British Columbia is hitting back at economic threats from the United States by introducing tools to impose fees on U.S. truck traffic travelling through the province on the way to Alaska.

Premier David Eby said the tolls may not be used, but warned that Canada needs to have tools available to fight the threat of tariffs coming from President Donald Trump until he backs down from his plans altogether.

Eby made the announcement on the lawn of the Legislative Assembly building in Victoria, which had been draped with a large Canadian flag, while members of his party chanted “Canada” in the background.

Eby said he was responding to an “unprecedented threat” from the United States, which he characterized as U.S. President Donald Trump wanting to “erase our border.”

“He wants to annex Canada and turn us into the 51st state,” Eby said. “That is a threat.”

WATCH | Eby says Trump has declared ‘economic war’ on Canada: B.C. premier says Trump wants to ‘eliminate the border’ Duration 3:01 B.C. Premier David Eby said Donald Trump is threatening Canada with his economic sanctions, which the premier described as a desire to annex the country.

The premier said he was unmoved by news that had come just moments earlier that 25 per cent tariffs on some Canadian goods sold into the United States had once again been placed on pause, this time until April 2, saying B.C. and Canada should not let up until the threat was removed altogether.

“It’s all a deliberate tactic to weaken our resolve, and it will not work,” he said.

“This is unacceptable, and we’re going to ensure that the Americans understand how pissed off we are, how unified we are, how committed we are to working as a country to stand up for each other,” Eby said. “And I say we don’t let up until the president takes the threat off the table.”

Eby said that legislation will be introduced in the coming days, allowing the province to levy fees onto commercial trucks moving through the province to and from Alaska.

The premier says he would also be introducing legislation giving the province the ability to remove interprovincial trade barriers between provinces and territories and mandating that low-carbon fuels added to gasoline and diesel be produced in Canada.

He also reiterated actions the province has already taken, including removing alcohol from Republican-leaning states from B.C. Liquor Store shelves, de-prioritizing U.S. contractors on government contract bids, and fast-tracking the process through which energy and resource projects are vetted for approval in order to improve the province’s self-reliance and trade relationships with other nations.

Eby says the tariffs imposed by Trump are a profound mistake and are hurting families on both sides of the border, and his team is working hard to ensure the province comes out stronger on the other side.

“Trump thinks he can bring us to our knees by threatening tariffs. Well, what he is seeing is that Canadians are standing tall [with] one voice.”

How would the fees work?

Eby did not share the details of how the new legislation will work.

He also did not share information on the practicalities surrounding the plan to specifically fine commercial truck traffic through the United States, saying it was still early days.

“It will not be implemented immediately but we will have it available if required,” he said when pressed on how the province would see the legislation working on the ground.

In Nova Scotia, Premier Tim Houston has doubled the cost of tolls at the Cobequid Pass for commercial vehicles from the United States. But that’s an area which already has checks and fines in place.

B.C., by contrast, does not have similar checkpoints, particularly along the Alaska Highway, which connects Alaska to Washington State through B.C. and Yukon.

Speaking to CBC News earlier in the day, Yukon premier Ranj Pillai said he’d heard from many of his constituents interested in imposing similar restrictions on U.S. truck traffic but pointed out it may be easier said than done, as it would require new infrastructure, such as toll booths and new personnel to staff it.

Eby said details of his plan would be revealed in the “coming days.”

What could the impact be?

The Alaska Highway is, in fact, a Canadian one. With its origin point in Dawson Creek, B.C., it extends more than 2,000 kilometres through Whitehorse, before ending just southeast of Fairbanks, Alaska.

It was built in the Second World War by the U.S. Army Corps of Engineers after the bombing of Pearl Harbour as a way to ensure the United States had land access to Alaska in order to serve as a defence against Pacific incursions. It was funded by the United States, with Canadian leaders allowing the build on the condition that it be turned over to Canada following the war.

The highway remains a popular tourist route and the only way for goods shipped by truck to reach Alaska.

Flags for British Columbia, Canada and the United States mark Mile 0 of the Alaska Highway in Dawson Creek, B.C. (Andrew Kurjata/CBC)

Pillai has also pointed out that the U.S. has recently committed more than $40 million to improving the road on the Yukon side of the border, and expressed concern that any limitations on its use imposed by Canada could threaten that investment.

Then there’s the question of retaliation: while U.S. truck traffic is currently able to travel through Canada to Alaska duty-free, the same agreement is in place for most Mexican goods coming to Canada the same way, said Andrea Bjorklund, a McGill University professor and an expert in international commercial law.

Having duties imposed on items going to and from Mexico could further harm the Canadian economy as Mexico is Canada’s third-largest trading partner, including more than $2 billion worth of fruits and vegetables coming into the country every year.

Alaska, meanwhile, is not actually as dependent on trucks from Canada as some might think, with most of its goods arriving by sea rather than road.

Figures from the Port of Alaska illustrates the state’s reliance on goods shipped by boat relative to those arriving via truck from Canada. (CBC News)

In fact, according to numbers from the U.S. Bureau of Transport Statistics, shared by University of Alaska economics professor Mike Jones, trucking only represents about one per cent of cargo entering the state every year, with the bulk coming through the Port of Alaska in Anchorage, primarily from U.S. destinations.

That doesn’t mean, though, that Alaskans wouldn’t be impacted. The state does rely on Canada for some goods, including construction material and, potentially, fuel, as local sources run out. Those living in border communities also use Yukon as a source of supplies, from groceries to health care.

To that end, Republican senator Cathy Giessel has advanced a joint resolution pushing the president to honour the trade relationship with both Canada and Mexico, proclaiming that the Alaska Legislature “opposes any restrictive trade measures that would harm the unique relationship betwen Canada and Alaska or negatively affect our integrated economies.”

Chronic Disease Prevalence in the US: Sociodemographic and Geographic Variations by Zip Code Tabulation Area

Gabriel A. Benavidez, PhD1; Whitney E. Zahnd, PhD2; Peiyin Hung, PhD3; Jan M. Eberth, PhD4 (View author affiliations)

Suggested citation for this article: Benavidez GA, Zahnd WE, Hung P, Eberth JM. Chronic Disease Prevalence in the US: Sociodemographic and Geographic Variations by Zip Code Tabulation Area. Prev Chronic Dis 2024;21:230267. DOI: http://dx.doi.org/10.5888/pcd21.230267.

PEER REVIEWED

Summary What is known on this topic? Chronic diseases are increasing in prevalence throughout the US and put a major strain on the health care system. What this study adds Areas affected by a high prevalence of multiple chronic diseases face a variety of socioeconomic and environmental barriers to achieving good health. Many risk factors for chronic disease are likely beyond the individual’s control and require large-scale policy change. What are the implications for public health practice? Preventing and managing chronic disease will require combatting poverty and socioeconomic inequality.

Abstract

Introduction

We examined the geographic distribution and sociodemographic and economic characteristics of chronic disease prevalence in the US. Understanding disease prevalence and its impact on communities is crucial for effective public health interventions.

Methods

Data came from the American Community Survey, the American Hospital Association Survey, and the Centers for Disease Control and Prevention’s PLACES. We used quartile thresholds for 10 chronic diseases to assess chronic disease prevalence by Zip Code Tabulation Areas (ZCTAs). ZCTAs were scored from 0 to 20 based on their chronic disease prevalence quartile. Three prevalence categories were established: least prevalent (score ≤6), moderately prevalent (score 7–13), and highest prevalence (score ≥14). Community characteristics were compared across categories and spatial analyses to identify clusters of ZCTAs with high disease prevalence.

Results

Our study showed a high prevalence of chronic disease in the southeastern region of the US. Populations in ZCTAs with the highest prevalence showed significantly greater socioeconomic disadvantages (ie, lower household income, lower home value, lower educational attainment, and higher uninsured rates) and barriers to health care access (lower percentage of car ownership and longer travel distances to hospital-based intensive care units, emergency departments, federally qualified health centers, and pharmacies) compared with ZCTAs with the lowest prevalence.

Conclusion

Socioeconomic disparities and health care access should be addressed in communities with high chronic disease prevalence. Carefully directed resource allocation and interventions are necessary to reduce the effects of chronic disease on these communities. Policy makers and clinicians should prioritize efforts to reduce chronic disease prevalence and improve the overall health and well-being of affected communities throughout the US.

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Introduction

An estimated 129 million people in the US have at least 1 major chronic disease (1) (eg, heart disease, cancer, diabetes, obesity, hypertension) as defined by the US Department of Health and Human Services (2). Five of the top 10 leading causes of death in the US are, or are strongly associated with, preventable and treatable chronic diseases (3). Over the past 2 decades prevalence has increased steadily, and this trend is expected to continue (4). An increasing proportion of people in America are dealing with multiple chronic conditions; 42% have 2 or more, and 12% have at least 5 (5). Besides the personal impact, chronic disease has a substantial effect on the US health care system. About 90% of the annual $4.1 trillion health care expenditure is attributed to managing and treating chronic diseases and mental health conditions (6).

The increasing focus on studying social determinants of health (SDOH) has generated several studies showing a connection between chronic disease and socioeconomic factors. One study found a significantly lower prevalence of asthma, arthritis, diabetes, hypertension, and obesity in affluent counties compared with the least affluent ones (7). Another study showed that people with less than a high school education had nearly twice the odds of having diabetes compared with those with a college degree (8). Across large US cities, rates of stroke and hypertension are concentrated in census tracts with a high proportion of Black residents, old homes with low market value, and people receiving government assistance (9).

Although many chronic diseases are partly the result of individual health behaviors, such as excessive drinking, smoking, sedentary lifestyles, and poor nutrition, structural socioeconomic and environmental factors play a role in their development, prevention, and management. Although poor nutrition may appear to be an individual choice, affordability and convenient access to high quality, nutritious food are often the primary determinants of food choice for people with a low income (10). Similarly, leisure-time physical activity is often framed as a simple behavior that people should incorporate into their daily lives. However, this framing puts the onus on the individual and fails to recognize time, financial, and built environment factors (eg, safe neighborhood, access to parks) that influence a person’s ability to engage in regular physical activity (11). Once a chronic condition develops, management of the disease requires the ability to afford health care and have physical access to it. Yet research increasingly shows that even when medically necessary, people often forgo costly medications and health care, hindering their ability to manage their disease (12,13).

Although the literature examining both area-level and individual-level chronic disease and SDOH is expansive, current literature has 3 major shortcomings: 1) on a national scale, work is often limited to county-level analyses, 2) most current research focuses on individual chronic diseases and their association with SDOH, and 3) studies often include metrics of only socioeconomic status, such as income or educational attainment, as an SDOH measure. These shortcomings are notable because of the potential for wide variation in chronic disease prevalence and SDOH factors within counties. An average US county population is over 100,000, but smaller geographic units, such as Zip Code Tabulation Areas (ZCTAs), which are generalized areal representations of the geographic coverage and distribution of the zip codes, have an average population of less than 10,000. One study of the Chicago, Illinois, area (14) aimed to fill some of this research gap by examining multiple dimensions of SDOH in relation to premature death at the census tract level and found that all their SDOH dimensions (eg, socioeconomic position, housing, transportation, physical disability) were associated with higher premature death. That study highlighted the growing interest among researchers in examining the multidimensional factors of SDOH and their effect on health outcomes. However, it was limited in scope because it examined 1 health outcome metric, that is, premature death, and in only 1 city.

To add to the body of literature and address the shortcomings of previous research, our study had 3 aims: 1) to create a community indicator of chronic disease prevalence at a subcounty level (ie, areas with high or low prevalence of multiple chronic diseases) by using ZCTAs, 2) to examine the geographic distribution of chronic disease prevalence, and 3) to examine the differences in socioeconomic and environmental characteristics of ZCTAs with low and high chronic disease prevalence.

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Methods

Data sources

Data on ZCTA-level chronic disease prevalence estimates came from the 2020 Centers for Disease Control and Prevention’s (CDC’s) PLACES data set. The PLACES project is designed to generate health data for small geographic units across the country (ie, county, census tract, and ZCTA) to enable informed decisions when planning public health interventions. The PLACES estimates are derived by using small-area estimation methodologies along with health-indicator data from the Behavioral Risk Factor Surveillance System (BRFSS) survey (15) adapted by each state, which were originally designed to produce valid state-level prevalence estimates. To produce estimates at the ZCTA level, CDC uses a multivariable approach that incorporates demographic and socioeconomic data. This generates reliable estimates of chronic disease prevalence for smaller geographic units. Details of the PLACES methodology are described elsewhere (16).

ZCTA-level sociodemographic and economic data were obtained from the American Community Survey (17). As recommended when examining small geographic units, we used 5-year (2015–2019) estimates for all included variables. Additionally, PLACES 2020 data estimates are derived mainly from the 2019 BRFSS but also include the 2018 BRFSS for some modules of questions that are not asked every year. We also obtained data on availability of health care services (hospital-based intensive care units [ICUs], emergency departments [EDs], federally qualified health centers [FQHCs], and pharmacies) from the 2019 American Hospital Association Annual Survey (18), the Center for Medicare and Medicaid Services Provider of Service Files (19), and the US Department of Agriculture Service Area Map Datasets’ Healthcare facilities file (20). Our analysis included 31,634 of the approximately 32,000 ZCTAs in the US. PLACES excludes ZCTAs with fewer than 50 people.

Chronic disease prevalence score

As an indicator of chronic disease prevalence, we created a composite score that allowed us to identify ZCTAs estimated to have a high prevalence of multiple chronic diseases. We used the 10 most prevalent and costly chronic diseases in the US (21): obesity, hypertension, high cholesterol, coronary heart disease, chronic obstructive pulmonary disease, asthma, chronic kidney disease, diabetes, cancer (excluding skin cancer), and depression. For each disease, we ordered all ZCTAs by disease prevalence and assigned a score of 0 to ZCTAs with a chronic disease prevalence in the bottom 25th percentile, 1 for ZCTAs in the middle (between the 25th and the 75th percentiles), or 2 to those in the top 25th percentile for each chronic disease (Table 1). We summed the scores for each chronic disease included together to provide a total score ranging from 0 to 20. A score of 0 indicated that the ZCTA was in the lowest 25th percentile for all 10 disease prevalence estimates, and those with a score of 20 were in the top 25th percentile for all 10 disease prevalence estimates. We then created 3 chronic disease prevalence categories by using quartile thresholds as before (in the bottom 25th percentile, between the 25th and 75th percentiles, and in the top 25th percentile): the lowest prevalence ZCTAs (score ≤6), moderate prevalence ZCTAs (score of 7–13), and highest prevalence ZCTAs (score ≥14).

Covariates

Sociodemographic and economic status, distance to health care services. We included variables in this analysis that encompassed multiple aspects of the Healthy People 2030 Social Determinants of Health domains (Figure 1) (22). By using data from the 2019 American Community Survey’s 5-year estimates (2015–2019) (17) we captured ZCTA-level characteristics that included economic status, defined as median income, median home value, and proportion of residents receiving Supplemental Nutrition Assistance Program (SNAP); racial and ethnic characteristics; access to health care (eg, proportion without health insurance); travel barriers (eg, proportion without a vehicle, proportion with >1 h employment commute); and proxies of social disadvantage (eg, proportion of residents elderly, in households without medical insurance, foreign born).

Figure 1.

Categories of variables included, based on Healthy People 2030 Social Determinants of Health framework (22), in study of sociodemographic and geographic variations of chronic disease prevalence in the US by Zip Code Tabulation Area. [A text version of this figure is available.]

All the health care service files we used provided physical addresses of the facilities that were being examined. We used the World Geocoding Service from ArcGIS Pro (Esri) to perform batch geocoding of facilities for all health care services included in our analysis. We used the GEODIST function in SAS version 9.4 (SAS) to calculate the distance in miles from each residential population-weighted ZCTA centroid to the nearest health care facility for all facilities included in our analysis (ICUs, EDs, FQHCs, and pharmacies).

Analysis. To examine the geographic distribution of chronic disease, we created choropleth maps of prevalence scores in ArcGIS Pro. We then performed a hot spot analysis, also in ArcGIS Pro, to see where ZCTAs with either high or low prevalence were clustered together. The hot spot analysis tool in ArcGIS Pro calculated the Getis-Ord Gi* statistic for each ZCTA in the analysis. A positive Gi* statistic indicated intensity of clustering around high values, and a negative Gi* statistic indicated clustering of low values. We also applied the default ArcGIS Pro false discovery rate correction to account for multiple testing and spatial dependency in the hot spot analysis.

By using the Kolmogorov–Smirnov goodness of fit test we determined that the use of the Kruskal–Wallis test — a nonparametric hypothesis testing — was most appropriate for our data. To further examine specific differences between chronic disease prevalence groups, we then used the Dwass, Steel, Critchlow-Fligner multiple comparison test to perform pairwise comparisons of groups. Of specific interest was determining whether there was a significant difference in the characteristics of ZCTAs with the highest and lowest chronic disease prevalence. A 2-tailed P value of <.05 was used to determine significance in all hypothesis testing.

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Results

Of the 31,634 ZCTAs included in our study, 8,576 (27.1%) fell into the least prevalent (Quartile 1) category, 14,670 (46.4%) into the moderate prevalence category (Quartiles 2 and 3), and 8,388 (26.5%) into the highest prevalence category (Quartile 4) (Figure 2). The hot spot analysis (Figure 3) showed that high chronic disease scores (hot spots) were predominantly clustered throughout the southeastern region of the US, with additional high score clustering in parts of Maine, Michigan, and the Pacific Northwest. Less apparent patterns emerged when examining clusters of low chronic disease scores (cold spots) because they are disbursed throughout the US but appear to cluster around major cities. For example, in Texas, the 3 cold spot clusters in the eastern part of the state are in the metropolitan areas of Dallas, Houston, Austin, and San Antonio. The largest cold spot in the state of Georgia is the area surrounding the Atlanta metropolitan region.

Figure 2.

Choropleth map of the US showing the geographic distribution of chronic disease prevalence scores by quartile across Zip Code Tabulation Areas (ZCTAs). Chronic disease prevalence scores ranged from 0 to 20 with a score of 0 meaning the ZCTA was in the 25th percentile and a score of 20 meaning the ZCTA was in the 75th percentile of prevalence for each chronic disease examined. [A text version of this figure is available.]

Figure 3.

Hot Spot analysis of chronic disease prevalence scores throughout the US calculated in ArcGIS Pro (Esri) showing significant spatial clusters of high chronic disease prevalence scores (red clusters = hot spots) and low chronic disease prevalence scores (blue clusters = cold spots). [A text version of this figure is available.]

Differences in ZCTA characteristic median values across all categories of chronic disease prevalence were significant (P < .05) for all included variables (Table 2). ZCTAs in the lowest prevalence category had higher population sizes (median, n = 12,955) than those in the moderate category (median, n = 2,270) or highest category (median, n = 1,398). ZCTAs in the lowest prevalence category had populations that were younger (median age, 38.4 y; interquartile range [IQR], 34.2–43.1 y) compared with the moderate category (median age, 42.6; y IQR, 37.8–47.7 y) or highest category (median age, 44.4 y; IQR, 39.3–51.2 y).

Pairwise comparisons to examine differences in characteristics between ZCTAs with the highest and lowest chronic disease prevalence also showed significant differences for all sociodemographic and economic factors. Compared with ZCTAs with the lowest prevalence, those with the highest prevalence of chronic disease had significantly higher proportions of people with a disability (19.6% vs 11.4%; P <.001) and who were uninsured (9.8% vs 5.3%; P <.001), unemployed (3.2% vs 2.8%; P =.008), received SNAP benefits (16.9% vs 6.4%; P <.001), had no personal vehicle (5.6% vs 3.5%; P <.001), or had to commute more than 1 hour for work (7.6% vs 6.4%; P = .004). ZCTAs in the lowest prevalence category also had significantly higher proportions of residents with post–high school education (68.1% vs 42.9%; P <.001) who were living in a home with internet access (83.0% vs 66.9%; P <.001) and had higher median incomes ($73,929 vs $40,750; P <.001) and higher home values ($257,000 vs $91,600; P <.001) than those in ZCTAs with the highest prevalence.

Racial demographics also differed. ZCTAs in the highest chronic disease prevalence category had a significantly higher proportion of Black residents (11.9% vs 6.6%, P <.001) and American Indian/Alaska Native residents (2.7% vs 0.7%, P <.001) compared with the lowest prevalence ZCTAs. In contrast, the proportions of Asian residents (5.7% vs 0.5%, P <. 001) and Hispanic (13.9% vs 4.8%, P <.001) residents were significantly lower in ZCTAs with the highest prevalence than in those with the lowest prevalence. The proportion of White residents (89.5% vs 92.7.5%, P = .10) did not differ significantly between ZCTAs with the highest and lowest prevalence.

Residents of ZCTAs with the lowest chronic disease prevalence lived significantly closer to health care services than those in ZCTAs with the highest prevalence (P 31.2–9.4–30.6–31.3–5.7–6.4–9.1–2.7–6.7–19.3–<23.9 23.9

Table 2. Characteristics of Zip Code Tabulation Areas by Category of Chronic Disease Prevalence Characteristic Lowest prevalence

(n = 8,576) Moderate prevalence

(n = 14,670) Highest prevalence

(n = 8,388) P value Low vs high P valuea Median (IQR) Median (IQR) Median (IQR) Demographics Population size, no. 12,955 (2501–29923) 2,270 (689–9118) 1,398 (453–4356) <.001 <.001 Median age, y 38.4 (34.2–43.1) 42.6 (37.8–47.7) 44.4 (39.3–51.2) <.001 <.001 Aged ≥65 y, % 13.8 (10.3–17.3) 18.1 (14.5–22.4) 20.2 (16.15–26.2) <.001 <.001 Foreign-born, % 8.8 (3.8–17.9) 2.5 (0.87–6.1) 1.3 (0–3.2) <.001 <.001 Have a disability, % 11.4 (7.9–12.1) 14.54 (11.8–17.6) 19.6 (15.9–24.3) <.001 <.001 Uninsured, % 5.3 (3.0–8.9) 7.2 (4.2–11.9) 9.8 (5.7–15.0) <.001 <.001 Unemployed, % 2.8 (1.9–3.8) 2.8 (1.5–4.2) 3.2 (1.5–5.1) <.001 .008 Have internet, % 86.3 (80.5–90.8) 74.0 (68.9–81.1) 66.9 (57.3–73.2) <.001 <.001 Median income, $ 73,929 (60,250–93,400) 53,851 (46,157–62,857) 40,750 (34,200–47,917) <.001 <.001 Median home value, $ 257,700 (180,200–393,000) 133,300 (98,000–177,800) 91,600 (72,800–121,500) <.001 <.001 Receive SNAP, % 6.4 (3.4–10.8) 10.7 (6.21–16.3 16.9 (10.2–24.1) <.001 <.001 Post high school education, % 68.1 (57.9–78.5) 52.2 (43.8–60.4) 42.9 (35.3–50.1) <.001 <.001 No personal vehicle, % 3.5 (1.7–6.3) 3.9 (1.7–6.9) 5.6 (2.3–9.7) <.001 <.001 More than 1-hour work commute, % 6.4 (3.3–12.4) 6.6 (3.45–10.8) 7.6 (3.8–13.7) <.001 .004 Race or ethnicity, % American Indian/Alaska Native 0.7 (0.1–1.1) 1.5 (0.9–1.8) 2.7 (1.7–3.0) <.001 <.001 Asian 5.7 (3.4–6.8) 0.9 (0.6–1.1) 0.5 (0.2–0.8) <.001 <.001 Black 6.6 (3.9–7.1) 5.9 (3.8–7.3) 11.9 (9.4–14.6) <.001 <.001 Hispanic 13.9 (10.8–16.7) 8.8 (4.3–11.2) 4.8 (2.6–6.3) <.001 <.001 White 89.5 (71.3–94.4) 94.7 (85.2–98.0) 92.7 (71.2–98.0) <.001 .10 Distance to nearest health care services, miles Nearest FQHC 4.6 (1.8–9.6) 9.4 (3.8–17.3) 8.7 (3.1–14.8) <.001 <.001 Nearest ICU 5.6 (2.6–10.8) 12.8 (6.6–20.8) 18.2 (10.9–27.4) <.001 <.001 Nearest ED 5.3 (2.4–9.8) 10.6 (5.5–16.5) 14.6 (8.7–21.8) <.001 <.001 Nearest pharmacy 0.9 (0.4–3.3) 4.3 (0.86–8.6) 5.7 (1.3–10.1) <.001 <.001

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How we did this This report examines key changes in the economic status of the American middle class from 1970 to 2023 and its demographic attributes in 2022. The historical analysis is based on U.S. Census Bureau data from the Annual Social and Economic Supplements (ASEC) of the Current Population Survey (CPS). The demographic analysis is based on data from the American Community Survey (ACS). The data is sourced from IPUMS CPS and IPUMS USA, respectively. The CPS, a survey of about 60,000 households, is the U.S. government’s official source for monthly estimates of unemployment. The CPS ASEC, conducted in March each year, is the official source of U.S. government estimates of income and poverty. Our analysis of CPS data starts with the 1971 CPS ASEC, which records the incomes of households in 1970. It is also the first year for which data on race and ethnicity is available. The latest available CPS ASEC file is for 2023, which reports on household incomes in 2022. The public-use version of the ACS is a 1% sample of the U.S. population, or more than 3 million people. This allows for a detailed study of the demographic characteristics of the middle class, including its status in U.S. metropolitan areas. But ACS data is available only from 2005 onward and is less suitable for long-term historical analyses. The latest available ACS data is for 2022.

Terminology Middle-income households are defined as those with an income that is two-thirds to double that of the U.S. median household income, after incomes have been adjusted for household size. Lower-income households have incomes less than two-thirds of the median, and upper-income households have incomes that are more than double the median. When using American Community Survey (ACS) data, incomes are also adjusted for cost of living in the areas in which households are located. Estimates of household income are scaled to reflect a household size of three and expressed in 2023 dollars. In the Current Population Survey (CPS), household income refers to the calendar year prior to the survey year. Thus, the income data in the report refers to the 1970-2022 period, and the share of Americans in each income tier from the CPS refers to the 1971-2023 period. The demographic attributes of Americans living in lower-, middle- or upper-income tiers are derived from ACS data. Except as noted, estimates pertain to the U.S. household population, excluding people living in group quarters. The terms middle class and middle income are used interchangeably in this report. White, Black, Asian, American Indian or Alaska Native, and Native Hawaiian or Pacific Islander include people who identified with a single major racial group and who are not Hispanic. Multiracial includes people who identified with more than one major racial group and are not Hispanic. Hispanics are of any race. U.S. born refers to individuals who are U.S. citizens at birth, including people born in the 50 U.S. states, the District of Columbia, Puerto Rico or other U.S. territories, as well as those born elsewhere to at least one parent who is a U.S. citizen. The terms foreign born and immigrant are used interchangeably in this report. They refer to people who are not U.S. citizens at birth. Occupations describe the broad kinds of work people do on their job. For example, health care occupations include doctors, nurses, pharmacists and others who are directly engaged in the provision of health care. Industries describe the broad type of products companies produce. Each industry encompasses a variety of occupations. For example, the health care and social assistance industry provides services that are produced by a combination of doctors, managers, technology and administrative staff, food preparation workers, and workers in other occupations.

The share of Americans who are in the middle class is smaller than it used to be. In 1971, 61% of Americans lived in middle-class households. By 2023, the share had fallen to 51%, according to a new Pew Research Center analysis of government data.

As a result, Americans are more apart than before financially. From 1971 to 2023, the share of Americans who live in lower-income households increased from 27% to 30%, and the share in upper-income households increased from 11% to 19%.

Notably, the increase in the share who are upper income was greater than the increase in the share who are lower income. In that sense, these changes are also a sign of economic progress overall.

But the middle class has fallen behind on two key counts. The growth in income for the middle class since 1970 has not kept pace with the growth in income for the upper-income tier. And the share of total U.S. household income held by the middle class has plunged.

Moreover, many groups still lag in their presence in the middle- and upper-income tiers. For instance, American Indians or Alaska Natives, Black and Hispanic Americans, and people who are not married are more likely than average to be in the lower-income tier. Several metro areas in the U.S. Southwest also have high shares of residents who are in the lower-income tier, after adjusting for differences in cost of living across areas.

Our report focuses on the current state of the American middle class. First, we examine changes in the financial well-being of the middle class and other income tiers since 1970. This is based on data from the Annual Social and Economic Supplements (ASEC) of the Current Population Survey (CPS), conducted from 1971 to 2023.

Then, we report on the attributes of people who were more or less likely to be middle class in 2022. Our focus is on their race and ethnicity, age, gender, marital and veteran status, place of birth, ancestry, education, occupation, industry, and metropolitan area of residence. These estimates are derived from American Community Survey (ACS) data and differ slightly from the CPS-based estimates. In part, that is because incomes can be adjusted for the local area cost of living only with the ACS data. (Refer to the methodology for details on these two data sources.)

This analysis and an accompanying report on the Asian American middle class are, in part, updates of previous work by the Center. But they offer much greater detail on the demographic attributes of the American middle class.

Following are some key facts about the state of the American middle class:

Who is middle income or middle class? In our analysis, “middle-income” Americans are those living in households with an annual income that is two-thirds to double the national median household income. The income it takes to be middle income varies by household size, with smaller households requiring less to support the same lifestyle as larger households. It also varies by the local cost of living, with households in a more expensive area, such as Honolulu, needing a higher income than those in a less expensive area, such as Wichita, Kansas. We don’t always know the area in which a household is located. In our two data sources – the Current Population Survey, Annual Social and Economic Supplement (CPS ASEC) and the American Community Survey (ACS) – only the latter provides that information, specifically the metropolitan area of a household. Thus, we aren’t able to adjust for the local cost of living when using the CPS to track changes in the status of the middle class over time. But we do adjust for the metropolitan area cost of living when using the ACS to determine the demographic attributes of the middle class in 2022. In the 2023 CPS ASEC data, which reports income for 2022, middle-income households with three people have incomes ranging from about $61,000 to $183,000 annually. “Lower-income” households have incomes less than $61,000, and “upper-income” households have incomes greater than $183,000. In the 2022 ACS data, middle-income households with three people have incomes ranging from about $62,000 to $187,000 annually, with incomes also adjusted for the local area cost of living. (Incomes are expressed in 2023 dollars.) The boundaries of the income tiers also vary across years as the national median income changes. The terms “middle income” and “middle class” are used interchangeably in this report for the sake of exposition. But being middle class can refer to more than just income, be it education level, type of profession, economic security, home ownership or social and political values. Class also could simply be a matter of self-identification.

Households in all income tiers had much higher incomes in 2022 than in 1970, after adjusting for inflation. But the gains for middle- and lower-income households were less than the gains for upper-income households.

The median income of middle-class households increased from about $66,400 in 1970 to $106,100 in 2022, or 60%. Over this period, the median income of upper-income households increased 78%, from about $144,100 to $256,900. (Incomes are scaled to a three-person household and expressed in 2023 dollars.)

The median income of lower-income households grew more slowly than that of other households, increasing from about $22,800 in 1970 to $35,300 in 2022, or 55%.

Consequently, there is now a larger gap between the incomes of upper-income households and other households. In 2022, the median income of upper-income households was 7.3 times that of lower-income households, up from 6.3 in 1970. It was 2.4 times the median income of middle-income households in 2022, up from 2.2 in 1970.

The share of total U.S. household income held by the middle class has fallen almost without fail in each decade since 1970. In that year, middle-income households accounted for 62% of the aggregate income of all U.S. households, about the same as the share of people who lived in middle-class households.

By 2022, the middle-class share in overall household income had fallen to 43%, less than the share of the population in middle-class households (51%). Not only do a smaller share of people live in the middle class today, the incomes of middle-class households have also not risen as quickly as the incomes of upper-income households.

Over the same period, the share of total U.S. household income held by upper-income households increased from 29% in 1970 to 48% in 2022. In part, this is because of the increase in the share of people who are in the upper-income tier.

The share of overall income held by lower-income households edged down from 10% in 1970 to 8% in 2022. This happened even though the share of people living in lower-income households increased over this period.

The share of people in the U.S. middle class varied from 46% to 55% across racial and ethnic groups in 2022. Black and Hispanic Americans, Native Hawaiians or Pacific Islanders, and American Indians or Alaska Natives were more likely than others to be in lower-income households.

In 2022, 39% to 47% of Americans in these four groups lived in lower-income households. In contrast, only 24% of White and Asian Americans and 31% of multiracial Americans were in the lower-income tier.

At the other end of the economic spectrum, 27% of Asian and 21% of White Americans lived in upper-income households in 2022, compared with about 10% or less of Black and Hispanic Americans, Native Hawaiians or Pacific Islanders, and American Indians or Alaska Natives.

Not surprisingly, lower-income status is correlated with the likelihood of living in poverty. According to the Census Bureau, the poverty rate among Black (17.1%) and Hispanic (16.9%) Americans and American Indians or Alaska Natives (25%) was greater than the rate among White and Asian Americans (8.6% for each). (The Census Bureau did not report the poverty rate for Native Hawaiians or Pacific Islanders.)

Children and adults 65 and older were more likely to live in lower-income households in 2022. Adults in the peak of their working years – ages 30 to 64 – were more likely to be upper income. In 2022, 38% of children (including teens) and 35% of adults 65 and older were lower income, compared with 26% of adults ages 30 to 44 and 23% of adults 45 to 64.

The share of people living in upper-income households ranged from 13% among children and young adults (up to age 29) to 24% among those 45 to 64. In each age group, about half or a little more were middle class in 2022.

Men were slightly more likely than women to live in middle-income households in 2022, 53% vs. 51%. Their share in upper-income households (18%) was also somewhat greater than the share of women (16%) in upper-income households.

Marriage appears to boost the economic status of Americans. Among those who were married in 2022, eight-in-ten lived either in middle-income households (56%) or upper-income households (24%). In contrast, only about six-in-ten of those who were separated, divorced, widowed or never married were either middle class or upper income, while 37% lived in lower-income households.

Veterans were more likely than nonveterans to be middle income in 2022, 57% vs. 53%. Conversely, a higher share of nonveterans (29%) than veterans (24%) lived in lower-income households.

Immigrants – about 14% of the U.S. population in 2022 – were less likely than the U.S. born to be in the middle class and more likely to live in lower-income households. In 2022, more than a third of immigrants (36%) lived in lower-income households, compared with 29% of the U.S. born. Immigrants also trailed the U.S. born in the shares who were in the middle class, 48% vs. 53%.

There are large gaps in the economic status of American residents by their region of birth. Among people born in Asia, Europe or Oceania, 25% lived in upper-income households in 2022. People from these regions represented 7% of the U.S. population.

By comparison, only 14% of people born in Africa or South America and 6% of those born in Central America and the Caribbean were in the upper-income tier in 2022. Together they accounted for 8% of the U.S. population.

The likelihood of being in the middle class or the upper-income tier varies considerably with the ancestry of Americans. In 2022, Americans reporting South Asian ancestry were about as likely to be upper income (38%) as they were to be middle income (42%). Only 20% of Americans of South Asian origin lived in lower-income households. South Asians accounted for about 2% of the U.S. population of known origin groups in 2022.

At least with respect to the share who were lower income, this was about matched by those with Soviet, Eastern European, other Asian or Western European origins. These groups represented the majority (54%) of the population of Americans whose ancestry was known in 2022.

On the other hand, only 7% of Americans with Central and South American or other Hispanic ancestry were in the upper-income tier, and 44% were lower income. The economic statuses of Americans with Caribbean, sub-Saharan African or North American ancestry were not very different from this.

Education matters for moving into the middle class and beyond, and so do jobs. Among Americans ages 25 and older in 2022, 52% of those with a bachelor’s degree or higher level of education lived in middle-class households and another 35% lived in upper-income households.

In sharp contrast, 42% of Americans who did not graduate from high school were in the middle class, and only 5% were in the upper-income tier. Further, only 12% of college graduates were lower income, compared with 54% of those who did not complete high school.

Not surprisingly, having a job is strongly linked to movement from the lower-income tier to the middle- and upper-income tiers. Among employed American workers ages 16 and older, 58% were in the middle-income tier in 2022 and 23% were in the upper-income tier. Only 19% of employed workers were lower income, compared with 49% of unemployed Americans.

In some occupations, about nine-in-ten U.S. workers are either in the middle class or in the upper-income tier, but in some other occupations almost four-in-ten workers are lower income. More than a third (36% to 39%) of workers in computer, science and engineering, management, and business and finance occupations lived in upper-income households in 2022. About half or more were in the middle class.

But many workers – about one-third or more – in construction, transportation, food preparation and serving, and personal care and other services were in the lower-income tier in 2022.

About six-in-ten workers or more in education; protective and building maintenance services; office and administrative support; the armed forces; and maintenance, repair and production were in the middle class.

Depending on the industrial sector, anywhere from half to two-thirds of U.S. workers were in the middle class, and the share who are upper income or lower income varied greatly.

About a third of workers in the finance, insurance and real estate, information, and professional services sectors were in the upper-income tier in 2022. Nearly nine-in-ten workers (87%) in public administration – largely filling legislative functions and providing federal, state or local government services – were either in the middle class or the upper-income tier.

But nearly four-in-ten workers (38%) in accommodation and food services were lower income in 2022, along with three-in-ten workers in the retail trade and other services sectors.

The share of Americans who are in the middle class or in the upper- or lower-income tier differs across U.S. metropolitan areas. But a pattern emerges when it comes to which metro areas have the highest shares of people living in lower-, middle- or upper-income households. (We first adjust household incomes for differences in the cost of living across areas.)

The 10 metropolitan areas with the greatest shares of middle-income residents are small to midsize in population and are located mostly in the northern half of the U.S. About six-in-ten residents in these metro areas were in the middle class.

Several of these areas are in the so-called Rust Belt, namely, Wausau and Oshkosh-Neenah, both in Wisconsin; Grand Rapids-Wyoming, Michigan; and Lancaster, Pennsylvania. Two others – Dover and Olympia-Tumwater – include state capitals (Delaware and Washington, respectively).

In four of these areas – Bismarck, North Dakota, Ogden-Clearfield, Utah, Lancaster and Wausau – the share of residents in the upper-income tier ranged from 18% to 20%, about on par with the share nationally.

The 10 U.S. metropolitan areas with the highest shares of residents in the upper-income tier are mostly large, coastal communities. Topping the list is San Jose-Sunnyvale-Santa Clara, California, a technology-driven economy, in which 40% of the population lived in upper-income households in 2022. Other tech-focused areas on this list include San Francisco-Oakland-Hayward; Seattle-Tacoma-Bellevue; and Raleigh, North Carolina.

Bridgeport-Stamford-Norwalk, Connecticut, is a financial hub. Several areas, including Washington, D.C.-Arlington-Alexandria and Boston-Cambridge-Newton, are home to major universities, leading research facilities and the government sector.

Notably, many of these metro areas also have sizable lower-income populations. For instance, about a quarter of the populations in Bridgeport-Stamford-Norwalk; Trenton, New Jersey; Boston-Cambridge-Newton; and Santa Cruz-Watsonville, California, were in the lower-income tier in 2022.

Most of the 10 U.S. metropolitan areas with the highest shares of residents in the lower-income tier are in the Southwest, either on the southern border of Texas or in California’s Central Valley. The shares of people living in lower-income residents were largely similar across these areas, ranging from about 45% to 50%.

About 40% to 50% of residents in these metro areas were in the middle class, and only about one-in-ten or fewer lived in upper-income households.

Compared with the nation overall, the lower-income metro areas in Texas and California have disproportionately large Hispanic populations. The two metro areas in Louisiana – Monroe and Shreveport-Bossier City – have disproportionately large Black populations.

Note: For details on how this analysis was conducted, refer to the methodology.

Alaska, also known as the Last Frontier, is on many Americans’ travel bucket lists. If you visit Alaska and fall in love with it, you may decide you want to move there. While Alaska famously offers an annual check to each of the state’s residents, the somewhat high cost of living may offset this perk. Despite some of its more exorbitant costs, there are no state income or sales taxes. If you’re considering the move you can prepare financially by working with a financial advisor.

Housing Costs in Alaska

In general, homes cost a little more in Alaska than in the rest of the U.S. According to Realtor.com, the state median home value is $385,000. This is a 10% increase from 2021 to 2022. Some of the bigger cities have even higher median values.

Perhaps the most disturbing trend regarding the Alaskan home market is its lack of solid appreciation rates prior to the recent housing boom. From 2013 to 2018, NeighborhoodScout data shows homes have averaged a 2.13% appreciation rate annually. While that might seem good, consider that California and Oregon have seen 7.55% and 8.61% appreciation rates, respectively, over that same period of time.

As far as rent goes, Alaska is ever so slightly cheaper than the U.S. as a whole. According to RentData.org’s site, the median rent for a studio and one-bedroom apartment in Alaska is $860 and $962. The state is mostly made up of homeowners, though.

Cost of Utilities

Alaska does not have cheap utilities. A cost of living study by the Anchorage Economic Development Corporation shows that overall monthly utility costs in Alaska are quite high. In fact, natural gas and electricity bills are 33% higher than the national average in Alaska. To further illustrate this, according to the U.S. Energy Information Administration (EIA), the average monthly electricity bill for Alaska residents was $127.83 in 2017. That’s just over $16 higher than the national average of $111.67.

For 2022, the average utility bill is around $240 while the average utility bill in Anchorage, Alaska is $270.28. This accounts for basic electricity, gas and water. As you can see, the utilities can be much more expensive than in other states and the extra monthly cost can really add up over time.

Food Costs

Alaskans have access to a lot of great local food items. King crab, Copper River red salmon and Kachemak Bay oysters are true Alaskan delicacies. In southern Alaska, fertile land and abundant summer sunlight produce great local produce. Some Alaskans supplement their food supply by fishing, hunting and picking berries themselves.

Unless you fully embrace the “subsistence” way of life, you’ll end up buying some food at the grocery store. In Alaska’s case, the food needs to travel far distances to get there. The same goes for the food served in restaurants.

As a result, food prices in Alaska are high. In Anchorage, the recommended minimum amount of money spent on food for one person is $451.71, according to Numbeo.com data from April 2019. Compare this to the national average of $323.72, and you can see how food can get pricey in the northernmost state.

Transportation in Alaska

Transportation works a little differently in the largest state in the country. To get around, driving is essentially a necessity as there are no subway systems, and most towns don’t have a bus option either. Without public transportation, the spread-out state is virtually impossible to navigate if you don’t have a vehicle. Plus, with the bad weather, it becomes important to have a vehicle that can make it well in the snow.

According to GasBuddy, the average price of a gallon of gas in Alaska is $3.36. That’s the sixth-highest average price in the nation. On the upside, an Insure.com report from 2019 shows that Alaska has a $1,183 average annual car insurance premium. This compares favorably to the $1,457 national average.

There are also plenty of places in Alaska that you cannot get to via road. To make your way there, you might have to take a boat or a small plane. Even Juneau, Alaska’s capital, is only accessible by plane or ferry. This makes it even more expensive if you’re looking to travel around the state.

Healthcare Costs

Alaskans pay a lot for healthcare compared to even other states in the country. According to a report from the Health Care Cost Institute, the overall healthcare prices in the Anchorage metro area are an astounding 82% higher than the national median. To make matters worse, Alaska private company employees contribute $99 more than the national average for single coverage healthcare, according to a report from the Agency for Healthcare Research and Quality. The scarcity of providers might be one of the major reasons for this.

Alaskan Taxes

Alaska has no state income tax and it doesn’t have a state sales tax, either, though some cities impose their own sales taxes up to 7.50%. The average effective property tax rate in Alaska is 1.19%, which is coincidentally the same as the national average. You should analyze how the lack of a state income tax can improve the cost of living depending on your personal situation.

Alaska Pays Its Residents

So how does the state make money if taxes are low? Resource-rich Alaska gets money for its oil wealth. Some of that money was put into a permanent fund that accumulates value on the stock market. This is called the Alaska Permanent Fund. Every year, the state distributes some of the fund’s earnings to each Alaskan in the form of a check. Based on data from the Alaska Department of Revenue, the payout has been more than $1,o00 per year in the past.

The Bottom Line

Alaska can be more expensive to live in than many people who live in the lower 48 states realize. The landscape and weather are closer to some of the more affordable places to live in the rest of the states so it’s natural to think that. However, without a good transportation solution and the added costs of things related to scarcity, you end up paying for the beautiful scenery in the state. It’s important to weigh that cost before moving.

Next Steps for Your Move to Alaska

Financial advisors can be a big help when you’re dealing with the financial stresses of moving to a new state. Finding the right financial advisor that fits your needs doesn’t have to be hard. SmartAsset’s free tool matches you with up to three financial advisors who serve your area, and you can interview your advisor matches at no cost to decide which one is right for you. If you’re ready to find an advisor who can help you achieve your financial goals, get started now.

If you’ll be starting a new job, your paycheck may end up looking quite different than it does now. Try using our Alaska paycheck calculator to estimate what your new take-home pay will be in “The Last Frontier.”

You can also check out our listing of the top financial advisors in Anchorage, Alaska if you’re interested in only reaching out to the financial advisors with the highest rankings who also serve the area.