In this episode of Lehigh University’s College of Business IlLUminate podcast, we are speaking with David Rea about how to increase fairness and justice in the delivery of basic needs services.
Rea is an assistant professor of decision and technology analytics (DATA) in Lehigh's College of Business. His research integrates predictive and prescriptive analytics methodologies, largely focusing on operational problems and the delivery of basic needs services.
He spoke with Jack Croft, host of the ilLUminate podcast. Listen to the podcast here and subscribe and download Lehigh Business on Apple Podcasts or wherever you get your podcasts.
Below is an edited excerpt from that conversation. Read the complete podcast transcript.
Jack Croft: It seems that one of the key underpinnings of much of your research is the difference between fairness and justice. I suspect a lot of people probably use those terms interchangeably. So what are some of the important distinctions between fairness and justice involved in the research you do?
David Rea: My view is simply that justice is a set of principles that guide decision-making, whereas fairness is the perceptions about some individual scenario. It's obviously much easier to agree on a set of principles versus the fairness of a particular outcome. Because I work with organizations, I typically view these problems through the lens of organizational justice.
Organizational justice breaks it down into three categories: procedural justice, where it's governing the processes, making sure they're consistent, appealable, or transparent; distributive justice, where you're thinking about how benefits or penalties are distributed across individuals; and then interactional justice, which is people's treatment during the decision-making process. And generally, research shows that adherence to these just principles leads to perceptions of fairness.
Croft: One of the most basic of the basic needs services is food. And during the holiday season, especially, doing something to help feed those in our communities dealing with food insecurity is on the minds of many. Being able to measure need accurately and in a timely manner in different locations and among different populations would seem to be essential to fair and just food distribution, getting food to those who need it most and are the most vulnerable. So what are some of the issues that you've identified that complicate achieving that goal?
Rea: I see two main issues here. We have lack of data, and we have siloed operations. So when we talk about lack of data, the way we measure food insecurity is through an annual survey. It's given in December, and it asks people to reflect back on the year and identify if they were worried about food or missed meals. And then this data is aggregated to a state level, and it's released with about a two-year delay.
This is in contrast with the hunger that people experience. That's an immediate problem that occurs on the order of hours. So you have emergency food organizations who are doing great work, but they're doing it essentially blind without the ability to understand if they're even moving the needle at all. They're addressing hunger, not necessarily food insecurity. This makes it difficult to develop best practices and even to make a case for funding for those organizations.
The second part is dispersed resources for families. The way that people access the things they need on the ground level for food is usually through food pantries, which are often run by one or two well-meaning individuals through churches, nonprofits, community centers, and so they don't really have the resources to collaborate and integrate their operations. This means that people who are food insecure and who already have issues with the amount of time they have, have to try and cobble together the resources from a bunch of different organizations. This can include things like the public schools, the public library, nonprofits, and food banks, along with using things like SNAP [Supplemental Nutrition Assistance Program] to go to the retail food system. This creates a really, really complex situation for families.
So we have organizations doing good work on one hand, but they're doing it blind, and we have people trying to cobble together a bunch of different disparate resources together to make a whole set of meals, which is a whole stressor in its own self, even if you never experience hunger.
Croft: The two-year lag is interesting because when you think about that in the context of what we've been living through over the last two years, … a lot of organizations are basing their feeding programs today on data that was collected before the pandemic hit. And obviously, that's changed so much in our society.
Rea: Yeah, exactly right. We're using these metrics as targets, but those targets were defined in what was essentially a different world. And this is one of the reasons that the group I've been working with in Cincinnati is trying to create a real-time data system so that you can actually monitor this on a hyper-local level, because we're talking about neighborhoods, not even states. So you have a time delay, and then you also have the geographic aggregation. How we handle this is by getting a bunch of organizations together, helping them integrate their data systems, and then using that data to drive real-time decision-making. That's where we're going. Obviously, it's still a work in progress, but I think it could be very useful information. And if there's any good thing that came out of the pandemic, it's creating the cause or the ability to integrate all these organizations together.
Croft: Recent research that you've also been involved in examined how scheduling shifts for emergency medicine physicians at a health system's main medical center and two community hospitals could be made more fair. What were some of the issues there that led some employees to believe they were being treated unfairly to begin with?
Rea: I'll take you back to the three principles of organizational justice. You have distributive justice and procedural justice, I would say, are kind of the two main factors. Distributive justice is you have people who are unable to work at the location they wanted, and at the time, that primarily was the main medical center. Various reasons. There are more interesting cases there. You get to work with residents. It may just be because you live closer to there, so it's nicer that you've [got] a shorter commute, compared to two community hospitals.
The other part is procedural justice. The original process was fairly opaque. It was done by a small group of senior physicians, and they were trying to schedule some 50-odd people across three locations, which is an extremely difficult task to do manually. But because the process is opaque, you can create perceptions of unfairness even if they're not intended or not even there. Automating this, I think, helped with, one, the transparency—helped with the procedural justice. Also, what we've showed through surveys is that it improved perceptions of distributive justice. That is, we're balancing equity and equality. That is not seeking solutions where everyone receives the same, but seeking solutions that reward individuals for their contributions without going to the extreme where those who are, say, the newest or haven't had the time to contribute as much are receiving all of the poor shifts.
In another set of my research, I'm thinking about inter-hospital transport problems. We have multiple stakeholders. We have the health system, we have patients, and we have the crews, and each is going to have a different goal for what they actually need. The system wants an efficient solution. It wants to minimize its costs so that it can keep its profits high and keep costs down for their customers, i.e., patients.
The patients themselves need equitable treatment, but as they have different needs, they need to be moved at different speeds. Maybe one person that has a more severe condition needs to move faster than someone else who can stay there in the hospital they're at for an extended period of time.
Then, you have the crews. They're all salaried. Generally, what you're trying to do is balance your workload, or you're trying to create equal solutions.
So we have three stakeholders, three different objectives. How do you balance this inside a model? Well, you use the same kind of method we did in the scheduling process. That is, you create an objective that seeks efficient solutions, but you discourage unequal and discourage inequitable solutions. And the key is trying to balance the treatment of your different stakeholders to find a solution that is acceptable to all.