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19 Following the scheme of the US. Census Bureau, class of worker had four categories in this data—employee of a private company, government employee, self-employed in own business, and unemployed.Google Scholar

20 The details of the coding of this variable are given in the code book for the Wheeler et al. project (available from the Inter University Consortium for Social and Political Research, Ann Arbor, Michigan).Google Scholar

21 US. Census Bureau codes were used.Google Scholar

22 If either indicated that an individual was a manager, he or she was so classed.Google Scholar

23 However, partners in companies were included in the employer category. We believe that the presentence investigations provide thorough information on matters such as work and financial history and that individuals who clearly own controlling interest in the companies for which they work are so identified and thus accurately coded.Google Scholar

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27 For a more detailed review of the study design, see Wheeler, et al., 47 Am. Soc. Rev. at 642 (cited in note 3). Hagan and Parker include only securities violators in their sample.Google Scholar

28 Those districts are southern New York, western Washington, central California, northern Georgia, northern Illinois, Maryland, and northern Texas.Google Scholar

29 See Wheeler, et al., 47 Am. Soc. Rev. at 644.Google Scholar

30 The Cronbach's alpha reliability coefficient of the new impeccability measure is 68. The zero order correlation between the two indicators is 78.Google Scholar

31 As noted earlier, however, we include the revised impeccability measure here. The Duncan scores for unemployed members of the sample are randomly allocated. See Wheeler, et al., 47 Am. Soc. Rev. at 649.Google Scholar

32 The logic response function for the probability of imprisonment (p of Imprisonment= 1/1 +e-xb) is approximated here by setting the value of xb at 0 and then allowing the variable being estimated (bi) to vary (xb=0+bixi).Google Scholar

33 Because of the skewness of sentences meted out by judges, the natural log of prison length was used as the response variable rather than the actual distribution of sentences. This served to pull the longest sentences closer to those of 6 or 12 months, better approximating the actual intervals of the decision the judges make (see Wheeler, et al., 47 Am. Soc. Rev. at 653 n.25).Google Scholar

34 Table 6 in id. at 654 is based on SEI scores from 60 to 960 (rounded to .001). The coefficient for the Socio Economic Index, when based on the range 6.0 to 96.0 is close to that which we report. The difference in significance between our model I results for status and those reported by Wheeler et al. derive primarily from changes in the impeccability index and the addition of lambda. Because the t coefficient for status in the original analysis was 1.97, these changes bring the significance of the variable below p >. 05..+05.>Google Scholar

35 The percentage increase in the months of imprisonment imposed caused by a one-unit change in an independent a variable may be calculated with the formula (ebi - 1) where bi, is the coefficient of the variable. See E. Tufte, Data Analysis for Politics and Policy 124 (Englewood Cliffs, N.J: Prentice-Hall, 1974).Google Scholar

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39 Comparing those prosecuted under statutes that allow for imprisonment of more than a year and those allowing only imprisonment up to one year, they find that the type of statutory charge is the only significant influence upon sentence severity.Google Scholar

40 As Berk observes, “In principle … there exists an almost infinite regress for any data set in which at some point sample selection bias becomes a potential problem. As for traditional specification errors and measurement errors, the question is not typically whether one has biased (or even consistent) estimates. The question is whether the bias in small enough to be safely ignored.”Berk, , 48 Am. Soc. Rev. at 392.Google Scholar

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