1. Do Men and Women Accumulate Assets in
Different Ways?
Evidence from Rural Bangladesh
Agnes Quisumbing
International Food Policy Research Institute
May 2011
Tuesday, June 21, 2011

2. 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)
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3. 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
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4. 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?
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5. 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
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6. 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
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7. At+1 At= At+1
f(At)
At
AL A* AH

8. • 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
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9. 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
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10. 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
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11. Empirical specification
Ait-AiB= ß0 + ß1 AiB + ß2AiB2 + ß3AiB3 + 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
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12. Survey design and data
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13. 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
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14. 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)
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15. Map of study sites of longitudinal study
Page 15

16. 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)
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Page 16

17. Page 17

18. 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
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19. 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)
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20. Poverty and poverty transition categories
Agricultural
technology
(1996-2006)
Poverty headcount
Poverty in baseline 70%
survey
Poverty in 2006/2007 18%
Poverty transitions
Chronic poor 16%
Falling into poverty 2%
Moving out of poverty 54%
Never poor 28%
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21. 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
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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 4.4 1.8 11.2 2.6 155.3 42.1
livestock)
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23. Distribution of area of owned land across ownership
categories
1996 2006
Joint Joint
Husband Husband
Wife Wife
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24. Distribution of nonland assets across ownership categories
1996 2006
Joint Joint
Husband Husband
Wife Wife
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25. Most common shocks experienced by households, 1996-
2006
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26. Proportion of households reporting negative shocks,
1997-2001 and 2002-2006
50
45
40
35
30
Illness, 1997-2001
Proportion of
25
households Illness, 2002-2006
20 Death, 1997-2001
Death, 2002-2006
15
Dowry and wedding, 1997-2001
10
Dowry and wedding, 2002-2006
5
0
Illness, Illness, Death, Death, Dowry and Dowry and
1997-2001 2002-2006 1997-2001 2002-2006 wedding, wedding,
1997-2001 2002-2006
Type of negative shock
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27. Proportion of households reporting positive events,
1997-2001 and 2002-2006
9
8
7
6
5
Proportion of households
4
Remittances, 1997-2001
3 Remittances, 2002-2006
2 Inheritance, 1997-2001
Inheritance, 2002-2006
1
Dowry received, 1997-2001
0 Dowry received, 2002-2006
Type of positive event
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28. Parametric specification
Ait-AiB= ß0 + ß1 AiB + ß2AiB2 + ß3AiB3 + 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
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29. Results
Page 30

30. Lowess plots for land (scale of axes not uniform across graphs)
Household land Jointly-held land
2000
1000
800
1500
Joint Land 2007
Own Land 2007
600
1000
400
500
200
0
0
0 500 1000 1500
0 500 1000 1500 2000
Own Land 1997 Joint Land 1997
Husband’s land Wife’s land
1500
150
Own Land Husband 2007
1000
Own Land Wife 2007
100
500
50
0
0
0 500 1000 1500 0 50 100 150
Own Land husband 1997 Own Land Wife 1997

31. Lowess plots for nonland assets
150000
Household Jointly-held assets
150000
Value of Joint Assets 2007
100000
100000
50000
50000
0
0
0 50000 100000 150000
0 50000 100000 150000
Value of Assets 1996 Value of Joint Assets 1996
Husband’s assets Wife’s assets
150000
150000
100000
Value of Wife Assets 2007
100000
50000
50000
0
0
0 50000 100000 150000 0 50000 100000 150000
Value of Husband Assets 1996 Value of Wife Assets 1996

32. 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 +)
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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
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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
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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 Negative
(weak)
02-06 Death Negative
02-06 Dowry and wedding Positive
02-06 Remittances Positive
02-06 Inheritance
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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?
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37. Impact of shocks on husband-wife growth differs by type of
asset and type of shock
Shocks and positive events Land Nonland
(ΔH -Δ W) 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
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38. Familial networks matter, but not in the same way
Land Nonland assets
Joint Husband Wife Joint Husband Wife
Husband‘s Negative
brothers
Husbands Negative Positive
sisters
Distance to Positive
husband‘s
village
Wife‘s Negative
brothers
Wife‘s sisters
Distance to Negative Negative
wife‘s village
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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)
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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
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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)
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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
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