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The problem of corporate crime rates has been the subject of debate, speculation and operationalization for decades, largely stemming from the complexity of measuring this type of crime. Examining corporate environmental crime poses challenges and creates opportunities for advancing the discussion of corporate crime rates, but criminologists are less familiar with environmental data. In the current paper, we review the strengths and weaknesses of existing environmental data that can be used to construct the components of an environmental crime rate. We also present a corporate environmental crime rate derived from data on violations of the Clean Water Act and describe problems with using it in real world data. Implications for theory, practice and future research are discussed.The problem of corporate crime rates has been the subject of debate, speculation, and operationalization for decades [6,16,10], largely stemming from the complexity of measuring this type of crime. For example, a single act of corporate crime may include individuals, the organizational entity and interdependencies between the two. In addition, firms and managers vary in opportunity for criminal activity according to the position of the corporation in the industry and the manager in the organization [16].Examining corporate environmental crime poses challenges to crime rate construction, in part because it is a relatively new analytic and legal concept that covers a wide range of illegal activity by individuals and organizations [3]. Theoretical definitions and typologies of environmental crime are virtually nonexistent and criminologists are less familiar with environmental data. In addition, Crime Law Soc Change (

Matched groups of subjects were used to test the learning and transfer effects that follow changes in the display, the muscular reactions and the directional relationship between stimulus and response in a tracking task. Two arrangements were compared in the relationship studies: one arrangement of the stimuli and reactions was similar, and the other was opposed to that used in many every-day skills. The familiar arrangement was easier to learn. There was high positive transfer from the unfamiliar to the familiar, and little transfer from the familiar to the unfamiliar. The physical dimensions of the display were varied to give two tasks with different stimuli. The initial learning times were equal for both tasks, and the transfer between them was high, positive, and equal. Two further tasks varied in the extent, speed and force of the required muscular movements. One task proved more difficult to learn initially, and there was greater transfer from the difficult to the easy task than from the easy to the difficult. A further experiment tested the effects of changing the difficulty of a tracking course, and it was found that learning was more rapid on the more difficult course. A difference in difficulty between two tasks, therefore, determined both the amount of transfer between them and the rate of learning the tasks. New measures were developed to test the transfer between tasks of unequal content, and the effect of such inequalities upon the rate of learning. The findings are discussed, as are their possible implications for transfer measurement and their bearing upon existing theories of transfer.