Simple random sampling in a wage and hour case

An article you may find useful:

http://knol.google.com/k/dwight-steward/simple-random-sampling/15hmam9ln4cjr/5#

Salary discrimination allegations and data

In contrast to cases involving hiring discrimination, employment cases involving discriminatory salary allegations tend to present relatively few special data issues for the statistical expert. Generally, employers tend to keep data records on compensation issues that are sufficient for most statistical employment analyses. In these types of cases, the statistical expert will generally be most concerned with the specification of the underlying compensation process.

For example, in many of the cases both the plaintiff and defendant statistical experts will utilize the same employer salary and generally use the same class of statistical methodologies. The differences between the opposing statistical experts opinions frequently revolves around the specification of the compensation model and the employment actors included in the model.

Hiring discrimination and data

Similar to cases involving discriminatory pay allegations, discriminatory employment termination type cases present relatively few data related issues for the statistical expert. Like pay cases, most of the differences revolve around the specification of the statistical model of the employer’s practices. However, determining which protected and non-protected group members were actually eligible for a given reduction in force action does tend to produce differing opinions

Statistics Saturday: Wage and Hour Statistics Tools

Sampling Error, n. the difference between the
true, and generally unknown, value of a statistic,
such as the mean average salary, of a parent
population and the value that is estimated from a
sample.

Discussion:

Sampling error in commonly presented in national
surveys. For instance, a survey may report that
27%, with a sampling error of +/-3.5%, of the
employees were interested in applying for a given
job.

The sampling error in this example would
mean that it can be expected that had the entire
population been asked the question, between
23.5% and 30.5% of the employees would have
reported that they were interested in the job in
question.

Our damages paper makes the top 10 downloaded list in August!

Our paper entitled, "Back Pay and Front Pay Calculations in Employment Termination Cases: Accounting for Re-Employment and Mitigation Efforts" was recently listed on SSRN's Top Ten download list for

EBCPL: Compensation Law (Topic),
EBCPL: Employee Benefits Law (Topic),
EBCPL: Pension Law (Topic) and EPELL: Theoretical Perspectives on Employment & Labor Law (Topic).

Discriminatory hiring allegations: Special Concerns

Statistical analyses involving hiring allegations tend to present a number of special issues. First, it is not uncommon for organizations to keep only cursory records on individuals who apply for a given job position. Consequently, special attention must be dedicated to the construction of the relevant applicant pool in these types of employment cases. In some instances in hiring analyses, the employer level hiring data is augmented with labor market availability information from government and other publicly available sources to provide a more probative analysis.

Second, in addition to the data issues in hiring cases, the definition of the relevant pool of qualified applicants is typically an issue of concern for the statistical expert. Generally speaking, it is important for the expert to ensure that the hiring analysis compares individuals who, but for their protected class status, would have a comparable probability of being hired. For example, in a case involving allegations of gender discrimination in faculty hiring at a university, factors such as the individual’s prior research and teaching experience would be factors to potentially consider in the construction of the qualified applicant pool.

Third, it is important to consider each of the stages that comprise a typical employer’s hiring process as well as the different factors that may effect the individual’s decisions at each stage of the hiring process. For example, many employers’ hiring processes are comprised of distinct stages that may include the application stage, interview stage, testing stage, and ultimately the selection stage. It is important to clearly define the hiring stages, as well as to determine the number of applicants in both the protected and non-protected group who are not only qualified but are truly interested, potential job candidates for the given position.

Household production and age

Once criticism that economists typically encounter when calculating the loss of household services is that it is unrealistic to use the average number of hours spent on household services because the number of hours that a person will spend performing household services will decrease as the person ages.

Data on the how people allocated their time within a given day (time use surveys) suggest that this criticism is unfounded. For instance, according to time use data from the U.S. BLS the opposite is true. That is the amount of time spent performing household services such as housework, food cooking, outdoor chores, home maintenance, and time spent obtaining goods and services actually increases as a person ages. For example, a male who is between 25 and 34 will spend 11.7 hours performing household services; where as a male between the ages of 65 and 74 will spend 21.3 hours performing household services.