My last post on addressing transit agencies’ labor ‘shortage’ got a bit of traction so, here is a follow-up focusing on the need for transit agencies to address the organizational trauma caused by COVID-19. Similar to other employers, in order to retain and recruit employees, transit agencies need to address the emotional impact of the pandemic on their workforce.
Clearly, the COVID pandemic has created immense stress in every community. And the ongoing pandemic has an acute impact on people in jobs that cannot be done remotely and in sectors where the work itself must not stop. The healthcare sector is highly visible coping with both challenges, and every week I see new stories of devastating burn-out.
The majority of public transit work also can’t be done remote and can’t stop. In March 2020, we at the MBTA could not predict what was going to happen, but we knew that we needed to keep running no matter what. Agencies have adjusted service levels, accounting for ridership changes and often in response to employee availability, but have kept operating day in and day out of the pandemic. (These changes take considerable work in and of themselves.)
Many transit workers on the frontlines got sick and hundreds died of COVID. Frontline transit workers have had to accept daily risk and fear of exposing their loved ones. And, to protect themselves and other riders, they have to proactively enforce mask mandates and cope with anger from some members of the public. Agencies had to plan for uncertainty of revenue sources and the possibility of service cuts and layoffs. And staff had to come to terms with the impact cuts would have on riders and the workforce.
Unfortunately, managing through crisis and trauma isn’t new to transit agencies. Just in the year before COVID-19, the MBTA had several major derailments (one impacting service for months), a bus operator fatality, and multiple people struck by trains. I used to say that working at T was like being on a crisis treadmill. During COVID, the other crises experienced by transit systems didn’t stop.
Transit employees are also first responders for passengers in crisis. Policy decisions, like the lack of affordable housing, mental health and drug treatment resources, and growing income inequality, are increasing passengers in need. These decisions, often outside a transit agency’s control, put increasing stress on agency staff to respond on top of their existing jobs.
All of this is layered on top of the everyday trauma of racism and all forms of oppression playing out in the workplace. And in the specific moment of 2020 the societal trauma of killings of Black people by the police and hate crimes against Asians.
By the time the calendar hit the middle of 2020, my emotional reserves from crisis were completely empty. For the rest of the year, I cried at work almost every day. And I was privileged to be working from the safety of my home, which offered the additional grace of being able to turn my camera off.
I left the T at the end of 2020 and it took me months to recover. I imagine that it is even more difficult to be two years into this pandemic with another surge underway. It is no wonder that many employees are at their breaking point or that it is hard to recruit.
Regardless of how long this pandemic lasts, transit agencies have work to do to recover beyond building back ridership and revenue sources. The collective trauma the agency experienced has to be acknowledged and addressed. And in the process, agencies should implement better strategies to deal with trauma in general.
Trauma impacts individuals, and also organizations, communities, countries, and right now the world. I find the concept of organizational trauma useful because it allows us to think about collective impacts and responses. Organizational trauma can start from single events or build over time and if not addressed it can become embedded in organizational culture.
Much of the writing on organizational trauma focuses on mission-driven nonprofits that serve people in need (e.g rape crisis centers). While some of the specific takeaways might not apply, there are parallels with the mission-driven nature of public transit agencies. Transit employees provide direct public service and their commitment to service can mean organizational crises become personal ones.
Workplaces often provide individual responses to trauma like HR hotlines or therapy referrals. Transit agencies often have in-house teams to provide employee services and support. While necessary, these services don’t solve problems that are ingrained in organizational culture and management practices. Recovery from the current level of trauma requires institutional changes.
After a major derailment in 2019 the MBTA Board (FMCB) hired an independent panel to review the safety practices and culture and make recommendations. At their kick-off meeting with the senior management team, the panel asked us what we thought we did well as a team. As we went around the room the most common answer was respond to crisis. This might be because we had so many, but at some point it becomes a bit of a self-fulfilling prophesy. When your identity as a leadership team is crisis response, who is focused on crisis prevention?
Almost* no matter what happens on a given day, transit agencies have to start over the next day and try to do the same thing- run the schedule. There is not much time for reflection and when it happens it is often focused on technical changes to make. (COVID did make some transit agencies more adaptive as they figured out how to adjust service levels in response to crowding on a daily basis.)
Even though the work is the same, the next day isn’t the same as the day before for the people running the schedule. People’s lives change, sometimes in acute ways. After the mass shooting at the VTA yard in San Jose in May 2021, a horrific trauma for their workforce, the acting GM Evelynn Tran said something that has stayed with me. She said, “Some of us get training on what to do when there is an active shooter event, but not about the aftermath.” In my years at the MBTA, I did a few trainings on active shooter events and the simulation ended when the shooting stopped. But an event like that is only the beginning of trauma; I never had a training on what to do after a crisis. As a manager responding to personal and collective crisis at work, I relied on the training and experience I received as a volunteer at a rape crisis center years ago.
When organizations don’t train their managers to respond to crisis and the aftermath, that means the work isn’t always happening when it should, leaving employees unsupported. And it means that the people who are doing this work aren’t being recognized or compensated for their emotional labor. They are the ones who are counseling colleagues when someone had a particularly bad day and their manager doesn’t want to hear about it. They are the people who mentoring and training new employees, even though it isn’t in their job description or rewarded with pay raises and promotions. They are the managers who speak up for their team, but have no one to turn to. Often women and people of color are over-represented in the group shouldering the organizational trauma. Before COVID I used to joke that I held therapy office hours from 5 to 6pm. But I don’t think it is funny anymore.
This group of care-givers is providing an emotional buffer for the rest of the organization, allowing others to not feel the full impact and keeping the organization emotionally afloat. Not only is the labor not recognized, but the responsibilities aren’t spread evenly reinforcing gender and race-based inequities. This is unhealthy for the individuals and for the organization. In times of mass trauma, like COVID, this informal system can no longer carry the load. Ways to address trauma and crisis have to be built into the systems and structures of the organization.
One very practical example of a system impacting employees at a transit agency is scheduling. Transit agencies have the ability to create better work-life balance for their frontline employees by how they schedule shifts, off-days, and breaks. I heard from a few folks after my last post that people are quitting because of how work is being scheduled. Efficiency has to be balanced with employees’ needs. This balance can be thrown off in the name of cost-savings and need adjusting. But even if the scheduling practices haven’t changed, I think the balancing point needs to shift to account for the trauma and general exhaustion of the past two years. I found some helpful content on LinkedIn on retention and scheduling.
Even after COVID has receded transit agencies will continue to face crises, and how they respond will determine if they are on a crisis treadmill. Resources and leadership attention have to be dedicated to preventing crisis and responding to the emotional aftermath of crises. Everyday management practices need to include sharing the emotional labor more equitably and prioritizing employee wellness in systems. They are probably pretty busy right about now, but there are experts in organizational trauma who can help.
Transit agencies are going to have to change to recover from the COVID pandemic. They need to change their service to better serve the public. Many will need new revenue sources. And transit agencies need to change their organizational culture to recruit and retain their workforce. Many of these changes needed to happen before, COVID made the need more visible. COVID also showed that transit agencies can respond and change more quickly.
*For example, the VTA shutdown their light rail service for months after the mass shooting. A decision I supported, but for which they faced public push back and had a big impact on some passengers.
In Part 1 I talked about my experience using quantitative research methods at the MBTA. Over the past almost a year I have thought (and read) more about qualitative research and data (and the difference between the two).
For transit agencies to really consider and use community voices and lived experience as data they will need to institutionalize qualitative data and research methods. This will require different data infrastructure, data collection, and analysis skills.
In general transit agencies gather qualitative data for a particular project, plan, or policy decision as one-off efforts. Each effort sometimes regathers the same input from the community as previous ones. This can be a burden on both the community and the agency’s time. Part of the problem is that transit agencies (transportation more widely) don’t have a qualitative data infrastructure.
As agencies started getting more automated data from technology systems they developed data infrastructure to clean and store data. They hired IT staff to build and maintain data warehouses and data scientists to analyze the data. They built dashboards that visualize the data. Part of what this data infrastructure allows is for multiple teams to use the same data to answer different questions. For example, ridership data is stored in one place and many departments (and agencies and organizations) can access it for their analyses. The data infrastructure also provides transparency with open data.
In my experience transportation agencies don’t have the same infrastructure for qualitative data. There isn’t a centralized location and a storage system so multiple teams can find out what riders on a certain route or neighborhood are saying. The data from the customer call center remains siloed in that system. The input for a specific project stays with that project team. Data from the transit agency isn’t shared with the MPO or City. Or the public in a standardized way.
At the MBTA we did create an infrastructure for survey data. My team wrote an internal survey policy to standardize practices and data sharing. Part of this effort was standardizing basic questions so we could gather comparable results across surveys and time. This ‘question bank’ also allowed us to save time and money by getting all of our standard questions translated into the six languages the MBTA uses (based on its Language Access Plan) once. We attempted to create a single repository for survey data. (Most of the work to do this was organizational, not technical.)
Creating this type of qualitative data infrastructure requires thinking through data formats, how to code qualitative data (by location, type of input, topics, etc), and how to share and tell the stories of the data. And whoever is doing that has to have authority to impact all of the ways the agency gets community input. So importantly creating this infrastructure is also about where community engagement lives in an agency and how it is funded.
(An advocate I spoke to recently suggested that maybe this consolidation of qualitative data shouldn’t even live at a transit agency. That it should cut across all transportation modes and live at the MPO or other regional body.)
To move beyond using qualitative data in quantitative analysis, agencies need people with research and data collection skills not always found in a transit agency. Lots of agencies have started hiring data scientists to analyze their ‘big’ quantitative datasets. Agencies also need sociologists, ethnographers and other research skillsets grounded in community. These researchers can design community data collection efforts that go beyond public comments in a public meeting.
Qualitative research also asks different questions. Instead of using data to explain who is in the tail of a quantitative distribution, qualitative research asks questions like why is the distribution like this in the first place and how do we change it. Qualitative research is able to bring in historical context of structural racism, explore the impact of intersectional identities, and allows power dynamics to part of the analysis. Check out more from The Untokening on the types of questions that need to be asked.
Transit agencies often aren’t asking the types of research questions that qualitative research answers. Not just because they don’t have the staff skillsets, but because these questions don’t silo people’s experiences with transit from their experiences and identities overall. Lots of agencies try to stay in their silos, so they aren’t forced to address the larger structural inequities. It is easier to focus on decisions you think are in your control. For example, quantitative Title VI equity analyses are confined to only decisions made by that agency in that moment in time. But equity is cumulative and no one is just a transit rider.
Valuing community voices as essential data means agencies will need to invest in data collection, storage, analysis, and visualization or story-telling for qualitative data in a manner similar to quantitative data. The executive dashboards and open data websites will need to incorporate both types of analysis and data. More fundamentally it means that agencies will have to breakdown their silo and take an active role in fixing the larger structural inequalities that impact the lives of their riders everyday.
This series is about the data used to make decisions; clearly who is making the decisions is also important to valuing community voices. I chose to write this series in a way that shows how my thinking about data has evolved over time and will no doubt continue to do so. In fact it evolved in the act of writing this! For insightful dialogue about revision in writing and life, check out this podcast between Kiese Laymon and Tressie McMillan Cottom.
One of The Untokening Principles for Mobility Justice is to “value community voices as essential data.” I have been thinking about how transit agencies can put this into practice.
This is a three-part series that shows my thinking about data over time. The prequel is the post I wrote on data back in 2017 that mostly focused on how messy quantitative data analysis is. In Part One I discuss my experience in a transit agency mixing quantitative and qualitative data for analysis using quantitative research methods. Part Two is my thinking now on the importance of qualitative research methods and what transit agencies need to do to put qualitative data on equal footing with quantitative data. (Note: I have found a distinction between qualitative data and qualitative research methods useful as my thinking has evolved.)
Quantitative transit data often comes from technology systems (e.g. automated passenger counters or fare collection systems) or survey datasets (e.g the US Census or passenger surveys). In both cases collecting quality data requires investment. The benefits of technology systems are datasets that contain almost all events (a population, not a sample) and the ability to automate some analysis. However, transit agencies can’t rely on technology systems alone, because there is so much information, quantitative and qualitative, that these systems can’t measure.
As a generalization, qualitative data is information that is hard to turn into a number. For quantitative transit analysis, it is needed to answer questions about how people experience transit, why they are traveling, trips they didn’t make, and how they make travel decisions. Qualitative data can come from surveys, public comments at meetings, customer calls, focus groups, street teams, and other ways that agencies hear from the public directly.
In the data team at the MBTA we knew we needed both quantitative and qualitative data, usually mixed together iteratively depending on the type of decision. As an oversimplification, we used data to measure performance, find problems, and to identify/evaluate solutions.
Before you measure performance, you have to decide what you value (what is worth measuring) and how you define what is good performance. Values can’t come from technology and should come from the community. At the MBTA the guiding document is the Service Delivery Policy. In our process to revise this policy, we used community feedback in the form of deep-dive advisory group conversations, a survey, and community workshops. Once we agreed on values, knowing what data we had to measure those values, we needed input to try to make the thresholds match people’s experiences.
For example, we valued reliability so wanted to measure that in order to track improvements and be transparent to riders. This brings us to the question of how late is late? Our bus operations team stressed that they need a time window to aim for due to the variability on the streets. From passengers we need to know their experiences like: is early different than late, do they experience late differently for buses that come frequently vs infrequently, and how they plan for variability in their trips. Then we worked with the data teams to figure out how to build measures using the automated vehicle tracking data to report reliability and posted it publicly every day.
Identifying problems can come from both community input and data systems. Some problems can only be identified through hearing from passengers. No automated system measures how different riders experience safety onboard transit or tells transit agencies where people want to travel but can’t because there is no service or can’t afford it. In some cases, automated data is far more efficient in flagging issues and measuring the scope and scale of problems. For example, we used automated systems to calculate passenger crowding across the bus network and where it is located in time and space.
The MBTA used quantitative data to identify a problem of long dwell times when people add cash to the farebox on buses. The agency decided on a solution of moving cash payment off-board at either fare vending machines (FVMs) or retail outlets. (I will admit more qualitative analysis should have been done before the decision was made.) It was critical to understand how this decision would impact the passengers who take the 8% of trips paid in cash onboard. We used quantitative data on where cash is used to target outreach at bus stops. We did focus groups at community locations. Talking to seniors we found that safety was a key consideration between using a bus stop FVM or retail location. This is the type of information we could have never gotten from data systems or survey that didn’t ask the right questions. The team used the feedback to shape the quantitative process for identifying locations.
A key question is at what points in a quantitative analysis process can agencies rely on quantitative data and when is qualitative data imperative. As a generalization, quantitative research methods aggregate data and people’s experiences. We aggregate to geographic units (e.g. census blockgroups) and to demographic groups. We look at the distribution of data and report out the mean or some percentile. Quantitative data analysts need to look at (and share) the disaggregate data by demographics/geography before assuming the aggregated data tells the complete story. And ask themselves, when do we need more data to understand the experience in the tail of the distribution and when is the aggregated experience enough for making a decision.
The question of the bus being late and the use of cash onboard illustrate this difference. Once we set the definition of reliability, service planners use quantitative data to schedule buses. Looking at a distribution of time it takes a bus to run a route, you know there is going to be a long tail (e.g. long trips caused by an incident or traffic). Even though the bus will be late some percent of the time, it is an efficient use of resources to plan for a percentile of the distribution. Talking to the people who experience the late trips would be useful, but likely wouldn’t change that service is planned knowing some trips will be late. (Ideally riders, transit agencies, and cities work together to reduce the causes of late trips!)
However, on the question of cash usage, looking at the payment data you can’t ignore the 8% of trips paid in cash. The experience of that small group of riders is critical. Likely riders paying in cash rely on transit, experience insecurity in their lives, and a decision to remove cash onboard is a matter of access. Without talking to riders, we have no data on why they pay in cash, what alternative methods to add cash would work best for them, and the impact of having to pay off-board.
In my current thinking, at a minimum, decisions that impact the ability of even a small number of people to access transit or feel safe require a higher threshold of analysis. Agencies shouldn’t rely solely on aggregate quantitative data and need qualitative data on the impacts. The role of transit (and government in general) is to serve everyone, including, and often especially, people whose experience fall in the tail of a distribution. (A very quantitative analysis view of the world, I know.)
The lived experience of the community is critical to transit agency decision-making. There are many types of data that can’t come from automated systems. In my experience transit agencies should mix qualitative data into quantitative data analysis, often iteratively as the data inform each other. In practice this means that the teams doing quantitative analysis and community engagement need to be working in tandem with the flexibility to adjust as new data changes the course of the analysis.
Transformative implementation of the infrastructure and federal budget bills will take a generation of public service to fix the machinery of government.
The theme of my work this year is The How, not the What. There is a lot of great work being done on what transportation policy changes are need to address equity and climate change. But how to make or implement policy changes can be much harder. Harder to do, and to research and learn from as often changes are obscured in political deals and implementation takes place inside complex government mazes.
This is a short video I made for a “poster” presentation at the virtual TRB Conference on Advancing Transportation Equity. I am still looking for examples and other theories of change, so please reach out if you have some to share.
I am going to start with the given that a major source of inequity in transportation is the prioritization in funding and building infrastructure for personal motor vehicles. Equity (and addressing climate change) require a shift in this resource allocation. The power to make these decisions are mostly outside individual transit agencies. However, the question of equity also exists within the allocation of resources for transit (and biking and walking). And transit agencies do have the power to make these decisions.
There are a number of ways to define and measure equity in public transit. One definition is essentially that people (or neighborhoods depending on your dataset) of all demographics (income, race, ethnicity, language, ability, age) have access to service that meets their transportation needs. Since ‘needs’ is hard to measure, most analysis measures sameness (equality). For example, do people living in Black neighborhoods have access to the same number of jobs within 45 minutes as white neighborhoods?
There is a lot of data showing these types of inequities across transit networks. The underlying problem is both discriminatory land use policies and transportation decisions. Transit agencies can and should use these types of metrics and data to reduce and eliminate these inequities. But these inequalities didn’t just happen. They are the result of past (and current) transit agency decisions – big and small.
In order to not repeat past inequitable decisions and to acknowledge the impacts caused by agency decisions, I think transit agencies need to do an accounting of how their system got inequitable. We need ‘active voice’ in transit agency equity plans that takes responsibility for their role in creating the problem.
Inequitable transit access can come from big Capital decisions, like where to invest in rail service, and incrementally as a series of small decisions, like where to put that one additional bus trip. No doubt political pressure by politicians representing white and higher income communities is a major factor in many decisions. But that pressure will continue in the backrooms until forced into the light and acknowledged as inequitable.
If you are with me so far that this is important, my question is how: how should transit agencies go about this accounting of past decisions? Here are few components I am thinking about.
Who should do the accounting? Quite literally what process should agencies take and who should lead and be involved in the process. To build new solutions to long-term problems the answer can’t be the agency hires the usual consultants to lead a study. How can agencies and communities collaborate so the process builds trust?
What is the scope? Some transit agencies in the US go back to private sector control and it would be overwhelming to analyze every decision. (The history of transit injustice goes back to the beginning- here is a timeline I put together for my master’s project on Atlanta.) Every agency and region will need to figure out their scope, but it seems important to pick a variety of decisions and look at how they happened and their impact.
What is the format for presenting the history and acknowledgement of equity impacts? Or what is a platform for ongoing analysis and discussion? One interesting example I found is an LA Metro blog post on one of their rail lines.
How should the outcome be used? How will the results be integrated into policy decision-making? And drive narratives and communications about equity to help push back on the forces of inequity? I have seen inequitable decisions as the result of political bullying, maybe talking about the past can help inoculate against those tactics in the future.
What are the challenges for government agencies admitting past injustices? Or even disclosing that they were wrong about something? Clearly the main challenge is if you admit a past wrong then you should do something about it and that requires shifting power and resources. But I also found a deep fear inside a government agency of admitting any mistakes, even small ones. We need to figure out ways that a governmental body can acknowledge they did something wrong in ways that doesn’t undermine trust in government and instead builds it.
(A side tangent, one of the reasons I started the data blog at the MBTA was to create a forum or platform for talking about data mistakes and errors. Data analysis is difficult and messy and even if there are no mistakes new data comes along that might change the results. But there wasn’t really a way for a matter of fact telling of what happened and why we think the new results are better. My hope is that talking about mistakes makes people more confident in the data analysis and the agency in general.)
I have a lot more questions than answers on this topic. And I don’t think I should be the one to have the answers and I know this idea isn’t new. So I am looking for examples or best practices of transit (or other government) agencies doing this type of accounting of past inequitable decisions. Please share if you have any and I will share what I learn!