The simple experiment that shows how easy it is for society to become segregated

Racial segregation in New York City. Image: Copyright, 2013, Weldon Cooper Center for Public Service, Rector and Visitors of the University of Virginia (Dustin A. Cable, creator).

It seems obvious: if we are tolerant of people who are different from us, then our friends should come from all sections of society, our neighbourhoods should include people from all different races and our workplaces should have a good balance of men and women.

But that’s not how society works. In reality, segregation is widespread: in residential neighbourhoods, at the workplace, in schools, even online. And segregation is not a good thing: people who are physically separated are unlikely to exchange ideas, share resources, or resolve problems. Segregation worsens inequality and conflict.

In the workplace, segregation by gender is one of the culprits for the persistent wage gap between men and women. Likewise, residential segregation by race and social class likely intensified the urban unrest we witnessed in the past decade, including the 2005 French riots, the 2011 riots in England and the recent turmoil in American cities.

Modelling segregation

So does this mean that we are intolerant? Is segregation persistent because people are racist, sexist or bigoted?

Not exactly. As the visualisation of New York above shows, segregation looms large even in the most tolerant countries, such as the USA, the UK and Sweden.

To explain why segregation might occur in otherwise tolerant societies, the Nobel Prize-winning economist Thomas Schelling proposed a model. Schelling imagined a world where two types of individuals (we’ll make them blue and yellow) are randomly located on a flat square world. In Schelling’s model, individuals prefer to have some similar neighbours, but they do not discriminate against different neighbours – in short, they are tolerant. If individuals are unhappy with their neighbourhood, they can freely move to a neighbourhood with a more preferable composition.

In the example below, the yellow individual is unhappy about her assigned location because she does not have enough yellow neighbours, so she decides to move to a new neighbourhood. But when she moves, the composition of both her old and new neighbourhoods change. As a result, an old yellow neighbour and a new blue neighbour also decide to move.

This causes a domino effect that leads neighbourhoods to separate into yellow and blue ghettos. In the end, although no single individual prefers it, everyone ends up in segregated neighbourhoods.

Domino effects in the Schelling model of segregation.

Schelling’s model suggests that people inevitably end up living in a segregated world, even if they are tolerant. Other economists and sociologists have taken this theory one step further, and shown that segregation is likely even if people actively seek diversity. You can test how different models lead to different patterns of segregation using our online simulator, or by playing Parable of the Polygons.

A futile exercise?

These models have a dangerous implication: namely, that public policies which promote openness and tolerance will never improve integration. Some economists went so far as to suggest that “the welfare effect of educating people to have preferences for integration might be adverse” because the “segregated outcome will be unsatisfying for the majority of people”.

Models are one thing, but real people are different. We decided to test different versions of the Schelling model using an interactive game. We went to 20 different high-school classrooms and let the students play a game, which involved moving yellow and blue circles.

We didn’t tell them that they were playing a “segregation” game, we just asked them to follow the rules. Some students were given incentives to find similar neighbours, while others were given incentives to look for mixed neighbourhoods.

In our experiment, students controlled a blue or yellow numbered avatar on a shared screen. They followed rules we provided for their ‘preferences’ for the colour of their neighbours.

Our results confirm the prediction of Schelling’s original model; that when people are simply tolerant, they still become segregated. But we also found that when people strive for diversity, they are able to achieve integration.


Models and mingles

To understand our results, think about how we behave at a social mingle. People often attempt to optimise the composition of the group they are talking to, trying to get a good mix of different, interesting people around them. But as everyone moves to achieve their own optimised mix, the group composition continually changes.

As a result, the group never settles down and the composition of groups is more or less random. And randomly composed groups are integrated, rather than segregated.

This is exactly what happened in our experiment. Students were unable to identify when no better locations existed, and continued moving in the pursuit of perfect happiness.

Our experiments reveal why we should be cautious when offering policy advice on the basis of theoretical models. The models fail because they assume that we are always perfectly informed about the best available options – and perfectly able to pursue them. In reality, we often face constraints when we gather information and make decisions. This can be the case, not only at mingles, but even for serious decisions with lifetime consequences such as moving house, choosing schools and changing jobs.

Mathematical models are important, but we need to test them empirically before applying them in practice. Segregation is not unavoidable, but there is a need to continue educating people in the benefits of diversity and to continue devising polices and incentives that prevent or ease segregation.The Conversation

Milena Tsvetkova is a postdoctoral researcher in Computational Social Science at the University of Oxford. David Sumpter is professor of applied mathematics at Uppsala University.

This article was originally published on The Conversation. Read the original article.

 
 
 
 

Urgently needed: Timely, more detailed standardized data on US evictions

Graffiti asking for rent forgiveness is seen on a wall on La Brea Ave amid the Covid-19 pandemic in Los Angeles, California. (Valerie Macon/AFP via Getty Images)

Last week the Eviction Lab, a team of eviction and housing policy researchers at Princeton University, released a new dashboard that provides timely, city-level US eviction data for use in monitoring eviction spikes and other trends as Covid restrictions ease. 

In 2018, Eviction Lab released the first national database of evictions in the US. The nationwide data are granular, going down to the level of a few city blocks in some places, but lagged by several years, so their use is more geared toward understanding the scope of the problem across the US, rather than making timely decisions to help city residents now. 

Eviction Lab’s new Eviction Tracking System, however, provides weekly updates on evictions by city and compares them to baseline data from past years. The researchers hope that the timeliness of this new data will allow for quicker action in the event that the US begins to see a wave of evictions once Covid eviction moratoriums are phased out.

But, due to a lack of standardization in eviction filings across the US, the Eviction Tracking System is currently available for only 11 cities, leaving many more places facing a high risk of eviction spikes out of the loop.

Each city included in the Eviction Tracking System shows rolling weekly and monthly eviction filing counts. A percent change is calculated by comparing current eviction filings to baseline eviction filings for a quick look at whether a city might be experiencing an uptick.

Timely US eviction data for a handful of cities is now available from the Eviction Lab. (Courtesy Eviction Lab)

The tracking system also provides a more detailed report on each city’s Covid eviction moratorium efforts and more granular geographic and demographic information on the city’s evictions.

Click to the above image to see a city-level eviction map, in this case for Pittsburgh. (Courtesy Eviction Lab)

As part of their Covid Resource, the Eviction Lab together with Columbia Law School professor Emily Benfer also compiled a scorecard for each US state that ranks Covid-related tenant protection measures. A total of 15 of the 50 US states plus Washington DC received a score of zero because those states provided little if any protections.

CityMetric talked with Peter Hepburn, an assistant professor at Rutgers who just finished a two-year postdoc at the Eviction Lab, and Jeff Reichman, principal at the data science research firm January Advisors, about the struggles involved in collecting and analysing eviction data across the US.

Perhaps the most notable hurdle both researchers addressed is that there’s no standardized reporting of evictions across jurisdictions. Most evictions are reported to county-level governments, however what “reporting” means differs among and even within each county. 

In Texas, evictions go through the Justice of the Peace Courts. In Virginia they’re processed by General District Courts. Judges in Milwaukee are sealing more eviction case documents that come through their courtroom. In Austin, Pittsburgh and Richmond, eviction addresses aren’t available online but ZIP codes are. In Denver you have to pay about $7 to access a single eviction filing. In Alabama*, it’s $10 per eviction filing. 

Once the filings are acquired, the next barrier is normalizing them. While some jurisdictions share reporting systems, many have different fields and formats. Some are digital, but many are images of text or handwritten documents that require optical character recognition programs and natural language processors in order to translate them into data. That, or the filings would have to be processed by hand. 

“There's not enough interns in the world to do that work,” says Hepburn.


Aggregating data from all of these sources and normalizing them requires knowledge of the nuances in each jurisdiction. “It would be nice if, for every region, we were looking for the exact same things,” says Reichman. “Instead, depending on the vendor that they use, and depending on how the data is made available, it's a puzzle for each one.”

In December of 2019, US Senators Michael Bennet of Colorado and Rob Portman of Ohio introduced a bill that would set up state and local grants aimed at reducing low-income evictions. Included in the bill is a measure to enhance data collection. Hepburn is hopeful that the bill could one day mean an easier job for those trying to analyse eviction data.

That said, Hepburn and Reichman caution against the public release of granular eviction data. 

“In a lot of cases, what this gets used for is for tenant screening services,” says Hepburn. “There are companies that go and collect these data and make them available to landlords to try to check and see if their potential tenants have been previously evicted, or even just filed against for eviction, without any sort of judgement.”

According to research by Eviction Lab principal Matthew Desmond and Tracey Shollenberger, who is now vice president of science at Harvard’s Center for Policing Equity, residents who have been evicted or even just filed against for eviction often have a much harder time finding equal-quality housing in the future. That coupled with evidence that evictions affect minority populations at disproportionate rates can lead to widening racial and economic gaps in neighborhoods.

While opening up raw data on evictions to the public would not be the best option, making timely, granular data available to researchers and government officials can improve the system’s ability to respond to potential eviction crises.

Data on current and historical evictions can help city officials spot trends in who is getting evicted and who is doing the evicting. It can help inform new housing policy and reform old housing policies that may put more vulnerable citizens at undue risk.

Hepburn says that the Eviction Lab is currently working, in part with the ACLU, on research that shows the extent to which Black renters are disproportionately affected by the eviction crisis.

More broadly, says Hepburn, better data can help provide some oversight for a system which is largely unregulated.

“It's the Wild West, right? There's no right to representation. Defendants have no right to counsel. They're on their own here,” says Hepburn. “I mean, this is people losing their homes, and they're being processed in bulk very quickly by the system that has very little oversight, and that we know very little about.”

A 2018 report by the Philadelphia Mayor’s Taskforce on Eviction Prevention and Response found that of Philadelphia’s 22,500 eviction cases in 2016, tenants had legal representation in only 9% of them.

Included in Hepburn’s eviction data wishlist is an additional ask, something that is rarely included in any of the filings that the Eviction Lab and January Advisors have been poring over for years. He wants to know the relationship between money owed and monthly rent.

“At the individual level, if you were found to owe $1,500, was that on an apartment that's $1,500 a month? Or was it an apartment that's $500 a month? Because that makes a big difference in the story you're telling about the nature of the crisis, right? If you're letting somebody get three months behind that's different than evicting them immediately once they fall behind,” Hepburn says.

Now that the Eviction Tracking System has been out for a week, Hepburn says one of the next steps is to start reaching out to state and local governments to see if they can garner interest in the project. While he’s not ready to name any names just yet, he says that they’re already involved in talks with some interested parties.

*Correction: This story initially misidentified a jurisdiction that charges $10 to access an eviction filing. It is the state of Alabama, not the city of Atlanta. Also, at the time of publication, Peter Hepburn was an assistant professor at Rutgers, not an associate professor.

Alexandra Kanik is a data reporter at CityMetric.