EXECU T IVE SU MMARY

Bangladesh Poverty Assessment
Facing old and new frontiers in poverty reduction

POVERTY AND EQUITY GLOBAL PRACTICE
SOUTH ASIA REGION

POVERTY AND EQUITY GLOBAL PRACTICE SOUTH ASIA REGION
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Contents

Foreword v

Acknowledgements vii

List of abbreviations ix

Executive Summary 11

Introduction 19

Part 1: Assessing performance from 2010 to 2016/17 21

Bangladesh continued the progress in poverty reduction 21

Robust economic growth drives poverty reduction but not as
effectively as before 24

Very little poverty reduction occurred in urban areas 26

Rural Bangladesh marked by East-West divide in poverty reduction 29

The Rohingya refugee crisis impact on poverty remains local 32

Part 2: Factors behind the trends 33

I. Household demographics and education have contributed to
consumption gains across urban and rural areas 37
Bangladesh continues to make progress in fertility, education,
and other non-monetary dimensions of well-being 37
Lower fertility and education gains fuel consumption growth 42
Progress in the rural West was slower, contributing to
the re-emergence of an East-West divide 46
Bangladesh Poverty Assessment

II. Rural income growth: poverty reduction was rural but
not predominantly agricultural 48
All economic sectors have contributed to poverty
reduction since 2000 48
From 2010 to 2016, households engaged in industry and services
led rural poverty reduction 50
Agricultural growth was slower and less poverty
reducing than in the past 52
In the West, the agricultural predominance of rural
livelihoods slowed progress 53

III. Urban income growth: gains in manufacturing,
but stagnation in the service sector 54
Industry, particularly the garments sector, led urban poverty reduction 54
Slow manufacturing job creation curbed poverty reduction
and reduced female labor force participation 57
Private returns to education fell in urban areas 59
The gains of agglomeration in Dhaka and Chittagong
are more limited for the poor 60

Part 3. Distilling the evidence and looking ahead 63

References 69

Data Description Annex 73

Annex Tables 78

iv
Foreword

Bangladesh has an inspiring story of reducing poverty and advancing develop-
ment. Since 2000, the country has reduced poverty by half. In the last decade and
a half, it lifted more than 25 million out of poverty.

The country’s economy remained robust and resilient even in the face of many
challenges. All sectors of the economy have contributed to poverty reduction.
This has been accompanied by enhanced human capital, lower fertility rates and
increased life expectancy, which have also significantly contributed to increase
households’ ability to earn more and exit poverty.

Yet, behind this progress, there are emerging contrasts. As the country is rapidly
urbanizing, its rural and urban areas did not experience the same level of poverty
reduction. The rural areas reduced poverty impressively, accounting for 90 per-
cent of the poverty reduction since 2010. But, in urban areas, progress has been
slower and extreme poverty has not decreased.

The country’s higher economic growth in the last decade has not led to a faster
poverty reduction. Specially, poverty has stagnated and even increased in the
Western divisions while the Eastern divisions fared better.

This report highlights the need for both traditional and fresh solutions. To end
extreme poverty by the next decade, Bangladesh will need to continue to build
on its successes, such as family planning, educational attainments, and growth
in agriculture and manufacturing. But at the same time, it will need solutions to
overcome new and re-emerging frontiers of poverty reduction.

v
Bangladesh Poverty Assessment

The world can learn from Bangladesh’s development and poverty reduction expe-
riences. But with about one out of four people still living in poverty, much needs
be done to create equal opportunities for all citizens. The country also faces new
questions where more evidence is needed. Answers will come from its own expe-
riences as well as from other countries that have followed similar transformative
paths.

I hope this report provides policy makers and researchers with sound empirical
evidence to decide on policy actions that will help Bangladesh become an upper
middle-income country.

Mercy Tembon
World Bank Country Director for Bangladesh and Bhutan

vi
Acknowledgements

This poverty assessment has been prepared by a team led by Ruth Hill (Lead
Economist, EA2PV) and Maria Eugenia Genoni (Senior Economist, ESAPV).
Joaquin Endara (Consultant, ESAPV), Faizuddin Ahmed (Consultant, ESAPV),
Yurani Arias Granada (Consultant, ESAPV), Kelly Yelitza (Consultant, ESAPV),
Nelly V. Obias (Program Assistant, ESAPV) and Shegufta Shahriar (Team Assistant,
SACBD) have been core team members. The report draws on eleven background
papers that are published in full in Volume 2 of the assessment. The authors of
the background papers (in addition to the above-mentioned core members)
include: Monica Yanez-Pagans (Senior Economist, HLCED), Nobuo Yoshida (Lead
Economist, EA1PV), Dipankar Roy (Project Director HIES, Bangladesh Bureau of
Statistics) and Abdul Latif (Deputy Project Director HIES, Bangladesh Bureau of
Statistics) who co-authored the methodological note about poverty measure-
ment using the Household Income and Expenditure Survey; Mohammad Yunus
(Senior Research Fellow, Bangladesh Institute of Development Studies) who
wrote the paper on poverty convergence across districts; Binayak Sen (Senior
Research Fellow, International Food Policy Research Institute, IFPRI) who led the
analysis on rural transformation; Hossain Zillur Rahman (Executive Chairman,
Power and Participation Research Centre, PPRC) who co-authored the analysis on
urban poverty; Wameq Raza (Economist, ESAPV and Aphichoke Kotikula (Senior
Economist, HGNDR) who led the work on female labor force participation; Markus
Poschke (Associate Professor, McGill University) who authored work on the
urban labor market; and Saurav Dev Bhatta (Senior Economist, HSAED), Uttam
Sharma (Consultant, HSAED), Buyant Erdene Khaltarkhuu (Statistician, DECDG),
and Laura Maratou-Kolias (Consultant, ESAPV) who co-authored the analysis on
education outcomes. Hossain Zillur Rahman, Binayak Sen and Mohammad Yunus
also provided comments on other aspects of the poverty assessment.

vii
Bangladesh Poverty Assessment

This work has benefited greatly from generous comments from several people.
Zahid Hussain (Lead Economist, ESAMU), Johannes Hoogeveen (Lead Economist,
ESAPV), and Christian Eigen-Zucchi (Program Leader, ESADR) commented on
early drafts of background papers and guided the team as the overview was put
together. Dean Jolliffe (Lead Economist, DECIS), Ambar Narayan (Lead Economist,
EPVGE), and Akhter Ahmed (Country Representative, IFPRI Bangladesh) provided
peer review comments on this volume. Background papers were reviewed by:
Dean Jolliffe (Lead Economist, DECIS), Nandini Krishnan (Senior Economist,
ESAPV), Sarosh Sattar (Senior Economist, EA2PV), Iffath Sharif (Practice Manager,
HAFS3), Gabriela Inchauste (Lead Economist, ELCPV), Tom Bundervoet (Senior
Economist, EA1PV), Johannes Hoogeveen (Lead Economist, ESAPV), Christina
Weiser (Economist, ESAPV), Urmila Chatterjee (Senior Economist, ESAPV), Alan
Fuchs (Senior Economist, EECPV), Sailesh Tiwari (Senior Economist, EEAPV),
Hiroki Uematsu (Senior Economist, ESAPV), Madhur Gautam (Lead Agriculture
Economist, SAGGL), Forhad Shilpi (Senior Economist, DECEE), Syud Amer Ahmed
(Senior Economist, HSASP), Ana Maria Munoz (Senior Social Scientist, EPVGE),
Sonya Sultan (Senior Social Development Specialist, SSASO), and Monica Robayo
(Economist, EECPV). Participants in seminars at the Bangladesh Institute of
Development Studies (BIDS), Center for Policy Dialogue (CPD), Dhaka University,
and the Bangladesh Club at the World Bank also provided excellent comments
that improved the work. In addition, the report benefited from discussions with
participants at a workshop organized by the General Economics Division, Planning
Commission. We would like to extend our thanks to Dr. Shamsul Alam, Senior
Secretary of the General Economics Division, Ministry of Planning, Government
of Bangladesh, for providing feedback on preliminary findings and chairing a
workshop for his staff at which the findings were presented and discussed. We
benefited immensely from all the comments and guidance received. This work
has been prepared under the direction and guidance of Benu Bidani (Practice
Manager, ESAPV), Qimiao Fan (Director, Strategy and Operations, GGEVP), Robert
Saum (Director, OPSPF), and Dandan Chen (Operations Manager, SACBD).

viii
List of abbreviations

ADB Asian Development Bank HDDS Household Dietary Diversity Score
BBS Bangladesh Bureau of Statistics HES Household Expenditure Survey
BDT Bangladesh Taka (currency) HIES Household Income and
Expenditure Survey
BES Bangladesh Enterprise Survey ILO International Labor Organization
BIDS Bangladesh Institute of IMPS Integrated Multiple-Purpose
Development Studies Sample
BMET Bangladesh Bureau of IOM International Organization for
Manpower, Employment and Migration
Training
BRAC Bangladesh Rural Advancement LFS Labor Force Survey
Committee
BUISBS Bangladesh Urban Informal NGO Non-Governmental Organization
Settlements Baseline Survey
CAFE Computer-Assisted Field-Based NIPORT National Institute of Population
Data Entry Research and Training
CBN Cost of Basic Needs PECS Post-Enumeration Check Survey
CC City Corporation PPP Purchasing power parity
CPI Consumer Price Index PSU Primary Sampling Units
DAM Department of Agricultural RMG Ready-Made Garment
Marketing
DDS Dietary Diversity Score SMAs Statistical Metropolitan Areas
EA Enumeration Area Tk Taka, Bangladeshi Currency
FAO Food and Agriculture TVET Technical and Vocational
Organization of the United Education and Training
Nations
FGT Foster Greer Thorbecke UN United Nations
FLFP Female Labor Force WASH Water, Sanitation, and Hygiene
Participation
GDP Gross Domestic Product WDI World Development Indicators

ix
Bangladesh Poverty Assessment

x
Executive Summary

Bangladesh has come a long way in Figure E1. Bangladesh achieved
a short time in its fight to end pov- strong poverty reduction from
erty. In 2016, about 1 in 4 Bangladeshi 2000 to 2016
were poor.1 The country has halved 60%
48.9%
poverty rates in a decade and a half, 50% 40.0%
lifting more than 25 million people out 40% 31.5%
of poverty (Figure E1). Between 2010 30% 24.5%
34.3%
and 2016, about 8 million Bangladeshi 20% 25.1%
were lifted out of poverty. 10% 17.6%
13.0%
0%
2000 2005 2010 2016
Substantial improvements in other
dimensions of wellbeing have also Poverty Extreme poverty
been recorded. Reductions in poverty
were accompanied by sustained drops Source: Staﬀ calculations using Household Income
and Expenditure Survey (HIES) 2000, 2005, 2010,
in fertility and child mortality, improve- and 2016.
ments in nutrition and life expectancy, Notes: Poverty and extreme poverty rates based on
the Cost of Basic Needs approach (Ahmed et al 2019).
enhanced access to electricity, clean
water and sanitation, broad based
expansions in education, and other improvements in non-monetary dimensions
of well-being. Bangladesh is not only one of the top performers in poverty reduc-
tion in the South Asia region, it is equally a top performer in improving these
non-monetary dimensions of welfare.

However, there is no room for complacency. The job of ending extreme poverty
is not complete. About 1 in 4 Bangladeshi still live in poverty, while almost half
of those living in poverty live in extreme poverty and are unable to afford a basic
food consumption basket. Using the international poverty line, a measure that
allows comparison with poverty levels in other countries, the rate of poverty in

1
Poverty headcount rates based on the official upper poverty line using the Cost of Basic Needs (CBN).

11
Bangladesh Poverty Assessment

Figure E2. Despite improvements, poverty and vulnerability remain high

a. International poverty headcount b. Poverty and vulnerability
in the region (%)
21.2 100%
19.6
15.0 14.8 80%

60%
7.3
3.9 40%
1.5 0.8
20%

0%
India 2011

Bangladesh 2010

Nepal 2010

Bangladesh 2016

Maldives 2009

Pakistan 2015

Bhutan 2017

Sri Lanka 2016

2000 2005 2010 2016

Headcount Vulnerable Middle class
and above

Source: Staﬀ calculations using HIES 2000, 2005, 2010 and 2016 and World Development Indicators.
Notes: The international poverty line has a value of US$1.90 purchasing power parity (PPP). Vulnerable
denotes the population living between the national poverty line and twice the national poverty line. Middle
class and above denotes the population living above twice the national poverty line.

Bangladesh is relatively high by regional standards (Figure E2a). In addition, more
than half of the population can be considered vulnerable to poverty, as their lev-
els of consumption are close to the poverty threshold (Figure E2b).

To sustain progress, potential spoilers demand attention

First, robust economic growth con- Figure E3. Consumption growth
tinued driving poverty reduction but across periods
not as effectively as before. Between 2.3 2.3
consumption growth (%)
Annualized per capita

2010 and 2016, GDP growth acceler- 1.8
1.4 1.6
ated while the pace of poverty reduc- 1.2
tion slowed. Higher economic growth
has not led to faster poverty reduction,
because average consumption growth
was slower and less equal than before. 2000-2005 2005-2010 2010-2016
From 2010 to 2016, consumption Bottom 40% All
growth for the poorest 40 percent was Source: Staﬀ calculations using HIES 2000, 2005,
slower than for the whole population, 2010, and 2016.
Notes: Bottom 40% denotes the poorest 40 percent
while the reverse was true in previous of the population based on households’ per capita
periods (Figure E3). consumption.

12
Executive Summary

Second, very little poverty reduction occurred in urban areas. Rural Bangladesh
spearheaded poverty reduction from 2010 to 2016, accounting for about 90 per-
cent of the drop. Even though the poverty rate fell in urban Bangladesh, the rate
of reduction was much slower than in previous periods (Figure E4). The national
slowdown in poverty reduction has occurred largely due to an inability of urban
Bangladesh to sustain progress.

Figure E4. Urban and rural poverty reduction
a. Rural poverty headcount (%) b. Urban poverty headcount (%)
60% 52.3% 40% 35.1%
35%
50% 43.8% 28.4%
30%
40% 35.2%
25% 21.3%
26.7% 20% 19.3%
30% 37.9%
20% 28.6% 15% 19.9%
21.1% 10% 14.6%
10% 15.0%
5% 7.7% 8.0%
0% 0%
2000 2005 2010 2016 2000 2005 2010 2016
Poverty Extreme poverty

Source: Staﬀ calculations using HIES 2000, 2005, 2010, and 2016.

Figure E5. Poverty progress was slower in Western Divisions
a. Poverty rate in 2016 by division b. Change in poverty between
2010 and 2016

10
Rangpur
Chittagong

5
Rajshahi

Sylhet
Khulna
Barisal

Rajshahi
(percentage points)

Dhaka
Sylhet

Dhaka

0
Rangpur

Khulna -5
Barisal
Chittagong

Bay of Bengal -10

47% 21-26%
28-29% 16-18% -15
Source: Staﬀ calculations using HIES 2010 and 2016.

13
Bangladesh Poverty Assessment

Third, Western divisions did not see the same gains as the East. Since 2010, pov-
erty has risen in Rangpur division, the historically poorer Northwest of the country; it
has stagnated in Rajshahi and Khulna in the West. The East and central Bangladesh
have fared much better: poverty has fallen moderately in Chittagong, and declined
rapidly in Barisal, Dhaka, and Sylhet (Figure E5). The stronger rate of poverty reduc-
tion in the Eastern regions widened a welfare gap between Eastern and Western
Bangladesh that had previously been narrowed between 2005 and 2010 (Jolliffe
et al. 2013). The re-emerging divergence between the East and West has occurred
largely in rural, instead of urban areas.

What lies behind these trends?

Lower fertility and education gains fuel consumption growth and reductions in
poverty. From 2010 to 2016, changes in household demographics, education gains,
and asset holdings contributed to about 50 percent of consumption growth. The
amount of consumption growth explained by these gains was very similar across
rural and urban areas, indicating that the different poverty progress between urban
and rural areas was not explained by these factors. However, within rural areas the
gains were not uniform: the rural West recorded slower progress on education and
demographic change, contributing to the re-emergent East-West divide.

The changing sectoral composition of economic growth also explains the differ-
ent progress in poverty reduction. Since 2000, there have been large shifts in the
sectoral composition and geographical focus of economic activity in Bangladesh,
accompanied by rapid structural trans-
Figure E6. Implied growth-poverty
formation and urbanization. Between
elasticities, 2000-2016
2010 and 2016, growth in the agricul-
tural and service sectors slowed down. 2000-2005 2005-2010 2010-2016
0
Growth in industry was strong, but
there was limited job creation in man- -0.5
ufacturing. During 2010-2016, growth
-1
in the agricultural sector became less
poverty reducing, while growth in -1.5
industry and services became more
-2
poverty reducing (Figure E6). The dif-
ferent performance of the economic Total Agriculture
Industry Services
sectors compared to previous years
affected the returns from working in Source: Staﬀ calculation using HIES and WDI.
Notes: Elasticities are calculated from GDP growth
different activities and shaped the data and sectoral poverty rates presented in Table 2.3.
changes in poverty. For more details see Hill and Endara (2019a).

14
Executive Summary

Poverty reduction was rural but not predominantly agricultural. Although 47
percent of rural households were primarily engaged in agriculture in 2010, such
households accounted for just 27 percent of rural poverty reduction between
2010 and 2016. This contrasts with the period 2005 to 2010, when 69 percent of
rural poverty reduction was among households primarily engaged in agriculture
(Figure E7). Most rural poverty reduction between 2010 and 2016, 59 percent,
occurred among households whose primary sector of employment was industry
or services (Figure E7). This reflects the slower growth in agriculture during this
period but also the fact that agriculture growth was less poverty reducing, com-
pared to the past and other sectors.

Figure E7. Poverty reduction across sectors in rural areas, 2005-2016
a.Sector share in 2010 b.Contribution to poverty reduction

Agriculture

Services

Industry

Not available

Population shift

Agriculture Services -20% 0% 20% 40% 60% 80%
Industry Not available 2005-2010 2010-2016

Source: Staﬀ calculations using HIES 2005, 2010, and 2016.
Notes: Results obtained from Ravallion and Huppi (1991) decompose changes in poverty over time into
intra-sectoral eﬀects, a component due to population shifts across sectors, and an interaction (not
displayed). Sector of employment defined based on reported hours of work in each sector.

The smaller share of rural households in the West pursuing non-agricultural live-
lihoods contributed to the re-emergent East-West divide after 2010. Both in 2010
and 2016, households in the West were more likely than households in the East
to report their main sector of work as agriculture. Structural transformation also
seems to have been faster in the East than in the West: the proportion of households
reporting agriculture as their main form of employment fell by 22 percent in the
East compared to 12 percent in the West. Thus, although some of the divergence in
poverty-reduction performance between East and West from 2010 to 2016 can be
explained by less favorable changes in education attainment and demographics,
differences in sectors of work also seem to have played an important role.

15
Bangladesh Poverty Assessment

In urban areas, industry, led by the garments sector, has driven urban poverty
reduction. In 2010, poverty rates for households mainly engaged in industry were
higher than for those engaged in services (26 percent compared to 17 percent). By
2016, poverty rates among households in industry were almost at the same level
as for households working in the services sector. This convergence was driven by
rapid poverty reduction among households in industry and no change in poverty
for households engaged in the service sector (Figure E8b). This contrasts with the
period 2005-2010, when both households in industry and services experienced
reductions in poverty (Figure E8a). The stagnation in poverty reduction in services
is concerning, given that around 44 percent of the poor in urban areas are part
of households primarily engaged in this sector. Within industry, most gains were
driven by the garment sector.

Figure E8. Poverty reduction across sectors in urban areas, 2005-2016

a. Sector share in 2010 b. Contribution to poverty reduction

Agriculture

Services

Industry

Not available

Population shift

-30% 20% 70% 120%
Agriculture Services
Industry Not available 2005-2010 2010-2016

Poverty rates increased most among the self-employed in services, which set
back overall progress in urban areas. The strongest contributor to overall prog-
ress was poverty reduction among wage and daily workers in industry. Good
progress was also seen for wage and daily workers in services. However, poverty
rates increased among the self-employed in the service sector in urban areas.

Slow manufacturing job creation curbed urban poverty reduction and reduced
female labor force participation. There has been little growth in the share of the

16
Executive Summary

Bangladeshi labor force engaged in industry, and this has limited the amount of
poverty reduction derived from the country’s industrial growth. The slowdown
in job creation in the garments and textiles sector is also likely responsible for
the diminishing rates of female labor force participation (FLFP). Between 2005
and 2010, overall labor force participation in urban areas increased because of a
substantial increase in FLFP. The expansion of the garment sector was an import-
ant force in raising FLFP, as 80 percent of employees in this sector are female.
Between 2010 and 2016, however, female labor force participation declined about
4 percentage points.

Distilling the evidence and looking ahead

This poverty assessment tells a story of continued remarkable progress that
started decades ago. Critical actions taken decades ago allowed Bangladesh to
perform economically and realize high levels of per capita GDP growth as well
as improve human development outcomes. Investments in human capital sup-
plied a rapidly transforming economy with the labor force capable of benefiting
from expanded job opportunities outside agriculture. These elements have been
important contributors for the current success in poverty reduction. Looking for-
ward, several suggestions can be distilled:

What has worked in the past may not in the future. Educational attainment,
lower fertility rates, agricultural growth, and international migration have
helped reduce poverty in rural areas. Growth in rural services and manufactur-
ing re-emerged as an important driver of progress. In urban areas, lower fertility
rates and welfare gains among manufacturing employees have been important.
However, if the country is to succeed in its ambitions to eradicate extreme poverty
by the end of the next decade, it will need to go above and beyond traditional
catalysts of poverty reduction. The overall smaller elasticity of poverty reduction
to GDP growth is an indication that such adjustments are needed (Figure E6).

Improving the targeting and quality of service delivery will become more
important. Gains in educational attainment were more limited in the rural West
and in urban centers across the country, and returns to education fell substantially
in urban areas. Closing education gaps remains important; however, increased
targeting as well as higher quality spending will become crucial for education
investments to continue supporting poverty reduction.

Agriculture must become more poverty reducing. In this regard, there is sig-
nificant potential to increase productivity and incomes by supporting more

17
Bangladesh Poverty Assessment

diversification in agriculture. Improved connectivity can also support more rapid
transformation of the rural West and increased access to opportunities outside
agriculture.

Urban is the frontier in poverty reduction. Even though 8 in 10 poor live in rural
areas, at current trends more than half of Bangladesh’s poor households will
live in urban areas by 2030. While economic density is much higher in Dhaka
and Chittagong, living standards and poverty rates do not reflect this difference.
Many poor urban households live in slums, facing poor housing, insecurity, and
overcrowding to be near work. Mobility is limited for the poorest households in
Dhaka, limiting the degree to which they can gain from the benefits of agglomera-
tion. There was no reduction in the poverty rate among urban dwellers engaged
in informal service sector activities, suggesting the importance of finding ways
to increase their productivity. Few jobs were created in the manufacturing sec-
tor, even as manufacturing delivered strong welfare gains for those it did employ.
Female labor force participation rates fell, thus, lifting economic and social con-
straints for female participation in labor markets arises as an important venue for
poverty reduction.

As the country is facing new and re-emerging frontiers of poverty reduction,
namely tackling urban poverty and poverty in the West, approaches that
uncover effective traditional and new solutions must be embraced. Building
on the past successes without falling into the trap of complacency will be key to
eradicate poverty. Policies to reduce poverty when poverty incidence is high are
different from those when poverty is lower. In the past, relatively straightforward
measures like the introduction of high-yielding rice varieties could kick-start a pro-
cess of welfare improvement; however, more sophisticated policies are needed to
reduce poverty over a sustained period and in a more complex economy. Such
policies need to be synergistic and are both drivers as well as consequences of
improved welfare outcomes. Evidence on the importance of that has been seen
for education and fertility programmes. Continuing Bangladesh’s practices of
innovative policy experimentation, as well as learning from other country experi-
ences of similar economic and development transformation, will be important to
tackle some of the challenges presented in this poverty assessment.

18
Introduction

This poverty assessment documents Bangladesh’s progress in reducing poverty
over the period 2010 to 2016/17. The country is at a juncture where it is important
to examine what has allowed progress to continue, but also what more needs
to be done to increase the inclusivity of growth and accelerate towards the goal
of ending extreme poverty. This poverty assessment seeks to contribute to this
debate. It looks at what has worked so far for Bangladesh in its drive to reduce
poverty and where work remains to be done.

The core analysis of poverty trends and patterns relies on four cross-sectional
rounds of the Household Income and Expenditure Survey (HIES) collected by the
Bangladesh Bureau of Statistics for 2000, 2005, 2010, and 2016/17. In addition,
the report uses Agricultural Statistics, the Economic Census, the Population and
Housing Census, the Labor Force Survey (LFS), and other specialized surveys and
administrative data. The analysis presented in the current volume draws from
eleven background papers which are included in an accompanying volume 2.

Part 1 of this volume presents the main poverty trends and the places and peo-
ple who have benefitted from poverty reduction, as well as the people and places
where progress has been less pronounced. Part 2 analyzes the key drivers of pov-
erty reduction and the factors that explain the different levels of progress across
the country. First, it examines the role of households’ assets in explaining consump-
tion growth. Second, it shows how the different sectoral composition of growth
has shaped poverty reduction in rural areas. Lastly, it focuses on urban areas and
explores some of the key elements behind the observed slowdown in urban poverty
reduction. Part 3 draws lessons from the preceding analysis to inform public poli-
cies and contribute to ending poverty in Bangladesh.

19
Bangladesh Poverty Assessment

20
Part 1

Assessing performance
from 2010 to 2016/17

Bangladesh continued progress in poverty reduction

Bangladesh represents a remarkable story of sustained progress in welfare. Poverty
headcount rates based on both upper and lower (extreme) poverty lines using the Cost
of Basic Needs (CBN) showed that Bangladesh continued reducing poverty. The pov-
erty rate fell by 1.2 percentage points per year from the beginning of the decade until
2016. By 2016, about 1 in 4 Bangladeshi were poor and 13 percent were extreme poor.2,3,4

2
The cross-sectional Household Income and Expenditure Survey (HIES) is the main official source of
information about household consumption, poverty, and income in Bangladesh. The HIES 2016/17
data was collected from April 2016 through March 2017. Previous rounds of HIES data were collected
in 2000, 2005, and 2010. In the remainder of this report, we refer to the yearly estimates as from 2000,
2005, 2010, and 2016, respectively. The 2016/17 HIES data can also provide quarterly poverty esti-
mates. The poverty assessment does not discuss these quarterly estimates as poverty rates are not
statistically different across quarters and more rounds of data would be needed to assess seasonality
of poverty. See Data Annex for additional details concerning the HIES data.
3
The official methodology used in Bangladesh to estimate poverty numbers is based on the Cost of
Basic Needs (CBN). The CBN method calculates the cost of obtaining a consumption bundle consid-
ered to be adequate to satisfy basic consumption needs. If a person cannot afford the cost of this
bundle, then the person is considered poor. The poverty rate is calculated using an upper poverty line
which is the cost of a bundle that includes basic food and non-food items. The extreme poverty rate
is measured using a lower poverty line which is the cost of a bundle that mostly includes food, along
with a small share of non-food items. For a full discussion of how poverty is measured in Bangladesh
and comparability across rounds of the HIES, see Joliffe et al. (2013) and Ahmed et al. (2017). The
standard errors for the poverty estimates are included in Table 1.1 to indicate the precision with which
poverty is measured in Bangladesh.
4
Note that this poverty assessment presents slightly different poverty rates than the official figures. The
difference reflects a correction in a misclassification of 13 enumeration areas from rural to urban found
in the HIES 2016/17 microdata (See Data Annex for more details). The difference between the poverty

21
Bangladesh Poverty Assessment

This shows a continuation of the progress recorded in the previous decade, when
Bangladesh reduced its poverty rate from 48.9 percent in 2000 to 31.5 percent in 2010
(Figure 1.1a). The poverty headcount, using the international poverty line of USD
1.90 purchasing power parity a day, shows the same sustained decline (Figure 1b).

Figure 1.1. Bangladesh achieved strong poverty reduction from
2000 to 2016
a. Poverty and extreme poverty b. International poverty headcount (%)
headcount (%)
60.0% 40.0%
48.9% 33.7%
50.0%
40.0% 30.0% 24.5%
40.0% 31.5% 19.6%
30.0% 24.5% 20.0% 14.8%
34.3%
20.0% 25.1%
17.6% 10.0%
10.0% 13.0%
0.0% 0.0%
2000 2005 2010 2016 2000 2005 2010 2016

Poverty Extreme poverty 1.90 2011 PPP

Source: Staﬀ calculations using HIES 2000, 2005, 2010, and 2016 and World Development Indicators.
Notes: The international poverty line has a value of US$1.90 purchasing power parity (PPP).

The reduction in poverty headcount rates also translated into a reduction in
the size of the population living in poverty. Between 2010 and 2016, about 8 mil-
lion Bangladeshi were lifted out of poverty and 5.6 million out of extreme poverty.
Depth (poverty gap) and severity (squared of poverty gap) of poverty also pre-
sented improvements of about 23 and 22 percent, respectively (Table 1.1).

Substantial improvements in other dimensions of wellbeing, such as educa-
tion and life expectancy, have also been recorded. Since 2010, literacy rates
for adults increased from 53 to 60 percent, life expectancy rose by 2.6 years and
infant mortality rates dropped by 12 infants per 1000 live births. The percentage of
households with electricity increased from 55 to 75 percent. Lower fertility rates,
reaching almost replacement levels, have supported smaller household sizes and
dependency ratios. A rapid transformation in the structure of economic activity
has accompanied these changes.5

rates presented here and the official figures is very small and statistically not different from zero. In addi-
tion, none of the conclusions of the poverty assessment are altered if the official statistics are used.
5
See Table 2.2. in section 2.

22
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Table 1.1: National poverty trends, 2000-2016
2000 2005 2010 2016
Poverty
Headcount 48.9 40.0 31.5 24.5
(1.2) (1.1) (1) (0.5)
Depth 12.8 9.0 6.5 5.0
(0.49) (0.33) (0.26) (0.15)
Severity 4.6 2.9 2.0 1.6
(0.2) (0.1) (0.1) (0.1)
Extreme poverty
Headcount 34.3 25.1 17.6 13.0
(1.21) (0.93) (0.76) (0.38)
Depth 7.5 4.7 3.1 2.3
(0.38) (0.23) (0.16) (0.08)
Severity 2.4 1.3 0.8 0.6
(0.2) (0.1) (0.1) (0.03)
Source: Staff estimates using HIES 2000, 2005, 2010, and 2016/17.
Note: Standard errors in parenthesis.

However, the job of ending extreme poverty is not done yet, and the evidence
cautions against complacency. About 1 in 4 Bangladeshi still live in poverty,
while almost half of those living in poverty live in extreme poverty and are unable
to afford a basic food consumption basket. Using the international poverty line, a
measure that allows comparisons of poverty with other countries, the rate of pov-
erty in Bangladesh is relatively high by regional standards (Figure 1.2a). In addi-
tion, more than half of the population can be considered vulnerable to poverty, as

Figure 1.2. Despite improvements, poverty and vulnerability remain high
a. International poverty headcount b. Poverty and vulnerability
in the region (%)
21.2 100%
19.6
15.0 14.8 80%

7.3 60%
3.9
1.5 0.8 40%

20%
India 2011

Bangladesh 2010

Nepal 2010

Bangladesh 2016

Maldives 2009

Pakistan 2015

Bhutan 2017

Sri Lanka 2016

0%
2000 2005 2010 2016
Headcount Vulnerable Middle class
and above
Source: Staﬀ calculations using HIES 2000, 2005, 2010 and 2016 and World Development Indicators.
Notes: The international poverty line has a value of US$1.90 purchasing power parity (PPP). Vulnerable
denotes the population living between the national poverty line and twice the national poverty line. Middle
class and above denotes the population living above twice the national poverty line.

23
Bangladesh Poverty Assessment

their levels of consumption are close to the poverty threshold (Figure 1.2b). The
following sections highlight that although much progress has been made there
are some spoilers of progress that require attention in order for Bangladesh to
end extreme poverty.

Robust economic growth drives poverty reduction but not as
effectively as before

Bangladesh’s progress in reducing poverty reflects sustained economic growth.
High and stable economic growth, in combination with lower population growth,
has supported poverty reduction. Between 2000 and 2016, average GDP growth
was 6 percent per year, and average GDP per capita growth was 4.4 percent per
year. This growth was in line with growth rates in South Asia.

However, economic growth has delivered less poverty reduction than in the
past. Between 2010 and 2016, GDP growth accelerated while the pace of poverty
reduction slowed. As a result, the amount of poverty reduction each percentage
point of growth per capita delivers (the elasticity of poverty reduction to growth)
fell from 0.88 to 0.73.6 At the extreme poverty line, the elasticity of poverty reduc-
tion to GDP growth per capita has fallen by a third, from 1.24 to 0.86. In general,
the elasticity of poverty reduction to growth per capita is higher at lower levels
of poverty (Ravallion 2012), so this elasticity decline cannot be explained by
Bangladesh’s progress in reducing poverty.7

Higher economic growth has not led to faster poverty reduction, partly because
average consumption growth did not keep up with GDP growth. Although, GDP
growth accelerated between 2010 and 2016, compared to years before 2010,
household survey data shows consumption growth has been slower (Figure 1.3).
The share of private consumption in total GDP declined from 74 percent in 2010
to 69 percent in 2016. For the poorest 40 percent of Bangladeshis, consumption
growth fell from 1.8 percent in 2005-2010 to 1.2 percent in 2010-2016.

6
The elasticity of poverty reduction to GDP growth per capita is given by the percent reduction in pov-
erty divided by GDP growth per capita. The values using the growth rate instead of growth per capita
are 0.70 and 0.58, respectively.
7
The elasticity of poverty reduction to growth per capita is higher at lower levels of poverty in part for
arithmetic reasons: it is easier to halve the poverty rate when going from, for example, 5 percent pov-
erty (this requires a 2.5 percentage point reduction in poverty) than from 50 percent poverty (which
would require a reduction of 25 percentage points) (Cuaresma, Klasen and Wacker 2016). To take this
into account, the semi-elasticity can also be considered, which is the percentage-point reduction in
poverty for each percent of GDP growth per capita. This indicator has fallen even more substantially,
from 0.35 in 2005-2010 to 0.23 in 2010-2016 (using the national poverty line).

24
P a r t 1 : Assess i n g pe r f o r m a n c e f r o m 2 0 1 0 t o 2 0 1 6 / 1 7

Consumption growth was not only slower but has also become more unequal.
From 2005 to 2010, consumption growth in Bangladesh was highest among poorer
households (Figure 1.3a). In contrast, from 2010 to 2016, poorer households
experienced slower consumption growth than richer households. More specifi-
cally, consumption growth was highest for people in the 40th to 75th consumption

Figure 1.3. Consumption growth has slowed and become more unequal
a. Growth incidence curves across periods b. Shared prosperity
4.0
2.3 2.3
3.5
Avg. Annualized Growth (%)

3.0 1.8

consumption growth (%)
Annualized per capita
2.5 1.6
1.4
1.2
2.0

1.5

1.0

0.5

0.0
0 1 2 3 4 5 6 7 8 9 10 2000-2005 2005-2010 2010-2016
Consumption percentile
2000-2005 2005-2010 2010-2016 Bottom 40% All

c. Inequality trends d. Growth and redistribution decomposition
of poverty changes (%)
0.31 0.31 0.30 0.31

2000-2005 2005-2010 2010-2016
2.0 0.7 0.8
0.18 0.19 0.17 0.19 0.0
-2.0
-2.3
-4.0
-6.0
-6.2 -7.0
-8.0 -7.8
-8.5
-10.0 -9.6 -8.9
-12.0

Gini Theil alpha==1 Growth Redistribution Total change
in poverty

Source: Staﬀ calculations using HIES 2000, 2005, 2010, and 2016.
Notes: Figure (a) presents growth incidence curves, which indicate the growth in consumption for people at
each level of consumption (from the poorest on the left to the richest on the right). Figure (d) presents the
results from Datt-Ravallion (1992) decompositions of changes in poverty into changes due to consumption
growth (or mean consumption) in the absence of changes in inequality (or consumption distribution), and
changes in inequality in the absence of consumption growth.

25
Bangladesh Poverty Assessment

percentiles and was lower for the poorest and for the richest. As a result, although
Bangladesh recorded healthy consumption growth among the bottom 40 percent
during this period, it did not make progress on measures of equality and shared
prosperity. Between 2010 and 2016, the national Gini coefficient increased by
one percentage point and the Theil index by two percentage points (Figure 1.3c).
Overall, progress in poverty reduction can be explained by consumption growth,
and not by changes in the distribution of consumption. Datt-Ravallion decompo-
sitions indicate that indeed all the poverty reduction was due to growth in con-
sumption (Figure 1.3d).

Very little poverty reduction occurred in urban areas

There are two divergent stories of progress for rural and urban Bangladesh. The
poverty rate fell in both rural and urban Bangladesh from 2010 to 2016, but the
rate of reduction was much slower in urban areas (Figure 1.4), reflecting signifi-
cantly lower consumption growth in those areas (Figure 1.5a and b). In fact, there
was no progress in reducing extreme poverty in urban areas over this period.
Given the country’s rapid urbanization (Box 1), there are now more people living
in extreme poverty in urban Bangladesh than in 2010.

Nearly all of Bangladesh's poverty reduction from 2010 to 2016, about 90 percent,
took place in rural areas. The national slowdown in poverty reduction has occurred
largely due to an inability of urban Bangladesh to sustain progress. Progress in rural
poverty reduction has been only marginally slower than in previous periods.

Figure 1.4. Urban and rural poverty reduction
a. Rural poverty headcount (%) b. Urban poverty headcount (%)
60.0% 40.0%
52.3% 35.1%
50.0% 35.0%
43.8% 28.4%
30.0%
40.0% 35.2%
25.0% 21.3%
37.9% 26.7% 19.3%
30.0% 20.0%
28.6% 15.0% 19.9%
20.0%
21.1% 10.0% 14.6%
10.0% 15.0%
5.0% 7.7% 8.0%
0.0% 0.0%
2000 2005 2010 2016 2000 2005 2010 2016
Poverty Extreme poverty Poverty Extreme poverty

Source: Staﬀ calculations using HIES 2000, 2005, 2010, and 2016.

26
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While rural areas reduced poverty, rural consumption growth was less equal
than in previous years. Compared to the period 2005-2010, the poorest 10 per-
cent did not fare well, and rich rural households experienced higher consumption
growth (Figure 1.5a). Due to the more unequal consumption growth, the Gini coef-
ficient increased about 2 points in rural areas. In contrast, in urban areas the slow-
down in consumption growth was observed across the entire distribution (Figure
1.5b). Consumption growth was thus equal, and that redistribution contributed
to poverty reduction. In urban areas inequality continued a declining trend and
the Gini coefficient declined 1 point (Hill and Genoni 2018).

Figure 1.5. Consumption growth across urban and rural areas
a. Growth incidence curve, rural areas b. Growth incidence curve, urban areas
4.0 4.0

3.0 3.0
Avg. Annualized Growth (%)

Avg. Annualized Growth (%)

2.0 2.0

1.0 1.0

0.0 0.0
0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10
Consumption percentile Consumption percentile
-1.0 -1.0
2000-2005 2000-2005
-2.0 2005-2010 -2.0 2005-2010
2010-2016 2010-2016

Source: Staﬀ calculations using HIES 2000, 2005, 2010, and 2016.
Notes: Figures present growth incidence curves, which indicate the growth in consumption for people at each
level of consumption (from the poorest on the left to the richest on the right).

Rapid urbanization in Bangladesh plays a modest role in poverty reduction.
Bangladesh is urbanizing quickly. The country recorded a 3 percent increase in its
urban population share between 2010 and 2016.8 The growth in the urban popu-
lation has been faster than most countries in the South Asia region, with an aver-
age 3.9 percent growth rate per year since 2000, compared to 2.7 for South Asia.9

8
This increase in urban share is not reflected in the official HIES 2016 reports published by BBS. As
work for this poverty assessment was undertaken, a mistake in the classification of urban areas was
identified. Here, we report the increase in urban share after correcting for this mistake. This error has
very little impact on the national poverty rate (See Data Annex).
9
United Nations, Department of Economic and Social Affairs, Population Division (2018). World
Urbanization Prospects: The 2018 Revision. Also see Annex Table A4.

27
Bangladesh Poverty Assessment

Sixty percent of internal migrants move to Dhaka and 16 percent to Chittagong
(Farole and Cho 2017). Using this information and the poverty rates in Dhaka City
Corporation (CC), Chittagong CC, and the rest of the country, we estimate that
migration to Dhaka and Chittagong contributed about 0.5 percentage points of
poverty reduction over the six-year period, as a result of direct impacts on house-
hold welfare.10 This suggests that the contribution of internal migration to poverty
reduction has been limited. District analysis suggests that in-migration has no
immediate impact on poverty in receiving districts, but lack of data prevented an
assessment of its impact on sending districts (Hill and Endara 2019a).

Box 1. Slower urban poverty reduction and design changes
in HIES 2016

Might the apparent slowdown in urban poverty reduction be an artefact of
measurement? Two important changes took place in the household survey
sampling frame between the last two rounds of the HIES (See Data Annex).
First, Bangladesh’s 2011 census provided for a new sampling frame for the
2016 HIES. Second, slums were included for the first time in the urban sam-
pling frame of the 2016 HIES.

Can either of these changes explain the slowdown in urban poverty reduc-
tion? The change in sampling frame is unlikely to have caused such a shift,
as the HIES definition of ‘urban’ has not been altered. The inclusion of
slums in the sampling frame for the first time could at most only explain
part of the slowdown. If one assumes that, in fact, no slum dwellers were
included in the survey in previous years and that poverty rates in slums are
three times the urban average throughout Bangladesh (based on the dif-
ference between slum and non-slum areas in Dhaka)11—both quite strong
assumptions—urban poverty from 2010 to 2016 would still have fallen at
half the speed of rural poverty during the same period and half the speed of
urban poverty reduction from 2005 to 2010.

10
This assumes that migrants have the average poverty rate of their sending district before migrating
and the average poverty rate of their receiving district after migrating.
11
Poverty rates in slums from the Bangladesh Urban Informal Settlements Baseline Survey (BUISBS)
2016. See Hill and Rahman (2019).

28
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Rural Bangladesh marked by East-West divide in poverty
reduction

There is a marked divide in poverty reduction between Eastern and Western
divisions of Bangladesh. Western divisions did not see the same gains as the
East.12 In general, over the period 2000-2016, poverty fell in all divisions, and
disparities fell across districts. Poverty rates decreased in all divisions over the
16-year period, except for Rangpur division after 2010 (Figure 1.6a). In addition,
higher poverty reduction was more likely to be observed in districts that were
poorer at the beginning of the 2000s (Yunus 2019 and Figure 1.6b).

Figure 1.6. Poverty changes across divisions and districts, 2000-2016
a. Divisions b. Districts
Barisal
70%
30
Percentage Points change 2000-2016 (%)

60%
50% 20
Sylhet Chittagong
40% 10
30% 0
20%
10% -10 0 20 40 60 80

0% -20
-30
Dhaka
Rangpur -40
-50
-60
-70
Rajshahi Khulna
Poverty Rate in 2000
2000 2005 2010 2016
Source: Staﬀ calculations using HIES 2000, 2005, 2010, and 2016.
Notes: Figures present the national upper poverty rate by division and district. The poverty rate for 2000 in
panel (b) was calculated using Small Area Estimation (SAE).

Since 2010, poverty has risen in Rangpur division, the historically poorer
Northwest of the country; it has stagnated in Rajshahi and Khulna in the West.
The East and central Bangladesh have fared much better. Poverty has fallen mod-
erately in Chittagong, and declined rapidly in Barisal, Dhaka, and Sylhet (Table
1.2). The stronger rates of poverty reduction in the Eastern regions widened a gap
between Eastern and Western Bangladesh that had previously been narrowed
between 2005 and 2010 (Jolliffe et al. 2013). Consumption growth was significantly
lower in the West than in the East, compared to the previous decade (Figure 1.7).

12
Western divisions are Khulna, Rajshahi, and Rangpur. In the 2013 Poverty Assessment, Barisal was
also included in the divisions referred to as Western.

29
Bangladesh Poverty Assessment

Figure 1.7. Comparing the incidence of consumption growth in the East
and West, 2005-2016
a. Growth incidence curve, 2005-2010 b. Growth incidence curve, 2010-2016
5.0 5.0

4.0 4.0

Avg. Annualized Growth (%)
Avg. Annualized Growth (%)

3.0 3.0

2.0 2.0

1.0 1.0

0.0 0.0
0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10
-1.0 -1.0
Consumption percentile Consumption percentile
-2.0 -2.0
National East West
Source: Staﬀ calculations using HIES 2005, 2010, and 2016.
Notes: West includes the divisions of Rangpur, Rajshahi, and Khulna. East includes the divisions of Barisal,
Chittagong, Dhaka, and Sylhet. Figures present growth incidence curves, which indicate the growth in
consumption for people at each level of consumption (from the poorest on the left to the richest on the right).

Table 1.2: Poverty reduction has been uneven across divisions
Poverty Rate Extreme Poverty Rate
2010 2016 2010 2016
Barisal 39.4 26.4 26.7 14.4
(3.3) (1.5) (3.2) (1.3)
Chittagong 26.2 18.3 13.1 9.0
(2) (1.2) (1.4) (0.9)
Dhaka 30.5 20.5 15.6 9.9
(1.6) (1.1) (1.1) (0.7)
Khulna 32.1 27.7 15.4 12.1
(2.3) (1.3) (1.6) (0.8)
Rajshahi 29.7 29.0 16.0 14.3
(2.1) (1.5) (1.6) (1)
Rangpur 42.3 47.3 27.7 30.6
(3.2) (1.3) (2.9) (1.2)
Sylhet 28.1 16.2 20.7 11.5
(3) (1.7) (2.5) (1.4)
Source: Staff calculations using HIES 2010 and 2016.
Note: Divisions are defined in a comparable way across time. Standard errors in parentheses.

In 2016, 25 percent of the Bangladeshi poor were concentrated in Dhaka,
because it is the most populous division, and another 20 percent were in
Rangpur, reflecting that division’s high poverty rates. Dhaka and Rangpur
divisions also concentrate the largest share of the extreme poor (48 percent

30
P a r t 1 : Assess i n g pe r f o r m a n c e f r o m 2 0 1 0 t o 2 0 1 6 / 1 7

combined). In addition, although there are poor districts in all provinces, poor
districts are much more likely to be found in the periphery of the country and are
more likely to be in the Northwest (Figure 1.8).

The re-emerging divergence between the East and West has occurred largely in
rural, not urban areas. Figure 1.9 depicts the difference in consumption between
households living in the East and West across the consumption distribution.13
From 2010 to 2016, households living in rural areas in the East of the country were
more likely to have higher levels of consumption (Figure 1.9b). In contrast, the
consumption “premium” for living in the East increased much less in urban areas.
Statistically, for urban areas, the Eastern premium remained the same in 2016 as
in 2010 for most of the consumption distribution.

Figure 1.8. Distribution of poverty across districts

a. Poverty rate b. Number of poor

2.6% - 13.5% 47348 - 249019
13.6% - 23.3% 260194 - 389126
23.4% - 30.5% 395033 - 634028
30.6% - 37.2% 648674 - 914376
37.3% - 70.8% 929431 - 2101556

Source: Staﬀ calculations using HIES 2016.
Notes: Upper poverty rates calculated using oﬀicial methodology.

13
This difference comes from a regression model that controls for other household characteristics.

31
Bangladesh Poverty Assessment

Figure 1.9. The consumption premium of living in the East
a. Rural areas b. Urban areas
3 3
Consumption premium

Consumption premium
2 2

1 1

0 0

-1 -1
0 2 4 6 8 10 0 2 4 6 8 10
Consumption deciles Consumption deciles
2005 2010 2016 2005 2010 2016

Source: Staﬀ calculation using HIES 2005, 2010 and 2016. For more details, see Hill and Endara (2019b).
Notes: Figure depicts the diﬀerence in per capita consumption between households living in the East and
West across consumption deciles, conditional on other household characteristics. Shaded color depicts the 95
percent confidence interval for the correlations.

The Rohingya refugee crisis impact on poverty remains local

Within the Chittagong division, the refugee influx in Cox’s Bazar had an immediate
impact on poverty, but the impact was highly localized. At the end of August 2017,
the district of Cox’s Bazar in Chittagong division experienced a dramatic increase
in the number of refugees arriving from Myanmar. Within a period of three months,
approximately 650,000 Rohingya refugees arrived to the two small upazillas of
Teknaf and Ukhia, more than doubling the population living in the area. This latest
influx of Rohingya refugees poses a considerable welfare challenge for both the ref-
ugees and the host population. The concentration of refugees in a small geographic
area is putting pressure on service delivery, local wages, and prices of commodities.
Short-term negative economic impacts on hosts were more linked to the deteriora-
tion of wages than to price increases (Endara et al. 2018). It is estimated that hosts’
average daily wage decreased by about 24 percent between August 2017 and May
2018, increasing poverty rates among the host population by approximately 52 per-
cent. However, the impacts have not been large enough to change national poverty
rates. Food products’ inflation has not significantly affected local poverty rates.

This section showed that behind the progress in poverty reduction in
Bangladesh contrasting tales emerge. Bangladesh continues to advance in its
fight against poverty. However, progress has been patchy, presenting a picture
far from uniform. In broad terms, progress has been more pronounced in the East
than the West and in rural rather than urban areas. It is a deeply divided picture.
The next part explores the factors behind these different patterns.

32
Part 2

Factors behind the trends

Earned income remains a key driver of poverty reduction. The 2013 World Bank
poverty assessment (Jolliffe et al. 2013) showed that changes in earned income,
rather than transfers, were at the core of poverty reduction in Bangladesh from
2005 to 2010.14 The evidence for the period 2010-2016 suggests this was still the
case. Available data indicate that a high share of income is earned through work—
on average 73.4 percent, and for the poorest 40 percent this share is 82 percent.15
Although the receipt of international remittances in Bangladesh is notable, few
households directly benefit from them (Box 2). A larger proportion of households
receives social protection transfers, particularly poorer households in rural areas.
However, the size of the transfers is small, and the share of households receiving
those transfers has been falling (Table 2.1 and Annex Table A1).

Table 2.1: Share of households receiving remittances and social protec-
tion transfers
2010 2016
All Bottom 40 All Bottom 40
International remittances 9.59% 4.10% 5.01% 2.50%
Internal remittances 12.30% 10.54% 13.09% 11.74%
Social protection transfers 24.58% 33.20% 21.39% 29.06%
Source: Staff calculations using HIES 2010 and 2016.
Note: Bottom 40 denotes the poorest 40 percent of the per capita consumption distribution.

14
This is consistent with the international norm of poverty reduction in low- and lower middle-income
countries (Azevedo et al. 2013)
15
There are quality concerns about the income data in the HIES, in previous survey rounds but partic-
ularly in 2016 (See Data Annex), thus the shares of earned income should be interpreted as indicative.

33
Bangladesh Poverty Assessment

What is behind the different performance in poverty reduction across
Bangladesh? This section aims to shed light on the reasons behind the different
rates of progress in rural and urban areas and the reemergence of the East-West
divide. The first subsection highlights the role of changes in household assets as a
driver of consumption growth, stressing the important role of demographics and
investments in education. The second subsection examines the returns to these
assets, particularly in rural areas, and details how non-agricultural sectors have
become a more important driver of poverty reduction in rural areas in the last
six years. Together these two sections explain why Western divisions that remain
highly agricultural with slower progress on household demographics and educa-
tion were unable to match the gains in poverty reduction that Eastern divisions
experienced from 2010 to 2016. The final subsection of Part 2 focuses on urban
trends, to understand the factors behind the urban poverty reduction slowdown.

Box 2. International migration and poverty reduction in
Bangladesh

International migration has become an important source of employment
and income for Bangladesh. In 2016, 757,000 Bangladeshis left the country
to work, and remittances contributed to 6.2 percent of GDP (Figure B2.1).
Migration and remittance flows fluctuated over the period 2000-2016, with
the years 2007 and 2008 showing the sharpest rise in overseas employment,
followed by high remittance flows (Figure B2.1). In 2016, about 8 percent of
households reported having a member who had migrated abroad in the
previous five years.16 Around two-thirds of all migrants work in the Persian
Gulf region, taking short-term employment contracts. Most surveys of
Bangladeshi migrants find that migrants tend to be young, married males
with moderate education levels (World Bank 2012).

International migration can be welfare improving for those receiving
remittances and can also support poverty reduction indirectly. Spending
on local goods and services with remittance income boosts local demand,
and wages can increase as local labor supply is reduced. From 2000 to 2016,
poverty reduction was faster in districts where international migration was
higher: for each additional 0.1 percent of a district’s population migrating
internationally, poverty in that district fell by 1.7 percent (Hill and Endara

16
According to the 2011 census, 6.1 percent of households had a member working abroad. The regional
distribution of migrants in the HIES is aligned with the one observed in the census data.

34
Part 2: Factors behind the trends

Figure B2.1. Overseas employment and remittances

Overseas employment (number of workers)
12.0 1,000,000
900,000
10.0
Personal remittances (% of GDP)

800,000
700,000
8.0
600,000
6.0 500,000
400,000
4.0
300,000
200,000
2.0
100,000
0.0 -
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Personal remittances, received (% of GDP)
Total overseas employment

Source: Remittances data from Word Development Indicators. Overseas employment numbers
from the Bangladesh Bureau of Manpower, Employment and Training (BMET).
Notes: Overseas employment measures the number of workers leaving per year.

2019a). Sen et al (2014) find that differential rates of urbanization and inter-
national migration can help explain the spatial pattern of poverty reduc-
tion across districts in Bangladesh.

The decline in remittances observed since 2012 is unlikely to have had
a large effect on national poverty rates or explain the slowdown in pov-
erty reduction. HIES data indicates that the amount of international remit-
tances that households report receiving has fallen significantly, confirming
the trend in national accounts remittance data (Figure B2.2). However, as
households at the bottom of the income distribution were less likely to have
migrants and receive remittances in the first place, this reduction is unlikely
to have affected overall poverty rates.17 Assuming that the share of house-
holds with international migrants and the size of remittances had remained

17
Existing evidence shows that migrants are less likely to be poor, partly because there are high out-
of-pocket costs of migrating. According to a survey from the International Organization for Migration
(IOM) in 2010, three-quarters of migrants spent anywhere from Tk 100,001- 300,000, with the average
migration cost being Tk 219,394.

35
Bangladesh Poverty Assessment

at 2010 levels, the annual rate of poverty reduction would have increased
only slightly, to 1.4 percentage points between 2010 and 2016. The fall in
remittances has particularly affected incomes of the top 60 percent, who
are more likely to benefit from international remittances. However, the
slowdown in remittances may have had some impacts at the local level,
due to indirect benefits of migration. More information is needed to assess
this hypothesis.

Figure B2.2. Remittances have fallen, with larger impacts on
better-off households
a. Proportion of households with an b. Value of remittances received
international migrant, by consumption (per capita), by consumption decile
decile
25.0% 2010 2016 1400 2016 if number of migrants
had stayed at 2010 levels
1200
20.0% 2016
1000
15.0% 2010
800
600
10.0%
400
5.0% 200
0.0% 0
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
Consumption decile Consumption decile

Source: Staﬀ calculations using HIES 2010 and 2016.
Notes: In the graph on the right-hand side, the grey line represents the value of international
remittances per consumption decile that would have been observed, if the number of migrants per
household had stayed the same as in 2010. It assumes the 2016 value of remittances per international
migrant.

The geographic pattern in access to international migration constrains
the potential of this process to reduce income disparities across the
country. The majority of international migrants come from Dhaka and
Chittagong, and this has changed little over time (Figure B2.3). The tem-
poral persistence of these geographic patterns is consistent with evidence
that migration is more likely in places where the stock of migrants is already
high, as prospective migrants can rely on the benefits of existing migrant
networks (Hanson 2010; Litchfield et al. 2015).

36
Part 2: Factors behind the trends

Figure B2.3. Spatial pattern of international migration,
2010 and 2016
100%
7 11
1
90% 7 1
5
80% 6 6
70%
60% 36 29

50%

40%
30%
38 45
20%
10%
0% 4 3
2010 2016
Barisal Chittagong Dhaka Khulna Rajshahi Rangpur Sylhet

Source: Staﬀ calculations from HIES 2010 and 2016.
Notes: International migration identified from household reports with a reference period of five years.

I. Household demographics and education have contributed to
consumption gains across urban and rural areas

Changes in households’ assets (e.g., human capital, physical assets, access to eco-
nomic services) and the income derived from those assets are a central element
behind consumption growth. This subsection examines progress in demographics,
education, and other non-monetary dimensions of well-being and how this prog-
ress has contributed to consumption growth. Since its independence, Bangladesh
has made remarkable strides in improving human development outcomes, includ-
ing life expectancy, fertility, infant and child mortality, education, access to housing
services and sanitation. Both government and non-governmental organizations
have been important for these achievements (World Bank 2006).

Bangladesh continues to make progress in fertility, education, and other
non-monetary dimensions of well-being

Fertility rates continued to decrease between 2010 and 2016, triggering a fall
in household size and the number of dependents per household. Bangladesh
has been an impressive example of demographic change, with fertility rates

37
Bangladesh Poverty Assessment

declining from more than six children per woman in the early 1980s to 2.1 chil-
dren per woman in 2017, almost reaching replacement levels. Between 2010 and
2016, household size and the average number of children per woman continued
to fall (Table 2.2), resulting in lower dependency ratios. Between 2010 and 2016,
household size fell similarly for both poor and non-poor households. Yet, poor
households are still significantly larger than non-poor households, so each work-
ing-age adult in a poor household must support a larger number of non-working-
age members, on average (Appendix Table A1).

The fertility declines in Bangladesh have been accompanied by a rise in life
expectancy and substantial reductions in infant and child mortality. Since 2000,
life expectancy at birth has increased 8.9 years for women and 6.2 years for men
(Table 2.2). In addition, infant mortality decreased sharply, from 64 to 27 infants per
1,000 live births. Today, Bangladesh is performing better in these dimensions than
other countries in the South Asia region, where average life expectancy is 69 years
and infant mortality is about 36.4 infants per 1,000 live births (Annex Table A4).

In addition, a continued increase in school attendance is creating a significantly
more educated adult population. Over the period 2000-2016, net school atten-
dance rates rose by 20 percentage points for primary school, 22 points for second-
ary, and 16 points for tertiary. The investments in children’s education over many
years are now translating into a more educated working-age population (Figure 2.1).

The expansion in schooling has been broad-based and has reduced inequali-
ties in gender. Even though adult females are overall less educated than males,
younger generations are reversing this disadvantage (Figure 2.2a). Faster prog-
ress in female educational achievement has resulted in young men now being
less likely to complete primary or secondary school than women, although they
still outperform women in tertiary schooling. Large increases in the number of
schools, targeted stipends programs for girls, and the growth of the ready-made
garment industry contributed to closing gender gaps in school enrollment.
Today net female primary school enrollment rates exceed the average in South
Asia and lower-middle income countries (Annex Table A4). Women’s educational
gains have also supported better labor market options for women and increased
female labor force participation, which in turn improved women’s fertility choices
and empowerment within the household (Heath and Mobarak 2014).

School attendance has also grown more rapidly among the poor, shrinking
differences in school achievement between socioeconomic groups. Comparing
across cohorts, there has been a reduction in the primary and secondary school

38
Part 2: Factors behind the trends

completion gaps between poor and non-poor, though the difference is still con-
siderable (See Figure 2.2b for primary-level completion). Between 2010 and 2016,
the difference in average years of education between poor and non-poor adults
fell from 3 to 2.2 years. However, literacy rates remain significantly lower among
heads of poor households (38 percent) than among heads of non-poor house-
holds (59 percent) (Annex Table A1).

Table 2.2: Progress in non-monetary dimensions of wellbeing
2000 2005 2010 2016
Household demographics
Average household size 5.2 4.8 4.5 4.1
Average number of children under 8 1.1 0.9 0.8 0.7
Household access to housing services and land ownership
% of households with tubewell water 51.5 57.8 57.7 59.1
% of households with piped water 6.8 7.6 10.6 12.0
% of households with electricity 31.2 44.2 55.2 76
% of households that own cultivable land 41.4 45.4 41.0 32.3
Education
Literacy (among adults older than 18 years) 43.0 49.6 53.9 60.1
Average years of education (adults older than 18 years) 3.3 4.1 4.4 4.7
School attendance (among 6-18 years-old) 63.9 66.6 73.9 80.4
Health
Fertility rate, total (births per woman) 3.2 2.7 2.3 2.1+
Life expectancy at birth, female (years) 65.7 68.7 71.5 74.6+
Life expectancy at birth, male (years) 65.0 67.3 69.0 71.2+
Life expectancy at birth, total (years) 65.3 67.9 70.2 72.8+
Mortality rate, infant (per 1,000 live births) 64.0 50.4 38.9 26.9+
Prevalence of stunting, height for age (% of children under 5) 50.8 45.9 41.4* 36.1**
Prevalence of underweight, weight for age
42.3 37.3 36.8* 32.6**
(% of children under 5)
Prevalence of undernourishment (% of population) 20.8 16.6 16.9 15.2
Source: Demographic, services and education indicators from HIES. Health indicators from WDI.
*2011; **2014; +2017

There is room for improvement in nutrition areas despite the progress that has
been achieved. An analysis comparing 2010 and 2016 indicates that the average
number of calories consumed by the population has fallen (by about 150 calories)
both in urban and rural areas. Comparing across the consumption distribution,
caloric intake remained at similar levels for the poorest 20 percent but fell for the

39
Bangladesh Poverty Assessment

Figure 2.1. Gains in education, 2000-2016
a. Net attendance rates b. School achievement, adults 15+
100% 2%
120 6% 6% 7%
6%
90% 4% 6% 5% 6%
3% 6% 6% 5%
100 93 80% 6% 5% 5% 6%
8% 7% 7% 9%
83 70%
78 7% 10% 9%
80 60% 10%
72 72
9% 11%
64 50% 13%
60 54 40%
50
30% 63%
51% 50%
40 20% 44%
10%
16
20 12 0%
7 2000 2005 2010 2016
0
0 No schooling Incomplete primary
Primary Secondary Tertiary Completed primary Incomplete JSC
Completed JSC Incomplete SSC
2000 2005 2010 2016 Completed SSC More than SSC

Source: Source: Staﬀ calculations using HIES.
Note: Junior Secondary School (JSC) refers to completion of Grade 8. Secondary school (SSC) refers to
completion of Grade 10. The HIES only collects information on whether the person is currently attending
school, therefore the attendance figures will be lower than oﬀicial enrollment rates.

Figure 2.2. Gains in educational achievement have been broad-based
a. Gap in school achievement between b. Primary school completion across
females and males age groups, poor and non-poor
0.10 100%
0.08 86%
0.06 80%
0.04 71%
0.02 60%
0.00
-0.02 42%
40%
-0.04
-0.06 19%
20%
-0.08
-0.10 No At least At least At least 0%
schooling primary JSC SSC Age Age Age Age Age
complete complete complete 15-19 20-25 26-30 31-40 41-50

2000 2005 2010 2016 Not poor Poor

40
Part 2: Factors behind the trends

rest of the distribution—particularly for better-off households (Figure 2.3a). The
reduction in calories is partly explained by a change in diets, which are becoming
more diverse. Part of the decline in calories can also be explained by an increase
in the share of food consumed away from home.18 Over the 2010-2016 period, the
share of calories derived from cereals has decreased, while the share of calories
derived from meat, eggs, and vegetables has increased. The average Household
Dietary Diversity Score (HDDS), which measures access to a variety of food groups,
rose 0.2 points between 2010 and 2016. The HDDS increased more rapidly for the
poorest quintiles (Figure 2.3b). Despite these positive trends, about 78 percent
of calories on average are derived from the consumption of cereals, exceeding
the recommended guidelines for diet quality (Pinzon and Wang 2019). Moreover,
the latest statistics still show very high levels of malnutrition in Bangladesh, with
more than a third of children under 5 stunted and 15 percent of the population (25
million people) undernourished.

Figure 2.3. Calories consumed and food diversity, 2010-2016
a. Daily caloric intake per capita b. Household dietary diversity scale
3500.0 12.0
Total calories per capita (average)

Dietary Diversity Scale (average)

11.5
3000.0
11.0

2500.0 10.5
10.0
2000.0 9.5
9.0
1500.0
8.5
1000.0 8.0
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
Consumption per capita decile Consumption per capita decile
2010 2016 2010 2016

Source: Staﬀ calculations using HIES.
Note: The household dietary diversity scale (HDDS), based on FAO definitions, is meant to reflect, in a
snapshot form, the economic ability of a household to access a variety of foods. The index is based on 12
food groups (cereals; tubers and roots; vegetables; fruits; meat; eggs; fish and other seafood; legumes,
seeds and nuts; diary; oils and fat; sweets; and spices and condiments). The potential range for the score is
0-12. The score measures how many food groups were consumed in the past two weeks (reference period
for the HIES) by the household.

18
The HIES data collects a basic question about foods away from home, which suggests increasing
importance of this component across time.

41
Bangladesh Poverty Assessment

Progress was also made in access to basic services such as electricity, water,
and sanitation. There were faster gains for the poor in terms of electricity, as well
as access to mobile phones. However, progress was more modest on access to
water and sanitation services (Figure 2.4).

Figure 2.4. Access to services for the poor and non-poor
a. 2010 b. 2016
Non-poor Poor Non-poor Poor

Mobile phone Mobile phone
100% 100%
80% 80%
60% 60%
40% 40%
Sanitary toilet

Sanitary toilet
Electricity

Electricity
20% 20%
0% 0%

Piped water Piped water

Source: Staﬀ calculations using HIES 2010 and 2016.
Note: Sanitary toilet, electricity, mobile phone, and piped water are calculated as the percentage of
households that report having access to the item. Literacy denotes the share of the adult population older
than 18 years that can write a letter. Poor and non-poor defined based on the national upper poverty rate.

Lower fertility and education gains fuel consumption growth

From 2010 to 2016, changes in household demographics, education, and other
asset holdings contributed to consumption growth. A decomposition analysis
shows that, if the correlation between household characteristics and consump-
tion remained unchanged, demographic changes and the accumulation of edu-
cation and other assets were sizeable enough to explain half of all consumption
growth over this period (Figure 2.5). The consumption growth derived from these
gains was similar before and after 2010. However, these factors explained a larger
share of consumption growth after 2010, as overall consumption growth has been
slower recently. In addition, the amount of consumption growth explained by
changes in demographics, education, and other assets between 2010 and 2016
is very similar across rural and urban areas (Figure 2.5), indicating that the slow-
down in urban poverty reduction cannot be explained by different progress on
these areas.

42
Part 2: Factors behind the trends

Figure 2.5. Contribution of household demographics, education,
and other assets to total consumption growth

a. Period 2005-2010 b. Period 2010-2016
10% 10%
8% 8%
6% 6%
4% 4%
2% 2%
0% 0%
1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 9
Consumption deciles Consumption deciles
c. Urban areas, 2010-2016 d. Rural areas, 2010-2016
10% 10%
8% 8%
6% 6%
4% 4%
2% 2%
0% 0%
1 2 3 4 5 6 7 9 1 2 3 4 5 6 7 8 9
-2% -2%
Consumption deciles Consumption deciles

Household demographics, education and other assets Total change

Source: Staﬀ calculations using HIES 2005, 2010, and 2016.
Note: Y-axis presents the logarithm of per-capita consumption growth over the period. “Household
demographic, education and other assets” refers to the estimated consumption per capita growth derived
from changes in household demographics, education and other assets (i.e., land ownership and electricity).
It is calculated as the sum of the changes in each characteristic between two years multiplied by the
coeﬀicient for that characteristic in the first year. For more details see Hill and Endara (2019b).

Reductions in fertility and family size were the most important contributors to
poverty reduction. Figure 2.6 shows that smaller household sizes and reductions
in the number of children significantly contributed to poverty reduction. This is
in part a measurement effect, as the welfare measure used for estimating pov-
erty in Bangladesh is total household consumption per capita. The per capita
measure does not account for any scale economies or for the fact that children
will consume less than adults. However, there are other reasons why fewer chil-
dren result in lower poverty rates. For instance, the amount earned by working
adults is shared among fewer household members. Also, if less time is devoted
to childcare, more time could be allocated to income-earning activities. Results
using consumption measures that allow for scale economies suggest that some

43
Bangladesh Poverty Assessment

Figure 2.6. Estimated contribution of demographic and asset changes
to consumption growth
a. 2005-2010 b. 2010-2016
0.080 0.080

0.060 0.060

0.040 0.040

0.020 0.020

0.000 0.000

-0.020 -0.020

-0.040 -0.040
1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9
Decile Decile

Electricity coverage in the village Household size
Average years of schooling Share household members
Land for agriculture younger than 18

Source: Staﬀ calculations using HIES 2010 and 2016.
Note: Y-axis measures the predicted consumption per capita growth over the reference period from
changes in household demographics and assets (i.e., location, education of adult members, household
demographics, access to services, and land ownership). X-axis measures the per capita consumption decile.
For more details see Hill and Endara (2019b).

of these dynamics were at work in Bangladesh during this time. Reductions in fer-
tility and family size also bring long-run benefits not captured here. For example,
per-child investments in nutrition and education can be higher, enabling future
generations to be more productive.

After demographic changes, gains in educational attainment contributed
most to poverty reduction. Increases in years of schooling contributed less to
consumption growth in 2010-2016 than in 2005-2010, but educational gains still
made substantial contributions to driving down poverty. Comparing urban and
rural areas, the estimated contribution of education to poverty reduction has
been much higher in rural areas. Keeping other household characteristics fixed,
the correlation between education and consumption is higher in urban areas
than in rural areas, suggesting higher returns to education and therefore a larger
gain to educational attainment in urban settings. However, educational attain-
ment has increased more rapidly in rural Bangladesh than in cities, explaining the
larger role of education in poverty reduction in rural areas (Figure 2.7). For most

44
Part 2: Factors behind the trends

Figure 2.7. The contribution of increased education to consumption
growth was larger in rural areas
a. Education attainment, 2000-2016 b. Contribution of education to
consumption growth, 2010-2016
Average years of education (ages 18+)

7.0 0.040
6.0 0.035
5.0 0.030
0.025
4.0
0.020
3.0
0.015
2.0
0.010
1.0 0.005
0.0 0.000
2000 2005 2010 2016 1 2 3 4 5 6 7 8 9
Decile
Rural Urban Rural Urban

Source: Staﬀ calculations using HIES 2000, 2005, 2010, and 2016.
Note: Average years of education calculated for adults older than 18 years. For more details see Hill and
Endara (2019b).

of the consumption distribution in urban areas, the average years of education
increased, but the gains were modest.

The reduction in educational disparities between urban and rural areas partly
reflects faster spending increases on education by poorer households and
progressive public spending. In the past two decades, Bangladeshi households
have substantially increased the amount they spend on education. Increases in
private spending on education occurred at a faster rate among poor households,
thereby significantly reducing the gap in education spending across socioeco-
nomic groups. While in 2000 the top quintile spent 22 times more on education
per student than the poorest quintile, in 2016 the richest quintile spent only
six times more than the poorest quintile (Genoni et al. 2019). In addition, pub-
lic spending, particularly for primary schooling, has emphasized investments
in the education of poorer children, helping reduce inequalities in education
investments per child. Recent estimations indicate that, for the median child in
primary school, about 57 percent of total spending comes from public resources.
For the median child among the poorest 20 percent, however, three out of four
takas spent on education come from public resources. For students in second-
ary school, 43 percent of spending is public, and for the poorest 20 percent of
households, the share is 60 percent (Figure 2.8). Stipend programs and tuition
waivers helped to improve the progressivity of public spending, though mainly
for primary level of schooling.

45
Bangladesh Poverty Assessment

Figure 2.8. Shares of public and private spending in total education
spending
a. Primary level b. Secondary level

23% 29%
45% 48% 43%
55% 60% 54%
65%
76%

77% 71%
55% 52% 57%
45% 40% 46%
35%
24%

1 2 3 4 5 1 2 3 4 5
Consumption quintile Consumption quintile
Private Public

Source: Genoni et al (2019). Calculations using Household Income and Expenditure Survey 2016 and BOOST
for fiscal year 2014.

Progress in the rural West was slower, contributing to the re-emergence of an
East-West divide

The gains in rural areas were not uniform: the rural West recorded slower progress
on education and demographic change, contributing to the re-emergent East-
West divide. Household size fell more slowly in rural areas of the Western divisions
than in the Eastern divisions. Gains in education in the rural West were half of those
achieved in the rural East: the average years of schooling of adult household mem-
bers increased by 0.88 years among rural households in Eastern divisions between
2010 and 2016, but by only 0.43 years in rural households in Western divisions.
This suggests that the likely contribution of education to consumption growth in
Western rural areas was also only half that observed in the rural East (Figure 2.9).

In urban areas in Western Bangladesh, there was faster progress made on edu-
cation and reducing family size. Household size decreased more rapidly and
educational attainment improved five times faster in Western cities, compared to
urban areas in Eastern divisions. This faster progress in urban areas of the West
compared to the East perhaps contributed in reducing the economic disadvan-
tage of living in urban areas of the West.

Rural households in Western divisions also saw a more rapid decline in the
average size of land holdings. Between 2010 and 2016 there has been a decrease
in the size of land holdings, particularly in the West (Figure 2.10). Households
with larger land holdings have higher consumption, which means the reduction

46
Part 2: Factors behind the trends

Figure 2.9. The contribution of changing demographics and assets to
consumption growth in the rural East and West, 2010-16

a. Rural East b. Rural West

0.100 0.100

0.080 0.080

0.060 0.060

0.040 0.040

0.020 0.020

0.000 0.000

-0.020 -0.020

-0.040 -0.040

-0.060 -0.060
1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9
Decile Decile

Share household members younger than 18 Household size
Average years of schooling Land for agriculture

in average land holdings has most Figure 2.10. Change in average size
likely worked against consumption of owned land from 2010-2016
growth. This is shown at the national Urban Rural
0.00
level in Figure 2.6, where the reduction -0.003
in the size of land holdings contributed -0.05
-0.05
negatively to consumption growth.
-0.10
That land-holding size fell faster in -0.09
Western divisions likely dampening -0.15
consumption growth there, relative -0.20
to the Eastern divisions. The greater
prevalence of agriculture work in the -0.25
-0.26
West also played a role in faster con- -0.30
sumption growth from 2005 to 2010 East West
and slower consumption growth from
Source: Staﬀ calculations using HIES 2010 and 2016.
2010 to 2016. This dynamic is analyzed Note: Change in land size expressed in logs. For
in the next subsection. more details see Hill and Endara (2019b).

47
Bangladesh Poverty Assessment

II. Rural income growth: poverty reduction was rural but not
predominantly agricultural

The changing sectoral composition of economic growth also explains the
different rates of progress in poverty reduction across the country. While
reduced fertility, increased education and other assets played a significant role
in poverty reduction, they only explain half the consumption growth registered
in Bangladesh from 2010 to 2016. Another element that determined consumption
growth is the returns obtained from those household characteristics. Growth in
returns can come from growth in the return a household earns while staying in the
same employment sector or from moving to a sector with higher returns.

All economic sectors have contributed to poverty reduction since 2000

Since 2000, there have been large shifts in the sectoral composition and geo-
graphical focus of economic activity in Bangladesh. Between 2000 and 2016,
the share of agriculture in GDP fell from 24 to 15 percent. Six percentage points
of this shift went to industry and three to services (Table 2.3). The structure of
employment changed even more dramatically, with 24 percent of the workforce
moving out of agriculture during this period—10 percentage points into industry
and 14 percentage points into services. Bangladesh has moved at a faster pace
than most other developing countries in this process of structural transformation,
with the share of employment in agriculture falling about 24 percentage points
compared to 14 points in the South Asia region (Annex Table A4).

Through the entire period 2000-2016, poverty reduction was faster in districts
and periods with high growth in agriculture and manufacturing. District-level
panel analysis shows that agriculture and manufacturing have been equally
important contributors to Bangladesh’s poverty reduction over the period 2000 to
2016. Poverty fell faster in areas and years when growth in the value of agricultural
output and the number of manufacturing firms was highest. The impact of agri-
cultural growth holds when instrumenting growth with local rainfall conditions,
suggesting that the positive relationship between agricultural growth and poverty
reduction is causal. Similarly, the impact of manufacturing growth is present when
proxying manufacturing growth using a Bartik instrument, suggesting causality in
the estimated relationship between manufacturing growth and poverty reduction.
The Bartik instrument is the share of employment in industrial subsectors multi-
plied by the sub-sectoral growth rate (Hill and Endara 2019a). The period from 2005
to 2010 was particularly positive for agricultural households, as they benefited
from high food prices. This was true both for own-account workers in agriculture

48
Part 2: Factors behind the trends

and those working as agricultural laborers, since agricultural wage rates increased
(Jolliffe et al. 2013). Growth in the service sector may have been important too, but
challenges in measuring growth in this sector accurately and the fact that growth
in the informal service sector is often spurred by growth in other sectors (Shilpi and
Emran 2016) make it difficult to quantify its poverty-reduction impact.

Bangladesh has been resilient in maintaining strong progress in poverty reduc-
tion, despite change in the sectoral nature of growth. Growth and employment
shifts across sectors have not occurred uniformly over time. This has implications
for how poverty reduction was achieved over time. Between 2000 and 2005, there
was low average growth in agriculture, high but jobless growth in industry, and
moderate, job-creating growth in services. The shift of employment from agricul-
ture to services during this period was notable. Poverty reduction in this period was
driven by service sector growth (World Bank 2008). From 2005 to 2010, there was
high growth in agriculture (which slowed structural transformation), high job-cre-
ating growth in industry, and very high growth in services but limited in job-cre-
ation. This period is notable for its very high agricultural growth and the start of
Bangladesh’s boom in the creation of manufacturing jobs. Poverty reduction during
this period was driven mostly by growth in agriculture (Jolliffe et al. 2013).

Table 2.3: Trends in key economic and demographic variables,
2000-2016
2001- 2005- 2010-
2000 2005 2010 2016
2005 2010 2016
Average per capita
Growth (1) Share of GDP
growth
Total GDP growth 5.1 6.1 6.5
Total GDP per capita growth 3.3 4.8 5.2
Agriculture growth 23.8 19.6 17.8 14.8 1.5 4.1 2.3
Industry growth 23.3 24.6 26.1 28.8 5.2 6.8 8.4
Services growth 52.9 55.8 56.0 56.5 2.3 8.4 4.9
Consumer Price inflation (1) 5.1 7.7 7.2
Annual percent points
Share in employment (%)
change
Sector of employment and place of residence
Agriculture (2) 64.8 48.1 47.3 41.1 -3.3 -0.2 -1.0
Industry (2) 10.7 14.5 17.6 20.8 0.8 0.6 0.5
Services (2)
24.5 37.4 35.0 38.0 2.6 -0.5 0.5
Urban population (%) (3)
23.6 30.4

49
Bangladesh Poverty Assessment

2001- 2005- 2010-
2000 2005 2010 2016
2005 2010 2016
Annual percent points
Poverty (4) Poverty rate (%)
change
National 48.9 40.0 31.5 24.5 -1.8 -1.7 -1.2
Agriculture 55.4 50.0 37.3 32.6 -1.1 -2.5 -0.8
Industry 49.0 40.3 34.3 24.8 -1.7 -1.2 -1.6
Services 41.1 33.1 26.6 20.2 -1.6 -1.3 -1.1
(1) WDI. Sectoral average per capita growth calculated using total population growth.
(2) International Labor Organization modelled estimates.
(3) Percentage of the total population living in urban areas. From National Population Censuses.
(4) Estimates from Household Income and Expenditure Survey (HIES). Households are assigned to
the economic sector based on share of hours worked.

From 2010 to 2016, households engaged in industry and services led rural
poverty reduction

The period from 2010 to 2016 was characterized by lower agricultural
growth, high growth in manufacturing, and moderate service sector growth.
Although poverty reduction was primarily rural in this period, it occurred
more among households in industry and services, rather than agriculture.
Although 47 percent of rural households were primarily engaged in agriculture
in 2010, such households accounted for just 27 percent of rural poverty reduc-
tion between 2010 and 2016. This contrasts with the period 2005 to 2010,
when 69 percent of rural poverty reduction was among households primarily
engaged in agriculture (Figure 2.11). Most rural poverty reduction between
2010 and 2016, 59 percent, occurred among households whose primary sector
of employment was industry or services (23 percent in industry and 36 per-
cent in services). Data that follows the same households over time during this
period documents the same trend: households with higher shares of non-farm
income were less likely to remain in or fall into poverty (Ahmed and Tauseef
2018). When taking into account the fact that people work in multiple sec-
tors, the poorer performance of households engaged in any agricultural activ-
ities—even if also engaged in other sectors—becomes clearer, suggesting it
was engagement in industry or services, instead of having multiple sources of
income, that contributed to poverty reduction (Figure 2.11b). Despite strong
growth in nonagricultural sectors, the share of the rural population primarily
engaged in nonagricultural activities reported in the HIES increased by only 3
percentage points. This shift contributed just 4 percent to poverty reduction
(Figure 2.11).

50
Part 2: Factors behind the trends

Figure 2.11. Poverty reduction across sectors in rural areas, 2005-2016

a. By main sector

Sector share in 2010 Contribution to poverty reduction

Agriculture

Services

Industry

Not available

Population shift

Agriculture Services -20% 0% 20% 40% 60% 80%
Industry Not available 2005-2010 2010-2016

b. Allowing for multiple sectors

Sector share in 2010 Contribution to poverty reduction

Agriculture

Services

Industry

Multiple Sectors

Not available

Population shift
Agriculture Services
-20% 0% 20% 40% 60%
Industry Multiple Sectors
Not available 2005-2010 2010-2016

51
Bangladesh Poverty Assessment

Agricultural growth was slower and less poverty reducing than in the past

The smaller role of agriculture in poverty reduction between 2010 and 2016
partly reflects lower agricultural growth. Between 2010 and 2016, agriculture
grew 2.3 percent per year, compared to 4.1 percent annually between 2005 and
2010. After 2010, slower agricultural growth fueled a renewed decline in agricul-
tural employment. The sector’s growth in recent years was affected by negative
weather shocks in 2012–13, which substantially reduced its overall growth rate. In
addition, the moderation in real prices since 2010 has led to a slowing of growth
in crops (Gautam and Faruqee 2016). The slowdown in agricultural growth also
reflects a decreased growth rate in rice production, from 5.1 percent per year
between 2005-2010 to 1.2 percent during 2010-2016.

In addition, during 2010-2016, agricultural growth became less poverty reduc-
ing, while industrial and manufacturing growth became more poverty reduc-
ing. Nonetheless, there was very little difference in the poverty-reducing impact
of growth in any of the sectors. This contrasts with the general trend, in which
agricultural growth had tended to be much more poverty reducing than growth
in other sectors (Figure 2.12). Before 2010, poverty fell by 1.5 percent among agri-
cultural households for each percent of agricultural GDP growth. This elasticity
almost halved, to -0.8, from 2010 to 2016. In this time period, Bangladeshi house-
holds attached to the industry and services sectors secured 0.6 and 0.8 percent
reduction in poverty for every percent of value added per capita in these sectors,
respectively.

Figure 2.12. Implied sectoral growth-poverty elasticities, 2000-2016

2000-2005 2005-2010 2010-2016
0

-0.5

-1

-1.5

-2
Total Agriculture Industry Services

Source: Staﬀ calculation using HIES and WDI.
Notes: Elasticities are calculated from GDP growth data and sectoral poverty rates presented in Table 2.3. For
more details see Hill and Endara (2019a).

52
Part 2: Factors behind the trends

In the West, the agricultural predominance of rural livelihoods slowed progress

The smaller share of rural households Figure 2.13. Share of households in
in the West pursuing non-agricul- agriculture, East and West
tural livelihoods contributed to the
60.0%
re-emergent East-West divide after
2010.19 Both in 2010 and 2016, house- 50.0% 47.9%
holds in the West were more likely 42.7%
than households in the East to report 4 0.0%
their main sector of work as agriculture 28.2%
30.0%
(Figure 2.13). Structural transformation 23.2%
also seems to have been faster in the 20.0%
East than in the West: the proportion
of households reporting their main 10.0%
sector as agriculture fell by 22 percent
0.0%
in the East compared to 12 percent in 2010 2016 2010 2016
the West. The correlation between land We s t E as t
ownership and consumption weakened
Source: Staﬀ calculations using HIES 2010 and 2016.
across the consumption distribution in Notes: Sector assigned based on hours worked.
the East, likely indicating the presence
of more economic opportunities with higher returns outside of agriculture (Figure
2.14). This has driven a reduction in land ownership gaps between poor and non-
poor households (Annex Table A1). In contrast, in the West, there was less expansion
of opportunities outside of agriculture, and the relationship between owning land
and consumption did not change, despite the slowdown in returns to agriculture.

In sum, although some of the divergence in poverty-reduction performance
between East and West from 2010 to 2016 can be explained by less favorable
changes in education attainment and demographics, differences in sectors of
work also seem to have played an important role. There is not conclusive evidence
to explain why the rural West has lagged in terms of structural transformation.
One element highlighted by the literature is the West’s gaps in connectivity and
access to the major urban centers of Dhaka and Chittagong (Gautam and Faruqee
2016). Evidence on previous connectivity investments (e.g., rural roads, Jamuna
bridge) highlights positive effects on agricultural productivity and non-farm sector
opportunities (Blankespoor et al. 2018; Khandker et al. 2006 and 2010). Increased
connectivity can also support non-farm labor incomes by decreasing the cost of
domestic and international migration, which in turn spurs remittance flows.

19
Sen (2019).

53
Bangladesh Poverty Assessment

Figure 2.14. Relationship between land ownership and
consumption growth
a. East b. West
6 6
Consumption premium

Consumption premium
4 4

2 2

0 0
0 2 4 6 8 10 0 2 4 6 8 10
Consumption deciles Consumption deciles
2005 2010 2016 2005 2010 2016

Source: Staﬀ calculation using HIES 2005, 2010, and 2016. For more details see Hill and Endara (2019b).
Notes: Graph depicts the correlation between land ownership and per capita consumption (at the household
level) for each consumption decile, conditional on other household characteristics. Shaded color depicts the
95 percent confidence interval for the correlations.

III. Urban income growth: gains in manufacturing, but stagnation
in the service sector

Understanding Bangladesh’s urban poverty story is key to explaining the slow-
down in the country’s poverty reduction. Clarifying the urban story is doubly
important, because poverty is increasingly urban in Bangladesh. Even though 8
in 10 poor live in rural areas, at current trends of urbanization and poverty reduc-
tion, more than half of Bangladesh’s poor households will live in urban areas by
2030. Although data and evidence on urban poverty is weak, this section consid-
ers what is known about recent trends in urban poverty reduction.

The focus is on understanding what drove changes in households’ earned
income. Labor income contributes about 76 percent of total household income
in urban areas, and 85 percent of the income of the poorest 40 percent of urban
households. Section 2.I showed that the slowdown in urban poverty reduction
cannot be explained by changes in demographic change, education and other
assets, so the focus is on understanding the returns households earn on their labor.

Industry, particularly the garments sector, led urban poverty reduction

Poverty reduction has been patchy across economic sectors in urban areas,
with poverty rates in industry falling substantially faster than in other sectors.
In 2010, poverty rates for households mainly engaged in industry were higher than
those in services (26 percent compared to 17 percent). By 2016, poverty rates among

54
Part 2: Factors behind the trends

households in industry were almost at the same level as among households working
in the service sector. This convergence was driven by fast poverty reduction among
households in industry and no change in poverty for those households attached
to the service sector (Table 2.4). This contrasts to the period 2005-2010, when both
households in industry and services experienced reductions in poverty (Figure 2.15).
The stagnation in poverty reduction in services is concerning, since about 44 percent
of the poor in urban areas are part of households primarily engaged in this sector.

Table 2.4: Poverty reduction has been uneven across sectors
in urban areas
2010 2016
Percent of urban population living in poverty with main sector of household work in:
Industry 26% 19%
Garment Sector 25% 16%
Other Manufacturing 23% 20%
Construction 44% 33%
Services 17% 17%
Agriculture 35% 33%
Not employed or sector data missing 10% 15%
Source: Staff calculations using HIES 2010 and 2016.
Note: Sector is defined by main economic activity using hours worked.

Within industry, most gains were driven by the garment sector, followed by
construction. Industry and services are broad categories capturing several differ-
ent sub-sectors. Poverty reduction in industry has been concentrated in garments
and (to a lesser extent) construction. The service sector is varied, including every-
thing from rickshaw drivers and street vendors to physicians and those employed
in the financial sector. Figure 2.16 shows that different sectors have fared quite
differently. For example, poverty reduction in the transport sector was strong, but
this comprises a small share of service sector workers. Progress was very slow in
commerce, and even increasing poverty rates were observed in other services.

Poverty rates increased the most among the self-employed in services, which
set back overall progress. Figure 2.17 decomposes poverty reduction from 2010
to 2016 based on main sector and type of work (wage and daily employment or
self-employed). The strongest contributor to overall progress was poverty reduc-
tion among wage and daily workers in industry. This might in part reflect new
minimum-wage legislation affecting the larger firms of the garment sector. Good
progress was also seen for wage and daily workers in services. However, poverty
rates increased among the self-employed in the service sector in urban areas.

55
Bangladesh Poverty Assessment

Figure 2.15. Poverty reduction across sectors in urban areas, 2005-2016
a. Sector share in 2010 b. Contribution to poverty reduction

Agriculture

Services

Industry

Not available

Population shift

Agriculture Services -30% 20% 70% 120%
Industry Not available 2005-2010 2010-2016

Figure 2.16. Progress of households in the garment sector contributed
most to urban poverty reduction
a. Sector share in 2010 b. Contribution to poverty reduction

Agriculture
Garment
Oth er Manufacturing
Construction
Oth er Industry
Commerce
Tra nsport
Agriculture Garment Oth er Services
Other Manufacturing Construction Not available
Other Industry Commerce
-30% 30% 80%
Transport Other Services
Not available 2005-2010 2010-2016

56
Part 2: Factors behind the trends

Figure 2.17. Poverty reduction was fastest among wage workers,
particularly in industry

a. Sector share in 2010 b. Contribution to poverty reduction

Agriculture
self-employment

Agriculture wage

Services
self-employment

Services wage

Agriculture self-employment Industry
self-employment
Agriculture wage
Services self-employment Industry wage

Services wage
Industry self-employment -40% -20% 0% 20% 40% 60% 80%
Industry wage 2005-2010 2010-2016

Source: Staﬀ calculations using HIES 2005, 2010, and 2016.
Notes: Results obtained from Ravallion and Huppi (1991) decompose changes in poverty over time into
intra-sectoral eﬀects, a component due to population shifts across sectors (not displayed), and an interaction
(not displayed). Sector of employment defined based on reported hours of work in each sector.

Slow manufacturing job creation curbed poverty reduction and reduced
female labor force participation

Those with industrial jobs benefited, but few had the opportunity. There has
been little growth in the share of the Bangladeshi labor force engaged in industry,
and this has limited the amount of poverty reduction derived from the country’s
industrial growth. Job creation in the ready-made garment (RMG) and textiles
sectors combined has fallen from over 300,000 new jobs per year between 2003
and 2010 to 60,000 annually since 2010. In recent years, many of the new RMG and
textiles jobs have been created in the periphery of urban centers, in areas which
may be classified as rural for administrative purposes (Farole and Cho 2017).

This lack of job creation has reduced productivity among the self-employed.
In urban Bangladesh, few poor households can afford to be unemployed, as
safety nets are limited. Unemployment rates are about 2 percent in the Labor
Force Survey (LFS). Those who cannot obtain wage work engage in subsistence

57
Bangladesh Poverty Assessment

self-employment. As the availability of wage work declines, more people are
engaged in subsistence self-employment, and this can have the effect of lowering
productivity among the self-employed, as more marginal activities are under-
taken. A labor market model of urban Bangladesh was developed to quantify this
trade-off (Poschke 2019). Increasing job creation can thus have a positive impact
on productivity among subsistence self-employed workers.

There is a trade-off between improving labor regulations in manufacturing and
creating more jobs in the sector. Improving work conditions and pay has been
essential for poverty reduction and for avoiding disasters such as the Rana Plaza
garment factory collapse, which killed 1,134 garment workers in 2013. Poschke
(2019) shows minimal negative impact on hiring resulting from the enforcement
of regulations highly valued by workers, such as Employment Injury Insurance. As
essential regulations are introduced to ensure workers receive a larger share of
the surplus they generate, it is important that the cost of other regulations that
firms face to establish and grow their businesses are reduced to mitigate any neg-
ative impacts on hiring.

The slowdown in job creation in the RMG and textiles sector is also likely respon-
sible for falling rates of female labor force participation (FLFP) affecting poor
households. Between 2005 and 2010, overall labor force participation in urban
areas increased due to a substantial increase in FLFP. The expansion of the gar-
ment sector was an important force in raising FLFP, as 80 percent of employees in
this sector are female. Between 2010 and 2016, FLFP declined about 4 percentage
points (Table 2.5). FLFP rates in poorer and slum areas of Dhaka are significantly
higher (58 percent), compared to the urban average (Kotikula et al. 2019). Even
though men earn substantially more than women in urban areas, women’s contri-
bution to total household income is not small, and their exit from the labor mar-
ket could have impacted household incomes. In poor and slum areas of Dhaka, for
instance, men’s average earnings are about BDT 13,000, compared to BDT 5,500
for women (Kotikula et al. 2019).

Supply side factors are also important in determining whether a woman works,
but it is not clear whether this can explain trends in FLFP. FLFP is strongly deter-
mined by demographics—age, marital status, having young children. Survey data
in poor areas of Dhaka show that having children of an age that requires childcare
is significantly correlated with women being outside the labor force. In addition,
women reported that access to childcare was a main constraint, while detailed
time-use data show that women are often looking after children even when they are
engaged in another activity, something that would be impossible to do if working

58
Part 2: Factors behind the trends

away from home (Kotikula et al. 2019). Women with more education in urban areas
are less likely to work, indicating a socio-economic gradient in the choice to par-
ticipate in the urban labor market. In addition, social norms are very different
between women that work and women that do not. In poor areas of Dhaka, half of
the women interviewed wear a burqa outside of their community, and 30 percent of
women report not feeling safe in their community. Women who do not feel the envi-
ronment outside of their house is safe are 10 percentage points less likely to partic-
ipate in the labor market. And those who wear burqas are 8 percentage points less
likely to engage in the labor market (Kotikula et al 2019). Taken together, however, it
is unclear what these patterns of FLFP imply for trends in the FLFP rate. Increasing
education levels could have contributed to lower rates of FLFP, but declining family
sizes would increase the FLFP rate. If social norms have changed over time, this
could have influenced FLFP, but without reliable data on trends in social norms, the
nature and direction of putative influences remain unclear.

Table 2.5: Female labor force participation has declined in urban areas
since 2010
Area 2002-03 2005-06 2010 2013 2015
National
All 57 59 59 57 59
Male 87 87 83 82 82
Female 26 29 36 34 36
Rural
All 58 59 60 57 60
Male 88 88 83 82 82
Female 26 30 36 34 38
Urban
All 57 56 57 57 56
Male 85 83 80 82 82
Female 27 27 35 33 31
Source: Bangladesh Bureau of Statistics from Labor Force Surveys.
Note: Percentage of the population older than 15 years.

Private returns to education fell in urban areas

Another concerning trend is that the private returns to education have fallen
in urban Bangladesh, particularly in the middle of the consumption distri-
bution. Figure 2.18 presents the correlation between years of education in
a household and household per capita consumption, controlling for other

59
Bangladesh Poverty Assessment

characteristics. The data shows that, although the returns to education are
higher in urban areas than in rural areas, they have fallen substantially since
2010. The fall has been sharpest in the middle of the consumption distribution,
where the proportion of households with some secondary education is largest.
This is consistent with estimates of the return to education derived from earn-
ings data in HIES (Saurav et al. 2019), which confirm that returns to primary and
secondary education fell from 2010 to 2016.20 The reduction in private returns
is concerning and indicates that very real constraints to entrepreneurship and
labor productivity may have been present in urban areas in Bangladesh in
recent years. ADB and ILO (2016) note that returns to education in Bangladesh
were already low by international standards in 2013.

Figure 2.18. The private return to education appears to have fallen
in urban areas
a. Rural b. Urban
1 1
Consumption premium

Consumption premium

08 08

06 06

04 04

02 02
0 2 4 6 8 10 0 2 4 6 8 10
Consumption decile Consumption decile

2005 2010 2016 2005 2010 2016

Source: Staﬀ calculation using HIES 2005, 2010, and 2016. For more details see Hill and Endara (2019b).
Notes: Graph depicts the correlation between education and per capita consumption (at the household level)
for each consumption decile, conditional on other household characteristics. Shaded color depicts the 95
percent confidence interval for the correlations.

The gains of agglomeration in Dhaka and Chittagong are more limited for
the poor

A second priority for urban poverty reduction is ensuring that the bene-
fits of agglomeration in the cities of Dhaka and Chittagong favor the poor.
Economic density is much higher in Dhaka than in the rest of the country but
living standards and poverty rates do not reflect this advantage. This is also true
for Chittagong. In 2013, greater Dhaka comprised 10 percent of the population

20
These results are also consistent with returns to education estimated using LFS data (ADB and ILO
2016), which indicate higher returns in urban areas than in rural areas.

60
Part 2: Factors behind the trends

and 36 percent of GDP, while Chittagong comprised 3 percent of the popula-
tion and 11 percent of GDP (Aparicio and Muzzini 2013). On average, residents
of Dhaka and Chittagong are 3.6-3.7 times more productive than the national
average. These cities are growing, attracting 60 and 16 percent of internal
migrants respectively (Farole and Cho 2017). However, the standard of living
does not reflect this higher level of productivity. The poverty rate in Dhaka and
Chittagong City Corporations is 9 and 12.1 percent respectively, compared with
24.3 percent nationally.

Many poor households in Dhaka live in slums, facing poor housing, insecurity,
and overcrowding to be near work. Slums have much higher levels of monetary
poverty, more children out of school, and lower levels of access to water and san-
itation services (Table 2.6). Stunting is also much more prevalent in slum areas
(Govindaraj et al. 2018), and almost half of slum residents fear eviction. Work was
the most common reason households in slums gave for moving to their current
residence. People often move to slums from other slums (39 percent), and work
was their main reason for moving (59 percent). This was more often the case for
female respondents.

Table 2.6: Poverty, education, and WASH in slums and non-slums, Dhaka
Dhaka CC Slums
Poverty rate 9.0 23.3
Can write a letter 76 47
Has no schooling 24 42
Some primary schooling 16 41
Some secondary schooling 37 14
Some post-secondary 25 3
Years of education 6.4 3.1
School attendance: overall (6-18 years) 77 57
School attendance: primary (6-10 years) 96 85
School attendance: secondary (11-15 years) 80 60
School attendance: high secondary (16-18 years) 44 20
Percentage of male adults who are earners (18 plus) 86 93
Percentage of female adults who are earners (18 plus) 28 49
Dependency ratio 0.51 0.62
Water is piped into dwelling 96 76
Share a toilet 62 91
Source: HIES 2016 and Bangladesh Urban Informal Settlements Baseline Survey (BUISBS) 2016.
Note: WASH denotes water, sanitation, and hygiene.

61
Bangladesh Poverty Assessment

Mobility is limited for the poorest households in Dhaka, limiting their ability
to gain from agglomeration. The poorest households predominantly commute
on foot, and their median commute is 40 minutes, which means they only have
access to jobs within a 4-5km radius from where they live (Hill and Rahman
2019). Poorer households thus have access to fewer jobs or else must change
their place of residence much more frequently than better-off households to
access work. This carries considerable monetary and non-monetary costs for
the poor. In unions where the number of garment jobs available is low (in the
bottom quintile), 26 percent of households report a member working in the gar-
ment industry. In unions where the number of garment jobs available is high
(in the top quintile), 61 percent of households report a member working in the
garment industry.

Women’s mobility is even more constrained. A distinct spatial pattern can be
observed regarding where working women live. Compared to men, women who
work are more likely to walk to their jobs, and they commute shorter distances.
Outside of work, mobility for women is also limited. While 84 percent of men in
slums and low-income communities go outside their community every day, only
40 percent of women do so. One-quarter of women in poor areas of Dhaka only
leave their community once a month, and one in ten women never leaves her
community. This means that there are many women in Dhaka that live life as if
they were in a remote village, even though they live in one of the biggest cities in
the world.

62
Part 3

Distilling the evidence
and looking ahead

This poverty assessment tells a story of remarkable progress that began
decades ago and continues today. Critical actions taken decades ago, allowed
Bangladesh to perform economically and realize high levels of per capita GDP
growth, as well as improve human development outcomes. Investments in human
capital supplied a rapidly transforming economy with the labor force capable of
benefiting from expanded job opportunities outside agriculture. These elements
have been important contributors for the current success in poverty reduction.

The evidence for the period 2010-2016 suggests that many of the traditional drivers
of poverty reduction in Bangladesh continue to play a role. Educational attainment,
lower fertility rates, agricultural growth, and international migration have helped
reduce poverty in rural areas. Growth in rural services and manufacturing re-emerged
as important drivers of progress. In urban areas, lower fertility rates and welfare gains
among manufacturing employees have been important for reducing poverty.

However, the evidence also points to the limits of some of these drivers in bring-
ing about progress in the last six years. Gains in educational attainment in the
West and urban Bangladesh were more limited and returns to education fell quite
substantially in urban areas. The West also made less progress on reducing fertil-
ity rates. Agricultural growth has been lower and less poverty reducing than in the
past, which was particularly challenging for the rural West where livelihoods are
concentrated in agriculture, and where slower progress on structural transforma-
tion was made. International migration and remittances also fell.

The rest of this section draws lessons from the preceding analysis to inform
public policies and contribute to ending poverty in Bangladesh. Of note, the

63
Bangladesh Poverty Assessment

analysis undertaken here only aims to provide high-level recommendations.
Subsequently, more specific sectoral analyses will allow for more detailed policy
prescriptions.

Agriculture remains central for the poverty story but must become more pov-
erty reducing. Growth in agriculture remains an important avenue for poverty
reduction and to reduce spatial disparities in income. A large share of the poor
in rural areas remain engaged in the agricultural sector. Fifty-six percent of poor
households are engaged in agriculture, compared to 45 percent of non-poor
households. In addition, even with the rise in non-farm employment, most house-
holds engage in agriculture at least part-time. Moreover, the reliance on agricul-
ture is still much higher in the West than the East. Despite the broader economic
transformation observed in rural areas, the overall structure of agriculture has not
changed much. Rice dominates agricultural production and has driven much of
the growth in productivity (Gautam and Faruqee 2016). There is significant poten-
tial to increase productivity and incomes by supporting more diversification to
non-rice crops and non-crop agriculture. For crops other than rice, there is sub-
stantial room to reduce current yield gaps (Mondal 2011). In addition, improved
connectivity (e.g., roads and bridges) can help more isolated and poorer areas
benefit from higher productivity – through better access to inputs but also due
to the incentives to diversify production in order to meet a more diverse demand
(Gautam and Faruqee 2016).

Yet, rural income growth and poverty reduction will center more on the growth
of non-farm sectors. Contrasting trends between Bangladesh’s West and East
underscore the importance of the non-farm sector for rural poverty reduction.
Key drivers of growth in the non-farm sector are connectivity and proximity to
urban areas. Households in the East living near the large urban centers of Dhaka
and Chittagong have shown the largest increase in non-agricultural labor income,
followed by households in districts well connected to other cities (Gautam and
Faruqee 2016). Better connectivity is a key factor to support non-farm employ-
ment and its productivity. Further improvements in fertility rates and education
can also support the transition to off-farm activities.

Even though poverty is still highly rural, it is urbanizing rapidly and that will
require new solutions. Nearly all analysis of poverty and income dynamics in the
country has been focused on rural poverty and mobility. This has important pol-
icy implications. For example, the graduation approach that was developed by
the Bangladesh Rural Advancement Committee (BRAC), and which has received
international recognition, is focused on physical asset transfer and livelihood

64
Part 3: Distilling the evidence and looking ahead

support that are well suited for rural Bangladesh but that have little applicability
in urban centers. Particularly for urban areas, there is a need for better data, more
rapid monitoring and evaluation of policies and progress, and more data-driven
decision making.

In urban areas, a focus on increasing productivity in the informal service sector
needs to complement a drive for job creation in manufacturing. Manufacturing
growth has been an important driver of rural and urban poverty reduction, but
more manufacturing jobs are needed in urban areas. This could also help increase
productivity in subsistence self-employment by drawing some people out of
such self-employment activities and increasing the marginal profitability of new
self-employment ventures. Manufacturing job creation needs to be pursued,
while continuing the recent growth in manufacturing wages and improvements in
factory working conditions. This means reducing other regulatory costs that firms
may face as they establish and grow their businesses. Other actions to increase
productivity growth in informal services are also needed, particularly in city areas
where access to manufacturing jobs is low. Policy experimentation on how best
to do this is essential for hastening urban poverty reduction. For example, pol-
icy experimentation will help identify the appropriate mix of infrastructure and
neighborhood investments relative to targeted skills training and access to credit.

In Dhaka, better public transportation and better housing close to employment
hubs can help make labor markets work better and reduce spatial disparities.
Spatial disparities are significant in Dhaka, and the cost of getting to work is an
important facet of such inequality. Transportation solutions can make it easier for
poor households to commute to manufacturing jobs in the center and northwest
of the city that are helping reduce poverty. Opportunities also exist to expand
access to affordable housing in eastern Dhaka and to improve the quality of ser-
vices in slums that are located close to employment hubs, offering quality hous-
ing to employees at affordable prices.

As labor income is the main source of income mobility for households, increas-
ing female labor force participation is important to support future poverty
reduction. Job creation in sectors that have traditionally favored women will
likely help reverse the declining trend in urban FLFP, while other factors that
may constrain FLFP can be addressed through targeted interventions. Childcare
emerges as an important constraint in urban areas. This is a challenge that
does not exist to the same extent in rural areas, where work is more often in
and around the house or farm. Improving access to affordable, quality childcare
services would facilitate women’s engagement in the labor force. Some women

65
Bangladesh Poverty Assessment

may also benefit from access to more remunerative self-employment opportuni-
ties that could be pursued from home. Technical and vocational education and
training (TVET) and “soft skills” are an important correlate of the nature of work
that women engage in. This points to the potential value of investments in tech-
nical and soft-skills training or mentorship programs. Such programs could help
women find jobs suited to their needs and goals and learn the skills (including soft
skills) required for success. Testing which interventions work will be important.
In addition, evidence from poor areas of Dhaka underlines the potential value of
approaches such as making travel safer, ensuring that public spaces are female-
friendly, and making female work more socially acceptable. Such strategies may
increase women’s ability to grow their families’ incomes and find greater personal
fulfillment, without fear.

Additional priorities include speeding progress on educational attainment, fer-
tility reduction, and structural change in the rural West, along with human cap-
ital investments in urban areas. Investing in human capital continues to be a pri-
ority to make the most of Bangladesh’s transforming economy and the changing
nature of work. More advanced cognitive and socio-behavioral skills will become
increasingly important in labor markets and will require solid human capital foun-
dations (World Bank 2019). Programs to improve the skills of working-age adults
can help, but there is also a need to address deficiencies in human capital invest-
ments for the next generation. Many urban children are out of school, and malnu-
trition rates among young children in urban areas are high. Progress in educational
investments in the rural West has lagged. An analysis of district public spending in
education provides insights on some policy priorities (Genoni et al. 2019).

Improving school attendance rates and learning outcomes in poorly performing
districts requires better-targeted education spending, as much as it requires
higher levels of spending. Currently, education spending per student for primary
and secondary levels presents large variations across Bangladesh, which is not
correlated with attendance rates and internal efficiency indicators. Only when
spending translates into lower student-to-teacher ratios do results appear to
improve. However, Bangladesh’s student-to-teacher ratios remain generally sub-
optimal compared to those in other countries (Genoni et al. 2019). This suggests
that higher-quality education spending, not just increasing the overall budget,
should be a priority for further progress in poverty reduction.

Addressing norms, expectations, and perceptions around the benefits of
schooling may reinforce progress. According to HIES 2016, many households
do not see value in education investments. About 51 percent of households with

66
Part 3: Distilling the evidence and looking ahead

primary-age children out of school report lack of interest or else the children’s age
as the main reasons for not attending. Similarly, four in ten secondary-age chil-
dren out of school report lack of interest or being too old to go back as their main
reasons. Work reasons follow (cited by one in four children not attending school),
particularly for males. Family chores and marriage become an important reason
for women not to attend secondary school (30 percent of women not attending).
Similar reasons are found at the tertiary level of schooling.

Safety nets could contribute more to poverty reduction in Bangladesh. A third
of poor households have access to social protection programs, compared to 18
percent of non-poor households. This suggests there is room to increase coverage
and improve the quality of targeting. Coverage in urban areas is particularly low
(Annex Tables A2 and A3), and safety nets for families with young children and
elderly members could have a strong impact in reducing urban poverty. There is
a natural life cycle to poverty, and well-designed safety nets can target support to
households when they need it most: when children are young and when elderly
household members must be cared for.

The need for better data and data-driven decision making is more important now
than before. This is in part because Bangladesh’s economy is becoming more com-
plex and in part because gains are much more dependent on addressing behavioral
constraints rather than traditional constraints of education and credit (female labor
force participation is a case in point). Bangladesh is now also an economy where
change is more rapid—three agricultural seasons is the norm now whereas three
decades ago it was one—and there is more to lose from learning slowly. The need
for policy experimentation has been noted at points in this section. The analysis
has also highlighted areas where the existing database has been lacking (e.g. lack of
data in urban areas, poor quality income data). There is a need for better data, more
rapid monitoring of policies and progress, and more data-driven decision making.

In sum, the country is facing new and re-emerging frontiers of poverty reduction—
tackling urban poverty and poverty in the West. What is needed today to push those
frontiers is a step change that will require both traditional and new solutions. Not
falling into the trap of complacency will be key to eradicate poverty in Bangladesh.

67
Bangladesh Poverty Assessment

68
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72
Data Description Annex

The Household Income and Expenditure Survey (HIES) is a comprehensive, nation-
ally representative survey used to measure monetary poverty in Bangladesh. The
HIES 2016/17 is the fourth round in the series of HIES conducted by the Bangladesh
Bureau of Statistics (BBS) in 2000, 2005, and 2010. Before 2000, BBS monitored
poverty using a smaller survey, the Household Expenditure Survey (HES), which,
as its name indicates, only collected data on expenditure.

In Bangladesh, divisions are the first-level administrative geographical par-
titions of the country. As of 2016, the country has eight divisions: Barisal,
Chittagong, Dhaka, Khulna, Mymensingh, Rajshahi, Rangpur, and Sylhet. For
the poverty assessment, seven divisions are adopted, to be comparable across
time. Each division is subsequently divided into 64 districts, or zilas. Each dis-
trict is further subdivided into smaller geographic areas, with clear rural and
urban designations. In addition, urban areas in the main divisions of Chittagong,
Dhaka, Khulna, and Rajshahi are classified into City Corporations (CCs), and
other urban areas.

A stratified, two-stage sample design was adopted for the HIES 2016/17 with
2,304 Primary Sampling Units (PSU) selected from the list of the 2011 Housing
and Population Census enumeration areas. Within each PSU, 20 households were
selected for interviews. The final sample size was 46,080 households (Ahmed et al.
2017). PSUs in the HIES 2016/17 were allocated at the district level. Therefore, the
sample was stratified at the district level. Since there were a total of 64 districts
in Bangladesh, the sample design included a total of 132 sub-strata: 64 urban, 64
rural, and four main CCs.

The HIES sample was also implicitly stratified by month. Data was collected over
a year to capture seasonal variations in expenditure, expenditure patterns, and
income. The HIES 2016/17 survey was launched on April 1, 2016, and field opera-
tions were completed on March 31, 2017. The previous HIES were also in the field
for a period of a year but were collected in the same calendar year. As in previous
years, there was an implicit temporal stratification of the sample with primary
sampling units distributed by sub-periods (called terms).

The samples of the previous three rounds of the HIES were designed to pro-
vide reliable annual poverty estimates for the country’s urban and rural areas

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Bangladesh Poverty Assessment

separately and the Statistical Metropolitan Areas (SMAs).21 However, the HIES
2016/17 was designed to produce reliable poverty estimates at three different lev-
els: (i) annual poverty estimates at the division level for urban and rural areas; (ii)
annual poverty estimates for the country’s 64 districts; and (iii) quarterly poverty
estimates at the national level. This change implied quadrupling the sample size
of HIES 2016/17, compared to previous rounds – from 12,240 in 2010 to 46,080
households.

The substantial increase in the sample size also required using a different sam-
pling frame to accommodate the larger number of PSUs. The PSUs for all the
previous rounds of the HIES were selected from the Integrated Multiple-Purpose
Sample (IMPS) – a master sample updated after each Housing and Population
Census. In the HIES 2016/17, the PSUs come from the list of Enumeration Areas
(EAs) used for Bangladesh’s 2011 Population and Housing Census.

The changes introduced in the HIES 2016 had implications for the comparabil-
ity with previous HIES, as well as the use of specific information. We highlight
the most relevant aspects for this poverty assessment. Overall, although these
changes need to be kept in mind, they are unlikely to affect the main findings of
this poverty assessment.

Changes in the sampling frame affected the comparability of the survey strata
across time and increased the likelihood of covering slum areas.

The Post-Enumeration Check Survey (PECS) conducted after the completion of
the 2011 Household and Population Census found that there was under cover-
age both in urban and rural areas, but this was more prevalent in urban areas.
BBS thus used a two-step approach to adjust the 2011 census estimates. First,
it reclassified urban and rural areas using the concepts of: (i) growth centers, (ii)
urban agglomerations, and (iii) other urban areas. Second, it inflated all urban
and rural counts from the 2011 Census of Population Areas to align with the PECS
results. These two adjustments estimated the share of the urban population at
28 percent, which is the number that BBS has been using since then to produce
official population projections and statistics. These adjustments (reclassification
of areas and re-weighting) were also done in the HIES 2016/17 data to ensure a
consistent urban share with the corrected 2011 census and with previous HIES
rounds. However, 13 out of 2,304 enumeration areas were classified as rural when
in fact they were urban. This classification error underestimates the urban share

21
In 2017 the country had 7 divisions: Dhaka, Chittagong, Barisal, Khulna, Sylhet, Rangpur, and Rajshahi.

74
D a t a Des c r i p t i o n A n n e x

of the population in HIES. The urban share that is calculated directly from the
HIES microdata is 27.3 percent of the population, which is actually lower than
the official share for 2011. The corrected share is 29.1 percent of the population,
which is more consistent with the urbanization process observed in Bangladesh
in the past years. The poverty estimates are also affected, though the changes are
not statistically different from zero. Correcting for this classification error implies
that the national poverty rate is 0.2 percentage points higher than the official
poverty estimates. The urban poverty rate increases from 18.9 to 19.3 percent,
and the rural poverty rate also increases from 26.4 to 26.7 percent. This poverty
assessment reports the estimates correcting for the urban misclassification of
enumeration areas.

Within urban areas, the comparability across time was affected as the concept
of SMA was abandoned in the 2011 census. The concept of SMA was replaced
by the concept of Rural/Urban/CC (RUC) in the 2011 Census of Population and
Housing. Of the 64 districts, only in three does the old SMA concept not match
perfectly with the new RUC: in the districts of Gazipur and Narayanganj in the
Dhaka division (districts 33 and 67, respectively), and the district of Khulna in the
Khulna division. For Gazipur and Narayanganj, a perfect match can be achieved
by replacing all SMA areas to Other Urban areas. For Khulna district, however,
a match is not straightforward, as the SMA area was divided into CC and Other
Urban areas. In addition, all of the PSUs from the Khulna district available from
the HIES 2010 come from SMA areas, and there is therefore no baseline for Other
Urban Khulna district. For the analysis within urban areas, a reclassification was
done to obtain a comparable trend for SMAs.

Another implication of the change in representativity and sampling frame is that
the geographic strata used to compute the poverty lines are not comparable
across time, and this implies that the price indices used to update the poverty
lines are calculated using different strata in 2016. This particularly affects the
update of the lines for the urban areas, especially the four strata that cover the
City Corporations. A robustness check using the comparable strata translates into
a higher urban poverty rate (24.7 compared to 24.3).22

Finally, the Bangladesh IMPS used to draw the samples from previous HIES
excluded some geographic areas, such as urban slums. Therefore, the HIES
2016/17 has a higher likelihood of capturing slum areas.23 The geographic informa-

22
For more information see Ahmed et al. (2019), in the second volume of this poverty assessment.
23
For details of the sampling design see Ahmed et al. (2017).

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Bangladesh Poverty Assessment

tion in HIES does not allow one to identify and separate the slum areas. However,
this should be kept in mind when comparing poverty estimates, as poverty rates
in slum areas are higher than other urban areas.

The expansion of the sample size affected the quality of income information and
limited its use for the poverty assessment

Income data is always challenging to collect, but it appears that in the 2016/17
HIES the income data suffered from the increase in sample size and the change in
the fieldwork protocols.24 An analysis of the quality of the income data in HIES in
2016/17 compared to 2010 was conducted for this poverty assessment (Hill and
Endara 2019b).

Information on whether the person is an earner can be obtained from the house-
hold roster (Section 1) and the employment roster (Section 4). In 2016, there is a
significantly higher share of people that report being earners in the household
roster but do not have information in the employment roster (7 percent com-
pared to 0.45 percent in 2010). In addition, a larger share of respondents are
listed as earners in the employment roster but are not classified as earners in the
household roster (3 percent in 2016 versus 1 percent in 2010). It is possible that
the earner or employment rosters are incorrect, but if not, there are about 13 per-
cent of households in HIES 2016 with incomplete income data.

Because of the increase in the proportion of households reporting no earners and
increased incompleteness of the data, there are more households reporting zero
labor income (16 percent in 2016 versus 4 percent in 2010) and zero hours worked
(12.4 percent in 2016 versus 9.5 percent in 2010). However, these zeros really
reflect missing data. Given the higher prevalence of missing data, it is important

24
The data collection, entry, and transfer process for the HIES 2016/17 was conducted using paper
questionnaires combined with CAFE (Computer-Assisted Field-Based Data Entry). The data entry
system was combined with a data monitoring system for a selected set of variables important for
poverty measurement. This data monitoring system fed from the compiled data to create a set of key
indicators that were tracked on a continuous basis. The indicators that were tracked by team, term,
division, and district included: number of households, household size, number of households with
incomplete food and non-food consumption, number of households with incomplete durable items,
number of daily food items consumed by households, number of weekly food items consumed by
households, and number of non-food items and durables consumed by households. This informa-
tion supported supervision of fieldwork and ensured that consumption data was complete and high
quality for poverty estimation. However, other variables collected were not monitored, including
income-related information. Ex-post analysis of the data indicate that the data entry of income-re-
lated variables suffered weaknesses due to lack of range checks and merging issues in the CSPro
data entry program.

76
D a t a Des c r i p t i o n A n n e x

to be very clear when data is missing, rather than assuming income from a given
source is zero when it is not reported.

To determine the type of bias the missing income data is likely to cause, we assess
whether the missing data is systematic and in what ways. We find that the missing
income data is systematic. Data is less likely to be missing in cities, presumably
because of the greater reliance on employment other than self-employment. Data
is more likely to be missing for better-off households in rural areas. Yet, there is no
relationship between missing income and consumption in urban areas.

The analysis also indicates that the quality of wage income data, hours worked,
self-employment income, and agriculture income is comparable with 2010.
However, daily labor income has more missing data and is noisier. More work
needs to be done to assess the quality of the crop production and income data.
Yield and price data seem reasonable compared to 2010.

Finally, there are many instances of negative net income being reported: 14.3 per-
cent of households reporting non-zero agricultural income have negative agricul-
tural income, and 15.1 percent of households reporting non-zero non-agricultural
income have negative non-agricultural income. The negatives do not seem to be
systematic, no relationship with consumption or location is evident.

Overall, no large systematic error was found that undermines the 2016/17 income
data entirely. The income data is less complete and noisier than the income data
collected in 2010, with coding errors also limiting the number of observations for
which accurate income data is recorded. Some households do not have complete
income data, in comparison to a smaller proportion of such households in 2010.
Richer households in rural areas in self-employment activities are more likely to
be missing income data. It is important to correctly code this income data as miss-
ing and be aware of the limits of the income data in conducting analysis.

Access to data
The data and codes used to replicate the analysis presented in this poverty assess-
ment can be accessed in https://github.com/worldbank/BGD_Poverty_Assessment

77
Bangladesh Poverty Assessment

Annex Tables
Table A1. Characteristics of poor and non-poor households (average)
Test of Test of
Non-poor Poor
difference(1) difference(2)
Demographics
Household lives in an urban area (%) 32.13% 22.72% *** ***

Household size 3.92 4.57 *** ***
Household dependency ratio [3]
0.61 0.89 *** ***

Age of household head 44.60 43.00 *** ***

Household head is female (%) 13.88% 10.73% *** ***

Household head is married (%) 90.91% 91.24% ***
Labor market
Share of adults who are earners 0.33 0.29 ***

Share of adults in agriculture 0.10 0.13 ***

Household head in agriculture (%) 28.19% 42.54% *** Ref. group

Household head in industry (%) 19.06% 16.13% *** ***

Household head in services (%) 31.54% 25.60% *** ***
Household member has a chronic illness/disability 31.54% 24.26% *** ***
Human capital
Household head is literate (can write a letter, %) 59.28% 38.54% ***
Household head has no education (%) 41.47% 62.65% *** ***
Household head has some primary education (%) 8.55% 10.04% *** ***
Household head has completed primary education (%) 11.98% 10.50% *** ***
Household head has at least some secondary
37.89% 16.63% *** Ref. group
education (%)
Assets
Household owns land (%) 35.20% 21.91% *** ***
Household owns a mobile phone (%) 93.93% 87.81% *** ***
Household has electricity (%) 80.72% 59.04% *** ***
Household has piped water (%) 13.92% 5.23% *** ***
Household has sanitary toilet (%) 28.79% 14.30% *** ***
Transfers and credit
Household receives international remittances (%) 5.85% 2.03% *** ***
Household receives domestic remittances (%) 13.54% 11.51% *** **
Household receives microcredit (%) 28.96% 33.56% *** ***

Household receives social protection program (%) 18.52% 31.69% *** ***

Source: Calculations using HIES 2000, 2005, 2010, and 2016. Note 1: Stars indicate whether mean for
non-poor and poor is significantly different using a Wald test. Significance at the *10%, **5%, and ***
1% level. Note 2: Significance values are calculated for each year separately including division fixed
effects. Significance at the *10%, **5%, and *** 1% level of probit regression correcting for the clus-
tered nature of the errors. Note 3: Dependency ratio was calculated as the population aged zero to 14
and over the age of 65, to the total population aged 15 to 65.

78
A n n e x T a b l es

Table A2. Characteristics of rural poor and non-poor households
(average)
Test of differ- Test of differ-
Non-Poor Poor
ence (1) ence (2)

Demographics

Household size 3.97 4.56 *** ***

Household dependency ratio [3] 0.65 0.89 *** ***

Age of household head 45.75 43.31 *** ***

Household head is female (%) 14.64% 9.89% *** ***

Household head is married (%) 90.55% 91.76% *** ***

Labor market

Share of adults who are earners 0.30 0.29 ***

Share of adults in agriculture 0.14 0.16 ***

Household head in agriculture (%) 38.08% 49.94% *** Ref. group

Household head in industry (%) 14.81% 13.76% ***

Household head in services (%) 25.42% 21.55% *** ***

Household member has a chronic illness/disability 33.86% 25.14% *** ***

Human capital

Household head is literate (can write a letter, %) 54.30% 37.54% ***

Household head has no education (%) 46.52% 63.75% *** ***

Household head has some primary education (%) 9.34% 9.93% ***
Household head has completed primary education (%) 12.53% 10.53% *** ***
Household head has at least some secondary
31.48% 15.65% *** Ref. group
education (%)
Assets
Household owns land (%) 40.41% 25.06% *** ***
Household owns a mobile phone (%) 92.50% 87.33% *** ***
Household has electricity (%) 73.23% 52.11% *** ***
Household has piped water (%) 2.13% 1.53% ***
Household has sanitary toilet (%) 21.54% 10.51% *** ***
Transfers and credit
Household receives international remittances (%) 6.71% 2.12% *** ***
Household receives domestic remittances (%) 14.71% 11.98% *** **
Household receives microcredit (%) 32.89% 34.58% * ***

Household receives social protection program (%) 24.18% 35.77% *** ***

Source: Calculations using HIES 2000, 2005, 2010, and 2016. Note 1: Stars indicate whether mean for
rural poor and non-poor is significantly different using a Wald test. Significance at the *10%, **5%,
and *** 1% level. Note 2: Significance values are calculated for each year separately including division
fixed effects. Significance at the *10%, **5%, and *** 1% level of probit regression correcting for the
clustered nature of the errors. Note 3: Dependency ratio was calculated as the population aged zero to
14 and over the age of 65, to the total population aged 15 to 65.

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Bangladesh Poverty Assessment

Table A3. Characteristics of urban poor and non-poor households
(average)
Test of Test of differ-
Non-Poor Poor
difference(1) ence (2)
Demographics

Household size 3.79 4.59 *** ***

Household dependency ratio [3]
0.52 0.87 *** ***

Age of household head 42.17 41.95 ***

Household head is female (%) 12.26% 13.62% ***

Household head is married (%) 91.67% 89.50% ** ***

Labor market

Share of adults who are earners 0.38 0.30 ***

Share of adults in agriculture 0.03 0.05 ***

Household head in agriculture (%) 7.31% 17.38% *** Ref. group

Household head in industry (%) 28.03% 24.20% ** ***

Household head in services (%) 44.46% 39.41% ** ***

Household member has a chronic illness/disability 26.64% 21.27% ** ***

Human capital

Household head is literate (can write a letter, %) 69.80% 41.93% ***

Household head has no education (%) 30.79% 58.90% *** ***

Household head has some primary education (%) 6.87% 10.40% *** ***
Household head has completed primary education
10.81% 10.41% ***
(%)
Household head has at least some secondary
51.43% 19.99% *** Ref. group
education (%)
Assets
Household owns land (%) 24.19% 11.17% *** ***
Household owns a mobile phone (%) 96.94% 89.44% *** ***
Household has electricity (%) 96.53% 82.60% *** ***
Household has piped water (%) 38.83% 17.83% *** ***
Household has sanitary toilet (%) 44.10% 27.19% *** ***
Transfers and credit

Household receives international remittances (%) 4.01% 1.72% *** ***

Household receives domestic remittances (%) 11.07% 9.93% **
Household receives microcredit (%) 20.73% 30.08% *** ***
Household receives social protection program (%) 6.55% 17.84% *** ***

Source: Calculations using HIES 2000, 2005, 2010, and 2016. Note 1: Stars indicate whether mean for
urban poor and non-poor is significantly different using a Wald test. Significance at the *10%, **5%,
and *** 1% level. Note 2: Significance values are calculated for each year separately including division
fixed effects. Significance at the *10%, **5%, and *** 1% level of probit regression correcting for the
clustered nature of the errors. Note 3: Dependency ratio was calculated as the population aged zero to
14 and over the age of 65, to the total population aged 15 to 65.

80
Table A4 - International comparisons
Low-income Lower middle-in-
Bangladesh India South Asia
countries come countries
Circa Circa Circa Circa Circa Circa Circa Circa Circa Circa
Series Name 2000 2000 2000 2000 2000
2011 2016 2011 2016 2011 2016 2011 2016 2011 2016
GDP per capita (constant
525 822 1062 827 1410 1874 800 1304 1696 486 644 700 1101 1716 2085
2010 US$)
Urban population (% of
23.6 31.2 35.1 27.7 31.3 33.2 27.4 31.2 33.1 26.2 29.9 31.7 33.0 37.4 39.6
total population)
Urban population growth
3.6 3.6 3.3 2.5 2.4 2.3 2.8 2.6 2.5 3.8 3.8 3.9 2.8 2.7 2.6
(annual %)
Agriculture, forestry, and
fishing, value added (% of 22.7 16.8 14.0 21.6 17.2 16.2 22.0 17.9 16.7 29.1 26.9 25.4 20.1 16.5 15.7
GDP)
Employment in agriculture
(% of total employment) 64.8 46.6 41.1 59.6 49.0 45.1 58.9 48.5 44.8 71.2 65.5 63.5 53.8 44.8 41.0
(modeled ILO estimate)
Life expectancy at birth,
65.7 72.1 74.3 63.4 68.3 70.2 63.7 68.5 70.3 55.3 62.4 64.7 63.9 68.1 69.7
female (years)
Life expectancy at birth,
65.0 69.3 70.9 61.8 65.8 67.1 62.1 66.0 67.2 52.2 59.0 61.1 61.1 64.7 66.0
male (years)
Life expectancy at birth,
65.3 70.6 72.5 62.6 67.0 68.6 62.9 67.2 68.7 53.7 60.7 62.9 62.5 66.4 67.8
total (years)
Mortality rate, infant (per
64.0 36.9 28.3 66.7 43.2 33.6 68.9 46.8 37.9 88.1 58.6 50.0 66.7 45.7 38.1
1,000 live births)
Fertility rate, total (births
3.2 2.3 2.1 3.3 2.5 2.3 3.5 2.7 2.5 5.9 5.0 4.7 3.5 2.9 2.8

81
A n n e x T a b l es

per woman)
82
Low-income Lower middle-in-
Bangladesh India South Asia
countries come countries
Circa Circa Circa Circa Circa Circa Circa Circa Circa Circa
Series Name 2000 2000 2000 2000 2000
2011 2016 2011 2016 2011 2016 2011 2016 2011 2016
Literacy rate, adult total
(% of people ages 15 and .. 58.8 72.8 .. 69.3 .. 57.7 65.5 71.0 50.7 56.8 60.6 66.7 72.2 76.4
above)
Prevalence of stunting,
height for age (% of children 50.8 41.4 .. .. .. .. 51.3 40.6 35.9 47.0 39.2 35.9 45.5 36.2 32.2
under 5)
Prevalence of undernour-
Bangladesh Poverty Assessment

20.8 17.0 15.2 18.2 17.4 14.8 19.5 17.7 15.7 36.8 27.1 28.1 19.3 15.4 14.0
ishment (% of population)
People using at least basic
sanitation services (% of 25.3 41.4 .. 21.7 38.2 .. 23.9 40.6 .. 19.9 27.2 .. 35.4 48.0 ..
population)
Access to electricity, rural
16.7 45.6 66.0 48.1 56.1 85.2 45.4 55.1 80.8 6.2 14.7 27.6 49.1 58.2 76.3
(% of rural population)
Access to electricity, urban
81.2 90.2 94.0 88.7 92.9 98.5 88.7 93.2 98.3 46.2 59.9 67.6 89.9 93.2 96.1
(% of urban population)
Access to electricity (% of
32.0 59.6 75.9 59.4 67.6 89.6 57.4 67.0 86.6 15.1 27.7 39.9 62.5 71.1 84.1
population)
School enrollment, primary, female
93.0 72.8 91.8 68.7 87.7 88.9 51.5 75.8 77.0 73.7 86.3 86.6
(% net)
School enrollment, primary, male
88.1 86.2 89.2 82.4 87.1 90.2 61.0 81.3 81.5 82.9 86.7 88.9
(% net)
Source: World Development Indicators.