Document Type

Book Chapter

Abstract

Although ethical rules and other laws governing lawyers apply to the entire legal profession, each lawyer develops a personal understanding of them through norms observed in fields of practice. These norms vary considerably in different geographic locations, office settings, and legal fields. Any ranking indicating which lawyers are “the fairest of them all” reflects the profession's historical biases, hierarchies of prestige, and differences across organizational settings and across fields of law. This chapter describes an ethical hierarchy of the legal profession based on lawyers’ views of other lawyers who work in those fields, using data from the University of Michigan Law School Alumni Survey. The Michigan data shows wide variation in lawyers’ observations of the ethical conduct of those with whom they work (outside of their own offices). Quantitative analysis of the data suggests this variation is due to the types of work that lawyers do, the clients they represent, and their organizational setting, legal fields, and office size. Perceptions of ethical conduct also appear to be related to lawyers’ job satisfaction and thus may affect the trajectory of lawyers’ careers as they self-select into more compatible areas of practice or organizational settings.

Comments

This is a draft chapter/article. The final version is available in Research Handbook on the Sociology of Legal Ethics edited by Scott L. Cummings, Tamara Butter, Ole Hammerslev, and Sergio Ivan Anzola Rodriguez, published in 2025, Edward Elgar Publishing Ltd
https://doi.org/10.4337/9781800880566.00026

It is deposited under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

Without limiting the author's and publisher's exclusive rights, any unauthorised use of this work to train generative artificial intelligence (Al) technologies is expressly prohibited.

Available for download on Tuesday, November 24, 2026

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