Racial and Ethnic Disparities in Earnings, Employment, and Education in Connecticut

Cynthia Willner | June 16th, 2022

Even as people across the state at all levels engage in efforts to ensure all of Connecticut’s residents have a chance to thrive, marked disparities still exist in educational attainment, employment and earnings across racial and ethnic groups in Connecticut. Understanding these disparities is vital to identifying where systemic inequities exist. These disparities exist due to historical and ongoing systemic racism including discriminatory federal and local housing and urban development policies that drive the concentration of Black and Latino residents into lower-opportunity communities. Indeed, Connecticut is one of the most racially segregated states in the nation. Promoting racial equity in educational and economic opportunities will be an important step toward ending the persistence of racial and ethnic disparities across generations.

In this brief report, we present data on racial and ethnic disparities in earnings, employment and education in Connecticut using the U.S. Census Bureau’s 5-year estimates from the American Community Survey (ACS) covering the period from January 2016 to December 2020. It is important to note here that we are focusing on broad summary measures across all members of each racial and ethnic group as defined by the U.S. Census Bureau, which mask substantial variation within each group.

To gain some insight into regional variations in these disparities, we also discuss racial and ethnic disparities in earnings and education within Connecticut’s five largest cities by population: Bridgeport, Stamford, New Haven, Hartford, and Waterbury.

Residents of these five cities constitute 18% of the state’s total population, but they constitute 47% of the state’s Black population and 39% of the state’s Hispanic or Latino population. The table below shows the percentage of residents identifying with the major racial and ethnic groups across Connecticut as a whole and for the five largest cities in the state in 2016-2020 (see the Methodological Notes below for details on how we categorize race and ethnicity in this report). In the five largest cities, White non-Hispanic residents ranged from just 15% of the population in Hartford to 49% of the population in Stamford, as compared to 66% of the population statewide.

Deep Disparities In Earned Income Across Racial And Ethnic Groups

The 2016-2020 ACS 5-year estimates reveal deep racial and ethnic disparities in earnings across Connecticut. Among residents aged 16 years and older with any earnings in the prior year (including any full-time, part-time, or part-year jobs), median annual earnings in 2016-2020 for those identifying as White non-Hispanic or as Asian were much higher than median annual earnings for residents of all other racial and ethnic identities.

• Hispanic or Latino residents’ median annual earnings was lower than that of all other racial/ethnic groups with the exception American Indian or Alaska Native, for whom the estimate was imprecise due to the small sample size.

  • Hispanic or Latino residents’ median earnings was 54% (± 6%) of that for Asian residents, 57% (± 1%) of that for White non-Hispanic residents, and 90% (± 2%) of that for Black residents.

• Black residents’ median earnings was 60% (± 5%) of that for Asian residents and 64% (± 1%) of that for White non-Hispanic residents.

• American Indian residents’ median earnings was 52% (± 9%) of that for Asian residents and 56% (± 10%) of that for White non-Hispanic residents.

A similar pattern of earnings disparities was present within each of Connecticut’s five largest cities in 2016-2020, although the size of the disparities varied across cities. Below we discuss the statistically significant differences in median earnings among Hispanic/Latino, Black, and White non-Hispanic residents of these cities, as these are the largest populations and therefore have the most precise earnings estimates within each city.

• Hispanic or Latino residents’ median earnings was significantly lower than that for White non-Hispanic residents in all five cities, and their median earnings was significantly lower than that for Black residents in each city except New Haven.

• Black residents’ median earnings was significantly lower than that for White non-Hispanic residents in each city except Bridgeport.

• Earnings disparities were particularly wide in Stamford, where White non-Hispanic residents’ median earnings was double that of Black residents and 2.5 times that of Hispanic or Latino residents.

Wage Disparities Play A Large Role In Annual Earned Income Disparities Across Racial And Ethnic Groups

Annual income disparities are influenced by both differences in wages / hourly earnings and in hours worked per year. Here, we approximate wage disparities using median annual earnings among those working full-time, year-round (see the Methodological Notes, below, for details and limitations).

The data suggest that wage disparities played a large role in the total earned income disparities that exist across racial and ethnic groups in Connecticut.

• Hispanic or Latino males working full-time year-round earned significantly less than all other racial/ethnic groups, including:

  • 51% (± 8%) vs. Asian males

  • 57% (± 1%) vs. White non-Hispanic males

  • 90% (± 4%) vs. Black males

• Hispanic or Latina females working full-time year-round earned significantly less than all other racial/ethnic groups except for American Indian females:

  • 57% (± 10%) vs. Asian females

  • 61% (± 2%) vs. White non-Hispanic females

  • 87% (± 4%) vs. Black females

• Black males working full-time year-round earned significantly less than all other racial/ethnic groups except Hispanic/Latino and American Indian males:

  • 56% (± 8%) vs. Asian males

  • 63% (± 2%) vs. White non-Hispanic males

• Black females working full-time year-round also earned significantly less than all other racial/ethnic groups except Hispanic/Latina and American Indian females:

  • 65% (± 9%) vs. Asian females

  • 70% (± 2%) vs. White non-Hispanic females

Causes of these wage disparities likely include racial/ethnic differences in the distribution of employment across occupations and industries as well as continued labor market discrimination. Notably, these racial and ethnic disparities in earnings for full-time workers were consistently greater among males than among females, who earned less than males within each racial and ethnic group. We will explore the gender wage gap further in a future blog post.

Black Males Had Low Full-Time Employment Rates While Black Females Had Relatively High Full-Time Employment Rates

Differences in full-time, year-round employment also contribute to racial and ethnic earnings disparities.

• Asian males were more likely to be working full-time, year-round compared to all other racial/ethnic groups.

• Hispanic or Latino males and females were both less likely to be working full-time, year-round than were White non-Hispanic males and females, respectively.

• Black males were less likely to be working full-time, year-round compared to all other racial/ethnic groups except for American Indian males.

• In contrast, Black females were more likely to be working full-time, year-round than were females of any other racial/ethnic group.

The higher full-time employment rate for Black females likely reflects in part their greater rates of serving as the sole or primary breadwinner for their families.

Large Educational Disparities Contribute To Earnings Disparities

Greater educational attainment is strongly associated with higher earnings. In Connecticut in 2016-2020, having a high school diploma or equivalent was associated with a 44% boost in estimated median annual earnings compared to those without a diploma (from $25,926 to $37,365), and having a bachelor’s degree was associated with a further 77% boost in median annual earnings (to $66,131).

Across Connecticut in 2016-2020, there were large disparities in educational attainment across racial and ethnic groups.

• White non-Hispanic residents were more likely than all other racial/ethnic groups to have at least a high school diploma.

• Asian residents were far more likely than all other racial/ethnic groups to have a bachelor’s degree or higher.

• Hispanic or Latino residents had the lowest rates both of having at least a high school diploma or equivalent and of having a bachelor’s degree or higher.

  • More than one in four (27%) Hispanic or Latino residents in Connecticut did not have a high school diploma or equivalent, versus only 5% for White non-Hispanic residents, 10% for Asian residents, and 13% for Black residents. This educational disparity limits employment opportunities and earnings potential for a substantial proportion of the Hispanic/Latino population in the state.

  • Only 18% of Hispanic or Latino residents had obtained a bachelor’s degree or higher, versus 66% of Asian residents, 45% of White non-Hispanic residents, 23% of Black residents.

• Black residents were less likely to have a high school diploma or a bachelor’s degree relative to White non-Hispanic and Asian residents.

  • Compared to White non-Hispanic residents, Black residents were 8% (± 0.8%) less likely to have a high school diploma or equivalent and 49% (± 2%) less likely to have a bachelor’s degree or higher.

• American Indian residents also had lower rates of having at least a high school diploma and of having a bachelor’s degree relative to White non-Hispanic and Asian residents.

  • Compared to White non-Hispanic residents, American Indian residents were 15% (± 6%) less likely to have a high school diploma or equivalent and 58% (± 8%) less likely to have a bachelor’s degree.

A similar pattern of racial and ethnic disparities in educational attainment emerged within the five largest cities in Connecticut.

• In each of the five largest cities in the state, Hispanic or Latino residents were much less likely to have received a high school diploma or equivalent relative to both White non-Hispanic and Black residents. In Hartford and Bridgeport, roughly 40% of Hispanic or Latino residents did not have a high school diploma or equivalent.

• In Hartford, Bridgeport, New Haven, and Waterbury, Black and White non-Hispanic residents had similar rates of having a high school diploma or equivalent (estimated differences of ~1 to 4 percentage points, which were only statistically significant in Bridgeport and New Haven).

• Hispanic or Latino residents had lower rates of having a bachelor’s degree relative to both White non-Hispanic and Black residents in each of the five largest cities in the state.

  • Compared to White non-Hispanic residents in each city, Hispanic/Latino residents were about 60% - 80% less likely to have a bachelor’s degree.

  • Compared to Black residents in each city, Hispanic/Latino residents were about 20% - 50% less likely to have a bachelor’s degree.

• In each of the five largest cities, Black residents were about 30% - 70% less likely than White non-Hispanic residents to have a bachelor’s degree.

What Might Account for Racial & Ethnic Disparities in Educational Attainment?

A 2009 survey by the Pew Foundation suggested that many Hispanic/Latino residents in the U.S. who cut their education short during or after high school may have done so to provide financial support to their family (in the U.S. or abroad) or due to poor English language skills. In addition, the relatively high percentage of Connecticut’s Hispanic or Latino residents who were born in another country - nearly 40% of those aged 25 years or older - contributes to, but does not fully explain, the lower rate of high school completion among Hispanic or Latino residents.

Custom tabulations of the 2020 ACS 5-year public use microdata sample reveal that 32% of foreign-born Hispanic or Latino residents of Connecticut aged 25 and older had not obtained a high school diploma or equivalent, versus 24% of those who were born in the U.S. (notably, 24% is still much higher than the percentage of other racial and ethnic groups in the state who had not obtained a high school diploma). In contrast, foreign-born Hispanic or Latino residents were no less likely to have obtained a bachelor’s degree relative to those born in the U.S. (18% vs. 17%, respectively).

Structural racism also plays an important role in racial disparities in educational attainment. The Connecticut school system is highly racially segregated.

Implications

Much work remains to be done to address deep racial and ethnic disparities in earnings, employment, and education in Connecticut. Education and job training are important levers toward higher earnings. The disproportionate growth in jobs requiring higher levels of education and professional training means that those without postsecondary educational credentials are likely to face increasingly limited labor market opportunities. Addressing racial segregation and racial funding inequities across Connecticut’s schools would be important steps toward enhancing racial equity in educational opportunities and earnings potential. Unfortunately, the COVID-19 pandemic has had a greater negative impact on the educational and employment experiences of Black and Latino families across the nation, further exacerbating pre-existing inequities with potentially lasting consequences unless actions are taken to mitigate these impacts.

Methodological Notes

Margins of error and statistical significance

The ACS is a survey of a sample of residents, not all residents. Therefore, ACS estimates have margins of error which provide information about the precision of the estimate. Roughly speaking, we have 90% certainty that the “real” value is around the estimated value plus-or-minus the margin of error. Smaller groups, such as racial and ethnic minority groups, typically have larger margins of error around their estimates, meaning that we have less certainty about the “true” value of their estimates. This is particularly problematic for the Native American and Alaska Native population, who are a relatively small but important minority group in Connecticut whose ACS estimates often have large margins of error.

In this report, we show the margins of error after each estimate and in the tables and graphs, and we only discuss differences between groups that are “statistically significant,” meaning that there is sufficient statistical evidence to conclude that the actual difference is different from zero.

Categorizing race and ethnicity

The measurement of race and ethnicity by the U.S. Census Bureau is a controversial and evolving topic, and the Bureau recognizes that the categories used no longer reflect many people’s preferences for how to identify themselves. In the ACS, respondents are asked to report first whether they are of “Hispanic, Latino, or Spanish origin” and then to select one or more racial categories with which they identify (e.g., White, Black or African American, etc.). “Hispanic or Latino” is thus treated as an ethnicity separate from one’s “race,” even though many people who identify as “Hispanic or Latino” consider it to be a racial identity as well.

In their public tables of 2020 ACS 5-year estimates, the Census Bureau publishes earnings, employment, and educational attainment estimates separately for:

1. Each racial category regardless of Hispanic or Latino origin but excluding individuals who selected two or more races (e.g., “Black or African American,” including individuals identifying as both Black and Latino but excluding individuals identifying as both Black and White),

2. Individuals of “Hispanic or Latino origin” regardless of their selected race(s) (note that individuals identifying as both Black and Latino would also be included in this category),

3. Individuals who selected two or more races (e.g., Black and White, White and Asian, etc.), and

4. Individuals identifying as “White alone, not Hispanic or Latino.”

When interpreting estimates for these racial and ethnic groups, we must keep in mind that there is overlap between the racial groups (e.g., Black or African American) and Hispanic or Latino ethnicity. Following the Census Bureau’s guidance, we report estimates for these overlapping racial and ethnic groups to enhance their inclusiveness. The exception is that we report estimates for White non-Hispanic residents, rather than for White residents including those identifying as Hispanic or Latino, due to the substantial disparities that exist between Connecticut residents who identify as White non-Hispanic and those who identify as White and Hispanic.

The pie chart below shows the racial identities of Connecticut residents of Hispanic or Latino origin in 2016-2020. Roughly half of all Hispanic or Latino residents identified as White (and are excluded from the White non-Hispanic group) and another 31% identified solely as “some other race.” Notably, 93% (± 0.5%) of all Connecticut residents of “some other race” also identified as Hispanic or Latino (see the bar chart below); for this reason, we do not report statistics separately for residents of “some other race.” Additionally, nearly half of (1) American Indian and Alaska Native residents and (2) residents selecting two or more races also identified as Hispanic or Latino, and therefore comparisons between estimates for these groups and for Hispanic or Latino residents should be interpreted with caution. In contrast, only 8% of Black or African American residents identified as Hispanic or Latino and there was very little overlap between Asian race and Hispanic/Latino ethnicity.

Multiracial individuals are only included in the estimates for the “two or more races” group. Although this is how the Census Bureau reports the 2020 5-year ACS estimates in their publicly available tables, it has the unfortunate result that multiracial individuals’ data are not reflected in the estimates for any of the races with which they identify.

Estimating wages using the ACS publicly available data tables

The Census Bureau does not provide public data tables on hourly wage estimates from the 2020 ACS. Therefore, we approximate wage disparities between groups by examining median annual earnings among those working full-time, year-round (which the Census Bureau defines as working 35+ hours/week for 50 or more weeks per year). However, we note that this does not account for differences between groups in the number of weekly hours constituting “full-time employment.” Additionally, this includes some non-wage income such as earnings from self-employment, bonuses, commissions, and tips.

About The Author

Cynthia Willner, Ph.D., is Senior Research Associate at the CTData Collaborative. At CTData, Dr. Willner analyzes public data to yield policy-relevant insights and inform data-driven decision-making. Dr. Willner holds a Ph.D. in Human Development and Family Studies from The Pennsylvania State University.