I'm Paul Thornton, The Times' letters editor, and it is Saturday, April 30, 2016. Two hundred and twenty seven years ago today, George Washington became the first president of the United States. We've come a long way, right? Speaking of presidents, regular readers of this newsletter probably expect to find in this space an introduction to an opinion piece on Donald Trump, Hillary Clinton, Bernie Sanders or some other character from the protracted presidential primary. But this week, I've decided to spare faithful subscribers a lengthy excerpt on the even lengthier campaign in favor of commentary on a topic sure to perk up your Saturday morning: the daunting prospects faced by older women looking for a job.
Companies speak with job seekers at a job fair in Pittsburgh. Companies speak with job seekers at a job fair in Pittsburgh. The federal Age Discrimination in Employment Act turns 50 this year -- about the age when many American workers begin to encounter the kinds of biases the law was intended to prevent. At this "milestone of middle age," quipped Victoria Lipnic, acting chair of the U.S. Equal Employment Opportunity Commission, the law is grappling with new forms of age discrimination in the Internet era. Research by EEOC, which received 20,857 claims of age discrimination last year, found that 65% of older workers say age is a barrier to getting a job.
It's important for older women seeking employment to understand the particular challenges they face in the labor market, says economist Teresa Ghilarducci. Editor's Note: For a recent Making Sen e segment, Paul Solman caught up with economist Teresa Ghilarducci to discuss why the job market is harder on aging women than aging men. We asked Ghilarducci to share some of her practical advice from her new book, "How To Retire With Enough Money and How To Know What Enough Is." The book also discusses retirement, savings, Social Security and why you should get rid of your financial planner. Below, Ghilarducci explains what older women face in the job market and some tips on how to beat the odds.
As a bipartisan consensus on the need for criminal justice reform has solidified in recent years, one of the changes advocates have pushed for is "banning the box"--that is, removing from job applications the box people must check if they have had a felony conviction. Ban-the-box laws don't prevent employers from asking applicants about a criminal record, but rather delay the questioning until later in the process, after an applicant has made it past that first hurdle. The idea is that applicants with criminal records can then get an honest opportunity for consideration, as opposed to being eliminated from the get-go. Ban the box has also been touted by civil rights groups as a way to reduce unemployment among young black men (who disproportionately have criminal records) and thereby to lessen the racial employment gap. Twenty-three states have passed ban-the-box laws that apply to public employers, while nine also apply the policy to private employers.
We develop tools for utilizing correspondence experiments to detect illegal discrimination by individual employers. Employers violate US employment law if their propensity to contact applicants depends on protected characteristics such as race or sex. We establish identification of higher moments of the causal effects of protected characteristics on callback rates as a function of the number of fictitious applications sent to each job ad. These moments are used to bound the fraction of jobs that illegally discriminate. Applying our results to three experimental datasets, we find evidence of significant employer heterogeneity in discriminatory behavior, with the standard deviation of gaps in job-specific callback probabilities across protected groups averaging roughly twice the mean gap. In a recent experiment manipulating racially distinctive names, we estimate that at least 85% of jobs that contact both of two white applications and neither of two black applications are engaged in illegal discrimination. To assess the tradeoff between type I and II errors presented by these patterns, we consider the performance of a series of decision rules for investigating suspicious callback behavior under a simple two-type model that rationalizes the experimental data. Though, in our preferred specification, only 17% of employers are estimated to discriminate on the basis of race, we find that an experiment sending 10 applications to each job would enable accurate detection of 7-10% of discriminators while falsely accusing fewer than 0.2% of non-discriminators. A minimax decision rule acknowledging partial identification of the joint distribution of callback rates yields higher error rates but more investigations than our baseline two-type model. Our results suggest illegal labor market discrimination can be reliably monitored with relatively small modifications to existing audit designs.