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!
In Part 4, I discussed some ideas for agency insiders to democratize their technical power and value experiential knowledge. In my theory of change, organizers outside are critical to changing government agencies’ policies and practices. Often people on the inside need political support from the outside to push change. And more importantly, most priorities for changes should come from impacted communities.
I decided to study transportation after I moved to Atlanta, Georgia without a car. One afternoon during my rail to bus transfer, I met members of a grassroots transit riders’ group passing out fliers at a MARTA station. They were in my neighborhood organizing riders to make the transit system work better for them. I went to their next meeting and for the next five years I would use what I was learning in grad school to provide technical support to Atlanta organizations with low-income, of color, and disabled leaders organizing transit riders and workers. I also became a person who transit insiders called when they needed something said they couldn’t say publicly themselves without risk. I supported a joint day of action in 2010 organized by the transit agency, the employee union, and a transit advocate organization (all groups led by Black women) that successfully pressured the Georgia legislature to make a needed change to save transit service levels.
A convergence of leadership, governance structure, and historical moment led to such public collaboration at this high level of an agency. However, the central tenet remains constant: change-makers inside and outside of the agency building power-with each other. In power-with change making, developing a common understanding of the role each plays in the partnership is necessary to achieve the shared goal.
In her book The Purpose of Power, Alicia Garza, one of the founders of Black Lives Matter, defines organizing as building relationships so that together you can build power to change the conditions hurting your community. She makes sure to point out that a condition of success is changing the relationships of power so that it is held by many instead of few (pg 56). I call power held by many ‘power-with’ and power held by few ‘power-over’. (Terms I picked up in a Women’s Studies class as an undergraduate.)
I have observed how effectively building relationships with people inside government agencies can help outside organizers push change. Internal change-makers can provide information that is hidden, sometimes on purpose, from the outside; can explain the complexity around problems; and can identify leverage or decision points. Insiders can do the creative problem-solving internally to find a way to implement changes. They can champion changes sought by communities.
The challenge is how to build those relationships using power-with tactics within the overall power-over system that internal change-makers work in. The circumstances vary on how much internal change-makers can build power internally. They are working in a system where others have power-over them. Sometimes insiders are putting their livelihood at risk when they collaborate with outside organizers, so relationships require trust.
This is not to let people in the public sector, especially in leadership positions, off the hook, but to say that we need the creative tension of accountability and support. Using this tension to make change requires understanding the role that individuals play in bureaucracies. Decisions are made by individuals in the context of systems and political structures. Individuals in systems need to be held accountable for the decisions within their purview and must be supported so they can take risks.
Instead of criticizing individuals (or organizations) for not making decisions they don’t have the power alone to make, outside organizers should work to build the conditions where, either that individual has that power (political cover), or that power is more widely held. Identifying where in the system decisions can be made, and illuminating what or who is preventing those decisions, allows organizers to hold government to account while maintaining trust in the institutions of government that are working within the existing system of power. (A corollary to this principle is that trying to hold institutions to account for decisions they can’t make, or positions they can’t take, can undermine those institutions in the eyes of the public.)
Accountability is two-sided. Internal change-makers should also be asking what makes their potential outside partners accountable and to whom. In transportation there tend to be advocate organizations that have experiential expertise and those that specialize in technical expertise. The authority of organizations with experiential expertise comes from their legitimacy with the communities they represent. For organizations based on technical expertise there is a tendency to base their legitimacy on the power of their ideas. However, this can set up a power-over dynamic where the argument becomes about whose ideas are better, instead of how to collaboratively make change based on shared goals.
Organizers with access to power can use the tools of power-over to try to force change, but that comes at a cost and does not build relationships. No matter who uses power-over techniques they still replicate the systems of inequality, even if organizers ‘win’ in the short-term. In my personal experience, organizations that are rooted in communities historically without power and led by women and people of color are well-versed in power-with tactics. I assume this is due to their lack of access to power-over decision-makers, and because people who have had power wielded over them are less likely to replicate these tactics. The movement to create a better world is practicing the world we want.
I wish that the public didn’t have to organize themselves to change how government operates and treats them. Government should exist to solve problems and improve the lives of everyone. But we have to start with the reality of the power-over world we live in. Making sustained change will require dedicated organizers inside and outside of government working collaboratively to build power-with each other in large and small ways.
As I mentioned in Part 2 my theory of change at government agencies is that usually it requires people inside and outside agencies working collaboratively or at least pushing in the same direction. A key contribution from people inside is working to democratize their power.
The first book I checked out of the library after quitting my job (and had time to read again) was Max Weber’s The Theory of Social and Economic Organization. I wanted to go back to the theory on the functioning of bureaucracy and its role. Weber postulates that bureaucratic administration gets its power from technical knowledge and they increase their power by acquiring additional knowledge specific to the functioning of their office (what I called complexity).
I hope, especially after the last 10 months of the COVID pandemic, we can agree on the value of experts in government. The complexity is real and it requires technical knowledge to implement programs and services and maintain infrastructure that works for the public. But the greater the complexity, the greater the potential for an unaccountable or inaccessible government. To counter this concentration of power, governments need to make technical information more widely available and to value experiential as well as technical knowledge in decision-making.
Open data is one way that bureaucracies are sharing some of their technical power with people outside. It is a transparency tool that allows people to do their own analysis. But, I do have to point out that data rarely tells the entire story of the complexity. Data alone isn’t information and can be misinterpreted (by insiders or outsiders). So in addition to open data, we need data literacy tools that can help people engage in policy decision-making on the same terms.
My hypothesis is that a collaboration between internal data experts and outside stakeholders is most likely to produce tools that help the public engage in (or even challenge) the technical basis of policy decisions. (Please share any examples you know of.) It can be hard to translate the complexity and the messiness of data for multiple audiences. For one project (more about accountability than decision-making) my team created a dashboard that used a layered approach so people could keep drilling down if they wanted more detail. We also created a data blog so we could tell the stories behind the data, explain past mistakes, and provide examples of the ambiguity in data analysis.
One of the reasons why I love working in transportation is that I have no problem talking to strangers, or rather, they have no problem talking to me. I just mention my job and everyone has an opinion about what needs to change. Everyone experiences transportation in a way that is more clear-cut than how we experience, for example, health care in this country. This doesn’t make transportation policy simple, but everyday experiences make it more accessible to everyone.
The lived experiences of the public are needed to turn data into knowledge. Policy decision-making should integrate technical and experiential knowledge. In a practical sense, this means getting public input throughout the data analysis or technical phases of a project or policy decision (instead of at the end). In a principled sense, this means valuing the public’s experiential knowledge, especially from people whose voices are not well represented inside the agency or at the decision-making table, as important as technical knowledge. Valuing experiential knowledge requires stretching beyond any stated principle of equity in decision-making to operationalizing this principle in ongoing relationships with communities. It is more complicated in practice than most technical analysis, but rarely is given adequate resources.
Taking steps to open data and lift up experiential knowledge can be very challenging for insiders who get their power from their technical expertise. Within complicated organizations controlling access to information, to the public or other employees, is one way the few retain power-over the many. By sharing information and integrating technical and experiential knowledge, agency insiders can build power-with outside partners to make change.
(Side note, a great example of how technical and experiential knowledge clashed and then combined to make better health care policy comes from the relationship between Dr. Fauci and AIDS activists in the late 1980s.)
We need the idealistic vision that a better world is possible and the pragmatic reality of how to make changes work starting in the world we have right now. A major challenge is how to keep your eyes on the former while deep in the weeds of the latter. I tried to address this by practicing accountability.
In 2005, I called my mother to tell her that I planned to go to graduate school in City Planning. Her exact response was, “How are you going to make sure you don’t sell out and become a liberal?” I don’t remember my response, but I understood what she meant. She was warning against become a professional whose decisions were driven by making a salary, instead by making structural change. (I like to joke wanting to make structural change is why I went on to get a Ph.D. in Civil Engineering.)
I figured out early on working inside an agency that I needed to regularly talk to outside organizers. I needed their input on what work to prioritize and which battles to fight to improve equity. I knew that as an upper middle-class white person my experience limits my knowledge of the most pressing issues and that priorities should be based on the needs of the most impacted. So I looked to community organizations that had a grassroots base in low-income communities and communities of color. Luckily I could draw on previous relationships with organizers and the transit organizing already happening when I arrived in Boston.
The first project I picked up was piloting a transit pass for low-income youth. Youth and transit advocates had been organizing for the Youth Pass for seven years and had just won a pilot when I started. My role was to work with municipal and community partners and the agency’s technical team to design and implement the pilot program. We negotiated, we compromised, and together we found a way through all of the complexity to make a program that worked for youth participants, program administrators, the transit agency, and would provide useful research data. It was an excellent lesson for collaboratively solving complex problems and built my credibility as someone who could get projects done.
In a government agency that is very public facing, like a transit agency, it is easy to fall into the fortress mentality. You get publicly attacked all the time and often the criticism doesn’t include a nuanced understanding of the challenges the agency faces on a daily basis. Statements abound like ‘why doesn’t the [agency] just do X.’ You and your colleagues either laugh or cry at the over-simplistic idea of ‘just’ being able to change any one thing without due consideration of the domino effect that one change has on intertwined systems, teams, and services. A group defense mechanism in the face of many critiques is the tendency to pull up the proverbial draw-bridge to the fortress, which also filters out the reasonable and justified criticism.
I saw myself starting to get defensive and I knew that I had to actively tether myself to people outside of the agency to remind myself to listen. Again I tried to make sure I was hearing the voices of people who didn’t have same experience as most leaders of my agency and whose lives would be most impacted by our policy decisions. I made sure to put myself in situations where I would hear other voices by creating space in formal public engagement settings and by riding the transit system (and not just on social media). And I tried to listen with the intent of real change: changing myself, my decisions, and the organization.
At the same time I felt that to make meaningful change I had to remain grounded in a pragmatic view of what change seemed possible, on what time-frames, based on my understanding of the existing complexities (political and technical). However, there is a struggle between understanding the challenges of implementation and these challenges being used as an excuse for inaction. The practice of pragmatic empathy helps combat the fortress mentality and the inertia that pervades complex organizations.
Agencies often need outside actors to push them. And at the same time, I want the public to understand the very real challenges. Without understanding the complexity and true cost of change within public agencies, an unintended consequence of criticism plays into the narrative of government being incompetent, which undermines public support for government providing solutions.
Similarly, public agencies struggle with how to admit past mistakes or inequity caused by previous policy decisions. Honest accounting of the past is necessary for accountability, to rebuild trust with communities, and as important context as agencies work to increase equity. I think agencies, public stakeholders, and the media need to create the space for discussing the past in order to move forward. (Maybe more on this later, still thinking about it. There are definitely existing good examples to draw on.)
I started my work focused on accountability to the people the agency serves and spent four years developing policies and teams working on external equity. Then I expanded my focus to the internal working of the organization and realized that accountability also applies to the employees whose voices were not heard in the decision-making process. I had hired talented diverse teams, but now I knew I needed to actively support and create space for employees of color. Before I left I helped start a process for improving equity and inclusion internally. This work caused me to reflect on how my experience as an internal change-maker was shaped by my whiteness. There is still more I need to learn.
The dilemma of pragmatism, especially in middle management, is how much risk to take by speaking truth to power knowing that your work to accomplish other goals might be jeopardized. The counterfactual that someone worse could be in your position is always true. It is a constant balancing act to determine when to compromise and when to keep pushing past the point of comfort for the harder outcome. There is a strong tendency toward not rocking the boat now in order to get the next job with more power, but the question of what is enough power is rarely answered. I tried to remain true to myself by rooting my power in my principles, and not my position. And I knew some day I might have to leave.
Sadly my mother passed away a few weeks after I accepted the job at the MBTA. Her voice echoed through my decisions and I am pretty sure she would appreciate how much I have grown.
Making government work for everyone is not easy, regardless whether you are pushing for change from the outside or the inside. In my experience some of the same skills and analytical tools are useful.
I have advanced degrees in transportation, but it was my experience as an organizer that I drew on the most to figure out how to make change at the MBTA. As a child, I attended local government meetings with my mother and on the way home, she and I would analyze the political alliances we’d observed and debate about strategy. I began facilitating meetings to build group consensus when I was a teenager. In my 20s I was part of social movements and spent a lot of time critiquing tactics and considering how to be more effective at making lasting change. In graduate school I provided technical support to transit organizing in Atlanta.
Just like organizing for progressive change in general, change inside government agencies requires multicultural coalitions with clear objectives and the strategies and tactics to achieve them. That means change-makers have to address institutional and societal problems preventing inclusion and equity. Multicultural in this context also means understanding the cultures of departments and offices within the agency or across agencies. Meetings have to be planned with this in mind, and sometimes ‘translators’ are useful to help different departments communicate with each other more effectively.
The coalition part means that efforts need to be welcoming and inclusive of people with different skill sets and roles. The experience and knowledge of the person deep in the technical weeds or on the front-lines is critical and their participation is needed to create solutions. The sweet spot for middle management is having the ear of leadership and the trust of the workforce. I found that open communication and transparent decision-making was critical to gaining that trust. A more cynical version of coalition building is that dysfunctional agencies run on the favor economy so it helps to be helpful to a lot of people.
The clear objectives part means having goals that others can fully comprehend along with a plan to achieve it. It took some reflection to understand that people inside the agency often gravitated to me because I could articulate what I was trying to get done, why, and how they could help. A proficient manager can divide a big project into different tasks and an effective organizer has a strategy and tactics to achieve their goal.
My social movement thinking includes having an analysis of power and theory of change. Analyzing power includes identifying who has power, leverage points, and how to build your own power. Multicultural coalitions centered on people who historically have not had power, require building ‘power-with’ instead of using ‘power-over’ tactics. Power-with lifts everyone involved while power-over uses the existing systems of power rooted in white supremacy and patriarchy. Power-with principles help mediate the corrupting influence of power; the ends usually don’t justify the means.
My theory of change inside government agencies is that change usually requires both inside and outside collaboration. An idea or new priority might come from employees or the public, but it will require employees to make it work and to do so, they often need outside political support. (More on this collaboration in an upcoming post.)
Sometimes my objectives were shared by leadership, but more often than not I had to make them into priorities. I took advantage of the vulnerabilities of the governance structure of my agency (and every crisis) in order to push the changes I wanted to make. Key to this was figuring out how priorities were set and using my privilege and relationships to get on the agenda. And then never giving up regardless if the challenges were political or technical, one of the best things said about me when I left my job was that I had tactful relentlessness. Most of my projects or policy changes took years to achieve.
(After 4 years I realized that the vulnerabilities I was using were unsustainable for the agency and so I spent the next 2 years trying to fix them. This proved more difficult and gave me a lot to think about. So perhaps more later on the dilemma between making specific policy/program changes and changing the organization itself.)
In addition to organizing skills, I found it useful to think about the functions and roles within large complicated organizations. Through conversations with colleagues I came up with the three-sided spectrum of ideas people, details people, and process people. An organization needs a balance of all three types of thinking and often is lacking in ‘process’ people. Process people provide the connective tissue between silos and often do the translating I mentioned previously between and across the organization.
I was able to build a talented multidisciplinary team of process people who coordinated across departments and pushed projects up the hill of complexity. This gave us power in the organization because we were designing decision-making processes and we had the time and space to problem-solve collaboratively.
I know that I benefited from luck/good timing, leadership, and talented colleagues. But my experience and training as an organizer, who thinks about organizational dynamics, really helped me strategically play the cards I had and know which battles to fight when.
Part 1: The Need and the Challenge
For those of us who are committed to addressing climate change, dismantling white supremacy, and reducing economic inequality in the U.S. and who believe that government has a critical role, these have been a difficult few years, or decades. But we hold out hope in the election, at all levels of government, of change-makers with ambitious policy proposals. Clearly, the grassroots organizing in support of these proposals (and officials) needs to continue for them to be adopted, but from my vantage point as a (former) public employee, a similar amount of work is needed to actually implement these policies.
In school, we learned how a bill becomes a law (at least in a sanitized procedural sense). But few curricula cover what comes next: How does a law or a policy become enforceable regulations or take shape as a new program or service?
My sense is that many people perceive government agencies (the bureaucracy, not the politicians) as black boxes. This leads people to assume that every problem is the result of incompetence or lack of political will. In reality, government agencies are a collection of people managing technical systems, infrastructure, and business processes tenuously connected together through years of patchwork and, in all likelihood, under investment. The systems of systems create large amounts of complexity.
Because of the complexity, implementing a policy change or a new program can be just as difficult as getting a decision made. The challenges in making change could come from needing to modify an old technical system, connecting multiple systems together, getting decisions made across the silos within the organization, or unintended consequences that have a ripple effect across multiple systems and processes.
The challenges are often harder in the public sector because services need to be sustainable, scalable, and they should serve everyone, in many cases every day of the year. My former colleagues would tell you that I am obsessed with edge cases, and that is because, unlike the private sector, the role of government is to work for the people on the margins. Working for everyone means figuring out how to make government services accessible in all definitions of the word (people with disabilities, people without internet access, cash users, and on snow days). This requires far more work than aiming to serve the 85th percentile or a chosen targeted market.
Implementation requires people with the skills and commitment to the slow slog of making change deep in the machinery of government. Policy implementation requires creativity and building internal coalitions, and sometimes external partnerships, to find a way. The work usually isn’t particularly visible; and most public sector employees don’t have a public voice. While it is very rewarding to see something you did impacting lives at a large scale, often there is limited public understanding of the immense amount of work to achieve changes and criticism is very easy to find.
If we believe that government can and should solve problems, we need change-makers embedded in all levels of government. So this is my humble call for an expanding squad of public employees ready to laugh and cry their way through the complexity to lasting change.
In this week of rebirth of government in the U.S., I will post some reflections on skills, accountability, and collaboration from my six years of implementing change at the MBTA/MassDOT. For those unfamiliar with my work, I focused on changes to fare policy and programs, and service policy and pilots to increase equity. I welcome feedback on this blog or email at laurelintransit via gmail.com.