Creaming (creaming off, or skimming) is the practice of serving a select client base in order to improve the efficiency or efficacy of a treatment. Creaming generally occurs in a response to incentives. For example, when hospitals receive a fixed reimbursement payment for treating a given ailment, they have an incentive to seek out the most healthy patients among those afflicted with that condition; this improves the apparent efficiency of their care because the most healthy can typically be treated at lower cost. Similarly, job training programs that are evaluated based on the success of their clients obtaining subsequent employment have an incentive to recruit clients who are most likely to succeed following the completion of their training; in doing so the programs appear highly effective. Scholars continue to debate whether specialized schools such as magnet or charter schools practice creaming by attracting the strongest students from within the larger pool of students who are eligible to attend.
The practice of creaming has both social and statistical consequences. When clients (patients, trainees, or students, for example) who are already relatively advantaged obtain the most favorable treatment, inequality of opportunity is exacerbated. In some cases, that may be a goal of the program, such as when the aim is to maximize the number of program completers who are able to perform a specific role following training. More often, however, creaming produces results that are considered undesirable because it allocates resources unequally in favor of those who are already privileged. As an example, school choice programs that allow greater access to specialized schools to children from more educated families tend to increase rather than reduce segregation and inequality.
The statistical consequence of creaming is that it tends to distort efforts to assess program impact. Viewed narrowly as intended for a select population, creaming may limit generalizability; that is, if the program is intended for a particular subgroup, its effects may be properly assessed on that subgroup, but the effects may not generalize to the larger population. This pattern may be uncovered through examination of disaggregated data to identify the relevant subgroups. Generally, however, creaming produces biased estimates of program effects when the estimates fail to take into account the selectivity of the population undergoing the favored treatment in contrast to those without such access. Anderson, Burkhauser, and Raymond showed in a 1993 study that the remarkable performance of the Job Training Partnership Act of 1982 (a federal employment program in the United States) in preparing unemployed persons for work reflected in part the selection of those trainees who were most likely to obtain employment irrespective of their training opportunities. However, the bias was not as large as the critics had supposed. Responses to bias caused by creaming include statistical adjustments, such as instrumental variables analyses and propensity score models, and design-based solutions, such as randomized controlled trials with random assignment of participants to treatment and control groups.
SEE ALSO Heckman Selection Correction Procedure; Inequality, Political; Instrumental Variables Regression; Sampling; Selection Bias; Statistics
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