Wages, Violence and Health in the Household

Transcription

1 Preliminary and Incomplete Wages, Violence and Health in the Household Anna Aizer Brown University April, 2005 Household bargaining theory predicts that as a woman s income increases relative to a man s, violence against her should decrease. We estimate the impact of women s relative wage on domestic violence. To account for the endogeneity of wage we take advantage of a tradition of sex-segregation by industry to construct a measure of relative wage that is based on wage differences in industries dominated by men versus women and thus more likely driven by changes in industry demand. We find that as the wage gap decreases, violence against women likewise decreases. The relationship between relative wages and domestic violence provides an alternative explanation for the well-established finding that child health improves when mothers control a greater share of the household resources. While previous work has largely attributed these improvements to women's greater material investments in their children, we provide evidence based on birth outcomes that reductions in violence brought about by an increase in women's relative wages are responsible for a greater share of the improvements in child health than are increases in material investments. This work sheds new light on the health production process as well as observed income gradients in health, suggesting that the higher rates of violence against poor women explains 15 percent of the observed income gradients in health. aizer@brown.edu. The author thanks Janet Currie, Pedro Dal Bó, Mark Duggan, Melissa Kearney and seminar participants at Brown University and the University of Maryland for helpful comments and suggestions.

2 I. Introduction Everyday roughly 14 thousand women in the US are battered and four are killed by their intimate partners, 1 prompting former Surgeon General C. Everett Koop to label domestic violence the single most important health issue in the US. The estimated costs of such abuse which include declines in productivity, medical care, social services, housing and criminal justice expenses, are high in excess of $67 billion annually (Laurence and Spalter- Roth, 1996). Despite the prevalence and high costs of domestic violence, much remains to be understood about the economic determinants of battering. In this paper we analyze the impact of women s relative wages on the prevalence of battering. Existing theory is ambiguous with respect to the impact of relative income on violence. An economic model of bargaining predicts that as a woman s resources increase relative to those of her husband, violence against her should decline though either a reduction in violence in intact marriages or a dissolution of the marriage. Other theories, notably theories of male backlash and exchange theory, predict the opposite. Existing empirical research examining the impact of women s wages on violence using survey data has generally found a negative relationship women with lower wages experience more violence. However, this work is limited in two main respects. First, it is based on data from household surveys which are prone to underreporting. In addition, because of the relatively low prevalence of domestic violence as reported in survey data (with annual rates of roughly 2 percent), very large samples are required to generate stable estimates. Second, these studies fail to establish a causal relationship between domestic violence and women s wages by, for example, failing to account for the potential for omitted variable bias. That is, the negative relationship they find may be attributed to omitted confounding factors (unobserved attributes of women that result in both violence and lower 1 CDC 2003 Costs of Intimate Partner Violence Against Women in The US. 2

3 wages). Examples of such attributes might include alcohol and drug abuse as well as a history of abuse as a child, both of which are correlated with abusive relationships and worse labor market outcomes. 2 Nor does previous work account for the potential for reverse causality: domestic violence may cause women s earnings to decline, not the other way around. We employ two strategies to overcome these obstacles. First, we explore the possibility of using alternative administrative datasets to develop measures of domestic violence. These data include: hospitalizations for assaults, arrests for domestic violence and intimate partner homicides. We review the advantages and disadvantages of these data relative to survey data later. To overcome the second obstacle and establish a causal relationship between earnings and domestic violence, we take advantage of the fact that certain industries have traditionally been dominated by women (eg, services) and others by men (eg, construction). Increases in demand in these sectors results in an arguably exogenous increases in female and male wages, respectively. Based on these measures of wages and violence, we find that as women s relative earnings increase, violence against them declines. We find that a ten percentage point increase in the female/male wage ratio leads to an eight percent decline in violence against women. Based on this finding, we explore the impact of domestic violence on maternal and child health and the potential mitigating effects of economic resources. Previous work based mostly on data from developing countries has shown that child health improves when mothers control a greater share of the household resources (see Thomas, 1990). This finding has been largely attributed to women s greater preferences for children and increased material investments in their children, though little evidence in support of this mechanism has 2 For example, Widom and Maxfield (2001) found that abuse and neglect as a child increased the likelihood of adult criminal behavior by 28 percent and violent crime by 30 percent. See Kalmuss (1994) for evidence of the intergenerational transmission of abuse. 3

4 been offered. This research suggests another potential mechanism: reductions in violence. Violence during pregnancy can adversely affect the developing fetus both directly (via blunt trauma to the maternal abdomen) and indirectly by affecting the health behaviors of expectant mothers. Using California natality data which includes information on birth outcomes and prenatal investments in children, we explore the importance of this alternative mechanism. We find that the reductions in violence are more likely responsible for the improvements in child health as measured by birthweight than are increases in prenatal investments. This work sheds new light on the health production process as well as the observed income gradients in health. Results presented here suggest that as much as 15 percent of the observed income gradients in birthweight may be attributed violence. The rest of this paper is laid out as follows: in section II we review the existing literature on domestic violence and discuss the shortcomings of existing empirical work, in section III we describe the data and analytic approach, in section IV we present the results with respect to the impact of relative wages on violence and in section V we explore the relative impact of violence and material investments on one measure of child health birth weight; section VI concludes. II. Background on Domestic Violence A. Measuring Domestic Violence Measuring the prevalence of domestic violence is difficult. Measures are typically derived from survey data and most believe domestic violence to be under-reported in survey data. In addition to under-reporting, estimates of domestic violence based on survey data are highly sensitive to the ways in which the surveys are conducted. In a review and comparison of three surveys of domestic violence conducted in Nicaragua 4

5 which produced estimates of the prevalence of domestic violence ranging from 28 to 69 percent, Ellsberg, et al (2001) concluded that estimates of violence based on surveys are highly sensitive to methodological factors, and that underreporting is a significant threat to validity. The three main household surveys that are used to generate estimates of domestic violence in the US are: the Department of Justice sponsored National Crime Victimization Survey (NCVS) and the National Violence Against Women Survey (NVAW) and the NIMH-supported National Family Violence Survey (NFVS). 3 In addition to underreporting and sensitivity to question wording, these three surveys suffer from other limitations. The NCVS, while a large ongoing- survey (50,000 households), is a survey of crime and criminal activity and to the extent that women do not consider intimate partner violence a crime, these data will underestimate its incidence. 4 The NVAW survey is perhaps the best source of survey data on intimate partner violence. Yet its relatively small size (8000 men and 8000 women) enables only national estimates; and because it has only been fielded once, it does not enable one to track changes over time. The NFVS was one of the first surveys designed specifically to capture intimate partner violence and was fielded twice, in 1976 and It too suffers from small sample size (n=6000), preventing one from establishing small area estimates of the prevalence of domestic violence. In addition, this survey includes only married/cohabiting women and thus does not capture any of the violence that occurs 3 Other surveys also exist. These typically consist of either 1) household surveys that include some questions on domestic violence but for which violence is not the focus and 2) small surveys of battered women. Perhaps the most important example of the former is the National Survey of Families and Households (NSFH) fielded in 1987/8, 1992/4 and 2001/2. Estimates of violence generated from these surveys are generally much lower than those from other surveys. Brush (1990) discusses methodological problems associated with using the NSFH for estimates of domestic violence. 4 Recent modifications to the survey have been made in an attempt to capture behavior that might not be considered criminal. As a result, estimated rates increased after

6 between non-cohabiting partners or ex-spouses, affecting the generalizability of these results. Given these limitations, it is not surprising that researchers have offered differing estimates of the prevalence of domestic violence. Arguably the most reliable estimates come from the NVAW survey. In an analysis of these data, Tjaden and Thoennes (1998) calculate an annual incidence of 2 percent and conclude that 25 percent of women have been raped or assaulted in their lifetime by an intimate partner and that one third of these assaults resulted in injury. In addition, they conclude that intimate partners are responsible for three fourths of all violence against women over the age of 18. As expected, estimates based on the NCVS put the estimates of domestic violence considerably lower (at.5 percent annually). However, the NCVS is the only survey that allows one to examine trends over time. Data from provided by the BJS and shown below suggest that the rates have declined considerably since 1993, from 10 per 1,000 to 5 per 1,000, though increasing slightly towards the end of the period. Interestingly, the rate for males is likewise declining over this period, but the initial levels of intimate partner violence among men are considerably lower than those for females. 5 5 Due to a change in survey design, estimates prior to 1993 are not comparable to those obtained post

7 This downward trend is likewise witnessed in the hospitalization and homicide data for California, but not in the rates of arrests for domestic violence which remain roughly constant (as discussed later). One possible reason for this is likely that conditional on abuse, women appear to have become increasingly more likely to report domestic violence to the police over this period. According to survey data, in 1993, 48 percent of women reported the abuse to the police and by 1998, the number had increased to 59 percent (Rennison and Welchans, 2000). A number of risk factors associated with domestic violence have been identified in household surveys. Based on data from the NCVS, Rennison and Welchans (2000) report that black women are at significantly greater risk of violence (35 percent higher rates), as are young women between the ages of 20 and 24 who report annual victimization rates of 21 per 1000 (compared to average rates of 4-6 per 1000). In addition those in the lowest income categories report rates of 20 per 1000 versus those in the highest income categories who report rates closer to 3 per These previous findings are consistent with trends in California hospital assault data, as discussed later. B. Theoretical Background Theory is ambiguous with respect to the nature of the relationship between women s economic dependence and domestic violence. Economic theories of bargaining generally do not incorporate violence. Two exceptions are Tuachen, Witte and Long (1991) and Farmer and Tiefenthaler, (1997). The latter yields the comparative static results that as a woman s financial resources increase relative to her husband s, so too does her utility at the threat point, thereby increasing the probability that she will leave 7

8 and thus end the violence, but also decreasing the amount of violence in intact families. In this model of non-cooperative bargaining, men are the only decision-makers. Their utility is a function of consumption and violence. That is, they take pleasure in battering their wives. Women s utility is likewise a function of consumption and violence and they derive negative utility from being battered. Men in essence purchase the right to batter their wives by transferring resources to them. Women choose to accept the transfer (and the battering) or end the relationship. Women s relative income affects the level of violence in the following manner: assuming declining marginal utility of consumption, as her own income increases, the price of violence likewise increases as she requires a larger income transfer for the same level of violence. Knowing this, men can either lower the level of violence within the relationship or the relationship will end. 6 Yet others have hypothesized that as women s financial independence increases, violence against them should increase. Two mechanisms have been put forth. The first is purely symbolic and can be characterized as a theory of male backlash against increasing female independence. According to Macmillan and Gartner (1999), a wife s independence signifies a challenge to culturally prescribed norm of male dominance and female dependence. Where a man lacks this sign of dominance, violence may be a means of reinstating his authority over his wife. (p949). The second mechanism follows from exchange theory and views domestic violence as one of the two sides of a reward/punishment approach to influence (Molm, 1989). Under this scenario, individuals possess two sources of power: transferring resources (rewards) and violence (punishment). As a husband s ability to influence his wife s behavior by transferring resources (rewards) diminishes when his income decreases relative to hers, he is more 6 This model is particular case of a bargaining model in the sense that men are the only decision-makers. 8

9 likely to rely on punishment which may include violence. In this way, an increase in women s income relative to men s may result in an increase in violence against them. Theories of male backlash and exchange theory which predict that increases in women s relative wage lead to an increase in violence are problematic because they ignore the individually rational constraint faced by women in abusive relationships. Women have no incentive to remain in relationships in which their partners abuse them and they do not receive financial transfers. That is, these women are likely to end the match if transfers decline and abuse continues or escalates. In the next section we review the results of previous empirical work that examines the relationship between income and violence. C. Previous Empirical Findings A number of researchers have examined the relationship between women s wages, women s relative wage, or employment and domestic violence, with differing results. Many rely on small, unrepresentative datasets. For example, Tauchen, Witte and Long (1991) find that for low and middle income families, increases in income lead to a reduction in violence, but for high income families in which women earn the bulk of the income, they serve to increase violence. Their sample consists of 125 California women who had been the victims of domestic violence. Farmer and Tiefenthaler (1997) analyze two datasets of victims of domestic violence. The first is a panel consisting of 165 women, the second a cross section of 340 observations. They find that women with higher monthly income experience fewer incidences of violence. Their results with respect to male income, however, are less consistent. Increases in husbands earned 9

10 income lead to a reduction in violence, but increases in unearned income lead to an increase in violence. Work based on larger, more representative samples of women include Gelles (1976) who finds that the fewer resources a woman has, the less likely she is to leave an abusive relationship based on the NFVS. Macmillan and Gartner (1999) look at the impact of women s employment status conditional on the employment status of her partner using a sample of 12,000 Canadian women. They find that for women, being employed increases the odds of being victimized when her husband is unemployed, but decreases the odds if he is employed. Finally, work by Bloch and Rao (2002) incorporate asymmetric information and signaling in a model of noncooperative bargaining to explain why women from wealthy families in India are subject to greater violence by their husbands in an effort to extract more resources from her family. Bowlus and Seitz (2005) find that female employment has a large negative and significant effect on abuse. Interestingly, they also find that men are more responsive to policies designed to reduce the gains to repeat abuse than women are to policies reducing the cost of leaving violence marriages. In related work, Stevenson and Wolfers (2003) examine the impact of an increase in access to divorce on domestic violence and suicide. They find that the adoption of unilateral divorce laws lead to a decline in rates of suicide and domestic violence, suggesting that the increase in access to divorce served to increase the exit threat point in a bargaining model, thereby lowering abuse. Fertig, Garfinkel and Mclanahan (2004) find that women in states with stricter child support enforcement suffer more violence at the hands of their partners. They argue that while stricter child support enforcement may increase women s bargaining power which should decrease violence, it also increases the 10

11 likelihood contract between a women and her violence partner, thereby increasing violence. Empirically, the latter appears to have the stronger effect. While some studies are based on larger, arguably more representative datasets, all are subject to the criticism that the measures of employment and income analyzed are endogenous and therefore subject to potential omitted variable bias or reverse causality. As such, no causal inference can be made. III. Data and Methods Impact of Wages on Violence The methods employed here are designed to overcome the two main drawbacks of the existing work on domestic violence and relative wages: 1) the small and often unrepresentative sample sizes and reliance on self-reports, and 2) the endogeneity of wages. To overcome the former, we use alternative measures that do not rely on selfreports of intimate partner violence and are based on a large sample of women. The measures of domestic violence are drawn from California administrative data and include: arrests for domestic violence, female intimate partner homicides and hospitalizations for assaults. These data are available for each of California s 58 counties and also include information on the race of the woman. California is the largest state in the US with a population of 34 million. California is comprised of 58 counties, 25 of which have populations in excess of 250,000 and eight of which have populations in excess of 1 million. For each county, we calculate multiple annual race-specific rates of violence for the period Summary statistics are presented in Appendix Table 1. 7 Rates are calculated by combining these data with data on annual population counts by county, gender, race and age provided by the California Department of Finance. Because evidence based on surveys suggests that domestic violence is most prevalent among women age 18-44, dropping considerably afterward, the denominators include women in this age range. Because of small sample sizes, rates are 11

12 Each source of data on violence has both advantages and disadvantages. Intimate partner homicides are relatively accurately reported but are a rare occurrence (1500 women annually in the US). 8 Arrest data will capture more of the abuse than homicide data, but will only capture women whose abuse was reported AND resulted in an arrest. Nearly half of all women fail to report their abusers to the police and conditional on reporting, fewer than 70% of reports to the police result in an arrest or formal report either due to lack of victim cooperation or police inaction (BJS, 1994). 9 In addition, over the last decade there have been substantial changes in policies regarding the arrest and prosecution of domestic violence, a point to which we return later. To counter the underreporting of the arrest and homicide data, a third data source hospital discharge data is employed. Beginning in 1990, California began collecting external cause of injury data for all hospital admissions. The external cause of injury codes include classifications for assaults by others, accidents and self-inflicted injuries. While a separate category for battering is included, we do not use this as the sole source of information on domestic violence because it is subject to the same under-reporting as survey data. Such a measure may be subject to reporting bias on the part of the victim or medical professional responsible for recording the cause of injury code. For example, medical professionals may be more likely to suspect (and report) domestic violence in women of color. Rather, we use all reports of assaults as a measure of domestic violence. 10 calculated for white, blacks and Hispanic only. Asians and all other races/ethnicities are excluded from this analysis. 8 The relationship between assailant and victim is missing for 20% of homicides. Criminologists have developed methods for imputing this missing information. 9 Mandatory arrest policies have recently been passed in a number of states and local jurisdictions. 10 Self-inflicted and unintentional injuries, such as injuries from motor vehicle accidents, are excluded. 12

13 However, this measure has two drawbacks. First, it will include assaults against women not inflicted by an intimate partner. To the extent that most violence against women is perpetrated by intimates (estimates from the NVAWS suggest 76 percent) and we can control for levels of non-intimate violence in our analysis (discussed later), we can limit any bias from such misclassification. Second, this measure will only include those assaults so severe as to require hospitalization. Data from the NVAW survey suggest that seven percent of all women who were injured as a result of physical assault by an intimate were hospitalized over night in First we compare trends in domestic violence measured with these three sources of administrative data to those calculated by the BJS based on survey data. The downward trend in domestic violence found in survey data and documented by the BJS is evident in California s hospitalization data for assaults and rates of intimate partner homicide. The rates of female hospitalization for assault decline by half over the same period from 27 per 100,000 to 14 per 100,000 (Figure 1A). Intimate partner homicide likewise declines from almost 5 per 100,000 in 1990 to just over 2 (Figure 1B). Data on arrests for domestic violence however, do not exhibit the same trend. Rather they appear to be fairly steady over this period, declining towards the end of the decade (Figure 1c). This is attributable to the fact that conditional on violence, reporting has increased over this period, as previously noted. This likely reflects important changes in policies strengthening the prosecution of domestic violence over this period, including the devotion of additional resources to police and prosecutors for dealing with domestic violence. 11 Trends for these three measures suggest that hospitalizations for assault may be the most accurate measure of domestic violence since arrests are subject to many 11 These include, for example, mandatory arrest policies that require police officers, once summoned, to arrest the offender. 13

14 changes in policy over this period for which we are unable to control and intimate partner homicides are too rare an occurrence, even in a state as large as California. What is striking about both the hospitalization rates and intimate partner homicide, however, is the over-representation of blacks: blacks were six times as likely to be admitted to the hospital for an assault as whites and were three times as likely to be killed as whites in 1990 (Figures 1A and 1B). This is likely due to a combination of factors including higher rates of domestic violence among blacks as reported in survey data (Rennison and Welcher, 2000) and even higher rates of severe violence as well as higher rates of non-intimate violence among blacks and blacks greater reliance on hospitals for medical care (Currie and Reagan, 2003). In Figure 1D we plot the ratio of intimate partner homicide to non-intimate homicide by race. Viewed in such relative terms, it is white women who appear to suffer higher rates of intimate partner violence, not black women. Many of the risk factors associated with domestic violence as captured by survey data are also apparent in the hospital discharge data. For example older women are much less likely to be admitted to the hospital for assaults (7.1 per women age versus 20 per women age Appendix Table 1). Women with Medicaid coverage, who make up less than half of all births in the state, are admitted to the hospital for assaults at rates three times that of women with private health insurance, consistent with survey data indicating that poverty is an important risk factor for abuse. In contrast, women with Medicaid are admitted to the hospital as a result of car crashes at lower rates than their privately insured counterparts. The table below contains correlations among these measures (expressed as rates per 100,000 women aged 15-44) for the period for LA county. As a check, 14

15 we also include correlations between measures of violence and admissions to the hospital for car-accidents. Assaults Arrests Homicides Car Crash Assaults Arrests Homicides Car Crash The measures of violence are all positively and strongly correlated, but not perfectly. Correlations between car crashes and the three measures of domestic violence are considerably lower, though less so for hospitalizations for assaults. The correlation between hospitalization for car accidents and assaults (0.5299) likely reflects overall downward trends in hospital utilization over this period. In Figure 1E we show trends in hospitalization for assaults and non-assault injuries. As is evident, both are declining considerably over this period, but hospitalizations for assaults more so. We account for the possibility that downward trends in the rate of hospitalizations for assaults may simply reflect declining rates of hospitalization in the analysis in two ways: we control for rates of admission for non-assault injuries in our analysis and we later redefine the measure of domestic violence as the share of total hospitalizations for injuries that are the result of an assault. Figure 1F shows that when we redefine the measure as such, the downward trend remains but is less strong. The regression results do not change (in fact, they are somewhat strengthened) when assault hospitalizations are so redefined. To overcome the second major drawback of existing work, the endogeneity of women s relative income, we take advantage of the history of sex-segregation by industry to construct a measure of relative wages that is based on wage changes in industries dominated by women (or men). Gender segregation by industry is well-established (Bayard et al, 1999). For example, 2000 Census data for California reveal that 72% of 15

16 service industry employees are women. In contrast, 90% of those employed in the construction industry are men. We develop a measure of female relative wage among the low-skilled (since domestic violence is most prevalent among this group) by dividing service wage by construction wage in each county and year for Figure 2A shows the time trend in this ratio. We argue that this is in fact an arguably more exogenous measure of the wage ratio than the actual female/male wage ratio as it reflects, primarily, changes in the demand for female and male unskilled labor and less the underlying productivities of females and males in a particular labor market. However, to control for other possible factors influencing this ratio such as an influx of migrants who are were predominantly low skilled over this period or incarceration rates that could also affect the supply of low-skilled men, we also include measures of (legal) immigration and incarceration rates by county, race and year over this period. To compare the female/male wage ratio predicted in this manner with the actual wage ratio, we plot the weekly wage ratio as calculated from the annual March CPS (Figure 2B). A comparison of Figures 2A and 2B suggest that actual and predicted female/male wage ratios follow roughly similar trends. The final dataset consists of a panel of 1914 observations (58 counties x 11 years x 3 racial groups). 13 The panel structure of the data enables identification of the impact of relative wages on domestic violence from changes within each county over time which implicitly control for all differences (observed and unobserved) between counties and enables one to avoid identification from comparisons across different counties which may 12 We also experiment with defining the male and female wages in each county by creating a weighted average wage based on how female (male) employees are distributed across occupations in each county (based on census data) and then recalculating the wage for each county and year based on wage growth in the industries dominated by females (males). The results are similar in magnitude when the wage ratio is calculated in this way. 13 There are some cells with no observations. 16

17 differ in other important ways, confounding the results. The following equation is estimated: DV cry = α + β 1 WAGERATIO cy + β 2 UNEMP cy + β 3 INC cy + β 4 RACE r + (1) β 5 VIOLENCE cry + β 6 LN(IMMIGRATION) cy + β 7 LN(INCARCERATION) cry + γyear y + θcounty c +ε cry In this equation, c indexes county, r race and y year. DV refers to the three measures of the rate of domestic violence by year, county and race (hospitalizations, arrests and homicides). WAGERATIO is the ratio of female wages to male wages, though alternative measures (the linear difference between male and female wages and the log of the wage ratio) are also considered. UNEMP is the unemployment rate (from the California Department of Finance) and INC is per capita income in the county and year. The latter two measures are included so that the impact of relative income can be identified separately from the impact of income or general economic conditions in the county. RACE is a vector of race dummies (White, Black and Hispanic white is excluded) and their inclusion controls for differences in rates of violence across races. VIOLENCE is the race-specific (non-intimate) homicide rate by county, race and year and is included to control for trends in underlying violence since previous work has established a positive correlation between non-intimate and intimate violence. Immigration and incarceration are also included to control for changes in the supply of low-skilled labor that may affect the wage ratio. Year and county fixed effects are also included to control for any unobserved fixed differences between counties and state-wide secular trends in domestic violence, respectively. The latter will control for all state-wide policy changes such as 17

18 welfare reform, changes in Medicaid eligibility or state laws regarding the prosecution of domestic violence that may affect rates of domestic violence. The above equation is estimated separately for each measure of violence and all regressions are weighted by cell size (female population by race, county and year). IV. Results Impact of Wages on Violence OLS estimates of equation (1) are presented in Table 1. The first column contains estimates of the impact of the wage ratio on the rate of assault hospitalizations (defined as the number of assaults per 100,000 women age 15-44). In the first panel of the table, we present the estimated effect of the female/male wage ratio. In the second panel of the table, the wage difference is measured as the natural log of the female/male ratio and in the third panel it is measured as the linear difference between male and female wages. Regardless of the way that the wage difference is measured, the results are the same: a decline in wage difference leads to a decline in the rate of hospitalization for assaults. The coefficient estimate in the first panel of suggests that an increase in the wage ratio of 0.06 (which was observed over this period as the wage ratio increased from 0.75 to 0.81) lead to a decline in the rate of female hospitalizations of 5.5 percent. Female hospitalizations for assaults declined by 48 percent over this period, suggesting that the increase in the female/male wage ratio accounts for roughly 11 percent of this decline. However, as evidenced by Figure 1E the number of hospitalizations for nonassault injuries is declining as well over this period, most likely reflecting (in part) changes in hospital utilization over this period. While the first column includes a control for the rate of hospitalizations for injuries that are not the results of assaults, perhaps a more appropriate measure of violence is the share of all hospitalizations for injuries that 18

19 are the result of an assault. In addition, this measure also protects against potential measurement error introduced by imprecision in the population counts. We present regressions based on this measure in column 2 of Table 1. Again, as the wage ratio increases, the proportion of hospitalizations that is the result of an assault declines. Interestingly, when this regression is run excluding controls for general trends in violent crime over this period (not shown), the estimate of the impact of the wage ratio increases by roughly a third. This suggests that part of the impact of the wage ratio on female assaults is working through more general reductions in violent crime over this period which are substantial. 14 In columns 3 and 4 we present regression estimates of the impact of relative wages on the arrest rate for domestic violence and the female intimate homicide rate. We find no significant effects on these measures and in fact, the point estimates are positive. In the case of arrests, this likely reflects the fact that consistent with a bargaining model, an increase in the wage ratio may lead to a decrease in violence but also an increase in reporting, conditional on violence. Findings based on survey data do suggest that over this period, reporting to the police conditional on violence has increased considerably. With respect to the non-significant results for intimate partner violence, this may reflect the relatively infrequent occurrence of intimate partner homicide and large variance in the data. To explore whether the results for assaults are sensitive to the linear functional form, we examine the impact of relative wage on the natural log of assaults. Because of zero values in small cells, the regressions are limited to cell sizes of at least 15,000. The results are presented in Table 2. In the first column we present estimates of the impact of the wage ratio (panel 1), log wage ratio (panel 2), and linear difference (panel 3) on the 14 In Table 3, we estimate the impact of the wage ratio on non-intimate violent crime and find a significant effect, though one much smaller proportionally than the effect on female hospitalizations for assault. 19

20 natural log of the female assault rate. The coefficient estimate suggests that an increase in the wage ratio of 0.06 lead to 5.5 percent decline in female hospitalizations. The coefficient estimate of in panel 2 of the same column can be interpreted as an elasticity, and is quite high. In the second column we present estimates of a regression of the natural log of the male assault rate on male-female wage differences. We do this in log form in order to facilitate comparison of the regression coefficients since the rate of male assaults is considerably greater than the rate of female assaults. 15 By taking the log form of assault rates we can interpret our coefficient estimates as the impact of increasing the wage ratio on the percent decline in assaults. As is evident from column 2 of the table, the effect is negative, considerably smaller than it is for female assaults and insignificant. In column three we regress the natural log of the ratio of female assaults to male assaults on the relative wage. The estimated coefficient declines slightly for all three measures of relative wage, but is not significant in panel 2. In Table 3 we present estimates of the impact of wage differences on four additional outcomes for which we expect small or no effects of wage differences: female suicide rates, rates of unintentional injuries, car crashes as well as non-intimate homicide rates. There does not appear to be any significant effect of the wage ratio on the female suicide rate, unintentional injuries or car crashes among women age (columns 1-3). In fact, the point estimates, though insignificant, are all positive. In contrast, there does appear to be a negative and significant effect on the rate of non-intimate homicides (column 4). However, the impact is roughly half the size of the impact on female hospitalizations for assault: the increase in the female to male wage ratio from.75 to 0.81 over this period leads to a 5.4 percent decline in female assaults but only a 3 percent 15 There is a negative and significant effect of the wage ratio on the rate of male assaults but the effect is smaller (proportionately) than for female assaults. 20

21 decline in non-intimate homicides. In the last column, column 5, when the natural log of the non-intimate homicide rate is regressed on the wage ratio, the coefficient estimate is smaller and is no longer precisely estimated. The finding that declines in the male-female wage gap lead to a decline in domestic violence suggests that in addition to more equitable redistribution of resources, policies that serve to narrow the male-female wage gap also reduce violence and the costs associated with it. In addition, the relationship between maternal income and violence may also provide an alternative explanation for the well-established finding that child outcomes improve when mothers control a greater share of the household resources. Previous work has largely attributed these improvements to women's greater material investments in their children (Thomas, 1990), though this work has primarily been conducted in the context of developing countries. Given existing evidence that suggests that violence during pregnancy has a negative effect on birth weight, the results presented here suggest an alternative mechanism linking maternal income and birth outcomes: reductions in violence. The relationship between income and violence, more generally, may also serve to explain some of the observed infant health gradients. In the next section, we explore the impact of domestic violence on maternal and child health. V. Women s Wages, Violence and Child Health A. Background A marital bargaining model that incorporates children yields important predictions regarding women s income and the allocation of household resources to children. In bargaining models, if mothers exert stronger preferences for their children than fathers, then as their income (and bargaining power) increase, household allocations 21

22 to children should likewise increase. Previous empirical work on intra-household allocation has largely supported bargaining models over common preference models. Seminal work by Thomas (1990) based on survey data from Brazil found that unearned income in the hands of a mother has a bigger effect on her family s health than income under the control of a father. The positive relationship is often attributed to increased expenditures on children: given equal increases in maternal and paternal income, the former results in larger expenditures on children than the latter. More recent work by Duflo (2000) found that an increase in pension payments among women in South African households lead to improvements in the health of girls in the household (as measured by height and weight for height), but not boys. In contrast, an increase in pension payments among men did not have any affect on the health of children. These findings, however, are based on data from developing countries. In Table 4, we show that birth outcomes in California (as measured by the proportion born low birth weight) follow a similar pattern among low income women: as the relative wage increases, the proportion born low birth weight declines. 16 The coefficient estimates on the interaction between the relative wage and share Medicaid and the relative wage suggest that if the wage gap were to increase by six percentage points in areas with all low-income births, the share of LBW births would fall by 3 percentage points (slightly less than half). The mechanism behind this relationship, however, is not clear. Previous papers linking the distribution of resources in the household to improvements in child health often assume or conclude that an increase in material investment is responsible. Some evidence that a reallocation of resources from the father to the mother results in an 16 Perry (2005) explores the relationship between women s earnings and fertility decisions. Her extension of the Gronau model of home production predicts that the substitution effect of a wage increase should be stronger among low-income women. Her empirical findings are consistent with this: the fertility of lowincome women falls when labor market conditions improve for them. Given the well-established child quantity-quality tradeoff, a reduction in fertility would be consistent with improvements in birth outcomes. 22

23 increase in material investments in children is provided by Lundberg, Pollak and Wales (1997). They find, based on survey data from the UK, that an exogenous increase in maternal income leads to an increase in expenditures of women and children s clothing. The results presented here which indicate a negative relationship between women s relative income and violence suggest that reductions in violence may be another mechanism behind the finding that an increase in women s relative income results in improved child outcomes which has not previously been considered. B. The Relationship Between Violence and Birth Outcomes Studies of pregnant women have provided varied estimates of the prevalence of domestic violence that range from 0.9% to 20.1% (Gazmararian, et al 1996). 17 In addition, a number of studies have suggested that violence often initiates or escalates during pregnancy. For example, Stewart and Cecutti (1993) and Helton, McFarlane and Anderson (1987) found that 12 and 14 percent of women reporting violence during pregnancy did not experience it any time prior to their current pregnancy. Another study by Amaro, Fried, Cabral and Zuckerman (1990) reported that 88 percent of women reporting violence during pregnancy did not experience it in the three months prior to their current pregnancy. Psychologists have offered one possible explanation for the increase in violence during pregnancy: sexual jealousy inspired by the uncertainty of paternity. In their article Pregnancy as a Stimulus for Domestic Violence authors Burch and Gallup interviewed 258 men convicted of spouse abuse and found that the frequency and severity of abuse directed toward pregnant partners was double that 17 Examples include Hillard (1985) who found that 3.9 % of 742 prenatal women reported abuse during pregnancy; Helton, McFarland and Anderson (1987) found that 8% of 290 pregnant women reported violence. Berenson, Stiglich, Wilkinson and Anderson (1991) found that 5.5% reported abuse during the current pregnancy and in a postpartum sample of 488 women, Campbell, Poland, Waller and Ager (1992) found that 8.3% of the women reported abuse during the pregnancy. 23

24 directed toward partners who were not pregnant. Sexual jealousy was also greater in the case of men with pregnant partners than those who were not. A number of studies in the medical literature have documented a positive correlation between domestic abuse during pregnancy and poor birth outcomes. For example, Valladeras (2002) found that 22 percent of mothers with LBW infants experienced physical abuse as opposed to 5 percent of full weight infants, controlling for potential confounders such as age, parity, socio-economic status and smoking. In a metaanalysis of 8 studies, Murphy el al (2001) found that women who reported abuse during pregnancy were more likely than nonabused women to give birth to a baby with LBW (OR 1.4). Evidence of direct and indirect mechanisms linking abuse and LBW have been identified in the medical literature (see Newberger et al 1992). With regards to the former, abuse resulting in blunt trauma to the maternal abdomen can cause abruptio placentae, fetal fractures, rupture of the maternal uterus, liver, spleen and antepartum hemorrhage. Trauma can also cause uterine contractions, premature rupture of membranes and infection. In a 1990 study of 205 pregnant women admitted to the hospital for non catashtrophic trauma (41 of whom were admitted as a result of assault and of those 36 were assaulted by an intimate partner), Goodwin and Breen found that roughly 9% resulted in pregnancy complications including premature labor, placental separation, fetal injury and death. Notably, the authors found that the complications were more likely if the trauma resulted from assault (17 %), as opposed to motor vehicle accidents or falls (6%) and that the complications were more severe for assaults than for other mechanisms of injury. Finally Newberger et al (1992) suggest that abuse may lead 24

25 to the exacerbation of chronic illnesses such as hypertension, diabetes or asthma which can negatively affect the fetus. With regards to the indirect effects of abuse during pregnancy on birth weight. Newberger et al (1992) outline four possible routes: (1) elevated stress, (2) inadequate access to prenatal care, (3) behavioral risks such as smoking, and (4) inadequate maternal nutrition. Indeed, abuse during pregnancy has been found to be correlated with low SES, stress, substance abuse, fewer prenatal care visits, poor maternal weight gain, anemia, and unhealthy diet and these variables have also been identified as risk factors for LBW (see for example, McFarlane and Parker, 1996). However, these studies fail to establish a causal relationship between abuse and LBW, but rather simply document correlations between abuse and behaviors known to results in poor birth outcomes. In this study, we seek to establish whether a causal relationship between violence and birth weight is supported by the data. To explore the relative importance of violence and prenatal investment as the mechanism behind the relationship between maternal income and child health and wellbeing, we turn to California vital statistics data for the period We consider prenatal care a measure of maternal investment in children. It typically represents a considerable time investment, and, to a lesser extent, a financial investment (given the high rates of insurance coverage for pregnant women over this period, out of pocket payments are minimal). Existing literature on paternal investment and child well-being suggests that time investments may be more important than material investments in developed countries (Mayer, 1997; Aizer, 2004). We estimate the relative impact of time investments (as measured by prenatal care) and violence on birth outcomes. 25

26 Though no one (to our knowledge) has examined the relationship between violence and birth outcomes, there is a large body of work examining the causal relationship between prenatal care and birth outcomes. Evans and Lien (2005) estimate a causal relationship by instrumenting for prenatal care using data on a bus strike in Pennsylvania. Arguing that the lack of public transportation is likely to exogenously affect women s ability to receive prenatal care, they provide IV estimates of the impact of prenatal care on birth outcomes using the bus strike as an instrument. They find that the point estimates are similar to those obtained with OLS, but less precisely estimated, suggesting little or no favorable selection. Other work by Joyce, Gibson and Colman (2004) concludes that positive estimates of the impact of prenatal care on birth weight are very much inflated by favorable selection on prenatal care. In this work we estimate the relative importance of investment (prenatal care) and violence on birth weight, instrumenting for both prenatal care and violence. C. Estimation 1. Data Natality data for the period for the state of California are aggregated and linked with data on violence at the race, county, and year level. The natality data for these years include between 525 and 600 thousand births per year for a final sample of 5 million of births and includes information on the county of residence of the mother, age, race, education, marital status and source of payment as well as information on the birth outcomes (gestation and birthweight). The data also include information on the month prenatal care was initiated and the number of prenatal visits. This is the only direct measure of maternal investment in the child available in the data. Because the number of 26