gender assets shocks_ifpri bbl may 2011

Tuesday, June 21, 2011

Do Men and Women Accumulate Assets in

Different Ways?

Evidence from Rural Bangladesh

Agnes Quisumbing

International Food Policy Research Institute

May 2011

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

Introduction

• This paper attempts to bring together two threads of the

literature on assets:

• (1) literature on asset dynamics and poverty traps

(Carter and May; Carter and Barrett; others)

• (2) literature on risk and intrahousehold allocation, that

suggests that risk are not pooled within the household

• Earlier work on (1) using data from Bangladesh focused

on hh asset dynamics; found no evidence for multiple

poverty traps, possibly because of reasonably well-

functioning factor markets (labor and credit markets,

although credit markets may discriminate against the

landless)


Why look at gender-differentiated asset dynamics?

• General: evidence that risk is not pooled within households

(Ethiopia—Dercon and Krishnan; Cote d‘Ivoire—Dulfo and Udry;

Ghana—Goldstein) and that risk perceptions may also differ

between men and women (East Africa--Doss, McPeak, Barrett)

• There is also evidence rejecting unitary model of the household

in many countries and specifically for Bangladesh—resources

are not pooled within the household

• Social norms favoring female seclusion lead women to be

systematically excluded from labor markets in Bangladesh

• Anthropological evidence (Thailand, Indonesia, Bangladesh)

suggests that men and women have different asset

accumulation strategies, and use their assets in different ways

to cope with shocks


Research questions

• Are asset dynamics different for joint and exclusively-

held assets? How do these differ from household asset

dynamics?

• Is the impact of negative events and processes (flood

shocks, dowries, illness, death) different on husband-,

wife- and jointly-owned assets? Are these mitigated by

positive events?

• …And a policy-related question

• What are the implications for the design of social

protection systems?


Presentation overview

1. Conceptual framework and methods

2. Survey design and data

• Assets and traps:The CPRC-DATA-IFPRI longitudinal study

• Gender and assets: The agricultural technology panel

3. Descriptives

4. Nonparametric results

5. Parametric results

6. Concluding remarks


Conceptual framework

• Barrett, Carter, others: theory of dynamic

poverty traps, empirically tested using data from

SSA

• Based on observation that it is easier to measure

assets than consumption expenditure or income

• Parametric and nonparametric methods used to

derive a dynamic asset frontier, showing

relationship between hh asset holdings in two

periods

f(At)

At

At+1 At= At+1

A* AHAL


• AL: stable low level equilibrium

• A*: unstable mid-level equilbrium

• AH: stable high level equilbrium

• Prediction is that hh‘s asset trajectories will

bifurcate, with hh‘s with A>A* tending toward the

high level equilibrium, and those with A<A*

tending to the low level equilibrium

• Some hhs will tend toward a chronically poor

state, and others toward relative affluence


How to estimate the dynamic asset frontier?

• Nonparametric methods: we use locally-

weighted scatterplot smoothing (Lowess)

(Cleveland 1979)

• Parametric methods: use a linear term in lagged

assets, plus higher order terms to allow

curvature

• What is different in this paper: we estimate this

for husband-owned, wife-owned, and jointly

owned land and nonland assets


General empirical specification

• We estimate the following nonparametrically:

• At+1 = ß (At) + εt

• The analogous parametric regression is:

• At+1 = (1 + α)At + θt, which is estimated in differenced

form as

• At+1 - At = αAt + θt

• Dynamic equilibrium: at least in expectation, asset stocks

do not change over time, that is,

• E[At+1 - At ] =0

• Condition for dynamic eqbm at A*:

- 2 < ∂ E[At+1 - At ]/ ∂ At │A* ≤ 0


Empirical specification

Ait-AiB= ß0 + ß1 AiB + ß2AiB2 + ß3AiB

3 + ß4AiB4+

ZiΓi + CiΛi + εit

Ait -ln AiB asset growth for asset owner i from

baseline survey period (B) to the most recent

survey (T)

AiB assets at baseline

Zi and Ci are time invariant individual, household,

and community characteristics and εit is the

error term

Tests for convergence: tests on coefficients


Survey design and data


Assets and traps: The CPRC-DATA-IFPRI Study--1

• Longitudinal study seeks to examine factors

behind movements out of poverty over the long-

term, as well as factors that make some

households and individuals unable to escape

poverty

• Builds on three evaluations undertaken by DATA

and IFPRI

• Microfinance (MFI) from 1994

• Agricultural Technology (AT) from 1996

• Educational Transfers (ET) from 2000


The CPRC-DATA-IFPRI Study--2

• A qualitative and quantitative methods study with 3 phases:

• Summer 2006: focus group discussions investigating causes of decline and improvement and the long term impact of 3 interventions (116 FGDs in 11 districts)

• Winter 2006-7: quant resurvey of panel households

(1787 core + 365 splits)

• Spring-Summer 2007: life history interviews and village histories in 8 districts (160 households –300 interviews)

Map of study sites of longitudinal study


Agricultural technology study: 1996-1997

• Only site with gender-disaggregated asset data at baseline

• 3 technologies/implementation modalities:

1. improved vegetables for homestead production, disseminated through

women‘s groups (Saturia)

2. fishpond technology through women‘s groups (Jessore)

3. fish pond technology targeted to individuals (Mymensingh)


To recap: Survey design and sampling

• Panel data set with gender disaggregated data includes

904 core households previously surveyed by DATA and

IFPRI in 1996-97, as well as household splits; estimation

sample is smaller (725 hhs with complete information on

husband and wife)

• Core households are those that were interviewed in

baseline and 2006/7 rounds

• Split households are those formed by children who

formed separate residences (tracked within district)—not

included in this analysis


Attrition

• Attrition is relatively low: 93.7 percent of original

households were reinterviewed, and the attrition rate is

about 0.4 percent per year in the agricultural technology

site

• Attrition rates for hhs are about 4-11% for households

across interventions, but for ―intact couples‖ attrition is

higher, 17% (over a 10-year period)

• To account for attrition, all regressions estimated with

inverse probability weights (Fitzgerald, Gottschalk, Moffitt

1998)


Poverty and poverty transition categories

Agricultural

technology

(1996-2006)

Poverty headcount

Poverty in baseline

survey

70%

Poverty in 2006/2007 18%

Poverty transitions

Chronic poor 16%

Falling into poverty 2%

Moving out of poverty 54%

Never poor 28%


Asset growth of core households over time

Asset holdings Household assets

Value (‗1000 taka, 2007 prices) 1996 2006 Annual

growth rate

Total nonland assets 27.0 49.7 8.4

Consumer durables 8.13 15.8 9.4

Ag durables 4.8 1.5 -6.9

Nonag durables 1.2 4.4 25.8

Jewelry 2.5 11.1 35.2

Livestock 10.5 17.0 6.3

Total owned land (decimals) 148.5 117.4 -2.1

Page 22


Asset growth over time , 1996 and 2006

(exclusively held assets)

1996 2006 % change

H W H W H W

Landholdings (decimals)

Homestead 10.3 .3 10.9 0.6 5.8 44.5

Cultivated 85.9 1.9 67.9 3.2 -21.0 39.7

Other land 5.4 .1 5.0 0.2 -8.9 6.1

Total owned land 101.7 2.4 83.8 4.0 -17.6 39.2

Nonland assets („000 taka, 2007 prices)

Consumer durables 2.2 .3 5.8 0.4 166.4 40.8

Ag durables 1.6 n.s. 0.6 n.s. -62.7 6.2

Nonag durables 0.5 n.s. 3.3 0.1 494.3 428.9

Jewelry n.s. 1.5 1.5 2.1 5262.2 38.5

Livestock 5.8 1.7 9.1 1.1 57.5 -31.9

Nonland assets (excl

livestock)

4.4 1.8 11.2 2.6 155.3 42.1

Page 23


Distribution of area of owned land across ownership

categories

1996

Joint

Husband

Wife

2006

Joint

Husband

Wife

Page 24


Distribution of nonland assets across ownership categories

1996

Joint

Husband

Wife

2006

Joint

Husband

Wife

Page 25


Most common shocks experienced by households, 1996-

2006

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 27

0

5

10

15

20

25

30

35

40

45

50

Illness, 1997-2001

Illness, 2002-2006

Death, 1997-2001

Death, 2002-2006

Dowry and wedding,

1997-2001

Dowry and wedding,

2002-2006

Proportion of households

Type of negative shock

Illness, 1997-2001

Illness, 2002-2006

Death, 1997-2001

Death, 2002-2006

Dowry and wedding, 1997-2001

Dowry and wedding, 2002-2006

Proportion of households reporting negative shocks,

1997-2001 and 2002-2006

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 28

Proportion of households reporting positive events,

1997-2001 and 2002-2006

0

1

2

3

4

5

6

7

8

9

Proportion of households

Type of positive event

Remittances, 1997-2001

Remittances, 2002-2006

Inheritance, 1997-2001

Inheritance, 2002-2006

Dowry received, 1997-2001

Dowry received, 2002-2006


Parametric specification

Ait-AiB= ß0 + ß1 AiB + ß2AiB2 + ß3AiB

3 + ß4AiB4+

ZiΓi + CiΛi + εit

Dependent var: Asset growth

Regressors: Lagged assets (linear, squared, cubed, fourth)

Covariate shocks (floods)

Idiosyncratic shocks (illness, death, dowry/wedding expenses)

Positive events (remittances, inheritance, received dowry)

HH demographic characteristics: age of head, age squared, hh size,

proportion in age-sex categories

Value of (assets) land at baseline [assets in land equation; land in

assets equation)

Thana dummies

Page 29

Results

Page 30

0

50

010

00

15

00

20

00

0 500 1000 1500 2000

Ow

n L

and

20

07

Own Land 1997

Household land

0

20

040

060

080

010

00

0 500 1000 1500

Join

t L

and

20

07

Joint Land 1997

Jointly-held land0

50

010

00

15

00

0 500 1000 1500

Ow

n L

an

d H

usba

nd

200

7

Own Land husband 1997

Husband’s land

050

10

015

0

0 50 100 150

Ow

n L

an

d W

ife 2

00

7

Own Land Wife 1997

Wife’s land

Lowess plots for land (scale of axes not uniform across graphs)

0

50

00

010

00

00

15

00

00

0 50000 100000 150000

Valu

e o

f A

ssets

200

7

Value of Assets 1996

Household

0

50

00

010

00

00

15

00

00

0 50000 100000 150000

Valu

e o

f Jo

int A

sse

ts 2

007

Value of Joint Assets 1996

Jointly-held assets

0

50

00

010

00

00

15

00

00

0 50000 100000 150000

Valu

e o

f H

usb

and

Asse

ts 2

00

7

Value of Husband Assets 1996

Husband’s assets

0

50

00

010

00

00

15

00

00

0 50000 100000 150000

Valu

e o

f W

ife

Assets

2007

Value of Wife Assets 1996

Wife’s assets

Lowess plots for nonland assets


Parametric results: Land

• Initial landholdings matter for jointly-owned and wife-

owned land but not husband-owned land (acquired

mostly through inheritance)

• Individual and hh characteristics significant for husband-

owned land: wife‘s schooling (+), proportion of older

males at baseline (+), nonland assets (+)

• Very few individual and household characteristics affect

growth of joint- and wife-owned land; wife owned land is

affected by wife‘s schooling (-) and location (Jessore +)

Page 33


Parametric results: Nonland assets

• Initial assets matter for joint and husbands‘ assets, but

not wife‘s assets

• Baseline characteristics matter:

• Joint assets: higher levels of schooling (+),

demographics

• Husbands‘ assets: hh size (-), wife‘s schooling (+)

• Wife‘s assets: age and age squared (life cycle factors)

• Location dummies important

Page 34


Impact of shocks on exclusively held land is not symmetric

Shocks and positive events Land

Joint Husbands Wives

“Earlier shocks”

97-01 Death

97-01 Dowry and wedding Positive

(weak)

97-01 Dowry receipts Negative

(weak)

“Later shocks”

02-06 Death Negative Positive

(weak)

02-06 Remittances Positive

(weak)

02-06 Dowry receipts Negative

Page 35


Impact of shocks on exclusively held nonland assets is not

symmetric either

Shocks and positive events Nonland assets

Joint Husbands Wives

“Earlier shocks”

97-01 Floods Negative

(weak)

97-01 Illness Positive

(weak)

97-01 Dowry and wedding Negative

97-01 Inheritance Positive Positive

“Later shocks”

02-06 Illness Negative

(weak)

Negative

02-06 Death Negative

02-06 Dowry and wedding Positive

02-06 Remittances Positive

02-06 Inheritance Negative NegativePage 36


Life-cycle events are clearly important determinants of

asset accumulation and decumulation

• Dowry and wedding expenses reduce husbands‘ assets

(dowry receipts increase joint assets, but reduce wife‘s

assets)

• Death reduces husband‘s assets

• Early inheritance increases assets, later inheritance

reduces it (associated with a death, or property division)

• Remittances associated with children growing up and

working tend to increase joint assets

• Question: are there better ways to prepare for (save

for?) these life-cycle events?

Page 37


Impact of shocks on husband-wife growth differs by type of

asset and type of shock

Shocks and positive events Land

(ΔH -Δ W)

Nonland

assets

(ΔH -Δ W)

Floods

97-01 Illness

97-01 Death Wife (+)

97-01 Dowry and wedding Wife (+)

97-01 Remittances Wife (+)

97-01 Inheritance Husband (+)

“Later shocks”

02-06 Illness

02-06 Death Wife (+)

02-06 Dowry and wedding

02-06 Remittances

02-06 Inheritance Wife (+) Page 38


Familial networks matter, but not in the same way

Land Nonland assets

Joint Husband Wife Joint Husband Wife

Husband‘s

brothers

Negative

Husbands

sisters

Negative Positive

Distance to

husband‘s

village

Positive

Wife‘s

brothers

Negative

Wife‘s sisters

Distance to

wife‘s village

Negative Negative

Page 39


Conclusions--1

• Very interesting evidence on gender-differentiated asset

growth over a decade

• Asset growth positive for men and women, but faster for

men (less unequal when looking at jointly held assets)

• Raises questions about control of jointly held assets

• Composition of men‘s and women‘s asset portfolios also

changing as households diversify into non-agriculture,

and as women get more involved in agriculture (possibly

owing to NGO interventions)

Page 40


Conclusions--2

• Shocks appear to have differential impacts--women‘s

assets are negatively affected by illness, men‘s by death,

illness, and dowry and wedding expenses

• Illness is the shock most frequently reported by

households=>implications for asset disposal?

• Life-cycle events (dowries, weddings, death) important,

how to better prepare for them?

• This analysis has also been done for disaggregated

assets, indicates that there is a lot of movement in

consumer durables and agricultural durables, also

livestock and jewelry

Page 41


Implications--1

• Need to devise social protection strategy to provide

insurance against shocks

• Health insurance may help protect asset stocks (as well

as individual health)

• Social safety nets (public works, income transfer

programs) may help prevent asset depletion, which

would also help protect future livelihoods

• What to do with dowries? Good question!

• Important to increase incentives to invest in women‘s

human capital as well as to increase returns to human

capital (e.g. remove barriers to labor market

participation)

Page 42


Implications--2

• Need to provide mechanisms for poor to save and build

up asset stocks, and to rebuild them after shocks

• Need to provide mechanisms to prepare adequately for

(anticipated) life-cycle events

• Women, in particular, need to be able to build up assets

(savings?) that they can control

Page 43

gender assets shocks_ifpri bbl may 2011

Documents

asset dynamics different

asset depletion

asset disposal

research questions

data assets

hh asset holdings

buildup asset stocks

dynamic asset frontier