The Non-participant across Borders: Linking Immigrant Political Behavior in the Country of Origin to the United States

Javier M. Rodriguez University of California, Los Angeles

Rafael A. Jimeno Arizona State University, Tempe

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Abstract

Objective. We investigate whether Latino immigrant non-electoral participation in the home country contributes to non-electoral participation in the United States. Method. We implement a Random Digit Dialing multistage sample technique to conduct a survey of Latino immigrants in the Phoenix metropolitan area. We use a chi-squared test, an extensive data analysis, and a sequenced logit regression analysis to determine the nature of the influence that participating in groups in the country of origin may have over immigrants’ engagement in said activity in the United States. Results. While non-electoral participation prior to immigration is a statistically significant predictor of the likelihood of non-electoral participation in the United States, the majority of our sample lacked the resources and skills to participate in their home countries thus diminishing their likelihood of participating in the United States. Conclusions. A lack of resources in the home country is transported across borders and continues to define living standards and opportunities.

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Introduction Experience matters. This is the case for human behavior broadly, and certainly for political

behavior in particular. For immigrants, possibly faced with a vastly different political landscape,

prior experience in politics could be determinant because processes of adjustment are inefficient,

thus different starting points often result in different outcomes (c.f. Tversky and Kahneman,

1974). A reliance on “lessons of the past,” and on particular historical analogies, helps to shape

judgments of current situations (Jervis, 1976; Khong, 1992; Levy, 1994; May, 1973;

Vertzberger, 1990). For the growing Latino demographic, which has been identified as a

potential force within the American electorate (although socio-political manifestations of this

potential have been few), this debate takes on special significance.

Research focusing on electoral turnout, for example, has shown that levels of

participation in the Latino community are significantly lower relative to other groups (Shaw, de

la Garza, and Lee, 2000; DeSipio, 1996). Further, we are reminded that other forms of

participation, such as voluntary associations, contacting government officials and financial

contributions may require more resources and thus be even more inaccessible to immigrants as a

means to convey their preferences (Rosenstone and Hansen, 1993; Verba, Schlozman, and

Brady, 1995).

With significant exceptions (Guarnizo, Portes, and Haller, 2003, Hritzuk and Park, 2000;

Jones-Correa, 1998a; Portes and Rumbaut, 1990), research has focused on the impact that

proximate variables, like socio-economic status and political context in the United States, have

on the political behavior of Latinos (Barreto, Segura, and Woods, 2004; Leighley, 2001; Álvarez

and Bedolla, 2001; Pantoja, Ramírez, and Segura, 2001). The absence of control variables that

ascertain the experiences of immigrants in their countries of origin is mainly due to a dearth of

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information. Both qualitative and quantitative investigations into migration processes have

depended on data narrowly based on immigrants’ immediate experiences in the United States.

The present study attempts to balance this situation; it provides a more comprehensive portrait of

immigrants in general, and of Latin American immigrants specifically, by incorporating to the

debate explanatory variables associated to immigrants’ pre-immigration experiences.

The main objective of this study is to test the impact that non-electoral participation in

the country of origin has on non-electoral participation in the United States. Due to the nature of

the sample I implement a methodological approach focused on the development of a stable,

robust model of non-electoral participation. I build on research that analyzes traditional factors

impacting Latino political behavior by investigating non-electoral participation, as measured by

participation in social, cultural, religious, or political groups. This relationship between pre-

immigration socio-political experiences with processes of re-socialization in the United States

has been largely assumed in current immigration research but has never been tested directly.

Non-electoral participation—a growing area of inquiry in Latino Politics (Ramakrishnan,

2006)—is particularly important because it is accessible to all immigrants, not just the

naturalized. As such, non-electoral participation is useful in sorting through the differential

impacts of both individuals’ interests in participatory politics, and their abilities to overcome

structural barriers and participate in spite of the known costs in terms of time, money, and

information.

Hypothesis and Empirical Framework

I hypothesize that those first-generation Latin American immigrants who do not participate in the

American political system are those who also did not participate in their countries of origin.

This, I argue, is a function of the fact that the actualization of Latin American immigrant political

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participation, more often than not, is impeded by certain systemic constraints and the nature of

migratory processes themselves. Consequently, the difficulties associated with immigrant socio-

political incorporation are in many ways correlated with the skills and resources with which

immigrants arrive.

The immigrant population of the United States is mostly composed of the underprivileged

classes of Latin American nations. Accordingly, “most immigrants enter the US at the bottom of

the socio-economic hierarchy” (Massey and Fischer, 2000: 673). It is my argument thus, that the

detrimental impact to political participation of this lack of resources in the country of origin is

transported across borders and continues to define the living standards and opportunities for

incorporation of a large number of immigrants.

In Mexico, by far the most important supplier of immigrant labor to the United States, the

Encuesta Nacional de la Dinámica Demográfica (1992), a face to face survey with a sample of

57,916 households and 277,552 individuals (INEGI, 1994), indicates that about 89 percent of the

Mexicans who emigrated between 1970 and 1992 to the United States were over the age of 19

years at the moment of departure. This evinces that Latin American immigrants generally arrive

after entering political life in their countries of origin.

By using the 1996 New Immigrant Survey Pilot (NIS-P) and the 1996 Current Population

Survey (CPS), Jasso et al. (2000) report that the average years of schooling completed among

Mexican legal immigrants who entered the United States between 1992 and 1996 is between 7.5

and 8.9; and that the percentage of total documented immigrants between 18 and 39 years of age

who do not speak English very well is 74 for the same period. These factors imply a pattern

within this community; namely, that many Latin American immigrants do not have the requisite

resources and skills to have been participants in their countries of origin and consequently arrive

with little, if any, political experience.

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In her study of Asian American and Latino immigrants in the United States, Wong (2000)

highlights the importance of an immigrant’s socialization into American politics, as time must

pass before first generation immigrants can learn the features and dynamics of the American

political system. I concur that processes of adaptation depend on crucial factors such as the

availability of, access to, and information about the resources extant in the host country, but

argue that the attainment of those resources is shaped by the stock of resources immigrants arrive

with. Shortly put, I contend that resource accessibility, for instance, is dependent on

“snowballing” processes in which resources are not spontaneous but cumulative.

The common treatment of Latino immigrants as a community whose political

incorporation is mainly a response to their current socio-economic status or political environment

in the United States presupposes that resources become available to them once in the United

States, ignoring many of the inherent difficulties involved in a process of incorporation. For

instance, Massey (1990) emphasizes the high costs of migration, especially for first-time

migrants, and particularly if it involves entering the United States without documents.

Additionally, incorporation is not likely to be monotonic due to high rates of settlement and

return. As the possibility of settling within the American border increases among legal

immigrants, circulation also increases with recurring and more extended visits to the country of

origin (Durand, Massey, and Zenteno, 2001).

Further, relocating across borders is a process fraught with difficulties. There is ample

literature that highlights credit constraints, unequal distribution of citizenship rights, and

phenotypic discrimination (Worswick, 1999; Crowley, 2001; Espino and Franz, 2002). Also,

work experience and education in the country of origin does not provide equivalent returns in the

host country (Baker and Dwaine, 1994; Reitz, 1998; Jones-Correa, 1998b; Shaafsma and

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Sweetman, 2001). There seems to be significant evidence that wages among immigrants are not

only the lowest but that they have been declining in the last decades establishing a pattern of

diminishing returns to assimilation (Borjas, 1995a, 1995b, 1985; Fairchild and Simpson, 2004;

Feliciano, 2001; Phillip and Massey, 1999). In this line of reasoning, Massey and Fischer (2000)

stress that “racial/ethnic segregation interacts with structural shifts in society to concentrate

poverty” (670) and that immigration is strongly related to the spatial isolation of the poor. These

factors contrast the will and efforts of immigrants to incorporate with the systemic forces that

isolate them in the host country, where the less well-to-do citizens hold an unduly lower share of

political power (Lijphart, 1996; Verba, Nie, and Kim, 1978).

Though social networks have been identified as important to the potential success of

immigrants in the United States (García and de la Garza, 1985; Verba, Schlozman, and Brady,

1995), I argue that these networks frequently compound the impact of pre-immigration levels of

resources and skills. Mobilization, on the part of organizations, has also been noted for its ability

to raise participation (Leighley, 2001; Michelson, 2003). In this regard, mobilization is also

commonly offered as a solution for socio-economic obstacles to participation, but it may be a

mixed blessing. Leighley (2001), for example, notes that “class is the most consistent predictor

of political mobilization [in the United States]” (124). In a comparative study of voting behavior

by the economically disadvantaged, Aguilar and Pacek (2000) conclude that the marginalized

“have been the focus of demobilization efforts on the part of parties and regimes representing the

better-off elements” (1011). Considering that many Latin American immigrants comprised the

impoverished classes in their countries of origin, it is to be expected that they arrive with little

political experience; and equally important, that once in the United States, they are subject to a

continuing cycle of under-representation and low levels of mobilization.

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Finally, other research into political behavior has investigated how naturalization, or the

difference between being a native-born or an immigrant impacts participation (Jones-Correa,

1998a; Barreto and Muñoz, 2003). Throughout this research, it is customary to find that those

Latinos who participate are more well-to-do in their communities. Apart from native-born

Latinos, it is perhaps those sophisticated Latin American immigrants with resources at their

disposal prior to immigration that can overcome systemic forces (Jimeno and Rodríguez, 2006),

thus facilitating their incorporation in the American system.

These preceding insights, developed mainly in Social Science disciplines other than

Political Science make it reasonable to argue that, as migration and incorporation processes are

intricately related to the country of origin, and as later socialization in adults is associated to

early socialization events during childhood and adolescence, to treat immigrants as blank slates

in the host country takes such processes and their units of analysis out of their own nature.

Survey and Sampling Method

To test the hypothesis advanced herein, a survey instrument was constructed based on an

adaptation of questions used in the Latino National Survey (LNS) to probe respondents regarding

their demographic characteristics, participation levels, and availability of, and access to,

resources in both their countries of origin and in the United States. This survey was carried out in

the Phoenix metropolitan area of Arizona.

This 2007 Phoenix-Metro Latino Survey (PMLS), has 80 questions which take account of

time-of-arrival related indicators, migratory statuses and processes, migration-specific

economics, and transnational activities. It also probed respondents on occupational

characteristics, language proficiency, socio-political participation levels, and political

knowledge, interest, and efficacy in their countries of origin and in the United States.

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A Random Digit Dialing (RDD) multistage sample technique was implemented based on

Cummings (1979) for Maricopa County, the fourth most populated county in the United States

and where the Phoenix metropolitan area is situated. There were a total of 6 area codes and 1130

prefixes for our area of interest (DEX Residential White Pages, 2005; Thedirectory, 2006). With

a sample frame of 11.3 million telephone numbers,1130 “primary clusters” (1 per prefix) were

generated. Each primary cluster was composed of 3 randomly sampled telephone numbers,

leading us to a primary sample of 3390 numbers.

In order to increase the probability of contacting Hispanic households, primary clusters

containing successful numbers were randomly sampled further (Waksberg, 1977). Implementing

this sampling technique, a total of 3955 phone-calls were carried out between January 10 and

March 18, 2007. From this sample, about one-fifth (18.6 percent) of the telephone numbers were

household numbers. From the total 737 households reached, 213 ended up in successful surveys

(response rate 28.9 percent); of these, 145 were with Hispanics. There were 114 surveys

conducted in Spanish and 99 in English. Answering machines and “ring, no-answer” numbers

accounted for 19.6 percent of the total sample, 12 percent were business numbers, and half of the

total sample (49.9 percent) corresponded to non-working numbers.

Methodology In order to determine if there is a direct relationship between non-electoral participation in the

countries of origin and non-electoral participation in the United States, I ran a simple chi-square

test of Latin American immigrants’ participation in groups on both sides of the border. For this

test, variables were coded 1 for participation and 0 for non-participation.

(Table 1. Participation in Social, Cultural, Religious or Political Groups, about here)

Respondents were asked about their participation in social, cultural, religious or political

groups in their countries of origin and about their participation in such groups in the United

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States.1 This preliminary chi-squared test shows that, out of 77 immigrants who did not

participate in groups in their countries of origin, 68 (88.3 percent) do not participate in groups

now in the United States. Moreover, of the 87 immigrants who do not currently participate in

groups (71.3% of the sample), 68 (78.2 percent) were those who did not participate in groups in

their countries of origin (p< 0.000, see Table 1, Appendix A).

The strength of this first piece of evidence suggests that there may be a statistical

relationship between the non-electoral participation of immigrants in their countries of origin and

non-electoral participation in the United States. I thus turn my attention to the empirical question

of whether this holds after controlling for the usual predictors of political participation.

Accordingly, my basic working model of participation is based on time of residence in the

United States, years of education, income, and other common variables of interest. Specific

variable transformations and relevant theoretical considerations are discussed in detail below.

Treatment of Variables

Time in the U.S. It is difficult to assess the impact that time has on the socio-political life of an

immigrant. Problems of validity arise when one considers that the variable “age” does not

capture an immigrant’s length of exposure to a new environment; that “time of residence” does

not capture the impact that age at the time of arrival may have had on an immigrant’s disposition

for incorporation; and that “age of arrival,” on its own, again, does not capture the length of

exposure. All of these considerations are of high relevance specially when considering that

processes of learning and information processing, for example, are dependent on factors

associated to the life cycle of humans. I thus rely on the percentage of the time the immigrant

has been living in the United States. This variable balances more efficiently the inconsistencies

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listed above and adds to the validity of the model by combining the three classical time-related

variables:2

=

−=

AgeTimeinUSA

AgeAgeArrivalAgelifeUS% x100

Years of Education. This variable relies on the number of years of education attained by

an individual in order to obviate the differences between the different educational systems

respondents may have participated either in their countries of origin and/or in the United States.

Examining the data, the distribution of this variable offers particular methodological challenges.3

This situation is due to the fact that at least two different subpopulations, the native-born and the

immigrant, are being collapsed into one distribution. Natives tend to have much higher levels of

education than immigrants, and there are many immigrants with drastically low levels of

education. These two groups create the tails of the distribution.

Theoretically speaking, it is also to be expected that years of education in the country of

origin would impact in a different fashion the levels of participation in the U.S. than years of

education in the U.S. because curricula are generally oriented toward knowledge and skills

necessary in the particular system where they are devised. In this regards, the native-born may

be receiving an education that guides them through their political socialization in the U.S. while

immigrants may attain an educational baggage more connected to the specific features of their

countries of origin. For this reason, to collapse the years of education in a regression model

without accounting for such a distinction could result in a biased estimate of the coefficient and a

destabilization of the model. In order to investigate if such a pattern of differentiation exists I

separated the three types of educational backgrounds (see Box-plot 1A, Appendix A).

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Looking at the three resulting distributions, it becomes evident that the variable “years of

education” indeed compresses three qualitatively distinct subpopulations. To compensate I

implemented the Box-Cox procedure, estimated the best normalizing power (1.3485), and then

created three interactive dummy variables. The first is for years of education received solely in

the U.S., the second for years of education received solely in a country different than the U.S.,

and the third for years of education received in both the U.S. and abroad. I interact these three

dummy variables with “years of education” to differentiate the impacts that these three types of

educational backgrounds may have over non-electoral participation in the U.S.

Income. The main theoretical consideration for “income” is that many immigrants tend

to have dependents not only in the U.S. but also in their countries of origin thus impacting their

disposable income in the U.S. (i.e. the income they may put at their disposal to participate).

Accordingly, if the variable “income” is included in the model as is, it will not account for this

particular feature of an immigrant’s economic calculus. To address this, I divided “income” by

the number of dependents; this mathematical maneuver, of course, does not alter the tendency for

the native-born.

The box-plot for “income/dependents” shows a very skewed distribution with highly

pronounced outliers (see Box-plot 2, Appendix A). This behavior is associated with a

geometrical distribution (i.e. not fitting the assumptions for generalized linear models). Because

of this, I logged the variable. In order to test for the appropriateness of the transformation, I once

again employed the Box-Cox procedure to get the best normalizing power (-0.14). Since this

value is very close to 0, which is the power for the natural logarithm, a logged variable is an

appropriate choice. The QQ-normal plot, in particular, illustrates that the transformed

distribution is fairly normal-looking and well-behaved (see “QQ-Normal,” Appendix A).

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Variables of interest. The two main variables of interest are non-electoral participation in

the U.S. and non-electoral participation in the country of origin. Both variables are measured

through participation in social, political, religious, and cultural groups. These are dichotomous

variables, coded 1 for participation and 0 for non-participation.

Other variables. There are two additional variables particularly germane to non-electoral

participation included in the PMLS (2007). These variables are expected to behave differently

for the native-born and the immigrant. These variables are: 1) Sex (dichotomous, coded 1 for

males and 0 for females), and 2) English proficiency.4

According to the extant literature, the variable “English proficiency” may have relevant

effects on participation. Nevertheless, most of the literature comments very little on the

methodological challenges of this variable. In the present study, English proficiency is

intricately related to the variable “percent of lifetime in the US.” For instance, the native-born

(maximum percent of lifetime in the US (100%)) are also those with the highest level of English

proficiency possible according to the measurement scale. This becomes evident in a simple

bivariate scatterplot, where the cases in the right-upper corner absorb the regression line inducing

an almost unavoidable bias to the estimations (see Scatterplot 1, Appendix A). The auxiliary

€

R2

is .74, reflecting the high level of correlation between the two variables. Because of these

reasons I decided not to include the variable English proficiency in the selection of the model

that follows. By not including this variable in the model I will diminish instability due to

multicollinearity.5

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Selection of the Model and Data and Sequential Logit Analyses

I began with a basis “kitchen sink” model for non-electoral participation in the U.S. (without the

variable of interest “non-electoral participation in the country of origin”). This model includes

all the theoretically suitable variables discussed above (see Table 1, Model 1, Appendix B).

Analyzing the deviances and the contribution that each of the variables bring to the

aggregate structure and fitness of the model, it is noticeable that the variables “sex” and “percent

of lifetime in the U.S.” are not adding any explanatory power to the model. For instance, the

Akaike Information Criterion (AIC) for the model that includes the variable “sex” is 225.57, and

without it is 225.73. This statistic suggests that the fitness of both alternative models (with and

without the variable “sex”) remains virtually the same independently from the inclusion (or the

exclusion) of the variable “sex.”

I also compared the nested models (with and without the variable “sex”) and noted the

change in the deviance statistic (see List of Deviances Tests (LDT), Test 1, Appendix B).

According to the calculations, the null hypothesis cannot be rejected; that is, the model that

includes “sex” is not more jointly statistically significant than the model without “sex” thus the

variable can be removed from the model for the sake of robustness and stability.6 A similar

outcome is obtained with the variable “percent of lifetime in the U.S.” thus the variable is

removed from the model, as well (see LDT, Test 2, Appendix B).7 The respective sequential

models are listed in Table 1, Appendix B.

Based on theoretical grounds we assumed that different origins of education had an

independent impact on non-electoral participation in the U.S. This assumption is central for the

internal coherence of my general interpretation of migratory processes: Socio-political

affections, behaviors, and cognitions (i.e. the main objects that compound the socio-political

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experiences of individuals) of immigrants in the host country are related to those they

experienced in pre-immigration stages. In this particular case, the statistical behavior of the three

types of education (that solely received in the U.S., in the country of origin, and in both the U.S.

and abroad) should be distinct.

Three scatterplots of non-electoral participation in the U.S. vs. “origin of education”

evince the patterns of their relationships (see Scatterplots 2A, 2B, and 2C, Appendix A). These

scatterplots bring to light how years of education are, in average, translated into non-electoral

participation in the U.S. (see regression trends). The scatterplots also illustrate how years of

education, especially those related to postsecondary education, increase non-electoral

participation in the U.S. notably, independently of their origin.8 This does not seem to be the

case for those years related to elementary education in the U.S. (where the lowess smoother

overestimates the impact of a few low education cases that participate), but more interestingly, to

the years of elementary education in the country of origin, whose impact over non-electoral

participation in the U.S appears to depend on different qualitative features of said education (see

lowess smoothers).

In accordance with the argument of this piece, two of these scatterplots deserve special

attention. First, when looking at the divergent pattern of non-electoral participation with respect

to the education acquired in the country of origin (Scatterplot 2B), it is clear that only those

immigrants who arrive in the United States with higher levels of education are more prone to

participate (see lowess smoother). Remarkably, the patter of participation of immigrants whose

last level of education was culminated in their country happens in the lower quadrants of the

scatterplot; that is, there is a tendency to increase participation in the U.S. as education increases,

but the probability of doing so is notably low. Worth noting, is that the general tendency is that

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of low participation due to the fact that an important fraction of immigrants arrive with low

levels of education. This is of high importance considering that most of Latin American

immigrants arrive to the United States with low levels of education and remain with the same

levels throughout their lives in the U.S., especially that fraction of immigrants who arrive at an

age that is associated with the productive stage of their lifespan and/or parenthood, and in which

returning to school does not seem a plausible option in the U.S.

Second, when looking at immigrants who got their education both in the U.S. and their

countries of origin, it is possible to see a steeper slope overall (Scatterplot 2C). Please note how

the pattern is influenced by those outliers who completed very high levels of education in the

U.S. and which coincide with those who participate the most. In the case of immigrants, this

may be the case of those who came to the United States precisely because of their pre-

immigration skills and resources (e.g. those who emigrated to continue onto graduate education).

Looking directly at the data it becomes clear that those immigrants who continue their education

in the U.S. are those who arrived with higher resources, most notably income and English

proficiency, in average. And contrarily, those who arrive with low levels of income and low

English proficiency in average (typically those with low level of education, as well) are those

who tend to participate less.

These depictions are in accordance with my theoretical expectations and with the general

grounds of my argument. Critical life experiences in the country of origin, such as education,

could not be equated with, and have an independent impact from, those in the United States.

These preliminary observations suggest that, it may be the case that an education abroad does not

yield the same economic remuneration that an American education yields, on average, but the

contributions of the skills associated with receiving a post-secondary education either in the

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United States or abroad remain similar. Alternatively, the evidence suggests that there is a

tendency in which Latin American immigrants who enjoyed higher levels of pre-immigration

resources are also those who received a better type of elementary and high school education thus

permitting a smooth transition onto the American educational system and finally participating at

higher levels. This is not the case, however, of those immigrants who were part of the poor and

marginalized populations in their countries of origin. These many Latin American immigrants

usually arrive with low-quality elementary and high school education, a situation that thwarts

their possibilities of continuing to acquire a post-secondary education in the United States, thus

their low non-electoral participation. These immigrants are in their majority non-participants

across borders.

In keeping with my search of higher levels of robustness and stability in the model, I

tested if the coefficients of the different sources of education are statistically different from the

other, especially considering the limited degrees of freedom the model is relying on.

Accordingly, I did three F-Tests, then I constructed three confidence ellipsoids (at a 95%

confidence level) in order to test the following joint null hypotheses:

€

Ho1 :β1 = β2[ ],

€

Ho2 :β2 = β3[ ] and

€

Ho3 :β3 = β1[ ] (see Confidence Ellipsoids 1A, 1B, and 1C, Appendix A).

The three confidence ellipsoids evince that all the joint values in which the coefficients

are equal to the other are within the frame of tolerance. Accordingly, the three joint null

hypotheses cannot be rejected; that is, the coefficients of the three origins of education are not

statistically different from the other at a conventional 0.05 level of confidence.9 Consequently,

for the sake of robustness and stability, I collapsed the three types of education into a single

category defined by the total years of education, indifferent to their origin (i.e. the initial

transformed variable of years of education) (see Table 1, Model 5, Appendix B).

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This model offers a lower Akaike Information Criterion (AIC), lower deviance, and a

shorter range of the deviance residuals than those of previous models. It is worth noting that the

AIC rewards models that fit the data better, but also penalizes overfitting. This model uses less

degrees of freedom, thus the lower AIC. These estimates confirm, nevertheless, that this is a

more efficient model and that it fits the data in a better fashion. Accordingly, I added the

variable of interest (Non-electoral participation in the country of origin) to this basis model (see

Table 1, Model 6, Appendix B).

The first change to be noticed is that the number of degrees of freedom diminished. This,

of course, is due to the fact that the native-born have never participated in a different country

than the U.S. This situation highlights the inconveniences of small samples when implementing

a generalized linear model.10 One of the symptoms of this situation can be perceived in this

“final model” by looking at the intercept’s coefficient, as well, in which the change of value and

standard errors are illustrative. One of the best ways to check for the stability of the model is

through a scatterplot of studentized residuals vs. fitted values (see Scatterplot 3, Appendix A).

This scatterplot shows that both the regression trend and the lowess smoother depict a

monotonous increase of residuals as the fitted values become larger (please also note how the

lowess smoother slightly deviates from 0). No powerful outliers are detected, which should be

the case for binomial data due to bounding of the dependent variable. In addition to this

evidence of stability, I checked for the impact that specific cases may have on the estimates by

developing a scatterplot of Cook’s distance vs. Leverage (see Scatterplot 4, Appendix A). This

scatterplot suggests that the combined effect of leverage and Cook’s distance of some particular

cases may be impacting the estimates. Due to the low sample size, I also checked this possible

inconvenience, in which cases such as 4, 45, and 88 (among others of less importance) may be

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destabilizing the model. Driving my attention to this possibility, I created a scatterplot that

combines the leverage, studentized residuals, and Cook’s distance (the circumferences are

proportional to Cook’s distance)11 (see Scatterplot 5, Appendix A).

This scatterplot, once again, brings to light the incidence of the three same cases: 4, 45,

and 88. Looking directly at the data, the peculiarities of these cases are evident. Case 4, for

instance, has a notably low income, very low level of education (completed in the country of

origin), and participated in the country of origin and continues doing so in the U.S. In plain

words, case 4 is special because she has afforded a persistent participation across borders in spite

of radically contradicting the forces for participation as described by the patterns captured by the

model, thus generating a high residual. Case 45 has a particularly high income, low level of

education (completed in the U.S.), and has not participated at either side of the border. This

inconsistency across indicators of participation is what makes case 45 quite singular, generating

a medium-sized leverage and negative residual. And finally, case 88 has very high income, the

highest level of education (initiated in the country of origin and culminated in the U.S.), and has

participated at both sides of the border. Case 88 is special because she enjoys the unusual

combined power of highest education, very high income, and participation in the country of

origin over non-electoral participation in the U.S., making her an outlier in a sample of usually

underprivileged Latin American immigrants.12

Another feature of the final model is that both the AIC and the residual deviance

diminished in comparison to previous models. In order to test if the model has more explanatory

power than those developed previously (especially the latter, the best fitted model without the

variable of interest (Model 5 in Table 2, Appendix B) I resorted to a statistical comparison of the

models (see LDT, Test 4, Appendix B). According to these calculations, the final model is

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jointly statistically significant and comparatively more suitable than all other models previously

implemented herein. It is to be interpreted, therefore, that the variable of interest (non-electoral

participation in the country of origin) adds important explanatory power to this model thus for

immigrants’ non-electoral participation in the U.S. across borders.

Considering that the statistical significance has little, if any, relationship to the

importance of a variable in a given model, I illustrate the “explanatory power” that the variables

included in the final model have over non-electoral participation of Latin American immigrants

in the U.S. The illustrations are based on the interaction of these variables (see Graphs 1, 2, and

3, Appendix C). This, of course, is accomplished by interpreting the value of the coefficients in

conjunction with their spread and their levels of statistical significance within the theoretical

framework that best explain the behavior of the variables under observation.

Variables and Coefficients Analyses

In order to assess the influence of the predictors over the dependent variable, I illustrated the

relationship between education, income, and non-electoral participation in the country of origin.

Graphs 1 through 3 depict the simulated interactive effects of the explanatory variables over the

probability of participating in groups in the U.S. The independent variables are settled in their

transformed units of measurement and fixed to their means unless stipulated. In Graphs 1 and 2

the independent impact of participation in the country of origin (for participants and non-

participants) is shown in separate trend-lines. In the case of Graph 3 the compound effect of

both education and income were collapsed under upper-middle and working class immigrants

(described as “socio-economic status”) and their effects are shown in separate trend-lines.

(Graph 1 about here)

21

Graph 1 shows a positive interactive effect of education and participation in the country

of origin over participation in the U.S. The convex and concave curvatures of the respective

lines evince a ceiling effect for those who participated in their countries of origin before

immigrating and a floor effect for those who did not participate. These specific effects captured

by the logit manifest that Latin American immigrants’ behavior in the United States could be

interpreted as two different subpopulations dependent on their pre-immigration experiences.

The first interesting observation is that the sole effect of participation in the home country

for those who did not participate before immigrating has almost the total effect of receiving or

not an education (participants in the home country without an education have very similar

probability of participating in the U.S. to those who did not participate in the home country and

has earned a maximum level of education). Looking at the effect of education on non-

participants over the probability of participating in the U.S. is worrisome. Education has been

depicted as the cornerstone for participation across literatures, disciplines, and public policies,

but according to this interaction effect, even those highly educated immigrants who did not

participate in their country of origin, would reach a probability of participating in the U.S. of

only about 40%. This finding highlights the main argument of my hypothesis: No participation

in the country of origin is a powerful predictor of Latin American immigrants’ likelihood to

continue not participating in the United States.

Also worth noting is the positive compound effect education and participating in the

home country has over the probability of participating in the U.S. According to Graph 1, a Latin

American immigrant who used to participate in her country of origin notoriously increases her

probability of participating in the U.S. with each year of education. In summary, Graph 1

exemplifies that the effects of critical predictors such as education over immigrants’ participation

22

in the U.S. is nested to the event that immigrants’ did or did not participate in the country of

origin before immigration.

(Graph 2 about here)

Graph 2 captures a particularly strong ceiling and floor effects reaffirming the qualitative

differences among Latin American immigrants according to their pre-immigration experiences.

The impact of income seems to be particularly strong.13 Interestingly, the effects of income over

participation in the U.S. are attenuated at the tails of the distribution (probability differences are

about 25%) and accentuated around its mid-range (where the probability differences are close to

50%).

It is worth noting that over 75% of the immigrant sample is located under a value of

logged income of “10,” that is, most of the reality for Latin American immigrants is illustrated in

the lower-left quadrant of Graph 2. Taking this into account, the effects of income on

participation in the U.S. for the majority of the sample share the characteristics of the effects

depicted above by education. Again, the graph does not predict a good participation prospect for

Latin American immigrants, much more drastically so for those who did not participate in the

country of origin before immigrating. A great portion of the sample shows a probability of

participating in the U.S. under 30% according to the compound effect of income and non-

participation in the home country.

The upper tails of the distributions (i.e. those unusual high income cases), however,

depict a pattern more similar to the general American public, in average. Finally, it is important

to consider the fact that levels of education acquired in the country of origin (specially the post-

secondary years) are not translated into income in the same proportion as happen among the

general American public. This being said, those immigrants situated at the upper tails of the

23

distribution in education are to be moved to the left, in average, when comparing their behavior

across Graphs 1 and 2. In general, Graph 2 offers a second piece of evidence that favors my

hypothesis and the general framework of the arguments advanced herein.

(Graph 3 about here)

In order to visualize the simultaneous effects captured by the simulations explained above

I developed Graph 3, which illustrates in a more parsimonious way the reality of most Latin

American immigrants. The graph shows how the probability of participating in the U.S. varies in

response to the immigrants’ probability of having or not participated in the country of origin, all

of these according to the socio-economic status they have attained in the United States. Their

“socio-economic status” has been fixed at their upper and lower quartiles of the respective

distributions; that is, a working class immigrant, for example, is that who has a maximum level

of education higher than 25% of her fellow immigrants and a maximum income level higher than

25% of her fellow immigrants, as well. In the same manner, an upper-middle class immigrant

has minimum levels of education and income superior to 75% of the sample.

Before interpreting Graph 3, it is important to remind the reader the findings exposed in

Table 1, which points out a strong tendency among Latin American immigrants to not participate

in groups across borders. This being said, the reader can appreciate that the majority of the

sample is represented by the patterns located in the lower-left quadrant of the graph, and more

specifically, by that trend depicted for the immigrant working-class.

In accordance with these considerations, Graph 3 reaffirms the troublesome situation

suggested by Table 1 and the one that motivated this study: Latin American immigrants’

probability of participating in the U.S. is intrinsically linked to their pre-immigration

participation. Considering that the majority of immigrants in the U.S. arrive with little resources

24

at their disposition thus becoming part of the American disadvantages communities, Graph 3

shows that the prospect for these immigrants to participate in the U.S. are very low. In

conjunction, the effects of low levels of education, low income, and low levels of participation in

the home country trap an important portion of immigrants in a socio-political stage of stagnation.

The combined effects of higher levels of education and income together with pre-immigration

participation seem to portrait the socio-political behavior of the elites and those found in the

average American public; unfortunately, a small portion within the immigrant community.

Finally, another general illustration of the patterns described above could be achieved by

calculating the main ratio of participation across borders that the model designed herein captures

as a whole. Succinctly, by calculating the Odds Ratio (OR), I am able to assess the odds of

participating in the U.S. for those who participated in the country of origin and for those who did

not participate in the home country. According to the odds ratio, the odds of participating in the

U.S. for those immigrants who participated in the country of origin are 7 times those for

immigrants who did not participate before immigrating. In other words, the odds of participating

in the U.S. are 7 times lower for those immigrants who did not participate in their country of

origin than those for the immigrants who participated in pre-immigration stages (for OR

calculation details see Appendix B).

In summary, these interpretations offer support for the hypothesis advanced in this study.

Clearly, there is a tendency among the Latin American immigrant community to not participate

in the U.S. and this pattern could be explained by the low levels of political experience they

arrive with. When considering the crosstabulation in Table 1 it is evident that many of the Latin

American immigrants who do not participate in the U.S. are those who did not participate in their

countries of origin before immigrating, and the illustrations of the coefficients are capturing this

25

pattern. An evident inconvenience, of course, is that the coefficients are capturing the effects of

“non-events;” a lack of variability, especially at the upper level of the sampling distributions of

non-electoral participation at both sides of the border, may be impacting the estimates.

Nevertheless, much effort has been devoted for the generation of a robust, stable model that

captures a general pattern that could be replicated under different hypotheses, along many

surveys to come, across immigrants of different origins. An interesting paradox is that the

powerful consistency of non-electoral participation across borders limits the statistical variance

required to demonstrate the very tenet that the hypothesis is trying to address. The independent

impact that not participating in the country of origin has on not participating in the U.S. is just

another example of the difficulties academic research face when trying to prove the reality of

socio-economic disadvantages: An interesting statistical conundrum that drowns in a sea of

clustered empirical evidence.14

These findings speak for the explanatory power that non-electoral participation in the

country of origin has over non-electoral participation in the U.S. when considered in conjunction

with other predictors. Since the beginning I have been focusing efforts on developing the most

robust and stable model possible according to the data. In addition to illustrating the interactive

impacts of the predictors over the dependent variable, I were most interested in assessing the

explanatory power that the model as a whole offers with respect to non-electoral participation of

Latin American immigrants in the U.S., especially when including a variable never tested before:

pre-immigration non-electoral participation.

Final Remarks and Conclusions

The model developed herein states that, considering the compounded effect of low levels of

education, low levels of “disposable income,” and non pre-immigration non-electoral

26

participation, it becomes difficult to advocate for a simple theory of incorporation. It has been

shown that the composite model (with the three predictors: years of education,

income/dependents, and pre-immigration non-electoral participation) is more stable, more robust,

fits the data in a better fashion, and is statistically more significant than all other working models

for non-electoral participation in the U.S. as permitted by the data.

Findings bring to light that pre-immigration non-electoral participation does not act in

isolation; the explanatory power of the predictors is more notorious when assessed in

conjunction than when considered independently. In this regard, the development and analysis

of a robust model for participation seemed particularly enlightening when considering the low

explanatory power that traditional predictive variables have had throughout cumulative research

on political behavior (Aldrich, 1993; Fowler, Baker, and Dawes, 2008; Matsusaka and Palda,

1999; Plutzer, 2002).

The analysis of the data makes plausible to state that the correlation between the low levels

of educational skills and resources and the scarce political stock Latin American immigrants

arrive with have a compound effect that is intermediated by their disposable income in the U.S.

Considering that many Latin American immigrants arrive in the U.S. at an age when the

continuation of their education takes a back seat to entering the work force, their possibilities of

acquiring American-like skills they could put at the service of participation are thwarted. One of

the main lessons out of this investigation is that pre-immigration socio-political experience of

Latin American immigrants adds explanatory power to models of non-electoral participation as it

acts in conjunction with both pre-immigration and post-immigration socio-economic indicators,

an empirical bridge never tested directly before.

Findings evince that the lack of resources and skills are transported across borders and

27

continue to define Latin American immigrants’ life styles and opportunities in the U.S. thus their

low participation levels relative to other groups in the United States. A process of

disenfranchisement that is initiated in their countries of origin is continued in their host country.

Variability of indicators such as education and income levels across borders is scarce; that is,

Latin American immigrants are similarly located within the relative socio-economic spectrums

on both sides of the border.15 Also worthy of emphasis is that this is not the pattern that the data

analysis presented above shows for native-born Latinos. Thus, on average, socio-political

incorporation is a phenomenon more proper among second and subsequent immigrant

generations, not necessarily the first Latin American immigrant generation.

Many immigrants arrive with little to no previous political experience, with little to no

appropriate types of education they can adequately apply in the host nation, and they arrive at an

age in which their life cycle prevents them from an accelerated and smooth incorporation. This

situation, at the end, is aggravated by the fact that this population also arrives with little to no

disposable income thus diminishing the probability of acquiring high-quality, useful information.

At least during their initial years in the U.S. their early socialization continues to situate them in

a perspective of the world that resembles the reality of their country of origin. The learning and

incorporation curves that would ultimately lead them to participate in the U.S. are subjected to a

re-socialization process that is shaped by their pre-immigration experiences.

Latin American immigrants present an accumulation of socio-political poverty that

unearths several methodological problems. In the present analysis, for example, this

accumulation of “non-events” is reflected in the lack of variability among the regressors.

Additionally, to localize members of a society whose primary characteristic is their level of

segregation and alienation is difficult, at best. This is reflected in the survey implemented

28

herein, where the analysis may be limited to the partial variability of those cases located at the

lower tail of the population distribution, thinning the reliability and generalizability of the

study.16 This does not mean, however, that these findings and suggestions are biased.

Conversely, in other (large n) studies, the impact of segregation and alienation experienced by

the segment of the population under analysis here may be drowned out precisely because these

groups are at times unreachable if one relies on voter rolls, for example. Thus, more than

reflecting a methodological pitfall, the analysis developed herein reflects an empirical trade off.

In general, this research should prompt further investigations regarding the influence that

politics in the country of origin has on Latin American immigrants in the United States. I believe

that this framework can be applied to the study of other immigrant groups, and perhaps even to

migrant groups within nation-states. Nonetheless, it must be understood that migration across

states within one country is in many ways a simpler proposition than migration across

international boundaries in terms of language, laws and social context, among other factors.

Further, despite the fact that the statistical analysis presented herein does not afford the

ability to make causal arguments, it is important to highlight that immigrants’ non-participation

in the United States is necessarily preceded by their political activity (or inactivity) in their

countries of origin. Thus, notwithstanding that causality per se cannot be determined via this

statistical analysis, the fact that the life of an immigrant in the country of origin precedes his or

her life in the United States, clarifies the causal direction of the variables involved. Theories of

early socialization, skills transferability, socio-economic mobility, and incorporation of

immigrant communities corroborate the findings, as well.

Finally, I think that a research agenda focusing on political behavior in the country of

origin would contribute to the understanding of not only Latinos specifically, but immigrants in

29

general, as well as processes of incorporation, transnational behavior, and mobilization. Issues

of participation and representation are arguably of great importance for the future of the

American democracy as immigrants (particularly those from Latin America) continue entering

the political system of the United States. In spite of this, there is no comprehensive public policy

program in the United States to incorporate Latin American immigrants into the political system

(Gerstle and Mollenkopf, 2001; Wong, 2006). Latino non-participants have little hope of being

taken into account even as laws that affect their lives are debated and passed daily (Michelson,

2003). The obstacles faced by this population upon entering the United States are numerous and

preexisting, and may not be easily diminished by proximal forces.

30

Footnotes

1 Specifically, respondents were asked: “Do you regularly participate in the activities of a social,

cultural, religious, or political group?” And to determine participation in the home country they

were asked: “Before coming to the U.S. did you regularly participate in the activities of a social,

cultural, religious, or political group?” These questions did not immediately follow each other in

order to ensure that an answer to one did not influence an answer to the other. The same is true

of all questions intended to compare behaviors across borders.

2 This variable is well behaved (please see Appendix A). The histogram in particular depicts that

for those cases other than native-born Americans (i.e. immigrants) the distribution looks quite

normal. The column over the number “1” are, of course, the native-born who have spent 100%

of their lifetime in the U.S. No powerful outliers are detected, and testing for normality of the

sampling distribution (and the possibility of a favorable transformation) shows no relevant

deviations. The estimated power for this variable implementing the Box-Cox procedure is 0.92,

close to its original state (power = 1); for this reason I maintained the variable in its original

format.

3 Looking at a box-plot of this variable a powerful accumulation of outliers becomes evident at

both tails of the distribution (see Box-plot 1, Appendix A). A transformation under such

circumstances is problematic because I risk developing a model under very unstable

circumstances in which a minimal variability at any of the tails could produce significant

differences on my estimators.

31

4 In keeping with Schrauf (2002) who states “most immigrants come to linguistic and

communicative competence in some second-language domains but not others” (101), the variable

“English proficiency” is an additive score of the levels of proficiency reported by the participants

in speaking, writing, listening, and reading the English language in the United States. For

instance, for spoken English, a participant was asked: “How well would you say that you speak

English?” These questions were individually scored from 0 for “not at all,” to 3 for “very well”.

Accordingly, the additive scale ranged from a minimum of 0 (when the respondent neither

speaks, reads, understands spoken English, nor writes in English) to a maximum of 12 (when the

respondent enjoys a “very well” level of proficiency in all of them).

5 Further evidence of instability was developed in other instances of this study. For instance, In a

separate exercise (not included herein) once the variable “English proficiency” was included into

the model, the sign of the variable “percent of lifetime in the U.S.” changed to negative, and the

coefficient became inflated accompanied by large standard errors. This common situation may

be due to multicollinearity, as suggested above. Alternative independent models were also

developed including either “percent of lifetime in the U.S.” and “English proficiency” resulting

in non-statistically different models of variation. Other transformations of the variable “English

proficiency” were implemented (including factor analysis and other composite indexes) with no

significant deviations from the patterns described above, especially those related to high levels of

multicollinearity. A two-tailed analysis (in order to capture the impact generated by the entire

variation of the distribution) and a one-tailed analysis (in order to capture the tendency to

increase English proficiency as time progresses) were also implemented with neither interesting

results nor an improvement on the fit of the model, in general. For these reasons, I decided to

32

keep the variable “percent of lifetime in the U.S.,” which may provide no different information

than the one provided by “English proficiency” (a particularity that is not sustainable if the

variable “English proficiency” is included instead). The theoretical parsimony of the variable

“percent of lifetime in the U.S.” also affords this study to develop a more robust, general

interpretation akin to the processes under observation. Nevertheless, in order to provide the

reader with a generalized test of robustness of the variable of interest, I am including the variable

“English proficiency” in a separate model (see Table 2, Appendix B).

6 The literature shows that women tend to engage in non-electoral participation at higher rates

than men (Hardy-Fanta, 1993). It is not my intention to address this topic in this paper; this

piece, however, is focused on analyzing what happens to participants (be they male or female),

and non-participants, after they emigrate. Thus the variable “sex” is only essential to a model

targeting the causes of participation; such endeavor is out of the frame of this piece. The

variable “sex,” nevertheless, is included in other models (offered to the reader) for the sake of

robustness (see Table 2, Alternative Models 3, 4, and 5, Appendix B).

7 Despite there is a methodological reason for the exclusion of the variable “percent of life in the

U.S.,” and this reason fits the aim of this paper (in accordance with the constraints offered by the

data), there is little theoretical ground for excluding the variable from the model without

incurring in a bias of the estimators. Again, the objective of this piece is to develop a robust,

stable model of participation across borders that captures the main pattern of the phenomenon

under observation, mainly for the sake of reliability. In this particular case, the variable “percent

of life in the U.S.” puts the study in the common statistical trade situation between bias and

33

stability. Again, for reasons of transparency this variable is included in other models that are

offered to the reader (see Table 2, Alternative Models 2 and 4, Appendix B). The reader can

notice, however, that the variable in question does not offer major threats in favor of biasedness

(with the probable exception of “income,” which showed to be confounded with this variable) as

it does for the stability of the model, in general (see LDT, Tests 2 and 3, Appendix B).

8 This inference is based on the fact that, for example, in the case of education received both in

the country of origin and the U.S., the first years of education of an immigrant who continued her

education in the U.S. were necessarily received in the country of origin.

9 Please note that finding that the three coefficients are statistically indistinguishable from the

other does not mean that the interpretation of the coefficients within their theoretical implications

(i.e. the importance of the variables at work) is useless. To the contrary, the analysis of the

patterns and relationships between the origins of education and non-electoral participation in the

U.S. developed in previous passages of this article provide an insight of inherent dynamics

attached to immigration processes never captured before. These are, precisely, some of the

advantages that a sequential logit analysis and the protocols of model selection can bring to this

type of research. It is worth noting, however, that the data analysis of said variables suggests

that, by increasing the degrees of freedom, the impact of different origins of education may reach

statistical significances independently from the others without compromising the stability and

robustness of similar models for participation.

34

10 In this particular case, most notably the lack of variance in the predictors (this variance,

therefore, may not be necessarily capable of capturing the true variance of the population of

interest), lack of efficiency (high deviances and higher standard errors, in general), and normality

becomes capricious. Outliers may affect drastically the estimates thus bringing instability, which

is, in fact, the main problem of small samples. Please note that despite these inconveniences, I

can be confident that the estimations do not suffer of biasedness; deviations from normality have

been successfully managed through appropriate variable transformations; the present model fits

adequately the data; and finally, the randomized sample highly resembles that of other high-N

probabilistic samples.

11 Namely, the square root of Cook’s distance.

12 One methodological suggestion would be to disregard these three cases. It seems clear,

however, that deleting these cases from the model is not proper: These “rare” cases do exist in

the real world, and by deleting them I would limit the level of reliability of the study and the

sample will not be as representative as it has been intended to be. In theory, a larger sample

would eventually incorporate them adequately into given sampling distributions. None of the

cases, accordingly, are to be eliminated from the model. Alternatively, a test for robustness

seems the most appropriate alternative to investigate if the final model is sensitive to these

influential cases. With this in mind, I rerun the final model after dropping the three cases (see

Table 2, Model 6, Appendix B). Results show that after dropping the influential cases the

variable of interest “non-electoral participation in the country of origin” remains highly

statistically significant and its impact is slightly affected. Additional tests of robustness show the

35

same level of reliability; the variable of interest is reliably statistically significant even after

controlling for additional variables and its coefficient vaguely varies, always within the ranges

depicted by its standard errors across models (see also LDT, Test 5, Appendix B).

13 According to the findings highlighted above with regard to the relationship between

“income/dependent” and the “percent of life” that an immigrant has spent in the U.S., this effect

may not solely be attributed to “income/dependent” alone. Data analysis has shown that an

immigrant’s income increases as she invests more of her life-time living in the United States. It

is important to note, however, that this effect is more noticeable among the young, in which the

“percent of life in the U.S.” increases at a higher rate per every year she spends in American soil

in comparison to older immigrants. This effect may be aggravated by the fact that older

immigrants may also have higher numbers of dependents at both sides of the border.

14 The most immediate solution for such an statistical unfeasibility is to counter-balance such

clustering at the lower level of the sampling distribution with enough cases at the upper level;

that is, a dramatic increment of the degrees of freedom for the sake of variability. The high costs

of such an endeavor are well known to researchers, and especially to those who concentrate their

efforts on studying underrepresented populations.

15 This is not to state that immigrants live in the U.S. in similar absolute conditions to those they

experienced before emigrating. The first absolute beneficiaries of immigration are, of course,

immigrants themselves. Findings emphasize, however, that their relative socio-political

positions across borders are similar, not their absolute socio-political positions when compared

36

to their pre-immigration conditions. In absolute values immigrants show to be more well-to-do

in the U.S., on average, than they used to be in their countries of origin.

16 An additional caution with regard to the generalizability of the study is due to the geographical

localization of the sample. It is well known, however, that the Latino population in the United

States lives in high concentrated areas; about 75% of the total Latino population lives in merely

seven states, and about 54% of the total Latino population lives in the four states that share their

borders with Mexico (Pew Hispanic Center, 2008). Accordingly, despite the geographical

location of the sample, I believe that there exists a good resemblance of the sample with the

majority of the Latino and Latin American immigrant population in general.

37

Appendix A

Table 1. Participation in Social, Cultural, Religious or Political Groups

Participated in the

country of origin

Did not participate in

the country of origin

Total

Participates U.S.

26 (74.3%) (57.8%)

9 (25.7%) (11.7%)

35 (28.7%)

Does not

Participate U.S.

19 (21.8%) (42.2%)

68 (78.2%) (88.3%)

87 (71.3%)

Total 45 (36.9%) 77 (63.1%) 122 (100%)

Pearson chi2 (1) = 29.4903 Pr = 0.000

38

39

40

41

42

43

44

45

46

47

Appendix B

Table 1 Sequential Logit Models

Basis “Kitchen

Sink”

Basis without “sex"

Basis without

"% life in US"

Basis without

"sex" & "% life in US"

Basis Final

Final Model

(1) (2) (3) (4) (5) (6) Intercept -3.31 -3.33 -3.18 -3.2 -3.46 -9.96* (2.30) (2.30) (2.28) (2.29) (1.87) (4.41) Education in CO 0.07* 0.06* 0.06* 0.06* (0.03) (0.03) (0.02) (0.02) Education in USA 0.07** 0.06** 0.07*** 0.07*** (0.02) (0.02) (0.02) (0.02) Education in USA & CO 0.09*** 0.09*** 0.09*** 0.08*** (0.02) (0.02) (0.02) (0.02) Education 0.08*** 0.05 (0.02) (0.03) Log-Income 0.02 -0.01 0.07 0.05 0.06 0.74 (0.26) (0.26) (0.26) (0.26) (0.22) (0.52) % life in US 0.97 1.06 (1.23) (1.22) sex -0.49 -0.51 (0.34) (0.34) N-E particp. in CO 1.95*** (0.50) Null deviance 246.95 246.95 247.83 247.83 248.72 144.18 Residual deviance 211.57 213.73 212.32 214.65 218.92 102.79 AIC 225.57 225.73 224.32 224.65 224.92 110.79 N 189 189 190 190 191 119 Significance codes: 0 ‘***’, 0.001 ‘**’, 0.01 ‘*’, 0.05 ‘’

48

Table 2 Alternative Models with Additional Controls (All applied to Final Model)

Alternative

Model 1 Alternative

Model 2 Alternative

Model 3 Alternative

Model 4 Alternative

Model 5

Robustness Test (without

cases) (1) (2) (3) (4) (5) (6) Intercept -4.67 -9.56* -9.95* -9.55* -4.77 -9.47* (5.10) (4.44) (4.47) (4.50) (5.16) (4.54) Education 0.02 0.05 0.05 0.05 0.02 0.07* (0.03) (0.03) (0.03) (0.03) (0.03) (0.03) Log-Income 0.06 0.64 0.77 0.66 0.1 0.64 (0.62) (0.54) (0.53) (0.55) (0.63) (0.54) English 0.30* 0.29 (0.15) (0.15) % life in US 1.5 1.64 (1.64) (1.69) Sex -0.53 -0.55 -0.44 (0.50) (0.51) (0.51) N-E particp. in CO 1.95*** 2.01*** 1.92*** 2.00*** 1.94*** 1.77*** (0.52) (0.52) (0.51) (0.52) (0.52) (0.51) Null deviance 144.18 143.48 144.18 143.48 144.48 138.536 Residual deviance 98.402 101.86 101.68 100.66 97.664 98.876 AIC 108.4 111.86 111.68 112.66 109.66 106.88 N 119 118 119 118 119 116 Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘’

49

List of Deviances Tests (LDT) Test 1: Basis “Kitchen Sink” vs. Basis without “sex”

84.3)05.0(1182183......

16.257.21173.213

→=−

=−=−=

=−=−

αsquaredChifdfdfd

withDD

AHNH

AHNH

Test 2: Basis “Kitchen Sink” vs. Basis without “% life in U.S.”

84.3)05.0(1184185......

75.057.21132.212

→=−

=−=−=

=−=−

αsquaredChifdfdfd

withDD

AHNH

AHNH

Test 3: Basis “Final” vs. Basis without “sex” & “% life in U.S.”

82.7)05.0(3185188......

27.465.21492.218

→=−

=−=−=

=−=−

αsquaredChifdfdfd

withDD

AHNH

AHNH

Test 4: Final Model vs. Basis “Final”

95.93)05.0(73115188......

41.11579.10292.218

→=−

=−=−=

=−=−

αsquaredChifdfdfd

withDD

AHNH

AHNH

Test 5: Final Model vs. Final Model (without cases 4, 45, and 88) (Robustness)

82.7)05.0(3112115......

91.388.9879.102

→=−

=−=−=

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ee

OCpartNonOCPart

50

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