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I spent a lot of time with my team at the MBTA/MassDOT thinking about how to think about data:  about what data we had and the data we didn’t, the decisions we made in the analysis and visualization process and their implications, and how to communicate with different audiences about our level of certainty. I left knowing we needed better tools for the public, transit advocates, and decision-makers to engage with how transit agencies use data.  

During my fellowship this year I wrote about it on this blog and explored what prioritizing qualitative data would look like in practice at transit agencies. I have turned some of this thinking into a website and training presentation. It focuses on questions to ask about quantitative data analysis, since that is my expertise, and points to when qualitative analysis is needed. I hope people find it useful and I welcome feedback! provides a framing around the use of large automated datasets in transit decision-making and gives transit advocates tools and questions to ask about the data agencies are using.

Transit recovery requires addressing organizational trauma

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.

Solving the operator ‘shortage’ by not running transit like a business

In my second dating app message to my now partner I shared a link to this New York Times article. I was responding to the question, “what are you doing today?”, and I said reading this article and thinking about how it applies to the public sector. The article compares the career path of a Kodak employee, who started in the 1980s as a janitor and become CTO, to a person who works now as a subcontractor janitor at Apple without employee benefits or promotional opportunities, to illustrate how changes in corporate practices contributed to income inequality. Lucky for me, we are still talking about the article together!

Right now I am thinking about it in the context of the current crisis in the public transit sector to hire enough frontline employees, especially bus operators, which is causing service reductions across the country. The problem of not enough bus operators existed before the pandemic, but as you can imagine the risk of COVID and increasing operator assaults makes the job even less attractive. There are many possible other factors like increasing opportunities as delivery drivers who don’t need to interact with passengers. (I also heard a theory about the impact of federally required random drug testing for transit employees.) I think to solve the problem holistically, we need to understand the changes in the US economy illustrated by the NYT article and implement long-term solutions that reverse these trends.  

Stories like Gail Evans’s rise at Kodak were always rare, but these stories were the exceptions that held up the dream of the US economy as a meritocracy. At this point they are almost non-existent in large private sector firms, but it is still possible in a public transit agency for someone to start at the frontlines (e.g. bus operator) and work their way to the top. In the past the MBTA had bus operators become General Manager. The current head of  King County Metro Terry White started at the agency as a telephone operator. At the MBTA I saw the immense value of working with people who had started at all levels of the agency and the knowledge they brought to the team.

The public sector in the U.S., which maintained higher unionization rates than the private sector, has historically served as a pathway to the middle class, especially for Black Americans. But throughout my lifetime (I was 1 when President Reagan fired the air traffic controllers) there has been increasing pressure on public sector unions and attempts to maintain/lower public sector costs in order to maintain/lower tax rates. It is commonplace to hear politicians and business leaders alike say that we should run government like a business.

During my time at the MBTA, mostly between 2015 and 2018, I got to see running government like a business in practice. There was a concerted effort to lower the cost curve by using strategies from the private sector that limit upward economic mobility. The most visible efforts were the attempts to outsource some internal jobs.  Outsourcing was successful for cash collection jobs, customer service agents (now the red jacket ambassadors), and warehousing. And attempts to contract out some bus operations and bus maintenance jobs were used as leverage in union negotiations, including the 2016 ATU contract that lowered starting wages for bus operators in exchange for not outsourcing.

As in the example at Apple, outsourcing meant that, along with any lower wages or benefits, workers in the outsourced positions likely have a limited career pathway with the private employer. Previously, an employee could have worked as a customer service agent during a long career at the MBTA, but today, being a T Ambassador is less likely to be a stepping stone in a career than a short stint in a string of low-wage jobs.

A less externally visible private sector practice in this time period was hiring more executives who were generalists (experts in management), instead of hiring experts in transit operations or training existing workers in management skills. To try to understand my new colleagues I did a lot of reading about the rise of the MBA. Most of it connected changing management strategies in the private sector to the shift from an economy based on producing things to consuming things (a financial and service sector economy). I found fewer resources on the impact/extent of MBA management in the public sector. (If you know of sources, please share them!)

I suspect that the desire to have ‘professional’ managers is, at least in part, to create an experience barrier between executives and workers under the theory that someone who worked their way up would be less inclined to cut costs in ways that impact the people following behind them. Regardless of the motivation, the impact is a limit on the upward career mobility of the workforce and decrease in the incentives to invest in their professional development. In transit agencies like the MBTA this increases the demographic divide between the frontline workforce, which is predominantly people of color, and management, which is significantly white.   

Riding a bus in 2017 I had a conversation with an MBTA bus operator who spoke pointedly about the operations and management divide. After seeing my employee badge, he opened up about how he had been at the T for 17 years and it used to be that someone could work their way up from bus operator to GM, but he couldn’t imagine it happening now. It felt like there was an increasing divide between operations where most managers had worked their way up and the executive team where most people didn’t know the business.  

Part of the larger economic shift illustrated in the Kodak and Apple story is the decreasing amount of time that people expect to stay with an employer. People stay longer in public sector jobs and I suspect it is partially due to availability of retirement and pension plans. I believe that if the pension disappeared tomorrow, the MBTA would lose even more talent and would have a bigger operator shortage. However, it isn’t a sign of a healthy, robust workplace if people are mostly staying for the pension.

With the pension, even with a lower starting wage, the MBTA is a better employment choice than driving for a private company if you plan to stay for 25 years. But I wonder how much that factors into people’s employment decision-making right now, especially if a transit agency doesn’t seem like a place you want to stay for a career. (I am not an expert on pension financing, but it is worth noting that at the MBTA, executives are given a choice between joining the pension or a retirement plan where you are fully vested in 5 years; but non-executive workers don’t have the retirement plan option.)

Another pivotal change in the post-1970’s U.S. economy was a shifting of the burden of job training from companies to individuals through higher education, which is increasingly financed by personal debt. With the drop in accessible pathways to secure middle-class jobs in the private sector came a marked increase in people from working class families attending post-secondary schools with aim to receive training and credentials that would allow them to access jobs with greater potential for economic advancement and stability. Not only did this shift create an epidemic of education debt, it under-emphasized the value of technical trade jobs leaving a shortfall in the workforce needed in the infrastructure sector in the US as the existing workforce retires.

The NYT article ends with the observation that in the 21st-century economy the reality for most workers is: “Rather than being treated as assets that companies seek to invest in, they have become costs to be minimized.” The Great Resignation brought on by the pandemic should be a clear sign that this economy is untenable. Public transit agencies should be in a good position to attract job seekers since they have the foundation for career pathways, benefit packages, and on-the-job training programs. Instead of borrowing a cost-cutting mentality from the private sector, they need to invest in their workforce. These investments need to be in long-term structural and culture changes, in addition to short-term measures like wage increases, work rule changes, and addressing immediate safety concerns.

The investment needs to start in K-12 education with curriculum about infrastructure careers and pathways through technical training as well as higher education. Infrastructure is literally community building and the rewards of a career in a public transit agency can be mixed into learning about civic participation, climate science, and technical skill building. Introducing Youth to America’s Infrastructure is developing and piloting curriculum as a model.     

Transit agencies need to invest in professional development at all levels so that not only is it possible, but it is a goal that people starting on the frontline reach management levels. Making transit agencies a place where everyone can advance (and want to stay for a career) also requires addressing underlying racism, sexism and toxic workplace cultures so they are healthy and safe places to work. I am encouraged by VTA’s actions after a workplace shooting earlier this year, King County’s equity and social justice workforce goals and SFMTA’s racial equity plan. And I know other agencies around the country are creating internal equity teams to do this hard culture changing work.  

Yes, these investments will require additional public resources for public services like transit. But these people focused investments outweigh the costs when we measure all the benefits. In addition to quality and reliable public services, pathways to economic mobility and equitable and inclusive public sector workplaces help address income and racial inequality in the US.

Part 2: Qualitative Data Infrastructure and Research Methods

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.  

Part 1: Mixing Qualitative Data into Quantitative Analysis

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.