The author(s) shown below used Federal funds provided by the U.S.

Department of Justice and prepared the following final report:

Document Title: Economic Distress, Community Context and

Intimate Violence: An Application and Extension

of Social Disorganization Theory, Final Report

Author(s): Michael L. Benson ; Greer L. Fox

Document No.: 193434

Date Received: March 2002

Award Number: 98-WT-VX-0011

This report has not been published by the U.S. Department of Justice.

To provide better customer service, NCJRS has made this Federallyfunded grant final report available electronically in addition to

traditional paper copies.

Opinions or points of view expressed are those

of the author(s) and do not necessarily reflect

the official position or policies of the U.S.

Department of Justice.
Economic Distress, Community Context and Intimate Violence: 

An Application and Extension of Social Disorganization Theory 

Final Report 

Submitted by: 

Co-Principal Investigators: 

Michael L. Benson 

University of Cincinnati 

Greer Litton Fox 

University of Tennessee 

This project was supported by Grant ## 98WTVXOO11 fiom the National 

Institute of Justice, Office of Justice Programs, U. S. Department of 

Justice. Points of view in this document are those of the authors and do 

not necessarily represent the official position or policies of the U. S. 

Department of Justice. 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
Table of Contents 

List of Tables ...................................................................................................................... 3 

Appendices .......................................................................................................................... 5 

CHAPTER ONE ............................................................................................................... 6 

INTRODUCTION ............................................................................................................. 6 

Overview of the Report ................................................................................................... 6 

Research Questions ......................................................................................................... 8 

CHAPTER TWO ............................................................................................................ 13 

METHODS ...................................................................................................................... 13 

Design and Content of the National Survey of Families and Households .................... 13 

Variables Abstracted fiom the NSFH ........................................................................... 15 

Conflict and Violence ............................................................................................... 16 

Economic Status and Experiences ............................................................................ 20 

Socio-Demographic Controls .................................................................................... 22 

Variables Abstracted fiom the U . S . Census ................................................................. 26 

Measures of Community Context ................................................................................. 27 

Violence Against Women in the NSFH ........................................................................ 31 

CHAPTER THREE ........................................................................................................ 36 

COMMUNITY CONTEXT AND THE PREVALENCE, FREQUENCY, 

SERIOUSNESS AND DURATION OF INTIMATE VIOLENCE ............................ 36 

Conclusions: Neighborhood Conditions and Violence Against Women in Intimate 

Relationships ................................................................................................................. 42 

CHAPTER FOUR ........................................................................................................... 45 

ECONOMIC DISTRESS AND INTIMATE VIOLENCE ........................................... 45 

Measures of Economic Distress .................................................................................... 47 

Conclusions: Economic Distress and Intimate Violence Against Women ................... 62 

CHAPTER FIVE ............................................................................................................ 63 

CHANGE IN ECONOMIC DISTRESS AND INTIMATE VIOLENCE .................. 63 

Conclusions: Change in Economic Distress and Intimate Violence ............................. 67 

CHAPTER SIX ............................................................................................................... 68 

ECONOMIC DISTRESS AND COMMUNITY CONTEXT ..................................... 68 

Community Context and Economic Distress ................................................................ 70 

Economic Distress. Community Context. and the Prevalence of Intimate Violence 

Economic Distress. Community Context. and the Seventy of Intimate Violence 

Against Women ............................................................................................................ 83 

CHAPTER SEVEN ......................................................................................................... 92 

COMMUNITY CONTEXT AND MINORITIES ........................................................ 92 

Community Context, Economic Distress, and Violence Against Women ..................... 9 

Against Women ............................................................................................................ 75 

Conclusion: Economic Distress. Community Context and Intimate Violence ............. 89 

Race. Community Context. and Intimate Violence ...................................................... 93 

A Model of Intimate Violence Against Women ......................................................... 102 

Conclusions: Community Context and Economic Distress ........................................ 114 

CHAPTER EIGHT ....................................................................................................... 120 

SUMMARY OF THE PROJECT AND POLICY IMPLICATIONS ...................... 120 

Summary ofthe Project .............................................................................................. 120 

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This document is a research report submitted to the U.S. Department of Justice.
Limitations of the Data ........................................................................................... 121 

Summary of Findings .............................................................................................. 123 

Policy Implications ..................................................................................................... 126 

References ....................................................................................................................... 13 1 

2 

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List of Tables 

2.1 Constructed Census Variables ..................................................................................Zg 

2.2 Frequency Distribution of Number of Episodes of Intimate Violence Against 

Women in the Past Year, Waves 1 and 2 of the National Survey of Families 

and Households ..................................................................................................... 32 

2.3 Frequency Distribution of Injuries through Intimate Violence Against 

Women, Waves 1 and 2 ......................................................................................... 33 

2.4 Trajectories in Intimate Violence against Women in Continuing Couples .........,.... 34 

3.1 Percentage of Couples with Intimate Violence against the Woman by the 

Index of Concentrated Disadvantage in Deciles ..................................................... 39 

3.2 Seventy of Intimate Violence against Women by Neighborhood Type for All 

Couples and Violent Couples only ......................................................................... 40 

3.3 Maintenance of Intimate Violence Against Women by Neighborhood Type 

for Continuing Couples ........................................................................................... 43 

4.la Descriptive Statistics on Forms of Economic Distress for Wave 1 Couples .......... 50 

4.lb Descriptive Statistics on Forms of Economic Distress for wave 2 couples ............ 51 

4.2a Percentage of Couples with Intimate Violence against Women by Ratio of 

Income to Needs, Waves 1 and 2 ............................................................................ 52 

4.2b Percentage of Couples with Intimate Violence Against Women by Level of 

Household Debts, Waves 1 and 2 ........................................................................... 54 

4.2~ Percentage of Couples with Intimate Violence against Women by Subjective 

Financial Strain, Wave 2 ......................................................................................... 54 

4.2d Percentage of Couples with Intimate Violence against Women by Number 

of Periods of Unemployment, Wave 2 .................................................................... 55 

4.3a Seriousness of Intimate Violence against Women by Ratio of Household 

Income to Needs, Wave 1 ....................................................................................... 57 

4.3b Seriousness of Intimate Violence against Women by Level of Household 

Debts, Wave 1 ......................................................................................................... 58 

4.3~ Seriousness of Intimate Violence against Women by Ratio of Household 

Income to Needs, Wave 2 ....................................................................................... 59 

4.3d Seriousness of Intimate Violence against Women by Level of Household 

Debts, Wave 2 .......... ..... .- ................... .......... ... .. ............. . .... .... .... . .. .. . ..... .. ..... .._ ...... 5G 

4.3e Seriousness of Intimate Violence against Women by Subjective Financial 

Strain, Wave 2 ......................................................................................................... 60 

4.3f Seriousness of Intimate Violence against Women by Number of Periods of 

Unemployment, Wave 2 ...... ........ .. .... .............. .. . ....... . ..... ....... . .. .. ..... . ..... . ....... . ....... 6 1 

3 

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This document is a research report submitted to the U.S. Department of Justice.
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5.1 Descriptive Statistics on Change in Selected Measures of Economic Distress .. for Continuing Couples ........................................................................................... 65 

5.2 Percentage of Continuing Couples with Intimate Violence against Women in 

Wave 2 by Change in Selected Measures of Economic Distress ............................ 66 

6.la Individual Economic Distress by Neighborhood Type. Wave 1 ............................. 72 

6 . lb Individual Economic Distress by Neighborhood Type. Wave 2 ............................ 73 

6.2a Intimate Violence against Women in Wave 1 by Income to Needs Ratio 

within Neighborhood Type ..................................................................................... 77 

6.2b Intimate Violence against Women in Wave 1 by Household Debts within 

Neighborhood Type ................................................................................................ 78 

6.2~ Intimate Violence against Women in Wave 2 by Income to Needs Ratio 

within Neighborhood Type ..................................................................................... 80 

6.2d Intimate Violence against Women in Wave 2 by Household Debts within 

Neighborhood Type ................................................................................................ 80 

6.2e Intimate Violence against Women in Wave 2 by Subjective Financial Strain 

within Neighborhood Type ............................. : ....................................................... 81 

6.2f Intimate Violence against Women in Wave 2 by Number of Periods of 

Unemployment within Neighborhood Type ........................................................... 82 

6.3a Seriousness of Intimate Violence in Wave 1 by Income to Needs Ratio 

within Neighborhood Type ..................................................................................... 85 

6.3b Seriousness of Intimate Violence in Wave 2 by Income to Needs Ratio 

within Neighborhood Type ..................................................................................... 86 

6.3~ Seriousness of Intimate Violence in Wave 2 by Subjective Financial Strain 

within Neighborhood Type ..................................................................................... 86 

6.3d Seriousness of Intimate Violence in Wave 2 by Number of Periods of 

Unemployment within Neighborhood Type ........................................................... 88 

7.1 a Levels of Economic Distress and Location in Neighborhood Type by Race 

and Ethnicity. Wave 1 ............................................................................................. 97 

7.1 b Levels of Economic Distress and Location in Neighborhood Type by Race 

and Ethnicity. Wave 2 ............................................................................................. 98 

7.2a Percentage of couples with Intimate Violence Race/Ethnicity within 

Neighborhood Type. Wave 1 ................................................................................ 101 

7.2b Percentage of Couples with Intimate Violence by Race.F !hicity within 

Neighborhood Type. Wave 2 

7.3a Logistic Regression Model of Violence in Wave 1 .............................................. 107 

7.3b Logistic Regression Model of Violence in Wave 2 .............................................. 109 

7.3~ Logistic Regression Model of Violence among Continuing Couples in Wave 

2 ............................................................................................................................. 112 

4 

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expressed are those of the author(s) and do not necessarily reflect the official

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This document is a research report submitted to the U.S. Department of Justice.
Appendices 

Appendix A. Variables Abstracted fiom the U. S. Census.. . . . . . . . . . . . . . .136 

5 

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expressed are those of the author(s) and do not necessarily reflect the official

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I 

CHAPTER ONE 

INTRODUCTION 

Overview of the Report 

Violence in intimate relationships poses a serious problem to the lives, the health, 

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and the emotional well being of individuals and families. Although both men and women 

engage in physical violence against their intimate partners, women are significantly more 

likely to be seriously harmed than men (Brush 1990; Sorenson et al. 1996). For some 

women, home is not a haven of emotional security and physical safety but a place instead 

where they are physically abused by the men who supposedly love them. National 

surveys show rates of severe husband-to-wife violence to be approximately 35 per 1,000 

couples (Gelles and Strauss 1986). Analyses of the first wave of the National Survey of 

Families and Households found that 4.9 percent of men report inflicting physical violence 

on their spouses or cohabitors in the preceding year (Brush 1990). Women face a greater 

risk of assault and injury in their own homes by members of their own families than they 

do at the hands of strangers on the street (Jasinski and Williams 1998). 

The consequences of intimate violence are significant for victims, their families, 

and the community at large. Besides physical harm, many victims suffer severe 

emotional affects, increased anxiety, loss of self-esteem, depression, feelings of 

worthlessness, increased risk of suicide, sleeping disorders, and alcohol and substance 

abuse (Dutton and Painter 1993; Gelles and Harrop 1989; Gleason 1993; Kaufman, 

Kantor, and Asdigian 1996; Orava et al. 1996; Pagelow 1984; Stets and Straus 1990; 

6 

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expressed are those of the author(s) and do not necessarily reflect the official

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This document is a research report submitted to the U.S. Department of Justice.
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Straus and Gelles 1990; Umberson et al. 1998). Families may suffer economically when 

victims lose time from work and financial resources must be allocated to medical or 

psychiatric treatment (Ratner 1998). Because the family is a major transmitter of 

subcultural values, children who witness violence in the home may be harmed 

emotionally and developmentally in ways that have long lasting effects. Witnessing 

violence in the home, moreover, increases the risk that children will engage in abusive 

behavior in their own relationships later in life (Steinmetz and Straus 1974). Finally, the 

community at large experiences increased costs because battered women require the 

services of community-sponsored shelters as well as criminal justice and mental health 

systems. 

Recognizing the serious problem posed by domestic violence, Congress passed 

the Violence Against Women Act in 1994. It directed the National Research Council to 

develop a research agenda on violence against women, and the Panel on Research on 

Violence Against Women (1 996 p. 90) subsequently recommended that researchers 

should focus on “factors associated with the initial development of violent behavior, its 

maintenance, escalation, or diminution over time and the influence of socioeconomic, 

cultural, and ethnic factors.” Accordingly, this project was designed to address these 

important issues identified by the Panel. In particular, we focused on the initiation and 

subsequent trajectories in violence against women in intimate relationships, the role of 

economic distress in influencing these trajectories, and the effects of community context 

on patterns i2 violence against women. 

To conduct our analyses, we merged data drawn from waves 1 and 2 of the 

National Survey of Families and Households (NSFH) (Sweet, Bwnpass, and Call 1988) 

7 

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with census tract level data from the 1990 U. S. Census. From the NSFH, we abstracted 

data on conflict and violence among couples in the NSFH, as well as data on their 

economic resources and well-being, the composition of the household in which the 

couple lived, and a large number of socio-demographic characteristics of the sample 

respondents. From the 1990 Census, we abstracted tract level data on the characteristics 

of the census tracts in which the NSFH respondents lived. These data reflected the 

aggregate social, demographic, and economic characteristics of the tracts. Merging the 

census tract data with the NSFH survey data enabled us to investigate contextual 

variation in and correlates of domestic violence. 

Research Questions 

We investigated five specific research questions: 

How do measures of community context correlate with the prevalence, fiequency, 

seventy, and duration of intimate violence? 

To what extent do different forms of economic distress influence the use of violence 

by men against women in intimate relationships? 

How do changes in economic distress influence the initiation, maintenance, 

desistence, and escalation of violence by men in intimate relationships and to what 

extent do known precursors of violence mediate the impact of changes in economic 

distress on violence? 

Does change over time in economic distress influence intimate violence 

independently of comrhunity context aid household characteristics or does it interact 

with these factors to produce varying risk levels for women located in different types 

of areas and households? 

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8 

position or policies of the U.S. Department of Justice.

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+ Are the effects of community context and economic distress on intimate violence 

more pronounced for minority women, or do they operate independently of race and 

other demographic characteristics? 

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Community Context, Economic Distress, and Violence Against Women 

Enhanced understanding of intimate violence against women requires multi-factor 

causal models (Panel, 1996). As the Violence Against Women Panel notes, such models 

should take into account both its structural and contextual causes as well as personality 

and cognitive factors. Numerous studies have investigated how personality factors shape 

intimate violence (Hamberger and Hastings 199 1 ; Hart et al. 1993; Dutton 1995). The 

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aspects of dyadic relationships that lead to violence by men against women have also 

received attention from researchers (Browne 1987; Holtzworth-Munroe and Anglin 199 1 ; 

Gottman et al., 1995; Jacobson and Gottman, 1998). But little is known about how life 

transitions (e. g., becoming unemployed) and situational level factors (e. g., the duration 

of a union) interact with community context to affect violent offending and victimization 

(Sampson and Lauritsen 1994). Longitudinal studies of how socioeconomic, cultural, 

and ethnic factors influence developmental trajectories of violence against women are 

particularly needed. 

Fagan and Browne (1 994) hypothesize that life transitions that cause economic 

distress (e. g. becoming unemployed) may be particularly important precipitating factors 

of violence by men against women in intimate relationships. Burgess and Draper (1989) 

likewise argue for the salience of ecological instability, which is generally indicated by 

economic constraints and job loss, in increasing the probability of marital violence under 

certain circumstances. There is some support for this hypothesis. Although intimate 

9 

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violence is found in all social classes, rates tend to be higher in families of lower 

socioeconomic status who are experiencing underemployment or unemployment (Straw, 

Gelles, and Steinmetz 1980). Recent research has found that economic distress predicts 

individual and family outcomes including marital dissatisfaction and family conflict 

(Conger et al. 1990; Fox and Chancey 1998); and this relationship is moderated by 

contextual risk and protective factors (Voydanoff and Donnelly 1998). 

Social disorganization theory posits that the aggregate characteristics of areas 

influence the likelihood of violent crime and victimization (Sampson and Groves 1989; 

Kornhauser 1978; Shaw and McKay 1942). Disorganization theorists investigate how 

aspects of neighborhood structure influence rates of crime and violence (Stark 1987; 

Bursik 1988). A long and distinguished line of research has shown that disadvantaged 

neighborhoods experience high crime rates (Byme and Sampson 1986). Neighborhoods 

that suffer from low socioeconomic status, high rates of residential mobility, high levels 

of ethnic heterogeneity, and high population density tend also to be plagued by street 

crime and interpersonal violence. 

Just as social disorganization is related to violence in general, there are a number 

of reasons why it also may be related to patterns and developmental trajectories in 

intimate violence against women. Disorganization theorists suggest that the ecological 

correlations between neighborhood characteristics and violence result from variation in 

the relative effectiveness of neighborhood informal and formal social control mechanisms 

(Smpson and Groves 1989; Bursik 1988; Komhauser 1978). The low levels cf infinrial 

social control that typify disorganized areas provide a fertile soil in which violence 

against women can flourish, because abusive men who reside in these areas are not 

10 

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expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

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cohstrained by strong normative expectations against violence. Hence, they are free to 

commit violence against their spouses and cohabitors without fear of social disapproval. 

Indeed, recent ethnographic research suggests that among some disorganized area 

residents, especially minority men, the use of violence in interpersonal relationships is 

viewed not only as normatively acceptable but as virtually required by what one observer 

has called the “code of the street” (Anderson 1990). Since by definition residents of 

socially disorganized areas are more likely to have weak social bonds to their neighbors, 

women involved with potentially abusive partners are more likely to be isolated and thus 

at greater risk of violent victimization (Stets 1991). To exit an abusive relationship 

requires resources. Women who want to leave men who beat them need money and 

friends. These resources are likely to be less accessible to women who live in poverty 

stricken and socially fragmented areas. In addition, women in disorganized areas are 

likely to live in overcrowded households, which often leads to pathological consequences 

for family relationships (Gove et al. 1979). Finally, official forms of social control are 

weaker in disorganized areas. Stark (1987) suggests that law enforcement is more lenient 

in these areas because the police view these neighborhoods as filled with morally 

disreputable people who deserve what they get. Extensive social disorganization may 

create particularly pervasive and persistent forms of violence against women. 

Although poverty and crime often go together, it would be a mistake to think that 

intimate violence is restricted to disadvantaged areas. Abusive men and victimized 

women also are found in well-to-do suburbs and small toms. Littie is k;.,own absui :IOW 

community context influences intimate violence in these places. In suburban and rural 

areas, community context may influence patterns in intimate violence in ways not 

11 

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ordinarily envisioned by disorganization theorists. For example, to explain aggregate 

level correlations between neighborhood structural characteristics and crime, 

disorganization theorists often posit intervening mechanisms, such as the ability and 

willingness of local neighborhoods to supervise the behavior of residents and strangers on 

neighborhood streets (Greenberg 1982; Sampson 1987). But intimate violence occurs 

more often, though not exclusively, in the home rather than on public streets. Because of 

the family’s special characteristics of intimacy and privacy, whether and how community 

context influences intimate violence remains an important question that h& not received 

adequate attention from researchers (Gelles and Straus 1979). 

It is also unclear exactly how economic distress is related to intimate violence. 

Economic distress may trigger intimate violence more often among well-to-do men who 

see their neighbors moving ahead while they are suffering financial difficulties than it 

does among disadvantaged men who see hard times all around them. Similarly, leaving 

an abusive relationship may be more difficult for middle class homemakers who are 

dependent on their partner’s greater earnings than it is for disadvantaged women who are 

economically equal to their disadvantaged partners. 

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position or policies of the U.S. Department of Justice.

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This report has not been published by the Department. Opinions or points of view

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CHAPTER TWO 

METHODS 

This project is a secondary analysis of data drawn from waves 1 and 2 of the 

National Survey of Families and Households (NSFH) and from the 1990 U. S. Census. 

In this section of the report, we provide a brief overview of the design and content of the 

NSFH and describe the specific variables that we abstracted from the NSFH and the 

Census for our analysis. We also describe the sub-sample of the NSFH that was used in 

this project. Both the NSFH and the U. S. Census are publicly available data sets. More 

information on the design and content of the NSFH and instructions on how to get NSFH 

data are available on the Internet at (www.ssc.wisc.edu/nsfh). Information on the U. S. 

Census can be found at (www.census.gov). 

Design and Content of the National Survey of Families and Households 

The National Survey of Families and Households (NSFH) is a nationally 

representative sample of American households. Completed in 1988, the first wave of the 

NSFH included interviews with a probability sample of 13,007 adult respondents, 

representing 9,637 households. Face-to-face interviews were conducted with a randomly 

selected primary respondent from each household. To facilitate the collection of 

sensitive information, portions of the interview with the primary respondent were selfadministered. The interviews lasted one hour and forty minutes on average. The primary 

respondent’s spouse or cohabiting partner, hereinafter referred to as the secondary 

respondent, was given a shorter self-administered questionnaire (Sweet, Bumpass, and 

Call 1988). In wave 2, completed in 1994, interviews were conducted with all surviving 

13 

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members of the original sample (n = 10,007) and with the current spouse or cohabiting 

partner of the primary respondent (n = 5,624). Interviews were also conducted with the 

spouse or partner of the primary respondent in cases where the relationship had ended (n 

= 789) (Sweet and Bumpass 1996). This project was based primarily on a sub-sample of 

households in which respondents were married or cohabiting during wave 1 or 2 or both, 

participated in both waves, and have complete data on the outcome variables. 

The NSFH has a number of design strengths that make it an especially 

advantageous data set to use to enhance understanding of the varying trajectories that 

intimate violence can take. Because of the sample size, an adequate number of 

households with intimate violence are available for analysis. Minority households and 

cohabiting couples were over sampled. The time lapse between panel waves is longer 

than in most previous longitudinal studies of intimate violence, which reduces the 

likelihood that temporary changes in behavior (such as when a husband stops abuse for a 

short time) will be mistaken for real changes in violence trajectories (Feld and Straus 

1989; Fagan and Browne 1994). The timing of the two waves of the NSFH is an added 

strength. Waves 1 and 2 closely bracket the 1990 census; thus the NSFH data can be 

merged with appropriately timed census tract information to permit the analyses of the 

tract-level socioeconomic contexts of intimate violence. Further, the period covered by 

the two waves of data collection saw pervasive deterioration in the economic fortunes of 

many American workers: stagnation or decline in earnings, flattened career trajectories, 

and substantial downsizing and outsourcing of jobs (Farley !996). Many respondents in 

the NSFH presumably experienced some degree of economic stress during the time kame 

14 

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of the survey, making the NSFH especially well suited for the investigations undertaken 

in this project. 

Another strength of the NSFH series for studying couples is its inclusion of 

replicate questions in interviews with spouses or cohabiting partners of primary 

respondents. Many question sequences include paired questions so that the resulting 

data set includes cross-referenced paired perspectives on an event or issue, allowing for 

an assessment of patterns of differential reporting of behavior. Differential reporting can 

cause methodological problems in assessments of marital violence and was taken into 

account in our development of measures of violent conflict (Szinovacz 1983; Szinovacz 

and Egley 1995; Browning & Dutton 1986). 

As noted above, the NSFH has a number of design strengths. Nevertheless, as 

with all data sets, it also has weaknesses. One particularly important shortcoming 

involves the number of households representing racial and ethnic sub-groups. Although 

minority households were over-sampled, except for African-Americans and Hispanics 

there are relatively few representatives of other racial and ethnic sub-groups in the data 

set. The small numbers of cases representing other groups makes it difficult to analyze 

their experiences individually. Hence, our treatment of minorities and intimate violence 

is unfortunately limited only to Afiican-Americans and Hispanics. 

Variables Abstracted from the NSFH 

The NSFH is an extremely rich data source, containing literally thousands of 

variables on a broad range of individual, couple-level, and household characteristics, 

events, and experiences. The interview instruments are long and complex. Different but 

overlapping instruments are used for married, cohabiting, and single primary 

15 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
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respondents, for partners and other household members, and for respondents with 

children and those without. To aid other researchers interested in replicating and 

extending our analyses, we have tried to provide suficient descriptive information so that 

the variables we used can be identified and our variable transformations reconstructed. 

For this project, we abstracted variables from both waves 1 and 2 relevant to our 

research questions. The variables can be grouped into three major categories: (1) 

indicators of conflict and violence in the couple, (2) indicators of the economic status and 

experiences of the couple, and (3) individual, couple-level, and household sociodemographic characteristics. Because of the complex nature of the NSFH survey design, 

extensive variable transformations were required before analyses could be undertaken. In 

this section, we provide an overview of the variables that we abstracted from the NSFH. 

In the following section, we describe how the variables were transformed and how 

measures were constructed for the analyses. 

Conflict and Violence 

To assess conflict and violence in a couple, the NSFH asks a series of questions of 

both partners in the couple. In Wave 1, both the primary and secondary respondents were 

asked if during the past year arguments became physical. Respondents were then asked 

how often during the past year fights with the respondent’s spouse or partner resulted in 

the respondent hitting, shoving, or throwing things at the spouse or partner, followed by a 

questions asking how often such fights resulted in the spouse or partner doing the same 

things to the respondent. There were five response categories, ranging fhm 0 to 4 or 

more. Follow up questions asked whether the respondent had been “cut, bruised, or 

seriously injured” in a fight with the spouse or partner and whether the spouse or partner 

16 

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was similarly harmed in a fight with the respondent (yes or no). It is important to keep in 

mind that for all of these questions the respondent may be either a male or a female. If 

the respondent is a male, the question asking whether the respondent hit or shoved his 

partner indicates male violence toward the female. However, the same question indicates 

female violence toward the male, if the respondent is a female. Thus, before the 

violence items can be used for analysis, they must be transformed so as to take account of 

the directionality of the violence. 

In Wave 2, the wording for the second violence item changes slightly. Rather 

than asking if the respondent hit, shoved, or threw things at the partner, the Wave 2 

question asked how often the respondent became “physically violent” with the partner. 

Otherwise the questions and response categories are the same in both waves. 

The variable wave I overdl violence indexes violence directed by the male 

against the female in a marriage or cohabiting partnership reported during the wave 1 

interview. It ranges from 0 to 4. As noted above, respondents were asked how often in 

the past year they ended up hitting, shoving, or throwing things at their partner and how 

often their partner did these things to them in the past year. Response categories were 

none, one, two, three and four or more. If the parties to the couple disagreed in their 

reports about violence, we used whichever respondent reported more violence. So, if the 

female reported that her husband had hit her twice in the past year while the male 

reported hitting only once, we used the female’s report. Because the distribution of 

violence is highly skewed in most of the analyses reported below, WB rely bni a collapsed 

version of the violence variables in both waves. Wave I violence dummy is a 

17 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
dichotomous measure constructed by collapsing the wave 1 overall violence measure. 

Identical measures were constructed for wave 2. 

The seriousness of violence against women in intimate relationships can vary 

fi-om a simple shove to homicide. The measures available in the NSFH are not wellsuited to capturing variation in seriousness. Nevertheless, because severity of violence is 

an important issue, we developed a measure to assess the seriousness of violence in the 

NSFH using the limited information available. Following the question on number of 

violent incidents in the past year, respondents were asked whether any incident had 

resulted in the victim being “cut, bruised, or seriously injured.” As before, this item had 

to be recoded so that it identified injuries to the female in the partnership. To assess the 

seriousness of violence, we created ordinal measures that combined information on the 

number of violent incidents against the female and on injuries for the female for both 

waves. The seriousness of violence index groups respondents into three categories ‘no 

violence’ ‘one time violence without injury’ and ‘two or more violent incidents or 

violence with injury.’ 

A trajectory is a sequence of linked states within a conceptually defined range of 

behavior or experience (Elder 1985). In regard to trajectories in violent behavior an 

increasingly well-documented and consistent finding is that violence at time 1 predicts 

violence at time 2 (Wolfgang, Figlio, and Sellin 1972; Sampson and Laub 1993; Nagin, 

Farrington, and Moffitt 1995). It is unclear whether intimate violence exhibits continuity 

to the same extent as other €oms ofviolenzc. RSying on data gathered from battered 

women in emergency shelters and hospitals, feminist scholars argue that escalation, that 

is, a patterned increase in the seriousness of assaults over time, is a common trajectory 

18 

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a 

(Dobash and Dobash 1979). In contrast, analyses of national survey data suggest that 

violence tends to be an episodic affair that is not continued over a long period of time in 

relationships (Johnson 1995). Unfortunately, however, national surveys such as the 

National Family Violence Survey (NFVS) and the recently completed National Violence 

Against Women Survey (NVAWS) are ill-suited to identifylng trajectories in intimate 

violence (Tjaden and Thoennes 1998) 

Trajectories in violence can only be created for couples that were together in both 

waves of the NSFH. We call these respondents “continuing couples.” For the continuing 

couples, we created trajectories in violence by cross-tabulating the dummy version of 

wave 1 violence by the dummy version of wave 2 violence. This cross-tabulation 

allocates each of the continuing couples to one of four trajectories: no violence (those 

reporting no violence against the women in either wave), maintenance (those reporting 

violence in both waves), desistance (those reporting violence in wave 1 but not wave 2), 

and initiation (those reporting violence in wave 2 but not wave 1). 

It is important to be cognizant of the limitations of the NSFH for studying 

trajectories in violence. Ideally, trajectories should be studied over relatively long 

periods of the life course and with fiequent repeated measures. Unfortunately, NSFH 

data on intimate violence are limited to two observations over a five year period and are 

both lefi-, right-, and middle-censored. Because only two data points are available and 

because they are separated by a five year gap, trajectories can only be identified in a 

crude sense. Violence in wave 1 may not k the firs: Lstmxe cf violence in1 a 

relationship, and the lack of violence in wave 2 may not mean it has ceased or will be 

forever absent. It is also possible that violence may have occurred sometime after wave 1 

19 

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expressed are those of the author(s) and do not necessarily reflect the official

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This document is a research report submitted to the U.S. Department of Justice.
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but before the one year reference period in the wave 2 interview. Despite these 

limitations, we believe it is important to present what information is available in the 

NSFH on patterns in violence over time. 

The specific survey items related to conflict and violence are found in the selfenumerated sections of both wave1 and wave 2. Self-enumerated means that these 

questions on the survey were self-administered. In wave 1, the variables related to 

conflict and violence are located in section SE - 6 for respondents who were currently 

cohabiting and section SE - 7 for respondents who were currently married. In xave 2, 

the appropriate variables are located in sections SE-5 and SE-6 for cohabiting and 

married respondents, respectively. 

Economic Status and Experiences 

With respect to economic status and experiences, we were primarily interested in 

variables related to economic distress. To measure economic distress, we drew upon 

Voydanoff s and Donnelly’s (1 988) four-fold typology of economic distress, which 

reflects both subjective and objective aspects of employment and income, including 

employment instability, employment uncertainty, economic deprivation, and economic 

strain. Employment instability was operationalized with self-report information from the 

respondent and respondent’s partner or spouse on employment and job search activity. 

Both waves of the NSFH include rich data on the timing and extent of waged 

employment from 1970 to the date of the second wave interview. Additional information 

is available on weeks dunemplopnent ai2 jub search hi S8~ ycar preceding each 

interview date. These data permitted construction of several measures of individual and 

couple-level employment patterns. 

20 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
Employment uncertainty is the subjective component of employment-linked 

economic distress, and as conceptualized by Voydanoff and Donnelly, refers to fears of 

job loss or finding a replacement job. Unfortunately, the NSFH provides no direct 

assessment of such job concerns on the part of either the main respondent or the 

spouse/partner for either wave. 

Economic deprivation refers to the insufficiency of current income to meet needs, 

while subjective economic strain refers to the perception of financial inadequacy and 

worries about economic resources (Voydanoff, 1990). As with employment information, 

both waves of the NSFH contain a wealth of information from each respondent and 

hisher spouse or partner on earned and passive income sources, including public 

assistance payments. When combined with available data on household size and 

composition, various income-to-needs ratios can be calculated, including assessments of 

each respondent’s own earnings as a percentage of the poverty threshold given the 

respondent’s household configuration. The latter measure can be used to model the 

contribution of relative income sufficiency on conflict escalation and violence 

reciprocation patterns in couples. The NSFH also asked respondents about real assets, 

loans, and debt load; this information was used to create a more rounded picture of family 

financial status. 

As with the subjective assessments of employment concerns, the NSFH is weaker 

in the availability of subjective indicators of financial adequacy. No specific measures 

are included in Wave 1 Wave 2 i~cllldes two items that tap into the respondent’s 

evaluation of hisher financial situation: degree of satisfaction with finances and extent of 

worry about income sufficiency. Further, both waves allow assessment of the extent to 

21 

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expressed are those of the author(s) and do not necessarily reflect the official

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which earning and spending money and other financial decisions are a source of marital 

arguments. The survey items related to individual and household economic conditions 

are located both in the main and self-enumerated sections of wave 1 and primarily in 

sections P and Q of wave 2. NSFH also provides a number of constructed variables 

related to respondent, couple, and household income variables for both waves. 

Socio-Demographic Controls 

We included a number of measures in our analyses as control variables either 

because of their known or suspected relationship with intimate violence, or because they 

represent basic demographic dimensions. As described below, these variables may relate 

either to an individual respondent, to a couple, or to some aspect of the household in 

which a couple resides. 

Marital status refers to whether the respondent was married, cohabiting, 

separated, divorced or widowed. Marital status was assessed at both wave 1 and wave 2 

in a series of questions asked of all primary respondents and, if present, their current 

spouse or partner. For some of the analyses reported below, we focus on what we call 

continuing couples. Continuing couples are those identified as being married or 

cohabiting in wave 1 who were still married or cohabiting with the same individual in 

wave 2. Continuing couples also includes couples that changed their legal status between 

waves 1 and 2, provided that they still reported being together. Thus, there are cases in 

which couples that were married at wave 1 divorced before wave 2 but nevertheless were 

still living together as cohabitors. In a few cases, couples that were cohabiting at wave i 

reported marrying, divorcing, and then continuing to cohabit at wave 2. 

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position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
Only the primary respondent’s ruce or ethnicity was assessed in wavelof the 

NSFH. Because there are no race or ethnicity questions in wave 2, and because 

secondary respondents were not asked about this issue, there are some respondents for 

whom race and ethnicity are not known. These respondents include all secondary 

respondents in wave 1, and all wave 2 respondents who were not part of the NSFH study 

at wave 1 but who joined the study at wave 2 by virtue of their either marrying or 

cohabiting with a wave 1 respondent. In these cases, we assumed that the race or 

ethnicity of all of the secondary respondents was the same as the race or ethnicity of the 

primary respondent. 

The NSFH question on race and ethnicity has nine response categories, including 

(1) African American, (2) white - not of Hispanic origin, (3) Mexican American, 

Chicano, Mexicano, (4) Puerto Rican, (5) Cuban, (6) Other Hispanic, (7) American 

Indian, (8) Asian, and (9) other. Of the 7,554 respondents who were either married or 

cohabiting at wave 1,78.6 percent were white and 12.3 percent were Afican-American, 

accounting for 90.9 percent of the total sample of couples. The next largest category was 

Mexican American, Chicano, or Mexicano with 5.2 percent. Because there are so 

relatively few respondents who are not either white or African American, we collapsed 

this variable for our analyses. Accordingly, race and ethnicity are measured as a 

trichotomy, and includes the categories white, African-American, and persons of 

Hispanic origin (Mexican American, Chicano, Mexicano, Puerto Rican, Cuban, and 

Other Hispanic). We use indicator coding with Africzi-Americx s t5e reference 

category. Because of the small number of couples representing the categories of 

23 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

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This document is a research report submitted to the U.S. Department of Justice.
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American Indian, Asian and “other”, these cases were excluded in all analyses that used 

race or ethnicity as a variable. 

As a measure of social class that is not related to our measures of economic 

distress, we use educational attainment. Only the primary respondent’s level of 

educational attainment was assessed at wave 1, but the primary and secondary 

respondent’s educational attainment was assessed at wave 2. In our multivariate models 

for wave 1, we use the variable “educat”, which is a variable constructed by NSFH staff 

to measure educational attainment of the primary respondent in wave 1, For the wave 2 

models, we constructed variables to measure both the male’s and the female’s 

educational attainment. The correlation between male and female education in wave 2 is 

moderately strong .605. 

Duration of union refers to how long the couple has been together. Duration of 

union is easy to measure for couples who were together in wave 1 and who stayed 

together in wave 2. In NSFH 1 couples were asked the year and month when they were 

married or began cohabiting. This data was recorded in century months. The century 

month of the interview was also recorded. Duration of union was calculated by 

subtracting the century month in which the union began from the century month of the 

interview. Unfortunately, couples who married or began cohabiting between wave 1 and 

wave 2 were not asked the precise date at which the union started. In these cases, we 

assumed that the union started at the mid-point between waves 1 and 2. Duration of 

union for these cases was calculated by subtrac:hg the century month for the midpoint 

between waves 1 and 2 from the century month for the wave 2 interviews. In addition to 

duration of union, we also included the age of both members in a couple. Age and 

24 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
duration of union are highly correlated in the NSFH. Hence, their separate effects on 

dependent variables cannot be disaggregated. Prior research by DeMaris on the NSFH 

indicates that couples who were violent in wave 1 are more likely to break up between 

waves (DeMaris 2000). Thus, duration in wave 2 is in part a hction of violence and 

should not be used as a predictor of violence. Accordingly, in the multivariate models we 

use age of the primary respondent as a control. 

Couples may reside in households in which children and other related or unrelated 

individuals also may live. Prior research indicates that the size of a family is related to 

the likelihood of intimate violence. Hence, we included a measure of the number of 

chiZdren under the age of 18 residing in the household in both waves 1 and 2. 

Our final control variable is alcohol use. Previous research consistently finds that 

violence in intimate relationships is strongly correlated with substance abuse (Jasinski 

and Williams 1998. We created measures of alcohol use for both waves. However, the 

measures are not identical, because different questions were asked regarding alcohol and 

substance use in the two waves. 

In wave 1, both the primary and secondary respondents were asked whether 

anyone in the household had a “problem with alcohol or drugs.” If the respondent said 

yes, then additional questions asked the respondent to indicate “who living here has a 

problem of drinking too much alcohol.” Respondents could indicate the identity of the 

person by circling “me” or “my husband/wife/partner” or someone else in the household, 

i. e., a child or parent. To measure alcohol problems in wave 1 we created a dummy 

variable, with 0 indicating that neither the primary respondent or the partner were 

25 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

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identified as having a drinking problem and 1 indicating that at least one member of the 

couple was identified as having a drinking problem. 

In wave 2, the measures of alcohol use are more extensive. Both primary 

respondents and their partners were asked whether they had had a drink in the past 30 

days. If the respondent indicated yes, follow-up questions asked the respondent to 

indicate the number of days he or she had had a drink, the typical number of drinks per 

day, and the number of days in which the respondent had five or more drinks. We 

created male and female versions of the alcohol variables and factor analyzed them, using 

principal components analysis with varimax rotation. The results showed that the 

questions on number of days drinking, average number of drinks, and number of days 

with five or more drinks tap underlying constructs of male and female drinking. Two 

factors emerged with eigenvalues greater than 1. Together the two factors explained 62 

percent of the total variance. The factor scores for male drinking habits were saved and 

used as the measure of alcohol use in the multivariate analyses of violence in wave 2. 

Variables Abstracted from the U. S. Census 

Abstracting variables fi-om the 1990 U. S. Census and merging them with the 

NSFH wave1 and wave 2 data was a complicated process. We began by contacting 

NSFH and obtaining lists of census tracts in which respondents resided in both waves of 

the study. In a small number of cases in wave 2, census tract locations were not available. 

These respondents had either moved overseas, gone to prison, or were for some other 

reason not located in a tracted area. After wi: had the list ofczrlscs tiacts from NSFH, we 

abstracted data fi-om the U. S. Census website for the appropriate tracts. A complete 

listing of the census variables that were abstracted can be found in Appendix A. The data 

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position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

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This document is a research report submitted to the U.S. Department of Justice.
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set containing the tract ids and the raw tract level data was then returned to NSFH. At 

NSFH, the census tract data were merged with the NSFH respondent IDS and the tract 

identifiers were removed. The data set containing the tract level information and case 

IDS was then returned to us by NSFH. We refer to this data set as the first level census 

data. Because the tract identifiers were removed by NSFH, it is not possible to determine 

the tract locations of any respondents. Nor is it possible to determine whether any two 

respondents are located in the same or different tracts. 

The next step was to transform the raw census data variables into indicators of the 

various dimension of neighborhood context that we were interested in investigating, 

thereby creating second level census data sets. By combining selected census variables 

we created a number of summary measures of neighborhood conditions and saved them 

in a separate SPSS system file. These measures tap a number of theoretically relevant 

dimensions of neighborhoods, such as rates of poverty, family disruption, unemployment, 

educational attainment, ethnic and racial composition, residential mobility and others. A 

list of the indicators that we created and their definitions is provided in Table 2.1. The 

second level census tract data sets, that is the ones with the summary measures of 

neighborhood dimensions for wave 1 and wave 2, are the ones that were then merged 

with the NSFH data and used to create our measures of community context. 

Measures of Community Context 

Our conceptualization and measurement of community context was guided by 

recent work by Sampson, Moremfc ax! colleagues (Sann?wn, Raudenbush, and Earls 

1997; Sampson, Morenoff, and Earls 1999). To measure community context, we created 

indexes of neighborhood disadvantage for both waves of the NSFH. Following the work 

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position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

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of Sampson and colleagues (Sampson et al. 1999), the indexes are based on five census 

tract measures that reflect concentrated disadvantage. They are defined by the percent of 

single parents, percent non-white, percent unemployed, percent of families on public 

assistance, and percent below the poverty line. After transforming the items to z-scores, 

we summed them and divided by the number of indicators to form the indexes of 

concentrated disadvantage for both waves, both of which have an alpha reliability of .92. 

The individual components of the index of concentrated disadvantage are related to 

intimate violence in the same way. Individually, they all have positive but weak zero 

order correlations with violence, ranging from .053 to .066. 

Consistent with a long tradition of research in the social disorganization 

perspective, we included residential mobility as a dimension of neighborhood context in 

many of our multivariate models. High rates of residential mobility have long been 

conceived as a negative factor in regards to neighborhood context. It is presumed that 

residential instability impedes the formation of close bonds between neighbors and the 

recognition of common values, both of which are assumed to undermine informal social 

control in neighborhoods. Recent research and theory, however, suggests that the 

meaning and effect of residential stability is contingent on other neighborhood factors. In 

very poor neighborhoods, low rates of residential mobility may be associated with 

negative outcomes for individuals (Ross, Reynolds, and Geis 2000)). Wilson (1987) 

suggests that in areas of concentrated poverty, neighborhood stability may not be a matter 

of choice but rather indicate the inability of residents to move away even though they 

would prefer to if given the choice. 

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position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
Table 2.1. Constructed Variables fkom U. S. Census 

Variable 

Name* Description 

Mbllyrl 

MblSyrl 

Crowd1 

Vacant1 

Urban1 

Sngpnt 1 

Pnwte 1 

Rheterl 

Ymales 1 

Pl8ovel 

Noedl 

Pl6ovel 

Noprofl 

Lowinc 1 

Unempl 

Nophnl 

Pubas1 

Idlyou 1 

% of occupied households in which the occupants moved in within the 

past year - 1989-1990. 

% of occupied households in which the occupants moved in within the 

past 5 years - 1985-1990. 

% of households with 6 + people living in them. 

% of vacant housing units. 

% of population living in urban area. 

% of single-parent households - with children living in them under 18. 

% of the population that is non-white. 

racial heterogeneity score using Blau's 1977 formula 

% of the population that are male ages 15 - 24. 

The number of people 18 or older - needed to compute education variable. 

% of the population 18 & over who have no college experience. 

The number of people 16 or older - needed to compute the occupation 

variable. 

% of the population who are not in professional or executive positions. 

% of the population whose income is 20k or less . 

% of males & females who are in the labor force and unemployed. 

owners and renters without phones in occupied housing units divided by 

occupied housing units. 

% of households on public assistance. 

% boys ages 16 - 19 who are not in Armed Services and not in school and 

unemployed or not in labor force. 

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Mmarnol 

Fmarnol 

Mheadl 

Nofinhsl 

Grphml 

Joblesl 

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Mjoblsl 

Povl 

Educ25 1 

ratio of never married males ages 15 and up to ever married males 15 and 

UP. 

ratio of never married females ages 15 and up to ever married females 15 

and up. 

% of men who are married. 

% of people living in non-family households. 

% of people living in group homes, shelters, and institutions. 

% of males and females 16 and up who are either unemployed or not in 

the labor force. 

% of males 16 and up who are either unemployed or not in the labor force. 

the poverty rate - ratio of income in 1989 to poverty level - scores less 

than 1 indicate people living under the poverty cut off. 

% of persons 25 or over without a college degree. 

U * Variable names for wave 2 are the same except that they end with numeral 2. 

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expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
Violence Against Women in the NSFH 

Before we turn to the research questions that motivated this study, we first present data 

on the amount and seriousness of violence against women in the NSFH and compare it to 

other surveys on violence against women. Table 2.2 shows the distribution of number of 

episodes of violence against women for both waves 1 and 2. As expected the distribution 

of this variable is highly skewed with the vast majority of couples not reporting any 

violence against the woman in the past year. In wave 1, the overall rate of violence was 

7.4 percent and in wave 2 the rate was 5.6 percent. Table 2.3 shows the distribution of 

injuries to women in the NSFH. In wave 1,2.7 percent of all women in couples were 

injured. This translates to an injury rate of 37.2 percent among women who were 

victimized by their partners in wave 1. Similarly, in wave 2,2.2 percent of all women 

were injured, constituting 39.0 percent of all women who were victimized. Taken 

together Tables 2.2 and 2.3 indicate that at least as it is measured in the NSFH, the oneyear victimization rate is less than 8 percent. However, of those who are victimized over 

one third are injured. Finally, Table 2.4 shows the distribution of trajectories in violence 

among the continuing couples. Among continuing couples, most of those who exhibited 

violence in wave 1 did not do so in wave 2. Of the 271 continuing couples that reported 

intimate violence in wave 1, 199 (73.3 percent) did not report violence in wave 2. 

Initiators constitute just under two-thirds of the violent couples in wave 2, with the 

remainder being maintainers. 

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Table 2.2. Frequency Distribution of Number of Episodes of Intimate Violence Against 

Women in the Past Year, Waves 1 and 2 of the National Survey of Families and 

Households. 

Number of 

Episodes Wave 1 % Wave2 YO 

None 6,827 92.6 5,811 94.4 

One 239 3.2 181 2.9 

Two 143 1.9 83 1.3 

Three 83 1.1 46 .7 

Four or more 83 1.1 36 .6 

N 7,375 6,157 

Missing cases 179 114 

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expressed are those of the author(s) and do not necessarily reflect the official

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Table 2.3. Frequency Distribution of Injuries through Intimate Violence Against 

Women, Waves 1 and 2 National Survey of Families and Households. 

Iniured Wave 1 YO Wave2 YO 

No 7,032 97.2 5,984 97.8 

Yes 204 2.7 135 2.2 

N 7,236 6,119 

Missing cases 3 18 152 

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expressed are those of the author(s) and do not necessarily reflect the official

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Table 2.4. Trajectories in Intimate Violence Against Women in Continuing Couples. 

Violence in Wave 1 

Violence 

In Wave 2 No Yes 

No 4,411 (91.6%) 199 (4.1%) 

(No Violence) (Desist) 

Yes 135 (2.8%) 72 (1.5%) 

(Initiate) (Maintain) 

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expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

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The results on overall levels of violence against women in the NSFH fall in the 

middle of the range of results from other surveys. In the recently completed, National 

Violence Against Women Survey, the annual rate of physical assault by an intimate 

partner against a women was 1.3 percent (Tjaden and Thoennes 1998). However, the 

1975 and 1985 National Family Violence surveys found that 1 1 to 12 percent of married 

and cohabiting women were physically by assaulted by their intimate partner in the 

preceding year (Straus and Gelles 1986). The rates of intimate violence against women 

observed here (7.4 and 5.6 for waves 1 and 2, respectively) do not appear to be out of line 

with previous investigations. Hence, we have some confidence that despite its 

limitations, the NSFH is a valuable source of information about the causes of intimate 

violence and its patterning. We turn now to our first major research question regarding 

the connection between neighborhood conditions and intimate violence. 

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position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
1 

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COMMUNITY CONTEXT AND THE PREVALENCE, FREQUENCY, 

SERIOUSNESS AND DURATION OF INTIMATE VIOLENCE 

Much of what is known about neighborhood economic conditions and rates of 

intimate violence against women comes from studies of calls to the police. They indicate 

that calls related to domestic violence incidents tend to come disproportionately from 

poor neighborhoods (Sherman and Berk 1984). Miles-Doan (1 998) found that 

neighborhoods suffering from great resource deprivation, as indicated by the absolute 

poverty level and income inequality, experienced hgher rates of officially reported 

intimate violence. 

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The theoretical significance of the observed correlation between neighborhood 

characteristics and officially reported intimate violence, however, is unclear. Although 

neighborhood context is theorized to influence domestic assault, there are two other 

possibilities. First, the higher rates of officially reported intimate violence in 

disadvantaged neighborhoods may be caused by differential reporting to criminal justice 

personnel, based on individual and neighborhood characteristics (Miles-Doan 1998). 

Differential reporting may be particularly troublesome for studies of intimate violence. 

Analyses of National Crime Victimization Survey data indicate, for example, that black 

I 

i 

women are more likely than white women to report intimate violence to the police 

(Greenfeld et al. 1998). One way to get around this problem is to embed questions about 

physical aggression in surveys of the general population, as is done in the NSFH. 

Hopehlly, those unwilling to report violence to official agencies or to the police would 

feel freer to report it in the context of an anonymous survey. Second, as noted above, 

B 

I 36 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
aggregate correlations may actually represent compositional effects. Rates of intimate 

violence in disadvantaged neighborhoods may be higher solely because the people who 

inhabit these areas for various reasons engage in intimate violence at a higher rate than 

people who live in advantaged areas. We address the issue of compositional effects in 

later chapters. 

To assess the connection between neighborhood context and various dimensions 

of intimate violence against women, we divided our index of concentrated disadvantage 

into deciles and computed the rate of intimate violence against women in each decile (see 

Table 3.1). Sampson and Wilson (1995) argue that the crime-related effects of 

community disadvantage are not linear across levels of disadvantage. Rather, they tend 

only to appear in the most distressed neighborhoods as "concentration effects" (Sampson 

and Wilson 1995). Following this line of reasoning, we investigated whether our indexes 

of concentrated disadvantage have a non-linear relationship with intimate violence. They 

appear to, but the pattern is more clear in wave 2 than in wave 1. We grouped the 

respondents into deciles based on their score on the disadvantage indexes and compared 

rates of intimate violence across deciles. In wave 1, among all couples the rate of 

violence fluctuates between 4.5 percent and 7.5 percent over the first five deciles, Over 

the last five deciles, the rates are generally higher, fluctuating between 8.3 percent and 

1 1 .O percent. In wave 2, over the first seven deciles the rate of violence fluctuates 

randomly between 2.7 and 6.9 percent for all couples. In the eighth decile the rate rises 

to 7.3 percent, then to 8.7 percent and 10.1 Fercent in the ninth and tenth deciles, 

respectively. Thus, the average rate for intimate over the first seven deciles is 4.3 

percent, and it doubles to an average of 8.7 percent in the last three deciles. Table 3.1 

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37 

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expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
also shows the relationship between the index of disadvantage and rates of violence in 

wave 2 among the continuing couples. As with all couples in wave 2, the relationship 

between the index of disadvantage and rates of intimate violence for continuing couples 

appears to be non-linear. The average rate of violence in the first seven deciles is 3.5 

percent compared to 6.35 percent in the last three deciles, an increase of 8 1 percent. 

Accordingly, for analyses involving the wave 2 and continuing couples, we dichotomized 

our indexes of concentrated disadvantage at the 70th percentile. Census tracts that fall 

into the upper 30 percent are considered disadvantaged. In wave 1, it appears to make 

more sense to split the sample at the 50th percentile. 

Table 3.2 shows the relationship between neighborhood types and the severity of 

intimate violence against women. In wave 1, women in advantaged neighborhoods 

experience lower rates of one time violence and repeated violence or violence with 

injury. This same pattern is repeated in wave 2 and among continuing couples. In all 

three samples, the differences in victimization rates between advantaged and 

disadvantaged neighborhoods are significant at the .01 level. It is notable that in all 

samples greater percentages of women experience repeat violence or violence with injury 

than one time violence without injury. However, if we limit our analysis to violent 

couples, the relationship between neighborhood type and severity of violence changes. 

Among violent couples in wave 1, there is no relationship between seventy of violence 

and neighborhood type. Of the women who were victimized, roughly equal percentages 

in both types of neighborhoods experienced repeat violence or violence that resiilied ic 

injury. 

38 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
Table 3.1. Percentage of Couples with Intimate Violence Against the Woman by the 

Index of Concentrated Disadvantage in Deciles. 

Level of Disadvantage 

Low High 

Sample 1 2 3 4 5 6 7 8 9 10 

Wave1 

Couples' 4.6 5.9 6.1 4.5 7.5 9.0 8.3 8.4 9.1 11.0 

Wave 2 

Couples' 4.0 2.7 3.9 5.4 4.3 6.9 3.0 7.3 8.7 10.1 

Continuing 

Couples3 3.7 2.3 3.4 4.5 3.4 5.1 1.6 6.8 5.3 7.8 

N = 7,375 

*N= 6,133 

N = 4,734 

39 

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This report has not been published by the Department. Opinions or points of view

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Table 3.2. Seventy of Intimate Violence Against Women by Neighborhood Type for All 

Couples and Violent Couples only. 

All Couples Violent Couples 

Neicrhborhood Type Neighborhood TyDe 

Wave 1 Advantaged Disadvantaged Advantaged Disadvantaged 

No Violence 94.3% 91.0% 

1 time, no injury 2.1 3.1 36.7% 34.1 % 

2 or more times or 

with iniury 3.6 6.0 63.3 65.9 

N 3,705 3,663 

X2 = 31.1, p < .01 

210 33 1 

X2 = .548 

Wave 2 Advantaged Disadvantaged Advantaged Disadvantaged 

No Violence 95.7% 91.5% 

1 time, no injury 1.9 2.7 43.7% 32.1% 

2 or more times or 

with iniury 2.4 5.8 56.3 67.9 

N 4,300 1,827 183 156 

X2 = 50.7, p < .01 X2 = 4.9, p = .026 

Continuing 

Couples Wave 2 Advantaged Disadvantaged Advantaged Disadvantaged 

No Violence 96.6% 93.6% 

1 time, no injury 1.6 2.3 46.6% 36.0% 

2 or more times or 

with iniury 1.8 4.1 53.4% 64.0% 

N 3,394 1,333 116 86 

X2 = 23.9, p < .01 x2 = 2.2 

40 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
I 

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A particularly important question is whether neighborhood type influences the 

likelihood that intimate violence against women is maintained over time. As Table 2.4 

showed above, relatively few of the continuing couples reported violence in both waves 1 

and 2 of the NSFH. Nevertheless, as Table 3.3 shows there is evidence that maintenance 

is more common in disadvantaged neighborhoods. To explore the relationship between 

neighborhood type and the maintenance of violence, it was necessary to modify our index 

of neighborhood type to take into account that couples may have moved between waves 

of the NSFH. Approximately 40 percent of the NSFH sample moved between waves. 

Because of this individual residential mobility, we modified our measure of community 

disadvantage so as to take into account the possibility that respondents may have moved 

into or out of a disadvantaged neighborhood between waves. Respondents who resided 

in advantaged neighborhoods in both waves were assigned a 0. Those who moved out of 

a disadvantaged neighborhood in wave 1 to an advantaged neighborhood in wave 2 

received a score of 1. Finally, those who moved into a disadvantaged neighborhood 

between waves 1 and 2 or who lived in such a neighborhood in both waves received a 2. 

Thus, this measure roughly captures length of exposure to disadvantaged neighborhood 

conditions. 

Table 3.3 shows that violence was maintained among 1.1 percent of the couples 

who resided in advantaged neighborhoods over the two waves of the NSFH. For couples 

who resided in a disadvantaged neighborhood at least part of the time, the rate of 

maintenance was 2.3 percent, whiie for t5osc v:ho resided in a disadvantaged 

neighborhood through the study, the rate was 1.7 percent. These differences are small 

41 

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expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

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but they do suggest that violence against women is more likely to persist in couples 

exposed to disadvantaged neighborhood conditions. 

Residential stability has long been recognized as an important dimension of 

neighborhood conditions. In the social disorganization tradition, it is treated as an 

important exogenous cause of neighborhood social disorganization (Sampson and Groves 

1989). We abstracted data on rates of residential mobility in census tracts, but 

preliminary analyses indicated that residential mobility is not related to intimate violence 

against women at the bivariate level or multivariate levels. Hence, we forego further 

discussion of it at this time. 

Conclusions: Neighborhood Conditions and Violence Against Women in Intimate 

Relationships 

The results presented in this chapter correspond with those based on studies of 

calls to the police (Miles-Doan 1998). Although violence against women in intimate 

relationships can occur anywhere, it is more common in disadvantaged neighborhoods. 

The relationship between neighborhood socio-economic disadvantage and violence 

against women in intimate relationships appears to be non-linear, especially in wave 2 of 

the NSFH. In the 70 percent of couples that we designated as residing in advantaged 

neighborhoods in wave 2, we observed no consistent relationship between the index of 

disadvantage and rates of intimate violence. However, there was a distinct increase in the 

rate of violence in the 30 percent of couples located in neighborhoods that scored highest 

on index of disadvantage, and the rate of violence was highest among the 10 percent of 

couples located in the very worst neighborhoods. This pattern also held for our measures 

of fiequency, seventy, and duration of violence. 

42 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
f 

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Table 3.3. Maintenance of Intimate Violence Against Women by Type of Neighborhood 

for Women in Continuing Couples 

Residency in TvDe of Neighborhood 

Advantaged Disadvantaged Disadvantaged 

Both Waves Wave 1 or Wave 2 Both Waves 

Maintenance 1.1% 2.3% 1.7% 

N 2,449 620 1,545 

43 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
In Wave 1 of the NSFH, the relationship between neighborhood disadvantage and 

intimate violence is not as clear as it is in Wave 2. In Wave 1, couples located in 

neighborhoods that scored in the upper half of the distribution of disadvantage had higher 

rates of violence on average than their counterparts in the lower half of neighborhoods. 

As in Wave 2, the 10 percent of couples located in the very worst neighborhoods had the 

highest rate of violence. As in Wave 2, seriousness of violence as indicated by multiple 

incidents of violence and injury is related to neighborhood socio-economic disadvantage, 

with women who reside in disadvantaged neighborhoods faring the worst. 

Contrary to other research in the social disorganization tradition, we did not 

observe any relationship between neighborhood rates of residential instability and 

intimate violence at the bivariate level. We return to this issue and examine in more 

closely in later chapters. 

44 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
CHAPTER FOUR 

ECONOMIC DISTRESS AND INTIMATE VIOLENCE 

Our next research question was “to what extent do different forms of economic 

distress influence the use of violence by men against women in intimate relationships?” 

Economic distress comprises objective and subjective aspects of employment and 

income. Objective conditions such as being unemployed or having insufficient income to 

meet the needs of one’s family indicate economic distress. In addition, subjective 

feelings of anxiety or worry about money or worry that one might lose one’s job also may 

indicate economic distress. Fagan and Browne (1 994) have theorized that economic 

distress may trigger intimate violence. They suggest that life transitions that cause 

economic distress (e.g. becoming unemployed) may be particularly important 

precipitating factors of violence by men against women in intimate relationships (Fagan 

and Browne 1994). There are several possible causal mechanisms. Financial problems 

may lead to feelings of stress and fnrstration that find expression in physical aggression 

by either partner (MacMillan and Gartner 1999). On the other hand, males experiencing 

job instability or poor earnings or both may be especially likely to become violent in 

verbal confrontations with their partners. Violence, in this case, substitutes for 

socioeconomic leverage as a means for men to establish their authority (Sugarman and 

Hotaling 1989; Babcock, Waltz, Jacobson, and Gottman 1993; MacMillan and Gartner 

1999). Others likewise argue for the salience of economic constraints and job loss in 

increasing the probability of marital violence under certain circumstances (Burgess and 

Draper 1989). 

45 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
It is certainly possible that couples experiencing economic difficulties become 

violent because they argue more over money matters, or because the stress and frustration 

induced by financial exigencies manifests itself in violent outbursts. On the other hand, it 

is also possible that both financial problems and intimate assault may simply represent 

different manifestations of an underlying individual level characteristic such as low self 

control (Gottfredson and Hirschi 1990). Unfortunately, the cross-sectional nature of most 

studies of intimate violence makes it difficult to determine whether economic distress 

precipitates violence. Stronger inferences about the role of economic distress in the 

etiology of violence require data that track couples over time and that control for prior 

violence. With this approach, the association between economic distress at an earlier time 

and subsequent trajectories in intimate violence can be examined. If economic distress is 

positively associated with initiation and negatively associated with desistance, a stronger 

case for its causal role can be made. 

There is some support for the hypothesis that economic distress affects intimate 

violence. Although intimate violence is found in all social classes, rates tend to be higher 

in families of lower socioeconomic status who are experiencing underemployment or 

unemployment (Straus, Gelles, and Steinmetz 1980). Recent research has found that 

economic distress predicts individual and family outcomes including marital 

dissatisfaction and family conflict (Conger et al. 1990; Fox and Chancey 1998). 

MacMillan and Gartner ( 1999) found that unemployed husbands married to employed 

wives exhibited greater coercive control and more physical aggression, compared tc? 

husbands in dual employment or dual unemployment couples. These authors suggest that 

it is not economic deprivation, but rather the symbolic significance of differential 

46 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
employment statuses among the partners that drives violence. They conclude that their 

work ". . .suggests that spousal violence against women reflect(s) efforts to dominate and 

control women in marital relationships" (MacMillan and Gartner 1999:957). 

In this chapter, we first present descriptive results on our measures of economic 

distress. We then explore how they are related to violence at the bivariate level. 

Measures of Economic Distress 

As noted in chapter two, we have both objective and subjective measures of 

economic distress. The objective measures of economic distress include the household 

income to needs ratio, number of household debts, and male job instability. The measure 

of job instability is available only for wave 2. 

The subjective measure of economic distress is called subjectivefinancid strain. 

It indexes the perception of financial inadequacy and worries about economic resources. 

Unfortunately, the NSFH is weaker in the availability of subjective indicators of financial 

strain. No specific measures are included in Wave 1. Wave 2 includes two items that tap 

into the respondent's evaluation of his or her financial situation. Both partners in a 

couple are asked to indicate how satisfied they are with their finances, with response 

categories ranging from "very dissatisfied" (7) to "very satisfied" (1). Respondents are 

also asked how often they worry about the sufficiency of their income. Response 

categories range from "worry all the time" (5) to ''never worry" (1). Transformed to zscores and summed these items form an index of subjective financial strain with an alpha 

coefficient of .79. Higher scores indicate greater strain. This measure appears to have 

some construct validity. In analyses not reported here, we investigated the relationship 

between the measures of objective economic distress and the index of subjective financial 

47 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

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strain using OLS regression. As expected, each of the objective indicators had a 

'significant effect on subjective strain in the expected directions. The results indicate that 

how couples interpret their finances is to some extent grounded in objective economic 

realities. The overall R2 for the model, however, was a modest .13, which suggests that 

much of the variation in interpretations is idiosyncratic in nature or caused by noneconomic factors. 

Tables 4.1 a and 4.1 b present descriptive statistics for the measures of economic 

distress in waves 1 and 2, respectively. In wave 1, the average number of household 

debts was 1.08, with over 60 percent of the sample reporting having zero or only 1 debt. 

Roughly 10 percent of the wave 1 sample had 3 or more debts. The mean for the 

household income to needs ratio is 4.54. As an income measure, it is not surprising that 

this variable is highly skewed, with a coefficient of skewness of 8.042. The measure 

ranges from 0 to 115.5. The median income to needs ratio is 3.50, indicating that in 

roughly half of the households in the sample the household income was more than three 

times the poverty level. One problem with the household income to needs ratio in wave 1 

is that there is a relatively large number of cases (1,507) with missing values. 

In wave 2, the mean number of debts is 1.2, with over 60 percent of the sample 

having 1 or fewer debts. About 15 percent of the sample reported having 3 or more 

debts. The mean household income to needs was 4.74, and as in wave 1 this variable is 

highly skewed and has a large range. The median was 3.8 1. The measure of subjective 

financial strain is available edy for wave 2. Its mean is -.019. The variable male job 

instability tracks the number of periods of unemployment experienced by the male in the 

relationship between waves 1 and 2 of the NSFH. As indicated in Table 4.1 b, the vast 

48 

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expressed are those of the author(s) and do not necessarily reflect the official

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majority of the sample enjoyed employment stability between waves, with over threequarters of the sample reporting no periods of unemployment between waves and almost 

95 percent of the sample reporting 1 or fewer periods of unemployment. Just over 5 

percent reported 2 or more periods of unemployment, with the mean being .32. 

To investigate the relationship between economic distress and violence against 

women in intimate relationships, we recoded our various indicators of distress into 

categorical variables based on the distribution of the variables. We created a five 

category variable for income to needs, which divided the sample into the following 

groups based on the relationship between the household income and the poverty level: (1) 

income less than half of the poverty level, (2) income half to equal to the level of poverty, 

(3) income up to two times the poverty level, (4) income two to four times the poverty 

level, and (5) income five or more times the poverty level. Table 4.2a shows the 

relationship between the ratio of household income to needs and rates of intimate 

violence for both waves of the NSFH. As expected, in both waves women in couples 

with relatively high household incomes have lower rates of victimization than those with 

low household incomes. The only exception to this pattern occurs in wave 2 in which 

women in the very poorest households have a low rate of victimization (4.3 percent). 

Otherwise, women in households where the income is no more than twice the poverty 

level have notably high rates of victimization than women in households with incomes 

that are more than twice the poverty level. 

49 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
Table 4.1 a. Descriptive Statistics on Forms of Economic Distress for Couples in Wave 1 

Standard 

Variable N Percent Mean Deviation 

Number of 

Debts 

0 

1 

2 

3 

4 

5 

6 

7 

Total 

Household 

Income to 

Needs Ratio 

2,844 

2,378 

1,470 

623 

207 

29 

2 

1 

7,554 

6,047 

37.6 1.08 1.10 

31.5 

19.5 

8.2 

2.7 

.4 

.o 

.o 

4.54 5.32 

50 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
Table 4. lb. Descriptive Statistics on Forms of Economic Distress for Couples in Wave 2 

Variable 

Number of 

Debts 

0 

1 

2 

3 

4 

5 

6 

7 

Total 

Number of 

Job Spells 

For Male 

0 

1 

2 

3 

4 

5 

Total 

Household 

Income to 

Needs Ratio 

Subjective 

Financial 

Strain 

Standard 

N Percent Mean Deviation 

2,217 

1,875 

1,263 

637 

222 

50 

5 

2 

6,271 

4,378 

942 

180 

68 

25 

40 

5,633 

6,2 17 

4,958 

35.4 1.20 1.18 

29.9 

20.1 

10.2 

3.5 

.8 

.1 

.o 

77.7 .32 .74 

16.7 

3.2 

1.2 

.4 

.7 

4.74 4.18 

-.019 .77 

51 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
Table 4.2a. Percentage of Couples with Intimate Violence Against Women by Ratio of 

Household Income to Needs, Waves 1 and 2. 

Ratio of Household Income to Needs 

Less than Half to Up to Two Two to Four Five or More 

Half of Equal of Times Times Times 

Poverty Poverty Poverty Poverty Poverty 

Sample Level Level Level Level Level 

Wave 1 10.4% 13.1% 10.0% 7.6% 5.7% 

Couples' (250) (290) (898) (2 9 697) (1,767) 

Wave 2 4.3% 11.8% 9.5% 6.0% 3.2% 

Couples2 (1 62) (178) (757) (2.9 16) (2.077) 

'X2 = 31.5,p< .01 

2X2 = 58.2,p< .01 

52 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
To assess the effect of debts on violence, we Qchotomized the measure of debts, 

with those reporting 2 or more debts categorized as having a high level of debt and all 

others categorized as having low debts. As shown in Table 4.2b, in Wave 1 , the 

relationship between number of household debts and violence is straightforward, with 

women in the high debt category being roughly 75 percent more likely to experience 

violence than women in the low debt category (10.6 percent to 6.0 percent respectively). 

This difference is highly significant. In wave 2, the effect of debts appears much weaker 

and is only barely significant (see Table 4.2b). 

Our final indicator of objective economic distress is job instability, which is 

measured as the number of periods of unemployment of at least six months between 

waves 1 and 2 for the male. Because there are very few cases with two or more periods 

of unemployment, we collapsed this variable into a trichotomy, ranging from 0 through 2 

or more periods of unemployment. Fagan’s and Browne’s hypothesis regarding the 

adverse effects of life transitions that cause economic distress is supported by the results 

for our measure of job instability (see Table 4-2c). The rate of violence rises steadily as 

the number of periods of unemployment increases. For women in couples where the 

male was never unemployed between waves, the rate of violence is 4.7 percent. It rises 

to 7.5 percent when the male experiences one period of unemployment and to 12.3 

percent when the male experiences two or more periods of unemployment. 

There is a strong relationship between subjective feelings of financial strain and the 

likelihood of violence agaimt a woman in an intimate relationship. The distribution of 

the index of financial strain is normal and symmetrical. Therefore, we dichotomized it at 

0. As shown in Table 4.2d, the rate of violence among couples with high levels of 

53 

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subjective financial strain is roughly three and a half times higher than among couples 

with low subjective strain (9.5 percent to 2.7 percent, respectively). 

Table 4.2b. Percentage of Couples with Intimate Violence Against Women by Level of 

Household Debts, Waves 1 and 2. 

Level of Household Debts* 

Sample Low High 

Wave 1 6.0% 10.6% 

Couples' (5,083) (2,292) 

Wave 2 5.2% 6.4% 

Couples2 (4,136) (2.02 1) 

* High equals 2 or more 

x2 = 47.3, p < .01 

2 x2 = 3.7, p = .053 

Table 4.2~. Percentage .of Couples with Intimate Violence Against Women by Subjective 

Financial Strain, Wave 2. 

Subiective Financial Strain 

Sample Low High 

Wave 2 2.7% 9.5% 

Couples' (2.602) (2.350) 

X2 = 104.0, p < .01 

54 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
Table 4.2d. Percentage of Couples with Intimate Violence Against Women by Number of 

Periods of Unemployment, Wave 2. 

Periods of Unemployment 

TWO 

Sample None One or More 

Wave 2 4.7% 7.5% 12.3% 

Couvles' (4,320) (932'1 (310) 

' X2 = 39.6, p < .OJ 

55 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
In Tables 4.3a through 4.3f, we examine the relationship between economic 

distress and the seriousness of intimate violence against women. Beginning with wave 1 

(see Tables 4.3a and 4.3b), we note that like the general prevalence rate of violence, the 

seriousness of violence also is related to the household income to needs ratio and to the 

number of debts in the household. Women in low income households have higher rates 

of repeated victimizations and victimizations with injury than women in high income 

households. The same is true with respect to high and low levels of debts. A similar 

pattern is observed for these two variables among wave 2 couples (see Tables 4.3~ and 

4.3d). 

In wave 2, the measures of job instability and subjective financial strain also are 

consistently related to the seriousness of violence. With respect to job instability, the rate 

of serious violence increases fkom 2.7 percent in households where the male experiences 

no periods of unemployment to 6.5 percent when the male experiences 2 or more periods 

of unemployment. Just over 5 percent of women in couples with high levels of subjective 

strain report experience repeat victimizations or being injured by their male partner 

compared to less than 2 percent of women in couples with low levels of strain. 

56 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
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Table 4.3a. Seriousness of Intimate Violence Against Women by Ratio of Household 

Income to Needs, Wave 1 

Ratio of Household Income to Needs 

Less than Half to Up to Two Two to Four Five or More 

Half of Equal of Times Times Times 

Seriousness Level Level Level Level Level Poverty Poverty Poverty Poverty Poverty 

No Violence 90.3% 87.2% 90.0% 92.4% 94.3% 

One Time 

Without 

Injury 3.2 1.7 

Two or 

More Times 

Or With 

3.6 3.1 1.9 

Iniury 6.5 11.1 6.5 4.5 3.8 

N 248 289 898 2,697 1,767 

x2 = 44.7,p< .01 

57 

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This report has not been published by the Department. Opinions or points of view

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Table 4.3b. Seriousness of Intimate Violence Against Women by Level of Household 

Debts, Wave 1. 

Level of Household Debts 

Seriousness Low High 

No violence 94.1 Yo 89.4% 

One Time 

Without 

Injury 1.8 4.3 

Two or 

More Times 

Or With Injury 4.1 6.2 

N 5,076 2,292 

X2 = 58.0, p < .01 

58 

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Table 4.3~. Seriousness of Intimate Violence Against Women by Ratio of Household 

Income to Needs, Wave 2 

Ratio of Household Income to Needs 

Less than Half to Up to Two Two to Four Five or More 

Half of Equal of Times Times Times 

Poverty Poverty Poverty Poverty Poverty 

Level Level Level Level Seriousness Level 

No Violence 95.7% 88.7% 90.6% 94.2% 96.8% 

One Time 

Without 

2.8 4.0 2.1 1.4 Injury 1.2 

Two or 

More Times 

Or With 

Injury 3.1 8.5 

N 

5.4 3.7 1.8 

162 177 756 2,9 12 2,077 

X2 = 60.4, p c .01 

59 

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1 

Table 4.3d. Seriousness of Intimate Violence Against Women by Level of Household 

Debts, Wave 2. 

Level of Household Debts 

Seriousness Low High 

No violence 95.4% 92.8% 

One Time 

Without 

WurY 1.8 2.6 

Two or 

More Times 

Or With Injury 2.8 4.6 

N 4,002 2,149 

X2 = 17.4, p < .01 

Table 4.3e. Seriousness of Intimate Violence Against Women by Subjective Financial 

Strain, Wave 2. 

Subjective Financial Strain 

Seriousness Low High 

No violence 97.3% 90.7% 

One Time 

Without 

Injury 1.1 3.8 

Two or 

More Times 

Or With 

Injury 1.5 5.5 

N 2,602 2,345 

X2 = 99.7,p < .01 

60 

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expressed are those of the author(s) and do not necessarily reflect the official

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Table 4.3f Seriousness of Intimate Violence Against Women by Number of Periods of 

Unemployment, Wave 2. 

Periods of Unemtlloment 

Seriousness 

No violence 

None 

97.3% 

One 

90.7% 

Two 

or More 

88.3% 

One Time 

Without 

Injury 1.1 3.8 5.2 

Two or 

More Times 

Or With Injury 1.5 5.5 6.5 

N 4,3 19 93 0 308 

X2 = 36.l,p<.Ol 

61 

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Conclusions: Economic Distress and Intimate Violence Against Women 

With only a few exceptions, the general pattern of results for our measures of 

economic distress is consistent with previous research. Women in couples with low 

incomes, high debt, and male job instability are more likely to be victimized by their 

intimate partner than women in couples that are more financially well off, and the 

seriousness of their victimizations are likely to be greater as well. How a couple 

subjectively interprets economic distress also appears to matter a good deal, with those 

who define their situations as womsome or unsatisfying experiencing more violence than 

those who hold more optimistic views of their personal finances. These patterns hold for 

couples in both waves of the NSFH. 

Our results on economic distress are consistent with a long line of research which 

shows violence against women is inversely related to women’s financial status. Past 

research has consistently found that poverty, unemployment, and low social class are 

related to violence against women. Although violence is spread across the entire range of 

the income distribution and some well-to-do women indeed are victimized, the risk of 

victimization is highest for women who are not well-off financially or whose partners are 

unable to hold down a job consistently. 

Because couples experiencing economic distress are more likely to reside in areas 

or neighborhoods that are also economically disadvantaged at the aggregate level, it is 

possible that some or all of the relationship between neighborhood socio-economic 

conditions and intimate violence reflects compositional ~2r,ilm thm coniextunl effects. 

This issue is explored later in this report in chapters 6 and 7. 

62 

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CHAPTER FIVE 

CHANGE IN ECONOMIC DISTRESS AND INTIMATE VIOLENCE 

In this chapter we focus on our third research question. “How do changes in 

economic distress influence the initiation, maintenance, desistance, and escalation of 

violence by men in intimate relationships and to what extent do the known precursors of 

violence mediate the impact of changes in economic distress?’’ Because our focus in this 

chapter is on change in economic distress, our analysis is limited to the 5,031 continuing 

couples in the NSFH. 

We were able to create change measures for debts and household income to need 

ratios, because these variables were available in both waves. Unfortunately, information 

on subjective financial stress is not available in wave 1, preventing any analysis of 

change in this dimension of economic distress. Because the measure of job instability 

refers to events that were happening between waves, we treat it as a change measure of 

economic distress for continuing couples. To create change measures of debts and 

household income to needs ratios, we subtracted wave1 scores from wave 2 scores for 

these variables. Couples were then grouped into one of three categories depending on 

whether their financial situation in wave 2 compared to wave 1 could be regarded as 

worse, better, or unchanged. 

Table 5.1 presents descriptive statistics on change in economic distress. Just over 

40 percent of the continuing couples pxperienced a decline in household income; slightly 

more (47.6 percent) saw an improvement; and in 8.5 percent of the couples there was no 

change in income. Unlike the bimodal distribution of change in income to needs, the 

63 

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distribution of change in debts is more uniform. Approximately equal number of couples 

had more debts as had fewer debts (28.3 percent to 30.2 percent, respectively); 41.5 

percent had the same number of debts in wave 2 as in wave 1. Finally, males in 

continuing couples experienced periods of unemployment to roughly the same degree as 

males in all wave 2 couples. Nearly 80 percent were never unemployed between waves, 

16.2 percent were unemployed only once, and 4.7 percent experienced 2 or more periods 

of unemployment between waves of the survey. 

Except for our measure of job instability, there no evidence that changes in 

economic distress are related to violence against women in intimate relationships. As 

shown in Table 5.2, neither change in income to needs or change in debts is related to 

rates of violent victimization. In both cases, the relationship is not statistically significant 

at the .05 level, and the pattern of results is not in the expected direction. The only 

measure of change in economic distress that is related to violence in continuing couples is 

job instability. The rate of violence against women in continuing couples is more than 

twice as high when the male has 2 or more periods of unemployment than when the male 

has 1 or fewer periods of unemployment (9.1 percent to 4.1 percent, respectively). This 

results mirrors the pattern reported in chapter 4 for all wave 2 couples. 

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position or policies of the U.S. Department of Justice.

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Table 5.1. Descriptive Statistics on Change in Selected Measures of Economic Distress 

for Continuing Couples 

Standard 

Percent Mean Deviation 

Change in Ratio of 

Income to Needs 

Between Waves 

Worse 

No Change 

Better 

Change in Debt 

Load Between Waves 

More Debts 

No Change 

Fewer Debts 

Number of Periods of 

Unemployment 

Between Waves 

None 

One 

Two or More 

-.07 4.92 

42.0 

8.5 

47.6 

(4,098) 

-.002 1.22 

28.3 

41.5 

30.2 

(5,03 1) 

.29 .70 

79.1 

16.2 

4.7 

(4,494) 

65 

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Table 5.2 Percentage of Continuing Couples with Intimate Violence Against Women in 

Wave 2 by Change in Ratio of Income to Needs 

Violence 

Yes 

N 

X2 = 5.6, p = .061 

Violence 

Yes 

N 

X2 = 4.9, p =.086 

Violence 

Yes 

N 

X2 = 11.8, p < .01 

Change in Ratio of Income to Needs 

Worse No Change Better 

3.6% 5.7% 4.9% 

1,675 420 1,926 

Change in Debt Load 

More No Change Fewer 

4.9% 3.5% 4.7% 

1,394 2,033 1,495 

Periods of Male Unemployment 

One or None Two or More 

4.1 Yo 9.1% 

4,225 208 

66 

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Conclusions: Change in Economic Distress and Intimate Violence 

The failure to find a relationship between two of our measures of change in 

economic distress and intimate violence against women is surprising, especially in 

regards to change in the income to needs ratio. Recall that the cross-sectional 

relationslup between income to needs and violence was significant for both wave 1 and 

wave 2 couples. In both waves, couples at the lower end of the income to needs scale 

experienced notably higher rates of violence than more well to do couples. Thus, we had 

expected that continuing couples who experienced a downturn in financial status between 

waves would be especially likely to have high rates of intimate violence. It is possible 

that our measure of change is simply too crude and that only very dramatic downturns in 

financial fortunes trigger an increase in domestic violence. Another possibility is that a 

downturn in financial fortunes has two different types of effects on couples. In the case 

of couples that are not strongly bonded emotionally, it may lead them to separate or 

divorce, because the marriage or partnership is not bringing the economic rewards that 

the participants had hoped to gain by entering into their relationship. In contrast, in 

relationships that are emotionally strong, financial problems may actually bring couples 

closer together to respond to the threat posed to their relationship by economic hard 

times. A reduction in income may represent an external threat that increases 

cohesiveness and solidarity among some couples. 

In the next two chapters, we explore whether the relationship between our 

indicaiors of economic distress and intimate violence is influenced by community contert 

and other known precursors of intimate violence. 

67 

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CHAPTER SIX 

ECONOMIC DISTRESS AND COMMUNITY CONTEXT 

Results presented in Chapters 3 and 4 indicated that rates of intimate violence 

against women vary systematically with community level socio-economic disadvantage 

and with some measures of household level economic distress. It is to be expected that 

household level economic distress and community level socio-economic disadvantage are 

systematically inter-related, because access to financial resources influences housing 

decisions. Couples who are financially advantaged are more likely to be able to afford to 

live in well to do neighborhoods than couples who are financially disadvantaged. With 

respect to intimate violence against women, the correlation between household and 

community level financial status raises important substantive and methodological 

questions. Substantively, the question is whether household economic distress and 

community context influence intimate violence independently of each other or whether 

they interact with one another to produce varying levels of risk for women located in 

different types of areas and households. Methodologically, does the relationship between 

community context and intimate violence represent a compositional or contextual effect? 

Social disorganization and other contextual theories hold that the structural 

characteristics of neighborhoods can either facilitate or hinder crime (Bwsik 1988) 

(Sampson et al. 1997). Several studies have documented associations between crime and 

the perception or fear of crime by residents md such ncighbcrhood features as 

community socioeconomic status, ethnic heterogeneity, residential stability, the extent of 

family disruption, housing deterioration, residential overcrowding, and population 

68 

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density (Greenberg, Rohe, and Williams 1982; Byrne and Sampson 1986; Sampson 

1986b; Sampson and Groves 1989). That these associations obtain despite a complete 

turnover in the populations of neighborhoods over time suggests to Stark (1 987) that the 

structural, or contextual, features of neighborhoods themselves must play some role in the 

etiology of deviance. 

Yet, neighborhoods are selective in terms of the sociodemographic profiles of the 

inhabitants they attract. Despite turnover in the actual residents of a neighborhood there 

may nonetheless be continuity in the people found there over time. Differences in 

aggregate crime rates between neighborhoods with different structural characteristics 

may be primarily due to differences in their population compositions (Bursik and Webb 

1982; Simcha-Fagan and Schwartz 1986; Reiss 1986; Sampson 1986a). For example, as 

Sampson notes, an "aggregate offense rate may be positively related to the percentage of 

the population that is black because blacks have a higher rate of offending than do whites 

(an effect of composition) or because blacks in cities with a large black population have 

higher offending rates than do blacks in areas where they are a minority (an effect of 

context)" (Sampson 1986a). Because it is often difficult to untangle contextual from 

compositional effects, this debate is still largely unresolved. 

From chapter 3 we know that neighborhood socio-economic status is correlated 

with rates of intimate violence against women. However, it is not clear whether these 

aggregate correlations represent compositional or contextual effects. Compared to more 

advantaged neighborhoods, rates of intiiixitz violcnc~ in diwlvantaged neighborhoods 

may be higher solely because the people who inhabit these areas have individual level 

characteristics that are related to intimate violence. In particular, they may be higher 

69 

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because the people in these areas are more likely to experience individual level strains 

associated with violence, such as economic distress. 

To address this issue, in this chapter we begin by presenting evidence on the 

relationship between our measures of household economic distress and community 

context. We then investigate the joint effects of household economic distress and 

community context on the prevalence and severity of violence against women in intimate 

relationships. 

Community Context and Economic Distress 

Table 6.1 a shows the relationship between neighborhood type and the two 

measures of objective economic distress in wave 1. As expected, in disadvantaged 

neighborhoods, the proportion of households with lower incomes is notably higher than 

in advantaged neighborhoods. For example, 17.4 percent of the respondents located in 

disadvantaged neighborhoods have household incomes at or below the poverty level 

compared to 6.2 percent of respondents in advantaged neighborhoods. Although 

household income is related to neighborhood type, number of household debts is not. 

Approximately, 30 percent of respondents report having 2 or more debts in both 

advantaged and disadvantaged neighborhoods. 

A similar pattern is observed in wave 2 between our indicators of objective economic 

distress and neighborhood type (see Table 6.lb). Just over 12 percent of the respondents 

in disadvantaged neighborhoods have incomes at or below the poverty level compared to 

3.2 percent of respondents in advantaged neighborhoods. On the other end of the scale, 

roughly twice as many respondents in advantaged neighborhoods have household 

incomes that are five times higher than the poverty level than in disadvantaged 

70 

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neighborhoods (39.6 percent to 19.8 percent, respectively). As in wave 1, there is no 

relationship between debts and neighborhood type in wave 2. Surprisingly, there is also 

little relationship between employment instability and neighborhood type. Slightly more 

respondents reported 2 or more periods of unemployment between survey waves in 

disadvantaged than advantaged neighborhoods (6.6 percent to 5.1 percent, respectively). 

Because of the large sample size, this difference is statistically significant, but it does not 

appear to be strong in a substantive sense. Finally, subjective financial strain is related to 

neighborhood type as expected. Almost three fifths of the respondents in disadvantaged 

neighborhoods report high levels of subjective financial strain compared to about two 

fifths of respondents in advantaged neighborhoods. 

The results presented in Tables 6.1 a and 6.1 b suggest that some forms of 

economic distress are related to community level socio-economic disadvantage but not 

others. Clearly, and unsurprisingly, in regards to objective economic distress poverty is 

more pervasive among respondents located in disadvantaged neighborhoods. It is also 

clear that there are differences in subjective economic distress between respondents 

located in disadvantaged versus advantaged areas. The former are more likely to feel a 

sense of subjective financial strain than the latter. Yet, contrary to our expectations, 

there are practically no differences in debts or employment instability between 

respondents located in advantaged versus disadvantaged neighborhoods. 

71 

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Table 6. la Individual Economic Distress by Neighborhood Type, Wave 1. 

Income to Needs 

Ratio 

Less than Half of 

Poverty Level 

Half to Equal to 

Poverty Level 

Equal to Two Times 

Poverty Level 

Two to Five Times 

Poverty Level 

More than Five Times 

Poverty Level 

N 

X2 = 450.8, p < .OOO 

Number of Debts 

Low 

High 

N 

X2 = 1.7, p = .19 

Neighborhood TvDe 

Advantaged Disadvantaged 

3.1% 7.6% 

3.1% 9.8% 

11.9% 23.4% 

46.1 % 44.1 % 

35.8% 15.2% 

4,277 1,741 

Advantaged Disadvantaged 

68.7% 70.2% 

3 1.3% 29.8% 

5,289 2,265 

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Table 6.1 b Individual Economic Distress by Neighborhood Type, Wave 2 

Income to Needs 

Ratio 

Less than Half of 

Poverty Level 

Half to Equal to 

Poverty Level 

Equal to Two Times 

Poverty Level 

Two to Five Times 

Poverty Level 

More than Five Times 

Poverty Level 

N 

X2 = 482.4, p < .OOO 

Number of Debts 

Low 

High 

N 

X2 = .05, p = .824 

Subjective 

Financial Strain 

Low 

High 

N 

X2 = 84.4, p < .OOO 

Neinhborhood Tvpe 

Advantaged Disadvantaged 

2.1% 5.1% 

1.1% 7.1% 

9.1% 20.6% 

48.1 % 47.5% 

39.6% 19.8% 

4,336 1,839 

Advantaged Disadvantaged 

65.3% 65.0% 

34.7% 35.0% 

4,373 1,874 

Advantaged Disadvantaged 

56.5% 42.0% 

33.5% 58.0% 

3,548 1,395 

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Unemdovment 

Low (1 or none) 

High (2 or more) 

N 

X2 = 4.7, p = .031 

Advantaged Disadvantaged 

94.9% 93.4% 

5.1% 6.6% 

3,952 1,663 

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Economic Distress, Community Context, and the Prevalence of Intimate Violence 

Against Women 

To assess the joint effects of household economic distress and community context 

on intimate violence against women, we use a series of three-way contingency tables. In 

each table the relationship between an indicator of household economic distress and 

intimate violence is 'examined controlling for neighborhood type. 

Table 6.2a shows rates of intimate violence by the income to needs ratio and 

neighborhood type in wave 1. As expected, rates of violence are generally higher in 

disadvantaged compared to advantaged neighborhoods within each of the income to 

needs categories, especially in the higher income brackets. For example, in the wealthiest 

households, that is those making more than five times the poverty level, the rate of 

violence is 7.8 percent in disadvantaged neighborhoods, but it is only 4.7 percent in 

advantaged neighborhoods. Within neighborhood types, income is related to rates of 

intimate violence in disadvantaged neighborhoods but not in advantaged neighborhoods. 

In disadvantaged neighborhoods, rates of violence are notably higher among respondents 

in the three lower income brackets compared to respondents in the two upper income 

brackets. A similar pattern is observed in advantaged neighborhoods but it is not as 

pronounced and is not statistically significant. These results would appear to indicate that 

the aggregate correlation between neighborhood type and intimate violence reflects in 

part a contextual effect and is not simply the result of compositional differences in 

populations between neighborhood types. 

Table 6.2b shows the joint effects of household debt and neighborhood type on intimate 

violence in wave 1. Rates of violence are higher among respondents with low debts in 

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disadvantaged neighborhoods than advantaged neighborhoods (7.7 percent to 4.8 percent, 

respectively). The same pattern holds for those with high debts. Within neighborhood 

types, rates of violence are higher among respondents with high debts compared to those 

with low debts. Thus, the overall pattern of relationships between debts and intimate 

violence appears to be the same regardless of neighborhood type. High debts are 

associated with higher rates of violence in both advantaged and disadvantaged 

neighborhoods in. Recall that neighborhood type is not related to the number of 

household debts. Thus, the higher rates of intimate violence in disadvantaged 

neighborhoods for respondents having similar levels of debt suggests a contextual versus 

compositional effect. 

Tables 6.2~ through 6.2f report the results of our analysis of economic distress 

and community context for wave 2. In regards to the income to needs ratio and the 

number of household debts, the patterns in wave 2 are similar to those observed in wave 

1. Regardless of the indicator of economic distress, couples in disadvantaged 

neighborhoods generally have higher rates of intimate partner violence than couples in 

advantaged neighborhoods. For example, for couples in advantaged neighborhoods that 

report household incomes that are more than five times the poverty level, the rate of 

violence is 2.8 percent, but it is 5.2 percent among similarly well off couples in 

disadvantaged neighborhoods. Couples with high levels of debt who live in 

disadvantaged neighborhoods have a rate of intimate violence of 10.5 percent, whereas 

for their counterparts in advantaged neighborhoods the rate is 5.9 percent. Lr both types 

of neighborhoods, the relationship between the two indicators of economic distress and 

violence is significant. Overall, there is no evidence that the relationship between these 

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indicators of economic, community context, and intimate violence against women 

changed between waves of the NSFH. 

Table 6-2a Effects of Income to Needs Ratio and Neighborhood Type on Intimate 

Violance Against Women, Wave 1. 

Income to Needs 

Ratio 

Less than Half of 

Poverty Level 

Half to Equal to 

Poverty Level 

Equal to Two Times 

Poverty Level 

Two to Five Times 

Poverty Level 

More than Five Times 

Poverty Level 

N 

Violence Rate by Neighborhood Tvpe 

Advantaged Disadvantaged 

5.6% 12.4% 

10.4% 13.9% 

7.6% 11.3% 

6.2% 8.9% 

4.7% 7.8% 

3,010 2,892 

X2 Advantaged Neighborhoods = 7.7, p = .lo5 

X2 Disadvantaged Neighborhoods = 10.6, p = .03 1 

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Table 6-2b Effects of Household Debts and Neighborhood Type on Intimate Violence 

Against Women, Wave 1 

Violence Rate by Neighborhood Type 

Number of 

Household Debts Advantaged Disadvantaged 

1 

I Low 4.8% 7.7% 

High 7.2% 13.4% 

N 3,707 3,668 

X2 Advantaged Neighborhoods = 12.5, p < .OOO 

X2 Disadvantaged Neighborhoods = 36.1, p < ,000 I 

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Tables 6.2e and 6.2f report the results for employment instability and subjective 

financial strain in wave 2. The patterns of results for these two new indicators of 

economic distress are similar to the ones observed earlier for different indicators in wave 

1 and wave 2. In all cases, respondents in disadvantaged neighborhoods have higher 

rates of intimate violence compared to their similarly situated counterparts in advantaged 

neighborhoods. However, some interesting variations in relationships are apparent. 

Looking first at subjective financial strain and couples with low levels of subjective 

strain, we note that there is little variation in rates by neighborhood type. Couples with 

low subjective strain and who live in disadvantaged neighborhoods report a violence of 

3.8 percent, while those who live in advantaged neighborhoods report a rate of 2.3 

percent. The combination of neighborhood disadvantage and high subjective strain 

appears to be particularly potent, as intimate violence against women is reported in 13.8 

percent of these cases, In contrast, violence is reported in only 7.3 percent of couples in 

advantaged neighborhoods who have high strain, a rate that is roughly half as high as 

observed in highly strained couples in disadvantaged neighborhoods. Within 

neighborhood types, the relative increase in violence over levels of subjective strain is 

roughly similar. For couples in disadvantaged neighborhoods, couples with high levels 

of subjective strain report violence at a rate that is 3.63 times higher than couples with 

low strain. In advantaged neighborhoods, the increase in violence rate between low and 

high levels of subjective strain is 3.1 7. 

79 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
Table 6-2c Effects of Income to Needs Ratio and Neighborhood Type on Intimate 

Violance Against Women, Wave 2. 

Violence Rate bv Neighborhood Type 

Income to Needs 

Ratio Advantaged Disadvantaged 

Less than Half of 

Poverty Level 3.8% 4.8% 

Half to Equal to 

Poverty Level 18.4% 8.7% 

Equal to Two Times 

Poverty Level 7.5% 11.7% 

Two to Five Times 

Poverty Level 4.6% 9.3% 

More than Five Times 

Poverty Level 2.8% 5.2% 

X2 Advantaged Neighborhoods = 42.9, p < .OOO 

X2 Disadvantaged Neighborhoods = 1 1.5, p = .02 1 

Table 6-2d Effects of Number of Household Debts and Neighborhood Type on Intimate 

Violence Against Women, Wave 2 

Violence Rate by Neighborhood Tvpe 

Number of 

Household Debts Advantaged Disadvantaged 

Low 3.5% 7.7% 

High 5.9% 10.5% 

N 4,303 1,830 

X2 Advantaged Neighborhoods = 14.4, p < .OOO 

X2 Disadvantaged Neighborhoods = 4.2, p = .041 

80 

position or policies of the U.S. Department of Justice.

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This report has not been published by the Department. Opinions or points of view

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E Finally, with respect to the prevalence of intimate violence, Table 6.2f reports results for 

employment instability in wave 2. Regardless of neighborhood type, couples in which 

the male experienced 2 or more periods of unemployment between waves have notably 

higher rates of violence against women than couples where the male had more stable 

employment. Among couples located in advantaged neighborhoods, the rate of violence 

increases from 4 percent when the male had stable employment to 10.6 percent when the 

male had unstable employment. This is a relative increase of 265 percent. In 

disadvantaged neighborhoods, the relative increase in violence is 190 percent. As with 

the results reported for other indicators of economic strain, the results for employment 

instability suggest that neighborhood context influences intimate violence against women 

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independently of levels of employment instability. 1 

Table 6-2e Effects of Subjective Financial Strain and Neighborhood Type on Intimate 

Violence Against Women, Wave 2 

Violence Rate by Neighborhood Type 

Subjective 

Financial Strain Advantaged Disadvantaged 

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Low 

High 

2.3% 3.8% 

7.3% 13.8% 

X2 Advantaged Neighborhoods = 49.2, p < .OOO 

X2 Disadvantaged Neighborhoods = 39.5, p < .OOO 

I 

81 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
Table 6-2f Effects of Periods of Unemployment and Neighborhood Type on Intimate 

Violence Against Women, Wave 2 

Violence Rate by Neihborhood Twe 

Number of Periods 

Of Unemdoyment Advantaped Disadvantaged 

Low (1 or none) 4.0% 8.2% 

High (2 or more) 10.6% 15.6% 

X2 Advantaged Neighborhoods = 19.5, p < .OOO 

X2 Disadvantaged Neighborhoods = 7.0, p = .008 

82 

position or policies of the U.S. Department of Justice.

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Economic Distress, Community Context, and the Severity of Intimate Violence Against 

Women 

In chapter 3, we showed that patterns in the severity of intimate violence varied 

with neighborhood type in wave 2 but not in wave 1. We defined one time violence with 

no injury as minor violence. Serious violence was defined as 2 or more violent incidents 

in the past year or 1 or more incidents with injury requiring medical treatment. Among 

all women who were assaulted in wave 1, nearly identical percentages in advantaged and 

disadvantaged neighborhoods experienced serious violence (63.3 percent and 65.9 

percent, respectively). In wave 2, the pattern was different. Among all women who were 

victimized in wave 2, those located in disadvantaged neighborhoods were significantly 

more likely to experience serious violence than those located in advantaged 

neighborhoods (67.9 percent to 56.3 percent, respectively). Wave 2 women in continuing 

couples were more likely to experience serious violence if they were located in a 

disadvantaged neighborhood than in an advantaged one, but the effect was not 

statistically significant. 

Tables 6.3a and 6.3b show the relationship between the income to needs ratio and 

seriousness of violence controlling for neighborhood type for waves 1 and 2. In wave 1, 

poverty appears to be related to seriousness of violence in advantaged neighborhoods but 

not in disadvantaged neighborhoods (see Table 6.3a). Couples with low incomes in wave 

1 who were located in advantaged neighborhoods have rates of serious violence that 

ranged from 5.6 percent to 1 1.8 percent across the three lowest income categories. 

Although low income couples in disadvantaged neighborhoods also have higher rates of 

serious violence than their more well to do counterparts, the difference is not significant. 

The rate of serious violence in the two upper income categories ranges from 6.3 to 6.6 

83 

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percent; in the three lower income categories it ranges from 7.3 to 10.5 percent. A 

similar pattern is observed in wave 2 (see Table 6.3b). Within the income to needs 

categories, couples located in disadvantaged neighborhoods generally have higher rates 

of serious violence than those located in advantaged neighborhoods. In both waves, 

women in couples that have low incomes and that are located in disadvantaged 

neighborhoods experienced the highest rates of serious violence. 

Neighborhood socio-economic disadvantage also combines with subjective financial 

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strain and employment instability to produce notably higher rates of serious violence for 

some women in wave 2 (see Tables 6.3~ and 6.3d). As shown in Table 6.3c, women who 

are located in disadvantaged neighborhoods and who have high levels of subjective 

financial strain experienced serious violence at a rate of 8.8 percent. This rate is notably 

I 

higher than those for women in couples with low subjective strain or who reside in 

advantaged neighborhoods. Likewise, couples where the male has high levels of 

I employment instability and that reside in disadvantaged neighborhoods have high levels 

of serious violence. Their rate of serious violence (10.1 percent) is more than twice that 

for couples with high male employment instability who reside in advantaged 

neighborhoods (4.6 percent). Overall, the combination of economic distress and 

neighborhood disadvantage appears to be particularly risky for serious intimate violence 

against women. 

84 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

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Table 6-3a Effects of Income to Needs Ratio and Neighborhood Type on Seriousness of 

Intimate Violence Against Women, Wave 1. 

Seriousness of Violence bv Neighborhood Type 

Income to 

Needs Ratio 

Advantaged Disadvantaged 

Minor serious Minor Serious 

Less than Half of 

Poverty Level 2.4% 5.6% 4.0% 7.3% 

Half to Equal to 

Poverty Level 2.4% 11.8% 1.2% 10.5% 

Equal to Two Times 

Poverty Level 3.4% 5.8% 3.8% 7.3% 

Two to Five Times 

Poverty Level 2.9% 3.8% 3.6% 6.3% 

More than Five Times 

Poverty Level 1.7% 3.3% 2.7% 6.6% 

X2 Advantaged Neighborhoods = 32.8, p < .OOO 

X2 Disadvantaged Neighborhoods = 6.7, p = .572 

85 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

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Table 6-3b Effects of Income to Needs Ratio and Neighborhood Type on Seriousness of 

Intimate Violence Against Women, Wave 2. 

Seriousness of Violence by Neighborhood Type 

Advantaged Disadvantaged 

Minor Serious Minor serious 

Income to 

Needs Ratio 

Less than Half of 

Poverty Level 2.5% 1.3% 0.0% 4.8% 

Half to Equal to 

Poverty Level 8.2% 10.2% 0.8% 7.2% 

Equal to Two Times 

Poverty Level 4.4% 3.1% 3.5% 7.9% 

Two to Five Times 

Poverty Level 1.5% 2.9% 3.5% 5.7% 

More than Five Times 

Poverty Level 1.5% 1.3% 1.4% 3.9% 

X2 Advantaged Neighborhoods = 52.9, p < .OOO 

X2 Disadvantaged Neighborhoods = 15.7, p = .047 

Table 6-3c Effects of Subjective Financial Strain and Neighborhood Type on Seriousness 

of Intimate Violance Against Women, Wave 1. 

Seriousness of Violence by Neighborhood Type 

Advantaged Disadvantaged 

Minor Serious Minor Serious 

Subjective 

Financial Strain 

Low 1.1% 1.2% 1.2% 2.6% 

High 3.4% 3.7% 4.7% 8.8% 

X2 Advantaged Neighborhoods = 46.4, p < .OOO 

X2 Disadvantaged Neighborhoods = 38.2, p < .OOO 

86 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

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position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
Table 6-3d Effects of Periods of Unemployment and Neighborhood Type on Seriousness 

of Intimate Violance Against Women, Wave 1. 

Seriousness of Violence by Neiczhborhood Type 

Advantaged Disadvantaged 

Periods of 

Unemployment Minor Serious Minor Serious 

Low (1 or none) 1.8% 2.2% 2.7% 5.4% 

High (2 or more) 5.1% 4.6% 5.5% 10.1% 

X2 Advantaged Neighborhoods = 15.9, p < .OOO 

X2 Disadvantaged Neighborhoods = 7.4, p = .025 

88 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

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Conclusion: Economic Distress, Community Context and Intimate Violence 

Some forms of economic distress appear to be more prevalent in disadvantaged 

neighborhoods but not all forms. Levels of poverty and subjective financial strain are 

notably higher in disadvantaged neighborhoods, but the levels of debt and employment 

instability are roughly similar in advantaged and disadvantaged neighborhoods. This 

result, especially in regards to employment instability, appears counterintuitive. As we 

noted in chapter two, during the period covered by the two waves of the NSFH the 

economic fortunes of many American workers deteriorated. Staglation or decline in 

earnings, flattened career trajectories, and substantial downsizing and outsourcing of jobs 

affected all levels of the economy (Farley 1996). In the face of job loss and the 

subsequent interruption in the household income stream, many families increase their 

debt levels to offset or compensate for income loss (Sullivan et ai, 2000). 

The results presented in this chapter consistently show that economic distress is 

related to the risk of intimate violence in both advantaged and disadvantaged 

neighborhoods. Regardless of neighborhood type, women in couples that have low 

household incomes are more likely to report intimate violence than their counterparts in 

couples with high household incomes. The same relationship holds for the other 

measures of objective economic distress - number of household debts and male 

employment instability. Violence is more common in households with high levels of 

debt and where the male has an unstable employment history. Subjective economic 

89 

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distress also influences the risk of violence regardless of neighborhood type. Couples 

that are located in either advantaged or disadvantaged neighborhoods are more likely to 

report intimate violence if they have high levels of subjective financial strain. Thus, 

residing in an advantaged neighborhood does not eliminate the harmful effects of 

individual level economic distress on the risk of intimate violence against women. 

Although economic distress increases the risk of intimate violence for women 

who are located in advantaged neighborhoods, it does so to an even greater degree for 

women located in disadvantaged neighborhoods. In both waves, we consistently found 

that regardless the level of economic distress, women in disadvantaged neighborhoods 

experienced higher rates of intimate violence than women located in advantaged 

neighborhoods. Indeed, an important finding is that women in couples with low levels of 

economic distress who are located in disadvantaged neighborhoods consistently have 

higher rates of intimate violence than women with low economic distress who reside in 

advantaged neighborhoods. This pattern holds for both objective and subjective 

measures of economic distress. It indicates that neighborhood disadvantage has an 

independent effect on the risk of intimate violence over and above that of economic 

distress. 

Neighborhood disadvantage also influences the seriousness of violence. 

Regardless of the type or level of economic distress, women in disadvantaged 

neighborhoods experience higher rates of serious intimate violence than women in 

90 

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advantaged neighborhoods. But the combination of high levels of economic distress and 

residence in a disadvantaged neighborhood is particularly risky for women. 

Taken together, the results presented here indicate that aggregate correlations 

between neighborhood characteristics and intimate violence are not solely a hction of 

compositional differences in population. At least this is true with respect to 

compositional differences in levels of economic distress. But neighborhood population 

compositions may differ on a host of other individual level characteristics that may also 

be associated with intimate violence. Hence, it is possible that other compositional 

differences in neighborhood populations account for the relationship between 

neighborhood characteristics and intimate violence. In the next chapter, we explore 

whether the effects of neighborhood type on intimate violence remain in the presence of a 

larger and more vaned number of controls for compositional differences. 

91 

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CHAPTER SEVEN 

COMMUNITY CONTEXT AND MINORITIES 

Our final research questions focus on the effects of community context and 

economic distress on intimate violence in minority couples. Are the effects of context 

and distress more pronounced for minority women, or do they operate independently of 

race and other demographic characteristics? In this final results chapter we investigate 

whether the relationship between community context, economic distress, and intimate 

violence is moderated by race and ethnicity. We also develop a comprehensive model of 

intimate violence that controls for many of the known precursors and demographic 

characteristics that influence the likelihood of violence intimate violence in couples. In 

chapter 6, we showed that the effects of community context on intimate violence 

appeared to operate independently of household economic distress. We interpreted the 

results as indicating that neighborhood disadvantage has a contextual effect on violence 

that is not reducible to the compositional differences between neighborhoods in levels of 

economic distress. But it is important to determine whether this contextual relationship 

holds when other individual level characteristics, besides economic distress, are included 

in the model. 

The importance of race as a correlate of domestic violence is frequently noted but 

rarely discussed. As William Julius Wilson has pointed out in regards to ordinary street 

crime, criminologists are reluctant to discuss race directly even though it is known to be 

highly correlated with rates of street crime offending and victimization. In this chapter 

we attempt to determine whether the higher rate of domestic violence among blacks 

92 

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I results fkom a combination of economic distress and their location in criminogenic 

ecological contexts. As Sampson has noted in regards to street crime “the correlations of 1 

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race and crime may be systematically confounded with important differences in 

community contexts” (Sampson 1993, p. 44). Is the same true with respect to violence 

against women in intimate relationships? 

Race, Community Context, and Intimate Violence 

For over two decades, criminologists and family researchers have observed 

marked variation in rates of domestic violence among demographically and socioeconomically defined populations. Domestic violence has been associated with race and 

poverty. Higher rates are observed among racial minorities and people of low socioeconomic status (Straus et al. 1980; Sugman and Hotaling 1989; Stets 1991; Magdol, 

Moffitt, and Silva 1998). 

Race, poverty, and community disadvantage have long been linked in the United 

States. Blacks are more likely to be poor than whites, and the poor are more likely to live 

in disadvantaged communities than the rich. Poor black people are particularly likely to 

live in disadvantaged areas. Despite the passage of the Fair Housing Act and other civil 

rights legislation, the United States remains a highly racially segregated society (Massey 

1990; Massey and Denton 1993). Residential segregation has the effect of isolating high 

levels of black poverty within black neighborhoods (Wilson 1987). Particularly during 

the last quarter of the 20th century pockets of ghetto poverty grew in the United States, 

afid the ccnwntration of poverty affected racial minorities more than whites (Hagan 

1994). The concentration of poverty in inner city ghettos suggests that the problem of 

domestic violence may be particularly severe in these areas. It is important to ascertain 

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whether the effects of individual poverty and community disadvantage on domestic 

violence are more pronounced for racial minorities, or whether these variables operate 

independently of race. Although variation in rates of domestic violence across race, 

economic status, and neighborhood context have been observed for some time, there has 

been little research on their joint effects. Whether and how the convergence of race, 

poverty, and community disadvantage influences domestic violence deserves thorough 

investigation. 

Surveys of the general population show higher rates of domestic violence among 

blacks than whites. For example, in the first wave of the National Family Violence 

Survey higher rates of family violence were found among blacks (Straus et al. 1980). An 

early analysis of National Crime Victimization Survey (NCVS) conducted in the 1970s 

found black women were more likely than whites to be victimized by intimates (Gaquin 

1977-1 978). These differences have persisted in the ensuing years. In the 1985 National 

Family Violence Survey married black women were 2.36 times as likely as white wives 

to experience severe partner violence (Hampton and Gelles 1994). More recent analyses 

of NCVS data reveal higher rates of violence by intimates among blacks, especially 

black women compared to white women, in the 1990s (Greenfeld et al. 1998). Finally, 

analyses of the National Survey of Families and Households also show significant race 

differences in violence against women in relationships (Stets 1991; Sorenson, Upchurch, 

and Shen 1996; Umberson, Anderson, Glick, and Shapiro 1998). 

We begin OUT investigation by first examining the relationship Setween location in 

a disadvantaged community, economic distress and racelethnicity. Before we discuss our 

results, however, it is important to reiterate again the limitations of our sample. By the 

94 

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standards of social science research, the NSFH has a relatively large sample, and great 

pains were taken to include minorities and to insure the sample’s representativeness. 

Nevertheless, it still does not contain large numbers of representatives of many racial and 

ethnic sub-groups. Our analysis is limited to whites, blacks, and Hispanics who 

constitute over 95 percent of the sample. Unfortunately, there are two few cases to say 

anything definitive about other groups. With this caveat in mind, we turn to our results. 

Table 7.1 a shows the relationship between race/ethnicity and income and between 

race/ethnicity and neighborhood type in wave 1. As expected, both blacks and Hispznics 

fare worse than whites in their economic status. Roughly a third of black couples and 

over half of Hispanics have low incomes, that is less than twice the poverty level, 

compared to about one fifth of white couples. And as expected, blacks and Hispanics are 

much more likely than whites to live in disadvantaged neighborhoods. 

For blacks and Hispanics their individual economic status often does not match 

the economic status of the neighborhoods they live in. For example, we note that 35.8 

percent of the black couples fall into the lower three income categories and may be 

considered economically disadvantaged, but 77.2 percent of blacks live in disadvantaged 

neighborhoods. Or viewed from the other direction, 64.2 percent of blacks have incomes 

that are more than twice the poverty level, but only 22.8 percent of blacks live in 

advantaged neighborhoods. A similar pattern is observed for Hispanics. Just over half 

(56.5 percent) of Hispanic couples have low incomes, but 71.7 percent live in 

disadvantaged neighborhoods. In contrast, whk cogpies arc such more likely to live in 

a neighborhood that matches their economic status. About one-fifth of white couples 

95 

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have low incomes (20.1 percent) and about one-fifth (1 8.6 percent) live in a 

disadvantaged neighborhood. 

Similar patterns are observed in wave 2 (see Table 7.1 b). Over three-quarters of the 

black and Hispanic couples are located in disadvantaged neighborhoods (78.7 percent and 

76.5 percent, respectively) compared to only one-fifth of white couples. Two-thirds of 

the black couples have incomes that are two or more times the poverty level and over half 

of the Hispanic couples are similarly well situated financially. Most white couples (85.4 

percent) earn this much. In wave 2, black and Hispanic couples experience significantly 

more subjective financial strain than white couples, but there are no differences between 

the race and ethnic groups in employment instability. Thus, black and Hispanic couples 

that are relatively well-off financially and that are similar to whites in employment 

stability are nevertheless much more likely than white couples to live in disadvantaged 

neighborhoods. 

96 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
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Table 7.1 a Levels of Economic Distress and Location in Neighborhood Types by Race 

and Ethnicity, Wave 1 

Race and Ethnicity 

Income to 

Needs Ratio 

Less than Half of 

Poverty Level 

Half to Equal to 

Poverty Level 

Equal to Two Times 

Poverty Level 

Two to Five Times 

Poverty Level 

More than Five Times 

Poverty Level 

N 

X2 = 389.5, p < .OOO 

Neighborhood Type 

Advantaged 

Disadvantaged 

N 

X2 = 1708.2, p < .OOO 

Black Hispanic White 

5.6% 12.1% 3.5% 

8.7% 19.0% 3.4% 

21.5% 25.5% 13.2 

46.4% 33.3% 46.5 

17.8% 10.0% 33.4 

675 32 1 4,795 

Race and Ethnicity 

Black Hispanic White 

22.8% 28.3% 8 1.4% 

77.2% 71.7% 18.6% 

926 3 92 5,934 

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Table 7. lb Levels of Economic Distress and Location in Neighborhood Types by Race 

and Ethnicity, Wave 2 

Income to 

Needs Ratio 

Less than Half of 

Poverty Level 

Half to Equal to 

Poverty Level 

Equal to Two Times 

Poverty Level 

Two to Five Times 

Poverty Level 

More than Five Times 

Poverty Level 

N 

X2 = 399.8, p < .OOO 

Subjective 

Financial Strain 

Low 

High 

N 

x2 = 77.9, p < .ooo 

Race and Ethnicity 

Black Himanic White 

6.7% 5.4% 2.3% 

6.0% 13.4% 2.0% 

19.9% 29.3% 10.4 

46.2% 42.8% 48.3 

21.2% 9.1% 37.1 

699 276 5,025 

Race and Ethnicity 

Black Hispanic White 

36.7% 38.5% 55.3% 

63.3% 61.5% 44.7% 

490 205 4,122 

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Periods of 

Unemdoment 

Low (1 or None) 

High (2 or More) 

N 

X2 = 4.1, p = .129 

Neighborhood TvDe 

Advantaged 

Disadvantaged 

N 

X2 = 1341.9, p < .OOO 

Race and Ethnicity 

Black Hispanic White 

93.7% 92.2% 94.8% 

6.3% 7.8% 5.2% 

616 244 4,600 

Race and Ethnicity 

Black Hispanic White 

21.3% 23.5% 80.0% 

78.7% 76.5% 20.0% 

705 285 5,056 

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Next, we examined the joint effects of race and neighborhood type on rates of 

domestic violence (see Tables 7.2a and 7.2b). In wave 1, there is a significant race effect 

on intimate violence in advantaged neighborhoods, but not in disadvantaged 

neighborhoods. In advantaged neighborhoods, the rate of intimate violence for blacks is 

significantly higher (1 1.8 percent) than it is for whites (6.3 percent) or Hispanics (7.3 

percent). However, in disadvantaged neighborhoods, the rate of intimate violence is 

essentially the same for all three race and ethnic groups. For whites the rate is 9.3 

percent, while blacks and Hispanics have only slightly higher rates (10.9 percent and 10.2 

percent, respectively). In wave 2, black couples have higher rates in both advantaged and 

disadvantaged neighborhoods than the other race and ethnic groups, but these differences 

are not statistically significant at the .05 level. In both waves, it is clear that for all three 

race and ethnic groups, women in couples located in disadvantaged areas experience 

higher rates of victimization than do their counterparts in advantaged neighborhoods. 

Taken together these results suggest that the effect of community context on intimate 

violence operates independently of race and ethnicity, and accounts in some measure for 

aggregate correlations between race/ethnicity and intimate violence. 

100 

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Table 7.2a Percentage of Couples with Intimate Violence Against the Woman by 

Race/Ethnicity and Neighborhood Type, Wave 1. 

Race and Ethnicity 

Neighborhood Type White Black 

Advantaged 6.3% 11.8% 

Disadvantaged 9.3% 10.9% 

Hispanic 

7.3% 

10.2% 

x2 advantaged = 9.6, p = .008 

x2 disadvantaged = 1.2, p = .548 

Table 7.2b Percentage of Couples with Intimate Violence Against the Woman by 

Race/Ethnicity and Neighborhood Type, Wave 2. 

Race and Ethnicity 

Neighborhood Tvpe White Black Hispanic 

Advantaged 4.2% 7.6% 1.5% 

Disadvantaged 7.7% 10.4% 7.6% 

x2 advantaged = 5.3, p = .072 

x2 disadvantaged = 3.5, p = .175 

101 

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A Model of Intimate Violence Against Women 

As we noted earlier, correlations between the aggregate characteristics of areas 

and rates of intimate violence have been observed before (Miles-Doan 1998). But the 

significance of these correlations remains in doubt. Whether the association of intimate 

violence with neighborhood economic conditions is due to the structural features of 

neighborhoods or to the composition of their resident populations is an open question. At 

the individual level, the connection between economic distress and intimate violence also 

raises difficult issues. Does individual level economic distress motivate this type of 

violence as may be deduced, for example, f?om general strain theory (Agnew 1992; 

Messner and Rosenfeld 1997) or are economic distress and intimate violence simply 

different manifestations of some underlying individual characteristic such as low selfcontrol (Gottfredson and Hirschi 1990)? Because people of low socioeconomic status 

tend to live in economically disadvantaged areas, separating the structural effects of 

neighborhood disadvantage from the compositional effect of individual economic distress 

can be complex. 

Social disorganization and other contextual theories hold that the structural 

characteristics of neighborhoods can either facilitate or hinder crime (Bursik 1988) 

(Sampson et al. 1997). Several studies have documented associations between crime and 

the perception or fear of crime by residents and such neighborhood features as 

corrinnmity socioeconomic status, ethnic heterogeneity, residential stability, the extent of 

family disruption, housing deterioration, residential overcrowding, and population 

density (Greenberg et al. 1982; Byrne and Sampson 1986; Sampson 1986b; Sampson and 

1 02 

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Groves 1989). That these associations obtain despite a complete turnover in the 

populations of neighborhoods over time suggests to Stark (1987) that the structural, or 

contextual, features of neighborhoods themselves must play some role in the etiology of 

deviance. 

Yet, neighborhoods are selective in terms of the sociodemographic profiles of the 

inhabitants they attract. Despite turnover in the actual residents of a neighborhood there 

may nonetheless be continuity in the types of people found there over time. Differences 

in aggregate crime rates between neighborhoods with different structural characteristics 

may be primarily due to differences in their population compositions (Bursik and Webb 

1982; Simcha-Fagan and Schwartz 1986; Reiss 1986; Sampson 1986a). For example, as 

Sampson notes, an "aggregate offense rate may be positively related to the percentage of 

the population that is black because blacks have a higher rate of offending than do whites 

(an effect of composition) or because blacks in cities with a large black population have 

higher offending rates than do blacks in areas where they are a minority (an effect of 

context)" (Sampson 1986a). Because it is often difficult to untangle contextual fi-om 

compositional effects, this debate is still largely unresolved. Another possibility is that 

the higher rates of officially reported intimate violence in disadvantaged neighborhoods 

are caused by differential reporting to criminal justice personnel, based on individual and 

neighborhood characteristics (Miles-Doan 1998). Differential reporting may be 

particularly troublesome for studies of intimate violence. Analyses of National Crime 

Victimization Survey data indicate, for example, that black women are more likely than 

white women to report intimate violence to the police (Greenfeld et al. 1998). One way to 

get around this problem is to embed questions about physical aggression in surveys of the 

103 

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general population, as is done in the NSFH. Hopefblly, those unwilling to report violence 

to official agencies or to the police would feel freer to report it in the context of an 

anonymous survey. 

Up to this point, we have presented suggestive evidence that correlation between 

community context and intimate violence results at least in part for contextual reasons 

and is not entirely due to compositional differences in neighborhood populations. 

However, we have concentrated primarily on the connections between economic distress, 

community context, and intimate violence. We have generally ignored other individual 

and household characteristics that are known to be associated with intimate violence. It 

is possible that there are compositional differences between neighborhood types that are 

unrelated to economic distress and that account for the higher rate of intimate violence in 

disadvantaged neighborhoods. To investigate this possibility, we developed a more 

comprehensive model of intimate violence, one that includes a wide range of individual 

level characteristics as well as indicators of community context. We then used logistic 

regression to test whether the significant effect of neighborhood type on intimate violence 

remained after these individual level correlates of intimate violence were included in the 

model. Because of variation in the availability of measures in the different waves of the 

NSFH, the models that we develop for wave 1, wave 2 and continuing couples are similar 

but not identical. 

Although contextual effects constitute measures of higher-level units4.e. census 

tracts-than measures of other couple characteristics, v:e P :re mt able io employ 

multilevel modeling techniques for two reasons. First, because of the way that NSFH 

distributes census tract data, we do not know the tract ids for the respondents. Second, 

1 04 

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even if we could obtain tract ids, stable estimates of parameters in multilevel models 

require at least several cases per secondary unit (Bryk and Raudenbush 1992). On 

average, there are only about 5 households per census tract in the NSFH, rendering robust 

estimation of household-level parameters unlikely in a multilevel framework. For this 

reason, we treat neighborhood characteristics as though they were measured at the 

household level. As others have noted, this approach “. . .is likely to be adequate for 

detecting all but the smallest contextual influences” (Miethe and McDowall 1993:785). 

The model for wave 1 includes concentrated disadvantage, residential instability, 

the income to needs ratio, age of primary respondent, education for the primary 

respondent, race, number of children under age 18 in the household, and man’s drinking 

problems. As noted above, previous research consistently finds that intimate violence is 

more common among blacks than whites or others (Greenfeld et al. 1998) and among 

couples of lower class standings (Straus et al. 1980). Hence, our model includes race and 

the primary respondent’s educational attainment, which serves as an indicator of social 

class that is independent of our economic measures. Previous research also indicates that 

intimate violence is inversely related to age. Accordingly, we include age of the primary 

respondent as a control. As a measure of household composition, we include the number 

of children under 18 in the household. Finally, drug and alcohol use have long been 

viewed as important risk markers for intimate violence (Straus et al. 1980; Jasinski and 

Williams 1998). Hence, we include a measure of the male’s drinking problems. The 

operationalization of the variables is described in d>d$tC; 2. 

Table 7.3a presents the results of the logistic regression analysis of intimate 

violence in wave 1. To clarify the effects of the different independent variables, they are 

105 

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entered as a series of blocks, beginning with the indicators of community context, 

followed by economic distress measures, and then the control variables. Model 1 

includes the measure of concentrated disadvantage and residential instability. The odds 

ratio for concentrated disadvantage is 1.85 and is significant at the .01 level. The 

coefficient for residential instability is not significant. In model 2, the income to needs 

ratio is added. It is not significant, but the coefficient for concentrated disadvantage 

remains significant at the .01 level. Finally, model 3 includes the control variables. 

Several have significant effects, including age, race/ethnicity, and male drinking 

problems. In the full model, the odds of victimization decline with age, and for white 

women the odds of victimization are about half those for black women. Male drinking 

problems increase the odds of victimization more than four fold. Importantly, the 

coefficient for concentrated disadvantage is significant at the .05 level in the final model 

but reduced in size (Odds ratio = 1.36). We interpret this as indicating that neighborhood 

type does have a contextual effect on the risk of violence against women in intimate 

relationships. 

The model for overall violence in wave 2 is substantially similar to that for wave 

1, but it includes two additional measures of economic distress - male job instability and 

subjective financial distress. Otherwise, the model for wave 2 includes the same 

variables as for wave 1. However, as described in chapter 2, two of the variables are 

operationalized differently. In wave 2, educational attainment refers to the male’s 

educational attainment rather than rhz educational attainment of the primary respondent. 

The measure of male drinking in wave 2 is a factor score based on a factor analysis of 

four items as described in chapter 2. 

t 106 

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Table 7.3a. Logistic Regression Model of Violence in Wave 1. 

Variables Model 1 Model 2 Model 3 

Concentrated 

Disadvantage 

Residential 

Instability 

Income to 

Needs Ratio 

Age 

Education 

Race' 

White 

Hispanic 

Number of 

Children 

Drinking Problem 

Odds 

Ratio 

1.36* 

.40 

1 .oo 

.92** 

.97 

SO** 

.76 

.95 

4.60** 

-2LL 2,423.1 2,420.7 2,112.8 

Model Chi-square 30.4** 32.8** 340.7** 

Block Chi-square 30.4** 2.4 308.0** 

N = 4,344 

* p < .05 

** p < .O1 

' The comparison category is Afiecn-Amei-kx. 

107 

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Model 1 of Table 7.3b includes only the contextual measures. Concentrated 

disadvantage has a significant positive effect on the odds of intimate violence (odds ratio 

= 2.16). The size of the effect of concentrated disadvantage is reduced in model 2, which 

includes the three measures of economic distress. Two of the measures of economic 

distress have significant effects in the expected directions. Greater male employment 

instability and greater subjective financial strain increase the odds of intimate violence. 

In model 3, the control variables are entered. Age, race, number of children in the 

household, and male drinking have significant effects. The smaller size of the odds ratio 

for male drinking in wave 2 compared to wave 1 does not indicate that drinking problems 

became less significant as a cause of intimate violence. Recall that the available 

measures of drinking problems are quite different in the two waves. Importantly, the 

effects of our measures of community context and economic distress remain significant in 

the full model and are only slightly diminished in size even after all of the control 

variables are included in the model. As in wave 1, this result suggests that concentrated 

disadvantage exerts an independent contextual effect on intimate violence. 

The results presented in Tables 7.3a and 7.3b suggest that the aggregate 

correlation between neighborhood type and intimate violence is not entirely due to 

compositional differences in resident populations. The results also suggest that some 

forms of economic distress exert independent effects on intimate violence. Yet, it is 

possible that cne other individual level characteristic that we have not yet taken into 

account may reduce or eliminate both of these apparent effects. That characteristic is 

prior intimate violence. An increasingly well-documented and consistent finding is that 

108 

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Table 7.3b. Logistic Regression Model of Violence in Wave 2. 

Variables 

Concentrated 

Disadvantage 

Residential 

Instability 

Income to 

Needs Ratio 

Male Job 

Instability 

Subjective 

Financial Strain 

Age 

Male Education 

Race' 

White 

Hispanic 

Number of 

Children 

Male Drinking 

Model 1 Model 2 Model 3 

Odds Odds Odds 

Ratio Ratio Ratio 

2.16** 1.67** 1.66** 

1.15 .97 .76 

.98 1.03 ------ 

1.28** 1.28** ------ 

-2LL 1,920.6 1,814.6 1,671.3 

Model Chi-square 33.2** 139.1** 282.4** 

Block Chi-square 33.2** 105.9** 143.3** 

N = 4,406 

* p < .05 

** p < .01 

' The comparison category is African-American, 

109 

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violence at time 1 

1993; Nagin et al. 

predicts violence at time 2 (Wolfgang et al. 1972; Sampson and Laub 

1995). Furthermore, longitudinal research on crime and violence has 

shown that many cross-sectional correlations between violence and other individual level 

characteristics, such as employment status and educational attainment, disappear or are 

substantially reduced in size once prior levels of violence are controlled for. This result 

is usually interpreted as indicating that violence reflects an underlying individual level 

characteristic that affects not only fbture violence but also fbture employment and 

educational attainment as well as other outcomes. It is possible that the same is true of 

intimate violence against women. If men with a high propensity to be violent against 

their intimate partners also are more likely to locate in disadvantaged neighborhoods, and 

if they also are more likely to experience employment instability and other forms of 

economic distress, then the cross-sectional correlations that we have observed in wave 1 

and wave 2 may be to some degree spurious. 

To investigate this possibility, we focus on the continuing couples in the NSFH. 

Recall that continuing couples are those who were married or cohabiting in wave 1 and 

who are still together in wave 2. For the continuing couples in wave 2, we know whether 

they were violent in wave 1. Hence, we can control for prior violence in these couples. 

Because the data are longitudinal, we evaluated whether there was evidence of selective 

attrition as a function of intimate violence. Probit analysis of participation in wave 2 

indicates that violence is not a predictor of exclusion fiom wave 2. However, because we 

are focusing on continuing couples, there is scme selectivity OR vhlence. Couples 

experiencing violence in wave 1 are more likely to disrupt between waves of the NSFH 

(DeMaris 2000). So, the sub-sample used here likely has been subjected to some 

110 

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selectivity. We believe that this selectivity weakens the effects of our most important 

theoretical predictors, because males who live in disadvantaged areas and who engage in 

violence against their partners are the ones most likely to separate and to not show up in 

our sample of continuing couples. Therefore, use of this sub-sample makes for a 

conservative test of the question whether community context and economic distress 

influence intimate violence. 

Table 7.3~ presents the results of our logistic regression of violence in wave 2 

among continuing couples. The model is the same as that used for all couples in wave 2 

except that it includes violence at wave 1. TQ maintain consistency with the form of the 

analyses for waves 1 and 2, we present a series of equations so that changes in the effects 

of variables may be observed. However, we concentrate on model 4 as this is the model 

which contains new information. As expected, violence in wave 1 has a sizeable and 

positive effect on the odds of violence in wave 2 (odds ratio = 8.58). This result is 

consistent with a cascade of research on other forms of violence. However, despite the 

strong effect of prior violence, concentrated disadvantage, employment instability, and 

subjective financial strain continue to have significant effects on the likelihood of 

violence against women in intimate relationships. This result confirms the importance of 

both neighborhood socio-economic disadvantage and individual level economic distress 

for our understanding of the problem of violence against women. Another notable result 

in model 4 involves race and ethnicity. This variable is not significant in the fill model. 

Among continuing couples, black women are not sipificmtly more Iiiiely to expenence 

violence in intimate relationships than white or Hispanic women. 

111 

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2. 

Variables 

Table 7.3~. Logistic Regression Model of Violence Among Continuing Couples in Wave 

Model 1 Model 2 Model 3 Model 4 

Odds Odds Odds Odds 

Ratio Ratio Ratio Ratio 

Concentrated 

Disadvantage 

Residential 

Instability 

Violence Time 1 

Income to 

Needs Ratio 

Male Job 

Instability 

Subjective 

Financial Strain 

Male Education 

Race’ 

Blacks 

Other Non-whites 

Number of 

Children 

Male Drinking 

2.15** 1.96** 1.65** 1.81** 

1,233.4 I,C9i.6 1,059.1 992. I. -2LL 

Model Chi-square 19.6** 161.4** 193.9** 260.8** 

Block Chi-square 19.6** 141.8** 32.5** 67.0** 

N = 3,455 

*p<.O5 

112 

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1 ** p < .01 

' The comparison category is white. I 

113 

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Because the insignificant finding for race and ethnicity is unusual we investigated 

it further. Additional logistic analyses revealed that race has a significant effect on 

intimate violence at the bivariate level among continuing couples. The effect remains 

significant even after we control for prior violence in wave 1, but it becomes insignificant 

once we control for concentrated disadvantage. This result points to the importance of 

Sampson’s observation cited earlier in this chapter that aggregate correlations between 

race and crime must be interpreted cautiously because they may be systematically 

confounded with differences in ecological contexts. 

Conclusions: Community Context and Economic Distress 

The results presented in this chapter provide the strongest evidence yet that 

community level socio-economic disadvantage and individual level economic distress are 

associated with intimate violence against women. The relationship between community 

context and intimate violence does not appear to result from compositional differences in 

neighborhood populations. Rather, they represent real contextual effects. The effect of 

individual level economic distress on intimate violence does not appear to be the result of 

some underlying individual propensity in men toward violence against their intimate 

partners. 

The effects of economic disadvantage on a large number of social pathologies 

clearly can be observed at the aggregate level. Decades of research have shown that 

economically distressed communities tend to suffer from high rates of other social 

pathologies, including crime, substance ahse, mental arid physical health problems, and 

family disruption (Wilson 1987; Sampson et al. 1997). As the connection between 

individual problems and the community context within which individuals live and work 

114 

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has engaged researchers and theorists (Sampson 1985; Wilson 1987; Hagan 1993), the 

detrimental effects of economic distress on individuals also have been increasingly welldocumented. Economically disadvantaged individuals are more likely to suffer a host of 

deleterious outcomes compared to more well off individuals. Physical and mental health 

problems are more common and severe among the poor compared to the more affluent 

(Robert 1999). The poor also are more likely to be victims of violent crimes (U. S. 

Department of Justice 1996). 

We have attempted to extend this line of research by investigating the effects of 

community economic disadvantage and individual economic distress on intimate violence 

against women. Our results show that intimate violence against women is more common 

in disadvantaged neighborhoods, corroborating other research on domestic violence that 

has relied on calls to the police (Miles-Doan 1999). In that our measures of violence are 

based on reporting in an anonymous survey, they should be less affected by differential 

reporting than are results based on studies of calls to police. At least part of the difference 

in fiequency of calls to the police appears to be due to real differences in intimate 

violence behavior across neighborhoods and not just differences in reporting behavior. 

Of course, it is possible that both types of effects are operating. Residents of 

disadvantaged neighborhoods may experience more domestic violence and may also be 

more lizely to call the police when such incidents occur. 

We found that the relationship between neighborhood disadvantage and wife 

assault remains even after cor,trois for pri~i vidcnce and other individual and couple 

level attributes are included in the model. This result suggests that higher rates of 

intimate violence in disadvantaged neighborhoods in part reflect contextual effects. The 

115 

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data at our disposal did not permit us to investigate whether these structural 

characteristics are associated with the social and cultural processes identified by the 

concept collective efficacy. Nevertheless, the results suggest that this would be a fiuitful 

avenue of future research. 

With the data at our disposal, we cannot tell whether the contextual effect of 

concentrated disadvantage on intimate violence against women is as strong as its effects 

on other forms of criminal victimization. The special characteristics of intimate violence, 

most notably its location in the home, would suggest that contextual effects mi@ be 

weaker. Social disorganization and collective efficacy theory posit that neighborhood 

rates of offending and victimization are a fbnction of the strength of informal social 

controls (Bursik 1988; Sampson and Groves 1989; Sampson et. al. 1997). In structurally 

disorganized communities, residents are theorized to be less willing and less able to 

supervise others, especially youth, in public places and on public streets. Hence, those 

predisposed to engage in crime and deviance are freer to indulge their proclivities than 

individuals in more organized communities. Intimate violence, however, tends to be 

perpetrated more in private than in public. The perpetrators may be less susceptible to 

influence by community-level informal social controls than are the perpetrators of other 

forms of criminal behavior. Thus, the family's special characteristics of intimacy and 

privacy may require the development of specialized theories for crimes that take place 

within that setting (Straus et al. 1980). In addition, recent research suggests that the 

personality factors associated wit11 m!m : be'liveen iuiidcs are generally not the same 

as those associated with general crime (Moffitt, Krueger, Caspi, and Fagan 2000). 

116 

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I Residential instability has long been associated with neighborhood disadvantage 

and traditionally viewed as an exogenous source of social disorganization (Shaw and 

McKay 1942; Sampson and Groves 1989). Hence, we expected to find that residential 

instability would be positively associated with intimate violence but instead found that 

instability reduces the odds of intimate violence. One explanation for this 

counterintuitive finding comes from recent theory and research on residential mobility. 

Wilson’s (1 987) theory of concentration effects suggests that extremely disadvantaged 

neighborhoods may have little population turnover because racial and economic 

discrimination prevent residents fiom leaving. Farley’s (1 996) analysis of 1990 census 

data finds that persons with college and advanced degrees are more likely to move than 

less educated persons. Hence, high levels of neighborhood residential mobility may no 

longer be closely tied to high levels of neighborhood economic disadvantage. 

Economically disadvantaged neighborhoods may be quite stable. The combina$on of 

low residential mobility and high economic disadvantage in neighborhoods, however, has 

deleterious effects on psychological well-being and increases the risk of street crime 

victimization (Wikstrom and Loeber 2000; Walsh 2000). Our results suggest that this 

combination also increases the risk of intimate violence. 

Consistent with the hypothesis of Fagan and Browne (1 994) regarding economic 

distress as a triggering factor in intimate violence, we found that two measures of 

economic distress have significant effects on intimate violence. More employment 

icstability and greater financial si~nk La-ease its likelihood. But measures of economic <I 

deprivation and change in deprivation did not have significant effects. The more 

I pronounced effect of employment instability as compared to the two other measures of 

117 

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objective economic distress suggests that something other than resource deprivation alone 

drives the relationship between economic distress and intimate violence. If reduced 

access to economic resources is the primary way in which economic distress triggers 

intimate violence, we would expect that employment instability would have little direct 

effect on intimate violence independent of our measure of household income to needs and 

our measure of change in the income to needs ratio (MacMillan and Gartner 1999). Yet, 

we find that it is employment instability rather than household income that has the 

significant effect. Following MacMillan and Gartner (1999), we suspect that male 

employment instability may increase the risk of intimate violence against women because 

it reduces men's sense of self-worth and represents a threat to their sense of masculinity. 

Being repeatedly fired or released from employment may provoke feelings of 

stigmatization and anger in males, who then may take out their frustrations on their 

partners. Men's sense of self-worth may be particularly vulnerable when they cannot 

hold a job, so they may become especially sensitive to affionts to their authority. Thus, 

the economic aspects of male unemployment may be a less important source of stress 

than its symbolic aspects. 

The idea that the subjective aspects of economic distress are potentially important 

sources of intimate violence is buttressed by our finding on perceptions of financial 

strain. How couples subjectively perceive and interpret their financial situations matters. 

Females in couples that interpret their financial situations pessimistically appear to be at 

greater risk of intimate violence. As dissatisfaction with finances and worry about 

income suficiency increase, the likelihood of male to female violence also increases. 

This conclusion should be tempered, however, by a major limitation in our measure of 

118 

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subjective strain. As this measure was only available in wave 2, it may well be 

endogenous to violence rather than a cause of it. That is, the ordering of the measures of 

violence and subjective strain in wave 2 does not preclude the possibility that violence in 

the year preceding the survey engendered a greater sense of financial strain. We await 

replication of this finding with more precise measures before placing too much emphasis 

on it. Financial strain was related both to overall violence in wave 2 and to the initiation 

of violence in wave 2. Unfortunately, because there are no measures of subjective strain 

in wave 1, we also cannot ascertain how much change there is in this dimension of 

economic distress over time or whether change in subjective strain affects the likelihood 

of violence. 

119 

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CHAPTER EIGHT 

SUMMARY OF THE PROJECT AND POLICY IMPLICATIONS 

Summary of the Project 

The primary goal of this project was to investigate how community level socioeconomic disadvantage and individual level economic distress influence violence against 

women in intimate relationships. The selection of this goal was motivated by the 

National Research Council's Panel on Research on Violence Against Women, which 

called upon researchers to focus on "the influence of socioeconomic, cultural, and ethnic 

factors" on violence against women. The Panel (1 996) noted that intimate violence is a 

complex phenomenon and that enhanced understanding of intimate violence against 

women will require multi-factor causal models. These models must take into account 

both its structural and contextual causes as well as individual level factors. Accordingly, 

we designed this project to focus on the structural and contextual dimensions of intimate 

violence while at the same time taking into account measures of individual level 

economic distress as well as other relevant individual level variables. 

The project was based on a data set that we created by merging waves 1 and 2 of 

the National Survey of Families and Households with census tract data fkom the 1990 U. 

S. Census. The resulting data set was particularly well-suited for an investigation of a 

multi-factor causal model of intimate violence against women. It enabled us to trace the 

experiences of women in intimate relationships over time and to explore the independent 

and joint effects of a host of individual level and contextual level factors. As far as we 

120 

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I 

I 

know, th~s data set is the only one available which permits such complex and multidimensional longitudinal, contextual, and situational analyses. 

Limitations of the Data 

As with all data sets, NSFH data are not perfect. Before we summarize our 

findings, it is appropriate to first identify and briefly discuss four important weaknesses 

in the data. One general shortcoming of the NSFH for longitudinal analyses is the 

relatively large time lag between waves 1 and 2. On average, about five years elapsed 

between interviews for most respondents. With respect to studies of trajectories in 

intimate violence, this gap is problematic because the reference period for the violence 

questions is one year. Hence, there is a period of approximately four years between 

waves about which no information on violence is available. It is possible, indeed 

probably quite likely, that some unknown proportion of the couples that we categorized 

as non-violent actually displayed violence during this period. In effect, the data on 

violence are middle-censored. Furthermore, because only two observation points are 

available the data also are both left and right censored. Couples may have exhibited 

violence before the reference period in wave 1 or after wave 2. We do not know to what 

extent this censoring may have influenced the findings. However, because the overall 

patterns of results for wave 1 and wave 2 are so similar, we believe that censoring did not 

dramaticaily affect the relationships that we observed. We also note that a third wave of 

the NSFH is scheduled to be conducted in the future. So, this issue can be adjudicated by 

future researchers. 

Another weakness of the NSFH concerns the measures of intimate violence. As 

we noted above, like other national surveys the NSFH is probably best suited to 

121 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
identifylng women who are experiencing common couple violence as opposed to 

patriarchal terrorism. In addition, because the NSFH items focus on physical violence 

related to arguments, they do not capture violence by men that does not arise out of 

arguments, as, for example, when a male gets drunk and attacks his partner without 

provocation. The NSFH items also are poorly suited to identifylng non-physical forms of 

violence against women such as verbal or emotional violence. As with the issue of data 

censoring, we do not know to what extent the findings have been influenced by the nature 

of the measures of violence available in the NSFH. 

There are problems with the measure of subjective financial strain. The main one 

being that it is available only for wave 2. The lack of a wave 1 measure of this concept is 

unfortunate, because it prevented us fkom investigating the effects of changes in 

subjective strain on intimate violence. It also prevented from exploring how changes in 

the objective dimensions of economic distress are related to changes in subjective 

economic distress. In addition, only two items are available to measure this important 

concept. 

Finally, we argued that concentrated disadvantaged may be related to intimate 

violence via the processes associated with collective efficacy. Unfortunately, with the 

data available in the NSFH we are unable to construct measures of this intervening 

concept. We believe our results show that community context is related to rates of 

intimate violence and that this relationship is not entirely due to compositional 

differences in neighborhood populations. Enwever. we recognize that the exact name of 

the connecting mechanism is unclear. 

122 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
I Despite its limitations, the NSFH remains an extremely valuable data set for 

investigating the phenomenon of intimate violence against women and family relations. 

As far as we know, it is the only data set available that permits both longitudinal and 

contextual analyses of a nationally representative sample of couples. It contains solid 

8 

measures of many of the variables known to be related to intimate violence, thus 

permitting the specification and analysis of complex multivariate models. Although they 

are not perfect, the questions pertaining to violence and many other important concepts in 

the NSFH are asked of both the male and the female members of couples. These 

replicate questions provide for more accurate and comprehensive measurement. For 

these reasons, we are confident that our findings are reliable, and we turn to a summary 

of them now. 

Summary of Findine 

The major findings of the study concern the effects of community context and 

economic distress on violence against women in intimate relationships. In capsule form, I 

1 

1 

8 

I 

8 

I 

I 

I 

we present the four major findings here and then discuss them below: 

1. Women in couples that are located in socio-economically disadvantaged 

neighborhoods are at a higher risk of experiencing violence at the hands of 

their male partners than women located in advantaged neighborhoods. 

2. The relationship between community context and intimate violence is not 

the result of compositional differences in neighborhood populations but 

rather represents a cmtextm! effeci. 

3. Different forms of economic distress are related to the likelihood of 

violence against women in intimate relationships. 

123 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
4. The correlation of race and ethnicity with intimate violence is confounded 

with differences in community context. 

The elevated risk of intimate violence to women in disadvantaged neighborhoods is 

substantial and appeared consistently in both waves of the NSFH. This finding, based upon 

self-reports of couples from a nationally representative random sample of households, 

corroborates similar findings based upon crime reports to police and victimization surveys. 

The greater vulnerability of women in disadvantaged neighborhoods appears to be real and not 

simply an artifact of the method used to obtain the reports of violence. 

The substantive significance of finding that living in a disadvantaged neighborhood 

entails a higher risk of intimate violence is enhanced by our second major finding. That is, the 

neighborhood effect is not reducible to differences in the socioeconomic makeup of 

neighborhood residents. Although we found that sample respondents living in neighborhoods 

of concentrated disadvantage had lower income-to-need ratios, more periods of job instability, 

and greater levels of subjective economic distress, once these measures of economic well-being 

were taken into account by holding them constant, rates of intimate violence still were higher 

for women living in disadvantaged neighborhoods. We can say with assurance that where a 

woman lives matters significantly to her risk of violent victimization at the hands of her 

intimate partner. 

’ Our analyses do not, unfortunately, allow us to say with certainty why this is so. 

However, we can speculate that many of the same social mechanisms that others have 

suggested to account for the greater risk of street crime in disadvantaged neighborhoucis maji 

also be operative here to account for the higher risk of intimate violence for women in 

disadvantaged neighborhoods. These include a greater tolerance for deviance among residents 

124 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
of highly disadvantaged communities (Anderson, 1990), a lower degree of collective efficacy 

and social capital to stave off or respond to criminal behavior in the neighborhood and among 

neighborhood residents (Coleman 1988; Sampson et a1 1997), and a lack of models for and ties 

to more conventional social behaviors (Wilson, 1987). Moreover, research on women in 

disadvantaged neighborhoods shows that such women have fewer neighborhood-based 

resources to draw upon to ensure the safety of their homes and children and often resort to 

isolating themselves from other residents and to withdrawal within their own quarters as the 

surest means of self-protection to stay safe (Brodsky 1996; Burton and Jarrett 2000; Fox 2000). 

In these neighborhood contexts there literally may be no one nearby whom a victimized 

woman might call upon informally for assistance with a violent partner, and there may be 

nowhere to turn to escape an abusive relationship, even for a brief period. 

Our third major finding was that different aspects of economic stress or well being - 

including having income beyond the family’s basic needs, the number of debts, and work 

stability of the male partner -- are systematically related to the risk of violence. It is important 

to underline that although some violence was found across all levels of our various measures of 

economic well being, the highest risks and the most severe violence were not evenly 

distributed across the economic spectrum but rather were concentrated among the women who 

were least well off in terms of our measures of economic well being. Further, our finding that 

the women at greatest risk are those with the fewest economic resources who also live in the 

I most disadvantaged neighborhoods highlights the limited degrees of freedom for women in 

economically precarious situations living in impoverishx! neighborhoods. In the decisionmaking calculus of women struggling on the margins, the importance of a partner’s economic 

I contributions to household survival - even when the contributions are only sporadic or minimal 

125 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
I 

I 

I 

8 

- may outweigh the risk of violence. Having food on the table, money for next month’s rent, 

and shoes for growing children may make the risk of severe violence and occasional injury 

seem a not illogical bargain. Thus, when the loss of partner contributions are so costly to 

economic survival, some women may be compelled to live with his violent behavior rather 

than take the almost universally recommended steps to exit such relationships. 

Our fourth major finding -- that the correlations of race and ethnicity with violence are 

confounded with neighborhood context - provides a very important insight into the links 

among race/ethnicity, socioeconomic context, and violence against intimates. Specifically, OUT 

analyses show that when controls for community context are in place, the association between 

race/ethnicity and violence weakens or disappears. That is, the effect of race and ethnicity on 

intimate violence is in large part a fbnction of the greater likelihood of Black and Hispanic 

couples to reside in neighborhoods with concentrated economic disadvantage. This suggests 

that economic and contextual explanations of intimate violence are likely to be more 

informative than are culturally or race-based explanations. An understanding of the social 

dynamics that lead to patterns of race and ethnic residential concentration in the economically 

and socially most impoverished neighborhoods will shed light on the commonly found link 

between race/ethnicity and intimate violence. 

Policy Implications 

Few doubt that social change and economic distress can have pathological consequences 

on communities, intimate couples, and individuals. Further, the impact of change and distress 

certainly varies with the community coctext in which it takes place. To develop effective 

prevention and intervention strategies, policy makers need to know how changes that produce 

distress influence violence against women and whether the causes of violence differ among 

126 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
raoial and ethnic subgroups. With respect to intimate violence, however, little has been known 

about the impact that change and distress can have on victimization risks or on how these risks 

may differ among subgroups. Little has been known about the connections between intimate 

violence and personal and community economic well being or about the ways in which 

community context may influence the reasons why women stay in abusive relationships or why 

they leave them. This project was designed to shed light on these issues and to help articulate 

the relationship between community context, demographic characteristics, economic distress, 

and intimate violence. We believe this knowledge will enable policy-makers to target 

intervention and prevention programs more effectively and to anticipate more accurately when 

the demand for such programs is likely to increase because of social change. 

Although our analyses do not allow us to make specific recommendations to 

policy makers and service providers, nonetheless we can suggest in more general terms 

some strategies for policy, services and research that grow out of this project. Intimate 

violence is not solely a matter of individual psychopathology or inadequate interpersonal 

skills, and responses to intimate violence should not be based solely on these individual 

level causes. We would be wise to broaden our thinking beyond individual level causes 

of intimate violence to include a larger spectrum of potential areas for strategic 

intervention and change. 

In this study, intimate violence was strongly linked to the economic well being of 

individual couples and to the community contexts in which couples were found. This 

suggests that econnmic practices and jobs policies may be important conditional 

influences on the risks of intimate violence to women. We note that job stability rather 

than employment per se was an important risk factor for violence against women in this 

I 

I 127 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
study. Giving preference to economic practices and job policies that balance transitory 

labor supply/demand ratios by preserving job security for workers rather than by 

widespread layoffs and periodic rehires exemplifies a policy initiative that takes into 

account the role of job stability in the risk of violence against women. For service 

providers, an implication of the findings from this study is to be vigilant about changes in 

local jobs markets in terms of their potential to cause short-term increases in the numbers 

of victims of intimate violence. 

Our findings about the central importance of community context to the risk of 

intimate violence shifts focus to the social dynamics of spatial location in violence 

against women. The continuation of already strongly entrenched patterns of residential 

segregation by race and ethnicity has been exacerbated by an increasing spatial 

concentration of affluence and extreme poverty (Massey 1996). These current 

demographic trends in residential location patterns suggest that increasing numbers of 

women in the US population will be exposed to the contextual effects we identified in our 

study. In that housing policies, mortgage and lending policies, and insurance regulations 

are all relevant in shaping the spatial dynamics of residential patterning, it is not 

inappropriate to suggest that all might be seen as potential strategic targets for altering the 

risks of violence against women. For service providers, the implications of our findings 

about community context and economic distress are two fold. If the goal is to target 

services where the risk of violence is greatest, then priority for services should be given 

to women in the most disadvantaged neighborhoods. Second, given the curllulation 0; 

risk from community context and individual economic distress, services to women in the 

most disadvantaged areas must address their straitened economic circumstances. For 

128 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
many women who seek to exit an abusive relationship, their correlative needs for 

immediate cash assistance to replace the economic contributions of their male partners 

become paramount. Failure to address their economic needs may render any other 

services merely palliative. 

We conclude with some recommendations for hture research on violence against 

women. Our project implies that fhre research on violence against women will be most 

valuable when the study design 

8 

e 

e 

8 

e 

is couple-based, including both partners as respondents; 

includes a sampling design that focuses on couples drawn randomly from 

areal sampling units that represent socioeconomically the most 

disadvantaged neighborhoods; 

relies upon rich measures of a broad range of potential causes, contexts, 

and consequences of intimate violence; 

relies upon rich measures of a range of types and circumstances of 

intimate violence; 

relies upon a multi-method design for data collection; 

is a multi-wave design, revisiting couples at least three times over the 

course of the project, with time intervals between waves of no more than 

twelve months duration; 

includes the collection of contextual information on the neighborhood so 

that a more finely drawn picture may be drawn of the social dynamics 

through which neighborhood contexts affect the risk of intimate violence 

against women. 

129 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
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135 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
Appendix A 

Census 

Variable Names 

(spss variable 

names) 

Variables Abstracted from the U. S. Census 

Variable Description 

(Explanations of some of the variables appear in the endnotes.) 

IC 

1 

E 

I. 

I 

PO060001 

PO060002 

IC 

II 

I. 

1 

I 

1 

I 

I Inside urbanized area 

Outside urbanized area 

6 

c 

I 

I 

PO060003 

PO060004 

These are the original variables that were extracted fiom the census Internet cite 

(http :/venus.census. gov). 

Farm 

Non-fm 

PO070001 

PO070002 

TRACT I FPS.TRACT90 I Census Tract Number 

Male 

Female 

NOTE: state, county and tract do not appear in files "utkwlm" or "utkw2m". Instead, these files 

begin with the case id numbers fiom the NSFH, and then proceed as follows. 

PERSONS / Universe: Persons 

1 P0010001 I Total 

HOUSEHOLDS ' / Universe: Households 

1 PO050001 1 Total I 

136 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
PO080001 

PO080002 

PO080003 

PO080004 

PO080005 

White 

Black 

Amencan Indian, Eskimo, or Aleut 

Asian or Pacific Islander 

Other race 

L PO090006 I Chinese (605-607,976) 1 

P009000 1 

PO090002 

PO090003 

PO090004 

PO090005 

White (800-869,971) 

Black (870-934,972) 

American Indian, Eskimo, or Aleut (000-599,935-970,973-975): 

Eskimo (935-940,974) 

Aleut (94 1-970.975) 

PO090007 

PO090008 

Filipino (608,977) 

JaDanese (611.9811 

PO090009 

PO09001 0 

PO09001 1 

Asian Indian (600,982) 

Korean (6 12, 979) 

Vietnamese (619, 980) 

, 

PO090024 1 Pacific Islander, not specified (681-699) 

PO090012 

PO0900 13 - 

1 PO090025 I Other race (700-799,986-999) 1 

Cambodian (604) 

Hmong(609) 

AGE I Universe: Persons 

I PO130001 I Under 1 vear 1 

PO0900 14 

PO09001 5 

Laotian (6 13) 

Thai (618) 

. - __ 

1 PO130005 i bvears 1 

PO0900 16 Other Asian (60 1-603,6 10,6 14-6 1 7,620-652,985) Pacific Islander 

(653-699,978,983,984): Polynesian (653-659,978,983): Pacific 

Islander (653-699,978,983,984): Polynesian (653-659,978,983): 

PO0900 17 Hawaiian (653,654,978) 

PO0900 18 Samoan (655,983) 

PO0900 1 9 Tongan (657) 

PO090020 

PO09002 1 Guamanian (660,984) 

PO090022 Other Micronesian (661-675) 

Other Polynesian (656,658, 659) Micronesian (660-675, 984): 

PO130002 

PO 130003 

PO 130004 

137 

~. 

1 and 2 years 

3 and 4 years 

5 vears 

PO 1 3 0006 

PO 130007 

PO 1 3 0008 

PO 130009 

PO 13001 0 

d 

7 to 9 years 

10 and 11 years 

12 and 13 years 

14 years 

15 years 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
1 PO130011 I 16 vears 1 

! 

PO1 3001 3 

PO1 300 14 

PO1 300 15 

PO 13001 6 

PO1 300 17 

PO1 300 18 

PO 13001 9 

PO130020 

PO1 30021 

PO130022 

PO130023 

PO 130024 

PO 13 bo25 

PO 130026 

PO1 30027 

PO 1 30028 

PO 130029 

PO 130030 

PO1 3003 1 

17 years 

18 years 

19 years 

20 years 

21 years 

22 to 24 years 

25 to 29 years 

30 to 34 years 

35 to 39 years 

40 to 44 years 

45 to 49 years 

50 to 54 years 

60 and 61 years 

62 to 64 years 

65 to 69 years 

70 to 74 years 

75 to 79 years 

80 to 84 years 

85 years and over 

55 to 59 ,,e,- 

RACE BY SEX BY AGE / Universe: White males 

PO 14A00 1 

PO 14A002 

PO 14A003 

PO 14A004 5 years 

PO 14A005 6 years 

P014A006 7 to 9 years 

PO 14A007 10 and 11 years 

P014A008 12 and 13 years 

P014A009 14 years 

P014A010 15 years 

P014A011 16 years 

PO 14A0 12 17 years 

PO1 4A0 13 18 years 

P014A014 19 years 

PO1 4A0 15 20 years 

PO1 4A0 16 21 years 

PO 14A0 17 

PO1 4A018 

PO 14A0 19 

PO 1 4A020 

Under 1 year 

1 and 2 years 

3 and 4 years 

- - 

22 to 24 years 

25 to 29 years 

30 to 34 years 

35 to 39 years 

PO 14A02 1 40 to 44 years 

138 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
I 

1 PO 14A022 

PO 14A023 

PO 14A024 

PO1 4A025 

c 

1 

1 

45 to 49 years 

50 to 54 years 

55 to 59 years 

60 and 61 vears 

P014A027 

PO 14A028 

P014A029 

PO 14A03 0 

PO 14A03 1 

~ ._ ~~ a 

P014A026 I 62 to 64 years J 

65 to 69 years 

70 to 74 years 

75 to 79 years 

80 to 84 years 

85 years and over 

I 

Universe: White females 

PO 14BOO 1 Under 1 year 

P014BOQ2 1 and 2 years 

PO 14B003 3 and 4 years 

P014B004 5 years 

P014B005 6 years 

PO 14B006 

PO 14B007 

PO 14B008 

PO 14B009 14 years 

P014B010 15 years 

P014B011 16 years 

PO 1 4B0 1 2 17 years 

PO 14B013 18 years 

PO 1 4BO 1 4 19 years 

PO1 4B0 15 20 years 

P014B016 21 years 

PO 14BO 17 

PO 1 4BO 1 8 

PO 14BO 19 

P014B020 I 35 to 39 vears I 

7 to 9 years 

10 and 11 years 

12 and 13 years 

22 to 24 years 

25 to 29 years 

30 to 34 years 

a 

PO 148026 I 62 to 64 years 1 

PO 14B02 1 

PO 14B022 

PO1 4B023 

P014B024 

PO 14B025 

- 

40 to 44 years 

45 to 49 years 

50 to 54 years 

55 to 59 years 

60 and 61 vears 

139 

P014B027 

P014B028 

PO1 4B029 

P 0 1 4B03 0 

PO 14B03 1 

~ 

65 to 69 years 

70 to 74 years 

75 to 79 years 

80 to 84 years 

85 years and over 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
1 

8 PO1 4CO02 

PO 14C003 

PO 14C004 

PO 14C005 

P014C006 

P014C007 

PO 14C008 

1 and 2 years 

3 and4years 

5 years 

6 years 

7 to 9 years 

10 and 11 years 

12 and 13 vears 

PO1 4C009 

PO 1 4C010 

, PO14COll 

P014C012 

P014C013 

- ~~ 

14 years 

15 years 

16 years 

17 years 

18 years 

s 

1 

t 

PO 14CO 14 

P014CO 1 5 

P014C016 

PO1 4C017 

PO 14CO 18 

~ ~ 

19 years 

20 years 

21 years 

22 to 24 years 

25 to 29 vears 

P014C019 

PO 14C020 

P014C021 

PO 14C022 

PO 14C023 

PO 14C024 

P014C025 

PO1 4C026 

30 to 34 years 

35 to 39 years 

40 to 44 years 

45 to 49 years 

50 to 54 years 

55 to 59 years 

60 and 61 years 

62 to 64 years 

PO 14C027 

P014C028 

PO 14C029 

65 to 69 years 

70 to 74 years 

75 to 79 years 

PO 14C030 

PO 14C03 1 

4 

PO 14D0 13 I 18 years 

80 to 84 years 

85 vears and over 

140 

PO 14D00 1 

PO 14D002 

PO 14D003 

PO 14D004 

PO 14D005 

Under 1 year 

1 and 2 years 

3 and 4 years 

5 years 

6 vears 

PO 14D006 

PO 14D007 

PO 14DOOb 

7 to 9 years 

10 and 11 years 

12 and 13 vexs L d 

PO 14D009 14 years 

PO1 4D010 15 years 

P014D011 16 years 

PO1 4D0 12 17 vears 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
[ P014D014 I19years 1 

PO 14D015 

PO 14D016 

I 

II 

- 20 years 

21 vears 

CI 

I 

1 

I 

c 

I 

1 

1 

1 d 

PO 14D017 

PO 14D0 1 8 

PO 14D0 19 

22 to 24 years 

25 to 29 years 

30 to 34 years 

c 

1 

P014D020 

PO 14D02 1 

35 to 39 years 

40 to 44 vears 

PO 14D022 

P014D023 

45 to 49 years 

50 to 54 vears 

P014D025 

PO 14D026 

PO 14D027 

P014D028 

~ a 

P014D024 I 55 to 59 years 

60 and 61 years 

62 to 64 years 

65 to 69 years 

70 to 74 vears 

PO 14D029 

PO 14D030 

PO1 4D03 1 

~ 

75 to 79 years 

80 to 84 years 

85 years and over 

PO 14EOO 1 

PO 14E002 

PO 14E003 

PO 14E004 

PO 14E005 

PO 14E006 

PO 14E007 

PO 14E008 

PO1 4E009 

PO 14E0 10 

P014E011 

P014E012 

PO 14EO 13 

PO 14E0 14 

141 

Under 1 year 

1 and 2 years 

3 and 4 years 

5 years 

6 years 

7 to 9 years 

10 and 11 years 

12 and 13 years 

14 years 

15 years 

16 years 

17 years 

18 years 

19 vears 

P014E018 

P014E019 

PO 14E020 

PO 14E02 1 

PO 14E022 

PO1 4E023 

P014E024 

PO1 4E025 

25 to 29 years 

30 to 34 years 

35 to 39 years 

40 to 44 years 

45 to 49 years 

50 to 54 years 

55 to 59 years 

60 and 61 years 

-_-- 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
~~ ~ I P014E026 I 62 to 64 years 

P014E027 

PO 14E028 

65 to 69 years 

70 to 74 vears 

PO 14E029 

P014E030 

PO 14E03 1 

~~ 

75 to 79 years 

80 to 84 years 

85 years and over 

PO 14F002 

PO1 4F003 

1 and 2 years 

3 and 4 vears 

PO 14F004 

P014F005 

PO 14F006 

5 years 

6 years 

7 to 9 vears 

PO 14F007 

PO1 4F008 

PO 14F009 

I P014F020 I 35 to 39 years J 

10 and 11 years 

12 and 13 years 

14 vears 

P014F010 

P014F011 

PO 14F0 12 

PO 1 4F0 1 3 

PO 14F0 14 

4 

PO 14F023 I 50 to 54 years 1 

15 years 

16 years 

17 years 

18 years 

19 vears 

PO1 4F015 

PO 14F016 

20 years 

21 vears 

142 

PO 14F017 

PO 1 4F0 1 8 

PO 14F019 

~~~ 

22 to 24 years 

25 to 29 years 

30 to 34 vears 

PO 14F02 1 

PO 14F022 

40 to 44 years 

45 to 49 vears 

PO 14F024 

PO 14F025 

55 to 59 years 

60 and 61 vears 

PO 14F026 

PO 14F027 

PO 14F028 

PO 14F029 

PO 14F03 0 

PO 14F03 1 

d 

62 to 64 years 

65 to 69 years 

70 to 74 years 

75 to 79 years 

80 to 84 years 

85 years and over - 

PO 14G00 1 

PO 14G002 

PO1 4G003 

PO 14G004 

PO 14G005 

Under 1 year 

1 and 2 years 

3 and 4 years 

5 years 

6 years 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
I 

1 

PO 14GO06 

PO1 4G007 

PO 1 4G008 

PO 14GO09 

PO 14G0 10 

- 

7 to 9 years 

10 and 11 years 

12 and 13 years 

14 years 

15 vears 

P014G011 

PO1 4GO12 

PO 14G0 13 

P014GO14 

P014G015 

~ ~~ 

16 years 

17 years 

18 years 

19 years 

20 years 

PO146016 

PO 14G0 17 

PO 14G0 18 

~- 

21 years 

22 to 24 years 

25 to 29 years 

PO 14G0 19 

PO1 4G020 

Universe: Asian or Pacific Islander females 

I P014H001 I Under 1 year I 

.- 

30 to 34 years 

35 to 39 years 

P014G021 

PO 146022 

PO 14G023 

PO146024 

PO 146025 

40 to 44 years 

45 to 49 years 

50 to 54 years 

55 to 59 years 

60 and 61 years 

143 

PO 146026 

P014GO27 

PO 14G028 

P014GO29 

P014GO30 

PO 14G03 1 

62 to 64 years 

65 to 69 years 

70 to 74 years 

75 to 79 years 

80 to 84 years 

85 years and over 

PO 14H002 

PO 14H003 

PO 14H004 

P014H005 

PO 14H006 

PO 14H007 

PO 14H008 

PO 14H009 

P014H010 

P014H011 

PO1 4H012 

PO1 4H013 

PO 14H0 14 

PO 14H0 15 

~ - ~~ 

1 and 2 years 

3 and 4 years 

5 years 

6 years 

7 to 9 years 

10 and 11 years 

12 and 13 years 

14 years 

15 years 

16 years 

17 years 

18 years 

19 years 

20 years L ., 

PO 14H016 21 years 

PO1 4H017 22 to 24 years 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
P014H018 

PO 14H019 

PO 14H020 

PO 14H02 1 

PO1 4H022 

PO 14H023 4 

PO 14H024 [ 55 to 59 years 

25 to 29 years 

30 to 34 years 

35 to 39 years 

40 to 44 years 

45 to 49 years 

50 to 54 vears 

PO 14H025 

PO 14H026 

60 and 61 years 

62 to 64 vears 

PO 14H027 

PO 14H028 

PO 14H029 

PO 14H030 

PO 14H03 1 

~~ d 

PO 141007 I 10 and 11 years 

~ 

65 to 69 years 

70 to 74 years 

75 to 79 years 

80 to 84 years 

85 years and over 

PO 141002 

PO 141003 

PO 141004 

PO 141005 

PO 141006 

~~ ~ 

1 and 2 years 

3 and 4 years 

5 years 

6 years 

7 to 9 vears 

PO 141008 

PO 141009 

PO14101 0 

PO14101 1 

12 and 13 years 

14 years 

15 years 

16 vears 

144 

PO 1 410 I 2 

PO1 4101 3 

PO1410 14 

PO 1410 1 5 

PO1 410 16 

PO1 410 1 7 

PO 1410 18 

PO1 410 19 

PO1 41020 

PO141021 

PO 141022 

d 

17 years 

18 years 

19 years 

20 years 

21 years 

22 to 24 years 

25 to 29 years 

30 to 34 years 

35 to 39 years 

40 to 44 years 

45 to 49 vears 

- -- PO141023 50 to 54 years 

PO 141024 

PO141025 60 and 61 vears 

55 to 59 years 

PO 141026 

PO141027 

PO 14102 8 

PO1 41029 

62 to 64 years 

65 to 69 years 

70 to 74 years 

75 to 79 years 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
1 

I 

I 

1F 

PO € 41030 

PO 14103 1 

~ 

80 to 84 years 

85 years and over 

PO1 45001 

P014J002 

PO1 45003 

P014J004 

PO 145005 

P014J006 

PO 145007 

PO1 45008 

PO 14J009 

PO 14J010 

P014J011 

PO 14JO 12 

Under 1 year 

1 and 2 years 

3 and 4 years 

5 years 

6 years 

7 to 9 years 

10 and 11 years 

12 and 13 years 

14 years 

15 years 

16 years 

17 years 

P014J013 

PO 14JO 14 

P014J015 

P014J016 

PO1 45017 

18 years 

19 years 

20 years 

21 years 

22 to 24 years 

4 

P014J03 1 I 85 years and over 

P014J018 

PO1 4501 9 

P014J020 

P014J02 1 

PO1 4J022 

PO145023 

25 to 29 years 

30 to 34 years 

35 to 39 years 

40 to 44 years 

45 to 49 years 

50 to 54 vears 

145 

PO 14J024 

P014J025 

P014J026 

P014J027 

PO 145028 

~- 

55 to 59 years 

60 and 61 years 

62 to 64 years 

65 to 69 years 

70 to 74 years 

P014J029 

P014J030 

75 to 79 years 

80 to 84 vears 

PO 16000 1 

PO 160002 

POI 6m03 

PO 160004 

PO1 60005 

PO 160096 

PO1 60007 

1 person 

2 persons 

3 persons 

4 persons 

5 persons 

6 persons 

7 or more persons 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
I 

8 

I 

I 

I 

I 

I 

I 

I 

I 

I 

1 

I 

E 

PO1 70001 

PO 170002 

I 

I 

8 

Householder 

Spouse 

PO 170003 

PO 170004 

PO 170005 

PO1 70006 

PO 170007 

Natural-born or adopted 

Step 

Grandchild 

Other relatives 

Nonrelatives 

I PO170012 1 Non-relatives 

I PO170013 I Institutionalized persons 

~n group quarters 3: 

PO 1 70008 

PO1 70009 

I PO170014 I Other persons in group quarters 

Living alone 

Not living alone 

HOUSEHOLD TYPE AND PRESENCE AND AGE OF CHILDREN / Universe: Households 

Family households: 

PO 17001 0 

PO170011 

Married-couple family: 

I PO190001 I With own children under 18 vears I 

Living alone 

Not living alone 

I PO1 90002 I No own children under 18 years 

Other family: 

PO 190006 

PO 1 90007 

Male householder, no wife present: 

1 PO190003 1 With own children under 18 vears I 

- - - ._ - 

No own children under 18 years 

Nonfamily households 

~~ I PO1 90004 1 No own children under 18 years 

PO270003 

PO270004 

Female householder, no husband present: 

I PO190005 I With own children under 18 vears I 

Separated 

Other 

146 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
PO270005 

PO270006 

1 PO270008 I Married, spouse present I 

Married. suouse absent: 

Widowed 

Divorced 

PO270009 

PO270010 

PO27001 1 

I PO270012 I Divorced J 

Separated 

Other 

Widowed 

PO400001 

PO400002 

Correctional institutions (201-241,271,281, 950 

Nursing homes (601-671) 

Graduate or urofessional deaee 

PO400003 

PO400004 

PO400005 

EDUCATIONAL ATTAINMENT / Universe: Persons 18 years and over 

I PO600001 I Less than 9th made 

Mental (Psychiatric) hospitals (451-480 

Juvenile institutions (01 1-051, 101- 121, 150 

Other institutions (001, 061-091, 131, 141, 161-191, 251,261,291- 

PO400006 

PO400007 

PO400008 

PO400009 

PO4000 10 

College dormitories (87N) 

Military quarters (96N-98N) 

Emergency shelters for homeless persons (82N, 83N) 

Visible in street locations (84N, 85N) 

Other noninstitutional group quarters (00N-8 lN, 86N, 88N-95N, 99N) 

147 

P 0 6 0 0 0 0 2 

PO600003 

PO600004 

9th to 12th grade, no diploma 

High school graduate (includes equivalency) 

Some college. no dem-ee 

PO600005 

PO600006 

PO600007 

Associate degree 

Bachelor's degree 

Graduate or professional degree 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
SCHOOL ENROLLMENT, EDUCATIONAL ATTAINMENT, AND EMPLOYMENT 

STATUS ’/ Universe: Persons 16 to 19 years 

In Armed Forces: 

Enrolled in school: 

PO6 1000 1 

PO6 10002 

High school graduate 

Not high school graduate 

PO6 10003 

PO6 10004 

1 

High school graduate 

Not high school graduate 

SEX BY EMPLOYMENT STATUS 5/ Universe: Persons 16 years and over 

Male: 

In labor force: 

PO6 10005 

PO6 10006 

I PO700001 1 In ArmedForces I 

Civilian: 

Employed 

Unemployed 

PO6 10008 

PO610009 

PO6 1001 0 

Civilian: 

1 PO700006 1 Emuloved 

Employed 

Unemployed 

Not in labor force 

PO61001 1 

PO61 0012 

PO61 0013 

Employed 

Unemployed 

Not in labor force 

148 

PO700002 

PO700003 

PO700004 

~ 

Employed 

Unemployed 

Not in labor force6 

~~~ - 

PO700007 Unemployed 

~ PO700008 Not in labor force 

PO78000 1 

PO780002 

Executive, administrative, and managerial occupations (000-042) 

Professional specialty occupations (043-202) 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
I Technical, sales, and administrative support occupations (203-402): 

PO780003 

PO780004 Sales occupations (243-302) 

, PO780005 

Technicians and related support occupations (203-242) 

Administrative support occupations, including clerical (303-402) 

PO780006 Private household occupations (403-412) 

PO780007 Protective service occupations (413-432) 

PO780008 

PO780009 

PO7800 10 Precision production, craft, and repair occupations (503-702) ~ 

Service occupations, except protective and household (433-472) 

Farming, forestry, and fishing occupations (473-502) 

PO7800 1 1 

PO7800 12 

1 PO780013 I Handlers, equipment cleaners, helpers, and laborers (864-902) 1 

Machine operators, assemblers, and inspectors (703-802) 

Transportation and material moving occupations (803-863) 

HOUSEHOLD INCOME IN 1989 / Universe: Households 

I PO800001 I Less than $5.000 

PO800002 

PO800003 

PO800004 

$5,000 to $9,999 

$10,000 to $12,499 

$12.500 to $14.999 

PO800005 

PO800006 

PO800007 

PO800008 

PO800009 

$15,000 to $17,499 

$17,500 to $19,999 

$20,000 to $22,499 

$22,500 to $24,999 

$25,000 to $27.499 

PO8000 10 

PO80001 1 

PO800012 

$27,500 to $29,999 

$30,000 to $32,499 

$32,500 to $34,999 

PO800013 

PO800014 

$35,000 to $37,499 

$37.500 to $39.999 

MEDIAN HOUSEHOLD INCOME IN 1989 / Universe: Households 

P080A001 I Median household income in 1989 1 

149 

PO800015 

PO80001 6 

PO80001 7 

$40,000 to $42,499 

$42,500 to $44,999 

$45.000 to $47.499 

PO80001 8 

PO80001 9 

PO800020 

~ 

$47,500 to $49,999 

$50,000 to $54,999 

$55.000 to $59.999 

PO80002 1 

PO800022 

PO800023 

PO800024 

PO800025 

$60,000 to $74,999 

$75,000 to $99,999 

$100,000 to $ i 24,999 

$125,OOOTo $149,999 

$150.000 or more 

.- - 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
I 

PO820001 

PO820002 

PO820003 

PO820004 

PO820005 

PO820006 

PO820007 

PO820008 

PO820009 

I 

I 

Less than $5,000 

$5,000 to $9,999 

$10,000 to $14,999 

$15,000 to $24,999 

$25,000 to $34,999 

$35,000 to $49,999 

$50,000 to $74,999 

$75,000 to $99,999 

$100,000 or more 

PO8200 10 

PO8200 1 1 

PO820012 

PO8200 13 

PO820014 

PO82001 5 

PO8200 16 

PO8200 17 

PO820018 

1 

Less than $5,000 

$5,000 to $9,999 

$10,000 to $14,999 

$15,000 to $24,999 

$25,000 to $34,999 

$35,000 to $49,999 

$50,000 to $74,999 

$75,000 to $99,999 

$100,000 or more 

150 

PO8200 19 

PO820020 

PO82002 1 

PO820022 

PO820023 

PO820024 

PO820025 

PO820026 

PO820027 

Less than $5,000 

$5,000 to $9,999 

$10,000 to $14,999 

$15,000 to $24,999 

$25,000 to $34,999 

$35,000 to $49,999 

$50,000 to $74,999 

$75,000 to $99,999 

$100,000 or more 

PO820028 Less than $5,000 

PO820029 $5,000 to $9,999 

PO820030 $10,000 to $14,999 

PO82003 1 $15,000 to $24,999 

PO820032 $25,000 to $34,999 

PO820033 - $35,000 to $49,999 

~ PO820034 $50,000 to $74,999 

PO820035 $75,000 to $99,999 

PO820036 $100,000 or more 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
PO820038 

PO820039 

$5,000 to $9,999 

$10.000 to $14.999 

r 

AGGREGATE PUBLIC ASSISTANCE INCOME IN 1989 / Universe: Households 

1 P1030001 1 Total 

PO820040 

PO82004 1 

PO820042 

PO820043 

PO820044 

WORKERS IN FAMILY IN 1989 / Universe: Families 

~ 

$15,000 to $24,999 

$25,000 to $34,999 

$35,000 to $49,999 

$50,000 to $74,999 

$75.000 to $99.999 

' PO950001 

PO950002 

PER CAPITA INCOME IN 1989 '/ Universe: Persons 1 P114A001 I Per capita income in 1989 1 

With public assistance income 

No public assistance income 

P1120001 

P1120002 

P 1 120003 

P 1 120004 

No workers 

1 worker 

2 workers 

3 or more workers 

~ ~~ I P1210008 I 1.85 to 1.99 

P12 10001 

P 121 0002 

P12 10003 

P12 10004 

1 P1210009 I 2.00 and over 

Under SO 

.50 to .74 

.75 to .99 

1.00 to 1.24 

HOUSING UNITS / Universe: Housing units 

1 HOOlOOOl I Total 1 

P12 10005 

P1210006 

P 12 10007 

OCCUPANCY STATUS / Universe: Housing units 

1 H0040001 1 OccuDied 

~ ~ 

1.25 to 1.49 

1.50 to 1.74 

1.75 to 1.84 

~ ~~ I H0040002 I Vacant 

TENURE / Universe: Occupied housing units 

151 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
H0080002 Renter occupied 

1 

1 

I 

H0200001 1. , H0200002 

H0200003 

1, detached 

1, attached 

2 

MEDIAN YEAR STRUCTURE BUILT / Universe: Housing units 

1 H025A001 1 Median year structure built I 

H0200004 

H0200005 

H0200006 

H0200007 

H0200008 

H0200009 

H02000 10 

3 or4 

5 to 9 

10 to 19 

20 to 49 

50 or more 

Mobile home or trailer 

Other 

I 

1 

H0280002 1985 to 1988 

H0280003 

H0280004 

H0280005 

H0280006 

TENURE BY TELEPHONE IN HOUSING UNIT / Universe: Occupied housing units 

Owner occupied: 

1980 to 1984 

1970 to 1979 

1960 to 1969 

1959 or earlier 

H0350001 With telephone 

, H0350002 No telephone ~~ 

Renter occutied: 

H0350003 

H0350004 

With telephone 

No telmhone 

H037000 1 

H0370002 

H0370003 

H0370004 ~ .. 

H0370005 14 

None 

1 

2 

3 

_- __- 

I H0370006 I 5 or more 

Renter occuDied: I H0370007 1 None 1 

152 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
a 

I H0370008 

H0370009 

H03700 1 0 

H0370011 

H0370012 

I 

1 

I 

8 

1 

I 

1 

e 

1 

2 

3 

4 

5 or more 

MEDIAN GROSS RENT / Universe: Specified renter-occupied housing units paying cash rent 

I H043A001 I Median gross rent 1 

MEDIAN VALUE / Universe: Specified owner-occupied housing units I H061A001 I Medianvalue i 

153 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
ENDNOTES : Endnotes come fkom http/:venus. census.gov. 

1. FIPS.STATES 

0 1, Alabama 

02, Alaska 

04, Arizona 

05, Arkansas 

06, California 

08, Colorado 

09, Connecticut 

10, Delaware 

1 1, District of Columbia 

12, Florida 

13, Georgia 

15, Hawaii 

16, Idaho 

17, Illinois 

18, Indiana 

19, Iowa 

20, Kansas 

2 1, Kentucky 

22, Louisiana 

23, Maine 

24, Maryland 

25, Massachusetts 

26, Michigan 

27, Minnesota 

28, Mississippi 

29, Missouri 

30, Montana 

3 1, Nebraska 

32, Nevada 

33, New Hampshire 

34, New Jersey 

35, New Mexico 

36, New York 

37, North Carolina 

38, North Dakota 

39, Ohio 

40, Oklahoma 

41, Oregon 

42, Pennsylvania 

44, %ode Island 

45, South Carolina 

46, South Dakota 

47, Tennessee 

48, Texas 

49, Utah 

50, Vermont 

5 1, Virginia 

5 3, Washington 

54, West Virginia 

55, Wisconsin 

56, Wyoming 

2. Household-A household includes all the persons who occupy a housing unit. A housing unit is a house, an 

apartment, a mobile home, a group of rooms, or a single room that is occupied (or if vacant, is intended for occupancy) 

as separate living quarters. Separate living quarters are those in which the occupants live and eat separately from any 

other persons in the building and which have direct access fiom the outside of the building or 

through a common hall. The occupants may be a single family, one person living alone, two or more families living 

together, or any other group of related or unrelated persons who share living arrangements. 

In 100-percent tabulations, the count of households or householders always equals the count of occupied housing units. 

In sample tabulations, the numbers may differ as a result of the weighting process. 

Householder--The data on relationship to householder were derived from answers to questionnaire item 2, which was 

asked of all persons in housing units. One person in each household is designated as the householder. In 

most cases, this is the person, or one of the persons, in whose name the home is owned, being bought, or rented and who 

is listed in column 1 of the census questionnaire. If there is no such person in the household, any adult household 

member 15 years old and over could be designated as the householder. 

Households are classified by type according to the sex of the householder and the presence of relatives. Two types of 

householders are distinguished: a family householder and a nonfamily householder. A family householder is a 

householder living with one or more persons related to him or her by birth, marriage, or adoption, The householder 

and all persons in the household related to him or her are family members. A nonfamily householder is a householder 

living alone or with nonrelatives only. 

3. Group Quarters--All persons not living in households are classified by the Census Bureau as living in group 

quters. Two general categories of persons in group quarters are recogmzed: 

(1) institutionalized persons and 

(2) other persons in group quarters (also referred to as 

"noninstitutional group quarters"). 

Institutionalized Persons--Includes persons under formally authorized, supervised care or custody in institutions at the 

time of enumeration. Such persons are classified as "patients or inmates" of an institution regardless of the availability 

154 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
I 

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of nursing or medical care, the length of stay, or the number of persons in the institution. Generally, 

institutionalized persons are restricted to the institutional buildings and grounds (or must have passes or escorts to 

leave) and thus have limited interaction with the surrounding community. Also, they are generally under the care of 

trained staff who have responsibility for their safekeeping and supervision. 

Type of Institution--The type of institution was determined as part of census enumeration activities. For institutions 

which specialize in only one specific type of service, all patients or inmates were given the same classification. For 

institutions which had multiple types of major services (usually general hospitals and Veterans' Administration 

hospitals), patients were classified according to selected types of wards. For example, in psychiatric wards of hospitals, 

patients were classified in "mental (psychiatric) hospitals"; in hospital wards for persons with chronic diseases, patients 

were classified in "hospitals for the chronically ill." Each patient or inmate was classified in only one type of institution. 

Institutions include the following types: 

Correctional Institutions--Includes prisons, Federal detention centers, military stockades and jails, police lockups, 

halfway houses, local jails, and other confirnement facilities, including work farrns. 

Prisons--Where persons convicted of crimes serve their sentences. In some census products, the prisons are 

classified by two types of control: 

0 

0 

(1) "Federal" (operated by the Bureau of Prisons of the Department of Justice) and 

(2) "State." Residents who are criminally insane were classified on the basis of where they resided at the time 

of enumeration: 

0 (1) in institutions (or hospital wards) operated by departments of correction or similar agencies; or 

0 (2) in institutions operated by departments of mental health or similar agencies. 

Federal Detention Centers--Operated by the Immigration and Naturalization Service (INS) and the Bureau of 

Prisons. These facilities include detention centers used by the Park Police; Bureau of Indian Affairs Detention 

Centers; INS Centers, such as the INS Federal Alien Detention Facility; INS Processing Centers; and INS Contract 

Detention Centers used to detain aliens under exclusion or deportation proceedings, as well as those aliens who 

have not been placed into proceedings, such as custodial required departures; and INS Detention Centers operated 

within local jails, and State and Federal prisons. 

Military Stockades, Jails--Operated by military police and used to hold persons awaiting trial or convicted of 

violating military laws. 

Local Jails and Other Confinement Facilities--Includes facilities operated by counties and cities that primarily 

hold persons beyond arraignment, usually for more than 48 hours. Also included in this category are work farms 

used to hold persons awaiting trial or serving time on relatively short sentences and jails run by private businesses 

under contract for local governments (but not by State governments). 

Police Lockups--Temporary-holding facilities operated by county and city police that hold persons for 48 hours or 

less only if they have not been formally charged in court. 

Halfway Houses--Operated for correctional purposes and include probation and restitution centers, pre- release 

centers, and community-residential centers. 

Other Types of Correctional Institutions-Privately operated correctional facilities and correctional facilities 

specifically for alcoholldrug abuse. 

Nursing Homes-Comprises a heterogeneous group of places. The majority of patients are elderly, although persons 

who require nursing care because of chronic physical conditions may be found in these homes regardless of their age. 

Included in this category are skilled-nursing facilities, intermediate-care facilities, long-term care rooms in wards 

or buildings on the grounds of hospitals, or long-term care rooms/nursing wings in congregate housing facilities. 

Also included are nursing, convalescent, and rest homes, such as soldiers', sailors', veterans', and fraternal or religious 

homes for the aged, with or without nursing care. In some census products, nursing homes are classified by type of 

ownership as "Federal," "State," "Private not-for-profit," and "Private for profit 'I 

Mental (Psychiatric) Hospitals-Includes hospitals or wards for the criminally insane not operated by a prison, and 

psycluatric wards of general hospitals and veterans' hospitals. Patients receive supervised medicaYnursing care fiom 

formally-trained staff. In some census products, mental hospitals are classified by type of ownership as "Federal," "State 

or local," "Private," and "Ownership not known." 

Hospitals for Chronically Ill--Includes hospitals for patients who require long-term care, including those in military 

155 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
hospitals and wards for the chronically ill located on military bases; or other hospitals or wards for the chronically ill, 

which include tuberculosis hospitals or wards, wards in general and Veterans' Administration hospitals for the 

chronically ill, neurological wards, hospices, wards for patients with Hansen's Disease (leprosy) and other incurable 

diseases, and other unspecified wards for the chronically ill. Patients who had no usual home elsewhere were 

numerated as part of the institutional population in the wards of general and military hospitals. Most hospital patients 

are at the hospital temporarily and were enumerated at their usual place of residence. (For more information, see 

"Wards in General and Military Hospitals for Patients Who Have No Usual Home Elsewhere.") 

Schools, Hospitals, or Wards for the Mentally Retarded-Includes those institutions such as wards in hospitals for 

the mentally retarded, and intermediate-care facilities for the mentally retarded that provide supervised medicalhursing 

care from formally-trained staff. In some census products, this category is classified by type of ownership as "Federal," 

"State or local," "Private," and "Ownership not known." 

Schools, Hospitals, or Wards for the Physically Handicapped-Includes three types of institutions: institutions for 

the blind, those for the deaf, and orthopedic wards and institutions for the physically handicapped. Institutions for 

persons with speech problems are classified with "institutions for the deaf." The category ''orthopedic wards and 

institutions for the physically handicapped" includes those institutions providing relatively long-term care to accident 

victims, and to persons with polio, cerebral palsy, and muscular dystrophy. In some census products, this category is 

classified by type of ownership as "Public," "Private," and "Ownership not known." 

Hospitals, and Wards for Drug/Alcohol Abuse--Includes hospitals, and hospital wards in psychiatric and general 

hospitals. These facilities are equipped medically and designed for the diagnosis and treatment of medical or 

psychiatric illnesses associated with alcohol or drug abuse. Patients receive supervised medical care fiom formallytrained staff. 

Wards in General and Military Hospitals for Patients Who Have No Usual Home Elsewhere--Includes maternity, 

neonatal, pediatric (including wards for boarder babies), military, and surgical wards of hospitals, and wards for persons 

with infectious diseases. Juvenile Institutions--Includes homes, schools, and other institutions providing care for 

children (short- or long-term care). Juvenile institutions include the following types: 

Homes for Abused, Dependent, and Neglected Children-Includes orphanages and other institutions whch provide 

long-term care (usually more than 30 days) for children. This category is classified in some census products by type of 

ownership as "Public" and "Private." Residential Treatment Centers--Includes those institutions which primarily 

serve children who, by clinical diagnosis, are moderately or seriously disturbed emotionally. Also, these institutions 

provide long-term treatment services, usually supervised or hected by a psychiatrist. 

Training Schools for Juvenile Delinquents--Includes residential training schools or homes, and industrial schools, 

camps, or farms for juvenile delinquents. 

I 

Public Training Schools for Juvenile Delinquents--Usually operated by a State agency (for example, department of 

welfare, corrections, or a youth authority). Some are operated by county and city governments. These public training 

schools are specialized institutions serving delinquent children, generally between the ages of 10 and 17 years old, all of 

whom are committed by the courts. 

Private Training Schools--Operated under private auspices. Some of the children they serve are committed by the 

courts as delinquents. Others are referred by parents or social agencies because of delinquent behavior. One difference 

between private and public training schools is that, by their administrative policy, private schools have control over 

their selection and intake. 

Detention Centers--Includes institutions providing short-term care (usually 30 days or less) primarily for delinquent 

children pending disposition of their cases by a court. This category also covers diagnostic centers. In practice, such 

institutions may be caring for both delinquent and neglected children pending court disposition. 

Other Persons in Group Quarters (also referred to as "noninstitutional group quarters")--Includes all persons 

who live in group quarters other than institutions. Persons who live in the following living quarters are classified as 

156 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
"other persons in group quarters" when there are 10 or more unrelated persons living in the unit; otherwise, these living 

quarters are classified as housing units. 

Rooming Houses--Includes persons residing in rooming and boarding houses and living in quarters with 10 or more 

unrelated persons. 

Group Homes--Includes "community-based homes" that provide care and supportive services. Such places include 

homes for the mentally ill, mentally retarded, and physically handcapped; druglalcohol halfway houses; communes; and 

maternity homes for unwed mothers. 

Homes for the Mentally Ill--Includes community-based homes that provide care primarily for the mentally ill. In some 

data products, this category is classified by type of ownership as "Federal," "State," "Private," and "Ownership not 

known." Homes which combine treatment of the physically handicapped with treatment of the mentally ill are counted 

as homes for the mentally ill. 

Homes for the Mentally Retarded--Includes community-based homes that provide care primarily for the mentally 

retarded. Homes which combine treatment of the physically handicapped with treatment of the mentally retarded are 

counted as homes for the mentally retarded. This category is classified by type of ownership in some census products, 

as "Federal," "State," "Private," or "Ownership not known." 

II 

I 

1 

I 

I 

Homes for the Physically Handicapped--Includes community-based homes for the blind, for the deaf, and other 

community-based homes for the physically handicapped. Persons with speech problems are classified with homes for 

the deaf. In some census products, this category is classified by type of ownership as "Public," "Private," or "Ownership 

U 

not known." I 

Homes or Halfway Houses for Drug/Akohol Abuse-Includes persons with no usual home elsewhere in places that 

provide community-based care and supportive services to persons suffering from a druglalcohol addiction and to 

recovering alcoholics and drug abusers. Places providing community-based care for drug and alcohol abusers include 

group homes, detoxification centers, quarterway houses (residential treatment facilities that work closely with 

accredited hospitals), halfway houses, and recovery homes for ambulatory, mentally competent recovering alcoholics 

and drug abusers who may be re-entering the work force. 

Maternity Homes for Unwed Mothers--Includes persons with no usual home elsewhere in places that provide 

domestic care for unwed mothers and their children. These homes may provide social services and post-natal care 

within the facility, or may make arrangements for women to receive such services in the community. Nursing services 

are usually available in the facility. 

Other Group Homes--Includes persons with no usual home elsewhere in communes, foster care homes, and job corps 

centers with 10 or more unrelated persons. These types of places provide communal living quarters, generally for 

persons who have formed their own community in which they have common interests and often share or own property 

I 

4 

jointly. 

Religious Group Quarters-Includes, primarily, group quarters for nuns teaching in parochial schools and for priests 

I 

living in rectories. It also includes other convents and monasteries, except those associated with a general hospital or an 

institution. 

College Quarters Off Campus--Includes privately-owned rooming and boarding houses off campus, if the place is 

reserved exclusively for occupancy by college students and if there are 10 or more unrelated persons. In census 

products, persons in this category are classified as living in a college dormitory. 

Persons residing in certain other types of living arrangements are classified as living in "noninstitutional group quarters" 

regardless of the number of people sharing the unit. These include persons residing in the following types of group 

quarters: 

0 College Dormitories--Includes college students in dormitories (provided the dormitory is restricted to students 

I 

157 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
0 

0 

0 

0 

0 

0 

0 

0 

who do not have their families living with them), fraternity and sorority houses, and on-campus residential quarters 

used exclusively for those in religious orders who are attending college. Students in privately-owned rooming and 

boarding houses off campus are also included, if the place is reserved exclusively for occupancy by college-level 

students and if there are 10 or more unrelated persons. 

Military Quarters--Includes military personnel living in barracks and dormitories on base, in transient quarters on 

case for temporary residents (both civilian and military), and on military ships. However, patients in military 

hospitals receiving treatment for chronic diseases or who had no usual home elsewhere, and persons being held in 

military stockades were included as part of the institutional population. 

Agriculture Workers' Dormitories--Includes persons in migratory farm workers' camps on farms, bunkhouses for 

ranch hands, and other dormitories on farms, such as those on !!tree farms." 

Other Workers' Dormitories-Includes persons in logging camps, construction workers' camps, firehouse 

dormitories, job-training camps, energy enclaves (Alaska only), and nonfarm migratory workers' camps (for 

example, workers in mineral and mining camps). 

Emergency Shelters for Homeless Persons (with sleeping facilities) and Visible in Street Locations--Includes 

persons enumerated during the "Shelter-and-Street-Night" operation primarily on March 20-2 1, 1990. Enumerators 

were instructed not to ask if a person was "homeless." If a person was at one of the locations below on March 20- 

2 1 , the person was counted as described below. (For more information on the "Shelter-and-Street-Night" operation, 

see Appendix D, Collection and Processing Procedures.) This category is divided into four classifications: 

Emergency Shelters for Homeless Persons (with sleeping facilities)--Includes persons who stayed overnight on 

March 20, 1990, in permanent and temporary emergency housing, missions, hotels/motels, and flophouses charging 

$12 or less (excluding taxes) per night; Salvation Army shelters, hotels, and motels used entirely for homeless 

persons regardless of the nightly rate charged; rooms in hotels and motels used partially for the homeless; and 

similar places known to have persons who have no usual home elsewhere staying overnight. If not shown 

separately, shelters and group homes that provide temporary sleeping facilities for runaway, neglected, and 

homeless children are included in this category in data products. 

Shelters for Runaway, Neglected, and Homeless Children-Includes sheltedgroup homes which provide 

temporary sleeping facilities for juveniles. 

Shelters for Abused Women (Shelters Against Domestic Violence or Family Crisis Centers)-Includes 

community-based homes or shelters that provide domiciliary care for women who have sought shelter from family 

violence and who may have been physically abused. Most shelters also provide care for children of abused women. 

These shelters may provide social services, meals, psychiatric treatment, and counseling. In some census products, 

"shelters for abused women" are included in the category "other noninstitutional group quarters." 

Dormitories for Nurses and Interns in General and Military Hospitals--Includes group quarters for nurses and 

other staff members. It excludes patients. 

Crews of Maritime Vessels--Includes officers, crew members, and passengers of maritime U.S. flag vessels. All 

ocean-going and Great Lakes ships are included. 

Staff Residents of Institutions--Includes staff residing in group quarters on institutio~l grounds who provide 

formally-authorized, supervised care or custody for the institutionalized population. 

Other Nonhousehold Living Situations-Includes persons with no usual home elsewhere enumerated during 

transient or "T-Night" enumeration at YMCA's, YWCA's, youth hostels, commercial and government-run 

campgrounds, campgrounds at racetracks, fairs, and carnivals, and similar transient sites. 

Living Quarters for Victims of Natural Disasters--Includes living quarters for persons temporarily displaced by 

natural disasters. 

Limitation of the Data--Two types of errors can occur in the classification of "types of group quarters": 

0 Misclassification of Group Quarters--During the 1990 Special Place Prelist operation, the enumerator 

determined the type of group quarters associated with each special place in their assignment. The enumerator ussd 

the Alphabetical Group Quarters Code List and Index to the Alphabetical Croup Quarters Code List to assign a 

two-digit code number followed by either an "I," for institutional, or an "N," for noninstitutional to each group 

quarters. In 1990, unacceptable group quarter codes were edited. (For more information on editing of unacceptable 

data, see Appendix C, Accuracy of the Data.) 

No Classification (unknowns)-The imputation rate for type of institution was higher in 1980 (23.5 percent) than 

in 1970 (3.3 percent). Improvements were made to the 1990 Alphabetical Group Quarters Code List; that is, the 

inclusion of more group quarters categories and an "Index to the Alphabetical Group Quarters Code List." (For 

158 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
1 

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more information on the allocation rates for Type of Institution, see the allocation rates in 1990 CP- 1, General 

Population Characteristics.) In previous censuses, allocation rates for demographic characteristics (such as age, 

sex, race, and marital status) of the institutional population were similar to those for the total population. The 

allocation rates for sample characteristics such as school enrollment, highest grade completed, income, and veteran 

status for the institutional and noninstitutional group quarters population have been substantially higher than the 

population in households at least as far back as the 1960 census. The data, however, have historically presented a 

reasonable picture of the institutional and noninstitutional group quarters population. 

Shelter and Street Night (S-Night)--For the 1990 census "Shelter-and- Street-Night" operation, persons well 

hidden, moving about, or in locations enumerators did not visit were likely to be missed. The number of people 

missed will never be known; thus, the 1990 census cannot be considered to include a definitive count of America's 

total homeless population. It does, however, give an idea of relative differences among areas of the country. Other 

components were counted as part of regular census procedures. 

The count of persons in shelters and visible on the street could have been affected by many factors. How much 

the factors affected the count can never be answered definitively, but some elements include: How well 

enumerators were trained and how well they followed procedures. 

How well the list of shelter and street locations given to the Census Bureau by the local government reflected 

the actual places that homeless persons stay at night. 

Cities were encouraged to open temporary shelters for census night, and many did that and actively encouraged 

people to enter the shelters. Thus, people who may have been on the street otherwise were in shelters the night 

of March 20, so that the ratio of shelter-to-street population could be different than usual. 

The weather, which was unusually cold in some parts of the country, could affect how likely people were to 

seek emergency shelter or to be more hidden than usual if they stayed outdoors. 

The media occasionally interfered with the ability to do the count. 

How homeless people perceived the census and whether they wanted to be counted or feared the census and 

hid from it. 

The Census Bureau conducted two assessments of Shelter and Street Night: (1) the quality of the lists of 

shelters used for the Shelter-and-Street-Night operation, and (2) how well procedures were followed by censustakers for the street count in parts of five cities (Chicago, Los Angeles, New Orleans, New York, and Phoenix). 

Information about these two assessments is available from the Chief, Center for Survey Methods Research, 

Bureau of the Census, Washington, DC 20233. 

Comparability-For the 1990 census, the definition of institutionalized persons was revised so that the definition of 

"care" only includes persons under organized medical or formally-authorized, supervised care or custody. As a result of 

this change to the institutional defintion, maternity homes are classified as noninstitutional rather than institutional 

group quarters as in previous censuses. The following types of other group quarters are classified as institutional rather 

than noninstitutional group quarters: "halfway houses (operated for correctional purposes)" and "wards in general and 

military hospitals for patients who have no usual home elsewhere," which includes maternity, neonatal, pediatric, 

military, and surgical wards of hospitals, other-purpose wards of hospitals, and wards for infectious diseases. These 

changes should not significantly affect the comparability of data with earlier censuses because of the relatively small 

number of persons involved. 

0 As in 1980, 10 or more unrelated persons living together were classified as living in noninstitutional group 

quarters. In 1970, the criteria was six or more unrelated persons. 

Several changes also have occurred in the identification of specific types of group quarters. For the first time, the 

1990 census identifies separately the following types of correctional institutions: persons in halfbay houses 

(operated for correctional purposes), military stockades and jails, and police lockups. In 1990, tuberculosis 

hospitals or wards are included with hospitals for the chronically ill; in 1980, they were shown separately. For 

1990, the noninstitutional group quarters category, "Group homes" is further classified as: group homes for 

drug/alcohol abuse; maternity homes (for unwed mothers), group homes foi the mentally ill, group homes for the 

mentally retarded, and group homes for the physically handicapped. Persons living in communes, foster-care 

homes, and job corps centers are classified with "Other group homes" only if 10 or more unrelated persons share 

the unit; otherwise, they are classified as housing units. 

In 1990, workers' dormitories were classified as group quarters regardless of the number of persons sharing the 

dorm. In 1980, 10 or more unrelated persons had to share the dorm for it to be classified as a group quarters. In 

1960, data on persons in military barracks were shown only for men. In subsequent censuses, they include both men 

159 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
1 

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8 

and women. 

In 1990 census data products, the phrase "inmates of institutions" was changed to "institutionalized persons." Also, 

persons living in noninstitutional group quarters were referred to as "other persons in group quarters," and the 

phrase "staff residents'' was used for staff living in institutions. 

In 1990, there are additional institutional categories and noninstitutional group quarters categories compared with 

the 1980 census. The institutional categories added include "hospitals and wards for drug/alcohol abuse" and 

"military hospitals for the chronically ill." The noninstitutional group quarters categories added include emergency 

shelters for homeless persons; shelters for runaway, neglected, and homeless children; shelters for abused women; 

and visible-in-street locations. Each of these noninstitutional group quarters categories was enumerated on March 

20-2 1, 1990, during the "Shelter-and-Street-Night" operation. (For more information on the "Shelter-and-StreetNight" operation, see Appendix D, Collection and Processing Procedures.) 

4. Visible in Street Locations--Includes street blocks and open public locations designated before March 20, 1990, by 

city and community officials as places where the homeless congregate at night. All persons found at predesignated street 

sites from 2 a.m. to 4 a.m. and leaving abandoned or boarded-up buildings from 4 a.m. to 8 a.m. on March 21, 1990, 

were enumerated during "street" enumeration, except persons in uniform such as police and persons engaged in obvious 

money-making activities other than begging or panhandling. Enumerators were instructed not to ask if a person was 

"homeless. " 

This cannot be considered a complete count of all persons living on the streets because those who were so well hidden 

that local people did not how where to find them were likely to have been missed as were persons moving about or in 

places not identified by locd officials. It is also possible that persons with homes could have been included in the count 

of "visible in street locations" if they were present when the enumerator did the enumeration of a particular block. 

Predesignated street sites include street comers, parks, bridges, persons emerging fiom abandoned and boarded-up 

buildings, noncommercial campsites (tent cities), all-night movie theaters, all-night restaurants, emergency hospital 

waiting rooms, train stations, airports, bus depots, and subway stations. 

5. Employment Status/Income--The data on employment status were derived from answers to questionnaire items 2 1, 

25, and 26, which were asked of a sample of persons. The series of questions on employment status was asked of all 

persons 15 years old and over and was designed to identify, in this sequence: (1) persons who worked at any time 

during the reference week; (2) persons who did not work during the reference week but who had jobs or businesses 

from which they were temporarily absent (excluding layoff); (3) persons on layoff; and (4) persons who did not work 

during the reference week, but who were looking for work during the last four weeks and were available for work 

during the reference week. (For more information, see the discussion under "Reference Week.") 

The employment status data shown in this and other 1990 census tabulations relate to persons 16 years old and over. 

Some tabulations showing employment status, however, include persons 15 years old. By definition, these persons are 

classified as "Not in Labor Force.". In the 1940, 1950, and 1960 censuses, employment status data were presented for 

persons 14 years old and over. The change in the universe was made in 1970 to agree with the official measurement of 

the labor force as revised in January 1967 by the U.S. Department of Labor. The 1970 census was the last to show 

employment data for persons 14 and 15 years old. 

Employed--All civilians 16 years old and over who were either (1) "at work"--those who did any work at all during the 

reference week as paid employees, worked in their own business or profession, worked on their own farm, or worked 15 

horn or more as unpaid workers on a family fmn ?r in a family business; or (2) were "with a job but not at work"-- 

those who did not work during the reference week but had jobs or businesses from which they were temporarily absent 

due to illness, bad weather, industrial dispute, vacation, or other personal reasons. Excluded fiom the employed are 

persons whose only activity consisted of work around the house or unpaid volunteer work for religious, charitable, and 

similar organizations; also excluded are persons on active duty in the United States Armed Forces. 

Unemployed-All civilians 16 years old and over are classified as unemployed if they (1) were neither "at work" nor 

160 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
"with a job but not at work" during the reference week, and (2) were looking for work during the last 4 weeks, and (3) 

were available to accept a job. Also included as unemployed are civilians who did not work at all during the reference 

week and were waiting to be called back to a job from which they had been laid off. Examples of job seeking activities 

are: 

0 

0 Meeting with prospective employers 

0 

0 Placing or answering advertisements 

0 Writing letters of application 

0 

Civilian Labor Force--Consists of persons classified as employed or unemployed in accordance with the criteria 

described above. 

Registering at a public or private employment office 

Investigating possibilities for starting a professional practice or opening a business 

Being on a union or professional register 

Experienced Unemployed--These are unemployed persons who have worked at any time in the past. 

Experienced Civilian Labor Force--Consists of the employed and the experienced unemployed. 

Labor Force--All persons classified in the civilian labor force plus members of the U.S. Armed Forces (persons on 

active duty with the United States Army, Air Force, Navy, Marine Corps, or Coast Guard). 

Not in Labor Force--All persons 16 years old and over who are not classified as members of the labor force. This 

category consists mainly of students, housewives, retired workers, seasonal workers enumerated in an off season who 

were not looking for work, institutionalized persons, and persons doing only incidental unpaid family work (less than 15 

hours during the reference week). 

Worker-This term appears in connection with several subjects: journey-to- work items, class of worker, weeks worked 

in 1989, and number of workers in family in 1989. Its meaning varies and, therefore, should be determined 

in each case by referring to the definition of the subject in which it appears. 

Actual Hours Worked Last Week--All persons who reported workmg during the reference week were asked to report 

in questionnaire item 2 1 b the number of hours that they worked. The statistics on hours worked pertain to the number of 

hours actually worked at all jobs, and do not necessarily reflect the number of hours typically or usually worked or the 

scheduled number of hours. The concept of "actual hours" differs from that of "usual hours" described below. The 

number of persons who worked only a small number of hours is probably understated since such persons sometimes 

consider themselves as not working. Respondents were asked to include overtime or extra hours worked, but to exclude 

lunch hours, sick leave, and vacation leave. 

Limitation of the Data 

The census may understate the number of employed persons because persons who have irregular, casual, or 

unstructured jobs sometimes report themselves as not working. The number of employed persons'*at work is 

probably overstated in the census (and conversely, the number of employed "with a job, but not at work" is 

understated) since some persons on vacation or sick leave erroneously reported themselves as working. This 

problem has no effect on the total number of employed persons. The reference week for the employment data is not 

the same for all persons. Since persons can change their employment status from one week to another, the lack of a 

uniform reference week may mean that the employment data do not reflect the reality of the employment situation 

of any given week. (For more information, see the discussion under ',Reference Week.") 

Limitation ofthe Data--Since questionnaire entries for income frequently are based on memory and not on records, 

many persons tended to forget minor or irregular sources of income and, therefore, underreport their income. 

Underreporting tends to be more pronounced for income sources that are not derived from earnings, such as Social 

Security, public assistance, or from interest, dividends, and net rental income. 

There are errors of reporting due to the misunderstanding of the income questions such as reporting gross rather 

than net dollar amounts for the two questions on net self-employment income, which resulted in an overstatement of 

these items. Another common error is the reporting of identical dollar amounts in two of the eight type of income 

161 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
items where a respondent with only one source of income assumed that the second amount should be entered to 

represent total income. Such instances of overreporting had an impact on the level of mean nonfarm or farm selfemployment income and mean total income published for the various geographical subdivisions of the State. 

Extensive computer editing procedures were instituted in the data processing operation to reduce some of these 

reporting errors and to improve the accuracy of the income data. These procedures corrected various reporting 

deficiencies and improved the consistency of reported income items associated with work experience and 

information on occupation and class of worker. For example, if persons reported they were self-employed on their 

own farm, not incorporated, but had reported wage and salary earnings only, the latter amount was shifted to net 

farm self-employment income. Also, if any respondent reported total income only, the amount was generally 

assigned to one of the type of income items according to responses to the work experience and class-of-worker 

questions. Another type of problem involved nonreporting of income data. Where income information was not 

reported, procedures were devised to impute appropriate values with either no income or positive or negative dollar 

amounts for the missing entries. (For more information on imputation, see Appendix Cy Accuracy of the Data.) 

In income tabulations for households and families, the lowest income group (e.g., less than $5,000) includes units 

that were classified as having no 1989 income. Many of these were living on income "in kind," savings, or gifts, 

were newly created families, or families in whch the sole breadwinner had recently died or left the household. 

However, many of the households and families who reported no income probably had some money income whch 

was not recorded in the census. 

The income data presented in the tabulations covers money income only. The fact that many farm families receive 

an important part of their income in the form of "free" housing and goods produced and consumed on the farm 

rather than in money should be taken into consideration in comparing the income of farm and nonfarm residents. 

Nonmoney income such as business expense accounts, use of business transportation and facilities, or partial 

compensation by business for medical and educational expenses was also received by some nonfarm residents. 

Many low income families also receive income "in kind" from public welfare programs. In comparing income data 

for 1989 with earlier years, it should be noted that an increase or decrease in money income does not necessarily 

represent a comparable change in real income, unless adjustments for changes in prices are made. 

Comparability 

The questionnaire items and employment status concepts for the 1990 census are essentially the same as those used 

in the 1980 and 1970 censuses. However, these concepts differ in many respects fiom those associated with the 

1950 and 1960 censuses. 

Since employment data from the census are obtained fiom respondents in households, they differ from statistics 

based on reports fiom individual business establishments, farm enterprises, and certain govemment programs. 

Persons employed at more than one job are counted only once in the census and are classified according to the job 

at which they worked the greatest number of hours during the reference week. In statistics based on reports from 

business and farm establishments, persons who work for more than one establishment may be counted more than 

once. Moreover, some tabulations may exclude private household workers, unpaid family workers, and selfemployed persons, but may include workers less than 16 years of age. 

An additional difference in the data arises from the fact that persons who had a job but were not at work are 

included with the employed in the census statistics, whereas many of these persons are likely to be excluded from 

employment figures based on establishment payroll reports. Furthermore, the employment status data in census 

tabulations include persons on the basis of place of residence regardless of where they work, whereas establishment 

data report persons at their place of work regardless of where they live. This latter consideration is particularly 

significant when comparing data for workers who commute between areas. 

Census data on actual hours worked during the reference week may differ from data from other sources. The census 

measures hours actually worked, whereas some surveys measure hours paid for by employers. Comparability of 

census actual hours worked data may also be affected by the nature of the reference week (see "Reference Week"). 

For several reasons, the unemployment figures of the Census i3ureau are not comparabk with published figures on 

unemployment compensation claims. For example, figures on unemployment compensation claims exclude persons 

who have exhausted their benefit rights, new workers who have not earned rights to unemployment insurance, and 

persons losing jobs not covered by unemployment insurance systems (including some workers in agriculture, 

domestic services, and religious organizations, and self-employed and unpaid family workers). In addition, the 

qualifications for drawing unemployment compensation differ from the definition of unemployment used by the 

Census Bureau. 

162 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
e 

0 

0 

Persons working only a few hours during the week and persons with a job but not at work are sometimes eligible 

for unemployment compensation but are classified as "Employed" in the cmus. Differences in the geographcal 

distribution of unemployment data arise because the place where claims are filed may not necessarily be the same 

as the place of residence of the unemployed worker. 

The figures on employment status from the decennial census are generally comparable with similar data collected in 

the Current Population Survey. However, some difference may exist because of variations in enumeration and 

processing techniques. Median Income--The median divides the income distribution into two equal parts, one 

having incomes above the median and the other having incomes below the median. For households and families, 

the median income is based on the distribution of the total number of units including those with no income. The 

median for persons is based on persons with income. The median income values for all households, families, and 

persons are computed on the basis of more detailed income intervals than shown in most tabulations. Median 

household or family income figures of $50,000 or less are calculated using linear interpolation. For persons, 

corresponding median values of $40,000 or less are also computed using linear interpolation. All other median 

income amounts are derived through Pareto interpolation. (For more information on medians and interpolation, see 

the discussion under "Derived Measures.") 

The income data collected in the 1980 and 1970 censuses are similar to the 1990 census data, but there are 

variations in the detail of the questions. In 1980, income information for 1979 was collected frompersons in 

approximately 19 percent of all housing units and group quarters. Each person was required to report: 

0 Wage or salary income 

0 Net nonfarm self-employment income 

0 Net farm self-employment income 

0 

0 Public assistance income 

Between the 1980 and 1990 censuses, there were minor differences in the processing of the data. In both censuses, 

all persons with missing values in one or more of the detailed type of income item and total income were 

designated as allocated. Each missing entry was imputed either as a "no" or as a dollar amount. If total income was 

reported and one or more of the type of income fields was not answered, then the entry in total income generally 

was assigned to one of the income types according to the socioeconomic characteristics of the income recipient. 

This person was designated as unallocated. 

In 1980 and 1990, all nonrespondents with income not reported (whether heads of households or other persons) 

were assigned the reported income of persons with similar characteristics. (For more information on imputation, see 

Appendix C, "Accuracy of the Data.") 

There was a difference in the method of computer derivation of aggregate income from individual amounts between 

the two census processing operations. In the 1980 census, income amounts less than $100,000 were coded in tens 

of dollars, and amounts of $100,000 or more were coded in thousands of dollars; $5 was added to each amount 

coded in tens of dollars and $500 to each amount coded in thousands of dollars. Entries of $999,000 or more were 

treated as $999,500 and losses of $9,999 or more were treated as minus $9,999. In the 1990 census, income 

amounts less than $999,999 were keyed in dollars. Amounts of $999,999 or more were treated as $999,999 and 

losses of $9,999 or more were treated as minus $9,999 in all of the computer derivations of aggregate income. 

In 1970, dormation on income in 1969 was obtained fiom all members in every fifth housing unit and small group 

quarters (less than 15 persons) and every fifth person in all other group quarters. Each person was required to 

report: 

Interest, dividend, or net rental or royalty income Social Security income 

Income from all other sources 

0 Wage or salary income 

0 Net nonfarm self-employment income 

0 Net farm self-employment income 

0 Social Security or Railroad Retirement 

0 Public assistance or welfare payments 

0 Income fiom all other sources 

If a person reported a dollar amount in wage or salary, net nonfarm self-employment income, or net farm selfemployment income, the person was considered as unallocated only if no further dollar amounts were unputed for 

any additional missing entries. 

In 1960, data on income were obtained from all members in every fourth housing unit and from every fourth person 

14 years old and over living in group quarters. Each person was required to report wage or salary income, net self163 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.
employment income, and income other than earnings received in 1959. An assumption was made in the editing 

process that no other type of income was received by a person who reported the receipt of either wage and salary 

income or self-employment but who had failed to report the receipt of other money income. 

For several reasons, the income data shown in census tabulations are not directly comparable with those that may 

be obtained from statistical summaries of income tax returns. Income, as defined for Federal tax purposes, differs 

somewhat from the Census Bureau concept. Moreover, the coverage of income tax statistics is different because of 

the exemptions of persons having small amounts of income and the inclusion of net capital gains in tax returns. 

Furthermore, members of some families file separate returns and others file joint returns; consequently, the income 

reporting unit is not consistently either a family or a person. 

The earnings data shown in census tabulations are not directly comparable with earnings records of the Social 

Security Administration. The earnings record data for 1989 excluded the earnings of most civilian government 

employees, some employees of nonprofit organizations, workers covered by the Railroad Retirement Act, and 

persons not covered by the program because of insufficient earnings. Furthermore, earnings received from any one 

employer in excess of $48,000 in 1989 are not covered by earnings records. Finally, because census data are 

obtained from household questionnaires, they may differ from Social Security Administration earnings record data, 

whch are based upon employers' reports and the Federal income tax returns of self-employed persons. 

The Bureau of Economic Analysis (BEA) of the Department of Commerce publishes annual data on aggregate and 

per-capita personal income received by the population for States, metropolitan areas, and selected counties. 

Aggregate income estimates based on the income statistics shown in census products usually would be less than 

those shown in the BEA income series for several reasons. The Census Bureau data are obtained directly from 

households, whereas the BEA income series is estimated largely on the basis of data from administrative records of 

business and governmental sources. Moreover, the defintions of income are Werent. The BEA income series 

includes some items not included in the income data shown in census publications, such as income "in kind," 

income received by nonprofit institutions, the value of services of banks and other financial intermediaries rendered 

to persons without the assessment of specific charges, Medicare payments, and the income of persons who died or 

emigrated prior to April 1,1990. On the other hand, the census income data include contributions for support 

received from persons not residing in the same household and employer contributions for social insurance. 

"Not in Labor Force" This category is defined as students, housewives, retired workers, seasonal workers who 

were not working during the reference period, institutionalized people, and people working less than 15 hours per 

week. 

7. "Per capita income" is the mean income computed for every man, woman, and child in a particular group. It is 

derived by dividing the total income of a particular group by the total population in that group. Mean Income--This is 

the amount obtained by dividing the total income of a particular statistical universe by the number of units in that 

universe. Thus, mean household income is obtained by dividing total household income by the total number of 

households. For the various types of income the means are based on households having those types of income. 

Care should be exercised in using and interpreting mean income values for small subgroups of the population. Because 

the mean is influenced strongly by extreme values in the distribution, it is especially susceptible to the effects of 

sampling variability, misreporting, and processing errors. The median, which is not affected by extreme values, is, 

therefore, a better measure than the mean when the population base is small. The mean, nevertheless, is shown in some 

data products for most small subgroups because, when weighted according to the number of cases, the means can be 

added to obtained summary measures for areas and groups other than those shown in census tabulations. 

164 

position or policies of the U.S. Department of Justice.

expressed are those of the author(s) and do not necessarily reflect the official

This report has not been published by the Department. Opinions or points of view

This document is a research report submitted to the U.S. Department of Justice.