Sara Mahabadi (McGill) interviewing Brittany Bond (MIT, now at Cornell), Winner of the Louis Pondy Best Dissertation Paper Award, for “Pride without Prejudice: The Burden of Under-Recognition in Organizations”
Sara Mahabadi (SM): Congratulations on winning the Best dissertation award!
Brittany Bond (BB): Thanks so much.
SM: How does it feel to win the award?
BB: It was super exciting. I was very surprised, quite shocked when I heard. This was in the middle of the spring. I couldn't believe it.
SM: Nice. Okay, so, I read your paper. But for those who have not had the opportunity to read it and will probably be reading this interview, can you tell us a little bit what your paper is about?
BB: Of course. The paper demonstrates that employees who are working for a great company to work for – very high status in its industry and its area – will still end up doing something like leaving voluntarily, quitting this employer, when they feel under-recognized. In my setting, I was able to rule everything else out, except just this sense of under-recognition. So, it wasn’t because they weren't getting bonuses that you can get when you get like recognition at the end of the year for performance. It had no career or reputational concerns. These people were just as exceptional as those that the company was able to recognize. But because the company enforced a strict limit on the number of employees who can get the recognition itself, some were arbitrarily denied. Everyone knew it was arbitrary. But despite all of that it just didn't sit well with these exceptional yet under-recognized employees to the point where they still incurred very costly actions to go and work for a different, likely less competitive, firm, despite receiving the highest bonuses the company gave out, in part in an effort to make up for the forced under-recognition, for this reason alone.
SM: I think that it is a very interesting phenomenon that you decided to look at. So, how did this project come about? what motivated you to look at the unintended consequences of public recognition for employees and organizations?
BB: I was in my third year by the time that this project came together. So, I had two years of course work and working with my advisor and my committee to go deep on theory and understanding what grasped me – what theoretical questions and tensions really caught my attention. And so, when I got access to this field site, it was late fall in my third year, so the pressure was on to find something that was going to get traction.
My advisor, Roberto Fernandez, had trained me for look for questions in partnership with an organization – to listen to their needs – because you're going to get the best access and the most help when there's that good match.
So, I did that through all my initial, like, meet-and-greets and getting to know people. I was asking them what kept them up at night and what were their biggest concerns. Mostly, because of the time of year, the company was getting ready to go into their performance evaluation season, which they do quite intensely, and this was a front-and-center concern with everyone I was talking to.
They were aware that there were some kinks to work out and having to fit to this very tight curb stresses a lot of people out, because every year they know they're not going to be able to give everybody the recognition they deserve. So, I dug a little deeper into that, especially because I started seeing questions on status, here in the form of these performance rating designations, which I could address with causal inference. Measuring the importance especially on career outcomes was something that I was really excited about. When I heard how this company was executing their performance evaluation process and I saw that the data could capture the arbitrary bouncing from recognition of truly exceptional employees, I got super excited, and I had a good feeling that this is going to be my thesis.
SM: Nice. So, you actually answered my follow-up question, which was about access to the data. You used a natural experiment in a pharmaceutical company, which is a unique setting, and I know that it is not very easy to get data from these types of companies. I believe you kind of addressed the question. But just to clarify, you connected with the setting through your supervisor, is that right?
BB: Yes, I was TAing for him in his courses as well. So, he was able to kind of have me ready at the wings for when an opportunity like this did arise, and so this opportunity did come through an executive MBA student that he was teaching, and who was in the class I was TAing for.
Luckily, the organization was excited, too, after I wrote up a brief thing about what I was interested in, and it was enough to have them see a match as well.
And what was great was that they also let me embed myself like an ethnographer. Come in, you know, hover around, shadow people, be a fly on the wall during the calibration sessions that bounced these employees from the recognition categories, in addition to getting access to the other data that was at the core of the project.
SM: That is actually very cool. A quantitative researcher usually prefers to work only with quantitative data. It's interesting to see a researcher like you who is willing to go on the site in person and shadow or talk to people.
BB: Yeah, I was a little nervous, but I recognized how valuable and, like, what a gift it was. At the same time, also how under-prepared I was to do qualitative research adequately. So I put myself on a learning curve where I had great mentors like Susan Silbey and other ethnographers at Sloan, including my peers / colleagues.
And so, I would not say I'm an ethnographer, by any means, but I made sure that the data that I was able to collect in this way was solid. I think it’s really great, and I'm excited to show that through the paper: what life is like there, and bringing some more voices and understanding, especially around the process of performance evaluation. Because I recognize that not everybody, not all my readers, will have had such, like, great access to organizations like the one I study.
SM: Yes. Very true. So, my next question is actually around the framing of the paper and if you experienced any challenges finding the framing and also analyzing the data.
BB: Sure. good questions. Actually, I did have a different framing at one point.
The paper is still very much about status and causally identifying and supporting how individuals react to even known arbitrary status allocations. But when I was presenting this early on, I would hear reactions like, “Oh, you should definitely quantify how painful this is – like what is the monetary price of status?” Which led me to an initial framing I tried around the idea of the reverse Matthew effect. Starting with the understanding that people are willing to pay for status and thinking about what I was finding which was that the opposite was not true – that there’s an infeasibility of compensating the denial of status.
So, the idea that I was playing around with was observing, identifying the effects of the other side of the award-winning process. And at the same time showing this other flip side of the monetary thing from the fact that the under-recognized in my study were making the most in merit bonus at the company in this period.
The idea that the denial of merited status is incommensurable with money is still an important part of the framing that stuck. But I was helpfully pushed to keep reflecting, to move past this sort of initial throat-clearing framing and come up with something even more central to the rich pattern I was unfolding.
So I much more directly let the nature of this natural experiment shine and just kept it at the pure outcome of how people really are hurt from this nominal arbitrary status distinction, nothing else, and being able to rule everything else convincingly out and thinking about how then to interpret the effects that this had in the reactions the under-recognized employees took.
Your second question was the data?
SM: Yes, so, now I'm thinking did that make you reanalyze your data in a different way?
BB: I wrote the methods section last. But it was because it was finished in my head. So, it never really changed. It just didn't get on the paper for a long time.
My thesis had a little eureka moment – my aha moment – that I've got something when I ran my first t-test. I had no controls, I just flagged the people that I observed had been nominated as exceptional, had all of this proof of exceptionality. But through rounds of this calibration process meant to try to weed out the real non-‘exceptionals’ had in fact just been bounced because of numbers.
And so, I just did this binary t-test of all the nominated exceptionals, those who got it and those who got bounced, looking at the outcome of who left by that point. No controls and all the significance that you could ask for – so many bounced exceptional employees had left, that I knew I had something.
SM: Awesome. Again, we talked a little bit about your decision to add some quotes from your observations to your project, which I think is super interesting. But do you see yourself doing more research on the same phenomenon like public recognition or under recognition, and if so, in which direction would you probably go, or in which setting?
BB: Thanks. Yes, I am excited to keep unpacking these kinds of challenging questions! I am embarking on a big project that is going to take a little bit of time even processing the data which is probably even more central to the idea of the importance of status recognition on career outcomes. And I have an RA who's qualitative helping me on it. So I'm excited, because I feel like it complements the project well.
And the initial question is, can we trace from initial status through infrequent but important moments in careers how initial starting status evolves and culminates through very public meetings / instantiations. Specifically, using annual conference data from the social sciences, the Allied Social Sciences Associations, the American Economic Association, things like this, and trace out qualitatively, adding some understanding to the basic status accumulation concepts – nothing yet causal, by any means. Rather, starting by looking at what are the main drivers that map on to who you are associated with when you come into the profession, what kinds of schools, what kind of relative status positioning early on, and what kind of indicators through these important syncopated occasions, over the course of whole careers and how they may or may not translate into higher-prestige positions. At the same time, we can look at the same outcomes for the associations and institutions themselves concurrently. So, several layers / units of analysis playing out via these status dynamics and accumulation processes and also in kind of a closed system sense that will be helpful for analytical traction.
So, a very big data project, very ambitious, but I am really excited about being able to trace things you can't necessarily see on a shorter timeframe, and also being able to interact sort of the network stuff into career progressions over time. And thinking about how can this answer some lingering status questions and tensions, especially on the theoretical front, using this kind of data as system itself and modeling status dynamics and especially career progression type questions therein.
SM: It is excellent that you are complementing your data from different sources and running different forms of analysis, which means that you will probably have more than one paper.
BB: Oh, definitely. I think the first are going to be very descriptive. That’s the way I am going to move forward. And I have sort of my favorite ideas that I'll be playing around with, but I keep thinking about something else every time I go back in.
SM: Awesome. So, what advice do you have for PhD students who are interested in this area and want to do research similar to yours? And what do you think other researchers should be doing to complement what you have been doing?
BB: I think my biggest takeaways up to this point of my career is, one – know what you're good at and know your strengths really well and kind of don't lose sight of that in all the education part of trying to figure out and learn what you don't know.
You know you're always confronted with all these skills other people have that you don't have and see how valuable they are and you get a little jealous and you want them, too.
And so, first to know what what's your specialty that gives you an edge, and then decide, and use that to figure out what you need to acquire to bring out the most of that for a question you're passionate about.
I think that's super important and sometimes hard to remember, especially when you do have a question that you're obsessed with, and you feel inadequate for whatever reason. It can be challenging to try to find how to get from point A, of not feeling ready to answer or having the time necessary to get it published or get a job from finding a way to make it click.
And then, second – what helped me a lot was having advisors that, one – recognized those things in me, too. This allowed me to kind of outsource a little the responsibility for that really important piece. And that, second – finding advisors able to guide me, in terms of what I should be focusing on, where to put my energy in terms of what I needed to learn, what I needed to do better.
And the complimentary question. Yeah. I mean, I don't even know where to start. I think it's one of those things where you get hooked on something, start seeing it pop up everywhere you look. You start interpreting everything through a single lens, as your brain is playing with all the components of the theory you’re thinking about.
I don't think that's bad. So, I would say, if you are that kind of person, lean into it because you could find super interesting questions where you least expected.
As an example of a paper that came up that way, which I co-authored with Ethan Poskanzer, a current student at MIT Sloan, began when I had been reading all of this status recognition stuff for my thesis – real classics, on the older side, and sort of social psychology like ‘Why Men Rebel,’ by Gurr.
This is when the Red Sox were in and won the World Series last. And I was completely new to baseball, never really watched it. But I was living in Boston and watching it, and I just started seeing this one pitcher, Price, get frustrated when he had to go up to bat because he never had to do that in his league, being a pitcher and with the different league rules.
But then after he gets to go back to the pitcher's mound, he was like channeling all of that anger out at the other team, and being really successful striking them out. And I was like, I wonder if that's a thing – like I wonder if pitchers do get frustrated and can channel that like the frustrated rebellious masses that Gurr was discussing.
Anyways, that became a paper which is actually further along than my thesis. It was one of those lucky things where the data was so strong, the evidence was so clearly in support of the idea that there is this causal connection, and surprisingly, even baseball Saber-metricians have not shown it yet. So we have kind of a twofer for OB literature and the sports world, hopefully.
SM: That is excellent advice. So, basically keep an open mind and look for signs of, what is related to your area of interest.
BB: Yes. Don't be afraid to lean into it. And, you know, sometimes we’re in a state of always thinking about our current project. Especially when you have different audiences because you learn how to think about it from different angles that keeps it fresh and keeps it very generative for, like, the next opportunity that latches on to this interest. It can work because it is obviously something you're passionate about, thinking about it all the time.
SM: Yeah, that's very true. So, do you have other projects that are underway that are based on the same data set, or maybe a different data set but around the same topic?
BB: Sure, yeah. So, my thesis was on the top end of the performance distribution and using this arbitrary cutoff at the very top, to create and leverage a very clean discontinuity.
And I was looking to really understand all of the dynamics going into that, as well as trying to make the most of my opportunity with being embedded in this organization which was equally interested in trying to figure out the optimal way to do performance evaluations of employees.
And so, that entailed looking at the bottom end of the distribution as well, in part because some of the pain that these top performers felt had to do with the fact that while they had a very strict enforcement at the top of the distribution, they did not enforce it all at the bottom. They had this sort of grade inflation at the bottom. So, these exceptional employees were lumped in with the lowest performers of the company. It was very hard to get managers to identify low performers. There were no enforcement mechanisms and so you saw that in the data. And so, one question was, can we unpack the causal thread, like what was causing managers to do this and net out all the obvious? So, I ran a vignette experiment where I was able to control for all of these obvious reasons why managers would inflate and kind of see what from economic sociology theory could be teased out that could also be driving some of these underlying habits. Also, looking at, what can we learn to minimize this habit managers recognize is a downside? It becomes a major cost because of how much time and investment goes into it.
So, I did that and I was able to use two main competing theories, one being embeddedness, so the theoretical implication being that the more closely tied relationally managers are to their employees the more likely they would be to inflate the performance evaluation of a bad employee. And then conversely, managers who were sort of by the book ‘checklist manifesto’ types trying to do things where they're more structured, more formal, would be less likely, even if they were close friends. That is what I found. Now it's sort of just trying to package all of these moving pieces so that the main point – that, is that managers can be very close with their team and foster all those things that make you more productive overall through tight relational embeddedness, and that so long as you also pay attention to the formal protocol, you can kind of have the best of both worlds. So, you've already done the groundwork that lets you give the objective but hard grading to these employees, without all the concern that's leading to these very relationally embedded managers doing all this inflating.
One interesting side point of that study was that 20% of all the managers who were responding to this hypothetical case study I gave them did inflate the performance evaluation, even though they knew it was hypothetical, they knew the right answer, and they knew the formal policy for the evaluation. That’s how strong the relationally-embedded pull is in the inflation tendency.
SM: Wow, that is super interesting. I forgot to ask, what is the status of the paper that won the award?
BB: Well, I haven't submitted it yet. I still want to sprinkle in those quotes, and I want to include a few more fancier analyses that I have run a thousand times in my head already but I haven't put into the paper yet.
I had kept it pretty clean and simple through job market process through now. Now I'm teaching for the first time, so, I’m going to need to find and block off a few days – but hopefully soon, when my students are on break.
SM: Awesome. How has COVID affected your research?
BB: That's a good question. I think because I planned to teach for this fall, so far, not terribly. I TA-ed actually in my last semester of my PhD. So, with COVID, that did preclude a lot of research and finally finishing up my thesis with the above, unfortunately. But I used that experience to learn how to teach in whatever modality I was going to face right now. So, I invested a lot in prepping to teach and to put out a different paper under review this summer.
I have the data for the next project I'll be doing, data crunching, already. It's data from my field site that I'm working with my advisor on right now, completely separate from my thesis work, much more something that we've been developing in terms of looking at equality and promotions and gender dynamics in organizations. So through COVID, I think I've actually been able to keep the relationship with this field site and its industry stronger. They remain concerned with the same questions that kept them up at night when we started our connection.
So, I think, I think it's impacted probably everything, all the other aspects of my life much more than it has on my research. But that’s probably a function of how I had been planning to use this time in my career transitions to begin with.
SM: That is true. We just hope that it comes to an end sometime soon, right?
BB: Yeah, I mean, I can't wait to actually meet so many of my new colleagues face to face.
SM: Are you in a different city now?
BB: We moved to Ithaca, New York, at Cornell, which helps, because I'm teaching in-person.
And my new colleagues are great! Everyone I'm working with, they are trying their best. It's, still, just takes so much more energy to coordinate one another. There’s no spontaneous interaction, sadly.
SM: I totally understand. You actually kind of answered the question that I had for the end of the interview, which is what is the best advice you have ever received?
BB: Oh, that's really great. Yeah, I mean I won't lie, I've actually struggled with that piece of advice above, a really long time. I think that's why it's my favorite. Most good pieces of advice are like, clearly unquestionable, you're like yeah and maybe in the moment it helps get you through something and that’s all you need that advice for.
But this one, I've like went back and forth with it for so much of my life now. I got this piece of advice early on in undergrad from a challenging course and professor and it came out of the blue, unrelated to what we were doing. And I haven't stopped thinking about it since. But the idea that you're going to get much farther in this life if you emphasize your strengths, minimize your weaknesses when you can, but focus on maximizing your strengths, every time, every time they come into conflict, every time it's one or the other, double down on what makes you great.
As somebody who loves learning new skills and likes doing stretch goals, it's something I've questioned, a lot. Yes. But I think that part of that questioning, that tension, helps me get the most out of it, making it the best piece of advice for me, at least.
SM: Yeah, and I think that instinctively, human beings are just always comparing themselves with others. So, no matter how good you are at what you're doing you are always thinking about what skills you're missing or what others are good at.
BB: Exactly! Yeah, it's all relative. That’s what's nice about this little piece of advice. It makes it relative within yourself, too. So, every time you're using it to decide to invest one way or another, you can see what's the tradeoff on the other side for yourself and decide if it's worth it, that way.
SM: Yeah. Very true. Okay, this is the end of my questions. Again, congratulations, and good luck on your new role at Cornell University. I'm sure that is not easy, especially during COVID, with all the virtual teaching.
BB: Thank you! It's a new day every day.
SM: But we will get through this, I think. And, and hopefully we will be back to like normal life and real classes!
BB: Yeah and hopefully real conferences.
SM: Real conferences. Yes, that's very true. It was really nice chatting with you, Brittany.
BB: Same! Thank you, Sara.