Wednesday, May 08, 2024

Families in the poorest communities are the least likely to obtain Empowerment Scholarship Account (ESA) funds...Data emerging from Arizona provide plenty of reasons for concern

  • In Arizona, more advantaged communities are securing a highly disproportionate share of the funds from the state’s Empowerment Scholarship Account program...Some of the takeaways from this analysis are clear. In Arizona, the state with the first and highest-profile “universal” ESA program, families in the wealthiest, most advantaged communities are obtaining ESA funds at the highest rates. Families in the poorest communities are the least likely to obtain ESA funds. Nothing in the analysis above even remotely suggests that this program is addressing inequities in school access by students’ socioeconomic status.

    What is less clear—and worthy of further study—is why these patterns exist. 

  • Here, we’ll examine who is getting public funds through Arizona’s Empowerment Scholarship Account, the oldest universal ESA program in the United States. We focus on whether the primary beneficiaries of these programs are families in need—a key question for judging whether universal ESA programs really are addressing inequities in school access.

    The evolution of ESAs in Arizona

    Arizona was an early adopter of both an education savings account program and, ultimately, a universal education savings account program. In 2011, Arizona launched the Empowerment Scholarship Account program, which allowed qualifying families to obtain the equivalent of 90% of per-pupil funding in an ESA. (Today, most scholarships provide $7,000 to $8,000 annually.) Initially, the program was restricted to students with disabilities and, through legislative action in 2013, capped at a small number of recipients. Over time, eligibility expanded slightly until, in 2022, Arizona lawmakers opened the program to all students, including those already attending private schools. EdChoice touts the current iteration of the program as the “first to offer full universal funded eligibility with broad-use flexibility for parents.”

    The point about broad-use flexibility is important. The list of allowable expenses for Arizona’s ESA program is long. It includes everything from tuition and fees to backpacks, printers, and bookshelves. Overall, about 63% of state funds are being spent on tuition, textbooks, and fees at a qualifying school, with “curricula and supplementary materials” (12%) being the next largest expense.

    Researchers, state officials, and advocacy groups have raised concerns about the program’s expansion. Some have pointed to wasteful spending from the lightly regulated program, while others have emphasized exploding costs and their potential impacts on public schools. An early report indicated that a disproportionate share of program beneficiaries appeared to be affluent.

    A closer look at who is getting ESA funds in Arizona

    We looked to publicly available data on Empowerment Scholarship Account recipients to get a clearer picture of who is receiving ESA funds. If, in fact, affluent families are securing the lion’s share of ESA funding, that would raise obvious questions about whether these programs are exacerbating rather than mitigating inequities in school access.

    To begin, we took the most recent executive and legislative quarterly report for the program (the 2024 Q2 report). That report lists the number of students enrolled in the program by the recipients’ home ZIP code. We converted those ZIP codes to ZIP Code Tabulation Areas (ZCTAs), which allows us to describe the communities where ESA enrollees reside using U.S. Census Bureau data.

    In the analyses that follow, we compare ESA participation rates by the socioeconomic status (SES) of Arizona communities. We use three measures of SES: poverty rates, median household income, and educational attainment. This allows us to see, for example, whether wealthier or poorer neighborhoods (ZCTAs) tend to receive a disproportionate share of scholarships.

    First, we examine ESA participation based on a measure of local poverty: the share of residents receiving public assistance income or SNAP/Food Stamps. For this chart (and others that follow), we divide the Arizona population into deciles, with each bar representing roughly 10% of the state population under the age of 18. In Figure 1, each bar shows the number of ESA recipients per 1,000 people under 18 years old. The leftmost bar represents the parts of the state with the lowest poverty rate (based on ZCTAs); the rightmost bar represents the decile with the highest poverty rate.

    We see a clear trend on this measure. As poverty rates increase from left to right, the share of children receiving ESA funding decreases. The highest ESA participation rate—75 ESA recipients per 1,000 children under 18—is for the population decile with the lowest poverty rate. The lowest ESA participation rate—14 ESA recipients per 1,000 children—is for the population decile with the highest poverty rate. (Statewide, we find an average of 45 ESA recipients per 1,000 children.)

    Next, we run a parallel analysis based on median household income. This allows us to examine the highest-income areas in ways that a chart based on poverty rates might obscure.

    Here, too, the results are clear. As seen in Figure 2, the lowest decile in median income has the lowest rate of ESA participation (20 recipients per 1,000 children), while the highest decile in median income has the highest rate of ESA participation (74 participants per 1,000 children).

    When we disaggregate by educational attainment, we see a similar story. Figure 3 shows rates of ESA participation disaggregated by the share of local residents who attended at least some college. ESA receipt is lowest where the fewest people have attended college (14 recipients per 1,000 children). It is highest where the most people have attended college (76 recipients per 1,000 children).

    In other words, regardless of the SES measure used (poverty rate, median income, or educational attainment), we see similar patterns in who is obtaining ESA funding. More advantaged communities are securing a highly disproportionate share of these scholarships.

    Perhaps these results are driven by varying ESA participation rates across different types of geographic areas? Maybe, for example, ESA uptake is low in rural areas with low population density and fewer nearby private schools. If these areas have relatively low income and college attainment, that could explain the patterns above.

    We do, in fact, see higher rates of ESA participation in suburbs (52 per 1,000) and urban areas (45 per 1,000) than in small towns (35 per 1,000) and rural areas (35 per 1,000). However, that isn’t the full story.

    Let’s take a closer look at the Phoenix metropolitan area. By focusing on one urban/suburban region, we can see whether ESA participation gaps by household income persist even where families have a similar set of schools close to home.

    Figure 4a maps ESA participation rates by ZCTA in the Phoenix area, with darker colors indicating higher rates of participation. Figure 4b maps median household income by ZCTA in the Phoenix area, with darker colors indicating higher median household income.

    The maps look remarkably similar to one another. Even within this relatively tight geographic area, it’s the wealthiest areas that are home to a lion’s share of ESA recipients.

    Learning from Arizona as more states turn to universal ESAs

    Some of the takeaways from this analysis are clear. In Arizona, the state with the first and highest-profile “universal” ESA program, families in the wealthiest, most advantaged communities are obtaining ESA funds at the highest rates. Families in the poorest communities are the least likely to obtain ESA funds. Nothing in the analysis above even remotely suggests that this program is addressing inequities in school access by students’ socioeconomic status.

    What is less clear—and worthy of further study—is why these patterns exist. There are many reasons why families in lower-SES areas might not participate in this program. Some families might be interested in obtaining ESA funding but are unaware of the program (information barriers) or unable to get to/from their preferred schools (transportation barriers). Some families may confront financial barriers, since the tuition at many private schools exceeds the value of the scholarship, leaving ESA-recipient families to cover the difference. Some families might just not be interested. They may feel better served by, or more welcome in, their neighborhood public schools.

    Regardless, if states that have adopted (or are considering) universal ESA programs are serious about using private school choice to address inequities in school access, they need to take a hard look at these programs. The data emerging from Arizona provide plenty of reasons for concern.

Arizona’s ‘universal’ education savi

ngs account program has become a handout to the wealthy



 

Maria Polletta
@mpolletta
New report from @BrookingsInst's Brown Center on Education Policy analyzes 'whether the primary beneficiaries of (Arizona's ESA program) are families in need.' It finds that 'families in the poorest communities are the least likely to obtain ESA funds.' www.brookings.edu/artic…
Posted on X · 11 hours ago

Amplifying the Global Value of Earth Observation: Driver for $3.8 Trillion in Economic Growth by 2030

For years, the EO industry has struggled to unlock barriers related to technical skills, awareness, policy and more that would fundamentally shift the rate of adoption of EO. The challenge of transforming information to insights and insights into action are not unique to EO; they persistently slow technology adoption, especially in “big data” applications. The potential is evident, and the technical feasibility has been confirmed, but there are not enough people using this technology. The question then arises: what would happen if they did? The global value of EO data is estimated to be worth $266 billion as of 2023. By 2030, that value could exceed $700 billion, with a cumulative $3.8 trillion contribution to global GDP between 2023- 2030. While driving significant economic impact, EO can also inform actions with the potential to eliminate 2 Gt of GHG emissions every year while contributing to nature-positive strategies.
Valuation · Research on the economic and climate value of key Earth observation use cases, including an Earth observation community survey on industry dynamics ...
The World Economic Forum · 4 days ago
$3.8 trillion contribution to global GDP between 2023-2030. 
The global value of EO data EO could add $703 billion to the global economy while eliminating 2 gigatonnes of GHG emissions in 2030.


Amplifying the Global Value of Earth Observation

For years, the EO industry has struggled to unlock barriers related to technical skills, awareness, policy and more that would fundamentally shift the rate of adoption of EO. 

The challenge of transforming information to insights and insights into action are not unique to EO; they persistently slow technology adoption, especially in “big data” applications. 

The potential is evident, and the technical feasibility has been confirmed, but there are not enough people using this technology. The question then arises: what would happen if they did? 

  • The global value of EO data is estimated to be worth $266 billion as of 2023. 
  • By 2030, that value could exceed $700 billion, with a cumulative $3.8 trillion contribution to global GDP between 2023- 2030. 
  • While driving significant economic impact, EO can also inform actions with the potential to eliminate 2 Gt of GHG emissions every year while contributing to nature-positive strategies. $3.8 trillion contribution to global GDP between 2023-2030. BOX 4 
Downstream use is the value multiplier EO data acquisition Value-added services* and end-user applications Value added from analysis and use ~150 times value 

These figures are the result of a bottom-up examination of the direct economic and climate benefits that can be ascribed to EO through dozens of unique applications and an extrapolation of those benefits across all regions and industries (see Figure 6). 

The maximum potential value of each EO application is then scaled down based on modelled adoption rates. 

Refer to Appendix 1 for more details on methodology.

Industries with the most value to gain
Six industries stand to capture 94% of the projected economic value.


SPACE NEWS: Search engine focused on Earth data gets new investors

David Rothzeid, vice president of Shield Capital, said harnessing data across multiple sources is a challenge for most industries. “Danti’s large language models enable a new kind of search engine that has the potential to democratize the world’s massive amounts of data.”
Defense and intelligence analysts are using Danti’s engine to “connect large, very distributed datasets that live across government servers, commercial servers, the open Internet, and allow users to use all of that information as if it were  all in the same place, all structured the same way. And they can interact with it, conversationally,” Kallman said. 
“You can’t do this with ChatGPT,” Kallman said of the popular chatbot. “We’ve adapted language model infrastructure that’s been specifically tuned and trained for this kind of information,” he said. “The U.S. government has already procured quite a lot of this geospatial intelligence content for their benefit. The question is, do they have access to it? Do they know about it?”
Search engine focused on Earth data gets new investors

Danti’s $5 million seed round led by defense tech VC firm Shield Capital
Sandra Erwin May 6, 2024
KISSIMMEE, Fla. — Danti, an AI startup that developed a search engine for Earth data, is gaining users within the Department of Defense and the intelligence community, said the company’s founder and CEO Jesse Kallman. 
Danti, based in Atlanta, announced last week it secured $5 million in seed funding, an investment led by venture capital firm Shield Capital, which focuses on artificial intelligence and defense technologies. This is Shield Capital’s first investment in Danti.
The company’s search engine leverages artificial intelligence to unlock insights from a vast array of Earth observation data. 
  • This includes satellite imagery, drone footage, social media information, and government databases. 
Users can ask questions in plain English and receive comprehensive answers that combine data points from all sources. This can be helpful for a wide range of users, from insurance underwriters assessing risk to military analysts tracking troop movements 
“There is a massive data overload, distribution, and knowledge gap problem that companies and governments alike are facing,” said Kallman. 
By leveraging AI and natural language processing, users of any skill level can get answers in a matter of seconds, he said. "

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