Using two survey rounds from IFPRI's Bangladesh Integrated Household Survey (BIHS), IFPRI researchers from the Bangladesh Policy Research and Strategy Support Program (PRSSP) analyze poverty dynamics between 2011/12 and 2015, as well as offer policy considerations.

1. Poverty Dynamics in Rural
Bangladesh
Akhter Ahmed and Salauddin Tauseef
International Food Policy Research Institute
Presented by: Salauddin Tauseef
Eliminating Hunger and Malnutrition:
Are Sustainable Solutions in Sight?
Workshop | Dhaka | 4 October 20171

2. Achieving SDG1: ELIMINATE POVERTY
• Goal1: End poverty in all forms
everywhere.
• To achieve this we need to look
at 
• What factors entrap people in
poverty over time?
• What factors prevent people from
falling into poverty?
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3. …requires simultaneous actions on
many fronts
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4. Prevalence of poverty in rural Bangladesh:
Percent of people living on less than
$1.25/day
Population living on less than PPP $1.25 a day fell by 4.98
percentage points (or by 15.49%):
❖32.12 % in 2012 27.15 % in 2015
➢Daily per capita consumption expenditures from 2011 and
2015 IFPRI household surveys were adjusted for inflation
using Rural Food Consumer Price Index (2005 base year)
obtained from the Bangladesh Bureau of Statistics (BBS);
➢Used the international poverty line of $1.25 per day,
measured at 2005 purchasing power parity (PPP) exchange
rate for Bangladesh: PPP$1.00=25.494 taka (World Bank);
➢Calculated local currency equivalent of PPP $1.25 a day
poverty line using 2011 and 2015 FCPI estimates.
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5. Concept of Poverty Dynamics
Never poor
Fell into
poverty
Non-poor Moved out
of poverty
Poor
Non-poor
• Poverty in static period – poor or non-poor;
• Inter-temporal poverty – chronic and transient
(moving out or falling into) poverty.
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Poor
2012 BIHS
Chronic
poverty
2015 BIHS
2012 BIHS 2015 BIHS Dynamics category
Poor Poor Chronic poverty
Poor Non-poor Moved out of poverty
Non-poor Poor Fell into poverty
Non-poor Non-poor Never poor

6. Dynamics of poverty in Bangladesh:
Changes from 2012 to 2015
6
58.5
16.6 15.5
9.4
-
10
20
30
40
50
60
Non-poor remained non-
poor
Poor remained poor Poor moved out of
poverty
Non-poor fell into
povery
Percentoftotalruralpopulation

7. Modelling poverty dynamics and data
• Modelling poverty dynamics requires panel data;
• Utilizing two rounds of the Bangladesh Integrated
Household Survey (BIHS);
• Since the BIHS has two time periods and we are
interested to examine factors driving chronic and
transitory poverty, we use a multinomial logit
model for our analysis of poverty dynamics;
• As a robustness test we also estimate a
simultaneous quantile regression calibrated to the
mean consumption expenditure of the four poverty
dynamics categories.
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8. Modelling poverty dynamics
• In our MNL model, the probability Pij that a household i, is in a
poverty status j, can be expressed as a function of the
independent variables xi as follows:
𝑃𝑖𝑗 =
𝑒
𝑥 𝑖
′ 𝛽 𝑗
1+ σ 𝑘=1
3 𝑒
𝑥 𝑖
′ 𝛽 𝑗
for j = 0, 1, 2, 3.
where βj are the set of coefficients to be estimated. To identify the
model β0 is set to zero (the base category), since if unidentified
there is more than one solution for β1 … βj that leads to same
probabilities for 𝑃𝑖𝑗 (Greene, 2003).
• Since the coefficients of the MNL model cannot be interpreted
directly (Greene, 2003), we report the relative risk ratios (RRR) or
exponentiated coefficient:
exp(𝛽𝑗) =
Τ𝑝𝑖𝑗
′
𝑝𝑖0
′
Τ𝑝𝑖𝑗 𝑝𝑖0
𝜋𝑟2
The RRR shows how the probability favoring an outcome i.e. a
poverty state j (compared to the base state) are multiplied per unit
increase in the value of the associated explanatory variable 𝑥, when
we control for the other variables in the model.
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9. VARIABLES Coeff. Std error
Age (in years) of household head -0.118*** (0.0330)
Age squared (in years) of household head 0.00107*** (0.000359)
Household size 1.367*** (0.206)
Household size squared -0.0585*** (0.0182)
Child dependency ratio (0-18 years) 0.00523*** (0.00108)
Aged dependency ratio (60+ years) 0.000873 (0.00258)
Categories of average years of education of members 18+ (male)
Less than primary -0.224 (0.167)
Primary -0.821*** (0.167)
Secondary or more -0.441* (0.227)
Categories of average years of education of members 18+ (female)
Less than primary 0.0102 (0.153)
Primary -0.334** (0.153)
Secondary or more -2.091* (1.151)
Value of savings (in thousand taka) -0.0412*** (0.00949)
Value of asset (in thousand taka) -0.111*** (0.0157)
ln (household's owned_land+1) -0.234*** (0.0392)
Number of dairy cows owned -0.0381 (0.0679)
Number of poultry owned -0.0229** (0.0110)
Owns a mobile phone; dummy (1=yes, 0=no) -1.149*** (0.136)
Access to electricity; dummy (1=yes, 0=no) -0.844*** (0.132)
No access to sanitary latrine; dummy (1=yes, 0=no) 0.569** (0.289)
Receives safety net transfer of atleast 1500 taka/month; dummy
(1=yes, 0=no) -2.268*** (0.766)
Share of non-farm income in total income -0.00352** (0.00155)
Women's empowerment score -0.852** (0.352)
Primary female decisionmaker faces domestic violence; dummy
(1=yes, 0=no) 0.491*** (0.149)
Primary female decisionmaker has freedom of mobility; dummy
(1=yes, 0=no) 0.112 (0.173)
PP_Always_poor
Factors associated with living in
chronic poverty
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Contains district fixed effects

10. Factors associated with living in
chronic poverty
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Results from the multinomial logit regression show that the main factors
that decrease the likelihood of remaining in poverty are:
❖ Higher levels of human capital in the household: at least primary
education is required to break chronic poverty;
❖ Higher levels of physical assets and savings: HHs with higher land
holding, assets and savings;
❖ Smaller household size: smaller number of young dependent members;
❖ Non-farm engagement: increase in nonfarm income share in total
income;
❖ Women’s empowerment: Increase in the women’s empowerment in
agriculture (WEAI) score;
❖ HH primary female not exposed to domestic violence;
❖ No access to electricity and no ownership of cell phone;
❖ Hygiene standards: HHs with sanitary latrine.

11. VARIABLES Coeff. Std error
Age (in years) of household head -0.0958*** (0.0261)
Age squared (in years) of household head 0.000871***(0.000275)
Household size 0.124 (0.114)
Household size squared 0.00476 (0.00937)
Child dependency ratio (0-18 years) 0.00224** (0.00101)
Aged dependency ratio (60+ years) 0.00128 (0.00223)
categories of average years of education of members 18+ (male)
Less than primary -0.0681 (0.174)
Primary -0.207 (0.148)
Secondary or more -0.473** (0.223)
categories of average years of education of members 18+ (female)
Less than primary 0.0120 (0.160)
Primary -0.409*** (0.155)
Secondary or more -0.834* (0.455)
Value of savings (in thousand taka) -0.0125** (0.00547)
Value of asset (in thousand taka) -0.00912 (0.00840)
ln (household's owned_land+1) -0.179*** (0.0350)
Number of dairy cows owned -0.00436 (0.0727)
Number of poultry owned -0.000674 (0.00269)
Owns a mobile phone; dummy (1=yes, 0=no) -0.459*** (0.128)
Access to electricity; dummy (1=yes, 0=no) -0.564*** (0.132)
No access to sanitary latrine; dummy (1=yes, 0=no) 0.674** (0.318)
Receives safety net transfer of atleast 1500 taka/month; dummy
(1=yes, 0=no) -2.101*** (0.802)
Share of non-farm income in total income -0.00452*** (0.00157)
FiveDE score of WEAI -0.294 (0.326)
Primary female decisionmaker faces domestic violence; dummy
(1=yes, 0=no) 0.266* (0.142)
Primary female decisionmaker has freedom of mobility; dummy
(1=yes, 0=no) 0.0767 (0.169)
Falling into poverty
Factors associated with falling into
poverty
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Contains district fixed effects

12. Factors associated with falling into
poverty
Key results from multinomial logit regression suggest that the
following factors tend to prevent households from backsliding
into poverty:
❖More schooling of male and females in HH – particularly higher
level of female education;
❖Higher value of asset holding and increase in savings;
❖Access to electricity and owns mobile phone;
❖Non-farm engagement: increase in nonfarm income share in
total income;
❖HH primary female not exposed to domestic violence;
❖For social safety net beneficiaries, if income transfer is at least
1,500 taka ($19) per month per household.
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13. Considerations
❖Invest in quality education – particularly for women:
❖Devise policies to incentivize education for rural
population – particularly prevent dropout of secondary
school girls.
❖Promote non-farm employment:
❖Promote agriculture-driven, non-farm activities (e.g.,
vocational training in repairs and servicing of ag
machineries, particularly for rural youth);
❖Gainfully employ rural youth in agricultural value chains
(e.g., packaging, transport).
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14. Considerations
❖Revamp social safety nets to reach the most vulnerable
to improve their livelihoods:
❖Improve the targeting performance;
❖Scale up effective programs and phase out high-cost,
inefficient programs;
❖Ensure sustainability of program benefits;
❖Increase the size of transfers to generate sizable impacts
on food security and nutritional outcomes.
❖Freedom from violence and subjugation of women:
❖Promote awareness and enforce law to reduce domestic
violence through empowering women.
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15. IFPRI-PRSSP Workshop 4 October 2017 15
Thank you!