Monday, February 18, 2019

Maternal Leave and Stalled Careers: Considerate Discrimination


In Norway and Sweden, the paid parental leave after childbirth has one component that is exclusive to the mother and one shared between parents. Advocates of gender equality in the workplace have been critical of the parents’ ability to give all the leave to the mother and encouraged by the fact that most parents decide to let the father take some time off to care for the child. But think about this for a moment: If paid maternal leave is a major accomplishment of the movement for equal rights and opportunities for women in the workplace, does it make sense to let fathers take some of it?

Research by Irene Padavic, Robin Ely, and Erin Reid in Administrative Science Quarterly suggests that sharing family leave benefits across genders is exactly the way to achieve greater equality in the workplace. Yes, much research has shown that women have disadvantages and that these grow greater after childbirth. The idea of a work-family conflict that needs to be addressed by providing various benefits to women and especially mothers is grounded in this observation. But firms unwittingly use these benefits in ways that stall women’s careers and prevent changes that could create less-demanding work conditions for both men and women as well as greater equality in opportunities.

First, the various benefits to make the workplace more flexible and adapted to provide work-family balance are typically oriented toward making it easier for working women to fulfill the wife and mother roles, which in a typical family handle much more of the housework than the husband and father roles. In practice, that means that the workplace allows women to work fewer hours and move to internal-facing roles, which in turn makes them less promotable and even stigmatizes them as recipients of benefits. In Padavic, Ely, and Reid’s research from a global consulting firm, women took 11 years to be promoted to partner, while men took 9 years. And this disparity applies only to those women who got as far as promotion: at the partner level, only 10 percent were women.

Second, it was widely recognized that women in this firm had less successful careers than men, but management in the firm emphasized that this stemmed from the nature of the work, which simply did not fit women’s needs to achieve a good work-family balance. Indeed, the firm was seen as accommodating these needs quite well because it offered women alternative job paths involving less demanding work schedules and less travel. The implication was that if women did the same work as men (putting in the same hours and accepting the same heavy travel requirements), they could do equally well.

Third, managers did not recognize that the firm’s work schedules, unrealistic deadlines, and demand for employees to be available 24/7 were as burdensome for men and equally disruptive to their family lives as they were for women. The belief that the bond between mother and child is special and much more important to maintain than the bond between father and child was so firm that evidence showing how the work hours and travel schedules disadvantaged male workers too was dismissed. Everyone “knew” that this was “women’s problem,” so the firm was doing enough by catering to women’s needs for family time.

Discrimination is actions based on beliefs. There is a distinction between the belief that women are not fit for work and the belief that women have greater needs for a work-family balance than men, but it is a small distinction. In both cases the result is discrimination and stalled careers, and it doesn’t help anyone that the second kind of belief and discrimination seems more considerate than the first.


Padavic, I., R. J. Ely, and E. M. Reid
2019. "Explaining the Persistence of Gender Inequality: The Work–family Narrative as a Social Defense against the 24/7 Work Culture." Administrative Science Quarterly, forthcoming.

Monday, February 4, 2019

Self-driving Cars and Surgery: Up with Robots, Down with Skills

Imagine that you have just purchased the latest self-driving car and you are on your way to work. The car is in control because the route is familiar and the car has all the modern sensing and decision-making functions. You assure yourself that you can take control of it at any time. But if there is a sudden dangerous situation—someone darts into the road or another vehicle is moving erratically—will you do so? I venture that the answer is yes if you are an experienced driver of old-fashioned cars that need to be driven—your instinct will be to rely on your own skills to avert problems. If you learned to drive after the self-driving technology was the standard? I’m guessing the answer might be no. And the answer could be very consequential.

Research by Matthew Beane published in Administrative Science Quarterly explores a situation with similarities to this one. He examined what’s happening in operating rooms around the world, where robots are increasingly used to perform surgeries. At least for the moment, the most effective robots do not make their own decisions, but they can be controlled quite effectively by a single surgeon at the control panel. That single surgeon often no longer needs a team performing different surgical tasks for the operation to go smoothly. And that surgeon is not likely to give up the controls during a challenging operation to a medical intern who needs to learn how to perform the surgery.

If you’re the intern, that leaves you in a position of scrambling for any opportunity you can get to practice with the new technology so you can gain the necessary skills to guide the robots. At the same time, you aren’t (as often, at least) part of a surgical team performing traditional surgeries, so you aren’t building the skills that are essential backups if something goes awry during the robotic surgeries you’re observing.

Surgeons don’t live forever. What happens when they are replaced by the interns who are currently receiving less hands-on training than they had? I guess we’re all going to find out.

We immediately understand that less training of surgical interns is a very bad idea, but that does not solve the dilemma. Surgeries using robots are performed best by the most experienced surgeons with minimal help, so actively involving residents adds time and risk. The residents can do little more than observe the surgeon controlling the robot. The onus was on the residents Beane studied to learn the skills with minimal costs to the attending physician in charge. And some of them did, showing significant ingenuity in the process.

Because the robots were only supposed to be used by the most qualified surgeons, only two approaches to learn were open to the residents. They could learn when the surgeon was not there, or they could learn as a favor from the surgeon. Learning when the surgeon was away could be done before they even became residents at the hospital, if they were lucky enough to be in a place with the right equipment and surgeons who allowed them to practice. It could also be done by watching recordings of earlier surgeries, though this type of training only showed what the robot did, not how to use the actual controls.

The other option is to learn from the surgeon during an actual operation. Usually that means getting control of the robot during the less critical part of the operation, and being watched over by the surgeon at every step. This approach is fast and accurate, but it also means that any mistake becomes very visible, and usually leads to the surgeon taking over the controls right away. It is the best way of learning, but also the scariest one. The risk was real because residents would be compared based on very few operations, and surgeons would prefer to continue working more with those who had done best – whether it was because of skill or because of luck.

The difficulty of learning when robotic equipment takes over a production process but still needs human control is not just in hospitals. The self-driving car is an example, and there are many examples of more subtle processes. The software that recommends whether an applicant can get a mortgage or not, and at what interest rate, is an invisible robot that can be controlled by the bank employee. But as the software gets smarter and the employees get less training, they have less foundation for checking its work, or even to be employed at all. Robots are smart, but organizations need to use them smartly.

that less training of surgical interns is a very bad idea, but that does not solve the dilemma. Surgeries using robots are performed best by the most experienced surgeons with minimal help, so actively involving residents adds time and risk. The residents can do little more than observe the surgeon controlling the robot. The onus was on the residents Beane studied to learn the skills with minimal costs to the attending physician in charge. And some of them did, showing significant ingenuity in the process.
Beane, Matthew. 2019. Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail. Administrative Science Quarterly, 64: 87–123.

There is also a TED talk on this research.