assets, wealth and spousal violence: insights from ecuador and ghana abena d. oduro, university of...
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Assets, Wealth and Spousal Violence: Insights from Ecuador and Ghana
Abena D. Oduro, University of GhanaCarmen Diana Deere, University of FloridaZachary Catanzarite, University of Florida
Prepared for the World Bank Workshop on Gender and Assets
June 14 2012
Introduction
• Several studies investigate factors that might increase women’s bargaining power and reduce the risk of abuse.
• Very few have considered the relationship between women’s asset (i.e. land and home)ownership and spousal violence– Women’s homeownership deters physical and psychological
abuse (Panda and Agarwal 2005, Bhattacharrya et al 2011)– Evidence on association of spousal abuse and women’s land
ownership is mixed (Bhattacharyya et al 2011, Ezeh and Gage 2000, Panda and Agarwal, 2005,)
Introduction (contd.)
• This study adds to the growing literature on spousal abuse in two ways:– It considers ownership of a wider range of assets, i.e. agricultural
land, home ownership and ownership of other real estate such as another residence, commercial building and non-agricultural plot.
– It investigates women’s ownership of assets relative to their partners.• Places emphasis on relative value of women’s assets as a measure of
their fall back position• Controls for the fact that different assets impact bargaining power
differently.• Allow us to determine whether the preventive impact of women’s share
of wealth varies along the wealth distribution
Context
Ecuador• Population: 14.7 million• HDI rank: 83• Law Against Domestic
Violence Towards Women and the Family (1995)
Ghana• Population 25 million• HDI rank: 135• Domestic Violence Act
(2007)
The Data
Ecuador• EAFF-Ecuador Household
Asset Survey conducted in 2010
• 2,892 Households• Two-stage sampling
procedure• Sample size for this study:
1,938 couples –married or in a consensual union
Ghana• GHAS-Ghana Household
Asset Survey conducted in 2010
• 2,170 Households• Two stage sampling
procedure• Sample size for this study:
886 couples – married or in a consensual union
Survey Instrument
• Designed to be similar in several respects• Domestic Violence Module- Respondents
were asked:– How common domestic violence was in their
community or neighbourhood?– Whether they had been abused physically, verbally
or psychologically– Who the perpetrator of the abuse was
Incidence of Spousal Violence During Previous 12 months (Currently partnered women aged
18-49 years)Type of Abuse Ecuador Ghana
N= 1,938 N = 886
Physical 3.3% 2.1%
Emotional 17.7% 11.2%
Any form of abuse
18.1% 12.0%
Notes: *Categories are not mutually exclusive. The percentages are weighted by the sample expansion factors.Sources: EAFF (2010); GHAS (2010)
The Models
• The Dependent variables- Woman’s report of:– Physical violence in past 12 months– Emotional violence, i.e. verbal and psychological abuse, in
past 12 months• Variable of Interest- Women’s asset ownership
measured as:– Women’s ownership of any of the following real estate:
agricultural land, place of residence, other real estate . Categorical variable that takes a value of 1 if owner, 0 if not
– Women’s share of couple’s gross value of physical and financial assets- continuous variable ranging from 0 to 1.
Other Explanatory Variables• Characteristics of Woman
– Age, education and number of children aged 13 years and younger
• Characteristics of the Couple– Age difference, difference in years of education, employment status
relative to spouse, relative spousal earnings
• Nature of the Relationship– Type of union (i.e. married or in a consensual union), occurrence of
financial disagreements in past 12 months
• Household Context– Socioeconomic status of household- gross value of assets, crowding,
location
• Community Context– Woman’s perception of the frequency of domestic violence in the
community
Methodology• Logistic regression
– Physical abuse– Emotional Abuse
• Baseline model:– Includes all explanatory variables except variable of interest.
• Model I: – Includes woman’s ownership of asset variable in the baseline
• Model II: – Includes woman’s share of couple wealth in the baseline
• Model III:– Includes woman’s share of couple wealth and interaction of woman’s
share of couple wealth and household wealth tertiles in the baseline.
DescriptivesEcuador Ghana
N=1,938 N=886
Woman a Major Asset Owner (Percent) 54.5 21.8
Female share of Couple Wealth (Mean, percent)
46.8 23.2
Woman’s Age (Years) 41.27 39.24
Spousal Age difference (Years) 4.09 7.95
Woman’s Years of Schooling 8.17 4.51
Spousal Schooling Difference (Years) 0.38 1.76
Consensual Union (Percent) 35.4 13.3
Monogamous Union (Percent) 75.5
Financial Disagreements (Percent) 15.1 13.3
Both Employed 58.2 85.6
Sources: EAFF (2010); GHAS (2010)
Logistic Regression Results for Physical Violence
Ecuador (N=1938) Ghana (N=886)
Model Variables Coefficient Standard Error Coefficient Standard Error
I Woman Owns Real Estate -0.177 0.295 -0.64 0.847
Likelihood Ratio Chi-Squared (df) 52.971 (18) 27.17(16)
Pseudo-R squared 0.200
II Share of Couple Wealth -2.766** 1.397 -3.91 4.282
Share of Couple Wealth Squared 2.210 1.415 5.63 5.26
Likelihood Ratio Chi-Squared (df) 57.096 (19) 28.13(17)
Pseudo-R squared 0.2075
III Share of Couple Wealth -2.293*** 0.932 -7.498 6.692
Share of Wealth*Tertile 2 1.793 1.1868 5.288 7.590
Share of Wealth*Tertile 3 2.957** 1.354 10.982 6.912
Likelihood Ratio Chi-Squared (df) 59.775(20) 33.07(18)
Pseudo-R squared 0.243
Other Significant Explanatory Variables
Ecuador• Financial Disagreements
(+ve)• Report of Community
Violence(+ve)• Employment: Man only
is employed
• Ghana• Financial Disagreements
(+ve)• Age of Woman (-ve)• Years of education of
woman (-ve)
Logistic Regression Results for Emotional Violence
Ecuador Ghana
Model Variables Coefficient Standard Error Coefficient Standard Error
I Woman Owns Real Estate -0.140 0.1753 -0.687* 0.379
Likelihood Ratio Chi-Squared (df) 137.939 (18) 104.98 (18)
Pseudo-R squared 0.197
II Share of Couple Wealth -0.451 0.899 -1.261** 0.668
Share of Couple Wealth Squared 1.051 0.846
Likelihood Ratio Chi-Squared (df) 143.269 (19) 105.24(18)
Pseudo-R squared 0.197
III Share of Couple Wealth 1.200** 0.521 0.863 1.306
Share of Wealth*Tertile 2 -0.580 0.692 -4.570** 1.913
Share of Wealth*Tertile 3 -1.224* 0.745 -1.509 1.604
Likelihood Ratio Chi-Squared (df) 144.437 (20) 111.41 (19)
Pseudo-R squared 0.209
Other Significant Explanatory Variables
Ecuador• Financial Disagreements
(+ve)• Perceptions of
community violence (+ve)
• Urban location (+ve)• Earnings: Woman earns
more than partner (+ve)
Ghana• Financial Disagreements
(+ve)• Perceptions of
community violence (+ve)
• Urban location (-ve)• Polygamous union (-ve)
Conclusion
• Asset variables behave differently across models and between the two countries.– Being an asset owner has a significant and negative
effect in Ghana– In Ecuador woman’s share of couple wealth has a
significant negative effect on physical abuse. – In Ghana woman’s share of couple wealth has a
significant deterrent effect for emotional abuse only. • Context Matters.
Conclusion contd.
• The deterrent effect of women’s share of wealth depends on the socioeconomic status of the household. Women in different socio-economic strata face different risks.
• Ecuador:– Woman in lowest third of household wealth with zero
share of couple wealth is predicted to be at risk from physical abuse but is buffered from emotional abuse.
– However, when she increases her share of couple wealth predicted likelihood of physical abuse declines whilst likelihood of emotional abuse rises.
Conclusion contd.
• Predictors of both types of abuse:– Both countries:
• Financial disagreements• Perception of community violence
• Deterrents: – Ecuador:
• Only male is employed, reduces likelihood of physical abuse• Man’s years of schooling exceeds that of partner reduces likelihood
of emotional abuse
– Ghana:• Age, Years of schooling of woman reduces physical violence• Polygamous marriage reduces emotional violence
Conclusion
• Correlates of physical and emotional violence are often different
• Common patterns across countries• Context matters• Impact of women’s share of couple’s wealth
on spousal violence is contingent on household socioeconomic status.