Funding from the Robert Wood Johnson Foundation’s Local Data for Equitable Communities program, and technical support from the Urban Institute, has enabled us to produce an interactive choropleth map aggregating 23 indicators — including demographics, housing production, building permits, traffic safety, infrastructure access, and air quality — across a variety of geographies.

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You can see how Ward 7 compares to Ward 3 on median household income, or how a specific ANC compares to its neighbors on fatal crash rates, or how much income-restricted, subsidized housing has been built in each planning area since 2015.

Using the map embedded above, or a full-screen version here, you can use the drop-down menus in the upper-right of the screen to select any of the 23 indicators and any of eight geographic levels. When you do, the map will then show a map of DC, at that geographic level, color-coded by that indicator. You can mouse-over a geography to see its name and a measurement of the indicator.

You can also click on a geography to see a comparison table of all the indicators, and you can click more geographies to add them to the table. Click a geography again, or click the ‘x’ at the top of the column, to remove it from the table. When you remove all geographies, the table will disappear from the screen until you click on another one. (You’ll probably find the comparison table easier to use in the full-screen version).

In addition to using the map itself, you can also download a spreadsheet of indicator values here.

In this post, I’ll share a few especially interesting results of the analysis the map enables. And, I’ll review the methodology behind it: the choices of geographic levels of analysis, the sources of data, and the means of calculating each of the 23 indicators.

A few interesting results

There’s a lot of data accessible in the indicators map. As you explore it, you’ll make your own discoveries, find different patterns, and maybe learn something about your own neighborhood. These are just a few of the interesting data points that stuck out to me while I was building the map.

Traffic crashes and traffic fatalities have different distributions. Traffic crashes are relatively evenly-distributed across the District, with a hotspot in downtown and lighter incidence in Ward 3. But traffic fatalities are anything but even. Fatalities are highly concentrated in Ward 7 and Ward 8, with rates nearly twice as high as anywhere else in the city. This aligns with findings shown in a 2023 GGWash research project, a report on transportation, racial inequities, and public health in Washington, DC, also funded by RWJF.

Frequent transit covers less of the District than you might expect. We defined frequent transit access in a relatively conservative way: within a quarter-mile of a Metrorail entrance, or within an eighth of a mile of a bus stop running at 10-minute headways or better. This is meant to represent the kind of service that someone can rely on, every day, without having to check schedules or walk out of their way. This high bar means that for an ANC or ward to have a high transit-access score, it needs to be thoroughly covered by high-quality transit. The highest-scoring ward is Ward 1, with two-thirds of residents living close to frequent transit. The lowest is Ward 5, with barely more than a quarter. As good as our transit is, there’s a lot of room for improvement.

Worse air quality tracks closely with high-injury roads. The corridors with high diesel PM and NO₂ exposure tend to be the same ones with elevated crash rates and High Injury Network roads. None of this is a surprise to anyone familiar with environmental-justice literature. But the map makes it clear how residents who live near major arterials suffer the double burden of traffic violence and air pollution.

The distribution of income-restricted, subsidized (“affordable”) housing is very uneven. ANC 3D, the Palisades, has only seven income-restricted, subsidized units built since 2015. ANC 8C, Congress Heights, has over 3,000. When dedicated affordable housing is so concentrated in specific neighborhoods, people with lower incomes have less residential choice and less ability to live close to their communities and their places of work or school.

The eight geographic layers

One of the reasons DC is difficult to analyze at the neighborhood level is that its governance runs through overlapping, non-nested geographic systems. ANCs don’t correspond to planning areas, and land-use zones don’t follow, either. We included eight layers, and the choice of which to use matters depending on the question you’re asking.

Eight wards are the primary unit of District residents’ political representation, each individually represented by one member of the DC Council. Because wards are the largest areas we analyze, they have the most statistically robust results. Ward boundaries were redrawn after the 2020 Census to equalize the population distribution across wards. Ward-level data is the most widely cited in local policy discussions, which makes it a useful baseline. It also makes ward comparisons easy to put in context. If you know DC at all, you know roughly what Ward 8 means in terms of income and demographics. Source: DC Open Data, 2022 boundaries.

46 advisory neighborhood commissions (ANCs) are DC’s hyperlocal elected bodies. Each ANC represents several thousand residents and is given great weight on zoning, development, and licensing decisions. For most housing and land use questions, ANCs are the more operationally relevant unit than wards, because they’re the geography at which development fights tend to happen. Source: DC Open Data, 2023 boundaries.

345 ANC single-member districts are the individual commissioner districts within each ANC, typically covering a few hundred to a few thousand residents. At this scale, Census estimates become noisy and should be interpreted carefully. We include SMDs because they’re the unit at which commissioners are elected. Source: DC Open Data, 2023 boundaries.

Ten DC Comprehensive Plan planning areas are the ten planning regions used by the DC Office of Planning in the Comprehensive Plan: Far Northeast/Southeast, Near Northwest, Central Washington, and so on. They don’t correspond to any elected body, but they’re the geography at which the District thinks about long-range land use capacity and neighborhood change. If your interest is in housing production trends and growth policy, planning areas are often the right level of analysis. Source: DC Office of Planning via DC Open Data.

953 zoning districts — raw are individual zoning parcels as designated under DC’s 2016 Zoning Regulations. This is the highest-resolution look at legal land use in the tool. At 953 features, individual parcels can be very small, and the Census-based indicators are correspondingly unreliable. Source: DC Office of Zoning via DC Open Data.

178 zoning districts — by label dissolves the raw parcels by their zoning designation, so all R-1-A parcels citywide merge into one geometry, all MU-4 parcels merge, and so on. This is the more analytically useful zoning view: it lets you compare conditions across land use types rather than individual parcels, which is what you usually actually want to do when asking questions like “do we build more affordable housing in mixed-use zones than in residential zones?” Source: same as above, dissolved.

1,648 Future Land Use Map — raw is the District’s FLUM at parcel resolution. The FLUM is the long-range land use vision embedded in the Comprehensive Plan, with which zoning cannot conflict. At 1,648 features, this is the finest-grained geography in the tool, and Census-based indicators at this level are more illustrative than rigorous. Source: DC Office of Planning via DC Open Data.

44 Future Land Use Map — By land-use code dissolves FLUM parcels by their combined land use code, producing 44 distinct categories citywide. Source: same as above, dissolved.

Indicator methodologies

Demographics

We include seven demographic indicators. These are the baseline descriptors that give any neighborhood analysis its context. You can’t interpret a crash rate or an affordable housing count without knowing who lives there and at what density.

• 1: Total population — ACS table B03002. The count of total residents allocated to each target geography.

• 2: Population density — Total allocated population divided by the target polygon’s area in square miles. This is gross density, meaning the full polygon area including parks and water, which will slightly understate density in geographies with significant non-residential land.

• 3: Share non-White (People of Color)— ACS table B03002. Calculated as 1 minus the non-Hispanic White alone population divided by total population. This is the broadest available ACS measure of racial diversity and includes all residents not identified as non-Hispanic White, regardless of specific race or ethnicity. We include it because racial demographics remain one of the strongest predictors of access to infrastructure, exposure to pollution, and neighborhood investment patterns in DC.

• 4: Share age 18 or younger — ACS table B01001. Because the ACS bins ages 18 and 19 together, this is technically the share under age 20 — a close proxy for under 18, and the difference is small in practice. Children’s share of a neighborhood’s population matters for school planning, park investment, and family housing policy.

• 5: Share age 65 or older — ACS table B01001, summing all bins 65 and above. Older adults have distinct infrastructure needs — transit access, sidewalk quality, proximity to services — that this indicator helps identify where they’re concentrated.

• 6: Share non-male — ACS table B01001. The ACS measures biological sex (male/female), not gender identity. We label this “non-male” to be precise about what’s being measured. DC’s neighborhoods are roughly evenly split, but meaningful variation exists, particularly in areas with large institutional populations.

• 7: Median household income — ACS table B19001. The ACS reports income in 16 bins at the block group level rather than as a direct median, so we reconstruct the median by finding the 50th percentile within the cumulative bin distribution. Because this ACS variable as capped at 200,000, the map is not able to represent variation at the upper end of the income distribution.

Source: US Census Bureau, American Community Survey 5-Year Estimates, 2022 vintage, retrieved at the block group level via the Census API. All indicators are allocated to target geographies by area-fraction overlay.

Affordable housing

This group of indicators tracks where income-restricted, subsidized housing production has occurred since 2015, including where it’s been built, where it’s still in the pipeline, and whether production has kept pace with population.

• 8: Affordable housing units completed since 2015 — Sum of TOTAL_AFFORDABLE_UNITS for projects with a “Completed 2015 to Date” status. This is the stock of subsidy-supported affordable units that have actually come online in the past decade.
• 9: Affordable housing units in the pipeline — Sum of TOTAL_AFFORDABLE_UNITS for projects currently listed as “Pipeline” or “Under Construction.” These units are committed but not yet built.
• 10: Affordable housing units completed or in progress since 2015 — The combined total. This is the broadest view of the production pipeline: where has the city’s affordable housing investment been directed?
• 11: Affordable units per resident (Built since 2015) — The combined count divided by total population. Raw unit counts favor larger geographies; this rate normalizes for population size and helps identify which neighborhoods have seen disproportionately high or low production relative to their existing residents.

This dataset counts affordable units within DHCD-tracked projects, not unique addresses, and it doesn’t distinguish between units affordable at 30 percent of AMI and those affordable at 80 percent. The count is a reasonable proxy for production activity, but it overstates the depth of affordability.

Source: DC Open Data, Affordable Housing dataset maintained by the DC Department of Housing and Community Development (DHCD). Each project is a geographic point, assigned to geographies by point-in-polygon.

Building permits

Building permits are a leading indicator of housing supply growth. They are imperfect, since not every permit becomes a building, but meaningful as a measure of where new construction has been attempted. The map shows exclusively new building permits, excluding renovations, additions, and repairs.

Across the ten-year period, the filtered dataset yields 3,830 permit records. After deduplication by address (to remove cases where a project appears in multiple annual files), we have 3,610 unique locations. Annual totals peaked in 2021 at 561 unique addresses and declined to 243 by 2024.

• 12: New building permits (2016–2024, deduplicated) — Count of unique permitted construction addresses per geography.

• 13: New building permits per resident — The count normalized by population, useful for comparing production intensity across geographies of different sizes.

Source: DC Open Data, building permit datasets, downloaded annually for 2015–2024. Filtered to PERMIT_SUBTYPE_NAME equal to “NEW BUILDING,” then deduplicated across years by normalized address.

Traffic crashes

The full dataset contains 342,539 records. After filtering to the 2016–2025 window and deduplication, we work with 223,960 crashes. Fatal crashes are defined as any record where at least one of five fatality fields (FATAL_DRIVER, FATAL_PEDESTRIAN, FATAL_BICYCLIST, FATALPASSENGER, or FATALOTHER) is greater than zero. By that definition, the dataset contains 354 fatal crashes over the period.

Traffic safety is the indicator group where the data tells the most direct story about who is being harmed and where. The raw counts tell you volume; the per-capita rates tell you risk; comparing the two tells you something about the character of the danger.

• 14: Traffic crashes (2016–2025) — Total count of crashes per geography over the full period.

• 15: Fatal crashes (2016–2025) — Count of crashes where any person was killed. At 354 total across ten years, this is a small number, and at the SMD level fatal crash counts will often be zero or one. Interpret with caution.

• 16: Traffic crashes per 10,000 residents — Normalizes crash volume for population size. High-traffic commercial corridors with few residential addresses will appear elevated on this measure, which is a feature rather than a bug: it surfaces where crash risk is concentrated relative to who lives nearby.

• 17: Fatal crashes per 10,000 residents — The same normalization applied to fatal crashes. At small geographies, a single fatality can produce an outsized rate. We cap the display value in the visualization to prevent a single outlier from dominating the color scale.

Source: DC Open Data, Crashes in DC dataset, covering reports from 2016 through 2025. Deduplicated by CRIMEID.

People near infrastructure

This indicator group shows the share of a geography’s residents living close enough to each piece of infrastructure to plausibly use it. “Close enough” is defined differently for each mode based on reasonable walking distances.

The methodology here works in two steps. First, for each Census block group, we calculate what fraction of its area falls within the relevant buffer zone. We multiply that fraction by the block group’s total population to estimate how many residents are “near” the infrastructure. Then we allocate those estimated counts to target geographies using the same area-fraction method used elsewhere, and report the share.

• 18: Share of residents near High Injury Network roads — Within 200 meters (roughly ⅛ mile) of DDOT’s High Injury Network, the set of road segments responsible for a disproportionate share of DC’s serious and fatal crashes. This is an exposure indicator, not an access indicator. Being near HIN roads is a harm, not a benefit. Source: DDOT via DC Open Data.

• 19: Share of residents near frequent transit — Within 400 meters (¼ mile) of a Metrorail entrance, or within 201 meters (⅛ mile) of a bus stop with median weekday headways of 10 minutes or better between 7 a.m. and 7 p.m., with at least 6 scheduled departures in that window. We use a larger buffer for Metro because station access typically involves a longer walk than a bus stop; the asymmetry reflects how people actually use the two modes. Headways are computed from WMATA’s GTFS feed. Source: WMATA GTFS; DC Open Data for Metro entrances.

• 20: Share of residents near protected bike lanes — Within 200 meters (⅛ mile) of a physically-protected bike lane (BIKELANE_PROTECTED or BIKELANE_DUAL_PROTECTED in DDOT’s bicycle lanes dataset). Painted lanes without physical protection are excluded. Protected infrastructure is meaningfully different from painted lanes in terms of who is willing to use it; treating them the same would overstate true cycling access. Source: DDOT via DC Open Data.

• 21: Share of residents near Capital Bikeshare — Within 200 meters (⅛ mile) of an installed bikeshare station. Bikeshare access is a reasonable proxy for first-/last-mile active transportation options in DC, and the station network is dense enough in some areas to make this indicator informative. Source: Capital Bikeshare station locations via DC Open Data.

Source: DDOT and WMATA data via DC Open Data and WMATA’s public GTFS feed. All population figures use ACS 2022 block group totals.

Air quality

EJScreen is the Environmental Protection Agency’s environmental-justice screening tool. It publishes modeled estimates of pollution exposure at the block group level, expressed as national percentiles.

A block group at the 80th percentile for NO₂ has higher modeled nitrogen dioxide exposure than 80 percent of block groups in the country. This framing makes DC’s neighborhoods comparable both to each other and to the national distribution, useful for a city that sometimes thinks of its pollution exposure as a solved problem when it isn’t.

• 22: NO₂ exposure (EJScreen national percentile) — EJScreen field P_NO2. Nitrogen dioxide is primarily associated with vehicle traffic and is a marker for respiratory risk, particularly for children and people with asthma. Aggregated to target geographies as a population-weighted mean of block group percentiles.

• 23: Diesel particulate matter exposure (EJScreen national percentile) — EJScreen field P_DSLPM. Diesel PM is associated with freight trucks, bus depots, and construction equipment. Like NO₂, it tracks closely with arterial corridors and industrial land uses. Aggregated the same way as NO2 exposure.

Source: EPA EJScreen 2024, archived via Zenodo (DOI: 10.5281/zenodo.14767363). Data at the Census block group level, allocated to target geographies by area-fraction overlay with population weighting.

How we built the map

The core aggregation method is area-fraction-weighted allocation. When source data comes in Census block groups and the target geography is, say, an ANC, we intersect the two, calculate what fraction of each block group’s area overlaps the ANC, and allocate numeric values proportionally. It’s the standard approach for areal interpolation when source and target geographies don’t align, which, in DC, they never do.

This is a classic issue in geospatial analysis, called the Modifiable Areal Unit Problem. When we allocate block group populations to target geographies by land area, we’re assuming people are distributed uniformly across the block group. They aren’t — people live in buildings — but the Census doesn’t publish finer population counts.

For large geographies like wards and planning areas, this is a reasonable approximation. For small geographies like SMDs and raw FLUM parcels, it can produce meaningful error when a block group overlaps many small target features with very different land uses. This means you should take our results for those small or low-density geographies with a big grain of salt. (For example, this is why we say that the McMillan Reservoir has an average household income of $156,000. This is ridiculous – nobody lives in the reservoir – but it’s the statistical result of the process we have to use to aggregate data.)

Point data (crashes, building permits, affordable housing projects) uses simpler point-in-polygon counting: Each point falls in exactly one target polygon, and we sum.

All indicator values are rounded so as not to overstate the precision of the findings. Percentage values are rounded to the nearest whole percentage point (e.g., 25.3% becomes 25%), and numerical values are rounded to two significant figures (e.g., 142,304 becomes 140,000). We hope this will reinforce the understanding that indicator values are approximate, especially for the smaller geographic units but also for the larger ones.

The software to generate the map is built in Python, using GeoPandas for spatial operations, the Census API for demographic data, and Leaflet for the web visualization. Every indicator gets computed by the same basic pipeline: fetch or load the source data, clean and project it to DC State Plane (EPSG:26985), aggregate to each of the eight geographic layers, and write the results to a GeoPackage. The visualization script then reads all eight GeoPackages, combines them with display metadata, and renders a single self-contained HTML file.

Limitations

There are a few key limitations that you should bear in mind when using the indicator map.

• Small-area Census estimates carry significant uncertainty. ACS five-year estimates at the block group level have margins of error that can be large relative to the estimate itself, especially for rare characteristics in small block groups. After allocating to small target geographies, those errors compound. Income estimates at the SMD level, in particular, should be treated as rough indicators rather than precise measures. We don’t display confidence intervals in the tool because the user interface would become unwieldy.

• The data comes from different years. Demographic figures reflect 2018–2022 conditions (the ACS 2022 five-year window). Crash data runs through 2025. EJScreen is a 2024 vintage. Affordable housing data reflects the current DC Open Data snapshot. These indicators don’t all describe the same moment in time, which means the tool isn’t suited for tracking how a neighborhood changed over a specific period.

• “Affordable” means income-restricted and subsidized, but covers a wide range. The housing indicator counts DHCD-tracked units with some affordability mechanism, but doesn’t distinguish between units restricted to 30% of AMI and those restricted to 80% — or between long-term deed restrictions and time-limited commitments. The count measures production activity of income-restricted and subsidized units, not depth of their affordability.

• A permit is not a building. Some permitted projects are never built, and some completed buildings were permitted years before the period this tool covers. The permits indicator is a leading indicator of construction intent, not a direct count of new housing units.

• EJScreen percentiles are national, not local. A DC block group at the 65th national percentile for NO₂ may be entirely typical for a dense East Coast city. The percentiles tell you where DC neighborhoods stand relative to the country, which is useful, but they don’t tell you whether DC’s absolute air quality levels are acceptable by a particular clinical or regulatory standard.

• The High Injury Network is a policy designation. DDOT determined the HIN using historical crash data, but it’s a discrete list of road segments, not a continuous function of crash severity. If DDOT updates the network, the indicator would change independent of any change in actual crash risk.

Top image: Scott Circle in Northwest DC (Ward 2, ANC 2B, SMD 2B04). Image by Joe Flood licensed under Creative Commons.

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I’ve been meaning to write this post for a while, and now the AI revolution has given me an excuse.

I was standing in a Whole Foods on Long Island, sometime in the early 2010s, staring at a cheese counter, when I had a sudden revelation. All my life, I realized, I had been told that it was people’s work that gave them meaning — that what you produce makes you special. Few people say this explicitly, but it’s baked into many elements of our culture.

In San Francisco, when you meet someone, the first question you typically ask is: “What do you do?” Some people will give teasing answers — “I race boats”, or “I take care of rabbits” — but everyone knows that the question is about your job. Nor is SF particularly unusual; Americans tend to identify each other by their occupation. “This is Steve, he’s a professor,” and so on. In Japan, people are more likely to identify by the company they work for, but their identity is still fundamentally about production.

Or think about movies and TV shows. Yes, there are some stories about people whose hobbies become the most important things in their lives — High Fidelity, Shall We Dance?, Schultze Gets the Blues, and so on. But in most narratives, it’s people’s career that defines their life objective, their success or failure as a person, and their identity as a character.

Consumption, on the other hand, is typically trivialized, or even denigrated. Every culture has words like “lazy”, “shiftless”, or “playboy” to make fun of people who spend their time consuming instead of producing. A “hobby” is far less noble than a “calling” or a “vocation”.

In fact, the value of work over play is one of the few ideas that traditionally united the political left and right. In the 20th century, leftists decried the “consumer society” and called for “workers” to be in control of society. Conservatives, meanwhile, value hard work and complain that the welfare state makes people lazy, while rightists view consumer societies as decadent and weak. The “degrowth” movement is all about reducing Westerners’ so-called “overconsumption”; it’s hard not to hear a moralistic message in addition to the environmental one. Production is virtuous, consumption is wicked.

Why should what you produce, rather than what you consume, be the most important thing about you? Why shouldn’t the fact that you race boats or watch anime or drink matcha lattes be what defines your identity? Why should I call myself a “writer” rather than a “science fiction fan” or a “rabbit dad”? Just imagining introducing myself as the latter makes me cringe a little. But why?

One seemingly obvious answer is that the market values production over consumption. In fact, this is almost the definition of the two terms — work is what you get paid to do, while consumption is what you have to give up something in order to enjoy. But this doesn’t explain why culture and society should give additional accolades to production over consumption. You already get paid for going to work; why should you get praised for it too?

One cynical answer is that praising people for their work ethic is a way of trying to lower labor costs, by paying workers in status instead of money. Plenty of research in both economics and psychology indicates that people will accept lower salaries in exchange for working at a job where they think they’re doing something good for society; this helps explain why wages at nonprofits are so low. This might be why tech companies traditionally tell their young employees that finding new ways to sell ads is “making the world a better place”; it might allow them to pay less than hedge funds for the same class of talent. Professors, meanwhile, often forego lucrative careers in industry in exchange for the pride and status that comes from being an academic.

A less cynical answer is that in premodern times, most work wasn’t rewarded by the market; families and villages had to persuade, bully, or cajole people into plowing the fields or cooking dinner, so they “paid” people for productive effort with compliments instead of wages; this traditional culture may have carried over into the modern day.

And there are reasons to make production the center of your identity beyond the fact that society praises you for it. Your productive power represents a key point of leverage over society; the more the world needs you to produce stuff, the less likely you are to have to depend on the largesse of others. Pride in your productive power means pride in your independence.

But at the end of the day, it’s consumption, not production, that defines you as an individual.

That might sound like an odd thing to say, especially if you’ve grown up believing — as my liberal parents taught me — that advertising is a form of mind control that greedy corporations use to force you to consume things you don’t really need. But you don’t have to trick people into buying most of what they buy; people all over the world want modern conveniences like dishwashers and cars and AI chatbots, and it doesn’t take a lot of ads to convince them to buy those things. Without ads, people would still watch movies and listen to music and wear nice-looking clothes.

The truth is that merchants advertising their wares to you are begging you to buy their brand instead of their competitors’. Everyone wants your money; you are the one who gets to choose who gets it.

That choice is yours and yours alone. Every day that you exist as a consumer in a capitalist society, you are forced to make dozens of decisions about what to spend your money on. Should you buy coffee at Starbucks or Peet’s? Should you buy a new skirt or a new pair of jeans? Should you go watch a Marvel movie or an indie film? Should you subscribe to Noahpinion or to Slow Boring?1

Each time you make one of those choices, you are forced to interrogate your own preferences. You are forced to look inside your heart/mind/soul/utility function/whatever and decide which brand of coffee you want, which type of clothing you want, which blog you want to read, and so on. It’s all about you.

There’s some research demonstrating this effect. Here’s Cheek et al. (2022):

Across six studies (total N = 3,549), we find that participants who were randomly assigned to choose from larger assortments thought their choices were more self-expressive, an effect that emerged regardless of whether larger sets actually enabled participants to better satisfy their preferences.

And here’s Nanakdewa et al. (2021):

When people think of their actions as choices, they feel larger and stronger than others, are attracted to ideas of independence, and feel empowered to voice their opinions. Choosing what to eat and which shampoo to buy may seem like trivial acts, yet the current research finds that the salience of choice alone can have a range of powerful psychological effects.

Is every choice of product or fashion going to reveal some deep truth about you to yourself? Of course not. But consumption choices force you to develop the habit of self-examination. And when you think about more complex life choices — what kind of personality to present to the world, how to behave in your romantic relationships, how to express yourself through art or music — that habit will come in handy. In fact, economists would say that social interaction, romance, and self-expression are also forms of (non-market) consumption.

Production is very different. Your decision of what to produce is not fully your own; the market gets to decide. If what you really want to do all day is carve wood, but the market doesn’t pay a woodcarver a living wage, then you’ll have to find something else to do for money. Lawyers and software engineers and brain surgeons undoubtedly take pride in their careers, but the high salaries society pays for those occupations were undoubtedly a reason they went into those fields. Those salaries are a reflection of someone else’s preferences — someone else’s demand for legal services, software, and brain surgery.

What about jobs that involve self-expression, like art and music and writing? In fact, these are simply bundles of production and consumption. I love writing, and I’m lucky enough to get paid for it. But if I really buckled down and spent a lot of effort building my brand, writing what the audience wanted to hear, covering every breaking topic before other writers did, and generally treating this blog more like work, I could make a lot more money doing it. The lower income I accept in exchange for greater self-expression is actually a form of consumption. It’s no coincidence that artists who “sell out” tend to enjoy their craft less.

To sum up: When you decide what to consume, you ask: “What do I want?”. When you decide what to produce, you ask: “What do other people want me to do?”. The former is a lot more individuating than the latter.

We can observe this effect at the level of whole societies. The World Values Survey shows that richer countries tend to value “self-expression” more:

And in general, surveys consistently find that as countries grow richer, they become more individualistic. Japan often gets stereotyped as a collectivist society, but surveys show that hasn’t been true since the early 1980s — the first generation brought up in affluence became the “me generation”. America’s Boomers were undoubtedly similar.

We can argue about how much individualism versus how much collectivism is good at the societal level. But on a personal level, it seems clear to me that the standard story we grow up hearing — that your job is what makes you you, while what you consume is dictated by corporations — has it exactly backwards. In fact, consumption shapes you into a unique individual, while your job exists at the whim of the collective.

This is what I realized, staring at all that cheese.

As I said, this idea has been rattling around in my head for a while, but it’s the advent of AI that finally made me decide to write it up. A lot of people have been saying that by disrupting traditional careers and devaluing lots of human capital, AI is going to cause a crisis of meaning in our society.

I do agree that this will probably happen. If you’re an economist who prides himself on being able to do the difficult algebra required to turn a conjecture into a concrete theory, and now suddenly AI comes along and can do that at the touch of a button, you might lose some of the pride and sense of meaning that your previously rare skills had given you, even if tenure protects you from losing your income. If you were a software engineer who prided himself on being able to write good code, and now your job consists of checking the code written by Claude, you might feel less meaning in your job, even if your salary is higher.

But I don’t think this has to happen. I think it’s possible for us to reorient our identities away from what we produce, and toward what we consume, and to find meaning in the latter.

That might sound speculative, but in fact I’ve lived in a society where people’s sense of self and their social position was defined at least in part by what they consumed. It was called college. Although I worked part-time during my school years, many people didn’t — and even with my job, I still had plenty of time for leisure. I spent that time learning what music I liked, learning how to make better friends, learning about romance, and learning how to express myself better.

That was all consumption. Even my coursework contained an element of consumption — yes, studying physics built up my human capital, but it was also just fun, and my anthropology and film electives certainly weren’t about increasing my future earning power. Because I went to an elite school, I was able to have some confidence that the signaling component of my school’s pedigree, along with the human networks I built up, would save me from the risk of poverty. So I could spend college having fun and expressing myself — and my fellow students, who mostly didn’t have jobs and lived entirely on their parents’ largesse, could do so even more.

If your reaction to that is “Well good for you, jerk,” I don’t blame you. The American university system is not fair, and I was very lucky to land where I did.2 But imagine a society where everyone could have a college experience like mine — a time of self-discovery and self-expression, where work was done for enrichment instead of for money. And now imagine a society in which people could keep living that college life far past the age of 22.

Doesn’t that sound a little like paradise? Well, perhaps with AI, we can make that a reality. If we create robust institutions to redistribute the material gains from the intelligence explosion, perhaps we can create a society where all of life looks like an elite American school — where people spend their day reading interesting books, doing math because it’s fun, going to parties, making cool outfits, learning about their friends, playing in amateur bands, or having long drunken conversations about the meaning of life until 4 AM without worrying about going to work in the morning.

That’s a vision of a consumption society, but not one that’s meaningless or empty. Instead, it’s a vision of technology freeing us to become more like ourselves. I don’t think this happy outcome is inevitable, but I think it’s getting ignored in most of the discussions about our future.

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"A very, very small quadcopter, one inch in diameter can carry a one- or two-gram shaped charge. You can order them from a drone manufacturer in China. You can program the code to say: “Here are thousands of photographs of the kinds of things I want to target.” A one-gram shaped charge can punch a hole in nine millimeters of steel, so presumably you can also punch a hole in someone’s head. You can fit about three million of those in a semi-tractor-trailer. You can drive up I-95 with three trucks and have 10 million weapons attacking New York City. They don’t have to be very effective, only 5 or 10% of them have to find the target.

There will be manufacturers producing millions of these weapons that people will be able to buy just like you can buy guns now, except millions of guns don’t matter unless you have a million soldiers. You need only three guys to write the program and launch them. So you can just imagine that in many parts of the world humans will be hunted. They will be cowering underground in shelters and devising techniques so that they don’t get detected. This is the ever-present cloud of lethal autonomous weapons.

They could be here in two to three years."

              — Stuart Russell, professor of computer science and engineering at the University of California Berkeley

...lethal autonomous weapons systems (LAWS): weapons that have the ability to independently select and engage targets... humans out of the loop — where the human releases the machine to perform a task and that’s it — no supervision, no recall, no stop function.

One of the very real problems with attempting to preemptively ban LAWS is that they kind of already exist. Many countries have defensive systems with autonomous modes that can select and attack targets without human intervention — they recognize incoming fire and act to neutralize it... Meanwhile, offensive systems already exist, too: Take Israel’s Harpy and second-generation Harop, which enter an area, hunt for enemy radar, and kamikaze into it, regardless of where they are set up. The Harpy is fully autonomous...

Among the lauded new technologies is swarms — weapons moving in large formations with one controller somewhere far away on the ground clicking computer keys. Think hundreds of small drones moving as one, like a lethal flock of birds...

I worry it will breed way more terrorist activities. You can call them insurgents, you can call them terrorists, I don’t care, when you realize that you can’t ever fight the state mano-a-mano anymore, if people are pissed off, they’ll find a way to vent that frustration, and they will probably take it out on people who are defenseless. 

In some GGWash endorsement processes, we’ve backed candidates who aren’t wholly on board with our top priorities. Sometimes, the best, most urbanist option has been someone who has fallen short on housing, because they’re the only one in the field who’s solid on transportation, or vice-versa.

Not this year.

Per their questionnaires, all our endorsed DC Council candidates on the June 16 ballot agree that there aren’t enough homes in the District; that it’s more important to enable more homes, and more types of homes, in more of our neighborhoods, even if that results in pushback for “changing a neighborhood’s character”; that inducing residents and visitors to drive less should be an explicit policy goal of the District; that there are too many cars; that the District should establish new housing-production targets on five-year cycles, and would propose targets themselves; agree that 15 percent of all homes in a planning area should be affordable, and commit to equitably distributing affordable housing throughout the District’s planning areas; support changing the District’s construction code and permitting processes to make it easier to produce more housing, and would introduce legislation to do so; agree apartments should be legal in all parts of all the District’s neighborhoods and would introduce an amendment to the Comp Plan that would do so; agree that new housing of all types should be built throughout all neighborhoods; support removing parking and travel lanes to build dedicated bus and bike lanes; support the implementation of road pricing and would introduce legislation to do so; would not find it acceptable if the District Department of Transportation director drove to work the majority of the time; and pledge to participate in the National Week Without Driving every fall.

The writing of our 2026 endorsements for candidates running for DC Council followed the Office of Planning’s release of a draft new Future Land Use Map. The FLUM, part of the Comprehensive Plan, determines where and what can be built in the District. OP’s newly released draft, which will enable only an additional 15,000 units by 2050, is devastatingly unambitious.

We expect our endorsee for mayor, Janeese Lewis George, to ensure the Comp Plan rewrite legalizes greater density, particularly apartments District-wide. If the executive falls through, we’ll look to our council endorsees to introduce and support amendments that make GGWash’s Comp Plan priorities possible.

The Comp Plan rewrite is the most substantial issue for us in the coming council period, which will run from 2027–2029. We also aim to work closely with our endorsees, should they win, to make progress on the design and implementation of a road pricing program.

Click the links below to jump to specific endorsements:

• Council chair: No endorsement
• At-large majority council seat: Lisa Raymond
• At-large minority council seat (special election): Elissa Silverman
• Ward 1: Aparna Raj
• Ward 3: No endorsement
• Ward 5: Zachary Parker
• Ward 6: Charles Allen

Council chair: No endorsement

Neither Phil Mendelson nor Jack Evans responded to our questionnaire, a prerequisite to be considered for our endorsement. We are, therefore, not endorsing in the council chair race.

At-large majority council seat: Lisa Raymond

Greater Greater Washington endorses Lisa Raymond for at-large councilmember in the Democratic primary. Kevin Chavous, Michael Graham, Fred Hill, Candace Tiana Nelson, and Oye Owolewa also responded to our questionnaire.

We believe that Raymond will most reliably champion more housing, more affordable housing, and fewer trips by car if elected to the at-large majority-party seat. She would “offer and support legislation prioritizing use[s] other than single…occupancy vehicles for precious public space” and “consider legislation that limits ‘bad faith’ appeals while still allowing for meaningful community input” on discretionary zoning cases.

Raymond has considerable experience within District government, which is likely to inform how she will handle nominees to, in particular, the Zoning Commission: She’ll “only support nominees who support the development of additional housing in the District with an emphasis on affordable housing,” because “in order for DC to thrive it must grow and zoning must adapt.”

Raymond tends to emphasize transit-oriented development when asked where the District should build more housing. Nonetheless, she said she would introduce an amendment to the Comp Plan that would legalize apartments in all parts of all the District’s neighborhoods, and we trust she understands the value of increasing density beyond transit corridors.

Raymond is one of the few respondents to our questionnaire who discussed how older adults and disabled people would benefit from improved public transit, including dedicated bus lanes. She has creative ideas to promote bikeshare ridership, and grasps how meaningful “a safe, simple, route to get to school” is for kids and parents.

The caliber of the field to succeed Anita Bonds is, overall, high. Though we determined that Raymond was best on our issues overall, we also believe Nelson and Owolewa would be supportive of building more housing and reducing trips by car if elected. Consider ranking both highly on your ballot.

Donate to and volunteer for Lisa Raymond’s campaign.

At-large minority council seat, special election: Elissa Silverman

Greater Greater Washington endorses Elissa Silverman for independent at-large councilmember in the June special election, which, like the Democratic primary, will be held on June 16, 2026. Doni Crawford also responded to our questionnaire.

Kenyan McDuffie, who had served in this role since 2023, resigned in January 2026 to run for mayor. The DC Council appointed Crawford, a longtime McDuffie staffer, to fill his seat until the special election. Whoever wins on June 16 will have to run again in the November 2026 general election if they want to serve a full four-year term.

We agree with Crawford that enabling the development of jobs, grocery stores, healthcare facilities, and childcare centers near where people live is critical, and appreciate her interest in closing more streets to cars. But we endorse candidates who are most likely to advance our issues, so her opposition to road pricing—which Silverman supports and would introduce legislation to enable—is a dealbreaker in a year in which a majority of those running for office are a yes on one of our top policies. We hope that, should Crawford remain on the council, we have the opportunity to work closely on housing production and transportation.

We endorsed Silverman when she ran for a second council term in 2018 and again in 2022, when she lost her bid for a third term to Kenyan McDuffie. She continues to be suitably forward-thinking, writing in response to our questionnaire, “We also should expect another population boom in the future, as the next presidential administration rebuilds the federal government after Trump. We should plan now for that expansion, so we have enough new housing across the city to not price out existing families.”

She also notes her support for Ward 1 Councilmember Brianne Nadeau and At-large Councilmember Robert White’s Housing Production Omnibus Amendment Act, which will reboot how the Department of Housing and Community Development funds income-restricted, subsidized housing projects. We’ve long valued Silverman’s attention to detail. If the omnibus passes, the agency will need that kind of constructive scrutiny to ensure that it is spending housing trust fund dollars as the council intends.

Silverman is and has been reliably pro-transit. She correctly identifies a lack of “willpower to stand up for safer streets and better transit” as the impediment to building bus and bike lanes, which “includes not confirming DDOT directors who will make decisions that benefit commuters over residents.”

Donate to and volunteer for Elissa Silverman’s campaign.

Ward 1: Aparna Raj

Greater Greater Washington endorses Aparna Raj for the Ward 1 DC Council seat in the Democratic primary. Rashida Brown, Terry Lynch, and Miguel Trindade Deramo also responded to our questionnaire. We recommend that you rank Raj first, Brown second, and Trindade Deramo third on your ballot.

The Ward 1 race is for an open seat for the first time in 44 years. Outgoing Ward 1 Councilmember Brianne Nadeau has been a steadfast champion of GGWash’s priorities since 2015 in ways too numerous to detail. We will miss her on the council dearly.

On urbanism, Ward 1 will be set regardless of whether Raj, Brown, or Trindade Deramo wins. While their styles vary, there is—to the ward’s benefit—little daylight between their stated stances on housing and transportation. Raj edges out Brown by not just supporting building-code reforms and road pricing, but also being willing to introduce legislation to advance them. That the former chair of the Democratic Socialists of America’s Metro-DC chapter writes most convincingly of the three about the need for more market-rate housing may raise eyebrows. But it is representative of an emerging pro-growth mentality on the left that GGWash believes is worthwhile to foster.

Brown and Trindade Deramo, too, deserve your vote. We recommend that Ward 1 voters rank Brown second and Trindade Deramo third. Both are advisory neighborhood commissioners, though Brown’s much-longer tenure on ANC 1E, formerly 1A, and unwavering commitment to the redevelopment of Park Morton and Bruce Monroe puts her ahead. ANC 1B chair Trindade Deramo, whose entry point to local affairs was I-83 and cleaning up Malcolm X/Meridian Hill Park, is a natural on transportation and has been a very quick study on housing production. However, as a relative newcomer to the local political landscape, he might benefit from more time navigating various relationships in the ward before serving as a councilmember.

Ward 1 is the District’s densest and, fittingly, its most urbanist. That shows in our three preferred candidates, who make for an outstanding pool. Raj, Brown, and Trindade Deramo represent the very best of what Ward 1 has to offer to those who support more housing, more affordable housing, and fewer trips by car. Regardless of the outcome in June, we anticipate all three will remain in local politics, and hope they are as committed to our priorities beyond the Ward 1 race as they say they are while in it.

Donate to and volunteer for Aparna Raj’s campaign.

Ward 3: No endorsement

Though Matthew Frumin and Adam Prinzo both responded to our questionnaire, Prinzo did not secure enough signatures to qualify for the ballot. Frumin, the incumbent Ward 3 councilmember, is now running unopposed. GGWash is not endorsing in the Ward 3 council race.

We endorsed Frumin in 2022, following the lead of our initial endorsee, Ben Bergmann, who, with some of his competitors, stepped back to consolidate the field. At that time, we wrote, “[w]hile not a candidate perfectly aligned with GGWash, Frumin shares many of our policy priorities.”

Per his responses to our questionnaire, this is still true: Frumin affirmed his support for prioritizing more homes above preserving neighborhood character and for inducing people to drive less.

But Ward 3—large, low-density, amenity-rich, segregated, and affluent—deserves a leader who is unabashedly pro-housing. Historically, a minority of Ward 3 residents have weaponized land use processes to protect its status as an exclusionary enclave. The Comp Plan rewrite is a singularly important opportunity to set Ward 3 on a more welcoming trajectory. Frumin is not so much of a fighter that we feel we can sign off on him in an uncompetitive race.

We have been, and remain, glad to find opportunities to work with Frumin when they present themselves. As he is on track to win a second term, we sincerely hope that he will, if not lead the charge, stand more prominently for a more inclusive, pro-growth Ward 3 when conflict arises.

Ward 5: Zachary Parker

Greater Greater Washington endorses Zachary Parker for the Ward 5 DC Council seat in the Democratic primary. Bernita Carmichael also responded to our questionnaire.

Parker, whom we endorsed in 2022, may not be the most active councilmember on GGWash’s issues, but his responses to our questionnaire demonstrate a grasp of why more housing and fewer trips by car matter not just to Ward 5, but the District overall. We value his belief that buses should move faster than traffic and that “car ownership is often a financial burden rather than a choice” for residents who live east of the Anacostia River.

There are some contradictions within Parker’s responses. For example, we are skeptical that it is possible to “ensure our land use framework supports both community voice and the homes our residents urgently need.” Aligning “street design, transit funding, and land use policy” so that we can lower household costs while improving mobility for everyone” requires moving at a faster clip than most District agencies have demonstrated they are capable of. Nonetheless, Parker has worked hard in his first term on council, and has consistently supported pro-transit legislation.

Donate to and sign up to volunteer for Zachary Parker’s campaign.

Ward 6: Charles Allen

Greater Greater Washington endorses Charles Allen for the Ward 6 DC Council seat in the Democratic primary. His opponents did not respond to our questionnaire.

Allen, who chairs the Committee on Transportation and the Environment, is a steadfast supporter of frequent and reliable transit, safer streets, and more housing at all price points. He has been the most frequent participant in our yearly Week Without Driving campaign; led on the STEER Act, which empowers the District’s attorney general to prosecute reckless out-of-state drivers; and established the e-bike incentive program. Allen has taken tangible steps to reduce the transportation time tax by funding bus-priority projects and supporting Clear Lanes.

In his questionnaire, Allen proposes further reducing the time tax by creating bus rapid transit and dedicated bus travel lanes. But what we’re most looking forward to working with him on is determining the feasibility of road pricing in the District, which—more so than nearly any other policy—would bring legitimate parity to transit trips and trips by car, while raising revenue necessary to sustain and expand public transportation.

We were disappointed in Allen’s yes-vote on RFK stadium deal after years of nodding in the direction of better uses of the site. Still, with the stadium underway, we expect Allen to hold the executive, and the team, accountable for the sustainability requirements he pushed for, and for the delivery of promised housing and transportation.

Donate to and sign up to volunteer for Charles Allen’s campaign.

For more information about how we’re making endorsements in 2026, see our 2026 endorsements process post. On our 2026 Elections Hub, you’ll find information about GGWash’s political work this year, including resources about voting and candidates, candidates’ responses to our questionnaires, our endorsements, and how you can help our endorsed candidates win their elections.

Distinctly political work, like our endorsements process, is not funded by grants. We are only able to make endorsements with support from individual donors. If you value our endorsements resources, consider a contribution, of any amount, to support GGWash in 2026 and beyond. You can do so here.

Top image: John A. Wilson Building in Washington, DC. Image by eric rogers used with permission.

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