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American Journal of  Environmental
Economics (AJEE) 

Exploring Diversity of  Hispanic Health Outcomes Through Prism of  Environmental Justice
Victor Vasnetsov1*, Catherine Vasnetsov1, Siona Pramoda1, Meghna Pramoda2

Volume 3 Issue 1, Year 2024
ISSN: 2833-7905 (Online)

DOI: https://doi.org/10.54536/ajee.v3i1.2845
https://journals.e-palli.com/home/index.php/ajee

Article Information ABSTRACT

Received: May 13, 2024

Accepted: June 15, 2024

Published: September 19, 2024

In environmental economics studies US Hispanics are frequently depicted as one large 
homogeneous group, despite diversity in their countries’ cultural traditions, genetic 
background and socioeconomic status (SES). This study aimed to compare Florida’s very 
diverse Hispanic population of  Caribbean, Mexican, Central, and South American ancestry.
Twelve independent variables were selected from environmental pollution, geospatial 
challenges, and SES categories. Dependent variables included the relative prevalence of  three 
major chronic diseases: asthma, diabetes, and heart disease. Analysis of  pairs correlations 
was supplemented by multi-variable linear regression. Majority-Hispanic areas have higher 
than average pollution, diabetes, proximity to disadvantaged neighborhoods, and lower SES. 
Despite lower pollution than in urban areas, Florida’s inland rural areas had higher rates 
of  all major diseases and lower life expectancies. Cuban and Puerto Rican populations of  
Miami and Orlando metro areas, respectively, have less favorable SES outcomes and higher 
diabetes. Columbian and Venezuelan communities have significantly higher education levels 
than other Hispanic cohorts, and they have the best relative outcome among US Hispanics in 
SES and health, although still lagging US averages. African-American community in Miami-
Dade County was by far the most disadvantaged, with significantly lower SES and health 
outcomes than any other group in the study, including the nearby Cuban-majority community. 
Our results confirmed the view that Hispanics are environmentally disadvantaged in Florida, 
with worse outcomes than the US and Florida average in health and economic factors. 
Furthermore, the study highlighted significant differentiation of  SES and health outcomes 
among various regional Hispanic cohorts.

Keywords

Environmental Justice, Health 
Disparities, Hispanic Diversity

1 EnviroJusticePR Research Center, Puerto Rico  360 C. Ángel Buonomo, San Juan, 00918, USA
2 Harvard University Massachusetts Hall, Cambridge, MA 02138, USA
* Corresponding author’s e-mail: v.v@cambridge-research.org.uk

INTRODUCTION  
In demographic surveys, economic and environmental 
justice literature (Rodriguez, 2012; Mikati, 2018; 
Casey, 2023; Josey, 2023), Hispanics are portrayed as a 
homogeneous ethnic group united by a common language 
and centuries of  Spanish colonial experience. This broad 
unified classification is a significant oversimplification of  
a complex and nuanced sociocultural reality, as it ignores 
the rich diversity and multifaceted identities of  distinct 
groups within the Hispanic community. Filling this gap 
in the environmental economics literature requires more 
granular methodological frameworks that recognize and 
investigate the diversity within Hispanic populations.
People of  different Latin American national backgrounds 
differ significantly within the category in terms of  
socioeconomic status (SES), cultural traditions, history 
and future trends. US residents of  Hispanic descent 
come from diverse countries, including Mexico, Central 
America, South America, and the Caribbean. Each of  
these regions has a distinct history, cultural expressions, 
and social dynamics. Mexicans are the largest by far 
subgroup of  US Hispanics (55%), with a distinct 
cultural heritage that combines indigenous traditions 
with Spanish colonial influences. This experience differs 
significantly from the cultural backgrounds of  Hispanics 
from Caribbean countries, where African, indigenous 
Caribbean and European heritages got intertwined. 

The Caribbean cohort on its path to the US residency 
has more differences than similarities. Puerto Ricans 
have the advantage of  automatic US citizenship, which 
provides them with complete and reversible flexibility in 
terms of  destination and time of  their immigration to 
the mainland US. Cuban immigrants have much more 
favorable US immigration status than any other Hispanic 
country, ever since the Fidel Castro regime took over in 
the 1960s. In contrast, immigrants from the Dominican 
Republic and other smaller Caribbean nations have a 
much more difficult path to the US, as they do not have 
the above-mentioned preferences of  Cubans and Puerto 
Ricans, and yet they do not have a land border as a transit 
route available for Mexicans and Central Americans. 
Central American countries such as Guatemala, El 
Salvador, and Nicaragua have a history of  extended civil 
unrest, consistent economic challenges, lower levels of  
education and cultural traditions that differ significantly 
from those of  South Americans in Argentina, Brazil, and 
Chile. South American countries developed significantly 
independently over the past century hence their 
population has a wide range of  cultural diversity, from 
the European-influenced southern cone to the more 
indigenous Andean countries. Consequently, educational 
attainment, income levels, and health outcomes can vary 
widely among Caribbean, Mexican, Central, and South 
American cohorts, reflecting the diverse challenges and 



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Am. J. Environ Econ. 3(1) 93-104, 2024

opportunities encountered by these communities over 
the past century (Burchard, 2005). Therefore, it is crucial 
to apply the prism of  Environmental Justice to the 
Hispanic community with emphasis on its largest diverse 
components from various countries.
Florida was chosen for the analysis of  Hispanic diversity 
for several compelling reasons. It is the third largest 
US state by population, with a ratio of  urban and rural 
population close to the US national average. Florida is the 
second most popular tourist destination (after California) 
and a magnet for US retirees (21.6% vs. 17.3% of  people 
>65 years old). However, Florida’s population has a 10% 
lower household income vs. the US national average 
and a slightly higher percentage of  poverty prevalence, 
12.7% vs. 11.5% for the US average (Shrider, 2023). The 
number of  Floridians without health insurance (13.9%) 
is significantly higher than 9.3% for the US average, 
in agreement with prior observations (Branch, 2022).  
These factors are very relevant to the health focus of  
our research. Compared to the US average, Florida has 
a higher African-American population (17% vs. 13.6%) 
and a lower Asian population (3.1% vs. 6.0%). The most 

significant difference is a larger Hispanic/Latino segment 
in Florida at 25.9% vs. 19.1% in the US.
Despite being the sixth state in the US in the percentage 
of  the Hispanic population, Florida has by far the most 
diverse Hispanic community by country of  origin, as 
shown in Table 1. The largest Hispanic states (California, 
Texas, New Mexico, Arizona, and Nevada) are quite 
similar to each other in terms of  Hispanics origin, with 
Mexican descendants being by far the largest contingent 
of  Hispanics in those states. Notably, US residents 
of  Mexican descent represent 55% of  the overall US 
Hispanics, 77% of  Texan, and 82% of  Californian 
Hispanics. In contrast, Mexicans comprise only 9% of  
Hispanics in Florida, being the fourth region of  Hispanic 
origin after the Caribbean (55%), South American (24%), 
and Central American (12%), as shown in Table 1. 
The main goal of  this study is to explore the impact of  
ancestral differences among Florida Hispanics in several 
aspects: community geospatial factors, personal socio-
economic status (SES), environmental burden, and 
ultimately health outcomes.

Table 1: Hispanics by Country of  Origin, % of  Total Population in USA, the states of  Texas and Florida.
USA TX FL

Mexican 55% 77% 9%
Caribbean 22% 2% 55%
Puerto Rican 9.2% 0.1% 18%
Cuban 6.6% 1.6% 33%
Dominican 5.8% 0.3% 4.2%
Central American 17% 13% 12%
Salvadoran 6.6% 6.0% 1.2%
Guatemalan 4.7% 2.0% 2.6%
Honduran 3.2% 3.5% 3.0%
Nicaraguan 1.2% 0.4% 2.9%
Costa Rican 0.4% 0.1% 0.3%
Panamanian 0.4% 0.1% 0.5%
South American 13% 5% 24%
Colombian 4.0% 1.6% 9.2%
Ecuadorian 2.2% 0.2% 1.4%
Peruvian 2.2% 0.5% 2.7%
Venezuelan 2.1% 1.6% 7.2%
Argentinean 0.9% 0.2% 1.5%
Spaniard 0.7% 0.3% 0.7%
Chilean 0.5% 0.1% 0.5%
Bolivian 0.4% 0.0% 0.2%
Uruguayan 0.2% 0.0% 0.3%
Paraguayan 0.1% 0.0% 0.0%
Hispanic as % of  total population of  a state 19.1% 39.4% 25.9%

Source: US Census Bureau, 2020 Census Redistricting Data



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LITERATURE REVIEW
Environmental Justice area of  economic research focuses 
on identifying discriminatory disparities in the allocation 
of  environmental hazards across different population 
groups, particularly for people with lower incomes and 
less societal voice, such as racial and ethnic minorities. 
The ecosystem is highly complex, with numerous 
channels of  influence and feedback loops connecting 
various components.  Human health has deep genetic 
roots stemming from those regions’ development history 
and a diverse ancestor population. In general, the current 
population of  the Americas Hispanic region includes 
descendants of  three distinct ethnic groups: indigenous 
Indian tribes, Spaniards, and Africans brought by the 
slave trade over three centuries starting from the 17th 
century. The relative mix varies significantly by country, 
depending on the history of  people’s movements over 
centuries. In the Caribbean, the indigenous population 
mostly vanished, and newcomers absorbed the remaining 
few natives.  A large number of  Africans were transported 
to the Dominican Republic and Haiti, but to a lesser extent 
to Cuba and Puerto Rico, defining their currently different 
racial mix. In Central America and the northern part of  
South America, indigenous people coexisted and blended 
with Spaniards, while Latin Africans represented less than 
3-5% of  the total population. The importance of  tracing 
racial roots stems from the fact that Europeans, indigenous 
Indians, and Latin Africans have quite different disease 
predispositions based on their genetics and cultural habits 
such as prevalent diet, as noted by Buchard (2005).
More granular intra-ethnic research could logically begin 
with a more detailed understanding of  the population’s 
genetic predisposition to specific types of  diseases based 
on their heritage (Comonos, 2015). For example, it is 
well known that African Americans have a much higher 
prevalence of  asthma than other Americans, which can 
be attributed to both genetics and living conditions (Pratt, 
2015; Josey, 2023). Meanwhile, even after controlling 
for age, gender, and income, US Hispanics have a 
significantly higher incidence of  diabetes than the US 
white population (Flegal, 1991; Schneiderman, 2014; 
Fernandez, 2021). Overall, Hispanics had the highest 
diabetes prevalence (22.1%), followed by non-Hispanic 

African Americans (20.4%), Asians (19.1%), and non-
Hispanic whites (12.1%). Furthermore, Cheng (2019) 
reported heterogeneity in the distribution of  diabetes 
among US Hispanic adults of  various national origins: 
Mexicans (24.6%) have the highest percentage, followed 
by Puerto Ricans (21.7%), Cubans/Dominicans (20.5%), 
Central Americans (19.3%), and South Americans 
(12.3%). Body Mass Index (BMI) is the most commonly 
used measure of  obesity. The diabetes prevalence trend 
was generally aligned with the BMI trend: the highest for 
Mexicans at 30.5 and the lowest for South Americans at 
26.5, with other groups’ BMI close to 29 (CDC, 2023). 
For calibration, BMI ranges from 25 to 30 indicates 
overweight, and BMI greater than 30 indicates obesity. 
Importantly, US residents of  South American origin 
had a much lower frequency (5.9%) of  very high BMI 
> 35, compared to other US Hispanics: Mexicans (20%), 
Puerto Ricans (15.4%), and Cubans (13.8%). Higher 
levels of  obesity were found to be positively correlated 
with diabetes, though specific causality is more difficult 
to determine (Chobot, 2018). 
The task of  identifying, quantifying, regulating and 
monitoring environmental pollution sources is performed 
by the US Environmental Protection Agency (EPA), with 
the help of  various state and local authorities, setting 
standards and rules and enforcing compliance. The next 
step is to quantitatively assess the health of  a population 
with different socioeconomic statuses (SES), both “at a 
point in time” and longitudinally, when possible. Table 2 
depicts several major US Hispanic groups. Hispanics of  
Mexican descent make up the majority of  US Hispanics 
(55%). The next largest subgroup is from the Caribbean, 
accounting for 17%, followed by a smaller group from 
Central America (9%), South America (5%), and others 
(US Census, 2020).
While the above differences among Hispanics were 
discussed in the medical literature, there is a clear gap in 
existing economic and environmental literature, that still 
continues addressing Hispanics as one large homogenous 
category, despite apparent heterogeneity in this diverse 
ethnic group. Our study aims at filling this gap with a 
more focused emphasis of  identifying Hispanic sub-
groups and classifying their SES and health outcomes.

Table 2: Diversity of  US Hispanics by Different Origins
Mexico Caribbean Central 

America
South 
America

Pu
er

to
 R

ic
o

C
ub

a

D
om

in
ic

an
 R

.

Sa
lv

ad
or

G
ua

te
m

al
a

H
on

du
ra

s

C
ol

um
bi

a

Ve
ne

zu
el

a

Pe
ru

Sp
ai

n

Population in native 
country, M 

127.5 3.2 11.2 11.2 6.3 17.4 10.4 51.9 28.3 34.1 47.4 

US residents vs Native 
Country, %

29% 181% 21% 21% 39% 10% 10% 3% 2% 2% 2%

Population in US, Million 37.2 5.8 2.4 2.4 2.5 1.8 1.0 1.4 0.6 0.7 1.0



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The lower section of  Table 2 compares various 
socioeconomic factors, highlighting a wide range of  
outcomes. The median annual household income ranges 
from $50-52K for Caribbean and Central American 
descendants to $69K for Hispanics from Columbia and 
Peru (compared to the US average of  $75K). Aside from 
average income for a given cohort, two other factors are 
more important in terms of  health outcomes: poverty and 
a lack of  health insurance. Poverty rates range from 20-
26% for Caribbean and Central American descendants, 
while it is 11-13% for South Americans, similar to 
the US average of  12.4%. Health insurance is a major 
determinant in maintaining good health, and its availability 
is typically defined by two interrelated factors: level of  
household income and employment at mid-to-large US 
companies that typically provide health insurance to their 
employees. According to the data, Central Americans 
have the most challenging socioeconomic status, whereas 
South Americans are relatively well-positioned among US 
Hispanics. As a result, the Hispanic community in the 
United States varies greatly depending on their country 
of  origin, which has an impact on health (Rodriguez, 
2012; Velasco-Mondragon, 2016; Yanez, 2016; Shaw, 
2017). This is critical background to understand before 
analyzing the effects of  environmental pollution on 
health.

MATERIALS AND METHODS
The most recent US Decennial Census (2020) provided 
extensive demographic data such as population race/
ethnicity, age, education, income, and poverty. Distinct 
terms are assigned to five racial groups and also two ethnic 
groups: Hispanic or Latino (of  any race). Hispanics are 
defined as descendants of  Spanish-speaking countries, 
whereas Latinos include Hispanics as well as Latin 
American countries that speak Portuguese (Brazil) or 
French (Haiti). The American Community Survey (ACS) 
provides additional US Census data on demographics, 
social, economic, and housing. The ACS and US Census 
databases differ in frequency (annual vs. one-in-decade) 
and data collection (ACS’ sample estimates vs. official 

counts of  US Census). As a result, ACS has a margin 
of  sampling error, which is typically between 0.3% 
and 0.5% for larger categories such as the Hispanic 
population. Table 3 shows that our research approach 
defined independent and dependent research variables 
from categories of  environmental pollution, personal 
socioeconomic status, and health outcomes.
The collected data was checked for completeness across 
all variables to assure comprehensive analysis. The size 
of  the entire dataset was very large (>50,000 datapoints) 
to provide for robust statistical significance in all aspects, 
including focused analysis of  various Hispanic regional 
cohorts. Established statistical research methods were 
utilized across the entire study, including pair-wise 
correlations and multi-variable liner regression analysis.

Selection of  Independent and Dependent Variables
The US Environmental Protection Agency (EPA) 
provided environmental pollution data on diesel 
particulate matter (DZL), hazardous waste sites (HAZW), 
the proximity of  Superfund NPL slides (NPL), and RPM 
facilities. The personal Socio-Economic Status (SES) 
dataset was obtained from the US Census and included 
data on linguistic isolation (LINGO), unemployment rate 
(UEMPL), poverty PVRT (% of  tract population below 
100% US Federal level), and education (WHSD, defined 
as a percentage of  adults above the age of  25 without a 
high school diploma). 
A variety of  potential variables were examined, and four 
the most relevant were selected in each category. In case 
of  environmental contaminants, concentration of  diesel 
particles in the air is a clear indicator of  proximity to 
high-intensity roads.  Diesel exhausts by trucks are well 
known as the worst type of  land-based vehicle exhaust 
pollution, in contrast to passenger cars run on a much 
cleaner gasoline fuel. In addition to dynamic pollution 
sources, three dimensions of  static pollution sources 
were selected: proximity to Superfund sites, proximity to 
more general hazardous waste sites and finally, proximity 
to less impactful but most numerous Risk Management 
Plant (RPM) facilities. For SES group, the selected factors 

Foreign born 29% 2% 53% 50% 53% 58% 63% 57% 76% 59% 12%
U.S. citizens 81% 99% 82% 78% 66% 58% 51% 79% 51% 79% 95%
Median age in years 27.9 31 40 30.1 30.3 26.6 26.9 36.1 36 38 34.2
Bachelor's degree or more 15% 24% 30% 22% 13% 11% 14% 38% 57% 36% 40%
English proficient 74% 83% 64% 61% 56% 51% 47% 66% 56% 65% 95%
Median household income, 
$K/year

$59 $52 $59 $50 $61 $52 $50 $69 $65 $69 $74 

Homeowners 53% 34% 56% 31% 46% 34% 31% 53% 39% 56% 63%
Living in poverty 18% 21% 14% 20% 17% 23% 26% 12% 13% 11% 13%
Without health insurance 20% 8% 12% 10% 24% 34% 40% 13% 15% 12% 7%

Lower average income, 
higher poverty

Higher average  income, 
lower poverty

Source:  Pew Research Center, based on US Census data



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are most commonly mentioned in the environmental 
literature (Collins, 2016; Mikati, 2018; Liu, 2021; Josey, 
2023): poverty, lack of  education, unemployment and 
linguistic isolation from the prevailing English language-
based US culture. The four dependent variables are 

recognized by the US health authorities as more common 
major diseases (Rodriguez, 2012; Shaw, 2017). As shown 
on Figure 1, Florida population has a very uneven 
distribution of  health outcomes, stemming in a large part 
from the selected chronic diseases.

Table 3: Independent and Dependent Variables
Description of  Independent Variables
# Abbrev. Environmental Pollution Burden # Abbrev. Socio-Economic Status (SES)
1 DZL Diesel Particles Matter in the air 5 LINGO Linguistic Isolation 
2 HAZW Proximity of  Hazardous Waste Sites 6 UEMPL Unemployment rate
3 NPL Proximity to Superfund NPL Sites 7 PVRT Poverty (%, income < 100% US level)
4 RPM Proximity to Risk Management Plan facilities 8 WHSD Adults without High School Diploma 
Description of  Dependent Variables
# Abbrev. Health Equity - Dependent Variables
1 ASTM Asthma 
2 DBTS Diabetes 
3 HART Heart disease
4 SHLF Shorter Life Expectancy

Independent variables are expressed as percentiles 
(compared to the overall US range), making it much easier 
to compare very different variables using the same scale 
from 1 to 100. The proximity of  a given population tract 
to geospatial challenges or pollution sources resulted in a 
higher value of  independent variables (i.e. higher values 
indicate worse outcome for population). According to the 
US Centers for Disease Control and Prevention (CDC), 
the prevalence of  major chronic diseases such as asthma, 
diagnosed diabetes, coronary heart disease, and shorter 
life expectancy was used to calculate the value of  health 
equity-dependent variables.
The Health Equity Index (HEI) is formulated in the 
following equation for a given US Census tract (i):
HEIi = β0+ β1* DZL + β2* HAZW + β3* NPL+ β4* 
RPM + β5* LINGO + β6* UEMPL + β7* PVRT + β8* 
WHSD + εi  

HEI 
Index is the equally weighted average values of  asthma, 
diabetes and heart disease. All data were expressed in 
values from 1 to 100, as percentiles of  outcomes within 
the US. The lowest value of  1 was ascribed to lowest 
pollution, unemployment and other factors, while the 
value of  100 was the highest percentile within US, and 
value of  50 being the median across the US.

Florida as Research Subject
Prior to investigating the impact of  Hispanic origin on 
health outcomes under environmental pressure, it is 
preferable first to calibrate the urban-rural dimension, 
as the availability and quality of  US healthcare varies 
significantly depending on population density. Figure 1 
depicts the relative ranking of  all 67 Florida counties by 

health outcomes, defined as the equal weighting of  length 
and quality of  life. All counties were ranked from 1 as 
calculated by the County Health Ranking (https://www.
countyhealthrankings.org/). Lighter colors correspond to 
better health outcomes, while darker colors to relatively 
worse outcomes.
The Hispanic population is unevenly distributed across 
Florida, as illustrated in Figure 2. Cubans primarily live 
in Miami-Dade and nearby Broward counties in the 
southeast coastal Florida, whereas Puerto Ricans are 
concentrated in the greater Orlando metro area. The 
majority of  Mexicans live in rural areas in the state’s 
mid-south and northwest. The colored areas represent 
counties with majority of  Hispanic population from 
Puerto Rico, Cuba or Mexico.
This unequal distribution of  Hispanics by country of  
origin appears counterintuitive at first glance, given 
that the majority of  current Hispanic immigrants 
arrived in Florida relatively recently, within the last 50-
70 years, without any regional settlement constraints or 
preferences imposed by state or federal authorities. The 
highlighted majority-Hispanic counties are not adjacent 
to their home countries (as is frequently the case in 
Europe), and there were no specific industries designed 
to attract workers from specific countries. As a result, this 
map demonstrates the long-term power of  social bonds 
in attracting new immigrants to areas with a large existing 
population of  their compatriots.
Table 4 shows that four areas of  Florida were chosen 
for the current study to represent Hispanic majority 
from a specific country: Cuba, Puerto Rico, Mexico, 
and the Northern part of  South America (Columbia 
and Venezuela).  In each case, a specific Hispanic group 
accounted for the majority of  the population, ranging 



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Figure 1: Health outcomes for Florida counties.  Florida has a large Hispanic population of  various origins.

Figure 2: Florida counties of  major distinct Hispanic origin



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from 51% of  Mexicans in the rural south-central area 
to 92% of  the Cuban community prevalence in parts 
of  the metropolitan Miami-Dade County. The objective 
of  this focused segmentation was to explore difference 
in health outcomes based on the Hispanic country 
oforigin, district’s population density and other factors. 

A fifth racial minority group was added for comparison: 
African-American residents of  Miami-Dade County who 
live nearby the Cuban community in different densely 
populated neighborhoods. Results for ethnic majority-
minority areas were compared to one another, as well as 
the averages of  Florida and the US.

Table 4: Description of  Floridian Hispanic groups different by national origin
Minorities

Region / County Origin Urban / Rural HISP BLACK H + B ASIAN Other WHITE
Miami-Dade Cuba Highly Urban 92% 2% 94% 0% 0% 4%
Osceola Puerto Rico Suburban 64% 12% 76% 2% 0% 19%
Broward & Orange S. America Suburban 57% 12% 69% 3% 0% 24%
Hardee & Hendry Mexico Rural 51% 5% 56% 0% 0% 37%
Miami-Dade & Duval Black Urban 11% 84% 95% 0% 0% 4%
US Average Mix 28% 12% 40% 6% 1% 50%
FL State Avg Mostly Urban 23% 15% 38% 2% 0% 56%

Source: US Census Bureau, 2020 Census Redistricting Data

RESULTS AND DISCUSSION 
Table 5 displays the median levels of  environmental 
pollution, highlighting a significant difference in the 
concentration of  diesel fumes (DZL) in metropolitan 
areas versus rural areas: close to the 90th percentage in 
urban areas vs. 18th percentile in rural areas. The other 
three independent variables of  environmental pollution 
showed similar contrast in pollution level between cities 
and rural areas. Notably, the Black community had the 
highest exposure to all pollutants in majority-Black 
areas. Meanwhile, the average values of  environmental 
pollutants in Florida and the USA were closer to the 50th 
percentile.
As shown in Table 5, there was a significant divergence 
of  median outcomes in Socio-Economic Status (SES).  
All Hispanic minorities in their majority-population areas 

naturally had very high linguistic isolation (LINGO) – a 
traditional measure of  social disadvantage. However, it 
may not be a disadvantage in South Florida, where the 
Spanish language is so widely spoken that it has become 
the primary language in many large areas. Unemployment 
(UEMPL) and poverty (PVRT) rates were higher for 
all minorities compared to the baseline in Florida and 
the United States, with the Black community being the 
most disadvantaged, with the highest (worst) SES values. 
All five minority groups had higher medians for adults 
without a high school diploma (WHSD) factor, likely 
contributing to lower wages and higher unemployment. 
Overall, Table 5 values confirm that Hispanic and Black 
minorities face disproportionately adverse environmental 
and socioeconomic conditions, as was noted by Grineski 
(2013) and Collins (2016).

Table 5: Differentiation among Floridian Hispanic groups
Environmental Pollution Socio-Economic 

Status (SES)
Region / County

O
rig

in

U
rb

an
/ 

R
ur

al

W
H

IT
E

D
Z

L

H
A

Z
W

N
PL

R
PM

LI
N

G
O

U
E

M
PL

PV
R

T

W
H

SD

Miami-Dade Cuba Highly Urban 4% 92 53 78 42 98 37 65 81
Osceola Puerto Rico Suburban 19% 94 19 31 52 89 57 70 68
Broward & Orange S. America Suburban 24% 88 32 65 69 93 64 63 63
Hardee & Hendry Mexico Rural 37% 18 10 15 10 87 61 81 91
Miami-Dade & 
Duval

Black Urban 4% 87 59 94 88 61 87 85 81

US Average Mix 50% 55 54 55 52 60 52 50 53
FL State Avg Mostly Urban 56% 60 31 51 53 60 51 51 49



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Health outcomes included a higher prevalence of  
three chronic diseases and a shorter than statistically 
expected lifetime. The diversity of  asthma outcomes 
was much wider than expected based on environmental 
or socioeconomic factors: median values ranged from 4 
for Cubans to 91 for the Black population, implying that 
racial and genetic factors played a disproportionally larger 

role than the degree of  environmental toxins exposure 
by itself. Diabetes prevalence was higher in all minority 
groups, consistent with previous findings (Schneiderman, 
2014; Tessum, 2019). Mexicans and Blacks had higher 
rates of  heart disease, and the Black community had a 
significantly lower life expectancy, in agreement with 
Daya (2019) and Liu (2021).

Table 6: Mean values of  dependable variables for Floridian Hispanic sub-groups
Health Equity

Region/Country Origin Urban/Rural  ASTM DBTS HART SHLF
Miami-Dade Cuba Highly Urban 4 89 57 18
Osceola Puerto Rico Suburban 29 85 53 43
Broward & Orange S. America Suburban 18 69 37 41
Hardee & Hendry Mexico Rural 45 85 81 46
Miami-Dade & Duval Black Urban 91 96 81 90
US Average Mix 41 49 45 45
FL State Avg Mostly Urban 29 62 62 49

Overall, the observed diversity of  health outcomes 
exceeded the diversity of  environmental pollution 
impacts, confirming the ecosystem’s complexity and 
the significant disadvantage faced by Florida’s majority 
Hispanic and Black communities. 
The analysis of  pair correlations among eight independent 
and four dependent variables revealed several interesting 
observations, as shown in Table 7. There is no correlation 
between the prevalence of  Hispanics and unemployment 
– in contrast with many Western countries where recent 
immigrants have higher unemployment. However, there 
were positive correlations between Hispanic prevalence 
and lower income, along with lack of  high school 
education. Environmental pollution factors correlated 
amongst themselves, indicating their prevalence in 

the most heavily industrialized and densely populated 
regions. Furthermore, there was a significant positive 
correlation between diabetes and heart disease, implying 
that a portion of  the population is in poor health and 
affected by both diseases. There was a significant positive 
correlation between diseases and socio-economic status 
(SES) and a positive correlation between unemployment 
and low SES. As expected, there was high inter-correlation 
among SES factors. Overall, Florida’s SES ranking is very 
similar to the US national average. Because all the data was 
expressed in percentile ranking within the United States, 
the results were very easy to interpret, as lower values for 
all environmental, SES or health variables corresponded 
to better outcomes for the impacted population group.

Table 7: Summary of  factors’ pair correlations among variables for the entire state of  Florida (%)
Factor 1 2 3 4 5 6 7 8 9 10 11 12 13

HISP 1 % Hispanic * 45 31 28 1 67 -5 20 37 -16 11 -29 -23
DZL 2 Diesel Particles * 58 61 54 49 2 14 10 2 -5 -30 -12
HAZW 3 Hazard Waste Sites * 47 39 30 4 23 15 15 -2 -17 7
NPL 4 Superfund (NPL) * 41 32 7 15 11 11 -5 -28 0
RPM 5 RPM Facilities * 20 9 18 11 21 0 -3 12
LINGO 6 Linguistic Isolation * 7 30 39 4 14 -19 -7
UEMPL 7 Unemployment * 43 37 48 31 18 37
PVRT 8 Poverty (<100%) * 68 71 50 26 53
WHSD 9 Without High School * 61 58 26 48
ASTM 10 Asthma * 37 18 66
DBTS 11 Diabetes * 80 35
HART 12 Heart disease * 28
SHLF 13 Shorter Life Expectancy *

MAX 100% 97 98 99 99 99 99 99 99 99 99 99 99
MIN 0 3 1 2 0 12 0 0 0 0 0 0 0



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MEAN 23 60 31 52 54 60 51 51 49 29 62 62 49
MEDIAN 14 62 27 54 56 63 52 51 49 20 67 67 50
St. Dev 23 24 19 28 27 27 28 26 27 26 26 29 29
St. Dev / 
Mean

1.02 0.40 0.62 0.54 0.50 0.45 0.56 0.51 0.56 0.9 0.41 0.47 0.59

Following the completion of  the analysis of  the entire 
state of  Florida, it is beneficial to concentrate on areas 
with predominantly (>70%) Hispanic population and 
investigate pair correlations and median outcomes 
for that particular segment of  the Florida population, 
as demonstrated in Table 8. A clear and significant 
correlation exists between the prevalence of  Hispanics 
and higher exposure to sources of  environmental 
pollution, as well as lower levels of  education, higher 
diabetes, and heart disease, in agreement with Fernandez 
(2021) and Errisuriz (2024).
The lower half  of  the Table 7 contains important statistical 

data for the entire Florida dataset. Because all data points 
have already been normalized on percentiles within the 
US total population, the expected mean and median 
points should be around 50 if  all distributions were close 
normal and most US states were reasonably homogenous. 
Instead, in some aspects, Florida population is positioned 
well below (i.e. better) than the US average: 27th percentile 
in proximity hazardous waste sites, due to relatively low 
prevalence of  industrial manufacturing in tourism-driven 
Florida. In other aspects Florida is meaningfully above 
(worse) than the US averages, in prevalence of  diabetes and 
heart disease (both at 67th percentile).

Table 8: Summary of  factors’ pair correlations for Florida areas with majority-Hispanic populations
Factor 1 2 3 4 5 6 7 8 9 10 11 12 13

HISP 1 % Hispanic * 30 33 37 37 9 7 30 53 17 45 45 8
DZL 2 Diesel Particles * 51 60 48 47 -1 39 35 7 41 46 16
HAZW 3 Hazard Waste Sites * 75 60 28 18 52 41 38 33 46 34
NPL 4 Superfund (NPL) * 81 27 12 47 36 35 26 44 32
RPM 5 RPM Facilities * 14 4 41 35 28 28 46 28
LINGO 6 Linguistic Isolation * 22 47 48 32 58 60 23
UEMPL 7 Unemployment * 31 30 40 24 26 23
PVRT 8 Poverty (income 

<100% US threshold)
* 57 63 52 65 31

WHSD 9 Without High School Diploma * 55 75 73 24
ASTM 10 Asthma * 40 51 35
DBTS 11 Diabetes * 88 20
HART 12 Heart disease * 25
SHLF 13 Shorter Life Expect. *

MAX 100% 97 92 98 99 99 95 96 98 57 99 99 78
MIN 81 57    22 40    11 12 0 0 0 0 14 1 0
MEAN 91 88 53 75 48 96 40 62 76 7 83 53 24
MEDIAN 92 92 53 78 42 98 37 65 81 4 89 57 18
St. Dev 5 9 19 17 27 7 24 22 19 9 18 29 20
St. Dev / 
Mean

0.06 0.10 0.37 0.23 0.55 0.07 0.61 0.35 0.25 1.27 0.22 0.53 0.84

When comparing the correlation of  the two datasets 
(total Florida and majority Hispanic areas) in Tables 
7 and 8, it is notable that the majority-Hispanic areas 
in Florida have significantly higher than US average 
exposure to environmental pollution, diabetes, proximity 
to disadvantaged neighborhoods, and somewhat lower 
SES. Prior medical research has shown a high prevalence 
of  diabetes in the Hispanic community (Rodriguez, 2012; 
Shaw, 2017; Errisuriz, 2024).  
A few highlighted datapoints in Table 8 show 

disproportionately high exposure of  Hispanic-majority 
areas of  Florida to diesel particles (92th percentile) and 
yet very low asthma (4th percentile). This combination is 
counterintuitive, and could be a subject of  further research. 
By the sample design of  selecting majority-Hispanics areas, 
linguistic isolation is very high (98th percentile). However, 
in many Florida areas, Spanish has actually become the 
dominant local language, hence linguistic isolation is more 
acute for local English-speaking residents, not for Spanish-
speaking majority in those areas.



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Am. J. Environ Econ. 3(1) 93-104, 2024

As shown in Table 9, linear multi-variable regression 
analysis was conducted to supplement the pairs’ 
correlation analysis using all four environmental pollution 
factors and two socioeconomic factors: absence of  high 
school diploma (WHSD) and unemployment (UEMPL). 
This approach is conceptually similar to analysis of  CO2 
emissions and economic factors (Sabroso, 2023) and in 
determining the effect of  unemployment (Nojeem, 2023). 

The dependent variable is the Health Equity Index (HEI), 
an equally weighted three diseases (asthma, diabetes, and 
coronary disease). The higher value of  HEI is detrimental 
to the population. The HEI shows a positive correlation 
with the lack of  high school education (WHSD) and 
unemployment (UEMPL) with a high level of  statistical 
significance (p<0.01). These results agree with findings 
by Hipp (2010) and Grineski (2013).

Table 9: Linear multi-variable regression analysis of  environmental hazards and health outcomes
Independent Variables Abbrev. Coeff. t Stat P-value P-Signif.
Intercept 30.9578
Diesel Particles DZL -0.2146 -14.01 0.0000 * * * 
Hazardous Waste HAZW 0.0202 1.23 0.2169 not signif.
Superfund NPL -0.0679 -6.03 0.0000 * * * 
RPM Facilities RPM 0.1237 11.01 0.0000 * * * 
Without High School Diploma WHSD 0.4258 42.85 0.0000 * * * 
Unemployment UEMPL 0.1587 16.68 0.0000 * * * 

Dependent variable is Health Equity Index = Equally weighted 3 diseases (Asthma, Diabetes and Coronary Disease). Note that 
P-values are marked as the following: * p < 0.1, ** p < 0.05, and *** p < 0.01.

A significant negative correlation between the HEI, diesel 
and Superfund factors could indicate that rural areas are 
less efficient in providing healthcare than urban areas, as 
suggested by Mikati (2018). Earlier in this report Figure 
1 illustrates this phenomenon, which was observed by 
Branch (2022) and Liu (2021).  
It is impossible to know how long people affected by 
pollution have lived in a particularly polluted area, which 
is a natural limitation of  both our study and the majority 
of  other “point of  time” studies. Such information is not 
gathered by the United States Census, which is conducted 
once every ten years across the country. This unidentified 
longevity of  residence factor is of  utmost significance 
because it is obvious that environmental pollution has a 
cumulative impact, albeit non-linear, on the length of  time 
an individual is exposed to toxic substances (Morello-
Frosch, 2011; Tessum, 2019; Josey, 2023). 

CONCLUSION
As a country of  (relatively) recent immigrants, the United 
States is a true “melting pot” of  various nationalities. As a 
result, one significant limitation of  using this nationality-
specific approach is identifying local areas with a 
sufficiently high percentage of  residents with heritage 
of  a given country or region. It is still possible, but it 
requires careful consideration of  the research topic and 
robust granular databases.
This study discovered that the diversity of  Hispanic 
origin leads to very different socioeconomic and health 
outcomes. A more nuanced understanding of  intra-
Hispanic group differences is required to develop more 
tailored and effective strategies in research methodologies, 
service allocation, and policymaking in general.

The results of  pair correlation tests revealed significant 
relationships among a number of  different factors, 
particularly among socioeconomic and health equity 
groups. The interdependence of  factors that influence 
health equity in Florida was highlighted by the findings 
of  a multivariable regression analysis, which revealed 
significant associations among groups of  factors: 
environmental pollutants, socioeconomic characteristics, 
and health outcomes.
The results demonstrate that the overall Hispanic 
communities in Florida are disproportionately affected 
by pollution and socioeconomic challenges and that 
these communities also experience significantly worse 
health equity outcomes: a higher disease prevalence and 
a shorter life span. Within Hispanic communities, South 
Americans and Cuban cohorts have better health and 
SES outcomes despite exposure to higher environmental 
pollution in their highly urban areas. Mexican descendants 
have cleaner air environments in rural Florida areas, but 
not as favorable health and SES outcomes. A comparison 
with the African-American majority community in Miami-
Dade County (adjacent to Cuban-majority communities 
in the same country) shows that African Americans in 
south Florida have the most challenging outcomes across 
all twelve researched factors. This study may encourage 
researchers to focus on intra-segmentation within large 
US ethnic and racial minority groups. This should help in 
identifying critical aspects of  diversity among Hispanics 
and African-American communities that were previously 
overlooked by many environmental economists. Thus, a 
new more granular approach should allow to see many 
distinct vibrant colors when demographics are viewed via 
the prism of  the environmental justice.



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