Nathan Mayo staff portraitNathan Mayo
Vice President of Operations & Programs
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Scientific research is a valuable resource for learning about charitable programs’ best practices.   Often, a well-designed study does a better job of confirming or disproving our assumptions than anecdotal observations or deliberate outcomes measurement. In that vein, this synopsis addresses the findings of a 2019 study entitled, The Effects of Gentrification on the Well-Being and Opportunity of Original Resident Adults and Children.

What question does this research answer?

“What is the effect of gentrification on the original residents of a neighborhood?”

Gentrification is generally understood as the in-migration of higher-income residents to a previously working-class neighborhood (though this study uses education level instead of income). Those seeking to serve the poor usually considered it a problem, on the grounds that it fractures existing communities and drives the displaced to a worse situation. Even the term is slightly derogatory as it facetiously refers to a neighborhood being taken over by “the gentry.” Previous evidence suggests that in-migration increases the average cost of rent and that many original residents end up leaving. 

However, previous studies only identified effects on the neighborhood as a whole, which obscures important findings on how individuals fare. This study used millions of data points to track individuals over time and compare their outcomes to people in similar neighborhoods who did not experience gentrification.

The findings offer a surprising vindication of gentrification as a natural tool for community development. Anyone in neighborhood-centric ministry needs to be aware of these stunning results. 

 

Study Design

Data Source: U.S. Census long form and the American Community Survey

Sample Size: 3 million individuals across the 100 largest metropolitan areas in the U.S.

Type of Study: 

This study is closest to a “natural experiment,” meaning researchers observe the apparent effect of an intervention while using some external event to simulate random assignment into treatment and control groups. This makes the study as close to a scientific laboratory experiment as can be achieved in the real world.

For example, if you want to determine whether higher levels of education cause higher incomes, a traditional experiment would take a group of similar high schoolers and assign some of them to go to college, while the remainder do not. Then we could measure differences in their lifetime earnings and determine the value of education.

Because such a study is impracticable, an alternative would be to compare people who gain entrance into a college by a single point on a standardized test versus those who are ineligible by a single point and do not attend. Since people with similar scores are very similar, this would allow researchers to simulate an experiment and potentially prove a college education causes higher income.

In the case of the 2019 gentrification study, researchers designated the natural control group as similar neighborhoods, some of which gentrified over the course of the study and some that did not. They then treated the people in non-gentifying working class neighborhoods as a comparison group, which gave a good sense of what resident outcomes would look like if gentrification never happened. 

Limitations: This wasn’t a pure natural experiment, since it’s possible some relevant characteristic differentiated the residents of gentrifying neighborhoods from similar non-gentrifying ones. However, there is no obvious logical reason why they should be dissimilar and researchers did their best to mitigate that potential flaw with an exhaustive set of statistical techniques.

That said, while the study focused on economic harms, it can’t account for unmeasured harms to holistic well-being (like the mental strain of being pressured to move to a new neighborhood). Still, economic harms (or lack thereof) are significant to note because most who oppose gentrification claim them as a key reason for their resistance.

Key Findings

 

 1.  Even in non-gentrifying neighborhoods, 70% of renting residents move out within a decade

One of the facts muddling previous analyses is that people routinely move out of non-gentrifying neighborhoods. Indeed, over the course of a decade 70% of renting residents move out of low-income neighborhoods where the income level of the community is stable over time. Homeowners move out at a lower rate of 40%. 

In non-gentrifying neighborhoods, those residents were backfilled by other working-class people. In gentrifying neighborhoods, higher-income people replaced the natural emigrants. While the neighborhood composition changed, it would be incorrect to say most of those who left were “forced out” because they were leaving anyway. Note that this study focused on major cities, so this may not be true in rural areas.

2.  Gentrification modestly increased outmigration but in many measurable ways movers end up no worse off 

Certainly some low-income people leave as a result of gentrification; just not as many as you would expect. The research estimates that over a decade, gentrification forces out four to six percent of the original residents. Compared to the 70% of renters that would have left anyway, that is not dramatic. 

The research also addressed what happened to those who left. On average, they ended up in similar non-gentrifying neighborhoods in the same city. Their rent, income, employment, and commute were otherwise unaffected. They would have incurred some moving expenses and perhaps some intangible costs such as being forced to move farther away from existing friends and family. 

3.  Less-educated residents who remain in gentrifying neighborhoods end up better off

Gentrification typically raises concerns about increased rent. Yet if a set of swanky apartments are built in your neighborhood, average rent may rise — but that doesn’t mean yours will. It turns out low-income renters do not see any increase in their rent due to gentrification. Instead, increases stay on course with equivalent non-gentrifying neighborhoods in their city.

There’s more good news. Less-educated homeowners see an average rise in property values of $55,000, of which $15,000 is due to gentrification.

And, neighborhood poverty decreases, which benefits adults’ physical and mental health as well as children’s lifetime education and earnings. While the majority of original residents don’t stick around long enough to see those benefits, the effects on those who do are so large that there is still a significant reduction in poverty exposure to the “average original resident.”

 

Practical Application 

Community development leaders often find themselves in a quandary: helping a run-down neighborhood improve often means higher-income people move in and many original residents leave. It’s hard to know whether to celebrate the change or lament it.

While this study confirms their instincts that the community changes, it also affirms there are net beneficial outcomes that should be maximized. Thus, they should consider the following approach:

  • Help as many original residents as possible stay, preferably by building paths to home ownership while home values are still rising.
  • Bridge relationships between new high-income arrivals and original residents. Welcome new arrivals and integrate them into efforts to build strong community bonds. This natural opportunity to build social capital may also help some of the originals decide (or be able) to stick around.
  • When possible, connect departing residents with resources to assist their transition to another community. But remember that nine out of ten would have left even if gentrification didn’t occur.   
  • Be ready to pack up shop. You may wake up one day and realize your calling has migrated across town. Don’t be so tied to a place that you can’t follow the need. Celebrate what’s happened — and move on.

Conclusion

The conventional wisdom on gentrification appears to be wrong. The best evidence shows it forces relatively few people to move, has a largely neutral impact on those who do, and brings large positive benefits to those who stay. It is a powerful force that community developers shouldn’t block but channel to do the most good for the greatest number of people.


Read the Study

The Effects of Gentrification on the Well-Being and Opportunity of Original Resident Adults and Children by Quentin Brummet, Davin Reed :: SSRN


 

Amanda Fisher
Community Engagement Director
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Scientific research is a valuable resource for learning about charitable programs’ best practices. Often, a well-designed study does a better job of confirming or disproving assumptions than anecdotal observations or deliberate outcomes measurement. This synopsis addresses the findings of a 2022 study entitled Social Capital I: Measurement and Associations with Economic Mobility.

What Question Does This Research Answer?

What impact does social capital have on upward economic mobility?

The study surveyed data across 21 billion friendships on Facebook to examine the effects of three kinds of social capital (or social connectedness) on upward economic mobility: cross-type economic connectedness (relationships between those with low vs high socioeconomic status); social cohesion (i.e., cliques in friendship networks and how many friends are friends with each other); and civic engagement (i.e., volunteering).

Study Design

Data Source: Facebook

Sample Size: The data is based on 72.2 million Facebook users ages 25-44 in the United States across 21 billion “friendships.”

Type of Study: This is an observational study, which means researchers observed the apparent effect of an intervention without determining who does and does not receive the intervention. 

Limitations: Because this is a natural experiment rather than a randomized control trial, the results can only suggest correlation rather than causation. Additionally, as correlations are made, one must be cautious of using Facebook to provide a comprehensive picture of one’s social network’s breadth and depth. 

Key Findings

  • Of the three types of social capital examined, cross-type economic connectedness is the most important in predicting upward economic mobility. “If children with low socioeconomic status parents were to grow up in counties with economic connectedness comparable to that of the [typical] child with high socioeconomic status parents, their income in adulthood increases by 20% on average.” 
  • Economic connectedness in a community is the single best population-level predictor of whether children who grow up there will be upwardly mobile. It is more significant than neighborhood median income, degree of racial segregation, income inequality, math test scores, and the ratio of single-parent households (which still matter, just to a lesser degree). In fact, the study suggests economic connectedness is such a significant factor that, regardless of ZIP code, nearly all variations in income mobility outcomes for children can be explained by economic connectedness.
  • In-group cohesion may have other benefits not evaluated by this study, but it’s not a significant driver of economic mobility.
  • Almost none of the lowest-income ZIP codes in the US exhibited high levels of economic connectedness. If there were a few higher socioeconomic status individuals in the vicinity, there appeared to be very little opportunity for people with low socioeconomic status to connect with them. This echoes the observation of sociologist Peter M. Blau that “persons cannot associate without having opportunities for contact.”

Practical Application 

Poverty is complex, so it’s refreshing to find a study that demonstrates factors other than one’s ZIP code that affect upward economic mobility. All else being equal, it’s common knowledge kids in poor neighborhoods have worse life outcomes than kids growing up in better ones. Therefore, it’s significant this study strongly suggests neighborhood effects can be entirely offset by increasing economic connectedness. Of equal importance, it provides a very tangible focus point to help neighborhood development organizations prioritize an overwhelming list of things they could help improve.

As well, the study supports a True Charity basic belief that relationships are a key antidote to poverty and are vital in breaking generational cycles of poverty. These findings should persuade those of higher socioeconomic status–especially within the church–to open themselves to bonding with those of lower socioeconomic status. Doing so will create an environment that fosters sincere, caring relationships.

Because these connections are essential for upward mobility, we must continue to emphasize bridging social capital through mentoring and other relationships. This study clearly shows the importance of implementing programs that promote economic connectedness, such as Circles USA or Faith and Finances

The research also supports the need for training volunteers to build relationships with people in need (as opposed to simply completing tasks on their behalf). 

Finally, one must be careful about insinuating higher socioeconomic status or upward economic mobility equals flourishing. Benefits to engaged, clustered societies beyond income mobility should be considered.

Conclusion

Churches and nonprofits must focus energy on building relationships with individuals struggling in poverty–specifically, bonding with those of a different socioeconomic status. Join the True Charity movement for practical tools and tips on how to get started or improve your current charitable efforts.

Read the Study

You can access it here:  Social Capital I: Measurement and Associations with Economic Mobility.

Also, explore the data for your specific area through this helpful tool.


 

SONYA STEARNS
Network Manager
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Scientific research is a valuable resource for learning about charitable programs’ best practices. Often, a well-designed study does a better job of confirming or disproving our assumptions than anecdotal observations or deliberate outcomes measurement. This synopsis addresses the findings of a 2023 working paper entitled Fighting Poverty One Family at a Time: Experimental Evidence from an Intervention with Holistic, Individualized, Wrap-Around Services.

What question does this research answer?

“How does holistic, individualized wrap-around intervention affect outcomes for a broad population of low-income individuals?”

This study examines the impacts of the Padua Program on individuals and families facing poverty. Its wrap-around services (or holistic support services) are characterized by a comprehensive, personalized approach that addresses participants’ primary needs (such as housing, employment, and financial stability). Padua’s case management is asset-based and relational, meaning case managers empower participants based on strengths and abilities they bring to the table. The findings reveal Padua participants experienced significant improvements in employment outcomes with higher rates of full-time employment and increased monthly earnings. It also positively affected housing stability and physical health; and reduced reliance on government programs. In short, the program demonstrates the value of interventions tailored to the unique needs of participants, emphasizing the importance of a holistic approach to poverty alleviation.

Study Design

Data Source:
Catholic Charities of Fort Worth (CCFW)
Padua Program asked Notre Dame’s Sheehan Lab for Economic Opportunities (LEO) to study participants who entered their program from February 2015 through October 2016.
Sample Size:
The study included 427 participants. Of those, 193 were placed in the treatment group and 234 in the control group. Participants were in the program for an average of seventeen months.
Type of Study:
This study was a “Randomized Controlled Trial (RCT),” which means researchers select a random sample of people who receive intervention (the treatment group) and a random group who do not (the control group).
For example, if your thesis is higher education causes higher income, you could provide free scholarships to a random group of students and compare their outcomes to students who do not receive a scholarship. RCT is considered the gold standard for proving intervention causes the outcome (versus being a correlation of it).
Limitations:
Due to the study’s small size, the components of the Padua program that drove positive outcomes could not be determined. Additionally, while the study proved the program’s efficacy, it did not settle the question of whether other organizations’ use of Padua would achieve similar results. Some elements, like the skill level of the case managers, could be difficult to replicate.

Key Findings

  1. Unemployed individuals saw favorable workforce outcomes from the Padua program.
    Participants who entered the program without employment were 67% more likely than the control group to be employed full-time after twenty-four months. They also experienced a 46% increase in earnings, earning $420 more per month than individuals in the control group.
  2. Families saw a stabilization in their situation through housing.
    Participants who entered the Padua program without stable housing saw a stabilization of their situation after 24 months. In fact, they were 60% more likely to achieve it than the control group.
  3. Intense, asset-based case management helped increase their ability to participate more fully in a community (through jobs, stable housing, education, etc.) and improved their health.
    In a qualitative survey, forty-three percent self-reported improved mental, physical, relational, and emotional health. Even without objective data, the individuals perceived better overall well-being, indicating improved mental and emotional stability.
  4. Targeted and longer durations of case management were initially more costly per individual but were likely more economical for service providers in the long run.
    Case management in the Padua program costs almost $23,000 per individual over two years. Initial upfront costs for relational case management are higher because each case is targeted (that is, each client’s unique assets, liabilities, and situation are considered). Pauda suggests that investment leads to fewer clients returning to the program, leading to cost savings over time.

Practical Application

The treatment group in the Padua Program achieved better long-term self-sufficiency than those in the control group. The success of the program suggests its holistic approach may produce similar results for your organization. Here are a few highlights to consider for your program:

  1. Intense case management and small caseloads are the foundation for success because they foster commitment and relationship.
    Our Case Management Toolkit provides everything you need to implement asset-based, relational case management in your own context.
  2. Prioritize stabilized living situations for job seekers.
    As demonstrated, those in the treatment group with stable housing secured jobs more readily and increased their wages more substantially than the control group. This suggests achieving overall stability is sequential–with housing a higher initial priority than boosting income. Long-term transitional housing can assist in meeting that need, and we’ve developed a toolkit to help you implement this model in your own setting. As well, it’s important to remember everyone can progress, but they will not do so at the same rate nor in every area simultaneously.

Conclusion

This research supports implementation of an asset-based, relational case management plan for individuals and families facing poverty. The findings highlight the value of tailoring care to meet the unique needs of participants, emphasizing the importance of using what we call subsidiarity to meet root cause needs. The Padua Program is a successful example of its effectiveness and thus a worthy model from which to learn. Its detailed assessment tools and case management plan support positive, long-term self-sufficient outcomes.

Read the Study
Fighting Poverty One Family at a Time: Experimental Evidence from an Intervention with Holistic, Individualized, Wrap-Around Services | NBER

 

 

 


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