 0.4% 0.7% 71.6% 4.7% 0.2% 0.5% 2.5% 18.3% 1.0% 0.0%
Unskilled labour 2.9% 0.5% 0.2% 74.0% 0.2% 0.1% 1.1% 20.3% 0.8% 0.0%
Factory Worker 0.0% 0.9% 0.0% 4.7% 79.9% 0.0% 2.4% 10.3% 0.9% 0.9%
Salaried Job 1.6% 0.4% 0.9% 2.2% 0.0% 78.8% 1.4% 14.1% 0.7% 0.0%
Small Business 1.8% 0.2% 0.0% 2.4% 0.0% 0.1% 77.4% 17.2% 0.8% 0.0%
Unemployed
0.6% 0.2% 0.0% 1.0% 0.0% 0.7% 0.5% 96.9% 0.0% 0.0%
Rickshaw puller 0.0% 1.2% 0.0% 6.1% 0.0% 0.0% 1.2% 11.4% 80.2% 0.0%
Housemaid 0.0% 0.0% 0.0% 3.3% 0.0% 0.0% 0.0% 54.2% 0.0% 40.8%
Workers’ Employment Status and Income Drop Relative to Pre-COVID
Poorest recovery for female
workers
Female workers are losing jobs and are less likely to return to work after lockdown
-60.21 -64.53
-29.46
-52.08
-90 -60 -30 0
Drop in monthly income relative to pre-COVID (%)
During lockdown July
Male
58.01
48.55 84.62
61.85
0 30 60 90
Male Female
% of Employees in February
During lockdown July
Female
Source: BIGD-Monash University, Australia’s COVID Response Research on small enterprises and their workers
Drop in Sales relative to pre-COVID by Initial Capital of Small Firms
Poorer light engineering firms are facing severe drop in sales after the lockdown
-48.59
-52.83
-44.36
-25.24
-60 -40 -20 0
Drop in sale relative to pre-COVID (%)
1st quintile 2nd quintile 3rd quintile 4th quintile
Source: BIGD-Monash University, Australia’s COVID Response Research on small enterprises and their workers
Small Business’s Access to Government Support
1226 out of 1960 enterprises (63%) know about the package 65 out of 1960 enterprises (3%) applied for the support
1 out of 1960 enterprises received the support
1000 out of 1960 enterprises (54%) don’t know how to apply
Source: BIGD-Monash University, Australia’s COVID Response Research on small enterprises and their workers
