A Deeper Dive into Texas Racial Patterns
Understanding Texas County Diversity
The Lone Star State spans over 268,000 square miles and has a population of over 29 million people. Texas is home to a diverse variety of cultures, traditions, and landscapes. From the fast paced cities of Houston and Dallas to the beautiful landscapes of the Hill Country, Texas contains a unique blend of urban cities and rural towns However, observing racial data brings to light a troubling reality of disparities in equality between Black, White, and Asian citizens.
The data highlights a significant difference between the percentages of Black, White and Asian populations in Texas counties. Across the state there is a notable contrast with a lower percentage of Black and Asian individuals compared to their White counterparts. Based on the census api data there are 1,511,069 Asian Americans, 3,552,579 Black Americans, and 17,293,460 White Americans in the state. This disparity becomes evident when observing the extremes of the distribution in the histograms. While only one county in the entire state has a Black population exceeding 30%, there are no counties with less than a 37% White population share. On top of this there is only one county in the state of Texas that has a greater than 20% Asian population. This wide margin seems to indicate significant issues in racial demographic patterns, with White people maintaining a significant portion of the population in just about every county in the state.
Plotting this data on a scatter plot reveals insights into patterns between these different races. Looking at White percentage vs Black percentage in all texas counties we can begin to see a few patterns. Higher percentages of Black people are typically found in counties with larger populations such as Austin, Dallas, and Houston. However, as the population decreases we tend to see a higher percentage of White people.
Similarly, when we plot White percentage vs Asian percentage we see a similar pattern where Asian citizens tend to have higher percentages of the population in and around metropolitan areas. Interestingly the two highest percentages of Asian Americans in Texas are located in counties that are suburbs of major cities. Fort Bend is located southwest of Houston with a population of 832,607 and Collin County is located north of Dallas. This could be a result of strong Asian communities formed there over the years.
Houston, the fourth largest city in the United States, boasts a population exceeding 4.7 million residents. Situated within Harris County, it stands as a diverse metropolis. Approximately 19% of its populace identifies as Black, with Asians constituting only 7%. White residents make up the majority at around 46%. Despite its size, Houston’s demographic makeup highlights the significant dominance of White residents, with Black and Asian populations collectively comprising just a quarter of its inhabitants.
The largest percentage of Black people are found in Jefferson County, located along the southeastern border of the state. Wedged between the Gulf of Mexico and Louisiana, Jefferson county contains a population of 254,942. Of that population White people still make up a majority with 47% with Black people making up 33% of the county’s population and Asian’s making up a mere 4%.
Observing the graphs we can infer that counties with a higher percentage of White people tend to be smaller and more rural. The top 5 counties in Texas with a percentage of White people greater than 80% all contain populations less than 10,000. The lowest percentage of White people are found within Hudspeth County. Hudspeth County is found in the western part of the state along the border of Mexico and contains a population of 3,329. The percentage of White people in this county is 37% and the Black and Asian population is almost non-existent at 0.007% and 1%. Further emphasizing the dominance White people have in rural counties.
In examining the demographic disparities within Texas Counties, it becomes apparent that historical and regional factors could likely play a significant role in the distributions. While these factors provide some insight, they fail to fully explain the variation observed. To dive deeper into these dynamics, a regression analysis was conducted. The results reveal an R-squared value of 0.112 and a coefficient of -0.1834 when comparing changes in the Black percentage to the White percentage. This suggests that approximately 11.2% of the variation in the Black percentage can be attributed to changes in the White percentage. While the analysis comparing Asian percentages to White percentages gave an R-squared value of 0.107 and a coefficient of -0.0617. Suggesting that 10.7% of the changes in the Asian percentage is a result of the White percentage .These findings indicate that there are additional significant factors at play in this relationship beyond just changes in the White percentage. This highlights the complexity of demographic dynamics and underscores the need for further exploration to fully understand the factors shaping Houston’s demographic landscape.
Socioeconomic factors could play a significant role in these racial disparities. Access to quality education, healthcare, housing, and employment opportunities are often unequal, with Black communities disproportionately affected by poverty, unemployment, and lack of access to resources. Looking deeper, I attempted to gain a better understanding of these issues by comparing the percentage of White, Black, and Asian citizens next to median income.
By observing the percentage of these three races compared to median income we can draw several conclusions. First, counties with a higher population tend to have higher median incomes. Second, counties with a lower population tend to have lower median incomes. By running a regression analysis on these variables we can start to quantify this data. For the White percentage, the model gave an R-squared value of 0.102, indicating that approximately 10.2% of the variation in median income can be explained by changes in the White percentage. The negative coefficient of -4.073e+05 suggests an inverse relationship between the White percentage and median income, implying that as the White percentage increases, median income tends to decrease. Conversely, the analysis on the Black percentage resulted in an R-squared value of 0.071, with a positive coefficient of 6.191e+05. This indicates that approximately 7.1% of the variation in median income is attributed to changes in the Black percentage, suggesting that as the Black percentage increases, median income tends to increase as well. Similarly, for the Asian percentage, the model gave an R-squared value of 0.257, with a positive coefficient of 3.42e+06. This indicates a strong relationship with approximately 25.7% of the variation in median income can be explained by changes in the Asian percentage, suggesting a positive association between the two variables.
OLS Regression Results
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Dep. Variable: Median Income R-squared: 0.102
Model: OLS Adj. R-squared: 0.099
Method: Least Squares F-statistic: 28.65
Date: Sat, 04 May 2024 Prob (F-statistic): 1.95e-07
Time: 15:51:55 Log-Likelihood: -3370.4
No. Observations: 254 AIC: 6745.
Df Residuals: 252 BIC: 6752.
Df Model: 1
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — —
White Percentage -4.073e+05 7.61e+04 -5.352 0.000 -5.57e+05 -2.57e+05
const 3.372e+05 5.6e+04 6.023 0.000 2.27e+05 4.47e+05
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Omnibus: 368.767 Durbin-Watson: 2.029
Prob(Omnibus): 0.000 Jarque-Bera (JB): 44477.212
Skew: 6.899 Prob(JB): 0.00
Kurtosis: 66.342 Cond. №13.2
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OLS Regression Results
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Dep. Variable: Median Income R-squared: 0.071
Model: OLS Adj. R-squared: 0.067
Method: Least Squares F-statistic: 19.18
Date: Sat, 04 May 2024 Prob (F-statistic): 1.74e-05
Time: 15:52:13 Log-Likelihood: -3374.7
No. Observations: 254 AIC: 6753.
Df Residuals: 252 BIC: 6761.
Df Model: 1
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — —
Black Percentage 6.191e+05 1.41e+05 4.380 0.000 3.41e+05 8.98e+05
const 3217.0759 1.25e+04 0.257 0.797 -2.14e+04 2.78e+04
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Omnibus: 373.204 Durbin-Watson: 2.091
Prob(Omnibus): 0.000 Jarque-Bera (JB): 45557.230
Skew: 7.057 Prob(JB): 0.00
Kurtosis: 67.073 Cond. №15.8
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OLS Regression Results
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Dep. Variable: Median Income R-squared: 0.257
Model: OLS Adj. R-squared: 0.254
Method: Least Squares F-statistic: 87.20
Date: Sat, 04 May 2024 Prob (F-statistic): 5.37e-18
Time: 15:52:08 Log-Likelihood: -3346.3
No. Observations: 254 AIC: 6697.
Df Residuals: 252 BIC: 6704.
Df Model: 1
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — —
Asian Percentage 3.42e+06 3.66e+05 9.338 0.000 2.7e+06 4.14e+06
const 350.9516 9148.717 0.038 0.969 -1.77e+04 1.84e+04
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Omnibus: 379.332 Durbin-Watson: 1.982
Prob(Omnibus): 0.000 Jarque-Bera (JB): 57099.434
Skew: 7.165 Prob(JB): 0.00
Kurtosis: 75.041 Cond. №45.6
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In conclusion, this data highlights significant differences between Black, White, and Asian populations across all Texas counties. White individuals maintain a significant portion of the population in almost every county. Larger cities like Houston tend to have higher percentages of Black and Asian residents, but even then they both still remain a minority in comparison to White people. Jefferson County stands out for its relatively high percentage of Black residents, while Harris county stands out for its high percentage of Asian residents. These disparities could reflect deep historical and socioeconomic factors, like systemic racism and unequal access to resources, however more research would need to be done to fully understand the complexities.
