Background
On average, intimate partner violence affects nearly one in three women worldwide within their lifetime. But the distribution of partner violence is highly uneven, with a prevalence of less than 4% in the past 12 months in many high-income countries compared with at least 40% in some low-income settings. Little is known about the factors that drive the geographical distribution of partner violence or how macro-level factors might combine with individual-level factors to affect individual women’s risk of intimate partner violence. We aimed to assess the role that women’s status and other gender-related factors might have in defining levels of partner violence among settings.
Methods
We compiled data for the 12 month prevalence of partner violence from 66 surveys (88 survey years) from 44 countries, representing 481 205 women between Jan 1, 2000, and Apr 17, 2013. Only surveys with comparable questions and state-of-the-art methods to ensure safety and encourage violence disclosure were used. With linear and quantile regression, we examined associations between macro-level measures of socioeconomic development, women’s status, gender inequality, and gender-related norms and the prevalence of current partner violence at a population level. Multilevel modelling and tests for interaction were used to explore whether and how macro-level factors affect individual-level risk. The outcome for this analysis was the population prevalence of current partner violence, defined as the percentage of ever-partnered women (excluding widows without a current partner), aged from 15 years to 49 years who were victims of at least one act of physical or sexual violence within the past 12 months.
Findings
Gender-related factors at the national and subnational level help to predict the population prevalence of physical and sexual partner violence within the past 12 months. Especially predictive of the geographical distribution of partner violence are norms related to male authority over female behaviour (0·102, p<0·0001), norms justifying wife beating (0·263, p<0·0001), and the extent to which law and practice disadvantage women compared with men in access to land, property, and other productive resources (0·271, p<0·0001). The strong negative association between current partner violence and gross domestic product (GDP) per person (–0·055, p=0·0009) becomes non-significant in the presence of norm-related measures (–0·015, p=0·472), suggesting that GDP per person is a marker for social transformations that accompany economic growth and is unlikely to be causally related to levels of partner violence. We document several cross-level effects, including that a girl’s education is more strongly associated with reduced risk of partner violence in countries where wife abuse is normative than where it is not. Likewise, partner violence is less prevalent in countries with a high proportion of women in the formal work force, but working for cash increases a woman’s risk in countries where few women work.
Interpretation
Our findings suggest that policy makers could reduce violence by eliminating gender bias in ownership rights and addressing norms that justify wife beating and male control of female behaviour. Prevention planners should place greater emphasis on policy reforms at the macro-level and take cross-level effects into account when designing interventions.
Introduction
Violence against women by a male intimate partner is both a violation of women’s human rights and a profound health problem that interferes with their full participation in society and their countries’ social and economic development.
Although violence affects many women’s lives, it does so unevenly. Research shows that the prevalence of violence differs greatly across settings—eg between countries, within countries, and across neighbourhoods and regions. The 12 month prevalence of partner violence (established with similar questions and methods between countries) varies from 4% in high-income countries such as Denmark, the UK, Ireland, and the USA to more than 40% of women in some low-income countries such as Ethiopia. In the WHO Multi-country Study on Women’s Health and Domestic Violence (referred to as the WHO Study), reports of current abuse by a partner varied from less than 4% in Yokohama, Japan, and Belgrade, Serbia to 53·7% in rural Ethiopia and 34·2% the Peruvian department of Cuzco. The average 12 month prevalence of partner violence across the 28 states of the European Union is likewise 4%. Even between neighbourhoods in a city or villages in a district, the prevalence of partner violence often varies substantially. This finding raises a crucial question: what accounts for these differences in levels of violence and can the geographical distribution of violence yield insights useful for violence prevention?
Research in context
Evidence before this study
Before initiation of this study, we did a comprehensive, but non-systematic, review of the scientific literature on macro-level factors associated with partner violence. Between July 1, 2014, and August 8, 2014, we searched Econlit, JSTOR, Scopus, NBER Working Papers, Medline, and Global Health using the search terms: “macro*”, “community*”, “ecological”, “determinant”, “cross-national“, “country-level”, “neighbourhood”, and various terms for partner violence (eg, domestic violence, wife abuse) and grey literature available on relevant websites. We searched only English language journals. Only 9 relevant studies were identified, all with substantial flaws in their methods.
Added value of this study
The current study is the first to analyse macro-level predictors of partner violence with a well defined and highly similar measure of partner violence across countries, on the basis of self-reported victimisation in population-based surveys, all with the same questions, survey methods, and ethical controls. It shows that gender-related factors at the country level and regional level—especially norms and property rights—predict the population prevalence of current partner violence (physical or sexual violence in the past 12 months). The study also shows that the macro-environment can potentiate or dampen the effect that individual-level factors have on the risk of partner violence.
Implications of all the available evidence
Our findings suggest that policy makers could reduce violence by elimination of gender bias in ownership rights and addressing norms that justify wife beating and male control of female behaviour. Prevention planners should place greater emphasis on policy reforms at the macro-level and take cross-level effects into account when designing interventions.
Feminists have long contended that the main drivers of partner violence are gender-related norms and hierarchies that shape relationships between men and women and structure women’s access to resources. These factors, combined with genetic predispositions, developmental pathways, and partner-related and relationship-related factors, determine the likelihood that a couple will experience violence and drive the overall level of partner violence in a setting. Feminist-informed theory acknowledges the role of individual life-course factors, but emphasises the importance of community and macro-level factors as fundamental in defining levels of abuse.
Research into intimate partner violence, however, has largely ignored the role of macro-level factors in affecting a woman’s risk of violence and the geographical distribution of abuse. Violence research is dominated by studies from North America and other high-income settings and these have emphasised the role of personality and relationship dysfunction, childhood trauma and developmental adversity, and antisocial behaviour as key risk factors for partner violence.10, 11, 12Efforts from US researchers to test the feminist hypothesis on the importance of gender norms and hierarchies at a state level have yielded equivocal results,13 leading many academics to argue that gender plays a minor part in the cause of abuse.14, 15
Hence this study has two goals: to test the gender hypothesis by assessment of the degree to which macro-level factors related to women’s status, gender inequalities, and norms of male authority and control are associated with population-levels of partner violence and to explore whether these factors interact with individual-level variables to predict a woman’s personal risk of partner violence. Specifically, we examine the following four questions: do macro-level gender variables correlate with the geographical distribution of partner violence in the directions feminist-informed theory would suggest? What best accounts for the apparent association between a country’s level of socioeconomic development and its overall prevalence of partner violence? Which factors remain important at the macro level when analysed in the presence of other macro-level and individual-level predictors of violence? Do important cross-level interactions exist between macro-level and individual-level factors that affect a woman’s personal risk of partner violence?
This analysis builds on and extends the fairly undeveloped scientific literature about macro-level predictors of population prevalence of violence against women. So far, only nine studies have sought to explore country-level or state-level predictors of partner violence and all have weaknesses in the methods they have used, especially with respect to the outcome variable utilised. One study21 derives a numerical measure of partner violence on the basis of qualitative descriptions in human rights reports and the remainder rely on data from a range of studies that used different definitions and measures of intimate partner violence. Our analysis is the first to analyse macro-level predictors of partner violence at the level of the country and survey year with highly similar outcome data.
Outcomes
The outcome variable for this analysis is the population prevalence of current partner violence, defined as the percentage of ever-partnered women (excluding widows without a current partner), aged from 15 years to 49 years who experienced at least one act of physical or sexual violence within the past 12 months.
Our analysis focuses on partner violence in the past year to address differences in inclusion criteria between the DHS and WHO studies. The DHS is restricted to violence perpetrated by a woman’s current or most recent partner, whereas the WHO study asks about violence perpetrated by any partner since the age of 15 years. By focusing on the previous 12 months for both surveys, we maximise similarity between them. Moreover, a comparison of how current macro-level factors affect present day rates of partner violence makes conceptual sense.
Our exposure variables represent various gender-related domains and control variables that offer alternative explanations for the geographical distribution of violence. The gender-related domains include women’s status, women’s economic participation and entitlements, women’s political participation and entitlements, gender inequality between men and women, and gender-related norms and attitudes. Additionally, we include variables to control for a country’s level of socio-economic development (natural log of gross domestic product [GDP] in purchasing power parity in 2011 constant US dollars) and the age structure of the population.
Table 1 summarises the individual data sources and variables used to represent each domain. All macro-level variables represent the mean level of that measure aggregated at the survey level (if derived from surveys) or a national-level measure, if taken from data banks maintained by multilateral agencies, such as the World Bank. Several of the indicators represent specialised indices of entitlements or discrimination created by academics or worldwide institutions to track gender-related trends. These include measures of women’s political and economic rights from the Cingranelli-Richards Human Rights Database (eg, women’s de jure and de facto economic entitlements) and two measures of gender inequality in family law and ownership rights created and maintained by the OECD as part of its Social Institutions and Gender Index (SIGI) database. In both indices, two experts independently assigned scores to countries on the basis of data from the US State Department’s Country Reports on Human Rights Practices, according to a detailed coding scheme. The SIGI family law index, for example, assesses the degree to which states discriminate against women on issues of child guardianship and custody, access to divorce, the minimum legal age of marriage, and the right to inherit property. Values range from 0 (no discrimination between men and women in law and practice) to 1 (high discrimination between men and women).
For each explanatory variable tested, we used data from the same year that the violence survey was undertaken. Where an exact match was not available, the closest year to the survey date was used, giving priority to data obtained before the date of the violence survey.
Statistical analysis
This study uses various different techniques to address our different research questions. Scatterplots, histograms, and linear and quantile regression were used to assess normality, identify outliers, and examine the potential associations between macro-level explanatory variables and partner violence at a country and survey level. The goal of this bivariate ecological analysis was to assess whether the population-level distribution of partner violence is associated in the predicted direction with macro-level variations in women’s status, gender inequality, and norms related to male authority and control. Quantile regression was used to check the robustness of our findings. Because quantile regression models the median rather than the mean, quantile regression generally yields more accurate coefficients for skewed datasets, with fewer covariates emerging as significant. It also can be used to assess whether a covariate exerts a differential effect at low versus high ends of an outcome distribution.29
Next, we ran the same ecological analysis with several variables. Standard errors are clustered at the country level to take into account that some countries have several surveys and therefore their observations are not fully independent. For this analysis, we used linear rather than quantile regression.
Our strategy for model building was to establish which variable from each domain dominated when taken together with the other variables selected to represent that domain. We selected the most robust measure for each domain (highest, most stable effect size), and then ran a set of structured regressions to establish whether the apparent association between a country’s aggregate GDP per person and its partner violence persisted in the presence of gender-related variables. All models include year-fixed effects. Robust p values are provided in parentheses. We regarded p values less than 0·10 to be statistically significant.
Multilevel analysis was used to examine whether the macro-level variables associated with the geographical distribution of partner violence were mainly a function of the characteristics of the individuals living there (a compositional effect) or suggestive of a higher order social process (a contextual effect). When we include the same variable at both levels, we essentially test whether there is an extra correlation between the macro factor and abuse in addition to that operating at the individual level.
Role of the funding source
The funder of the study had no role in the study design, data collection, data analyses, or data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
Results
Our sample included 66 surveys and 88 survey years from 44 countries, representing 481 205 women. When including only surveys that did not have any missing values for our exposure variables, we had 30 countries and 56 survey years. Surveys are defined as a combination of country and year because some countries have more than one survey. The distribution of surveys by country is shown in theappendix.
Table 2 summarises the bivariate associations between gender-related macro factors and the geographical distribution of partner violence. As indicated by the arrows, all the ecological associations are in the direction that feminist-informed theory would predict (with the exception of political rights) although some did not achieve statistical significance, including early marriage, and the ratio of earned income between men and women. In addition to being associated with women’s status and other gender-related variables, levels of partner violence seem lower in high-income countries than in low-income countries. For every log increase in GDP per person, the prevalence of partner violence decreases by 5·5%. These findings are robust to quantile and logistic regression and are largely similar in urban and rural samples.
With respect to the association between discrimination in women’s ownership rights and current partner violence, the presence of ownership laws and practices that privilege men over women were robustly and significantly associated with higher levels of violence (0·313, p=0·006). The SIGI ownership index seemed to be the strongest predictor of aggregate levels of partner violence of all variables in the gender inequality domain. When the index was broken down into its component parts, only women’s access to land and other property were significant, suggesting that ownership of assets rather than access to credit or banking drives the association (model 8). Additional analysis of urban versus rural samples further suggested that it is gender discrimination in ownership among women from rural areas that mostly accounts for the association present at a country level (appendix).
Table 4 shows the results of the same analysis but with multilevel regressions. Multilevel coefficients represent the risk of partner violence to individual women in the presence of macro-level factors. The advantage of multilevel regressions compared with ecological associations is that they show how factors are important at different levels in the social ecology. Table 4 shows that completion of secondary education (–0·080, p=0·007) and being older than 34 years (–0·049, p=0·001) significantly reduce a woman’s personal risk of partner violence. Living in countries or regions where acceptance of wife beating and male authority are high remains significantly associated with partner violence at the 10% level in the presence of these compositional controls.
Table 4 shows that women who accept wife beating as a man’s right and who have a controlling partner are at a significantly higher risk of violence at the bivariate level (data not shown) and in multilevel analysis (0·046, p<0·0001 and 0·079, p<0·0001). Macro-level norms of acceptance and male authority are no longer significant in the presence of these individual-level factors, but they might be on the causal pathway between norms and intimate partner violence. Table 4 also shows that living in a country that discriminates against women in access to land and other property remains a strong driver of abuse-related risk (0·132, p=0·015 and 0·155, p=0·003), even in the presence of a range of individual factors.
Table 5 splits the sample between surveys in countries with a high acceptance of partner violence and a low acceptance of violence, showing potential cross-level interactions. High acceptance is defined as above the median of the survey mean of those accepting at least one justification for wife beating. Table 5 shows that the level of overall acceptance of violence affects the effect of individual age-related and education-related variables on women’s risk of partner violence. The coefficient for education is greater in settings with high acceptance compared with lower acceptance of violence, suggesting that education is more protective in countries or regions where justification of wife beating is greater. Being in the age range of 15–24 years is also more risky in countries with high acceptance of violence (the p value of the difference is 0·064 for being 25–34 years compared with being younger than 25 years). All interaction p values are available in the appendix.
