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Endogenous social effects on intimate partner violence in Colombia
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Social Science Research 32 (2003) 335–345
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Endogenous social effects on intimate partner violence in Colombia Michael J. McQuestion* Department of Population and Family Health Sciences, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, E-4142 Baltimore, MD 21205-2179, USA
Abstract Intimate partner violence (IPV) is now recognized as a worldwide public health problem. Most theories ascribe IPV to individual, family, or cultural factors. Empirical support for these theories is inconsistent; however, community-level clustering of IPV behaviors is commonplace, indicating these behaviors are autocorrelated. In this study I use social interaction models and DHSIII data from Colombia to jointly model household and neighborhood effects on individual reports of beatings and forced sex. I find the probability a Colombian woman ever experienced IPV is dependent on her union status, schooling, number of live births, area of residence, and her partnerÕs schooling. Net of these factors, the odds of ever being beaten were 64% higher for a woman living in a cluster where the proportion of other women reporting beatings exceeded the sample mean. These results show that endogenous social processes affect IPV levels and indicate the need for community-level IPV control efforts. Ó 2002 Elsevier Science (USA). All rights reserved.
1. Introduction Intimate partner violence (IPV) is now recognized as a worldwide public health problem. Surveys in developing countries show that as many as half of all women ever in unions have been beaten and/or raped by their partners (Fischbach and Herbert, 1997; Heise et al., 1999). Besides the obvious traumatic effects, battered women are more likely to report chronic and mental illnesses and less likely to seek needed
* Fax: 410-955-2303. E-mail address: [email protected].
0049-089X/02/$ - see front matter Ó 2002 Elsevier Science (USA). All rights reserved. doi:10.1016/S0049-089X(02)00062-5
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M.J. McQuestion / Social Science Research 32 (2003) 335–345
care for themselves (Heise et al., 1994; Plichta and Falik, 2001) or their children (Rosales Ortiz et al., 1999). Although a number of individual-level IPV risk factors have been identified the underlying causes are not well understood. In this paper I explore the social dimensions of the problem. I depart from the intriguing observation that IPV is differentially distributed across localities and that this clustering persists even when household and individual risk factors are controlled (Counts et al., 1999). At the community level, unemployment rates, per capita income, presence of social organizations, and other exogenous characteristics are known to affect individual IPV probabilities (Koenig et al., 1999; OÕCampo et al., 1995), but these factors do not fully explain the clustering. Other social forces must be at work. The variable IPV patterns, suggest some researchers, differentiate places where prevailing gender norms are or are not enforced (Campbell, 1999; Straus, 1998). Though norms are unobservable and difficult to operationalize, the fact that they are socially produced and reproduced explains why normative behaviors are autocorrelated (Bunton et al., 1991). This behavioral interdependence points to a prior but testable research question: Do the revealed IPV experiences of nearby women affect the likelihood any one woman experiences IPV? In other words, are IPV outcomes clustered because those specific behaviors in that specific context are autocorrelated? In this study I use recently developed social interaction models to test this proposition. The data are from a 1995 Demographic and Health survey in Colombia, a rapidly developing country with a well-documented IPV problem that has undertaken significant IPV control efforts in recent years (Ordonez et al., 1995). Below I sketch three theoretical explanations of why IPV might be autocorrelated.
2. IPV risk factors Field research in Latin America and elsewhere shows that IPV victims tend to be young, impoverished, poorly educated, socially isolated, multiparous women who are dependent on their abusive spouses for their survival (Fournier et al., 1999; Gonzales de Olarte and Gavillano Llosa, 1999). The perpetrators are also likely to be poor and uneducated, to use alcohol and to firmly control household decisionmaking (Ellsberg et al., 2001; Heise et al., 1999). Women in consensual versus formal unions are at greater risk, a reflection perhaps of less long-term commitment to the relationship (Hotaling and Sugarman, 1990). The observation that IPV tends to be more common among adults who were themselves abused, or who witnessed similar behavior in the home as children, suggests it is learned behavior (Kalmuss, 1984; Moreno Martin, 1999). Feminist, cultures of violence and exchange theories have all been invoked to explain IPV as a social phenomenon; however, empirical findings do not consistently support any of these (Campbell, 1999). What is consistent is the observation that IPV is differentially distributed across social space within cultures. There are several substantive theories that might explain this clustering. Differential association theory (Sutherland, 1947; Sutherland and Cressey, 1978) holds that deviant behaviors are learned and reinforced wherever the individual receives an excess of criminal versus conventional definitions from others in the community. It
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presumes society contains heterogenous groups generating normative conflict. Social disorganization theories, in contrast, maintain that deviance is more common in neighborhoods where neighbors do not know one another and do not act in unison to maintain social order (Sampson et al., 1997; Shaw and McKay, 1942). Both theories emphasize the situational aspects of deviance—the idea that certain places are more criminogenic than others. However, both impose the strong assumption that actors understand that IPV behavior is deviant. Alternatively, neighborhood effects on IPV behaviors could reflect a more general social learning process (Bandura, 1971). In this view, high-IPV neighborhoods are places where individuals observe influential others being rewarded for engaging in the behavior and adopt it themselves. A low-IPV neighborhood is one where IPV perpetrators are socially ostracized or otherwise punished, making it less likely others will reproduce the behaviors. Montgomery and Casterline (1993) invoked social learning theory to explain why contraceptive choice is often heterogenous across adjacent communities. Late adopters observe the outcomes of early adopters and emulate their choices, leading to a particular mix of methods within a neighborhood that persists over time. Though the data do not permit a direct test of any of these theories, differential association, social disorganization, and social learning theories all have in common the concept of reciprocal behavior, or endogenous social effects, wherein individualsÕ choices both determine and are determined by the reference groupÕs prior choices. Endogenous effects, loosely defined, would cause IPV behaviors to be autocorrelated. Little quantitative work has been done on the social aspects of IPV. In a study of 76 Baltimore census tracts, OÕCampo et al. (1995) found moderate clustering of IPV risk among pregnant women. Tract-level unemployment and per capita income measures were correlated with individual IPV risk, suggesting that stressful living conditions increase IPV incidence. Theoretically, such stress erodes menÕs status and diminishes their coping resources, increasing the likelihood they will abuse their partners (Gelles, 1993). Other evidence suggests that struggles to redefine gender norms determine IPV risk. A second multilevel study of 179 rural communities in two different areas of Bangladesh also found clustered IPV behaviors (Koenig et al., 1999). The proportion of community women belonging to savings and credit associations and the mean score on a womenÕs empowerment measure—proxies for gender norms—were both negative IPV predictors. These contextual effects were present only in the less traditional area, where overall IPV levels were lower. On the individual level, neither association membership nor empowerment affected IPV risk; however, both were positive IPV predictors in the culturally more conservative area. The authors conclude that women are at greatest risk in areas where gender norms are in flux. Ideally, one could use police or other contextual data to test whether a womanÕs IPV reports are conditional on the reported IPV behaviors of others in her community. In their absence I use a within-sample measure, the non-self group mean, to represent the level of IPV in each study community. Recent empirical work using mean group behaviors to predict individual behaviors include studies of household rice consumption in Indonesia (Case, 1991), crime in American cities (Case and Katz, 1991) and delivery care and child immunization in Colombia and Paraguay (McQuestion, 2001). In each of these studies the independence assumption was
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relaxed and behavioral dependencies were explicitly modeled using cross-sectional data. Below I describe such a model.
3. Methods 3.1. Model I conceptualize norms as one class of endogenous social effects wherein an individualÕs choice is conditional on the revealed behaviors of influential others (Erbring and Young, 1979). I operationalize this feedback process as a neighborhood-level latent variable or random effect. Recently, Durlauf (2001) demonstrated that such endogenous social effects are estimable in nonlinear models using observational data and can be represented by the group mean outcome. The latter represents a rational actorÕs beliefs about the choices other actors have made, given her information set. This expected average choice effect is weighted by the magnitude of direct interactions she has with these actors and is independent of individual preferences, attributes, and exogenous group characteristics. Specifying a binary model obviates the identification problem endogeneity poses in linear models (Manski, 1995). Following Durlauf, I use maximum likelihood1 to fit the following two-level random intercept logistic model: yij ¼ pij þ eij ; logitðpij Þ ¼ b0j þ b1 X1ij þ b2 WYji ; b0j ¼ c00 þ c01 z1j þ u0j ; eij Nð0; 1Þ; covðXij ; eij Þ ¼ 0; u0j Nð0; r2u Þ; covðz1j ; u0j Þ ¼ 0; covðeij ; u0j Þ ¼ 0; where pij is the probability respondent i in cluster j experiences IPV outcome yij , eij is an individual error term, and b2 and b1 are parameters measuring, respectively, the social interaction effect and individual effects due to covariates Xij . b0j is a random cluster-level intercept, c00 and c01 are parameters, z1j is a dummy variable and u0j is a cluster-level random effect. W is a weightingPmatrix assigning the mean outcome Yji to each respondent i in cluster j such that wi1j is in the interval ½0; 1. I thus use geographical proximity to represent social distance: Responses of those in the same sample cluster (census tract) are weighted one; all others are weighted zero. This nonself mean measure avoids the simultaneity bias that could result from having the same individualÕs outcomes on both sides of the equation. 1
I used the Stata macro gllamm (StataCorp, 2002).
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My analytical strategy is to decompose, then identify the random effect. If u0j is insignificant in this model there is no cluster-level autocorrelation of that behavior. If the non-self mean measure is insignificant then there are no substantive grounds for inferring an endogenous social effect. However, if both u0j and b2 are significant and if u0j diminishes when WYji is added to the model, then, by deduction, the revealed behaviors of others explain some of the clustering of outcome Y in that collectivity. Such a finding would support a theory in which social norms or related endogenous social processes determine individual IPV outcomes. 3.2. Variables I analyze data from ColombiaÕs 1995 Demographic and Health Survey (DHSIII). The survey incorporated a two-stage sampling strategy in which census tracts (about 1000 population) were randomly chosen from a population-weighted random sample of municipios. Within each sampled tract households were systematically sampled until the requisite number of 10 reproductive-aged women were interviewed. This sampling design makes it likely that most or all respondents in the same sample cluster know and to some degree monitor each othersÕ behaviors—a necessary assumption for inferring endogenous social effects. Interviewers posed a series of IPV questions to all women currently in unions.2 I model two of these: ‘‘Has your current husband/partner ever hit you?’’ and ‘‘Has your current husband/partner ever forced you to have sex?’’ Responses to both questions were observed for 6116 women in 991 of 1005 clusters. An additional 15 women declined to answer the beating question; I include their responses to the coerced sex question. I represent the two outcome variables as binary indicators coded one if the woman reported the behavior, and zero otherwise. Independent variables in both models include the respondentÕs age, number of live births, an indicator for consensual (versus married) union status, and levels of schooling for both partners. The schooling variables are coded 0 for no formal education, 1 for completed primary, 2 for completed secondary, and 3 for any higher attainment. I include occupational prestige scores for respondents and their partners as measures of employment and social status.3 The scores assign the following values: agricultural self-employed ¼ 1; unskilled and agricultural wage labor ¼ 2; services and skilled labor ¼ 3; clerical and sales ¼ 4; administrative, and professional ¼ 5. I represent household socioeconomic level with a dummy variable indicating a dirt floor. To control for geographic variation I include DHS-defined indicator variables for rural–urban residence and region ðn ¼ 5Þ.
2
Given their sensitivity, the IPV questions were asked confidentially. However, in a few cases ðn ¼ 191Þ interviewers were forced to ask them in the partnerÕs presence. Ellsberg et al. (2001) reported significant negative biases in IPV responses when partners were present a similar household survey in Nicaragua. To test this I added an indicator variable for partnerÕs presence but it was insignificant in all models. 3 The DHS incorporates two-digit occupational categories developed by the Colombian Department of Administration and Statistics (DANE). I arbitrarily combined these into the five categories.
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Table 1 Descriptive statistics for IPV respondents currently in union, Colombia DHSIII (1995) Variable
Mean
SD
Ever coerced sex Ever beaten Consensual union RespondentÕs age (yrs) Live births Respondent’s level of schooling None Primary Secondary Higher Partner’s level of schooling None Primary Secondary Higher Respondent employed Respondents occupational prestige PartnerÕs occupational prestige Dirt floor Rural residence Non-self cluster mean coerced sex Non-self cluster mean ever beaten
0.05 0.19 0.47 32.5 2.83
0.216 0.395 0.499 8.469 2.124
0.04 0.43 0.44 0.09
0.200 0.495 0.496 0.280
0.06 0.41 0.40 0.13 0.53 1.80 2.78 0.17 0.31 0.049 0.194
0.229 0.491 0.489 0.337 0.50 1.819 1.097 0.373 0.461 0.104 0.194
ðn ¼ 6131Þ
The social interaction measures are the numbers of other women in each sample cluster who responded positively to each IPV item, divided by the total number of respondents in the cluster. I compute binary indicators from these proportions. Seventy-two percent of respondents lived in clusters where no other women reported coerced sex and 33% lived in clusters where no one else reported beatings. Given the rarity and skewed distribution of coerced sex, I coded this indicator ‘‘1’’ for all who lived in a cluster where at least one other woman reported coerced sex. Beating was more common and not as skewed so I coded this indicator ‘‘1’’ if the cluster proportion was above the sample mean. I expect both these terms to be signed positively. To aid estimation I center all continuous variables by subtracting individual values from their sample means. Descriptive statistics for all variables are shown in Table 1.
4. Results 4.1. Coerced sex Models 1 and 2 in Table 2 show multivariate logistic regression results (odds ratios) for the forced sex outcome. Looking first at the household-level model (Model
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Table 2 Logistic IPV models, women 15–49 currently in unions, Colombia DHSIII, 1995 Coerced sex Model 1 Married (comparison) Consensual union RespondentÕs age (yrs) No. live births RespondentÕs level of schooling PartnerÕs level of schooling RespondentÕs occupational prestige PartnerÕs occupational prestige Durable floor (comparison) Dirt floor Urban residence (comparison) Rural residence Region No reports (comparison) One or more other reports of coerced sex Below sample mean (comparison) Cluster beating mean above sample mean ru )2log-like n
Beating Model 2
Model 3
Model 4
1.000 1.472 1.003 1.070 0.970 0.830 1.054
1.000 1.448 1.003 1.060 0.976 0.834 1.053
1.000 1.154 0.983 1.168 0.784 0.939 1.011
1.000 1.142 0.985 1.159 0.799 0.953 1.014
1.015 1.000 0.702 1.000 0.709 1.070
1.017 1.000 0.702 1.000 0.733 1.066 1.000 1.349
0.922 1.000 0.902 1.000 0.689 1.039
0.931 1.000 0.898 1.000 0.712 1.034
1.000 1.643 0.270 )1170.22 6131
0.000 )1169.24 6131
0.233 )2909.86 6116
0.000 )2893.05 6116
*
p < :10. p < :05. *** p < :01. **
1), the log-odds of coerced sex were 47% higher if the union was consensual, and the odds increased by 7% with each live birth. Maternal education had a negative but insignificant effect; however, the more educated the partner, the less likely he ever forced the respondent to have sex. Neither occupational prestige measure was significant. Interestingly, the odds of forced sex were 30% lower for women whose homes had dirt floors and for those living in rural areas. The random effect was marginally significant in Model 1 under a two-tailed test ðp ¼ :08Þ, but its inclusion did not significantly improve model fit when compared to a fixed-effect model (not shown). Model 2 includes the social effects measure. Its effect was significant and positive. Net of all other factors, the odds of coerced sex for a woman living in a cluster where at least one other respondent reported coerced sex were 35% higher than those of a woman in a cluster where no one else reported coerced sex. Adding the social interaction measure slightly moderated the effects of consensual union and rural residence but the other fixed effects remained unchanged. Adding the mean outcome term reduced the random effect to zero-evidence of a social interaction effect. However, the likelihood ratio test between Models 1 and 2 shows that adding the interaction measure does not improve model fit.
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4.2. Beating Models 3 and 4 of Table 2 show the corresponding beating models. The strong random effect in Model 3 shows that beating was also clustered at the community level. In contrast to the coerced sex model, specifying the random effect significantly improved model fit (not shown). As with forced sex, consensual union status and each live birth increased the likelihood of beatings. However, the effect of consensual union status on forced sex was three times greater. Two of the significant sex model factors—partnerÕs schooling and dirt floor—were not associated with beating. Conversely, respondentÕs age and education conferred protective effects for beating but not for sex. Each level of respondentsÕ schooling decreased their odds of beating by 22%. Likewise, the higher the partnersÕ occupational prestige,4 the less likely they ever beat their women. Rural residence had the same negative association with beatings as with coerced sex. This accords with other studies which have shown IPV is more frequent in urban areas (Kaufman and Zigler, 1993).5 Overall, the fixed effects agree with those reported in past studies. The similarity of risk factor effects in both sexual coercion and beating models suggests these two forms of IPV are related. In Model 4 the social interaction indicator is both positive and significant and again reduces the random effect to insignificance. For a woman in a locality where the beating level exceeded the sample mean, the odds of beating were 64% higher—double the corresponding social effect on coerced sex. Adding the measure significantly improved model fit, supporting the endogenous social effects hypothesis for this outcome.
5. Discussion The results of this study show that the likelihood a woman experienced coerced sex or beatings by her current partner is associated with the proportion of adjacent women who experienced the same phenomenon. This social interaction effect explains a significant amount of unexplained variance in the case of beatings. The magnitudes of these social effects are as large or larger than that of any individual control variable. Secondly, the social interaction terms are positively signed. Comparing the two behaviors, beatings are both more common and subject to stronger endogenous social effects. Though the autocorrelation of these behaviors is clear, the theoretical implications are ambiguous. On the one hand the endogenous social effect could reflect conformity to pro-IPV norms, implying that Colombian actors learn and emulate the observed IPV behaviors of adjacent others. This would be compatible with differential association and social learning theories. On the other hand, the endogenous so4
Status inconsistency theory predicts more IPV in households where a woman has higher status than the man. I tested variables indicating greater female occupational prestige and educational attainment but these were insignificant in all models. 5 The literature is unclear on whether urban women are more socially isolated or simply more willing to report IPV.
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cial effect might signify the absence of anti-IPV norms in particular neighborhoods, due perhaps to social disorganization. Or, following Koenig et al. (1999), it may be that gender norms are being contested in these places and not others. Unfortunately, these results cannot distinguish which, if any, of the theories is correct. The fact IPV behaviors are socially mediated implies that social-structural changes may affect their incidence. Rising female employment, migration, and cross-cultural exposure, for example, could lead to diffusion of more egalitarian gender concepts across collectivities (Strang and Meyer, 1994). Although Colombia certainly has undergone such structural changes in recent decades, there is little evidence of IPV behavioral change. The mean IPV levels I report here are close to those reported in ColombiaÕs 1990 DHSII survey.6 It may be unrealistic to expect a measurable change in these behaviors over such a short period. Moreover, the reported IPV experiences could have occurred any time in the past. There are a number of methodological shortcomings in this study. The data may be affected by recall and interviewer bias. Memories of actual IPV episodes and the willingness to report them are inherently problematic (Ellsberg et al., 2001); both would be affected by disturbances in gender norms. The apparent stability of IPV may mask countervailing trends in the actual behaviors and willingness to report them. A second problem is that IPV questions were restricted to women currently in unions, selecting out those who may have left abusive relationships (Campbell and Soeken, 1999). True IPV levels are probably higher than those reported here. Thirdly, the mean outcome indicators capture revealed behaviors but they are, at best, imprecise measures of IPV norms. Weighting observations by simple propinquity ignores the complex social networks in which real actors are embedded. In addition, the measure is unlagged because the temporal sequence of IPV behaviors in each sample cluster is unknown. Establishing temporal order, argued Erbring and Young (1979), would assure that each actorÕs behavior was conditioned solely by the prior behaviors of others. Though perhaps unfeasible for a representative national sample, qualitative research or panel data would generate better measures that would enable clear causal inferences to be drawn and perhaps support a particular theory of IPV behaviors. Despite these shortcomings, the results of this study demonstrate the feasibility of modeling endogenous social effects using observational data. They also show that IPV is partly, if not largely, a social phenomenon. Efforts to reduce the problem should therefore aim to shift IPV behaviors through community-level interventions.
Acknowledgments The author gratefully acknowledges Michelle Hindin, Michael Koenig, David Bishai, and two anonymous reviewers for their constructive comments. This study was supported by Grant P30 HD06268 to the Hopkins Population Center from NICHD.
6 In DHSII, which posed the same questions, 20% of Colombian women ever in unions reported having been beaten and 10% had been forced to have sex (PROFAMILIA, 1991).
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References Bandura, A., 1971. Social Learning Theory. General Learning Press, New York. Bunton, R., Murphy, S., Bennett, P., 1991. Theories of behavioral change and their use in health promotion: some neglected areas. Health Education Research 6 (2), 153–162. Campbell, J., 1999. Sanctions and sanctuary: wife battering within cultural contexts. In: Counts, D.A., Brown, J.K., Campbell, J.C. (Eds.), To Have and to Hit, second ed.. University of Illinois Press, Chicago, pp. 261–285. Campbell, J.C., Soeken, K., 1999. WomenÕs responses to battering over time: an analysis of change. Journal of Interpersonal Violence 14, 21–40. Case, A.C., 1991. Spatial patterns in household demand. Econometrica 59 (4), 953–965. Case, A.C., Katz, L.F., 1991. The Company You Keep: The Effects of Family and Neighborhood on Disadvantaged Youths. NBER Working Paper No. 3705. National Bureau of Economic Research, Cambridge, MA. Counts, D.A., Brown, J.K., Campbell, J.C., 1999. To Have and to Hit, second ed. University of Illinois Press, Chicago, IL. Durlauf, S., 2001. A framework for the study of individual behavior and social interactions. Sociological Methodology 30, 47–87. Ellsberg, M., Heise, L., Pena, R., Agurto, S., Winkvist, A., 2001. Researching domestic violence against women: methodological and ethical considerations. Studies in Family Planning 32 (1), 1–14. Erbring, L., Young, A.A., 1979. Individuals and social structure. Sociological Methods and Research 7 (4), 396–430. Fischbach, R.L., Herbert, B., 1997. Domestic violence and mental health: correlates and conundrums within and across cultures. Social Science and Medicine 45 (8), 1161–1176. Fournier, M., De los Rios, R., Orpinas, P., Piquet-Carneiro, L., 1999. Estudio multicentrico sobre actitudes y normas culturales frente a la violencia (Proyecto Activa): metodologia. Revista Panamericana de Salud Publica 5 (4/5), 222–231. Gelles, R.J., 1993. Through a sociological lens: social structure and family violence. In: Gelles, R.J., Loseke, D.R. (Eds.), Current Controversies on Family Violence. Sage Publications, Newbury Park, CA. Gonzales de Olarte, E., Gavillano Llosa, P., 1999. Does poverty cause domestic violence? Some answer from Lima. In: Morrison, A.R., Biehl, M.L. (Eds.), Too Close to Home: Domestic Violence in the Americas. InterAmerican Development Bank, Washington, DC, pp. 35–49. Heise, L.L., Pitangui J., Germain, A., 1994. Violence Against Women: the Hidden Health Burden, World Bank Discussion Papers # 255, The International Bank of Reconstruction and Redevelopment, The World Bank, Washington, DC. Heise, L., Ellsberg, M., Gottemoeller, M., 1999. Ending violence against women, Population Reports, Series L., No. 11, Johns Hopkins Bloomberg School of Public Health, Baltimore. Hotaling, G.T., Sugarman, D., 1990. A risk marker analysis of assaulted wives. Journal of Family Violence 5 (1), 1–13. Kalmuss, D.S., 1984. The intergenerational transmission of marital aggression. Journal of Marriage and the Family 46, 11–19. Kaufman, J., Zigler, E., 1993. The intergenerational transmission of abuse is overstated. In: Gelles, R.J., Loseke, D.R. (Eds.), Current Controversies on Family Violence. Sage Publications, Newport Park, CA. Koenig, M.A., Hossain, M.B., Ahmed, S., Haaga, J., 1999. Individual and community-level determinants of domestic violence in rural Bangladesh, Hopkins Population Center Paper on Population, No. WP99-04, Johns Hopkins Bloomberg School of Public Health, Department of Population and Family Health Sciences, Baltimore. Manski, C.F., 1995. Identification Problems in the Social Sciences. Harvard University Press, Cambridge, MA. McQuestion, M.J., 2001. Correlated health behaviors and the Latin American mortality transition. Notas de Poblacion 72, 189–228.
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Montgomery, M.R., Casterline, J.B., 1993. The diffusion of fertility control in Taiwan: evidence from pooled cross-section time-series models. Population Studies 47, 457–479. Moreno Martin, F., 1999. La violencia en la pareja. Revista Panamericana de Salud Publica 5 (4/5), 245– 258. OÕCampo, P.O., Gielen, A.C., Faden, R.R., Xue, X., Kass, N., Wang, M.-C., 1995. Violence by male partners against women during the childbearing year: a contextual analysis. American Journal of Public Health 85 (8), 1092–1097. Ordonez, M., Ochoa, L.C., Ojeda, G., 1995. Encuesta Nacional de Demografia y Salud 1995. Informe Final, Macro International, Calverton, MD. Plichta, S.B., Falik, M., 2001. Prevalence of violence and its implications for womenÕs health. Womens Health Issues 11 (3), 244–258. PROFAMILIA, 1991. Colombia: Encuesta de Prevalencia, Demografia y Salud 1990, Macro International, Columbia, MD. Rosales Ortiz, J., Loaiza, E., Primante, D., Barberena, A., Blandon Sequera, L., Ellsberg, M., 1999. Encuesta Nicaraguense de Demografia y Salud 1998. Informe Final, Instituto Nacional de Estadisticas y Censos, Managua. Sampson, R.J., Raudenbush, S.W., Earls, F., 1997. Neighborhoods and violent crime: a multilevel study of collective efficacy. Science 277 (15), 918. Shaw, C.R., McKay, H.D., 1942. Juvenile Delinquency in Urban Areas. University of Chicago Press, Chicago, IL. Strang, D., Meyer, J.W., 1994. Institutional conditions for diffusion. Theory and Society 22, 487–512. Straus, M., 1998. The controversy over domestic violence by women: a methodological, theoretical and sociology of science analysis. In: Arriaga, X., Oskamp, S. (Eds.), Violence in Intimate Relationships. Sage Publications, Thousand Oaks, CA, pp. 17–44. Sutherland, E.H., 1947. Principles of Criminology, fourth ed. J.B. Lippincott, Philadelphia, PA. Sutherland, E.H., Cressey, D.R., 1978. Principles of Criminology, tenth ed. J.B. Lippincott, Philadelphia, PA.
Social Science Research 32 (2003) 335–345
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Endogenous social effects on intimate partner violence in Colombia Michael J. McQuestion* Department of Population and Family Health Sciences, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, E-4142 Baltimore, MD 21205-2179, USA
Abstract Intimate partner violence (IPV) is now recognized as a worldwide public health problem. Most theories ascribe IPV to individual, family, or cultural factors. Empirical support for these theories is inconsistent; however, community-level clustering of IPV behaviors is commonplace, indicating these behaviors are autocorrelated. In this study I use social interaction models and DHSIII data from Colombia to jointly model household and neighborhood effects on individual reports of beatings and forced sex. I find the probability a Colombian woman ever experienced IPV is dependent on her union status, schooling, number of live births, area of residence, and her partnerÕs schooling. Net of these factors, the odds of ever being beaten were 64% higher for a woman living in a cluster where the proportion of other women reporting beatings exceeded the sample mean. These results show that endogenous social processes affect IPV levels and indicate the need for community-level IPV control efforts. Ó 2002 Elsevier Science (USA). All rights reserved.
1. Introduction Intimate partner violence (IPV) is now recognized as a worldwide public health problem. Surveys in developing countries show that as many as half of all women ever in unions have been beaten and/or raped by their partners (Fischbach and Herbert, 1997; Heise et al., 1999). Besides the obvious traumatic effects, battered women are more likely to report chronic and mental illnesses and less likely to seek needed
* Fax: 410-955-2303. E-mail address: [email protected].
0049-089X/02/$ - see front matter Ó 2002 Elsevier Science (USA). All rights reserved. doi:10.1016/S0049-089X(02)00062-5
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care for themselves (Heise et al., 1994; Plichta and Falik, 2001) or their children (Rosales Ortiz et al., 1999). Although a number of individual-level IPV risk factors have been identified the underlying causes are not well understood. In this paper I explore the social dimensions of the problem. I depart from the intriguing observation that IPV is differentially distributed across localities and that this clustering persists even when household and individual risk factors are controlled (Counts et al., 1999). At the community level, unemployment rates, per capita income, presence of social organizations, and other exogenous characteristics are known to affect individual IPV probabilities (Koenig et al., 1999; OÕCampo et al., 1995), but these factors do not fully explain the clustering. Other social forces must be at work. The variable IPV patterns, suggest some researchers, differentiate places where prevailing gender norms are or are not enforced (Campbell, 1999; Straus, 1998). Though norms are unobservable and difficult to operationalize, the fact that they are socially produced and reproduced explains why normative behaviors are autocorrelated (Bunton et al., 1991). This behavioral interdependence points to a prior but testable research question: Do the revealed IPV experiences of nearby women affect the likelihood any one woman experiences IPV? In other words, are IPV outcomes clustered because those specific behaviors in that specific context are autocorrelated? In this study I use recently developed social interaction models to test this proposition. The data are from a 1995 Demographic and Health survey in Colombia, a rapidly developing country with a well-documented IPV problem that has undertaken significant IPV control efforts in recent years (Ordonez et al., 1995). Below I sketch three theoretical explanations of why IPV might be autocorrelated.
2. IPV risk factors Field research in Latin America and elsewhere shows that IPV victims tend to be young, impoverished, poorly educated, socially isolated, multiparous women who are dependent on their abusive spouses for their survival (Fournier et al., 1999; Gonzales de Olarte and Gavillano Llosa, 1999). The perpetrators are also likely to be poor and uneducated, to use alcohol and to firmly control household decisionmaking (Ellsberg et al., 2001; Heise et al., 1999). Women in consensual versus formal unions are at greater risk, a reflection perhaps of less long-term commitment to the relationship (Hotaling and Sugarman, 1990). The observation that IPV tends to be more common among adults who were themselves abused, or who witnessed similar behavior in the home as children, suggests it is learned behavior (Kalmuss, 1984; Moreno Martin, 1999). Feminist, cultures of violence and exchange theories have all been invoked to explain IPV as a social phenomenon; however, empirical findings do not consistently support any of these (Campbell, 1999). What is consistent is the observation that IPV is differentially distributed across social space within cultures. There are several substantive theories that might explain this clustering. Differential association theory (Sutherland, 1947; Sutherland and Cressey, 1978) holds that deviant behaviors are learned and reinforced wherever the individual receives an excess of criminal versus conventional definitions from others in the community. It
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presumes society contains heterogenous groups generating normative conflict. Social disorganization theories, in contrast, maintain that deviance is more common in neighborhoods where neighbors do not know one another and do not act in unison to maintain social order (Sampson et al., 1997; Shaw and McKay, 1942). Both theories emphasize the situational aspects of deviance—the idea that certain places are more criminogenic than others. However, both impose the strong assumption that actors understand that IPV behavior is deviant. Alternatively, neighborhood effects on IPV behaviors could reflect a more general social learning process (Bandura, 1971). In this view, high-IPV neighborhoods are places where individuals observe influential others being rewarded for engaging in the behavior and adopt it themselves. A low-IPV neighborhood is one where IPV perpetrators are socially ostracized or otherwise punished, making it less likely others will reproduce the behaviors. Montgomery and Casterline (1993) invoked social learning theory to explain why contraceptive choice is often heterogenous across adjacent communities. Late adopters observe the outcomes of early adopters and emulate their choices, leading to a particular mix of methods within a neighborhood that persists over time. Though the data do not permit a direct test of any of these theories, differential association, social disorganization, and social learning theories all have in common the concept of reciprocal behavior, or endogenous social effects, wherein individualsÕ choices both determine and are determined by the reference groupÕs prior choices. Endogenous effects, loosely defined, would cause IPV behaviors to be autocorrelated. Little quantitative work has been done on the social aspects of IPV. In a study of 76 Baltimore census tracts, OÕCampo et al. (1995) found moderate clustering of IPV risk among pregnant women. Tract-level unemployment and per capita income measures were correlated with individual IPV risk, suggesting that stressful living conditions increase IPV incidence. Theoretically, such stress erodes menÕs status and diminishes their coping resources, increasing the likelihood they will abuse their partners (Gelles, 1993). Other evidence suggests that struggles to redefine gender norms determine IPV risk. A second multilevel study of 179 rural communities in two different areas of Bangladesh also found clustered IPV behaviors (Koenig et al., 1999). The proportion of community women belonging to savings and credit associations and the mean score on a womenÕs empowerment measure—proxies for gender norms—were both negative IPV predictors. These contextual effects were present only in the less traditional area, where overall IPV levels were lower. On the individual level, neither association membership nor empowerment affected IPV risk; however, both were positive IPV predictors in the culturally more conservative area. The authors conclude that women are at greatest risk in areas where gender norms are in flux. Ideally, one could use police or other contextual data to test whether a womanÕs IPV reports are conditional on the reported IPV behaviors of others in her community. In their absence I use a within-sample measure, the non-self group mean, to represent the level of IPV in each study community. Recent empirical work using mean group behaviors to predict individual behaviors include studies of household rice consumption in Indonesia (Case, 1991), crime in American cities (Case and Katz, 1991) and delivery care and child immunization in Colombia and Paraguay (McQuestion, 2001). In each of these studies the independence assumption was
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relaxed and behavioral dependencies were explicitly modeled using cross-sectional data. Below I describe such a model.
3. Methods 3.1. Model I conceptualize norms as one class of endogenous social effects wherein an individualÕs choice is conditional on the revealed behaviors of influential others (Erbring and Young, 1979). I operationalize this feedback process as a neighborhood-level latent variable or random effect. Recently, Durlauf (2001) demonstrated that such endogenous social effects are estimable in nonlinear models using observational data and can be represented by the group mean outcome. The latter represents a rational actorÕs beliefs about the choices other actors have made, given her information set. This expected average choice effect is weighted by the magnitude of direct interactions she has with these actors and is independent of individual preferences, attributes, and exogenous group characteristics. Specifying a binary model obviates the identification problem endogeneity poses in linear models (Manski, 1995). Following Durlauf, I use maximum likelihood1 to fit the following two-level random intercept logistic model: yij ¼ pij þ eij ; logitðpij Þ ¼ b0j þ b1 X1ij þ b2 WYji ; b0j ¼ c00 þ c01 z1j þ u0j ; eij Nð0; 1Þ; covðXij ; eij Þ ¼ 0; u0j Nð0; r2u Þ; covðz1j ; u0j Þ ¼ 0; covðeij ; u0j Þ ¼ 0; where pij is the probability respondent i in cluster j experiences IPV outcome yij , eij is an individual error term, and b2 and b1 are parameters measuring, respectively, the social interaction effect and individual effects due to covariates Xij . b0j is a random cluster-level intercept, c00 and c01 are parameters, z1j is a dummy variable and u0j is a cluster-level random effect. W is a weightingPmatrix assigning the mean outcome Yji to each respondent i in cluster j such that wi1j is in the interval ½0; 1. I thus use geographical proximity to represent social distance: Responses of those in the same sample cluster (census tract) are weighted one; all others are weighted zero. This nonself mean measure avoids the simultaneity bias that could result from having the same individualÕs outcomes on both sides of the equation. 1
I used the Stata macro gllamm (StataCorp, 2002).
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My analytical strategy is to decompose, then identify the random effect. If u0j is insignificant in this model there is no cluster-level autocorrelation of that behavior. If the non-self mean measure is insignificant then there are no substantive grounds for inferring an endogenous social effect. However, if both u0j and b2 are significant and if u0j diminishes when WYji is added to the model, then, by deduction, the revealed behaviors of others explain some of the clustering of outcome Y in that collectivity. Such a finding would support a theory in which social norms or related endogenous social processes determine individual IPV outcomes. 3.2. Variables I analyze data from ColombiaÕs 1995 Demographic and Health Survey (DHSIII). The survey incorporated a two-stage sampling strategy in which census tracts (about 1000 population) were randomly chosen from a population-weighted random sample of municipios. Within each sampled tract households were systematically sampled until the requisite number of 10 reproductive-aged women were interviewed. This sampling design makes it likely that most or all respondents in the same sample cluster know and to some degree monitor each othersÕ behaviors—a necessary assumption for inferring endogenous social effects. Interviewers posed a series of IPV questions to all women currently in unions.2 I model two of these: ‘‘Has your current husband/partner ever hit you?’’ and ‘‘Has your current husband/partner ever forced you to have sex?’’ Responses to both questions were observed for 6116 women in 991 of 1005 clusters. An additional 15 women declined to answer the beating question; I include their responses to the coerced sex question. I represent the two outcome variables as binary indicators coded one if the woman reported the behavior, and zero otherwise. Independent variables in both models include the respondentÕs age, number of live births, an indicator for consensual (versus married) union status, and levels of schooling for both partners. The schooling variables are coded 0 for no formal education, 1 for completed primary, 2 for completed secondary, and 3 for any higher attainment. I include occupational prestige scores for respondents and their partners as measures of employment and social status.3 The scores assign the following values: agricultural self-employed ¼ 1; unskilled and agricultural wage labor ¼ 2; services and skilled labor ¼ 3; clerical and sales ¼ 4; administrative, and professional ¼ 5. I represent household socioeconomic level with a dummy variable indicating a dirt floor. To control for geographic variation I include DHS-defined indicator variables for rural–urban residence and region ðn ¼ 5Þ.
2
Given their sensitivity, the IPV questions were asked confidentially. However, in a few cases ðn ¼ 191Þ interviewers were forced to ask them in the partnerÕs presence. Ellsberg et al. (2001) reported significant negative biases in IPV responses when partners were present a similar household survey in Nicaragua. To test this I added an indicator variable for partnerÕs presence but it was insignificant in all models. 3 The DHS incorporates two-digit occupational categories developed by the Colombian Department of Administration and Statistics (DANE). I arbitrarily combined these into the five categories.
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Table 1 Descriptive statistics for IPV respondents currently in union, Colombia DHSIII (1995) Variable
Mean
SD
Ever coerced sex Ever beaten Consensual union RespondentÕs age (yrs) Live births Respondent’s level of schooling None Primary Secondary Higher Partner’s level of schooling None Primary Secondary Higher Respondent employed Respondents occupational prestige PartnerÕs occupational prestige Dirt floor Rural residence Non-self cluster mean coerced sex Non-self cluster mean ever beaten
0.05 0.19 0.47 32.5 2.83
0.216 0.395 0.499 8.469 2.124
0.04 0.43 0.44 0.09
0.200 0.495 0.496 0.280
0.06 0.41 0.40 0.13 0.53 1.80 2.78 0.17 0.31 0.049 0.194
0.229 0.491 0.489 0.337 0.50 1.819 1.097 0.373 0.461 0.104 0.194
ðn ¼ 6131Þ
The social interaction measures are the numbers of other women in each sample cluster who responded positively to each IPV item, divided by the total number of respondents in the cluster. I compute binary indicators from these proportions. Seventy-two percent of respondents lived in clusters where no other women reported coerced sex and 33% lived in clusters where no one else reported beatings. Given the rarity and skewed distribution of coerced sex, I coded this indicator ‘‘1’’ for all who lived in a cluster where at least one other woman reported coerced sex. Beating was more common and not as skewed so I coded this indicator ‘‘1’’ if the cluster proportion was above the sample mean. I expect both these terms to be signed positively. To aid estimation I center all continuous variables by subtracting individual values from their sample means. Descriptive statistics for all variables are shown in Table 1.
4. Results 4.1. Coerced sex Models 1 and 2 in Table 2 show multivariate logistic regression results (odds ratios) for the forced sex outcome. Looking first at the household-level model (Model
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Table 2 Logistic IPV models, women 15–49 currently in unions, Colombia DHSIII, 1995 Coerced sex Model 1 Married (comparison) Consensual union RespondentÕs age (yrs) No. live births RespondentÕs level of schooling PartnerÕs level of schooling RespondentÕs occupational prestige PartnerÕs occupational prestige Durable floor (comparison) Dirt floor Urban residence (comparison) Rural residence Region No reports (comparison) One or more other reports of coerced sex Below sample mean (comparison) Cluster beating mean above sample mean ru )2log-like n
Beating Model 2
Model 3
Model 4
1.000 1.472 1.003 1.070 0.970 0.830 1.054
1.000 1.448 1.003 1.060 0.976 0.834 1.053
1.000 1.154 0.983 1.168 0.784 0.939 1.011
1.000 1.142 0.985 1.159 0.799 0.953 1.014
1.015 1.000 0.702 1.000 0.709 1.070
1.017 1.000 0.702 1.000 0.733 1.066 1.000 1.349
0.922 1.000 0.902 1.000 0.689 1.039
0.931 1.000 0.898 1.000 0.712 1.034
1.000 1.643 0.270 )1170.22 6131
0.000 )1169.24 6131
0.233 )2909.86 6116
0.000 )2893.05 6116
*
p < :10. p < :05. *** p < :01. **
1), the log-odds of coerced sex were 47% higher if the union was consensual, and the odds increased by 7% with each live birth. Maternal education had a negative but insignificant effect; however, the more educated the partner, the less likely he ever forced the respondent to have sex. Neither occupational prestige measure was significant. Interestingly, the odds of forced sex were 30% lower for women whose homes had dirt floors and for those living in rural areas. The random effect was marginally significant in Model 1 under a two-tailed test ðp ¼ :08Þ, but its inclusion did not significantly improve model fit when compared to a fixed-effect model (not shown). Model 2 includes the social effects measure. Its effect was significant and positive. Net of all other factors, the odds of coerced sex for a woman living in a cluster where at least one other respondent reported coerced sex were 35% higher than those of a woman in a cluster where no one else reported coerced sex. Adding the social interaction measure slightly moderated the effects of consensual union and rural residence but the other fixed effects remained unchanged. Adding the mean outcome term reduced the random effect to zero-evidence of a social interaction effect. However, the likelihood ratio test between Models 1 and 2 shows that adding the interaction measure does not improve model fit.
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4.2. Beating Models 3 and 4 of Table 2 show the corresponding beating models. The strong random effect in Model 3 shows that beating was also clustered at the community level. In contrast to the coerced sex model, specifying the random effect significantly improved model fit (not shown). As with forced sex, consensual union status and each live birth increased the likelihood of beatings. However, the effect of consensual union status on forced sex was three times greater. Two of the significant sex model factors—partnerÕs schooling and dirt floor—were not associated with beating. Conversely, respondentÕs age and education conferred protective effects for beating but not for sex. Each level of respondentsÕ schooling decreased their odds of beating by 22%. Likewise, the higher the partnersÕ occupational prestige,4 the less likely they ever beat their women. Rural residence had the same negative association with beatings as with coerced sex. This accords with other studies which have shown IPV is more frequent in urban areas (Kaufman and Zigler, 1993).5 Overall, the fixed effects agree with those reported in past studies. The similarity of risk factor effects in both sexual coercion and beating models suggests these two forms of IPV are related. In Model 4 the social interaction indicator is both positive and significant and again reduces the random effect to insignificance. For a woman in a locality where the beating level exceeded the sample mean, the odds of beating were 64% higher—double the corresponding social effect on coerced sex. Adding the measure significantly improved model fit, supporting the endogenous social effects hypothesis for this outcome.
5. Discussion The results of this study show that the likelihood a woman experienced coerced sex or beatings by her current partner is associated with the proportion of adjacent women who experienced the same phenomenon. This social interaction effect explains a significant amount of unexplained variance in the case of beatings. The magnitudes of these social effects are as large or larger than that of any individual control variable. Secondly, the social interaction terms are positively signed. Comparing the two behaviors, beatings are both more common and subject to stronger endogenous social effects. Though the autocorrelation of these behaviors is clear, the theoretical implications are ambiguous. On the one hand the endogenous social effect could reflect conformity to pro-IPV norms, implying that Colombian actors learn and emulate the observed IPV behaviors of adjacent others. This would be compatible with differential association and social learning theories. On the other hand, the endogenous so4
Status inconsistency theory predicts more IPV in households where a woman has higher status than the man. I tested variables indicating greater female occupational prestige and educational attainment but these were insignificant in all models. 5 The literature is unclear on whether urban women are more socially isolated or simply more willing to report IPV.
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cial effect might signify the absence of anti-IPV norms in particular neighborhoods, due perhaps to social disorganization. Or, following Koenig et al. (1999), it may be that gender norms are being contested in these places and not others. Unfortunately, these results cannot distinguish which, if any, of the theories is correct. The fact IPV behaviors are socially mediated implies that social-structural changes may affect their incidence. Rising female employment, migration, and cross-cultural exposure, for example, could lead to diffusion of more egalitarian gender concepts across collectivities (Strang and Meyer, 1994). Although Colombia certainly has undergone such structural changes in recent decades, there is little evidence of IPV behavioral change. The mean IPV levels I report here are close to those reported in ColombiaÕs 1990 DHSII survey.6 It may be unrealistic to expect a measurable change in these behaviors over such a short period. Moreover, the reported IPV experiences could have occurred any time in the past. There are a number of methodological shortcomings in this study. The data may be affected by recall and interviewer bias. Memories of actual IPV episodes and the willingness to report them are inherently problematic (Ellsberg et al., 2001); both would be affected by disturbances in gender norms. The apparent stability of IPV may mask countervailing trends in the actual behaviors and willingness to report them. A second problem is that IPV questions were restricted to women currently in unions, selecting out those who may have left abusive relationships (Campbell and Soeken, 1999). True IPV levels are probably higher than those reported here. Thirdly, the mean outcome indicators capture revealed behaviors but they are, at best, imprecise measures of IPV norms. Weighting observations by simple propinquity ignores the complex social networks in which real actors are embedded. In addition, the measure is unlagged because the temporal sequence of IPV behaviors in each sample cluster is unknown. Establishing temporal order, argued Erbring and Young (1979), would assure that each actorÕs behavior was conditioned solely by the prior behaviors of others. Though perhaps unfeasible for a representative national sample, qualitative research or panel data would generate better measures that would enable clear causal inferences to be drawn and perhaps support a particular theory of IPV behaviors. Despite these shortcomings, the results of this study demonstrate the feasibility of modeling endogenous social effects using observational data. They also show that IPV is partly, if not largely, a social phenomenon. Efforts to reduce the problem should therefore aim to shift IPV behaviors through community-level interventions.
Acknowledgments The author gratefully acknowledges Michelle Hindin, Michael Koenig, David Bishai, and two anonymous reviewers for their constructive comments. This study was supported by Grant P30 HD06268 to the Hopkins Population Center from NICHD.
6 In DHSII, which posed the same questions, 20% of Colombian women ever in unions reported having been beaten and 10% had been forced to have sex (PROFAMILIA, 1991).
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