Poverty Dynamics IRRI

7/31/2019 Poverty Dynamics IRRI

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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



Background: Rice Prices, 2000-2011 (2000 US$)

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Thai Export Bangladesh Wholesale



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



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



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



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



64 Rural Villages

in Bangladesh

~20 HH per village

+ their offshoots

Four rounds

collected: 1987,

2000, 2004, 2008



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



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



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



• 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



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



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”



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



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



Turning our attention to poverty…

• But first, any questions?



Measuring Poverty



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.



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



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



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.



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.



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



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)



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.



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



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))



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.



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.



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



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



Maraming salamat po!

Questions?

Poverty Dynamics IRRI

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