Generational Shifts in Language Use among U.S.

Latinos:

Mobility, Education and Occupation

By Jeremiah Spence, Joseph Straubhaar, University of Texas at Austin;

Viviana Rojas, University of Texas at San Antonio

Abstract

The role of language and linguistic assimilation among Latinos has a

direct impact on both education and occupation in terms of social

mobility. The relationship can be examined with a generational

context as language usage changes from first generation immigrants to

third generation immigrants. The specific question being addressed

herein is whether language or ethnicity had more impact on Latinos'

mobility in terms of educational achievement and occupational

prestige. Results presented in this paper imply the importance and

impact of the maximization of the accumulation of linguistic capital

in order to accelerate the acquisition of educational attainment.

Keywords: Latinos, Language, Education, Occupation

1

Generational Shifts in Language Use among U.S.

Latinos:

Mobility, Education and Occupation

The role of language and linguistic assimilation among Latinos has a

direct impact on both education and occupation in terms of social

mobility. The relationship can be examined with a generational

context as language usage changes from first generation immigrants to

third generation immigrants. The specific question being addressed

herein is whether language or ethnicity had more impact on Latinos'

mobility in terms of educational achievement and occupational

prestige. Results presented in this paper imply the importance and

impact of the maximization of the accumulation of linguistic capital

in order to accelerate the acquisition of educational attainment.

This study focuses on language issues, as they are connected to

Hispanic ethnicity and to success in educational and occupational

mobility. This study employs a fairly novel methodological approach,

incorporating socioeconomic genogram techniques for analyzing family

social mobility trajectories over time. While the study attempted to

directly measure the media use by previous generations in the family

system via the socioeconomic genogram, those were not reliable enough

to use in the statistical analysis. Results presented in this paper

imply the importance and impact of the maximization of the

2

accumulation of linguistic capital in order to accelerate the

acquisition of educational attainment.

Literature Review

In order to understand how immigrants attempt to facilitate the

accumulation and augmentation of their social networks and knowledge

in the acculturation process, this study relies on the construct of

social capital. Pierre Bourdieu (1983, p. 243) defined social capital

as "the aggregate of the actual or potential resources which are

linked to possession of a durable network of more or less

institutionalized relationships of mutual acquaintance and

recognition." This was utilized as the dominant framework for this

research.

Within the framework of social capital, developing predominantly out

of sociological literature, varying capitals were examined, including

economic, cultural and linguistic capitals. Bourdieu posits that the

role of capital is a defining variable in the orientation of agents

within an overall social space, which is used in the “first dimension,

according to the overall volume of capital they possess and, in the

second dimension, according to the structure of their capital, that

is, the relative weight of the different species of capital: social,

economic and cultural, in the total volume of their assets” (Bourdieu

1989, p. 17).

As referenced in the works of Pierre Bourdieu (1990a), Daniel Bertaux

(1994), Paul Thompson (1994), and Jorge Gonzalez (1995), the uses of3

varying types of media (old vs. new) were examined within the context

of generational stratification. Daniel Bertaux and Paul Thompson

focus much of their joint research on families and social mobility

building upon years of intergenerational interviews in Britain and

France (1997). These have served to demonstrate how “some families

transmit particular occupations while most try to maintain their

social positions by adaptations, how mobility may be a consequence of

family discord as well as ambition, and the different paths followed

by men and women” (Bertaux and Thompson 2005, p. 1).

Bertaux and Thompson (1997) found the distinctions made by Bourdieu

between the three main kinds of family assets or ‘capital’ (economic

capital, cultural capital and social capital) that discuss concepts of

reproduction and transmissibility within families particularly useful

in their work. Transmissibility and the family systems concept lie at

the core of Bertaux and Thompson’s work (1993; 1997; 2005). Bertaux

and Thompson found the family systems approach to be considerably

useful in the understanding of transmission in practice. González

integrated the use of family histories into his research on cultural

fields among varying publics in Mexico (1997). The integration of

family histories allowed for the observation of social trajectories

(occupational, spatial, familial and educational) across at least

three generations. Each accumulated family history allowed the

researchers to identify and examine dozens of successful and

unsuccessful trajectories (González 1995). In addition to the basic

family history, Bertaux, Thompson, and González all used genograms, a

family tree with key items of information on social mobility and, in

4

the case of González, cultural resource/media use, included about

ancestors.

Building upon both the family systems structure utilized by González

and Bertaux, as well as revisiting the original genogram literature in

the field of family counseling (McGoldrick and Gerson 1999), a new

genogram tool was constructed and implemented.

Immigration and Generation

This work roughly aligns with Herbert Gans’ (1992) “bumpy-line theory

of ethnicity”, which provides the following context for the

understanding of assimilating immigrating ethnic groups to the United

States: roughly that there is a general trend towards assimilation

which can be framed by an overall generational dynamic (although

outside factors or barriers can interfere with the assimilation

process) (Alba and Nee 1997). Alejandro Portes and Dag McLeod (1999)

note that the face of modern America has changed drastically due to

successive waves of immigration (mostly from developing countries,

particularly in Latin America) and points out that the Latino

originated population increased 53 percent from 1980 to 1990 to a

total of 22.4 million (U.S. Bureau of the Census 1993). Portes and

McLeod emphasize that the long-term success of these waves of

immigration will hinge on the experiences of the second-generation

immigrants, and continue, “their economic and social fate is bound to

have a lasting influence on the character of the ethnic communities

created by contemporary immigration” (Portes and McLeod 1999, p. 374).

5

Literature in the field has indicated growing schisms in educational

attainment between differing immigrant ethnic groups where Asians

immigrants are moving ahead and Latinos are falling behind or

“confronting serious educational handicaps” (Portes and McLeod 1999,

p. 374; Hirschman and Wong 1986; Matute-Bianchi 1991; Suarez-Orozco

1987; Zhou and Bankston 1998). Portes and McLeod emphasize the

relative importance of educational achievement and subsequent economic

and social adaptation. Additionally, there exist contextual barriers

that transcend the individual because of societal prejudices and

subsequently inhibit the capacity of the individual to successfully

assimilate or adapt to the United States. One of many prejudices that

affect Latino immigrants is that while many Mexican immigrants enter

the country illegally, all Mexicans - even those who entered the

country legally- are subjected to harassment by immigration

authorities and are targeted by nativist prejudices (Chavez 1988;

Cornelius 1982).

The first-and-a-half generation (in the case of a parent immigrating

at a very young age) or the second generation immigrant is the key

point of analysis for Portes and McLeod, which they explain is the

crucial point in the assimilation trajectory where a family either

fully assimilates or fails to assimilate. This is, of course, mostly

born out in resulting economic and social success in larger society.

The educational achievement study of Portes and McLeod (1999) clearly

outlines that Mexican-American children do significantly worse than

their non-Mexican-American peers in both public and private schools.

While their article does not provide a conclusive explanation of the

6

reason for this variance, the research conducted for this study will

hopefully provide some insight into this trend.

Social Capital: A Defining Construct

This research thus attempts to frame the question of language and

linguistic assimilation within the larger scheme of social, cultural,

and economic factors through family trajectories (Black 1996; Acosta

2001). The family history methodologies dwell in the theories of

social mobility and stratification, transmissibility and family status

of Daniel Bertaux (1994). They are very useful for examining minority

and immigrant groups’ efforts to achieve social mobility and adapt to

new communities while transmitting their values to their children.

The family trajectory approach takes the family as the unit of

analysis, as the site where social status and social mobility is

constructed for its members. Parents attempt to achieve or obtain

various capitals and resources, which they try to pass on to their

children, but “… while the expectations of status achievement that are

projected on to children are obviously related to (parent’s) social

status, the concrete resources they are able to pass on to their

children, especially the key resources of insider’s information about

the rules of the game and interpersonal connections, are clearly situs

bound” (Bertaux, Thompson et al. 1997). Bourdieu utilizes the concept

of situs in his explanation of fields and capital, where he frames situs

as the present and potential situation of agents or institutions

within networks of objective relations (Wacquant 1989, p. 37).

7

Family status can be described in terms of the amount of economic,

social, and cultural capital accumulated by the family as a unit that

cannot be passed as such from parents to children. Parents can only

provide access to or transmit certain resources or assets to their

children. Transmissibility of an element of status as a resource is

directly proportionate to its degree of objectivation, and reversely

proportionate to its degree of subjectivation (Bertaux, Thompson et

al. 1997). Because many elements have a low degree of

transmissibility, transmissions of status are frequently implemented

by transforming a resource into a condition of action, such as

choosing a better school or the right place to find someone to marry

(Bertaux, Thompson et al. 1997).

Obtaining access to information about multiple generations of a family

permits a sustained analysis of the transmission of status within

families and provides a standardized structure that allows for the

comparison of class trajectory among families and demographic cross

sections. The capacity for making comparisons across demographic

groups is key for understanding the differences between the

transmissibility trend between Hispanic and non-Hispanic immigrant

families. More specifically, the vast majority of the variables

collected in the socioeconomic genogram data collection process allow

for the direct or indirect measurement of trends, trajectories and

transmissibility between generations. One example is the trend in

educational achievement. It is possible to map changes in educational

achievement from generation to generation within a specific family,

and to compare trends between families and/or groups of families.

8

The socioeconomic genogram technique was a concerted effort to attempt

to create a decidedly quantitative aspect to the examination and

understanding of the different strategies that individuals have taken

to overcome structural constraints such as poverty, illiteracy,

segregation and so forth. The variables that will be most involved in

this analysis are measures of educational and occupational prestige,

as well as measures of ethnicity and language consumption.

Additionally, the variables that measure immigrant generation and age

served as filtering variables in the pre-analyses process.

Anthony Giddens (1993), in contrast, provides the concept of

structuration that stresses individual agency over social structural

conditions to understand how individuals can use the resources

available for survival and/or upper social mobility: “…human agency

and social structure are in a relationship with each other, and it is

the repetition of the acts of individual agents which reproduces the

structure. This means that there is a social structure - traditions,

institutions, moral codes, and established ways of doing things; …”

(Rogers and Gauntlett 2003, p. 5).

Linguistic Capital

Jeroen Smits and Aye Gunduz-Hogor (2003) describe language as an

essential element of both the identity of an ethnic group and the

capacity of the cultural group to transmit that identity to subsequent

generations. However, they also note that a ‘multi-linguistic’9

structure of a nation may place ethnic and language-minority groups at

a significant disadvantage. Bourdieu (1991) describes the ability

to speak the dominant language of a country with fluency as a social

resource that can be measured and transmitted to subsequent

generations. Linguistic capital (as Bourdieu defines it) can be

transferred into other forms of capital, such as economic or social

resources, and, in as much, assist those who obtain a functional

degree of linguistic capital in achieving social and economic success.

Smits and Hogor (2003) explain that although Bourdieu emphasizes the

symbolic value of linguistic capital and the related symbolic barrier,

there exists a very real socio-economic barrier to those with a low or

diminished degree of linguistic capital. This research falls on the

line between these two concepts.

The impact of language ideology in society has been theorized by

Pierre Bourdieu (1990a) in his sociology of practice. Bourdieu

conceives language as being part of a larger system that he calls the

field of power. He argues that behind the unity of most standard

languages lie power relations, unifying administrations, economy and

state formation, or governance (Hezfeld, 1996). Bourdieu explains that

there is a linguistic market whenever someone produces utterances that

receivers are capable of assessing, evaluating, and setting a price on

them (Bourdieu 1993). The price a language competence will receive in

the market will depend on the laws of price formation specific to that

market. More than linguistic competences, individuals participate in

the linguistic market with different degrees of linguistic capital,

allowing them to obtain different degrees of linguistic profits.

Bourdieu states that linguistic capital is power over the mechanism of10

linguistic price formation; the power to make the laws of price

formation operates to one’s advantage and to extract the specific

surplus value.

The process of English-skills acquisition among Hispanics has been at

the center of studies about immigrant assimilation in which language

is often used as a proxy for acculturation (Cuellar, Arnold &

Maldonado, 1995). Studies reveal that the Spanish and English

languages overlap in Latinos’ everyday lives. For example,

approximately 75 percent of Latino adults routinely watch television

in English and Spanish (DeSipio, 2003). However, Latino migrants’

English proficiency does not take place in a vacuum. There is more

depth to this problem than simply having competence in the language of

the new land. Linguistic minorities negotiate their way through a

majority-language world with a different exchange of power in the

linguistic market of the dominant society. Bourdieu presents the

concept of linguistic marketplace or exchange in relationship to an

economic metaphor of linguistic exchange, “which is established within

a particular symbolic relation of power between a producer, endowed

with a certain linguistic capital, and a consumer (or a market), and

which is capable of procuring a certain material or symbolic profit”

(Thompson 1991, p. 66; Silver 2004). It is precisely this conflict

and demands that the linguistic marketplace in the United States

places upon linguistic-minority immigrants; as a result, immigrants,

particularly the newly arrived and the older generations, are

constructed as communities who do not pursue assimilation (Huntington,

2004). However, the burden on both society and the immigrant families

is on the second generation immigrants in order to guarantee that they11

are adequately integrated or assimilated into the linguistic

marketplace. The data presented in the analysis will demonstrate this

is essential to the increase of both occupational and educational

prestige. The general tendency in all immigrant groups is for English

to become the dominant language by the second generation, with fluent

bilingualism being the exception rather than the rule (Portes and

Rumbaut 1990, p. 219; Rumberger and Larson 1998).

Bourdieu conceives the linguistic market as a site of domination where

linguistic legitimacy is privileged and where excluded individuals are

subject to the effect of censorship particularly when major political,

social and cultural stakes are involved (Bourdieu 1993). Language also

opens channels to acquire cultural capital in places such as schools

where language can be a severe barrier.

J.R. Slate, M. Manuel, and J.R. Brinson (2002) have indicated that the

language spoken at home impacts the attitudes toward and the use of

Internet and computer technology among Hispanic college students.

Students whose primary language at home was Spanish were more likely

to learn how to use the Internet through class, journals, books,

friends and colleagues or library instruction lessons than students

whose primary language at home was English (who were more likely to

learn how to use the Internet at home). The authors stated that this

finding was consistent with Latino students’ lower rate of home

computer ownership and internet access. The researchers suggested the

importance of developing more qualitative studies to further

understand how first-generation college students negotiate with the

new technologies. 12

In our own study with Latino informants in Austin, we found that

language has a great impact in the fields of education and occupation,

and that it can help to explain the difference in educational and

occupational achievements between Mexicans born in Mexico, and

Mexican-Americans and Latinos born in the United States. We also found

that English-language skills acted as a barrier in the use of

information and communication technologies (ICTs), primarily in the

case of the first and second generations of Latino migrants. These

findings reinforce the general notion that language can act as a

factor of discrimination and conditional assimilation among migrants

equipped with less cultural capital. However, this pattern prompts us

to think of practical ways we could contribute to improve the current

status of newly arrived immigrants and older Latinos.

Some scholars consider that efforts to increase computer literacy in

underserved communities must go beyond physical access and

connectivity and consider the role of cultural factors. M. Warschauer

(2002) explains that content, language, literacy and education, as

well as community and institutional structures must all be taken into

account if meaningful access to new technologies is to be provided.

The Socioeconomic Genogram: Building Upon a Theme

A family genealogies perspective also informs the use of socioeconomic

genograms (Spence 2006; 2007), a methodology that offers a path for

collecting, organizing and analyzing socio-economic, human capital and

media use information about three generations of a family from a

single informant or from three generation interviews, as done by13

Bertaux and Gonzalez above (Spence 2006). The socioeconomic genogram

provides insights into the comparative rates of transmission of class

prestige, educational status, and possibly cultural and/or social

capital over several generations across a spectrum of families. The

major differentiation between the socioeconomic genogram and that of

previous genogram implementations is a shift away from the utilization

of the data collected from the genogram interview in either a visual

(McGoldrick and Gerson 1999) or qualitative context (Bertaux 1994;

Gonzalez 1995). Rather, the socioeconomic genogram, from a

methodological perspective, focuses on how the underlying data

structure or family data matrix can be utilized in a distinctly

quantitative manner to inform complementary research projects.

The socioeconomic genogram technique was designed utilizing a family

systems perspective (Bertaux and Thompson 2005; Bowen 1966; Spark

1974; Menniger 1985; Whitaker 1970; Boszormenyi and Spark 1973),

which, as Bertaux articulates, transmits family, myths, models, and

denials and, “provide[s] for most people part of the context in which

their crucial life choices must be made, propelling them into their

own individual life paths” (Bertaux 2007, p. 36). This family systems

perspective provides a framework with the objective of being able to

study a wide array of vectors within a multigenerational family

history or family trajectory framework. While the quantitative

interpretation of the genogram or social genealogy as developed in the

socioeconomic genogram was designed with the broad aspiration of

developing a technique for measuring the rates of development and

transmission of not only social capital, but other capitals, as well,

this thesis, however, will focus on a single implementation and14

resulting analysis that focuses on the role of linguistic capital

versus ethnicity in relation to social trajectory.

Thus, using the data collected and developed using the socioeconomic

genogram technique, the first specific research question that will be

examined here is: what, if any, is the relationship between linguistic

capital and immigrant generation status (i.e. how many generations has

the family or individual in the family been in the United States) in

Hispanic immigrant families? The second specific research question

is: which has the most significant relationship with the achievement

of educational and occupational prestige, language (being primarily

English speaking or Spanish speaking) or ethnicity (being Anglo,

Hispanic or Mexican--born in Mexico) in relation to changes in social

class trajectory over three generations?

In order to explore the relationship between linguistic capital,

ethnicity and social class, a series of means tests was devised

utilizing educational and occupational prestige scales as the point of

comparison between ethnicity and language. Additionally, to explore

the relationship between media use and Hispanic immigrants, both means

tests and crosstabs were used to provide a base of analysis. These

ANOVA-derived means tests and related crosstabs informed the

construction of two multiple regressions that provide the basis for

both the analysis and resulting conclusions presented in subsequent

chapters.

Data collection

15

The interview typically takes place with a single informant who is

also the index person (IP) on the genogram and requires 30 – 45

minutes to complete. The informant is guided through the

socioeconomic genogram form by the interviewer. The socioeconomic

genogram as applied at the University of Texas at Austin collected the

following data: First name, Year of birth, Place of birth, Level of

education (measured in years – i.e. high school grad = 12 years),

Ethnicity/race, Religion, What generation immigrant to the US is the

individual (i.e. a person who is born outside the U.S. and dies in the

U.S. is a first generation immigrant), Primary occupation, Three

primary places of residence, Primary language, Secondary language,

Primary media in youth and Primary media in adulthood.

The data collection form utilized in the data collection portion of

the technique is in a tabular format in which the columns represent

the aforementioned fields and the rows represent extended family

members of whom the interviewer will collect the data. The current

socioeconomic genogram dataset is built upon 56 interviews. The

interviewer collects all of the above information about the following

family members in relation to the Index Person (IP):

Index person (primary individual or informant)

Spouse of index person

Mother of index person

Mother’s mother (maternal grandmother of IP)

Mother’s father (maternal grandfather of IP)

Father of index person

Father’s mother (paternal grandmother of IP)

Father’s father (paternal grandfather of IP)16

Sibling(s) of index person

Spouse(s) of sibling(s)

Sibling(s) of father

Spouse(s) of sibling(s) of father

Sibling(s) of mother

Spouse(s) of sibling(s) of mother

There are also several additional lines to be used as needed for step-

families, second or third spouses, adopted children, or whatever other

out of the ordinary/linear family dynamic that may present itself.

The collected data then forms a matrix which provides a three

generation snap-shot of a family, including spouses. When genograms

from various families and informants are combined in a single data

base, then substantial coverage of a variety of kinds of families is

provided. In the initial application of the socioeconomic genogram to

56 individuals the resulting dataset consisted of 904 individuals,

almost equally divided between male and female, covering a time period

from 1870-2006, 17 countries, and 7 languages.

Coding and Organization of the Data

In order to attempt to attain the maximum level of compatibility and

applicability of the research data, we made a decision to attempt to

harmonize the coding of the data to international norms and well-

tested scales whenever possible. One of the major alignments that we

sought was with the International Stratification and Mobility File

(ISMF) maintained by the Free University of Amsterdam and University

of California – Los Angeles (Ganzeboom and Treiman 2005).17

Additionally, a secondary level of coding was initiated implementing:

the International Socio-economic Index of Occupational Status (ISEI)

(Blau and Duncan 1967; Wegener 1992; Grusky and Van Rompaey 1992).

This index provides numeric interpretation of the “prestige” or

relative value of each occupation in society as calculated by the

research teams. The general conceptual base of the prestige scales

developed by Blau, Duncan and Treiman, who are referred to as

“gradationalists” (Grusky 2002, p. 37), has been to map a system of

categorized careers or occupational categories and then map these

occupational categories into abbreviated prestige or socioeconomic

scores. Additionally, these prestige scales are adjusted for standard

deviation errors. The result is that a researcher can match the

careers or occupations of the subjects in a study with the careers in

the prestige scale to arrive at an occupational prestige score for

each individual in the study.

Finally, the USA66e educational scores provide numeric scores for

expressing the weighted value of the levels of education (Program

1966). The USA66e educational prestige score is a tool developed by

the U.S. Bureau of Census to create a standardized scale that creates

a 1 – 20 scale where “no education” receives a score of 1 and a Ph.D.,

M.D. or J.D. receives a score of 19.

Analysis

In order to explore the relationship between linguistic capital,

ethnicity and social class, a series of ANOVA-based means tests

followed by multiple regressions were developed utilizing educational18

and occupational prestige scales as the dependent variables in a point

of comparison between ethnicity and language to analyze trends found

in a dataset collected utilizing the socioeconomic genogram technique

(Spence 2006; 2007) with an immigrant and non-immigrant respondents in

Austin, Texas. While the correlations resulting from both regressions

indicate that the language variable is more influential on their

relative dependent variables, the correlations suggest that language

is more influential on educational attainment than occupational

attainment. This result implies the importance and impact of the

maximization of the accumulation of linguistic capital in order to

accelerate the acquisition of educational attainment, which is a core

element in the formation / acquisition of other forms of capital.

The first analysis that was undertaken was the means tests that were

performed. They utilized the ANOVA one-way means comparison technique,

by which the mean values of two or more independent groups or

variables (each of which follow a normal distribution and have similar

samples) can be analyzed and evaluated to arrived at the relative

variability between the variables compared with the variability within

the groups (Rosner 1995). In this analysis, the groups compared were

the educational and occupational prestige scores with primary language

use (figure 5.1) and ethnicity (figure 5.2).

Figure 5.1 - Anova Based Means Comparison of Educational /

Occupational Prestige versus Primary Language Usage

Educational Occupational

Prestige (ISEI)19

Prestige Mean

(scale = 1-20)

Mean

(scale = 1-100)English 12.83 (+ 1.64)* 49.07 (+ 3.44)Spanish 7.71 (- 3.48) 36.47 (- 9.16)Total (Mean across

the entire

population)

11.19 (0.00) 45.63 (0.00)

*The values in parenthesis are the standard deviation values derived from the

distance of the mean score from the total mean score across the entire population.

Table 5.1 shows the comparison of the mean scores between English and

Spanish speakers for both occupational and educational prestige

scores. Most important to note is that Spanish speakers are much

further from the mean score for both educational and occupational

prestige scores. This should indicate that there exists a significant

difference between the English and Spanish speaking populations in

relation to both the educational and occupational prestige scores. All

comparisons in table 5.1 were statistically significant at the .000

level, in the ANOVA analysis of the variables. A secondary test of

multiple comparisons of variables with the Bonferroni method showed

that the comparison of English, Spanish and Educational Prestige was

statistically significant at the .000 level. Likewise, the same test

showed that the comparison of English, Spanish and Occupational

Prestige (ISEI) was statistically significant at the .000 level.

Figure 5.2 - Anova Based Means Comparison of Educational /

Occupational Prestige versus Race / Ethnicity

20

Educational

Prestige Mean

(scale = 1-20)

Occupational

Prestige (ISEI)

Mean

(scale = 1-100)Anglo 13.08 (+ 1.87)* 50.16 (+ 4.24)Hispanic 9.99 (- 1.22) 41.38 (- 4.54)Mexican 8.48 (- 2.73) 33.45 (- 12.47)Total (Mean across

the entire

population)

11.21 (0.00) 45.92 (0.00)

*The values in parenthesis are the standard deviation values derived from the

distance of the mean score from the total mean score across the entire population.

Table 5.2 shows the comparison of the mean scores between Anglo,

Hispanic (born in the U.S. of Mexican or Latin-American descent) and

Mexican (born in Mexico) populations for both occupational and

educational prestige scores. Most important to note is that Mexicans

(born in Mexico) were furtherest from the mean score for both

educational and occupational prestige scores. Hispanics were somewhat

below the mean and Anglos somewhat above the mean. This should

indicate that there exists a significant different between the Anglo,

Hispanic and Mexico-born populations in relation to both the

educational and occupational prestige scores. All comparisons in

table 5.2 were statistically significant at the .000 level in the

ANOVA analysis of the variables. A secondary test of multiple

comparisons of variables with the Bonferroni method showed that the

comparison of Anglo, Hispanic, Mexican and Educational Prestige was

statistically significant at the .000 level. Likewise, the same test

showed that the comparison of Anglo, Hispanic, Mexican and21

Occupational Prestige (ISEI) was statistically significant at the .000

level.

Comparing the two Anova tests, it seems that language differences lead

to greater mean differences in occupational and educational prestige

scores, compared to ethnic differences. This seems to indicate a

greater impact for language. However, the comparison is not very

precise in statistical terms as it does not allow for a clear cross

analysis of the two tests.

The multiple regression data analysis technique allows for the cross

analysis of the impacts of multiple independent variables and

compensates for structural problems within the dataset – such as the

wide variation in age of the sample population. However, there arises

a problem in the implementation of the multiple regression data

analysis technique in relation to the socioeconomic genogram dataset

in that within the dataset (especially the independent variables that

are needed to execute the analysis) are categorical variables.

Therefore, it was necessary to transform the categorical variables

into binary (dummy) variables. On the advice of consultants from

statistics and sociology, this was done using the Hardy (1993)

technique where rather than having an on/off variable (such as 1 =

anglo and 2 = not anglo), the Hardy binary variable model indicates

that the variable provides two different, distinct, options – such as

1 = Hispanic and 2 = Anglo. According to Hardy, this compensates for

a number of mathematical subtleties within the multiple regression

equation that, if left in the classical dummy variable form, may cause

minute errors.22

In order to facilitate the multivariate regressive analysis, the

following new variables were created in the dataset: Race1 –

1=Hispanic; 2=Anglo; Race2 – 1=Hispanic; 2=Mexican; Language1 –

1=Spanish; 2=English. Additionally, the immigrant generation variable

was refined so that the new variable is an ordinal/scalar variable and

is structured as follows:

1=1st generation immigrant (born outside the US, died in the US)

2=2nd generation immigrant (one parent born outside the US)

3=3rd generation immigrant (one grandparent born outside the US

4=4th generation and multi-generation immigrants were combined

into this category

Non-immigrants were removed from this new variable in order to

create an ordinal/scalar variable that would function in the

multivariable regressive analysis.

Using these four variables along with age, two 4-model multivariate

regressions were constructed. The first regression had the education

prestige index as the dependent variable1 and the second regression

had the occupational prestige index as the dependent variable2. The

choice to structure the regression with the different variables as

separate models was a decision made by trial and error. In previous

1 SPSS Syntax for 4-model multivariate regression with educational prestige as the dependent variable: REGRESSION /DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA COLLIN TOL CHANGE /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT USA66e_weighted /METHOD=ENTER age_2 /METHOD=ENTER immnumtr /METHOD=ENTER race1 race2 /METHOD=ENTER prilangred .2 SPSS Syntax for 4-model multivariate regression with educational prestige as the dependent variable: REGRESSION /DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA COLLIN TOL CHANGE /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT isei /METHOD=ENTER age_2 /METHOD=ENTER immnumtr /METHOD=ENTER race1 race2 /METHOD=ENTER prilangred .

23

versions of the syntax, the significance was markedly lower than in

the final version of the syntax than is being examined herein.

Educational Prestige Regression

The educational prestige 4-model multivariate regression provided

strong and insightful results into the relationships examined in this

model.

Figure 5.3 - Educational Prestige Regression: Pearson

Correlation*             

 

Educati

onal

Prestig

e Age

Immigrant

Generation

(1st, 2nd,

3rd, 4th+)

Hispa

nic

vs.

Anglo

Hispan

ic vs.

Mexica

n

Spanis

h vs.

Englis

h

Educational

Prestige 1.000

-

0.3

83 0.286 0.292 -0.181 0.526

Age -0.383

1.0

00 -0.303 0.011 0.116 -0.320Immigrant

Generation

(1st, 2nd,

3rd, 4th+) 0.286

-

0.3

03 1.000 0.283 -0.384 0.452Hispanic

vs. Anglo 0.292

0.0

11 0.283 1.000 -0.253 0.493Hispanic -0.181 0.1 -0.384 - 1.000 -0.335

24

vs. Mexican 16 0.253

Spanish vs.

English 0.526

-

0.3

20 0.452 0.493 -0.335 1.000*The values highlighted in yellow display the highest degree of significance in this

table.

The Pearson correlations for the educational prestige regression

(figure 5.3) show which relationships have the highest level of

correlation as shown by Pearson’s r. (Weisstein 2006) They also

indicate which relationships require extended attention: Spanish vs.

English / Educational Prestige; Spanish vs. English / Immigrant

Generation; Spanish vs. English / Hispanic vs. Anglo.

Figure 5.4 - Educational Prestige Regression: Significance (1-

tailed)             

 

Educati

onal

Prestig

e Age

Immigrant

Generation

(1st, 2nd,

3rd, 4th+)

Hispa

nic

vs.

Anglo

Hispan

ic vs.

Mexica

n

Spani

sh

vs.

Engli

shEducational

Prestige -

0.0

00 0.000 0.000 0.000 0.000Age 0.000 - 0.000 0.412 0.010 0.000Immigrant

Generation

0.000 0.0

00

- 0.000 0.000 0.000

25

Hispanic

vs. Anglo 0.000

0.4

12 0.000 - 0.000 0.000Hispanic

vs. Mexican 0.000

0.0

10 0.000 0.000 - 0.000Spanish vs.

English 0.000

0.0

00 0.000 0.000 0.000 -

The significance calculation for the educational prestige regression

(figure 5.4) shows that all relationships are significant within

the .000 range with the exception of the relationship between age and

race. This, of course, provides a solid indicator for the use of this

model for the evaluation of the relationship between educational

prestige and the other variables.

Figure 5.5 – Educational Prestige Regression: Model Summary

The model summary (figure 5.5), which shows the relationships between

the various models (or combination of variables attempted by the

software) shows that the fourth model, which is the examination of

age, immigrant generation, race and language together, has the largest

26

R square value (.335) compared to the other models and thus provides

the strongest statistical relationship.

Figure 5.6 – Educational Prestige Regression: Model 4

Coefficients

 

Unstandardized

Coefficients

Standardized

Coefficients

T

sig

.

Collinearity

Statistics

B

Std.

Error Beta ToleranceVIF

(Consta

nt =

Educati

onal

Prestig

e) 11.397 0.887   12.854

0.0

00    

Age -0.049 0.008 -0.258 -5.733

0.0

00 0.823 1.216

Immigra

nt

Generat

ion

(1st,

2nd,

3rd,

4th+) 0.019 0.214 0.004 0.088

0.9

30 0.701 1.426

Hisp

vs.

Anglo

0.946 0.451 0.102 2.101 0.0

36

0.710 1.409

27

comp

Hisp

vs. Mex

comp 0.149 0.829 0.008 0.179

0.8

58 0.813 1.231

Spanish

vs.

English

Speaker

s

(Primar

y

Languag

e) 4.006 0.543 0.394 7.379

0.0

00 0.584 1.713

When examining the values in the coefficient table for the educational

prestige regression in model 4, as presented in figure 5.6, it is

worth noting first: the relationship between “Hispanic versus Mexican”

variable and the dependent variable (educational prestige) is well

outside the range of significance at .858; second: the relationship

between “immigrant generation” and the dependent variable is even

further outside of the range of significance at .930. These indicate

that there is no discernable relationship within the dataset between

either immigration or non-immigrant Latinos and rise in educational

status. In contrast, there is a negative relationship between age and

educational prestige, which is significant at the .000 level and

reflects the dynamic of the sample – that being the wide age range

included, which includes populations from time periods and locations

that predate modern forms of public education, and possibly reinforces28

the understanding that until recently, immigrants, especially

immigrants from Latin America, were actively discouraged from

attending public schools in the southern U.S. states, including Texas.

(Black, 1996)

However, what is most important to note in this regression is the

relationships between the “Hispanic versus Anglo” and “Spanish versus

English speakers” variables and the dependent variable (educational

prestige), as well as between each other. The “Hispanic versus Anglo”

variable is significant at the .036 level, which is within an

acceptable range for this type of variable, and both the t (2.101) and

beta (.102) levels are well above all of the other variables with the

exception of the language variable. The “Spanish versus English

speakers” variable is significant at the .000 level, which is ideal,

and the t (7.379) and beta (0.394) levels are the highest of all the

independent variables in the regression. This indicates not only that

these two variables are the most influential independent variables in

the educational prestige regression, but that it is also possible to

clearly identify the differences between the degrees of influence

between the two variables. When utilizing the beta and t values as a

measure, the language variable is greater than three (3) times more

influential on the dependent variable (educational prestige) than the

race/ethnicity variable, which can be interpreted to mean that

linguistic capital is more than three (3) times more influential on

educational prestige.

Occupational Prestige Regression

29

The occupational prestige 4-model multivariate regression also

provided strong and insightful results into the relationships examined

in this model.

Figure 5.7 – Occupational Prestige Regression: Pearson

Correlation*             

 

Occupati

onal

Prestige Age

Immigrant

Generation

(1st, 2nd,

3rd, 4th+)

Hispa

nic

vs.

Anglo

Hispan

ic vs.

Mexica

n

Spanis

h vs.

Englis

h

Occupationa

l Prestige 1.000

-

0.10

5 0.127 0.236 -0.184 0.285

Age -0.105

1.00

0 -0.303 0.011 0.116 -0.320Immigrant

Generation

(1st, 2nd,

3rd, 4th+) 0.127

-

0.30

3 1.000 0.283 -0.384 0.452Hispanic

vs. Anglo 0.236

0.01

1 0.283 1.000 -0.253 0.493Hispanic

vs. Mexican -0.184

0.11

6 -0.384

-

0.253 1.000 -0.335

Spanish vs.

English 0.285

-

0.32

0 0.452 0.493 -0.335 1.000*The values highlighted in yellow display the highest degree of significance in this

table.

30

The Pearson correlation for the educational prestige regression

(figure 5.7) shows which relationships have the highest level of

correlation as shown by Pearson’s r (Weisstein 2006) and also

indicates which relationships require extended attention: Spanish vs.

English / Immigrant Generation; Spanish vs. English / Hispanic vs.

Anglo.

Figure 5.8 – Occupational Prestige Regression: Significance

(1-tailed)             

 

Occupati

onal

Prestige Age

Immigrant

Generation

(1st, 2nd,

3rd, 4th+)

Hispa

nic

vs.

Anglo

Hispan

ic vs.

Mexica

n

Spani

sh

vs.

Engli

shOccupationa

l Prestige -

0.0

17 0.005 0.000 0.000 0.000Age 0.017 - 0.000 0.412 0.010 0.000Immigrant

Generation

(1st, 2nd,

3rd, 4th+) 0.005

0.0

00 - 0.000 0.000 0.000Hispanic

vs. Anglo 0.000

0.4

12 0.000 - 0.000 0.000Hispanic

vs. Mexican 0.000

0.0

10 0.000 0.000 - 0.000Spanish vs. 0.000 0.0 0.000 0.000 0.000 -

31

English 00

The significance calculation for the occupational prestige regression

(figure 5.8) shows that all relationships are significant within

the .000 range with the exception of the relationship between age and

race. This, of course, provides a solid indicator for the use of this

model for the evaluation of the relationship between occupational

prestige and the other variables.

Figure 5.9 – Occupational Prestige Regression: Model Summary

The model summary (figure 5.9) which shows the relationships between

the various models (or combination of variables attempted by the

software) shows that the fourth model, which is the examination of

age, immigrant generation, race and language together, has the highest

R square value (.103) and thus provides the strongest statistical

relationship. However, it is important to note the difference between

the R square value of this regression (.103) and the previous

regression based on educational prestige (.335), suggesting that the

occupational prestige regression is significantly weaker than the

educational prestige regression.

32

Figure 5.10 – Occupational Prestige Regression: Model 4

Coefficients

 

Unstandardi

zed

Coefficient

s

Standardized

Coefficients

t sig.

Collinearity

Statistics

B

Std.

Error Beta ToleranceVIF

(Constant

=

Occupatio

nal

Prestige)

43.3

51 4.336   9.998 0.000    

Age

-

0.03

8 0.042 -0.048 -0.912 0.362 0.823 1.216

Immigrant

Generatio

n (1st,

2nd, 3rd,

4th+)

-

0.94

1 1.047 -0.051 -0.898 0.370 0.701 1.426

Hisp vs.

Anglo

comp

5.04

9 2.203 0.129 2.292 0.022 0.710 1.409

Hisp vs.

Mex comp

-

7.71

8 4.052 -0.100 -1.905 0.058 0.813 1.231

Spanish 8.37 2.655 0.196 3.156 0.002 0.584 1.71333

vs.

English

Speakers

(Primary

Language) 8

When examining the values in the coefficient table for the

occupational prestige regression (model 4, as presented in figure

5.10), it is important to first note that the age, immigrant

generation and “Hispanic versus Mexican” variables are outside the

ideal significance range. The “Hispanic versus Anglo” variable is

peripherally within the acceptable significant range with a value

of .022, which allows the comparison of the variable with the language

variable. The “Spanish versus English speakers” variable is

significant at the .002 level, which is just shy of ideal, and

indicative that it is the most influential variable in this

regression.

Further, when examining the language variable in relation to the

“Hispanic versus Anglo” variable the t and beta variables of the

language are slightly higher at 3.156 (t) and .196 (beta) for the

language variable as compared to 2.292 (t) and .129 (beta) for the

ethnicity variable. This reinforces the conclusion that when comparing

the influence of these two variables on the dependent variable

(occupational prestige), the language variable is notably more

influential than the ethnicity variable. This can be interpreted to

mean that linguistic capital is notably more influential on

occupational attainment than ethnicity.34

However, it is equally interesting to recognize the relative

differences between the two regressions. While the correlations

resulting from both regressions indicate that the language variable is

more influential on their relative dependent variables, the

correlations suggest that language is more influential on educational

attainment than occupational attainment. This result implies the

importance and impact of the maximization of the accumulation of

linguistic capital in order to accelerate the acquisition of

educational attainment (a core element in the formation / acquisition

of other forms of capital).

Conclusion

In order to better understand the ability and inclination of Latino

immigrants to use Spanish language and/or English language in the

process of social mobility in the United States, this study focuses on

language issues, as they are connected to Hispanic ethnicity and to

success in educational and occupational mobility. This study employs a

fairly novel methodological approach, incorporating socioeconomic

genogram techniques for analyzing family social mobility trajectories

over time. The study tried to get direct measures of media use by

previous generations in the family system via the socioeconomic

genogram, but those were not reliable enough to use in the statistical

analysis.

This study shows that language, notably being mono-lingual in Spanish,

had much more negative impact on educational attainment and

occupational achievement reflected in occupational prestige than did35

Hispanic ethnicity. This implies that to achieve greater mobility in

education and work, immigrants will tend to try to acquire English

relatively quickly. This research confirms what previous studies have

shown, that acquisition of English is important for social mobility,

in terms of education and occupation. What this study shows more

clearly than some earlier studies, is that language is a greater

barrier to social mobility than ethnicity.

When we compared the impacts of language and ethnicity on occupational

and educational attainment, or mobility, we see a further

clarification of this result. The correlations resulting from both

regressions indicate that the language variable is more influential

than the ethnicity variable on both dependent variables. However, the

correlations suggest that language, as a causal variable, is more

influential on educational attainment than it is on occupational

attainment. The correlation scores and beta weights were higher, as

were the significance levels. This shows that educational outcomes

tend to be more sensitive to language skills than occupational

outcomes, per se.

In the longer run, we would expect to see that educational achievement

is converted into higher status work, as predicted by Bourdieu and

others. An element of cultural geography may be important. Since we

are studying generations of families who ended up in Central Texas,

the work market in this increasingly Latino part of the USA may be

more open to Spanish speakers than is the field of education. As

Bourdieu notes each field of endeavor, language, education,

occupation, sports, etc. has different rules and different patterns of36

competition (1983). This study helps reinforce Bourdieu's point that

the field of language has competitive dynamics that need further study

(1993). This study also shows the continuing utility of analyzing

separately the various forms of capital, linguistic, educational and

occupational, that Bourdieu (1983, 1990a) identified. Hopefully it

adds something to the further definition of linguistic capital, its

impacts and how they may be studied.

This result still implies, however, the importance and impact of the

maximization of the accumulation of linguistic capital in order to

accelerate the acquisition of educational attainment (a core element

in the formation / acquisition of other forms of capital). Hopefully

this study will contribute toward the clarification of how language

attainment and acquisition of language capital is central to social

mobility for recent immigrants and other Latinos in the USA, arguably

-- on the basis of our results -- more important than ethnicity, per

se.

This study also builds upon both the family systems structure utilized

by González and Bertaux, as well as revisiting the original genogram

literature in the field of family counseling (McGoldrick and Gerson

1999), to construct and implement the new socioeconomic genogram tool.

It introduces the approach of the socioeconomic genogram as a useful

methodological approach for examining social mobility among immigrants

over time. It can easily be added to interviews that focus on a

variety of aspects of life history. It has the potential to become a

useful addition to more purely qualitative life history research, with

immigrants and others.37

38

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