Measuring Racial Discrimination (2004) / Chapter Skim
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12 Research: Next Steps
12 Research: Next Steps
Pages 247-253
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From page 247... ...
Some laboratory and field experiments, statistical analyses of observational data, evaluations of natural experiments, and survey measures of discriminatory attitudes and reported experiences of discrimination have produced useful results pertaining to particular types of possible discrimination within a domain or process. To make further progress, we believe it will be necessary for funding and program agencies to support studies that cut across disciplinary boundaries, make use of multiple methods and types of data, and analyze racial discrimination as a dynamic process rather than as a point-in-time event.
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From page 248... ...
Because resource limitations will necessarily constrain research and data collection, program agencies should subject their list of priority research areas to careful evaluation regarding feasibility and costs. We strongly urge that agencies not limit their determination of feasible priority projects to a particular disciplinary perspective or type of analytical method or data.
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From page 249... ...
In Chapter 7, we argued that statistical information on racial gaps in outcomes will rarely be adequate to support conclusions about the role of racial discrimination in the absence of a detailed understanding of the decision processes of decision makers, including information on what knowledge is available to them and what knowledge they bring to bear in making particular types of decisions. In the labor market example, this would mean understanding the processes by which hiring or promotion occurs and the information available to employers in making employment or promotion decisions.
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From page 250... ...
Consideration of appropriate concepts and review of pertinent laboratory results should help suggest the types of data that are most needed for informative analyses of observational data with statistical models. For example, case studies might justify adding questions to cross-sectional and longitudinal surveys on self-reports of discrimination, or they might suggest collecting information on specific characteristics related to the decision process, such as (again, a labor market example)
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From page 251... ...
RESEARCH AGENCIES We suggest that research funding agencies, such as the National Science Foundation, the National Institutes of Health, and private foundations, can best leverage their resources by addressing areas of research on racial discrimination that are less apt to be considered by program agencies. They also have a comparative advantage in supporting more basic research and data infrastructure, including support for rich longitudinal data collections.
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From page 252... ...
They could consider supporting studies of longer-term discrimination over lifetimes and generations. Such cross-process, cross-domain, cross-generation types of research will necessarily require bringing together researchers from multiple disciplines and perspectives and using various data sets and methods -- for example, using laboratory experiments to develop theoretical constructs for paths and mechanisms by which cumulative disadvantage could occur; using case studies and ethnographic research to obtain very rich data on perceptions and experiences of discrimination in a particular population group or community; and using rich panel data to follow population cohorts over time.
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From page 253... ...
Panel Data We have stressed in several chapters the need for rich longitudinal data sets that follow individuals over time and hence permit studies of cumulative disadvantage, as well as studies that delineate paths by which disadvantage -- and possible discrimination -- occurs. Statistical agencies fund some of the major panel surveys, such as the National Longitudinal Surveys of Labor Market Behavior of the Bureau of Labor Statistics, but many panel surveys are funded by public and private research agencies.
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