Index
A
Accuracy issue, 109
Across-domains cumulative effects studies, 251–253
cumulative disadvantage and racial discrimination, 224
Across-generations cumulative effects studies
cumulative disadvantage and racial discrimination, 223–224
Adarand Constructors, Inc. v. Peña, 53
Administrative records
indicators of discrimination from, 9–10
and the reporting on multiple races, 220
Adverse impact discrimination, detecting, 160–161
African Americans, 15
Agencies
program, 248–251
research, 251–253
Ambiguity
of prejudice, 60
of race, 33–34
Ambivalent attitudes about race, 60, 183–185
Anti-Defamation League, 174
Assessment
of racial discrimination causal inference, 77–89
of traffic-violating behaviors, 193–194
Attacks, physical, 58
Attitudes Toward Blacks Scale, 100
Attitudinal indicators of discrimination, 9–10, 162–185
black and white Americans’ perceptions of discrimination and ambivalent attitudes about race, 182–185
challenge of direct measurement of discrimination, 163–165
imperfect relationship to behaviors, 168–169
scale measures used in surveys, 175–180
sources of observational data, 165–175
Audit studies
accuracy issue, 109
combining features of laboratory and audit studies, 110–112
limits of, 108–114
methodology, 104–105
validity issue, 109–114
Auditor heterogeneity, 113–114
Automatic discrimination, 58–61
Avenues through which cumulative discrimination may occur
cumulative disadvantage and racial discrimination, 227–233
Avoidance, 57
B
Behavior
discriminating, effect of psychological mechanisms on measures of, 100–101
Behavioral indicators of discrimination, 9–10, 162–185
black and white Americans’ perceptions of discrimination and ambivalent attitudes about race, 182–185
challenge of direct measurement of discrimination, 163–165
scale measures used in surveys, 175–180
sources of observational data, 165–175
Behaviors
assessment of traffic-violating, 193–194
imperfect relationship of attitudes to, 168–169
Benefits of profiling, 200–201
Biases, reporting, 170–171
Biological definition of race, 25–26
Black Like Me, 78n
Blacks’ perceptions of discrimination, 182
BLS. See Bureau of Labor Statistics
Brookings Institution, 48
Brown v. Board of Education, 152
Bureau of Labor Statistics (BLS), 45, 173, 209, 216, 253
C
Causal inferences
counterfactuals and potential outcomes, 78–81
drawing, 78–88
illustrating causality, 80–81
roles of randomization and manipulation, 83–85
smoking and lung cancer, 86–87
study design and statistical methods, 81–83
weighing evidence from multiple studies, 85–88
Causation, not prediction, 199–200
Census data, 191–192
Citizen surveys, 194
Civil Rights Act of 1964, 16, 48, 87
Title VI, 152
Civil Rights Act of 1968, 106
Civil Rights Act of 1991, 52
Civil rights cases, rules for combining multiracial data for, 218
Civil War, 228
Combining features of laboratory and audit studies, 110–112
Combining multiracial data for civil rights cases
rules for, 218
Commissioned papers, 19
Committee on National Statistics, 16, 210
Comparisons, internal departmental, 194
Concept of cumulative discrimination, 225–227
cumulative disadvantage and racial discrimination, 225–227
current legal standards, 226
defining, 225
episodic discrimination, 226
transmission of, 226
See also Cumulative discrimination
Concepts, 23–70
defining discrimination, 4–5, 39–54
theories of discrimination, 55–70
Consequences of a racially biased society
cumulative disadvantage and racial discrimination, 231–233
Constitutional Convention of 1787, 27
Contexts in which cumulative discrimination may occur
cumulative disadvantage and racial discrimination, 227–233
Costs of profiling, 200–201
Counterfactuals and potential outcomes, 78–81
CPS. See Current Population Survey
Criminal justice, 46–47
life-course theory of cumulative disadvantage, 233–234
Cumulative disadvantage, 68–69
across generations, 223–224
across processes within a domain, 224
avenues through which cumulative discrimination may occur, 227–233
broader consequences of a racially biased society, 231–233
concept of cumulative discrimination, 225–227
cumulative discrimination across generations, 227–228
discrimination across processes within a domain, 228–229
effects cumulating across generations and through history, 68
effects cumulating through an individual’s life across different domains, 68–69
effects cumulating through an individual’s life sequentially within any one domain, 69
life-course theory of, 233–234
measuring cumulative discrimination, 238–245
models and theories of cumulative disadvantage, 233–238
and racial discrimination, 11–12, 223–246
Cumulative discrimination, 11–12
across generations, 227–228
Cumulative effects
across generations and through history, 68
through an individual’s life across different domains, 68–69
through an individual’s life sequentially within any one domain, 69
within-domain and across-domain, 251–253
Current outcomes estimating from past events, 243–244
Current Population Survey (CPS), 8, 210, 220
D
Data access and use, facilitating, 250–251
Data collection and research, 203–253
cumulative disadvantage and racial discrimination, 11–12, 223–246
measurement of race by the U.S. government, 5–11, 205–222
research—next steps, 12–13, 247–253
sources on racial profiling, 189–191
Decennial census, 30
Decomposition as racial discrimination, 128
Definitions of discrimination, 4–5
movement from episodic to dynamic, 68–69
role of cumulative disadvantage in, 68–69
Departmental comparisons, internal, 194
Desert Palace v. Costa [No. 02-679], 120
experimental, 82–83
Differences-in-differences approach, 149
Differential outcomes by race, 44–49
criminal justice, 46–47
education, 44–45
employment and income, 45–46
health care and health outcomes, 47–48
housing markets and mortgage lending, 47
interpreting, 48–49
Direct measurement of discrimination, 163–165
Disadvantaged racial groups, 4n, 17, 42
Discriminating behavior effect of psychological mechanisms on measures of, 100–101
Discriminating firms, model of, 134
Discrimination
across domains, 229–231
across processes within a domain, 228–229
blacks’ perceptions of, 182
classic laboratory experiment on, 96–97
disparate impact, 51–52
versus disparities, 195–196
domains operating in, 66–68
episodic, 226
examples of natural experiments to study, 149–153
intentional and explicit, 56–58
organizational processes of, 63–65
statistical discrimination and profiling, 61–63
structural, 63
subtle, unconscious and automatic, 58–61
theories of, 55–70
types of, 56–65
whites’ perceptions of, 183
See also Racial discrimination
Discrimination defined, 4–5, 39–54
differential outcomes by race, 44–49
legal definition, 49–53
limiting the discussion, 42–44
Discrimination in Elementary and Secondary Education, 21
Discrimination law regarding governmental actions, 52–53
Disparate impact discrimination, 4, 40, 51–52
Disparate outcomes
data sources on racial profiling, 189–191
establishing in profiling situations, 189–195
Disparate selection rates, methods for estimating, 191–195
Disparate treatment discrimination, 4, 40, 50–51
Disparities versus discrimination, 195–196
Domains in which discrimination operates, 66–68
map of potential points of discrimination within five domains, 67
See also Cumulative effects
Dynamic definitions of discrimination, role of cumulative disadvantage in, 68–69
E
Eastern European Jews, 2
Ecosocial theory of cumulative disadvantage in public health, 234–236
Education, 44–45
example of a natural experiment to study discrimination, 152–153
EEOC. See Equal Employment Opportunity Commission
Effective profiles, 197–200
developing, 197–200
inadequate data, 198–199
prediction, not causation, 199–200
Effectiveness, standards for, 200
Embodied social signification, 26
Employment and income, 45–46
Episodic definitions of discrimination, role of cumulative disadvantage in, 68–69
Episodic discrimination, 226
Equal Employment Opportunity Commission (EEOC), 153
Estimating an effect of discrimination, inferential target, 109–113
Estimating current outcomes from past events, 243–244
Estimating disparate selection rates
assessment of traffic-violating behaviors, 193–194
census data, 191–192
citizen surveys, 194
internal departmental comparisons, 194
methods for, 191–195
observational data, 192–193
Ethnicity in 2000 (C2SS), household data on, 215
Evidence from multiple studies, 85–88
Experimental designs, 82–83, 90–91
Experimental effects, translating, 102
Experimental methods for assessing discrimination, 90–117
laboratory experiments, 6, 92–102
in surveys about race, 168
Experiments using to measure racial discrimination, 91–92
Explicit discrimination, 56–58
Explicit racism, measures of, 179–180
Exposure to discrimination, identifying over time, 241–242
Extermination, 58
External validity, 82
F
Facilitating data access and use, 250–251
Fair Housing Act of 1968, 47–48, 106
FBI, 46n
Federal classification standards, 30–33
Federal Executive Order 11246, 48
Federal statistics, racial categories in, 29–33
Feedback models of cumulative
disadvantage, labor market, 237–238
audit or paired-testing methodology, 104–105
design, 103
housing audits, 106–107
key examples, 105–108
limits of audit studies, 108–114
Firms
discriminating, 134
nondiscriminating, 134
Fisher, R.A., 84
G
Gallup Organization, 182
Gender segregation of jobs, 136
Generations. See Cumulative effects
Government data on race and ethnicity, 213–217
2000 census, 213–216
ongoing research, 216–217
race in other U.S. government surveys, 216
Governmental actions, discrimination law regarding, 52–53
Governmental administrative data, 173–174
Griffin, John Howard, 78n
Griggs v. Duke Power Co., 40n
GSS. See General Social Survey
H
HDS. See Housing Discrimination Study of 2000
Health care
example of a natural experiment to study discrimination, 151–152
and health outcomes, 47–48
Health Interview Survey (HIS), 216, 219
High School and Beyond data, 239
Hiring decisions in the labor market, 130–137
model of a discriminating firm, 134
model of a nondiscriminating firm, 134
HIS. See Health Interview Survey
“Hispanic,” 30
Hispanic origin population
in the United States in 2000, 213
History. See Cumulative effects
“Honorary” whites, 29n
Household data on race and ethnicity in 2000 (C2SS), 215
Housing audits, 106–107
Housing Discrimination Study of 2000 (HDS), 105
Housing markets and mortgage lending, 47
HUD. See U.S. Department of Housing and Urban Development
I
Identification. See Self-identification of race
Illustrating causality, 80–81
Improving survey measures, 171–173
methodological improvements, 171–172
other improvements, 172–173
In-depth interviews, 175
Inadequate data, 198–199
Income and employment, 45–46
Inconsistent reporting, 34–36
Indicators of discrimination from surveys and administrative records, 9–10
Indirect prejudice, 59
Individual rights, 197n
Inferential targets estimating an effect of discrimination, 109–113
Inferring discrimination from statisticalanalysis of observational data, 128–137
Information on the occurrence of discrimination, using identifying, 244–245
Institute of Medicine, 48
Institute on Race and Poverty, 190n
Intentional discrimination, 56–58
Internal departmental comparisons, 194
Internal validity, 82
Interpersonal relations surveys, 165–173
Interpreting decomposition, 123–125
Italians, 2
J
Jews, Eastern European, 2
Jobs, gender segregation of, 136
L
Labor markets
example of a natural experiment to study discrimination, 149–151
feedback models of cumulative disadvantage, 237–238
hiring decisions in, 130–137
See also Past labor market discrimination
Laboratory experiments, 6, 92–102
classic laboratory experiment on discrimination, 96–97
design, 92–93
key examples, 95–99
limitations of laboratory experiments, 99–102
measuring racial discrimination, 94–95
strengths of, 93–94
translating experimental effects, 102
Legal definition of discrimination, 49–53
discrimination law regarding governmental actions, 52–53
disparate impact discrimination, 51–52
disparate treatment discrimination, 50–51
Legal standards
comparison with the four types of discrimination, 65–66
current, 226
Life-course theory of cumulative disadvantage, criminal justice, 233–234
Limitations
of audit studies, 108–114
effect of psychological mechanisms on measures of discriminating behavior, 100–101
of laboratory experiments, 99–102
of natural experiments, 153–154
Litigation, statistical analysis for, 119–120
Longitudinal data used to draw inferences about discrimination, 148–154
Lung cancer and smoking, 86–87
M
Manipulation, role of, 83–85
Mass killings, 58
Matching score methods, 146–147
Matrix of race, 37
Measurement of race, 33–37, 189–196
ambiguity of race, 33–34
disparities versus discrimination, 195–196
establishing disparate outcomes in profiling situations, 189–195
of explicit racism, 179–180
inconsistent reporting, 34–36
of modern racism, 176–179
multiple indicators of racial identification, 36–37
regarding race, 33–37
self-identification of race, 36
Measurement of race by the U.S. government, 5–11, 205–222
government data on race and ethnicity, 213–217
history, 206–208
issues in the reporting of data on multiple races, 217–221
standards for the collection of race and ethnicity data, 208–213
Measuring cumulative discrimination, 238–245
cumulative disadvantage and racial discrimination, 238–245
difficulties measuring cumulative discrimination, 239–240
estimating current outcomes from past events, 243–244
identifying exposure to discrimination over time, 241–242
tabulating outcomes over time, 240–241
using identifying information on the occurrence of discrimination, 244–245
Measuring racial discrimination, 5–11, 94–95
field experiments, 7
indicators of discrimination from surveys and administrative records, 9–10
laboratory experiments, 6
racial profiling as an illustrative example, 10–11
statistical analysis of observational data and natural experiment, 7–9
Methodological factors, 71–202, 169–170
attitudinal and behavioral indicators of discrimination, 9–10, 162–185
causal inference and the assessment of racial discrimination, 77–89
experimental methods for assessing discrimination, 90–117
illustration of methodological complexity—racial profiling, 10–11, 186–202
improving, 171–172
measurement issues, 189–196
profiling in the context of terrorism, 196–202
racial profiling as an illustration of, 10–11, 186–202
statistical analysis of observational data, 7–9, 118–161
“Mexican,” 30
Mexican-American Legal Defense and Educational Fund, 174
Models of cumulative disadvantage, 233–238
criminal justice—life-course theory of cumulative disadvantage, 233–234
labor market—feedback models, 237–238
public health—ecosocial theory, 234–236
Moderator variables, 93
Modern racism, measures of, 176–179
Modern Racism Scale, 176–178
Mortgage lending and housing markets, 47
Moving to Opportunity studies, 230
Multi-City Study of Urban Inequality, 135n, 185
Multiple indicators of racial identification, 36–37
Multiple races, reporting data on, 217–221
Multiple studies, weighing evidence from, 85–88
Multiracial survey data publication, 219
Murphy, Susan, 242
N
National Center for Education Statistics (NCES), 210, 212
National Center for Health Statistics (NCHS), 210, 216, 219–220
National Conference of State Legislatures, 187n, 190n
National Content Survey, 210–211
National Fair Housing Alliance (NFHA), 173
National Institutes of Health, 5, 89, 251
National Longitudinal Survey of Youth, 10, 12, 181, 239, 246
National Longitudinal Surveys of Labor Market Behavior, 253
National Race and Politics Study, 168
National Research Council, 210
National Science Foundation, 5, 88–89, 251
Natural experiments to study discrimination, 148–154
education, 152–153
health care, 151–152
labor market, 149–151
limitations of, 153–154
statistical analysis of, 7–9
NCES. See National Center for Education Statistics
NCHS. See National Center for Health Statistics
Next steps, 12–13
NFHA. See National Fair Housing Alliance
Nondiscriminating firms, model of, 134
Nongovernmental data, 174–175
O
Observational data, 192–193
detecting adverse impact discrimination, 160–161
effects of discrimination in other domains, 156–15
effects of past labor market discrimination on factors in hiring, 155–156
governmental administrative data, 173–174
in-depth interviews, 175
inferring discrimination from statistical analysis of, 128–137
nongovernmental data, 174–175
possible solutions to problems of using statistical models to infer discrimination, 141–154
problems with measuring discrimination by fitting statistical models to, 137–141
sources of, 165–175
statistical analysis for research and litigation, 119–120
statistical analysis of, 7–9, 118–161
statistical decompositions of racial differences, 121–128
surveys of interpersonal relations and racial discrimination, 165–173
Observational studies, 83
Office of Civil Rights, 248
OMB. See U.S. Office of Management and Budget
OMB revised standards of 1997 for the collection of race and ethnicity data, 212–213
OMB standards of 1977 for the collection of race and ethnicity data, 208–209
Omitted variables bias, 8, 137–140
using an indicator of productivity to address, 142–145
Organizational processes, 63–65
Outcomes tabulating over time, 240–241
P
Paired-testing methodology, 104–105
Panel data, 251–253
methods for assessing, 147–148
Panel Study of Income Dynamics, 10, 12,181, 239, 246
Papers commissioned, 19
Past labor market discrimination, effects on factors in hiring, 155–156
Physical attacks, 58
Pitfalls in statistical decomposition, 125–128
Points of discrimination within five domains, map of potential, 67
Police Foundation, 190n
Population controls, reporting data on multiple races, 220–221
Possible solutions to problems using statistical models to infer discrimination, 141–154
Potential outcomes and counterfactuals, 78–81
Prediction, not causation, 199–200
Princeton Survey Research Associates, 182
Priority research topics, 248–250
Problems, measuring cumulative discrimination, 239–240
Problems, measuring discrimination by fitting statistical models to observational data, 137–141
omitted variables bias, 137–140
sample selection bias, 140–141
Simpson’s paradox, 138–139
Processes within a domain, cumulative disadvantage and racial discrimination, 224
Profiling in the context of terrorism, 61–63, 196–202
costs and benefits of profiling, 200–201
data sources on, 189–191
developing effective profiles, 197–200
establishing disparate outcomes in specific situations, 189–195
illustration of methodological complexity, 10–11, 186–202
as an illustrative example, 10–11
trade-offs, 201–202
Program agencies, 248–251
facilitating data access and use, 250–251
priority research topics, 248–250
Propensity score methods, 104, 146–147
Psychological mechanisms, effect on measures of discriminating behavior, 100–101
Public health, ecosocial theory of cumulative disadvantage, 234–236
Publication and release of data on multiple races, 218–219
R
Race
in 2000 (C2SS), household data on, 215
ambivalent attitudes about, 183–185
differential outcomes by, 44–49
and Hispanic origin population in the United States in 2000, 213
matrix of, 37
self-identification of, 36
social construction of, 26–27
in U.S. government surveys, 216
in the United States, 27–29
Race and Ethnic Targeted Test, 210, 212
biological definition, 25–26
measurement issues, 33–37
race in the United States, 27–29
social construction of race, 26–27
Race-specific intercepts with regression models, 121–123
Racial categories in federal statistics, 29–33
decennial census, 30
federal classification standards, 30–33
in U.S. census, 31
Racial classifications
objective, 26
subjective, 27
Racial differences, statistical decompositions of, 121–128
Racial differentials in compensation, case of professional athletes, 145
Racial discrimination, 11–12, 39–42, 223–246
across generations, 223–224
across processes within a domain, 224, 228–229
broader consequences of a racially biased society, 231–233
cumulative discrimination across generations, 227–228
decomposition and residual “effects” as, 128
measuring, 5–11
surveys of, 165–173
Racial profiling. See Profiling in the context of terrorism
Racially biased society, broader consequences of, 231–233
Racism
measures of explicit, 179–180
measures of modern, 176–179
Randomization, role of, 83–85
Recommendations, 3, 6–7, 9–10, 12, 116–117, 160, 181, 222, 246
Regression models
race-specific, 122–123
with race-specific intercepts, 121–122
used to decompose racial differences, 124
Reporting biases, 170–171
Reporting of data on multiple races, 217–221
administrative data, 220
population controls, 220–221
publication and release of data, 218
publication of multiracial survey data, 219
rules for combining multiracial data for civil rights cases, 218
time-series data, 220
Research agencies, 251–253
panel data, 251–253
within-domain and across-domain cumulative effects studies, 251–253
Research on race and ethnicity
cumulative disadvantage and racial discrimination, 11–12, 223–246
by federal statistical agencies, 209–212
measurement of race by the U.S. government, 5–11, 205–222
priority topics, 248–250
program agencies, 248–251
statistical analysis for, 119–120
See also Data collection and research
Residual “effects” as racial discrimination, 128
Roper Center, 168
Rules for combining multiracial data for civil rights cases, 218
S
Sample selection bias, 8, 140–141
Scale measures used in surveys, 175–180
measures of explicit racism, 179–180
measures of modern racism, 176–179
Score methods matching and propensity, 146–147
Segregation, 57–58
Self-identification of race, 36
Seniority issues, 64
Sequential effects. See Cumulative effects
Seuss, Dr., 77–78
Simpson’s paradox, 137–139
Smith, Thomas, 162–173
Smoking and lung cancer, 86–87
Sneetches, The, 77–78
Social-cognitive approach, 26
Social construction of race, 26–27
Social Security Act of 1935, 152
Social signification embodied, 26
Solutions to problems of using statistical models to infer discrimination, 141–154
matching and propensity score methods, 146–147
natural experiments, 148–154
panel data methods, 147–148
racial differentials in compensation—case of professional athletes, 145
use of longitudinal data to draw inferences about discrimination, 148–154
using an indicator of productivity to address the omitted variables problem, 142–145
Sources of observational data, 165–175
Standards for effectiveness, 200
Standards for Maintaining, Collecting and Presenting Federal Data on Race and Ethnicity, 212
Standards for the collection of race and ethnicity data, 208–213
OMB revised standards of 1997, 212–213
OMB standards of 1977, 208–209
research by federal statistical agencies on race and ethnicity, 209–212
See also Legal standards
Statistical analysis of observational data, 7–9
developing statistical models, 129–130
examples—hiring decisions in the labor market, 130–137
gender segregation of jobs, 136
inferring discrimination from, 128–137
for research and litigation, 119–120
Statistical decompositions of racial differences, 121–128
decomposition and residual “effects” as racial discrimination, 128
interpreting the decomposition, 123–125
over time, 126–127
race-specific regression models, 122–123
regression models with race-specific intercepts, 121–122
statistical decompositions over time, 126–127
two pitfalls in statistical decomposition, 125–128
use of regression models to decompose racial differences, 124
Statistical Directive Number 15, 208
Statistical discrimination, 61–63
Statistical methods, 81–83
Statistical models, developing, 129–130
Structural discrimination, 63
Study design and statistical methods, 81–83
experimental designs, 82–83
observational studies, 83
Subtle discrimination, 58–61
Survey limitations, 169–171
methodological factors, 169–170
reporting biases, 170–171
Survey measures, means of improving, 171–173
Surveys
citizen, 194
indicators of discrimination from, 9–10
key examples of, 166–168
Surveys of interpersonal relations and racial discrimination, 165–173
design and strengths, 165–166
experiments in, 168
imperfect relationship of attitudes to behaviors, 168–169
key examples of surveys, 166–168
means of improving survey measures, 171–173
survey limitations, 169–171
T
2000 census, 213–216
household data on race and ethnicity in 2000 (C2SS), 215
household data on race and ethnicity in 2000 (C2SS—long form), 214, 215
race and Hispanic origin population in the United States in 2000, 213
Tabulating outcomes
over time, 240–241
Targets
inferential, 109–113
Teacher’s College Record, 19
Terrorism
costs and benefits of profiling, 200–201
developing effective profiles, 197–200
profiling in the context of, 196–202
trade-offs, 201–202
Theories of cumulative disadvantage, 233–238
criminal justice—life-course theory of cumulative disadvantage, 233–234
labor market—feedback models, 237–238
public health—ecosocial theory, 234–236
Theories of discrimination, 55–70
comparison of legal standards with the four types of discrimination, 65–66
domains in which discrimination operates, 66–68
moving from episodic to dynamic definitions of discrimination—role of cumulative disadvantage, 68–69
types of discrimination, 56–65
Time-series data and the reporting on multiple races, 220
Trade-offs in profiling, 201–202
Traffic-violating behaviors assessment, 193–194
Translating experimental effects, 102
Transmission
of cumulative discrimination, 226
Types of discrimination, 56–65
comparison of legal standards with, 65–66
U
Unconscious discrimination, 58–61
Uniform Crime Reports, 46n
U.S. census racial categories, 31–33, 209
U.S. Constitution, 27, 40, 206–207
Fifth Amendment, 52
U.S. Department of Education, 19, 32, 45, 248
U.S. Department of Housing and Urban Development (HUD), 16, 105–107, 115, 117, 173, 230, 250
U.S. Department of Justice, 106, 173
U.S. Equal Employment Opportunity Commission, 248
U.S. government surveys, race in, 216
U.S. Office of Management and Budget (OMB), 30–32, 36, 206, 208–209, 212, 218–219, 220–221
U.S. Supreme Court, 51, 53, 64, 120
V
Validity issues, 109–114
auditor heterogeneity, 113–114
external, 82
inferential target—estimating an effect of discrimination, 109–113
internal, 82
Variables
moderator, 93
See also Omitted variables bias
Verbal antagonism, 56–57
Voting Rights Act of 1965, 48
W
Washington Post, 182
Whites, “honorary,” 29n
Whites’ perceptions of discrimination, 183
Within-domain and across-domain cumulative effects studies, 251–253
Within-domain cumulative effects studies, 251–25
Workshop on Measuring Racial Disparities, 21