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

The Odds of Physical Violence and Women’s Share of Couple Wealth by Tertile, Ecuador and Ghana

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

The Odds of Emotional Violence and Women’s Share of Couple Wealth by Tertile, Ecuador and Ghana

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.

Thank you for your attention