Assets, Wealth and Spousal Violence: Insights from Ecuador and Ghana Abena D. Oduro, University of Ghana Carmen Diana Deere, University of Florida Zachary Catanzarite, University of Florida Prepared for the World Bank Workshop on Gender and Assets June

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 ,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 ,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 years) Type of AbuseEcuadorGhana N= 1,938N = 886 Physical3.3%2.1% Emotional17.7%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.

Descriptives EcuadorGhana N=1,938N=886 Woman a Major Asset Owner (Percent) Female share of Couple Wealth (Mean, percent) Woman’s Age (Years) Spousal Age difference (Years) Woman’s Years of Schooling Spousal Schooling Difference (Years) Consensual Union (Percent) Monogamous Union (Percent) 75.5 Financial Disagreements (Percent) Both Employed Sources: EAFF (2010); GHAS (2010)

Logistic Regression Results for Physical Violence Ecuador (N=1938)Ghana (N=886) ModelVariablesCoefficientStandard ErrorCoefficientStandard Error IWoman Owns Real Estate Likelihood Ratio Chi-Squared (df) (18)27.17(16) Pseudo-R squared0.200 IIShare of Couple Wealth-2.766** Share of Couple Wealth Squared Likelihood Ratio Chi-Squared (df) (19)28.13(17) Pseudo-R squared IIIShare of Couple Wealth-2.293*** Share of Wealth*Tertile Share of Wealth*Tertile ** Likelihood Ratio Chi-Squared (df)59.775(20)33.07(18) Pseudo-R squared0.243

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 EcuadorGhana Model VariablesCoefficientStandard ErrorCoefficientStandard Error I Woman Owns Real Estate *0.379 Likelihood Ratio Chi-Squared (df) (18) (18) Pseudo-R squared0.197 II Share of Couple Wealth **0.668 Share of Couple Wealth Squared Likelihood Ratio Chi-Squared (df) (19)105.24(18) Pseudo-R squared0.197 III Share of Couple Wealth1.200** Share of Wealth*Tertile **1.913 Share of Wealth*Tertile * Likelihood Ratio Chi-Squared (df) (20) (19) Pseudo-R squared0.209

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