Insights from the field—Economic conditions in low- and moderate-income communities

By Nishesh Chalise, Violeta Gutkowski, Steven Howland, Becky Kropf
August 19, 2025
This two-part report provides an overview of economic conditions in low- and moderate-income (LMI) communities and among the entities serving them based on a national survey conducted by the Federal Reserve during April and May 2025. This information is intended to help equip decision-makers with key insights for developing approaches to foster economic security in LMI communities.
This report addresses economic conditions in LMI communities, while the second report focuses on the health of the organizations that work in them. Throughout each report, we offer quotes from those serving LMI communities to provide additional context to the survey results.
Overall, entities responding to the 2025 Community Perspectives Survey reported poor economic conditions for LMI populations. Among all the sectors we asked about, housing and personal finance were among the worst performing indicators. Respondents rated employment and education conditions slightly better, driven by people’s ability to find work and the uptake of adult education programs. While the survey did not specifically ask about uncertainty, it was a broad theme raised in open-ended questions by respondents that was affecting communities, businesses, and entities negatively.
“Even among those earning just above minimum wage, the rising cost of housing, transportation and food is stretching budgets to the breaking point. Many are just one car repair or missed paycheck away from a crisis.”
a. April 2025 (N=440)
b. Expectations for the year ahead (N=429)
Economic mobility in this survey refers to whether respondents see the conditions—for all the various factors that contribute to economic mobility—as leading to upward or downward mobility. The question is not intended to measure whether economic mobility is occurring; rather, we are interested in this measure to speak to overall economic conditions in LMI communities and the potential economic outcomes of those conditions.
Conditions for economic mobility among LMI populations had a downward effect on economic mobility according to 57% of respondents (Figure 2). Nearly two-thirds of respondents said conditions had worsened over the past year. Sixty-seven percent expect conditions to worsen into next year, which was much more negative than expectations in last year’s survey.
Mobility was reported to be highly sensitive to labor conditions, which respondents indicated mixed prospects for workers (Table 1). Comments suggested there were differing economic experiences likely behind it. Some comments reported significant employment opportunities, while others reported labor market tightening and some reports of layoffs.
The highest-rated negative factor was government assistance, and nearly a third of comments noted considerable concern about the future of support programs on which LMI populations rely. Housing continues to be a significant negative factor for economic mobility into next year; cost and availability are common issues.
“Finding affordable housing is increasingly difficult, as the cost of rent is outpacing growth in wages. In addition, finding work that includes benefits and provides full-time schedules is increasingly difficult for many low-wage workers.”
a. April 2025 (N=440)
b. Expectations for the year ahead (N=429)
Positive Factors (N=424) | |
---|---|
1. | Employment (21.0%) |
2. | No positive factors (14.0%) |
3. | Housing (12.0%) |
Negative Factors (N=420) | |
1. | Government assistance (33.0%) |
2. | Housing (25.0%) |
3. | Employment (12.0%) |
Note: Respondents were asked to choose one top positive and negative factor affecting conditions for economic mobility. |
Conditions for finding work worsened from last year. Over 40% of respondents said that conditions for finding work were poor or very poor at the time of the survey (Figure 3). Almost 30% of respondents said conditions were good. These shares showed a more dichotomous job market than last year’s survey, when 43% of respondents said conditions for finding work were neither good nor poor. Most respondents expect conditions for finding work to worsen through 2026 (Figure 4). While the availability of jobs is a top positive factor affecting conditions for finding work into the next year, the affordability of child care continues to be a significant negative factor (Table 2). Comments consistently matched challenges of child care and transportation for LMI workers with both getting and keeping jobs.
“Compounding systemic barriers, such as costly and unreliable transportation, costly and unavailable child care slots and job cuts, negatively impact upward economic mobility.”
Job quality has also worsened over the last year, with 44% of respondents saying conditions in April and May were poor (Figure 3). However, respondents were much more mixed on how they expect job quality to change over the coming year (Figure 4). Respondents were also polarized about how wages would impact job quality over the next year (Table 2). Comments continued to express that while higher-paying jobs can be found, there are significant barriers preventing LMI workers access to them, and LMI workers’ wages have not kept up with rising costs, particularly for housing.
a. Finding work (N=87)
b. Job quality (N=57)
a. Finding work (N=87)
b. Job quality (N=56)
Positive Factors | |
---|---|
Factors affecting conditions for finding work (N=78) | |
1. | Availability of jobs (49.0%) |
2. | Availability of job search assistance (9.0%) |
3. | Ability to get to available jobs (6.4%) |
Factors affecting conditions for job quality (N=50) | |
1. | Wages (46.0%) |
2. | Employer-provided health insurance (2.0%) |
3. | Advancement opportunities (4%) |
Negative Factors | |
---|---|
Factors affecting conditions for finding work (N=81) | |
1. | Lack of affordable child care (27.0%) |
2. | Barriers to entry (e.g., drug and background checks, skills/credentials) (16.0%) |
3. | Availability of jobs (14.0%) |
Factors affecting conditions for job quality (N=52) | |
1. | Wages (38.0%) |
2. | Lack of advancement opportunities (6.0%) |
3. | Lack of employer-provided health insurance (6.0%) |
Note: Respondents were asked to select their top 3 positive and negative factors. Overall ranking was determined by a ranked choice weighting. Percentages are the share of respondents ranking the item as the No. 1 factor. |
Over 75% of respondents reported conditions for renters and homeowners to be poor or very poor (Figure 5). Most respondents do not expect conditions to improve into 2026 (Figure 6). There was little positive sentiment around expectations for rental conditions into the next year, while conditions for homeowners found some positivity in availability of downpayment assistance (Table 3). Prices and availability are significant drags on expectations for both rental and owner housing conditions.
“Folks may look for months for a unit they can afford. Or they find one they can afford and have to drive 20-30 minutes with unreliable transportation.”
Many respondents noted that affordable rental housing is often in poor condition or located far from jobs. Similarly, substandard housing is a considerable concern within homeowner conditions, and comments noted that financing and assistance were insufficient to address repair needs. Several comments also noted that insurance and taxes for LMI homeowners are continuing to grow significantly faster than their incomes, leading to heightened concern about broader financial instability. None of the comments mentioned a growing concern about foreclosures or evictions at this time.
a. Renters (N=85)
b. Homeowners (N=98)
a. Renters (N=83)
b. Homeowners (N=98)
Positive Factors | |
---|---|
Factors affecting conditions for renters (N=71) | |
1. | No positive factors (37.0%) |
2. | Prices (17.0%) |
3. | Availability of affordable, non-subsidized units (14.0%) |
Factors affecting conditions for homeowners (N=92) | |
1. | Availability of down payment assistance (22.0%) |
2. | Availability (17.0%) |
3. | Ability to get financing for purchase (9.0%) |
Negative Factors | |
---|---|
Factors affecting conditions for renters (N=76) | |
1. | Prices (57.0%) |
2. | Availability of nonsubsidized units (14.0%) |
3. | Availability of subsidized units (9.0%) |
Factors affecting conditions for homeowners (N=96) | |
1. | Prices (66.0%) |
2. | Availability (11.0%) |
3. | Ability to get financing for purchase (5.0%) |
Note: Respondents were asked to select their top 3 positive and negative factors affecting their future expectations. Overall ranking was determined by a ranked choice weighting. Percentages are the share of respondents ranking the item as the No. 1 factor. |
Nearly 80% of respondents said that conditions for financial stability were poor or very poor (Figure 7). A smaller share (63%) said personal credit conditions were poor or very poor, but very few said conditions were good. Conditions for both are expected to worsen over the next year (Figure 8). While access to banking and credit are both expected to be positive contributors to financial conditions into next year, cost of living and debt are expected to negatively affect conditions (Table 4). Use of alternative financial products like payday loans is also highly ranked as a negative factor for conditions in the next year.
“Many in the community lack any substantial savings, seemingly due to their low wages and high cost of living.”
Comments were nearly entirely negative and mirrored the ranked negative factors. However, aside from concerns about continued cost-of-living increases, from wages that have not kept up with inflation and from accumulating debt, comments also brought up increasing economic uncertainty and concerns about job stability. Those comments typically also coincided with concerns about cuts to social support programs and how LMI populations will weather economic shock in the near term.
a. Financial stability (N=89)
b. Personal credit (N=48)
a. Financial stability (N=89)
b. Personal credit (N=47)
Positive Factors | |
---|---|
Factors affecting conditions for financial stability (N=80) | |
1. | Access to banking and financial services (16.0%) |
2. | Income (21.0%) |
3. | Financial management practices (16.0%) |
Factors affecting conditions for personal credit (N=41) | |
1. | New products focused on helping LMI borrowers access credit (29.0%) |
2. | Credit availability (17.0%) |
3. | No positive factors (22.0%) |
Negative Factors | |
---|---|
Factors affecting conditions for financial stability (N=84) | |
1. | Cost of living (46.0%) |
2. | Income (24.0%) |
3. | Government assistance programs (e.g., TANF, SNAP, housing voucher) (15.0%) |
Factors affecting conditions for personal credit (N=43) | |
1. | Credit card debt (16.0%) |
2. | Cost of credit (e.g., fees and interest rates) (23.0%) |
3. | Use of alternative financial services (e.g., payday loans, pawn shops, “cash for _“) (16.0%) |
Note: Respondents were asked to select their top 3 positive and negative factors affecting their future expectations. Overall ranking was determined by a ranked choice weighting. Percentages are the share of respondents ranking the item as the No. 1 factor. |
Almost half of survey respondents rated small-business operating conditions as poor or very poor (Figure 9), and nearly two-thirds expect conditions to worsen into 2026. While the top positive factor was availability of technical assistance, the second-highest rating was “no positive factors” (Table 5), which supports the strong negative sentiment on expectations. Meanwhile, respondents said the top negative factor affecting business operating conditions over the next year will be operational costs. In the comments, several respondents reported that small-business owners are struggling with increasingly thin revenue margins and difficulties finding and retaining reliable employees. Economic uncertainty is also a drag on small-business operations; several respondents reported slowing sales and growing concerns around customers’ ability to absorb price increases.
“The cost of materials and supplies is increasing, the cost of living is increasing, but wages are staying stagnant. This makes it difficult for LMI businesses to attract and retain employees and keep their prices affordable for the communities they serve.”
Over half of respondents said small-business credit conditions are poor or very poor (Figure 9). Sixty-six percent expect those conditions to worsen into 2026 (Figure 10). The top positive factors were services that small-business support organizations typically provide, while the negative factors were cost of credit and lack of startup capital (Table 5). Respondents noted that many traditional lenders are increasing lending standards because of high economic uncertainty. Many respondents also mentioned a growing number of businesses closing due to poor business practices rather than lack of revenue.
“Many banks have increased their lending standards, making LMI communities rely on CDFIs [community development financial institutions]. But CDFIs have had to increase interest rates, and some have seen corporate donations decrease given economic uncertainty.”
a. Business operations (N=46)
b. Business credit (N=49)
a. Business operations (N=45)
b. Business credit (N=50)
Positive Factors | |
---|---|
Factors affecting conditions for small-business operations (N=39) | |
1. | Availability of technical assistance for day-to-day operations (36.0%) |
2. | No positive factors (26.0%) |
3. | Ability to find reliable employees (10.0%) |
Factors affecting conditions for small-business credit (N=45) | |
1. | Availability of technical assistance for credit applications (22.0%) |
2. | Availability of micro/small-dollar loans (13.0%) |
3. | Access to banking and financial services (18.0%) |
Negative Factors | |
---|---|
Factors affecting conditions for small-business operations (N=40) | |
1. | Operational costs (33.0%) |
2. | Ability to find reliable employees (23.0%) |
3. | Ability to source materials/products (18.0%) |
Factors affecting conditions for small-business credit (N=47) | |
1. | Cost of credit (e.g., fees and interest rates) (13.0%) |
2. | Lack of start-up capital (23.0%) |
3. | Availability of credit (17.0%) |
Note: Respondents were asked to select their top 3 positive and negative factors affecting their future expectations. Overall ranking was determined by a ranked choice weighting. Percentages are the share of respondents ranking the item as the No. 1 factor. |
Over 70% of respondents rated public health conditions as poor or very poor (Figure 11), and most respondents do not expect that to improve (Figure 12). The plurality of respondents expects conditions to worsen substantially. Respondents had mixed comments about insurance coverage saying that while health care access has increased through insurance, many are still without quality health care, which is likely why they rated it as the most positive and most negative factor (Table 6).
“With the number of doctors retiring in the next 5-10 years, [and] the number of nurses expected to leave nursing in the next 5 years—the access issues will only get worse, especially in rural areas. On top of that, you add in an aging population, who often has more health care needs than the younger population, and you are looking at a medical coverage gap that is about to compound every other access issue we already face.”
Several respondents noted that there has been an increase in preventable disease in LMI populations due to poor housing conditions, lack of education about preventive care, lack of nutritious food, and substance abuse. Additionally, several respondents noted that other expenses, such as rent and groceries, are being prioritized over health care costs. They mentioned that preventive care checkups and screenings are being neglected since many people cannot afford to lose hours at work, allowing conditions to go untreated.
“Emergency departments are seeing increased visits for preventable complications, mental health crises and advanced-stage diagnoses.”
A smaller share of respondents reported health care access as poor (42%), and over 30% said health care access was good or very good (Figure 11). Access to telehealth options was rated as the top positive factor, while insurance coverage and cost were the top negative factors (Table 6).
Respondents were primarily concerned that potential funding changes to public health insurance could affect LMI populations’ ability to afford future health care. Additionally, comments reported an increasing difficulty in finding health care providers, especially in rural areas where, they also said, demand for health care is increasing because of aging populations.
a. Public health/general health conditions (N=58)
b. Access to health care (N=41)
a. Public health/general health conditions (N=53)
b. Access to health care (N=41)
Positive Factors | |
---|---|
Factors affecting conditions for overall health (N=45) | |
1. | Insurance coverage (private, employer-provided, Medicaid or Medicare) (31.0%) |
2. | No positive factors (27.0%) |
3. | Availability of nutritious food (9.0%) |
Factors affecting conditions for health care access (N=37) | |
1. | Access to telehealth options (22.0%) |
2. | Insurance coverage (private, employer-provided, Medicaid or Medicare) (24.0%) |
3. | Availability of free/low-cost clinics (11.0%) |
Negative Factors | |
---|---|
Factors affecting conditions for overall health (N=48) | |
1. | Insurance coverage (private, employer-provided, Medicaid or Medicare) (38.0%) |
2. | Mental health needs (17.0%) |
3. | Substance abuse (10.0%) |
Factors affecting conditions for health care access (N=39) | |
1. | Insurance coverage (private, employer-provided, Medicaid or Medicare) (46.0%) |
2. | Cost (18.0%) |
3. | Availability of primary care providers (10.0%) |
Note: Respondents were asked to select their top 3 positive and negative factors affecting their future expectations. Overall ranking was determined by a ranked choice weighting. Percentages are the share of respondents ranking the item as the No. 1 factor. |
Forty-six percent of respondents reported conditions in pre-K through 12th-grade education as poor or very poor (Figure 13). A similar share expects conditions for those grades to worsen (Figure 14). Respondents ranked availability of early childhood education as the top positive factor (Table 7), but many of the comments were about concerns about funding for early childhood education into next year.
Related to their negative factor rankings, several respondents expressed concerns about funding uncertainty and the ability to find and retain teachers (Table 7). In particular, they noted fewer people were becoming teachers, and many teachers were leaving over salary and job security concerns. Some respondents also mentioned that they were concerned about students’ home lives affecting their ability to perform at school.
Respondents were more evenly split on adult education conditions, with 45% reporting poor or very poor conditions and 32% reporting good or very good conditions (Figure 13). They were also more evenly split on expectations (Figure 14). While respondents saw enrollment and employer partnerships as being positive factors on their expectations into next year, they rated support for program completion and costs as drags on expectations (Table 7).
Some respondents commented that it is becoming easier for people to access job training programs because of increased availability of online courses; however, there were also concerns for those who did not have access to the internet. Additionally, respondents noted that many vocational training opportunities require in-person learning and so were not accessible since many LMI people are unable to afford child care or could not take time off work to attend.
“They are focused on making ends meet with multiple jobs; they don’t have time for additional training to increase their wages.”
a. Pre-K to 12th grade education (N=50)
b. Adult education (N=56)
a. Pre-K to 12th grade (N=49)
b. Adult education (N=55)
Positive Factors | |
---|---|
Factors affecting conditions for pre-K to 12-grade education (N=45) | |
1. | Availability of early childhood education (13.0%) |
2. | Student performance (attendance, graduation rate, test scores) (16.0%) |
3. | School funding (20.0%) |
Factors affecting conditions for adult education (N=51) | |
1. | Participation in skills training and degrees (12.0%) |
2. | Partnerships with employers (10.0%) |
3. | Support for program completion (e.g., child care, transportation, tutoring, mentoring) (14.0%) |
Negative Factors | |
---|---|
Factors affecting conditions for pre-K to 12-grade education (N=50) | |
1. | School funding (32.0%) |
2. | Teacher hiring and retention (6.0%) |
3. | Student home life (20.0%) |
Factors affecting conditions for adult education (N=55) | |
1. | Support for program completion (e.g., child care, transportation, tutoring, mentoring) (16.0%) |
2. | Cost (24.0%) |
3. | Internet availability at home (15.0%) |
Note: Respondents were asked to select their top 3 positive and negative factors. Overall ranking was determined by a ranked choice weighting. Percentages are the share of respondents ranking the item as the No. 1 factor. |
Over half of respondents reported conditions in human services and emergency assistance as poor or very poor (Figure 15). Nearly 80% expect conditions to worsen over the coming year, with half of those expecting conditions to worsen substantially.
“More lower- to middle-class people are seeking food assistance for the first time. More people are seeking food assistance across our network of feeding sites.”
Echoing the poor conditions reported in the sector, the plurality of respondents said there were “no positive factors” affecting conditions for the next year (Table 8). However, there was some positivity in seeing coordination among entities. Their negative factors leaned heavily on economic factors and funding. Nearly 40% of comments stated concern about program funding cuts, and 30% commented on rising demand for services. Unlike last year, many comments also mentioned economic uncertainty and increasing fear about economic conditions.
“In the communities we serve, the demand for human services and emergency assistance among low- and moderate-income (LMI) individuals has reached a critical level. Basic needs like food, shelter, clothing, utility relief, and transportation are overwhelming the capacity of providers like ours. What used to be short-term crises are becoming long-term, recurring needs. For example, our food pantry, which once served primarily emergency cases, now supports hundreds of families on a regular basis who rely on it to supplement monthly groceries. Many of these households include working adults who simply cannot keep up with rising food prices and stagnant wages. We’ve seen seniors skipping meals to afford medications and parents feeding children first while going hungry themselves.”
a. April 2025 (N=89)
b. Expectations for the year ahead (N=89)
Positive Factors (N=81) | |
---|---|
1. | No positive factors (43.0%) |
2. | Coordination among entities (27.0%) |
3. | Funding (9.0%) |
Negative Factors (N=84) | |
---|---|
1. | Economic conditions (36.0%) |
2. | Funding (27.0%) |
3. | Housing Security (10.0%) |
Note: Respondents were asked to select their top 3 positive and negative factors affecting their future expectations. Overall ranking was determined by a ranked choice weighting. Percentages are the share of respondents ranking the item as the No. 1 factor. |
Over half of respondents rated internet access as poor or very poor (Figure 16), and the plurality does not expect changes in internet access over the next year (Figure 17). While service availability was a top positive factor, their positive ranking of availability of subsidized internet access was caveated due to lack of awareness of the programs and insufficient funding (Table 9). Comments suggested that internet service access was improved through an increased reliance on cellphones for the internet. However, respondents were also concerned about the growing use of cellphones for internet access because of data limitations and restrictions on using cellphones for job or benefit applications. There were also several comments noting an ongoing challenge with poor internet access in rural areas.
“[The] cellphones this population [uses] have limited data to utilize apps; individuals of this demographic … cannot afford tablets or computers; cost to learn skills is prohibitive; libraries do a very good job of helping, but they are limited in number of computers available, and some individuals are unable to access during open hours.”
Respondents were slightly more concerned about access to technology, with 59% saying that conditions are poor or very poor (Figure 16), and they expected increased reliance on cellphones to be a negative influence on access to technology over the next year (Table 9). Comments reported seeing significant limitations in digital skill development in those who used only cellphones. However, they noted there has been an increase in job training programs including courses on computer skills.
“Community members primarily use cellphones for accessing digital services, which leads to limited digital literacy and skills. Most do not own computers and have limited time to access computers outside the home. Additionally, many in the community possess limited digital literacy when it comes to issues of online safety (e.g., avoiding fraud and misinformation).”
a. Internet Access (N=150)
b. Technology Access and Digital Skills (N=189)
a. Internet Access (N=150)
b. Technology Access and Digital Skills (N=189)
Positive Factors | |
---|---|
Factors affecting conditions for internet access (N=130) | |
1. | Internet service availability (24.0%) |
2. | Availability of subsidized internet service (29.0%) |
3. | Access internet primarily through their cellphones (19.0%) |
Factors affecting conditions for digital skills and technology access (N=177) | |
1. | Basic digital device skills (e.g., computer and cell phone skills) (25.0%) |
2. | Availability of services to improve digital skills (10.0%) |
3. | Ability to get to places with computers or technology (8.0%) |
Negative Factors | |
---|---|
Factors affecting internet access (N=139) | |
1. | Availability of subsidized internet service (32.0%) |
2. | Awareness of subsidized internet options (12.0%) |
3. | Internet service availability (14.0%) |
Factors affecting conditions for digital skills and technology access (N=175) | |
1. | Use of cell-phone primarily leading to fewer digital skills (25.0%) |
2. | Funding (17.0%) |
3. | Availability of computers for home use (16.0%) |
Note: Respondents were asked to select their top 3 positive and negative factors affecting their future expectations. Overall ranking was determined by a ranked choice weighting. Percentages are the share of respondents ranking the item as the No. 1 factor. |
Does the entity you represent offer services directly to individuals and families? | |
---|---|
No | 17.0% |
Unsure | 3.0% |
Yes | 80.0% |
To which type of geographic area does your entity dedicate the most resources? | |
Equal across all geographic areas | 41.5% |
Metropolitan | 40.0% |
Rural (including frontier) | 18.5% |
What type of geographic area does your entity serve? | |
Nationwide | 11.0% |
Statewide or multiple states | 24.0% |
Within a county or some counties within a state | 37.0% |
Within a metropolitan statistical area (MSA) | 28.0% |
The Federal Reserve System performs five key functions that serve all Americans and promote the health and stability of the U.S. economy and financial system. Understanding the obstacles that may hinder lower-income and under-resourced communities’ greater participation in the economy offers the Federal Reserve valuable insight into the challenges they, and the entities that serve them, face. To that end, from 2020 to 2023, the Federal Reserve System conducted surveys, called “Perspectives from Main Street,” to better identify the range of challenges facing LMI communities as an effect of the COVID19 pandemic. In 2024, the survey was renamed “Community Perspectives,” and it continues as a national survey aimed at reporting the economic conditions of LMI communities and the health of entities serving them. This survey has two objectives:
The most recent survey was open from April 14, 2025, to May 23, 2025. Responses were collected through a convenience sampling method that relied on contact databases to identify representatives of nonprofit organizations, financial institutions, government agencies and other community organizations. These representatives were invited to participate in the survey via emails, newsletters and social media posts. The survey had a total of 440 responses from entities that serve LMI communities. However, the total number of responses across sectors could differ since not all entities serve in all areas. Respondents were asked to select a topic area based on their entities’ top programming areas and to answer questions related to that sector specifically and not others.
Views expressed are those of the report team and do not necessarily represent the views of the Federal Reserve System.
Please cite this report as: Chalise, Nishesh, Violeta Gutkowski, Steven Howland, and Becky Kropf. “Community Perspectives Survey: Insights from the Field -Economic Conditions in Low- and Moderate-income Communities,” August 2025.
Economic mobility: A combination of factors (such as employment conditions, community safety, or digital access) that may lead to improvements in economic position through changes in income and wealth.
Employment—finding work: Conditions affecting LMI people’s ability to find work if they are looking. Both the economy and a firm’s hiring criteria affect how easy it is for LMI people to find work.
Employment—job quality: The ability of LMI people to hold jobs that have high levels of job quality. Entities considered LMI people who have jobs and those looking for jobs when answering.
Housing—renters: Conditions affecting LMI renters such as price and availability of rental housing.
Housing—homeowners: Conditions that both current and prospective LMI homeowners are facing.
Household budget and credit—financial stability: Conditions affecting LMI people’s financial stability such as financial management skills and savings. This includes their access to banking services.
Household budget and credit—personal credit: Credit conditions (e.g., amount of debt and access to credit) affecting LMI communities.
Small business—operations: Operating conditions (e.g., business activity, costs and ability to find employees) being faced by LMI-owned small businesses.
Small business—credit: Credit conditions influencing LMI-owned small businesses.
Health—public health/general health conditions: The overall health of LMI people in the communities served by the entity.
Health—access to health care: LMI people’s ability to access health care in the communities served by the entity.
Education—pre-K through 12th grade: The ability of pre-K through 12th-grade education to meet the needs of LMI children and prepare them for careers or college.
Education—adult: Conditions shaping adult education and its ability to advance the skills of LMI people for career advancement. This includes skills certifications and post-secondary education.
Human services and emergency assistance: The demand for human services and emergency assistance, and the ability of providers to meet that demand. Services may include food, housing, counseling, legal aid, and other services with a goal of improving the quality of life for those with few resources and of helping them get by in times of emergency.
Internet access: LMI people’s ability to access the internet. This includes the availability of internet access at home, quality of internet service available, and price.
Technology access and digital skills: Access to technology for finding employment, business development, and skill training, among other economic factors. This can include access to cellphones, computers, software programs, specialized technology, and the training necessary to use them.
The Federal Reserve’s community development function seeks to promote the economic resilience and mobility of low- to moderate-income and underserved households and communities across the United States. We thank the following survey team members for their contributions:
Sydney Diavua, Federal Reserve Bank of St. Louis
Karen Leone de Nie, Federal Reserve Bank of Atlanta
Nick Sly, Federal Reserve Bank of Kansas City
Surekha Carpenter, Federal Reserve Bank of Richmond
Nishesh Chalise, Federal Reserve Bank of St. Louis
Violeta Gutkowski, Federal Reserve Bank of St. Louis
Steven Howland, Federal Reserve Bank of Kansas City
Matthew Klesta, Federal Reserve Bank of Cleveland
Becky Kropf, Federal Reserve Bank of Kansas City
Lisa Nelson, Federal Reserve Bank of Cleveland
Whitney Felder, Fed Communities
Crystal Flynn, Fed Communities
Natalie Karrs, Federal Reserve Bank of Cleveland
Melissa Kueker, Federal Reserve Bank of St. Louis
Nicholas A. Ledden, Federal Reserve Bank of St. Louis
Derek Stacey, Federal Reserve Bank of Cleveland
Allyson M. Sykora, Federal Reserve Bank of St. Louis
Amy Brewer, Federal Reserve Bank of Richmond
Suzanne Cummings, Federal Reserve Bank of Boston
Michelle Dailey, Federal Reserve Bank of St. Louis
Steven Howland, Federal Reserve Bank of Kansas City
Molly Hubbert Doyle, Federal Reserve Bank of Dallas
Kellye Jackson, Federal Reserve Bank of New York
Elizabeth Kneebone, Federal Reserve Bank of San Francisco
Susan Longworth, Federal Reserve Bank of Chicago
Grace Meagher, Federal Reserve Bank of Atlanta
Ryan Nunn, Federal Reserve Bank of Minneapolis
John Rees, Federal Reserve Bank of Atlanta
Edison Reyes, Federal Reserve Bank of New York
Brianna Smith, Federal Reserve Bank of Chicago
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