The Fruits of Our Labors: An Empirical Study of the Distribution of Income and Job Satisfaction Across the Legal Profession

Journal of Legal Education

Volumn 49, Issue 3, 1999, Pages 342-360

  Dau Schmidt, Kenneth G ^a   Mukhopadhaya, Kaushik ^b  

^a INDIANA UNIVERSITY   (United States)

^b EMORY UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0442328628     PISSN: 00222208     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (11)

1. See Lewis A. Kornhauser & Richard L. Revesz, Legal Education and Entry into the Legal Profession: The Role of Race, Gender, and Educational Debt, 70 N.Y.U. L. Rev. 829, 835-46 (1995); Robert G. Wood et al., Pay Differences Among the Highly Paid: The Male-Female Earnings Gap in Lawyers' Salaries, 11 J. Labor Econ. 417 (1993); Cathleen Donnell et al., Gender Penalties: The Results of the Careers and Compensation Study, Colorado Women's Bar Association (Denver, 1998).
2. See Lewis A. Kornhauser & Richard L. Revesz, Legal Education and Entry into the Legal Profession: The Role of Race, Gender, and Educational Debt, 70 N.Y.U. L. Rev. 829, 835-46 (1995); Robert G. Wood et al., Pay Differences Among the Highly Paid: The Male-Female Earnings Gap in Lawyers' Salaries, 11 J. Labor Econ. 417 (1993); Cathleen Donnell et al., Gender Penalties: The Results of the Careers and Compensation Study, Colorado Women's Bar Association (Denver, 1998).
3. See Lewis A. Kornhauser & Richard L. Revesz, Legal Education and Entry into the Legal Profession: The Role of Race, Gender, and Educational Debt, 70 N.Y.U. L. Rev. 829, 835-46 (1995); Robert G. Wood et al., Pay Differences Among the Highly Paid: The Male-Female Earnings Gap in Lawyers' Salaries, 11 J. Labor Econ. 417 (1993); Cathleen Donnell et al., Gender Penalties: The Results of the Careers and Compensation Study, Colorado Women's Bar Association (Denver, 1998).
4. Glen G. Cain, The Economic Analysis of Labor Market Discrimination: A Survey, in 1 Handbook of Labor Economics, eds. Orley C. Ashenfelter & Richard Layard, 693 (New York, 1986).
5. U.S. Bureau of the Census, Statistical Abstract of the United States: 1983-1997 at 417 (Washington, 1998).
6. For example, one might hope to sort out whether women choose or are shunted into the lower-paying types of practice by looking at survey responses as to what type of practice they had hoped to go into when they entered law school, before their possible disappointment in the labor market. The women in the Michigan survey do indeed report a desire, during law school, to go into government work or public interest work more frequently than their male counterparts. David L. Chambers, Accommodation and Satisfaction: Women and Men Lawyers and the Balance of Work and Family, 14 Law & Soc. Inquiry 251, 271 n.76 (1989). But this approach could be criticized on the basis that the women's preferences during law school might be affected by the extent of discrimination they anticipate in each of the types of practice they might choose.
7. Robert S. Pindyck & Daniel L. Rubinfeld, Economic Models and Economic Forecasts 6 (New York, 1976).
8. Use of a discontinuous variable as the dependent variable in a linear regression model technically violates one of the assumptions of the model: that the residual errors of the equation will be normally distributed. Pindyck & Rubinfeld, supra note 9, at 55. Violation of this assumption can result in biased coefficient estimates and the undermining of the tests of statistical significance that are performed on the coefficients. This can be a serious problem for binary dependent variables, but our dependent variable can take on seven different values and thus constitutes a reasonable approximation of a continuous variable. However, future work using logistic regression for the job satisfaction variable is warranted. Id. at 247.
9. As with Regressions 1 and 2, work hours squared is included in Regressions 3 and 4 because experimentation with the form of the regression suggests that the relationship between job satisfaction and hours worked is nonlinear and our regression estimates are improved by inclusion of the work hours squared variable.
10. Chambers, supra note 8, at 270, 276 n.88.
11. Because of the interdependent relationship between job satisfaction and work satisfaction and the interdependent relationship between job satisfaction and income, it is probably more appropriate to estimate the relationships among these variables through a two-stage least squares process involving three first-stage equations and three second-stage equations. Pindyck & Rubinfeld, supra note 9, at 144. The first-stage regressions would estimate each of the three "endogenous" variables - income, job satisfaction, and family satisfaction - as functions of "predetermined exogenous" variables that did not include the endogenous variables. The second-stage regressions would then estimate each of the three variables - income, job satisfaction, and family satisfaction - as functions of the predetermined variables and estimates of the appropriate endogenous variables from the first-stage regressions. For example: income is probably a function of job satisfaction, hours worked, type of practice, etc.; job