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7/31/2019 Poverty Dynamics IRRI
http://slidepdf.com/reader/full/poverty-dynamics-irri 1/32
Did the commodity price spike
increase rural poverty: Evidence
from BangladeshJoseph V. Balagtas, Purdue University
Humnath Bhandari, IRRI
Sam Mohanty, IRRIEllanie Cabrera, IRRI
Mahabub Hossain, BRAC
7/31/2019 Poverty Dynamics IRRI
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Background: Rice Prices, 2000-2011 (2000 US$)
0
100
200
300
400
500
600
700
800
J
a n - 0 0
A
u g - 0 0
M
a r - 0 1
O
c t - 0 1
M
a y - 0 2
D
e c - 0 2
J u l - 0 3
F e b - 0 4
S e p - 0 4
A
p r - 0 5
N
o v - 0 5
J u n - 0 6
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a n - 0 7
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u g - 0 7
M
a r - 0 8
O
c t - 0 8
M
a y - 0 9
D
e c - 0 9
J u l - 1 0
F e b - 1 1
Thai Export Bangladesh Wholesale
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Effects of the Global Rice Turmoil (and
other events) on Rural Poverty
• Rice market turmoil has potentially mixed
effects on rural poor:
– higher price of staple food harms consumers
– higher price of crop potentially boosts income for
farm households
• Directly for those farms with marketable surplus
•Indirectly for those farms consuming own production
• Net effect is an empirical question
7/31/2019 Poverty Dynamics IRRI
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Research Question and Approach
• How did the market events of 2007-08 affect
rural households in Asia?
– Focus on household income and poverty
• Long-term household survey of rural
households in Bangladesh allows crucial
insights
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Approach: Evidence from Panel of HH in
Bangladesh
• We update and extend work by Nargis & Hossain(2006) who analyze 1988-2004 data – Find incidence and depth of poverty declined over
that time
– Find that diversification away from agriculture,overseas migration strongly associate with higherincome
• We use a new round (2008) of the same survey
to: – document further changes in poverty
– evaluate determinants of income and poverty
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Data
• Repeat survey of nationally representativesample of rural households in Bangladesh
– Designed specifically to track rural livelihoods
• Multistage random sampling – 1 union randomly selected from each district (64)
– 1 village in each union selected based on land sizeand education
– Census of the village, followed by selection of households stratified on land ownership & tenure
7/31/2019 Poverty Dynamics IRRI
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64 Rural Villages
in Bangladesh
~20 HH per village
+ their offshoots
Four rounds
collected: 1987,
2000, 2004, 2008
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The Policy Setting
• Last survey round (2008) occurs at the peak of
the rice market turmoil
• Also other macroeconomic events
– Financial crisis/global recession. Potential impact
on income opportunities overseas, as well as
returns to domestic business/services
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Select Sample Statistics I
1988 2000 2004 2008
Sample size 1,238 1,872 1,927 2,010
Farm size (ha) 0.62 0.54 0.48 0.47
Nonfarm HH (%) 34 42 39 43Area under tenancy (%) 22 33 40 14
Ag workers (no.) 1.2 0.9 1.0 0.8
Domestic migrants (no.) 0.2 0.4 0.4 0.5
Overseas migrants (no.) 0.01 0.1 0.1 0.2
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Select Sample Statistics II
1988 2000 2004 2008
HH income (‘000 04 Taka) 65 78 82 95
Per cap income (‘000 04 Taka) 11 14 16 19
agriculture (%) 58 43 44 43rice (%) 26 16 15 15
trade/business (%) 9 21 19 15
remittances (%) 5 13 14 23
Phys. Capital (‘000 04 Taka) 17 22 36 44
agricultural (%) 47 37 48 32
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• The data reflect diversification away from
agriculture:
– Labor moving from farms to cities and abroad
– Balance of phys. capital tilting away from ag
• How do these and other demographic shifts
affect income?
– Estimate an income regression to assess
determinants of income
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Determinants of log HH income, 2008
Independent variable Estimated coefficient
Owned land (ha) 0.20***
Non-farm HH -0.08*
No. of nonag workers 0.16***Edu. of nonag workers 0.03***
Migrant workers, domestic 0.03***
Migrant workers, overseas 0.50***
Rice land in modern varieties 0.06***
N = 2010
R2 = 0.54
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Determinants of HH Income
• Results similar for all four survey waves
• Consistent with previous literature: income
diversification raises income
• Corollary policy recommendation: invest in
human and physical capital that allows HH to
tap non-ag income opportunities (and
diversify away from agriculture)
– A “pathway out of poverty”
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A potentially important caveat
• CS studies potentially suffer from endogeneity
– Unobserved HH household characteristics (e.g.,
ability, industriousness) potentially confound
identification of causal effects
• We estimate a fixed effects model on a subset
of the sample that we observe in all four
survey waves (~ 1000 households) – Uses dummy variables to control for time-
invariant, unobserved HH characteristics
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FE Estimates of Income Determinants
Independent variable 2008 CS Fixed Effects
Owned land (ha) 0.20*** 0.18***
Non-farm HH -0.08* -0.12***
No. of nonag workers 0.16*** 0.194***Edu. of nonag workers 0.03*** -0.004*
Migrant workers, domestic 0.03*** 0.03**
Migrant workers, overseas 0.50*** 0.29***
Rice land in modern
varieties
0.06*** 0.03
N = 2010 N = 964, T = 4
R2 = 0.54
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Turning our attention to poverty…
• But first, any questions?
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Measuring Poverty
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Choice of poverty threshold, z
• We calculate z based on FAO concept of
“moderate poverty” – 2100 kcal per day per person
– Data on rural food prices
– 30% of total income for non-food expenditures
• This approach assumes no price response in
consumption. Probably ok for staple goodsand broad-based price increases. But worth
noting.
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Rural Poverty, Bangladesh, 1988-2004
1988 2000 2004
Per cap. income (‘000 04 Taka) 11 14 16
Est. poverty line (‘000 04 Taka) 8.3 7.6 8.3
Poverty head count (%) 62 48 44Poverty gap 26.4 19.1 16.5
(Poverty gap)2 14.4 10.2 8.5
1. Incomes rising, poverty line steady2. Poverty incidence decreasing
3. Depth of poverty decreasing
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Rural Poverty, Bangladesh, 1988-2008
1988 2000 2004 2008
Per cap. income (‘000 04 Taka) 11 14 16 19
Est. poverty line (‘000 04 Taka) 8.3 7.6 8.3 11.9
Poverty head count (%) 62 48 44 56Poverty gap 26.4 19.1 16.5 21.9
(Poverty gap)2 14.4 10.2 8.5 11.1
1. Incomes rising, but higher food prices drive poverty threshold up by 30%2. Poverty incidence rises by 25%
3. Depth of poverty worsens
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The Commodity Price Spike and
Rural Poverty
• 12 percentage point increase in poverty
incidence
• Approx. 13 million people fall into poverty in
rural Bangladesh
• Suggests huge impact of macro events on
rural poverty in Asia
– We don’t quite have the information to make the
causal link.
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How did the macroeconomic events
affect poverty?• Do they close off pathways out of poverty, i.e., increase
the incidence of “chronic poverty”
• Do they increase transitional poverty?
• What HH characteristics or activities influence theoutcome? Did diversification away from ag help?
• To answer these questions we look at a subsample of our data, ~ 1000 HH that we observe in all four surveyyears.
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The Poverty Mobility Matrix
• Tracks poverty mobility between two years
• Four categories
– poor that stay poor
– poor that move out of poverty
– nonpoor that fall into poverty
– nonpoor that remain nonpoor
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Poverty Mobility Matrix, 2000-2004
2004 (row %)
2000
(col %) Poor (row %) Non-poor (row %) Total (row %)
Poor
(col %)
270 (68)
(60)
176 (31)
(40)
446 (46)
(100)Non-poor
(col %)
127 (32)
(25)
391 (69)
(75)
518 (54)
(100)
Total
(col %)
397 (100)
(41)
567 (100)
(59)
964 (100)
(100)Of 446 poor HH in 2000, 60% remain poor in 2004; 40% escape poverty.
Of 518 nonpoor HH in 2000, 25% backslide into poverty by 2004.
Net reduction in poverty incidence (446 HH to 397 HH)
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Poverty Mobility Matrix, 2004-2008
2008 (row %)
2004
(col %) Poor (row %) Non-poor (row %) Total (row %)
Poor
(col %)
255 (58)
(64)
142 (27)
(36)
397 (41)
(100)Non-poor
(col %)
183 (42)
(32)
384 (73)
(68)
567 (59)
(100)
Total
(col %)
438 (100)
(45)
526 (100)
(55)
964 (100)
(100)Increase in chronic poverty from 60% to 64%.
Among non-poor in 2004, 7 percentage point (28%) increase in transitory poverty.
Net increase in poverty incidence.
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Summarizing the Impact of the
2007-08 Events on Rural Poverty
• Increased incidence of poverty
– Less upward poverty mobility
– more transitional poverty….the commodity price
spike and other macro events pulls more non-poorinto poverty
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Additional research questions
• In the paper we show the chronic poor are
more reliant on ag. Did the chronic poor’s
reliance on agriculture reduce their ability to
escape poverty?
• A probit model of poverty:
Pr[Poor(t)] = f(Poor(t-1), Ag(t-1), X(t-1))
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Probit model of Poverty in
2004, Marginal Effects
Variable 2004
Poverty(t-1) 0.19***
Ag Income share(t-1) 0.18**
Land owned(t-1) -0.10***Overseas migrant(t-1) 0.03*
Nonfarm HH(t-1) -0.02***
Tenant HH(t-1) 0.17***
Being poor in 2000 increases the chance you’ll be poor in 2004.
Having more of your income from agriculture in 2000 also makes it more likely
you’ll be poor in 2004.
Consistent with previous work that suggests diversification away from ag
Is a pathway out of poverty.
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Probit model of Poverty in
2004 and 2008, Marginal Effects
Variable 2004 2008
Poverty(t-1) 0.19*** 0.22***
Ag Income share(t-1) 0.18** -0.14**
Land owned(t-1) -0.10*** -0.14**Domestic migrant(t-1) 0.03* -0.07***
Overseas migrant(t-1) -0.02*** -0.22***
Nonfarm HH(t-1) 0.17*** 0.12**
Tenant HH(t-1) 0.10* 0.19***
Migrant labor significantly shielded households from the 2007-08 events.
But ag income also appears to have reduced poverty in 2008.
Problem for renters.
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Summary of Key Findings I
• Rice price spike + global recession reversal
of long trend out of poverty
• Increased poverty incidence
– More chronic poverty and more transitory poverty
• Chronic poor are more reliant on agricultural
income, and increasingly so
– Nonfarm households and land renters tend to be
poor
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Summary of Key Findings II
• Appears that ag income and land shielded households from
commodity price spike and global recession
– Either as income source, or as a hedge against high price of
staple food
– This worked better for landowners; problem for renters
• Determinants of income/poverty are not time-invariant
–
A cautionary tale for those recommending non-ag incomesources as pathway out of poverty
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Maraming salamat po!
Questions?