HDG #014: Your neighborhood as a kid shapes your opportunities as an adult

Read time: 3 minutes

Greetings, Gurus! Today we discuss an intriguing aspect of social mobility: the influence your neighborhood as a youth has on your opportunities and outcomes as an adult.

Do you quantifiably know how much the momentum of your neighborhood as a child compares to your life as an adult? If you’re currently working in the health or community domain and living in a region you didn’t grow up in, do you quantifiably know what the residents of the area are “up against” so you can better help address it?

I’ve been playing around with the Opportunity Atlas, which exists solely to answer these questions.

Specifically:

Which neighborhoods in America offer children the best chance to rise out of poverty?

According to the site:

“The Opportunity Atlas answers this question using anonymous data following 20 million Americans from childhood to their mid-30s. Now you can trace the roots of today's affluence and poverty back to the neighborhoods where people grew up. See where and for whom opportunity has been missing, and develop local solutions to help more children rise out of poverty.”

So I looked my neighborhood up

In a nutshell, the tool lets you explore data on economic outcomes for individuals who grew up in different neighborhoods across the United States. Using the tool, I discovered that the average combined household income for females my age, from my Census tract, with parents also in the "middle" income bracket, is about $40,000.

Interesting, right? That figure represents the average median household income for females with similar conditions to mine at the time. Not exactly my individual situation, but close enough to give me pause. The tool also shows that 69% of individuals from my census tract have stayed within the same "commuting zone" all their lives, as adults.

While all of these figures surprised me initially, when I stopped to think about it, I realized it did somewhat track with my own observations (anecdotally anyway).

Opportunity Atlas offers a wealth of additional outcomes to explore, including teenage birth rates (18%), married by 35 (38%), incarceration rates, average spousal income, and more.

We are more than just dots on a map

It's important to remember that while these statistics provide valuable context, they do not define us. We are not mere products of our neighborhoods, resigned to following a predetermined path. Our individual choices, circumstance, and other factors play crucial roles in shaping our lives, and that arguably matters more than any census tract data ever could.

Still, it's impossible to ignore the value of this tool. Even though we're not defined by our odds, it's hard not to acknowledge that they exert a strong pull. As much as we might resist, we can't escape the fact that the neighborhood inertia, the momentum we absorb growing up, society and culture, experience, generational trauma, generational wealth (or lack thereof), and the people around us exert a significant influence over our opportunities and actions as adults.

And consider this: even if you're not personally affected by those things, you are still impacted indirectly. For example, local schools funded by property taxes might lack resources, leading to a lower quality of education. Similarly, lack of access to healthcare, public transportation, or fresh food can have cascading effects on health, well-being, and job availability.

Data is a tool, not a destiny

Data like this helps us understand the world, reveals societal patterns and systemic trends, and most importantly: offers a starting point to design intentional change. If we're truly committed to making a difference, we need to acknowledge and address these ingrained inequities. Only then can we start to reshape the odds and create environments where every person has a fair chance at economic success, no matter where they grew up.

All in all, this data is fairly limited and somewhat outdated, but I still found it very eye-opening, and I like it because it follows people over time vs. a snapshot in time of Census tract averages, which is the usual schtick.

But it got me thinking:

What if we could also start layering in other information to correlate with this, such as public funding and improvement programs, technology access, types of industries that drive the economies in these regions, etc?

What could that tell us?

. . .

Actionable Idea of the Week:

I encourage you to explore the Opportunity Atlas and think about how you can use this data to design better programs, policies, and help foster more opportunities for those who need them the most.

If you’re working in a non-profit or community-based organization (CBO), check out the neighborhoods that you serve. What are they facing? If you’re in healthcare, how does your patient mix compare to these values? What can you do to design programs that help address some of these issues and move them upstream? If you’re a policy-maker, consider how the programs/policies you are advocating for might move some of these dials.

If you want to see another sobering visualization related to income mobility and disparities, I HIGHLY suggest this article by the NYTimes that has a few animated graphics showing income mobility, or, said another way: how much black and white boys/girls who grew up rich/poor tend to make as adults, and how they transcend their income quintiles (either positively or negatively—you’d be surprised and possibly saddened).

In the animated visualization (shown as a static screenshot here), each dot represents a person who starts up in the “Grew up rich” lane as a kid. As time progresses, you can watch every one of these dots either fall or rise to their final quintile, representing their income as adults. The article also has a similar viz for girls, those who started off in poor families (vs. rich), and other races.

. . .

As we’ve said before, change starts with awareness. The more we understand the challenges, the better equipped we are to help change them.

As always, let me know your thoughts. How else could we use data like this?

See you next week!

-Stefany

(P.S. Here is the difference between inertia and momentum, for anyone interested)

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