Village Green: Revitalizing Cincinnati's Historic Over-the-Rhine (Part 3 - exciting progress portends a national model)
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A lesson in the elasticity of demand, prices and urban congestion. It looks like Uber, Lyft and other ride sharing services are swamping the capacity of New York City streets
Every day, we’re being told, we’re on the verge of a technological revolution that will remedy our persistent urban transportation problems. Smart cities, replete with sensors, command centers, and linked to an Internet of Things, will move us every more quickly and effortlessly to our destinations, traffic lights and even traffic will become a thing of the past. The most fully deployed harbinger of the change that such technology has wrought is clearly the app-based ride-hailing services, including Uber, Lyft and others. A few weeks back, we took to task claims from one study that a couple thousand autonomously piloted 10-passenger vans could virtually eliminate traffic congestion in Manhattan.
A new report from New York should give the techno-optimists reason to pause. Transportation consultant Bruce Schaller, who previously served as New York City’s deputy transportation commissioner, has sifted through the detailed records of New York City’s Taxi Licensing Commission and painted a stark picture of growing traffic congestion in the city–thanks to to increasing proliferation of app-based ride hailing services, which the report refers to as transportation network companies (TNCs). Schaller has a shorter op-ed summarizing his report in the New York Daily News, but you’ll want to have a look at the entire 38-page report, entitled “UNSUSTAINABLE? The Growth of App-Based Ride Services and Traffic, Travel and the Future of New York City,” The full report is worth a read, but here are some highlights.
- The total number of miles driven in New York City by TNCs vehicles has increased by 600 million miles in the past three years.
- Trips taken by the combination of taxis and TNCs has outpaced the increase in trips taken by transit; for the precdeing 24 years (since 1990) growth in person trips has been led by increased transit ridership.
- The additional vehicle traffic associated with TNCs amounts to about a 7 percent increase in traffic levels in Manhattan, about the same amount of traffic that the city’s cordon-pricing proposal was supposed to reduce.
- The bulk of the increase in traffic associated with TNCs has been in the morning and evening peak hours.
- While TNCs initially grew mostly by taking traffic from yellow cabs, increasingly they are taking riders from transit, and in some cases stimulating additional travel.
Bottom line: the growth of TNCs is increasing the volume of vehicles on New York City streets, and adding to congestion and delays. Interestingly, this key finding is a turnaround for Schaller, who acknowledges that just a year ago (in January 2016), he was part of a team that concluded on behalf of Mayor de Blasio that the added traffic from ride hailing services wasn’t increasing the city’s congestion. The growth in TNC volumes over the past year has apparently changed his mind.
What’s changed? In short, the price of car travel has fallen in New York City, relative to the alternatives. Previously, a combination of high taxi fares and limited entry (a fixed number of medallions) coupled with prohibitively expensive parking rates in most of Manhattan, meant that taking a private car cost four or five times more times as much as the average transit fare. But with Uber and Lyft charging much lower rates, and flooding the market with additional vehicles, there’s been a noticeable uptick in traffic volumes. This is a fundamental lesson in economics: increasing the supply of vehicles and lowering prices is going to trigger additional demand. And in the face of limited street capacity, congestion is likely to increase. And we should keep in mind that the TNCs are just a dress rehearsal for fleets of autonomous vehicles. Subtract the cost of paying drivers, and they promise to be even cheaper and more plentiful than Uber and Lyft are today, especially in high density urban markets.
Ultimately, the solution to this problem will come from correctly pricing the use of the city’s scarce and valuable street space, particularly at rush hour. Schaller makes this very clear:
As they steadily cut fares, TNCs are erasing these longstanding financial disincentives for traveling by motor vehicle in Manhattan. If TNC growth continues at the current pace (and there is no sign of it leveling off), the necessity of some type of road pricing will become more and more evident.
The detailed data from Uber and Lyft however, point up the major limitations of the proposed cordon-pricing scheme that was suggested for New York City a decade ago (under cordon pricing, vehicles entering lower Manhattan, below 96th or 110th street, or crossing the Hudson or the East River would pay a daily toll). But because so much traffic and so many rides begin and end within those boundaries, the cordon pricing scheme does nothing to disincentivize travel in the center, once the vehicle has paid the toll.
Consequently, if pricing is going to work in Manhattan, it will probably have to be some kind of zoned, time-of-day pricing, charging higher rates for travel in and through Manhattan during peak hours, which much lower fees for travel in out-lying boroughs and at off-peak hours. In effect, Uber’s much maligned “surge” pricing is proof of concept for this model; it just has to have prices reflect the scarcity and value of the publicly owned roadway rather than just the momentary scarcity of Uber’s privately owned vehicles. The GPS and mobile Internet technology that’s now been proven in taxis and TNC vehicles, shows a such a system is technically quite feasible.
Without some form of road pricing, the high concentration of profitable fares in denser neighborhoods and at peak hours, coupled with the additional financial inducement of surge pricing bonuses could lead ever greater volumes of TNC vehicles to clog city streets. As New York transportation expert Charles Komanoff puts it, the Schaller report settles the question that TNCs are making the city’s gridlock worst. He effectively calls the report a must read:
It touches on virtually every consequential transportation trend and policy question facing the five boroughs and stands as the most thoughtful and thorough analysis of New York City traffic and transportation issues since the Bloomberg years.
But New York is just on the leading edge of a range of technological and policy issues that every city is going to have to confront in the years ahead. If you want to get ahead of the curve–and think about where the expansion of TNCs, and ultimately autonomous vehicles is taking us–here is a good place to start.
Big data provides little insight
Cue the telephoto lens compressed photo of freeway traffic; it’s time for yet another report painting a picture of the horrors inflicted on modern society by traffic congestion. This latest installment comes from traffic data firm Inrix, which uses cell phone, vehicle tracking and GPS data to estimate the speed at which traffic moves in cities around the world.
Two words summarize our reaction to the new Inrix report: tantalizing and aggravating. The tantalizing part is the amazing data here: Inrix has astonishingly copious and detailed information about how fast traffic is moving, almost everywhere. The aggravating part: its essentially just being used to generate scary–and inflated–statistics about traffic that shed precious little light on what we might do to actually solve real transportation problems. Its main purpose seems to be to generate press headlines: “Los Angeles Tops Inrix Global Congestion Rankings,” “Atlanta Traffic Among Worst in the World, Study Finds,” and other scary stories.
Same as for the last congestion report. (Atlanta Journal-Constitution)
One one level, its a truly impressive display of big data. Inrix has compiled 500 terabytes of data, for hundreds of thousands of roadway segments, from hundreds of millions of sources on more than a thousand cities around the globe. That’s a real wealth of information. Inrix casually slips in the factoid that average speeds on New York streets are 8.23 mph, versus 11.07 mph and 11.54 mph in L.A. and San Francisco respectively. But unfortunately, in this particular report, it has chosen to process, filter and present this data in a way that chiefly serves to generate heat, rather than shed any light on the nature, causes and solutions to urban traffic problems. If “big data” and “smart cities” are really going to amount to anything substantial, it has to be more than just generating high tech scare stories.
We’ve read through the report, examining its key findings and comparing it to previous work by others. We think there are four fundamental problems that readers should be aware of: the report has a new methodology, which while more detailed than previous reports, is neither comparable to them, nor a major improvement. Like other reports, the definition of congestion is unrealistic, and its cost estimates are exaggerated (with no acknowledgement that building enough capacity to reduce congestion would be even more expensive, and likely be ineffective). Most importantly, like all travel time index measures, the Inrix methodology ignores differences between average travel distances in cities, which effectively penalizes denser, more compact cities. Its disappointing to see so much data providing so little insight into what we might do to understand and solve these problems.Methodology: New and non-comparable, but not significantly different or better
First off, Inrix has revised its methodology and definitions for computing and presenting metro level congestion statistics. They’ve segmented their data by time of day and trip characteristics, which in theory provides a more nuanced view than earlier work. But it also means that results in this year’s studies aren’t comparable at all to the data (and claims) made in earlier Inrix reports (which we’ve raised some questions about). Inrix has also changed the geographic definition of what constitutes a city or urbanized area. As a result, this report can’t tell us whether congestion is getting worse or better, or shed light on the strategies and investments that may have actually led to reductions in congestion in different cities around the world.
The failure to report data on a consistent basis over time undercuts our ability to use it to make sense of the world. For a long while, Inrix actually gathered and reported its data on a consistent monthly basis in a way that allowed independent observers to view congestion and travel time trends. This data actually showed traffic congestion easing in most metropolitan areas in the United States from 2009 2014. Inrix stopped reporting this data in 2014, and scrubbed the links to it from its website (although the original data still live on in a Tableau server you can see here).
But while the numbers are new and non-comparable, they appear to mostly be saying same thing that we were told in earlier congestion reports. So for example, we’ve graphed the 2016 Inrix Congestion Index (ICI) against the 2012 Texas Transportation Institute Travel Time Index (TTI). The two indices are calculated in different ways, and TTI runs from about 1.15 to 1.45 for each metro area while ICI runs from 1 to 19. (In both cases, the travel time index measures how much longer a trip takes due to congestion than it would during free-flow conditions; ICI omits an implied 1.0.) But the two measures are highly correlated (r-squared of .69), which says they’re really measuring the same thing in pretty much the same way, making ICI in many ways simply a gussied up, really-big-data version of the Texas Urban Mobility Report’s Travel Time Index, with all its attendant flaws.An unrealistic definition of congestion
Second, the definition of congestion is a novel and expansive one: Any time travel speeds fall below 65 percent of free-flow speeds, Inrix regards this as being “congested.” Inrix says it determines free-flow speeds using actual traffic data. As we and other have noted, this approach often results in using speeds that exceed the posted legal speed limit for a roadway as the baseline for determining whether a road is congested. For example, if “free flow” speeds on a posted 55 mile per hour road are 60 or 65 miles per hour, Inrix would presumably use this higher baseline for computing congestion. This has the curious implication that the inability of a motorist to engage in an illegal behavior constitutes a “cost.” Also: its worth noting that roadways achieve their maximum throughput (number of vehicles moved per hour on a roadway segment) and speeds that are usually much lower than free flow speeds. (At higher speeds, drivers increase their following distance and the road carries fewer cars per hour). So in many cases, these lower speeds (say 40 miles per hour on a 55 mile per hour roadway, where free-flow speeds are 60 miles per hour), may actually be more efficient. As we pointed out in our essay “The Cappuccino Congestion Index,” no one expects businesses to have has much capacity to provide the same service at peak hours that they do in slack times.Exaggerating costs
Third, while Inrix claims to have estimated the “cost” of congestion to travelers, these estimates are suspect for a number of reasons. Inrix uses a value higher than most other studies–almost $20 per hour for commuter travel time (a $12.81 wage rate, multiplied by 1.13 occupants per vehicle multiplied by 1.37 to reflect the aggravation of congestion). But real world experience shows that commuters actually value travel time savings at something more like $3 per hour. It also appears that there’s been a major shift in the monetization of congestion costs: Older studies like TTI, estimated dollar costs based on the additional time spent on a trip due to congestion: So if a trip that took ten minutes in un-congested traffic took a total of 15 minutes in a congested time period, they would monetize the value of the five minutes of additional time spent. The Inrix report appears to monetize the total value of time spent in congested conditions, i.e. anytime travel speeds fell below 65 percent of free flow speeds. It’s actually hard to tell exactly what they’ve done, because their explanation is at best somewhat cryptic:
The direct costs are borne directly by the car driver through their use of the roads in congestion, and include the value or opportunity cost of the time they spent needlessly in congestion, plus the additional fuel cost and the social cost of emissions released by the vehicle. (Page 8)
In our example, if congestion were evenly distributed over this same 15 minute journey, it appears than Inrix would monetize the entire 15 minutes as “time spent in congestion.” This has the effect of greatly increasing the estimated “cost” of congestion.
As we’ve pointed out before, despite the impressive sounding estimates of the value of time lost to congestion, the key question that the Inrix report begs is whether the the cost of building enough roadway capacity to eliminate congestion would somehow be less expensive than the supposed value of lost time due to congestion. Its likely the cost of building enough capacity to eliminate congestion would dwarf travel time savings–and that’s before considering the induced traffic that added capacity would add. We know from thorough academic studies like Duranton and Turner’s fundamental law of road congestion, and practical experience with freeway widening projects in Los Angeles and Houston, that spending billions of dollars on more capacity doesn’t reduce congestion, it increases traffic.Ignoring distance, discounting accessibility
Fourth, the Inrix Congestion Index, like the TTI travel time index still has the major flaw of overlooking the differences in average travel distances between cities. Some cities have much shorter commutes than others (usually because of much more compact development patterns), and while a larger percentage of trips may occur during “congested” time periods, the total duration of trips is far shorter in these more compact metro areas. Consider two cities, one with a five mile average commute and the second with a ten mile average commute. Suppose that in both cities, drivers drove an average of two miles in “congested” conditions: Inrix would tell us that in the first city 40 percent of commuter travel was “congested” while in the second only 20 percent (2 of 10 miles) was congested. Even though both sets of commuters experienced the same amount of congestion, the more compact city had trips that were half as long. As we’ve shown this percentage-based congestion index is profoundly biased against compact cities with short average travel distances. Its frequently the cases that the average commuter in a city with a high congestion score will have a shorter duration commute time than someone living in a city with a low congestion score because they don’t have to travel as far.Why can’t big data tell us something useful?
Think of Inrix as a test case for big data and smart cities. It has to do something more than simply serve as a high powered tool for p.r. and pro-road talking points. It should be an analytical tool which helps us figure out what works, and what doesn’t, what to do more of and what to stop doing altogether.
For example, this kind of data , should help us judge which cities are doing well, and why, and how we can learn from their successes. According to Inrix, Birmingham and Oklahoma City have some of the lowest levels of traffic congestion (at least as measured by their flawed ICI) of any of the 50 or so largest metro areas. Is there actually something that these cities are doing, or some keen insight other cities can learn that will show how to reduce traffic congestion? Absent a framework for connecting this data to policy–and for correcting the biases against compact, accessible development that are implicit in the travel time index/ICI–this data isn’t terribly useful for setting transportation policy or deciding on how best to invest in transportation infrastructure.
As we’ve argued at City Observatory, you can’t address transportation policy without a clear model of why we have congestion in the first place. There’s overwhelming evidence that roads get congested at peak hours because we’ve set too low a price for road use. When we actually charge even a modest price for road use, congestion problems evaporate (see our story about Louisville). The report’s authors, economists Graham Cookson and Bob Pishue, clearly understand that there’s something more to the traffic problem than the scary stories and big numbers presented in this report. In blogs at Inrix, both highlighted the importance of demand side strategies, specifically including road pricing. Graham Cookson wrote that we need to be “encouraging the efficient use of our roads through wider adoption of road user pricing.” Pishue acknowledges that pricing roads would reduce congestion, but apparently frets that this requires a change in behavior: “Demand-side strategies like road user pricing and flexible work schedules can be effective, yet rely on changing driver and economic behavior.” Unfortunately there’s no reference to these policies in the Inrix report itself; the word “pricing” simply doesn’t appear anywhere in its 44 pages.
Last year, we gave Inrix a grade of D for the last iteration of this report. This year we’re dropping that down to an incomplete. Inrix clearly has a wealth of data that could tell us a lot about how well our transportation systems perform, but so far, it appears that they’re chiefly interested in generating headlines, rather than providing the kind of analytical tools that could help inform policy choices. We hope they’ll do better in the future.
One of the things that gets us most excited about ADUs is the financial math. Here’s why. Cities across the nation are struggling to find ways to provide more affordable housing to meet growing demand, both little ‘a’ affordable housing and big ‘A’ Affordable Housing. Little ‘a’ housing is often also called workforce housing. This housing is intended to be accessible to people making up to 80% of the area median income (AMI in housing speak). Housing for police, firefighters, teachers, recent college grads with a lot of student debt. For Atlanta, this translates into monthly rents of $764 for an efficiency, $820 for a one bedroom, and $949 for a two bedroom.
Let’s talk about approximate costs for the ADUs we are designing. While we are still working through costing with our builder, we are expecting that the one bedroom version should cost somewhere between $95,000-$115,000 depending on specific site conditions. The two bedroom is expecting to cost somewhere between $125,000-$145,000. These numbers are the all-in cost. Design, permitting, construction, utility hookups, etc. etc. are included in these numbers.
Home equity loans are currently running at between 4%-5% depending on a variety of borrower factors. This means the following: A monthly payment for the one bedroom unit would come in between $450-$615 per month (best case construction cost and rate versus worst case). Add in an extra $100 per month for estimated taxes and insurance and you have an all in cost of $550-$715 a month. The two bedroom unit would range $700-$900 all-in. All this assumes that you as the borrower are putting no money into the transaction, you are borrowing 100% of the costs.
So let’s compare the workforce housing rent goals listed above versus the cost of an ADU. Worst case cost of $715 per month for a one bedroom and $900 for a two bedroom. With target rents of $820 and $949 respectively, both designs are cash flow positive with the worst case scenarios. Now, let’s set workforce housing goals aside for a moment and ask what a cute, one bedroom carriage house would rent for in your neighborhood. Most in town neighborhoods, and particularly those close to MARTA, these units would command a much higher rent rate. $950-$1,400 a month is not outrageous, particularly considering that new apartments being built typically offer studios, not one bedrooms, for upwards of $1,200 per month.
There are a couple basic reasons why the math works. Large apartment buildings choose to provide a multistory parking deck for vehicle storage. The cost of this deck often adds as much as $200-$300 in additional monthly rent to cover those costs. There is also essentially no extra land cost to build an ADU, and the storm water requirements are much less than for an apartment building. Construction cost is higher for the ADU, eliminating some of the benefits, but you still net out at a significant competitive advantage.
Lastly, we haven’t even mentioned Air BnB. We are seeing significant demand for Air BnB units close to MARTA, with apartments, spare bedrooms, and whole houses in constant demand. Running an Air BnB requires much more engagement than an apartment, but the returns are significantly more. We are not advocating for or against the merits of Air BnB, but we do recognize it exists, and provides an opportunity for a homeowner to capture additional income. This income can be a critical component of housing security for older residents that have limited options to earn more to pay for increasing property taxes in rising neighborhoods.
Big picture, we see the vast stretches of “single-family” homes and neighborhoods that are under the impression that they should ONLY have single family homes based upon a flawed understanding of our current zoning. Our zoning specifically permits these guest houses/ADUs, and these are a critical tool to address our growing housing demands without outside subsidy.
Image source: http://blog.newavenuehomes.com/index.php/2014/09/23/what-are-the-true-costs-of-an-accessory-dwelling/
San Francisco is enlisting developers to help reduce driving by requiring them to provide amenities like on-site bike parking, pictured here at 388 Fulton. Photo by Christopher Ulrich for SPUR.
How do you keep people moving and avoid gridlock in a city that’s poised to add 190,000 jobs and 100,000 households over the next 25 years? For San Francisco, solving this problem is not a thought experiment — it’s reality. To address this issue, the city is enlisting developers in making sure their new projects don’t add up to thousands more car trips.
Last week, the San Francisco Board of Supervisors passed the Transportation Demand Management (TDM) Ordinance. The TDM program, which the ordinance establishes, is the third prong of the city’s Transportation Sustainability Program, a set of three policy initiatives focused on improving the transportation system to accommodate new growth.
A joint effort of the San Francisco Municipal Transportation Agency, the San Francisco Planning Department and the San Francisco County Transportation Authority, the program requires developers to provide on-site amenities to help limit the number of car trips that residents, workers and visitors will make to and from the property. It applies to most new buildings, as well as to buildings where a change in land use will impact the estimated number of vehicle trips. While the program drew inspiration from TDM regulations across the country, it is the first to cover both residential and non-residential uses.
To help developers meet the requirement, city staff members have developed a menu of more than 60 TDM tools — things like discounted transit passes, free shared bikes, on-site car sharing and bicycle parking — that are proven to reduce driving. Each measure is allocated a point value based on its ability to reduce the number of vehicle miles people collectively travel per year to get to and from the property. Providing showers and lockers earns a developer 1 point, and offering car share parking and/or membership can earn up to 5 points. New developments will be assigned a point target based on the type of land use and the number of parking spaces their project proposes. To reach the target, developers will need to choose a selection of TDM measures from the menu. The agencies created an online tool to help developers navigate the process.
A sample section of the TDM menu, courtesy of the San Francisco Planning Department
There’s a lot to like in the TDM program. It’s strategic, transparent and consistent. Historically, if a new development included anything beyond the standard requirements of car-sharing and bicycle parking — like a bicycle repair station, transit contributions or other TDM measures — it was usually voluntary, at the request of the Planning Commission or required as a mitigation following an environmental review. Now, it’s a priority for all projects; amenities that help reduce car trips must be incorporated into a project from the start or else the developer won’t get the green light to build. The program is data driven and offers flexibility: developers can select whatever assortment of amenities they think will best meet the needs of their building’s future inhabitants, as long as they meet their points target. The program stands out for its inclusion of family-friendly amenities like loaner cargo bikes, on-site day care and providing storage for car seats near car-share parking.
Crucially, the TDM program doesn’t ignore monitoring and evaluation. It will require developments to demonstrate ongoing compliance, and city officials will study the impacts of the different amenities on people’s transportation choices and use the results to tweak the program. Because the buildings in the program will be located throughout the city, city staff will be able to collect fine-grain data on what amenities work best where, as well as learn how new development impacts the transportation system. The resulting trove of data will provide real information that will be vital for future planning efforts.
All too often programs call for monitoring and evaluation but don’t include the funding for it. The TDM Program involves two fees to cover costs: an upfront TDM plan application fee and an on-going monitoring/compliance fee. The later fee will fund needed staff.
TDM programs tend to focus on the commute, but the bigger win is when people turn to biking, walking and transit for all sorts of trips, not just getting to work. The amenities in San Francisco’s TDM program are trip agnostic; they don’t care if they support a work trip or a trip to the grocery store or a to friend’s house. An increase in sustainable trips benefits not only the local neighborhood, but the city and region as a whole.
The TDM program won’t eliminate congestion in San Francisco, but it’s a piece of the puzzle. Ultimately, if growth and mobility are to coexist, city officials will need to employ a range of tools to make transit more convenient and biking and walking safer. Research shows that policies such as congestion pricing would help, as would transit-only lanes, protected bike lanes and more walkable streets. Planners should watch the TDM program closely, especially to see what happens when we make not driving the easiest, most obvious choice. The lesson of “defaulting” could provide an important framing for other aspects of transportation planning.
Race and ethnicity 2010: San Francisco, Oakland, Berkeley. Red is white, blue is black, green is Asian, orange is Hispanic, yellow is other, and each dot is 25 residents. Map by Eric Fischer with data from Census 2010. Image courtesy flickr user Eric Fischer.
In January, two new laws were introduced in Congress to limit public information on the racial makeup of communities and the race of those struggling to afford housing.
The two bills – H.R. 482 (Paul Gosar, R-AZ) and S. 103 (Mike Lee, R-UT) – would prohibit the use of federal funding “to design, build, maintain, utilize, or provide access to a Federal database of geospatial information on community racial disparities or disparities in access to affordable housing.” These proposals could impact how the U.S. Census collects data for the 2020 decennial Census and the ongoing American Community Survey (ACS), part of which is specially tabulated to inform the allocation of housing assistance to those who need it most.
The intent of these bills is unstated, but their effects would include jeopardizing progress on racial equity. The most obvious problem is that they would effectively blot out our ability to measure racial disparities across neighborhoods. And that would mean we could no longer track institutional racism — or measure our progress to reverse it. For example, without housing and community data tabulated by race, we would not have recently learned that gains in black homeownership since the passage of the Fair Housing Act of 1968 were completely eroded between 2000 and 2015. (See the Urban Institute’s figure below). The disproportionate impact of the housing bubble and Great Recession on the wealth of black Americans would be less visible and measurable, and perhaps vulnerable to dismissal, without quantitative proof.
These two bills aren’t isolated efforts to limit demographic data. The ACS — the 70-question form sent to 3.5 million U.S. households every year — has been under repeated attack in recent years. Those who oppose it cite that the constitution only requires a basic count of people to ensure proportional representation in Congress. They say other questions on the ACS — like the number of flush-toilets in homes or whether or not family members have disabilities — are intrusive, as is the threat of a fine if they aren’t answered. But in making these claims, opponents fail to acknowledge that all survey forms are scrubbed of things like names and addresses and that fines have never been levied.
Some have warned that the ACS could be vulnerable to cuts in the next round of federal budget negotiations. After all, to prepare for the 2020 decennial census, the Census Bureau has submitted a request for 20 percent more funding in the coming year compared to 2016. A Republican-controlled congress looking to limit the size of government and a White House that prefers to generate its own facts could spell trouble for the ACS.
Thankfully, support for the ACS is widespread. Workers in government agencies use ACS data to allocate over $500 billion in federal spending. Conservative policy groups use it to conduct analysis. Large companies and numerous national business groups support it for its importance to conduct market research, plan businesses and create jobs.
If the ACS goes away, no other agency would have the resources to consistently collect detailed data, including racial data, for every neighborhood in the United States. The Census Bureau’s data collection provides us with some of the western world’s most robust and up-to-date public information, including local demographics as they relate to housing, commute and work patterns. It is the only source of these data that is consistent enough to allow comparisons across time and place.
In the era of “big data,” it may seem that we have access to more information than ever. Large companies are tracking where we take our cellphones, how many steps we take each day and what we buy with our credit cards, but they could not replace census data for three very important reasons:
- Big data relies on information passively transmitted from devices like cell phones and credit cards, which people who are very poor, homeless or elderly may not use. Relying on these data alone to understand our communities or the effect of different policies would mean that entire segments of people literally wouldn’t count.
- Big data are often only available for purchase. This means even basic research could be expensive, which could stifle innovation, as well as our ability to evaluate policy and ensure governmental accountability for all communities.
- Things like racial data simply aren’t collected along with all other big data. Without this layer of information, we can’t study how policies and economic changes affect different people.
Without the ACS and data on race, how else will we measure and ensure a that everyone in the country has equal access to housing, homeownership and countless other components of wellbeing and progress? Knowing and using this information is foundational in building and maintaining the democratic society we all deserve.