EHA Final Proposal
Bachelors Attainment Between Hispanics and Non Hispanic Whites - Exploring the Hispanic Experience
Introduction
The US has seen its overall Hispanic population increase by almost a quarter between the years of 2010 - 2020 (Passel et al., 2022). Over many decades it has been shown the Hispanic population has increased as the non-Hispanic white population continues to show a steady decline (Poston & Bouvier, 2017). This population increase has occurred primary along those southern border states in particularly Texas were it has benefited from its Hispanic led population growth shown in the 2020 Census which awarded them 2 new Congressional seats (Passel et al., 2022; Wadington & Santucci, 2021).
Aside from the political implications to be had from this increase in this ethnic population nationally it is important to understand the current status of the US Hispanic population over time. This is to help further understand the Hispanic Experience concerning educational attainment and the ability to be able to achieve their academic aspirations without experiencing systematic/cultural barriers. This research will observe the current educational outcomes of those earning a bachelors degree between Hispanics and non-Hispanic whites. These groups will be compared across different variables that will help observe this Hispanic Experience.
To be able to help ensure that those Hispanics are able to achieve their complete educational aspirations every generation since migration will help the overall generational upward mobility helping build family cumulative wealth.
Literature Review
The Hispanic Experience in the US
When observing the historical treatment of Latinos in the US it has been very negative treating them as second class citizens (Takaki, 2008; Telles & Ortiz, 2008). When observing those who migrate into the US many Latinos potentially experience downward assimilation being growing further from obtaining educational attainment hurting their chances of economic stability (Portes & Rumbaut, 2001). When observing educational attainment since-generation of migration you observe declines in educational outcomes by the 3rd and 4th generation especially for Mexican Americans (Telles & Ortiz, 2008). This experience of downward assimilation that negatively effects educational outcomes of immigrant children and those generations thereafter for Latinos is due to racialization of those Latinos.
This racialization that creates stereotypes for Latinos as violent criminalize usually occurring for those with darker skin color compared to their lighter skin counterparts (Lamas, 2018; Vasquez, 2010). Those who are self-ascribed Latinos, may be perceived by others as a different ethnic group due to their outwardly appearance or due to their physical facial features, skin color, and other attributes of their appearance (Lamas, 2018; Vasquez, 2010; Vazquez et al., 1997). The journey as a migrate also differs from many other ethnicities being that many of those Latinos are extremely poor seeking asylum in the US meaning that they have much less overall human capital compared to other migrants of other ethnic groups who may have higher capital. (Portes & Rumbaut, 2001; Telles & Ortiz, 2008)
Barriers are then created for those who are children of migrant parents and those children who are migrants themselves. Many fear legal repercussions so they do not pursue higher education, fear of facing stigmatization due to the negative political policies that are placed on those of Latin origin, and many of those migrant families do not know how to navigate the school system to help receive resources for their child to help the go to college. (Arellanes et al., 2019, Perez et al., 2009; Portes & Rumbaut, 2001)
Theoretical Paradigms
The following theoretical paradigms are presented to help explain this Hispanic Experience.
Status Attainment Theory
Status Attainment Theory states how socioeconomic variables mainly parental educational attainment, fathers occupation, and family income significantly predict the educational status of their children (Blau & Duncan, 1978; Hauser, 1972). This prediction in educational status also went hand in hand with other human capital variables that led to also predicting the child’s occupational and financial status (Blau & Duncan, 1978; Hauser, 1972).
Status Attainment Theory shows how upward mobility can be passed on from the parents to their children creating generational upward mobility and overall cumulative family resources. The other effect of this theory shows how those of Hispanic descent are at a disadvantage because most migrants coming to the US are poor with little human capital, are poorly educated, and have to attempt to assimilate into a non-Hispanic culture (Portes & Rumbaut, 2001; Telles & Ortiz, 2008). Due to having little human capital these migrants tend to live in impoverished neighborhoods that are usually low quality in housing and public schooling (Rodriguez, 2013; Telles & Ortiz, 2008). Also, with the lack of parental education they’re children do not have access to professional social networks that children of more highly educated parents may have access to (Telles & Ortiz, 2008).
Segmented Assimilation Theory
With segmented assimilation theory it is described as 4 key factors, “1) the history of the first generation; 2) the pace of acculturation among parents and children and its bearing on normative integration; 3) the barriers, cultural and economic, confronted by second-generation youth in their quest for successful adaptation; 4) the family and community resources for confronting these barriers.” (Portes & Rumbaut, 2005:986)
This theory helps to begin to observe those first initial generations that live in the US. From the type of starting point that these Hispanics undergo it is much further behind other ethnic groups that are already have US citizenship. Yet as its shown for those 3rd and 4th generations since immigration for Mexican Americans they have higher rates of dropping out of high school compared to earlier generations (Telles & Ortiz, 2008). This decline in generational educational attainment is also evident for the worsening mental and physical health outcomes of those migrants that move to the US as duration lived in the US increases overtime (Garcia Coll & Marks, 2009; Williams, Mohammed, Leavell, & Collins, 2010). This is a constant deleterious effect that is found for those ethnic minorities of color who migrate to the US and stay.
Self-Efficacy Theory
Self-efficacy theory focuses on the individual experience of students. It observes the mastery individuals undergo that differentiate by outcomes due to either positive or negative reinforcement and expectations of them (Bandura, 1977).
When observing self-efficacy for the Hispanic student, its seen on a spectrum of the ability to pass as non-Hispanic white and those who can not pass as those of having European descent (Vasquez, 2010). With the term Hispanic it is a blanket term for those of Latin origins meaning anything from being Cuban, Mexican, Brazilian, Honduran, and all other ethnic origins that are tied with Latin America (Telles & Ortiz 2008). Observing amongst Hispanics due to their ethnic origins they experience different levels of positive and negative interaction when migrating to the US.
The interaction is based on the stereotype that is placed on them because of their Latin country of origin, the ability to pass as a non-Hispanic white, citizenship status, and sex. Regarding country of origin those of Cuban descent are traditionally seen as refugee’s fleeing communism so are received in a more of a positive light compared to those from Mexico or other ethnic origins that are racialized as being “Mexican” and stigmatizing them as “illegals” “unintelligent”, and “criminals”. (Lamas, 2018; Telles & Ortiz, 2008; Vasquez, 2010)
Academic Momentum Theory
Academic Momentum Theory (Adelman, 2006) which observes how a students mastery over time are affected by the student’s high school background, academic courses taken, and ones progress at the college level that affect one’s trajectory of finishing a bachelor’s degree.
Perna (2006) Student College Choice
The conceptual model created by Perna (2006) is the multi-level design of what affects a students decision base on policy characteristics, institutional characteristics, structural supports and barriers, cultural capital, supply of resources, and expected costs and benefits as some examples of the varying layers. This conceptual design captures every level that affects the college choice of a student.
Each of the proposed levels by Perna (2006) observes different aspect that effect the ability of the student of choosing to go to college. Much of the levels present are those more systematic and macro level determinate of being able to go to college for that student. Of these macro level determinates would be Layer 3 which is the spatial context of the school itself and the emphasis the school places on colleges and the emphasis that’s placed on the school for college recruitment.
Layer 4 is the macro portion of the diagram that shows how the demographic characteristics, economic characteristics, and public policy effect those college choices.
With these two presented levels they are both at the school/district level as well as state level determinants.
Methods
For this analysis of the Hispanic Experience I will observe the possible differences in educational attainment between Hispanics and Non Hispanic Whites. The outcome variable to help measure educational attainment will be whether one received a Bachelors degree or not. It is hypothesized that
h1) those who are Non Hispanic whites will be at greater risk of earning a Bachelors degree as opposed to Hispanics.
The data being used will be the National Longitudinal Study of Youth (NLSY) following the cohort from 1997. For this analysis three waves of data are observed: 2004, 2010, and 2019. In the year of 1997 many of the respondents are still in high school and they are followed throughout high school graduation throughout the rest of their life course. This sample is representative of the national population.
I begin our study by observing the year of 2004. This is about a few years after the study began for the 1997 cohort. Those students in 1997 are in high school and junior high school. A lag in observation is allowed for time to pass for those respondents to go through high school and to be able to have enough time to earn a Bachelors degree.
I present three survival curves for each wave of data to observe those cross sectional relationships between the age when the respondent had earned a Bachelors degree. Next a series of Cox Regression models will be conducted to observe those possible interaction effects that may occur effecting the risk of the age of earning a Bachelors degree between Hispanics and Non Hispanic whites. The different variables that will be observed will be sex, if the father earned a Bachelors degree, if the mother earned a Bachelors degree, the respondents perceived skin tone, and the region of residence of the respondent in 2019.
The Variables
Time Constant Variables
Year of Bachelors 2004
Bachelors degree or higher = 1 & all lesser educations are labled 0. This variable is a part of the outcome variable to be able to observe those who earned a Bachelors in 2004.
Year of Bachelors 2010
Bachelors degree or higher = 1 & all lesser educations are labeled 0. This variable is a part of the outcome variable to be able to observe those who earned a Bachelors in 2010.
Year of Bachelors 2019
Bachelors degree or higher = 1 & all lesser educations are labeled 0. This variable is a part of the outcome variable to be able to observe those who earned a Bachelors in 2019.
Hispanic
Hispanics are coded as 0 & Non Hispanic whites are coded as 1, all other ethnicities are excluded. This will be the key indicator variable of the study.
Sex
Women are coded as 0 & Men are coded as 1. It is important to control and observe across sex because they are perceived differently. Hispanic men are seen as threatening and violent whereas Hispanic women are seen as exotic and sexualized thus having a more positive reception in the US. (Vasquez, 2010)
Bachelors Degree of the Father
Dads Bachelors degree or higher = 1 & all lesser educations are labeled 0. This is to help observe the importance of the educational attainment of the parents where its found that those Latinos who have atleast one parent that has a bachelors degree they are more likely themselves to earn a degree, especially for the 1st and 2nd generation since immigration (Telles & Ortiz, 2008).
Bachelors Degree of the Mother
Moms Bachelors degree or higher = 1 & all lesser educations are labeled 0. This is to help observe the importance of the educational attainment of the parents where its found that those Latinos who have atleast one parent that has a bachelors degree they are more likely themselves to earn a degree, especially for the 1st and 2nd generation since immigration (Telles & Ortiz, 2008).
Respondents Skin Tone
Respondents perceived skin tone. 0 to 3 are those perceived with whiter skin tones. Those 4 to 10 are those perceived to have darker skin tones. This is to help observe possible differences between lighter and darker skinned Hispanics which are treated and perceived differently (Lamas, 2018; Vasquez, 2010).
Respondents Region of Residence in 2019 living in the Southern US.
Respondents region of residence in 2019. Those 1 (Northeast), 2 (North), and 4 (West)coded into 0 and 3 (South) coded into 1.
Being able to observe these southern states historically were extremely racist towards people of color victimizing Hispanics by various lynchings, the “zoot suit” riot, and the recent Family Separation Policy that was implemented by the Trump Administration (Obinna & Ohanian, 2020; Telles & Ortiz, 2008). These same southern regions being molded by Jim Crow, school segregation, red-lining, and gentrification has its effects on Hispanics living in this region effecting their experience in the US (Telles & Ortiz, 2008).
Censoring
This is to censor for those who do not have a bachelors degree yet. Starting at the year of 2004 which was done to allow time to pass for those students in 1997 to be able to graduate through high school and give them enough time to complete a bachelors degree.
Time varying variables
The two time varying variables that were created were for the year 2010 and 2019. This analysis observes 3 waves of data (2004, 2010, 2019) from this 1997 cohort.
Results
Initial Survival Analysis Observations
fit %>%
ggsurvfit()+
xlim(18, 25)Warning: Removed 2 row(s) containing missing values (geom_path).
## Wave of 2004This first survival model observes the year 2004. In this first survival model we observe really no difference between Hispanics and Non Hispanics whites regarding the risk of earning a Bachelors degree. This observes the age interval 18 - 25.
fit1 %>%
ggsurvfit()+
xlim(18, 30)Warning: Removed 15 row(s) containing missing values (geom_path).
## Wave of 2010The second survival model that is observed is for the year 2010. The age intervals are now between 18 - 30. From this model we can see early really no difference between the age of 18 to about 23 years old. After this age the difference begins to widen with Hispanics being at greater risk of not earning a Bachelors degree compared to Non Hispanics whites. This relationship was not present in the first survival model observing the year 2004.
fit2 %>%
ggsurvfit()+
xlim(18, 45)Warning: Removed 2 row(s) containing missing values (geom_path).
## Wave of 2019The third survival model that is observed is for the age intervals between 18 - 45 for the year of 2019. This continued statistically significant difference in the risk of earning a Bachelors degree; with Hispanics being at greater risk of not earning a Bachelors compared to Non Hispanic whites. Although from this model it seems to come close to a state of no statistical difference at the age of 40.
Risk Set
Pivot
Cox Regression Hispanics and Skin Tone
f1 <- survfit(Surv(time = age_enter, event = BAtran)~his1+skintone, data=e.long1)
f1%>%
ggsurvfit()
#Fit the model
fitskintone<-svycoxph(Surv(time = age_enter, event = BAtran)~his1*skintone, design=des2)
summary(fitskintone)Stratified 1 - level Cluster Sampling design (with replacement)
With (168) clusters.
svydesign(ids = ~psu, strata = ~strata, weights = ~wt, data = e.long1,
nest = T)Call:
svycoxph(formula = Surv(time = age_enter, event = BAtran) ~ his1 *
skintone, design = des2)
n= 1758, number of events= 638
coef exp(coef) se(coef) robust se z
his1Non Hispanic -0.29195 0.74681 0.13857 0.15224 -1.918
skintone1 0.12395 1.13196 0.29353 0.38821 0.319
his1Non Hispanic:skintone1 0.04464 1.04565 0.44616 0.50153 0.089
Pr(>|z|)
his1Non Hispanic 0.0551 .
skintone1 0.7495
his1Non Hispanic:skintone1 0.9291
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
his1Non Hispanic 0.7468 1.3390 0.5541 1.006
skintone1 1.1320 0.8834 0.5289 2.423
his1Non Hispanic:skintone1 1.0457 0.9563 0.3913 2.794
Concordance= 0.516 (se = 0.006 )
Likelihood ratio test= NA on 3 df, p=NA
Wald test = 3.96 on 3 df, p=0.3
Score (logrank) test = NA on 3 df, p=NA
(Note: the likelihood ratio and score tests assume independence of
observations within a cluster, the Wald and robust score tests do not).This first Cox Regression is used to measure the risk of earning a Bachelors degree between Hispanics and Non Hispanic whites in association with skin tone. When observing the model; the variable Hispanic was found with a marginal effect with those who are Hispanic about 26% less likely to earn a Bachelors degree compared to Non Hispanic whites. When placed together with the interaction term (*) the marginal effect is lost.
Cox Regression Hispanics and region of residency in the South
f2 <- survfit(Surv(time = age_enter, event = BAtran)~his1+south, data=e.long1)
f2%>%
ggsurvfit()
#Fit the model
fitSouth<-svycoxph(Surv(time = age_enter, event = BAtran)~his1*south, design=des2)
summary(fitSouth)Stratified 1 - level Cluster Sampling design (with replacement)
With (168) clusters.
svydesign(ids = ~psu, strata = ~strata, weights = ~wt, data = e.long1,
nest = T)Call:
svycoxph(formula = Surv(time = age_enter, event = BAtran) ~ his1 *
south, design = des2)
n= 1758, number of events= 638
coef exp(coef) se(coef) robust se z Pr(>|z|)
his1Non Hispanic -0.28344 0.75319 0.16428 0.19090 -1.485 0.138
south1 -0.09592 0.90854 0.08482 0.10466 -0.916 0.359
his1Non Hispanic:south1 0.05319 1.05463 0.26314 0.23421 0.227 0.820
exp(coef) exp(-coef) lower .95 upper .95
his1Non Hispanic 0.7532 1.3277 0.5181 1.095
south1 0.9085 1.1007 0.7400 1.115
his1Non Hispanic:south1 1.0546 0.9482 0.6664 1.669
Concordance= 0.524 (se = 0.011 )
Likelihood ratio test= NA on 3 df, p=NA
Wald test = 4.84 on 3 df, p=0.2
Score (logrank) test = NA on 3 df, p=NA
(Note: the likelihood ratio and score tests assume independence of
observations within a cluster, the Wald and robust score tests do not).This second Cox Regression is used to measure the risk of earning a Bachelors degree between Hispanics and Non Hispanic whites in association with living in the Southern region of the US. When observing the model; non of the variables in the model are statistically significant.
Cox Regression Hispanics only
f3 <- survfit(Surv(time = age_enter, event = BAtran)~his1, data=e.long1)
f3%>%
ggsurvfit()
#Fit the model
fithis<-svycoxph(Surv(time = age_enter, event = BAtran)~his1, design=des2)
summary(fithis)Stratified 1 - level Cluster Sampling design (with replacement)
With (168) clusters.
svydesign(ids = ~psu, strata = ~strata, weights = ~wt, data = e.long1,
nest = T)Call:
svycoxph(formula = Surv(time = age_enter, event = BAtran) ~ his1,
design = des2)
n= 1758, number of events= 638
coef exp(coef) se(coef) robust se z Pr(>|z|)
his1Non Hispanic -0.2695 0.7637 0.1282 0.1390 -1.939 0.0526 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
his1Non Hispanic 0.7637 1.309 0.5816 1.003
Concordance= 0.515 (se = 0.006 )
Likelihood ratio test= NA on 1 df, p=NA
Wald test = 3.76 on 1 df, p=0.05
Score (logrank) test = NA on 1 df, p=NA
(Note: the likelihood ratio and score tests assume independence of
observations within a cluster, the Wald and robust score tests do not).The third Cox Regression is used to measure the risk of earning a Bachelors degree between Hispanics and Non Hispanic whites only. When observing the model; Hispanics are found to have a marginal effect with Hispanics about 24% less likely to earn a Bachelors degree compared to their Non Hispanic white counterparts.
Cox Regression Hispanics and Bachelors Degree of the Father
f4 <- survfit(Surv(time = age_enter, event = BAtran)~his1+DADBA, data=e.long1)
f4%>%
ggsurvfit()
#Fit the model
fitDADBA<-svycoxph(Surv(time = age_enter, event = BAtran)~his1*DADBA, design=des2)
summary(fitDADBA)Stratified 1 - level Cluster Sampling design (with replacement)
With (168) clusters.
svydesign(ids = ~psu, strata = ~strata, weights = ~wt, data = e.long1,
nest = T)Call:
svycoxph(formula = Surv(time = age_enter, event = BAtran) ~ his1 *
DADBA, design = des2)
n= 1758, number of events= 638
coef exp(coef) se(coef) robust se z Pr(>|z|)
his1Non Hispanic -0.14940 0.86123 0.15119 0.20259 -0.737 0.461
DADBA1 0.38760 1.47344 0.08091 0.07996 4.848 1.25e-06
his1Non Hispanic:DADBA1 -0.25017 0.77867 0.29109 0.33251 -0.752 0.452
his1Non Hispanic
DADBA1 ***
his1Non Hispanic:DADBA1
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
his1Non Hispanic 0.8612 1.1611 0.5790 1.281
DADBA1 1.4734 0.6787 1.2597 1.723
his1Non Hispanic:DADBA1 0.7787 1.2842 0.4058 1.494
Concordance= 0.546 (se = 0.012 )
Likelihood ratio test= NA on 3 df, p=NA
Wald test = 25.43 on 3 df, p=1e-05
Score (logrank) test = NA on 3 df, p=NA
(Note: the likelihood ratio and score tests assume independence of
observations within a cluster, the Wald and robust score tests do not).The fourth Cox Regression is used to measure the risk of earning a Bachelors degree between Hispanics and Non Hispanic whites in association with Fathers who have earned a Bachelors degree. When observing the model; Fathers who have earned a Bachelors are statistically significant at the p<001 level (***) with those respondents who have a Father that has a Bachelors degree are about 47% more likely to earn a Bachelors degree themselves as opposed to those respondents whos Father did not earn a Bachelors degree. The variable Hispanic is not statistically significant in this model. When these two variables are placed with a interaction term that significance is lost.
Cox Regression Hispanics and Bachelors Degree of the Mother
f5 <- survfit(Surv(time = age_enter, event = BAtran)~his1+MOMBA, data=e.long1)
f5%>%
ggsurvfit()
#Fit the model
fitMOMBA<-svycoxph(Surv(time = age_enter, event = BAtran)~his1*MOMBA, design=des2)
summary(fitMOMBA)Stratified 1 - level Cluster Sampling design (with replacement)
With (168) clusters.
svydesign(ids = ~psu, strata = ~strata, weights = ~wt, data = e.long1,
nest = T)Call:
svycoxph(formula = Surv(time = age_enter, event = BAtran) ~ his1 *
MOMBA, design = des2)
n= 1758, number of events= 638
coef exp(coef) se(coef) robust se z Pr(>|z|)
his1Non Hispanic -0.11864 0.88813 0.15090 0.16979 -0.699 0.484708
MOMBA1 0.35608 1.42772 0.07984 0.09342 3.811 0.000138
his1Non Hispanic:MOMBA1 -0.39651 0.67266 0.29044 0.32562 -1.218 0.223336
his1Non Hispanic
MOMBA1 ***
his1Non Hispanic:MOMBA1
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
his1Non Hispanic 0.8881 1.1260 0.6367 1.239
MOMBA1 1.4277 0.7004 1.1888 1.715
his1Non Hispanic:MOMBA1 0.6727 1.4866 0.3553 1.273
Concordance= 0.552 (se = 0.012 )
Likelihood ratio test= NA on 3 df, p=NA
Wald test = 21.44 on 3 df, p=9e-05
Score (logrank) test = NA on 3 df, p=NA
(Note: the likelihood ratio and score tests assume independence of
observations within a cluster, the Wald and robust score tests do not).The fifth Cox Regression is used to measure the risk of earning a Bachelors degree between Hispanics and Non Hispanic whites in association with Mothers who have earned a Bachelors degree. When observing the model; Mothers who have earned a Bachelors are statistically significant at the p<001 level (***) with those respondents who have a Mother that has a Bachelors degree are about 42% more likely to earn a Bachelors degree themselves as opposed to those respondents whose Mother did not earn a Bachelors degree. The variable Hispanic is not statistically significant in this model. When these two variables are placed with a interaction term that significance is lost.
Cox Regression Hispanics and sex
f6 <- survfit(Surv(time = age_enter, event = BAtran)~his1+sex11, data=e.long1)
f6%>%
ggsurvfit()
#Fit the model
fitsex<-svycoxph(Surv(time = age_enter, event = BAtran)~his1*sex11, design=des2)
summary(fitsex)Stratified 1 - level Cluster Sampling design (with replacement)
With (168) clusters.
svydesign(ids = ~psu, strata = ~strata, weights = ~wt, data = e.long1,
nest = T)Call:
svycoxph(formula = Surv(time = age_enter, event = BAtran) ~ his1 *
sex11, design = des2)
n= 1758, number of events= 638
coef exp(coef) se(coef) robust se z
his1Non Hispanic -0.31902 0.72686 0.17542 0.20024 -1.593
sex11Women -0.03268 0.96785 0.07875 0.08456 -0.386
his1Non Hispanic:sex11Women 0.10524 1.11098 0.25660 0.28409 0.370
Pr(>|z|)
his1Non Hispanic 0.111
sex11Women 0.699
his1Non Hispanic:sex11Women 0.711
exp(coef) exp(-coef) lower .95 upper .95
his1Non Hispanic 0.7269 1.3758 0.4909 1.076
sex11Women 0.9679 1.0332 0.8200 1.142
his1Non Hispanic:sex11Women 1.1110 0.9001 0.6366 1.939
Concordance= 0.506 (se = 0.012 )
Likelihood ratio test= NA on 3 df, p=NA
Wald test = 4.09 on 3 df, p=0.3
Score (logrank) test = NA on 3 df, p=NA
(Note: the likelihood ratio and score tests assume independence of
observations within a cluster, the Wald and robust score tests do not).The final Cox Regression model is used to measure the risk of earning a Bachelors degree between Hispanics and Non Hispanic whites in association with the respondents sex. When observing the model; none of the variables in the model are statistically significant.
Grambsch and Therneau (1994) Test
Overall, none of the models are statistically significant meaning that all variables are implying proportionality of effect. Being that none of the variables are correlated with the timing of the transition. This furthers the reliability of the observed Cox Regression Models.
skintone
fit.test1<-cox.zph(fitskintone)
fit.test1 chisq df p
his1 5.24e-04 1 0.98
skintone 1.00e-03 1 0.97
his1:skintone 5.83e-05 1 0.99
GLOBAL 1.48e-03 3 1.00plot(fit.test1, df=2)
South Region Residency
fit.test2<-cox.zph(fitSouth)
fit.test2 chisq df p
his1 5.68e-04 1 0.98
south 4.92e-06 1 1.00
his1:south 4.03e-05 1 0.99
GLOBAL 6.11e-04 3 1.00plot(fit.test2, df=2)
Hispanics
fit.test3<-cox.zph(fithis)
fit.test3 chisq df p
his1 0.000536 1 0.98
GLOBAL 0.000536 1 0.98plot(fit.test3, df=2)
Bachelors Degree for Fathers
fit.test4<-cox.zph(fitDADBA)
fit.test4 chisq df p
his1 7.54e-04 1 0.98
DADBA 7.53e-05 1 0.99
his1:DADBA 1.84e-05 1 1.00
GLOBAL 8.16e-04 3 1.00plot(fit.test4, df=2)
Bachelors Degree for Mothers
fit.test5<-cox.zph(fitMOMBA)
fit.test5 chisq df p
his1 7.40e-04 1 0.98
MOMBA 9.06e-06 1 1.00
his1:MOMBA 1.56e-04 1 0.99
GLOBAL 8.64e-04 3 1.00plot(fit.test5, df=2)
sex
fit.test6<-cox.zph(fitsex)
fit.test6 chisq df p
his1 5.62e-04 1 0.98
sex11 2.78e-04 1 0.99
his1:sex11 1.18e-05 1 1.00
GLOBAL 9.25e-04 3 1.00plot(fit.test6, df=2)
Conclusions
From the findings overall the hypothesis (h1) was not supported by analysis of the various Cox Regression Models. Only at a couple of time was there only but a marginal effect had between Hispanics and Non Hispanic whites concerning the risk of earning a Bachelors degree. With model one observing skin tone it is interesting that the skin tone variable was not able to take away the marginal effect had on the Hispanic variable. Meaning that evening accounting for the respondents skin color they are still slightly facing a educational disparity in terms of earning a Bachelors degree over the life course in the US. This is controlled for with the interaction term. This marginal effect with Hispanics is found again with only observing Hispanics in the model.
The two variables with the largest statistical significance would be the variables of observing the Mother’s and the Father’s educational attainment of whether or not they earned a Bachelors degree. Both variables showed over 40% of their children more likely to earn a Bachelors degree. When these variables are placed into interaction effects with Hispanics the statistical significance is lost for both variables in both models respectively.
The Grambsch and Therneau (1994) Tests conducted for each model further solidifies the reliability of the results that were generated.
When observing Hispanics between the years of 2004 and 2019 although there may be found some slight differences in educational attainment. That difference is lost especially as the cohort ages. The importance of parental educational attainment is found throughout theses models for both the Mother and the Father.
Future research is to be done for the different instances of entering into college to observe any possible barriers in college enrollment and application rejections. The quality of education that the Hispanic individual would receive to help observe different instances of “undermatching”. The type of occupation after graduation would be interesting to observe any possible differences in occupational types between Hispanics and non-Hispanic whites as well as occupation status or possible income differences.
Also for future research this 1997 cohort should be compared to the 1979 cohort to observe any potential differences. As seen in the 1997 cohort the difference of earning a bachelors degree seemed to not be statistically significant from the data but this may change for the 1979 cohort. It is important to observe all sides of the Hispanic Experience to properly understand how we’ve come to our current state of the Hispanic population concerning educational attainment.
To continue to observe the those rates of dropping out of high school is also of great importance. Although it is important to observe those Hispanics applying and undergoing the college path but to also observe those Hispanics who do not take this path is just as much of importance. If continued high school drop out rates continue to persist especially for Mexican Americans as generations pass a persistently growing percentage of the US population will be with a high school diploma that will struggle to compete with a ever growing collegiate educated population in the US. This would create poverty almost impossible to get out of for this population.
To continue to observe the Hispanic Experience is crucial as more of the US population becomes Hispanic. We must ensure that all Hispanics have the opportunity to go to college so that true family wealth is created and sustained for many generations.
References
Arellanes, Viramontez Anguaino, R. P., & Lohman, B. J. (2019). Bettering the educational attainment for Latino families: How families view the education of their children. Journal of Latinos and Education, 18(4), 349–362. https://doi.org/10.1080/15348431.2018.1426465
Bandura, A. 1977. Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215. doi: https://doi-org.libweb.lib.utsa.edu/10.1037/0033-295X.84.2.191
Blau, Peter, and Otis Dudley Duncan. 1978. The American Occupational Structure. New York: Free Press.
García Coll, C., & Marks, A. K. 2009. Immigrant stories: Ethnicity and academics in middle childhood. New York: Oxford University Press.
Hauser, Robert M. 1972. “Disaggregating a Social-Psychological Model of Educational Attainment.” Social Science Research 1 (2): 159–88.
Lamas, A., & Briscoe-Smith, A. 2018. The Salient Effects of Colorism Among Mexican American College Males (ProQuest Dissertations Publishing). Retrieved from http://search.proquest.com/docview/2120421031/
Obinna, & Ohanian, M. M. Z. 2020. Uncertain aspirations: Latino students and dropout in the United States. Race, Ethnicity and Education, 23(5), 654–672. https://doi.org/10.1080/13613324.2018.1497967
Passel, Jeffrey S., Lopez, Mark H., & Cohn, D’Vera. 2022. U.S. Hispanic population continued its georgraphic spread in the 2010s. Pew Research Center.
Perna. 2006. Studying College Access and Choice: A Proposed Conceptual Model. In Higher Education (pp. 99–157). Springer Netherlands. https://doi.org/10.1007/1-4020-4512-3_3
Perez, W., Espinoza, R., Ramos, K., Coronado, H. M., & Cortes, R. 2009. Academic resilience among undocumented Latino students. Latino Journal of Behavioral Sciences, 31(2), 149–181.
Poston, & Bouvier, L. F. 2017. Population and society : an introduction to demography (Second edition.). Cambridge University Press.
Portes, & Rumbaut, R. G. (2005). The Second Generation in Early Adulthood: Introduction: The Second Generation and the Children of Immigrants Longitudinal Study. Ethnic and Racial Studies, 28(6), 983–999. https://doi.org/10.1080/01419870500224109
Rumbaut, Rubén, and Alejandro Portes, editors. 2001. Ethnicities: Children of Immigrants in America. University of California Press.
Rodriguez. 2013. Unpacking the black box: Estimating the high school-level effects of undermatching among underrepresented students. ProQuest Dissertations Publishing.
Takaki, Ronald. 2008. A different mirror: a history of multicultural America (1st rev. ed.). Back Bay Books/Little, Brown, and Co.
Telles, E., & Ortiz, V. 2008. Generations of exclusion : Mexican Americans, assimilation, and race . New York: Russell Sage Foundation.
Vasquez, J. 2010. Blurred Borders for Some but not “Others”: Racialization, “Flexible Ethnicity,” Gender, and Third-Generation Mexican American Identity. Sociological Perspectives, 53(1), 45–72. https://doi.org/10.1525/sop.2010.53.1.45
Vazquez, L. A., Garcia-Vazquez, E., Bauman, S. A., & Sierra, A. S. 1997. Skin color, acculturation, and community interest among Mexican American students: A research note. Hispanic Journal of Behavioral Sciences, 19(3), 377-386. doi: 10.1177/07399863970193009
Wadington, Katie & Santucci, Jeanine. 2021. Texas will gain 2 congressional seats. Seven states to lose 1 seat, Census Bureau data shows. USA Today.
Williams DR, Mohammed, SA, Leavell J, & Collins C. 2010. Race, socioeconomic status, and health: Complexities, ongoing challenges, and research opportunities. Annals of New York Academy of Sciences 1186: 69-101.