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Hispanic Youth in the Labor Market: An Analysis of High School and Beyond Roberto Fernan(lez INTRODUCTION The number of people of Spanish origin in the United States rose from 9.1 million in 1970 to 14.6 million in 1980 (Bureau of the Census, 1982:Table 3.21. In addition to this growth in absolute numbers, the relative share of the population accounted for by Hispanics grew from 4.5 percent in 1970 to 6.4 percent in 1980. Although part of these increases probably reflect changes in Census Bureau enumeration procedures (see Jaffe et al., 1980:311-313 and Appendix A) and an undercount of Hispanics in 1970 (Bureau of the Census, 1979a; U.S. Commission on Civil Rights, 1974), it is clear that Hispanics are a substantial and growing part of the population of the United States. Hispanics tend to be younger than non-Hispanic whites. According to the March 1977 Current Population Survey, the median age of the Spanish-origin population was 22.1 years versus 30.0 for non-Hispanic whites (Bureau of the Census, 1979b:Table C). Since Hispanics are disproportionately young, they are more likely than non-Hispanic whites to suffer the employment problems that youth in general face in the labor market, e.g., low employment and low labor force participation rates. In fact, the data show that regardless of age, rates of employment and labor force participation are lower for Hispanics than for non-Hispanic whites, but not as low as for native Americans or non-Hispanic blacks (U.S. Commission on Civil Rights, 1978:Table 3.1~. However, differences in population-age profiles cannot explain why Hispanic youths are less successful than white majority youths in the labor market. For example, among those aged 16-19 in 1981, Hispanics had an unemployment rate of 24.1 percent and a civilian labor force participation rate of 46.3 percent compared with 17.3 and 59.0 percent, respectively, for whites and 41.5 and 37.4 percent, respectively, for blacks (National Commission for Employment Policy, 1982:Table l). Data from the March 1980 Current Population Survey show that Hispanic youths encounter other barriers in the labor market, as well. Among those l Roberto M. Fernandez is assistant professor in the Department of Sociology, University of Arizona. 410

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411 aged 14-19, Hispanics performed worse than non-Hispanic whites and blacks on three out of four indicators of "underemployment," i.e., Hispanic youths are more likely to experience involuntary part-time employment, live in households whose incomes fall below the poverty line, and receive inequitable pay than are non-Hispanic whites and blacks, although blacks are more likely than Hispanics to be inter- mittently employed (U.S. Commission on Civil Rights, 1982:Table 5.4; also see Clogg, 1979; Sullivan, 1979~. While it is clear that Hispanic youths are less successful than non-Hispanic whites in the labor market, the reasons underlying these disadvantages are less obvious. Determining the causes of Hispanic underachievement has important practical implications; the choice of relevant policies to ameliorate those conditions depends on under- standing the factors that lead Hispanics to fare less well than non-Hispanic whites in the labor market. In this paper, I undertake two tasks. First, I document the extent of the employment difficulties of Hispanics compared with non-Hispanic whites and blacks using data from High School and Beyond, a national longitudinal study of high school sophomores and seniors in 1980. Because respondents in this survey were enrolled in school in 1980, labor force statistics derived from the survey will not be directly comparable with statistics based on household surveys of the labor force, e.g., the Current Population Surveys. However, because respondents in High School and Beyond all started in high school, the survey is ideal for studying the transition of youths from school to work. Although past research has found that Hispanic youths fare less well than non-Hispanic white youths on many indicators of labor market success (e.g., wages, family income; see Mayers, 1980), I will focus on two important measures, i.e., labor force participation and unemployment rates. Also, because of the interdependency between youths' leaving school and their employment decisions during the school-to-work transi- tion (see National Commission for Manpower Policy, 1976; Stevenson, 1978b), I discuss the indicators of labor force status by school status, i.e., by high school dropout versus in-school youths for the sophomore cohort, and by out-of-school versus attending postsecondary institution for the senior cohort. My second task is to examine some of the presumed causes of the difficulties of Hispanic youths in the labor market. As with the descriptive analyses, labor force status will be studied in conjunction with school enrollment. Therefore, as a dependent variable, labor force participation has four categories: participating in the labor force and enrolled in school, participating in the labor force and out of school, out of the labor force and enrolled in school, and out of the labor force and out of school. Employment status is treated simi- larly and also has four categories: employed and enrolled in school, unemployed and enrolled, employed and out of school, and unemployed and out of school. Using logistic regression analysis, I predict these labor force and enrollment status indicators with measures of family background, school performance, language, immigration history, and other demographic variables.

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412 The remainder of this paper addresses six topics: (1) the extant knowledge on the labor market status of Hispanic youth, (2) the char- acteristics of the High School and Beyond data set and the advantages of using this survey for studying Hispanic youths' achievement, (3) the findings of descriptive analyses of the various subpopulations under study, (4) the findings of causal analyses of labor force and enroll- ment status indicators, (5) the results of empirical analyses of labor force participation and employment, and (6) recommendations for policies to improve the status of Hispanic youths in the labor market. LABOR MARKET STATUS OF HISPANIC YOUTHS As the Hispanic share of the population has increased, the socio- economic achievement of Hispanics has increasingly become the object of policy discussion (see e.g., National Center for Education Statistics, 1980; National Commission for Employment Policy, 1982~. Unfortunately, research on Hispanics in general, and Hispanic youths in particular, has been hampered by a lack of suitable data (see Estrada, 19801. For this reason, information on the labor market status of Hispanic youths is poor relative to that available on non-Hispanic white and black youths (see, e.g., Freeman and Wise, 1982~. Because much research suggests that the decisions young people make on participating in the labor force and continuing in school are interdependent (see B. Duncan, 1965; Edwards, 1976; Ornstein, 1976), it is important to examine the causes of Hispanics' educational difficul- ties when considering the determinants of their underachievement in the labor market. These causes can be divided into two types: general and specific. General factors, such as sex and family socioeconomic status, are potentially important for explaining the school and labor market achievements of everyone in the United States, regardless of their race or ethnicity. Specific factors are characteristics that are particu- larly salient for some minority groups and are expected to affect those groups disproportionately. For Hispanics, specific factors are language skills and immigration history. Distinguishing between the effects of general and specific factors on the labor market achievements of Hispanics is important for policy purposes. For example, if Hispanics' labor market disadvantages are due primarily to their lower levels of family socioeconomic status, then general policies designed to help all poor people would help improve Hispanics' labor market status. However, if specific factors, such as language background, account for a large portion of Hispanics' school or labor market difficulties, then general policies are apt to do little to improve Hispanics' performance in school or in the labor market. In this case, policy instruments, such as bilingual education, may have to be targeted specifically on the Hispanic population to improve Hispanics' labor market achievements.

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413 General Factors Recent studies identify Hispanics' low levels of education as one of the most important general factors that explain Hispanic youths' underachievement in the labor market (National Commission for Employment Policy, 1982~. Indeed, there is much evidence that Hispanics experience considerable educational difficulties. At each age level, school enrollment rates for Hispanics lag those for whites (National Center for Education Statistics, 1980:Table 1.08~. Hispanics also have significantly lower rates of high school completion than non-Hispanic whites (National Center for Education Statistics, 1980:Table 1.09~. Among those who remain in school, Hispanics are much more likely to have to repeat a grade as they progress through school than non- Hispanic whites (National Center for Education Statistics, 1980:Table 2.211. Hispanic educational difficulties extend to the postsecondary level, as well. Hispanics are underrepresented in undergraduate, graduate, and professional programs relative to their share of the population (National Center for Education Statistics, 1980:Table 3.01) and underrepresented among the nation's degree recipients (National Center for Education Statistics, 1980:Table 3.21~. There is much research, however, that suggests that these educa- tional difficulties are, in turn, caused by other general factors. In other words, Hispanics' low levels of education are an endogenous cause of their labor market difficulties. Other factors that influence Hispanics' educational attainments may also influence their labor market achievements directly, or indirectly through educational attain- ment. The most important of these factors is family socioeconomic background (Blau and Duncan, 1967; O. Duncan et al., 1972; Jencks et al., 1972~. This is generally interpreted to mean that higher income families, in which parents have high educational and occupational statuses, are more likely to support their children in educational endeavors. Less affluent families may not emphasize education for their children as much because the relative cost of college and higher education relative to the prospective returns on this investment do not justify the expenditure. In addition to the indirect effects of family background on labor market outcomes through education, most studies have also shown direct effects of family background on offsprings' labor market success (e.g., Blau and Duncan, 1967~. Unfortunately, the mechanisms by which these direct effects operate are not well understood in the case of occupa- tional status and earnings. A number of complicated and sometimes crosscutting processes appear to be operating to convert family background into occupational status and earnings (see Jencks et al., 1979:Ch. 3~. However, in the case of youths' labor force participation and employment, it has been shown that children of poorer families are likely to enter the labor force at earlier ages than offspring of wealthier families (Neugarten and Hagestad, 1976), even after the effects of educational attainment are controlled (Hogan, 1981:Ch. 5~. The direct effects of family background on labor force participation and employment have also been documented for high school students (Lewin-Epstein, 1981~.

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414 A number of recent studies of various Hispanic subgroups have come to the same conclusion as the studies of the general population: family socioeconomic background is an important determinant of Hispanics' educational achievements (Aspire, 1976; Fligstein and Fernandez, 1982, 1985; Nielsen and Fernandez, 1982) and occupational achievements (see Tienda, 1981; McLaughlin, 1982; Stolzenberg, 1982~. Although there has been very little empirical research on the topic, family background factors have also been cited as important determinants of Hispanic youths' labor market difficulties (National Commission for Employment Policy, 19821. The most important of these background factors is thought to be family income (see, e.g., Aspira, 1976; Briggs et al., 1977~. Hispanics are much poorer than non-Hispanics. In 1977, the median family income of Hispanics was Sll ,421 compared with $16,284 for non-Hispanics (Bureau of the Census, 1979b). Hispanic families also tend to be larger than non-Hispanic families (3.88 persons versus 3.31; see Bureau of the Census, 1979b). Researchers argue that to help ease the family's financial burdens, Hispanic youths are more likely to enter the labor force than non-Hispanics. However, as Hispanic youths become increasingly involved in the world of work, they are correspon- dingly drawn out of school. Hence, they are presented with a self- reinforcing situation wherein they leave school to work, and then their lack of schooling becomes a major obstacle to their success in the labor market. Specific Factors Language problems often head the list of specific factors that may disproportionately affect Hispanics' educational and labor market achievement (U.S. Department of Health, Education and Welfare, 1974; Barrera, 1979; National Commission for Employment Policy, 1982~. For youths entering school from non-English language backgrounds, limited English proficiency can certainly constitute a barrier to effective learning in English-only school systems. Students who cannot understand what is being taught through the medium of the English langu- age are likely to have both psychological and substantive difficulties in their interactions with teachers and in their studies. AS a conse- quence, it is often argued, these students tend to have lower scholastic performance and are more likely to drop out of school (see, e.g., Hirano-Nakanishi and Diaz, 1982; Steinberg et al., 1982a). Survey research in this area tends to support these notions. For example, Lopez (1976) found that U.S.-born Mexican-Americans raised in Spanish- language environments had lower educational attainments than their U.S.-born Mexican-American counterparts raised in English-language environments. To the extent that Hispanics speak only or predominantly Spanish when they complete their schooling, studies suggest negative effects on work-related variables (Lopez, 1976; Chiswick, 1978; veltman, 1981; Garcia, 1983~. Because effective communication is an important component of any production activity, Spanish monolinguals' inability to communicate in English may make them less attractive to employers.

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415 In addition, Spanish monolinguals are likely to receive lower wages (see Stolzenberg, 1982; McManus et al., 1983; Tienda, 1983) and to be underemployed and unemployed (Carliner, 1981~. For Spanish-dominant bilinguals, there is some evidence to suggest that accented or non- standard English may result in employers consciously or unconsciously showing bias against Spanish users (Garcia, 1983; Lopez, 1976~. The use of Spanish, or any non-English language, however, may not be intrinsically harmful to bilinguals' educational and work-related achievement. In fact, the effects of using Spanish, controlling for English proficiency, have been subject to debate. One argument empha- sizes the cost of bilingualism. In this view, the coexistence of two lexicons and two syntaxes in the mind of the bilingual represents a drain on a finite amount of mental energy, and less mental energy will be available, for example, for intellectual tasks in school. Another harmful consequence of bilingualism may be that the languages interfere with one another. This process is known as "code switching n (Albert and Obler, 1978~. In this view, Spanish proficiency and use should retard achievement in English-language schools. On the other hand, other studies have found that bilingual proficiency is an asset or does not hinder bilinguals either in school (Peal and Lambert, 1962; Lambert and Tucker, 1972; Cummins, 1976, 1977; Veltman, 1980; Fernandez and Nielsen, 1984) or in the labor market (Lopez, 1976; Tienda, 1981:Ch. 8; Garcia, 1983~. The fact that bilinguals have two codes for every concept may help them to realize that codes are arbitrary. Therefore, bilingualism may serve to stimulate intellectual development for abstract reasoning tasks, which should be expressed in higher scholastic achievement. Regarding the labor market, some studies have suggested that bilingualism is a form of human capital that may yield returns in the labor market (Carliner, 1976; Tienda, 1982~. Therefore, in areas where there is a demand for workers who can communicate in more than one language, bilinguals will be in an advantageous position in the labor market. Also, Lopez (1976) suggests that the knowledge of Spanish may aid bilinguals to find jobs in blue-collar job markets. Results from research on the effects of immigration patterns on achievement have been inconsistent. A substantial body of work docu- ments the fact that despite an initial lack of familiarity with language and customs, immigrants sometimes achieve higher educational and occupational levels than nonimmigrants (Blau and Duncan, 1967~. Chiswick's research (1977, 1978, 1979, 1980a, 1980b, 1982) tends to support these findings, although he shows that an initial adjustment period is needed before immigrants' attainments overtake those of nonimmigrants. Carliner's (1980) analyses support Chiswick's initial adjustment period: recent immigrants generally receive lower wages than second-generation workers, but second-generation workers receive higher wages than do third-generation workers. These findings have been taken to be indicative of a selection process whereby immigrants' high level of motivation manifests itself in higher socioeconomic attainment. Nielsen and Fernandez (1982) speculate that this high level of motivation may be passed on to the immigrants' children, thus explaining why progeny of more recent immigrants perform better in high school.

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416 However, when considering Hispanic immigrants specifically, others (e.g., Featherman and Hauser, 1978; Borjas, 1982; Tienda, 1983) find that Hispanic immigrants are at a socioeconomic disadvantage (relative to long-time residents), which these researchers attribute to dif- ficulties of language, cultural adjustment, and transferability of skills. In addition, using census data, Jaffe et al. (1980) have shown that Hispanic immigrants have lower levels of education than other immigrants, which can result, through the general mechanisms described above, in lower educational and occupational achievements for themselves and their children. In addition to the above research, which focuses on the characteris- tics of immigrants that lead them to achieve well or poorly in the United States, a number of researchers have emphasized that the political and economic climate of the United States at the time of immigration may be an important determinant of how well and how quickly immigrants are assimilated. The Cubans are an example here. It has been argued that the particular historical circumstances under which the initial wave of Cuban immigration took place--the climate of general acceptance by the host population, the legal status of Cubans as political rather than economic migrants (Pedraza-Bailey, 1980; Wilson and Portes, 1980), and supportive governmental policies at the time of Cuban settlement (see, Rogg, 1974; Pedraza-Bailey and Sullivan, 1979; Sullivan and Pedraza-Bailey, 1979; Jorge and Moncarz, 1980~-- explain Cubans' relative advantage over other Hispanic subgroups (see, e.g., Borjas, 1982; Nielsen and Fernandez, 1982; Portes, 1982~. A number of researchers have also argued that the fact that Cuban immigrants have largely settled in an ethnic enclave (Miami) made up of previous immigrants (see Wilson and Portes, 1980; Wilson and Martin, 1982) who own about 10 percent of the businesses and employ 50 percent of Cuban males in the area (see Clark, 1977; Portes et al., 1977, 1981) has had beneficial effects on Cubans' socioeconomic achievements (see Portes and Bach, 1980; Portes, 1982~. Finally, there is a substantial literature that suggests that ethnicity, viewed as analytically separable from language and immigra- tion factors, is related to lower achievement among Hispanics. Akin to arguments regarding the disadvantages that blacks face, it is often argued that racial-ethnic prejudice or cultural and socialization differences between majority-minority groups help to explain achievement differentials (see, e.g., Carter and Segura, 1979; Noboa, 1980; for a review, see Duran, 1983~. Although measuring the effects of racial or cultural discrimination in school or in the workplace is extremely difficult, discrimination is often cited as a major reason for Hispanic youths' school and labor market difficulties (see Carter and Segura, 1979; National Commission for Employment Policy, 1982~. In the case of labor market discrimination, inferences have been made on the basis of the different earnings returns to education for whites and Hispanics (National Commission for Employment Policy, 1982~. Such Hispanic-white differentials in returns to education have also been offered as a reason for Hispanic youths' lower levels of schooling: Hispanic youths are less likely to judge each additional year of schooling to be worth the investment, and hence, they are more likely to drop out.

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417 DATA AND VARIABLES The High School and Beyond Data Base The data analyzed in this paper are from the first two waves (1980 and 1982) of the National Center for Education Statistics (NCES) study, High School and Beyond, a longitudinal study of U.S. high school sophomores and seniors in 1980. The data were collected for NCES by the National Opinion Research Center at the University of Chicago. The base-year (1980) sample consists of 30,030 sophomores and 28,240 seniors in 1,015 high schools; the overall response rate of 84 percent. Of the respondents, 25,875 sophomores and 10,815 seniors were surveyed again in 1982. Hispanic schools were oversampled in the base year, and respondents in those schools had very high probabilities of being included in the follow-up sample (see Frankel et al., 1981~. Three features of High School and Beyond make it ideal for studying Hispanic youths' labor market achievements. First, because it is a longitudinal study of the sophomore and senior high school classes in 1980, respondents can be tracked through their transition from school to work. In addition to providing information on respondents' labor force status, the study provides detailed data on respondents' educational backgrounds and on how respondents combine their school and labor force activities. Second, because Hispanics were oversampled, the study contains sufficient numbers of Cubans, Puerto Ricans, and Mexican-Americans for separate analyses. This is important because past research has shown that Hispanic subgroups differ in their school and labor market achievement profiles (Newman, 1978; Jaffe et al., 1980; National Center for Education Statistics, 1980; National Commission for Employment Policy, 1982; Nielsen and Fernandez, 1982~. Third, High School and Beyond is rare in that it includes many detailed questions about the linguistic practices of the respondent and his or her family (see Nielsen, 1980:App. B and C, for descriptions and discussions of the language data available from the survey). The study also provides information especially relevant to Hispanics, such as nativity and length of U.S. residence. Definition of Comparison Groups One of the main goals of this paper is to provide statistics showing how Hispanic youths compare with non-Hispanic youths on different measures of employment status. To this end, I have divided both the sophomore and senior samples into groups of Hispanics, non-Hispanic whites, and non-Hispanic blacks. Self-identification was used in the survey to classify respondents' ethnic identity." This was done for both theoretical and practical - iDetailed coding information on the definition of the comparison groups and both the dependent and independent variables can be found in the appendix.

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418 reasons. First, the use of self-identification to define ethnic identification is in agreement with the emerging theoretical consensus on what constitutes "ethnic" identity (Barth, 1969~. Second, self- identification of ethnicity is particularly well suited for use in surveys. Smith (1980) has shown that of the various methods of classification (i.e., natal definitions, such as those based on the respondent's country of birth; behavioral definitions based on some objective cultural criterion, such as the use of a language other than English; and subjective criteria involving self-identification by the respondent), self-identification is the most efficient technique for eliciting a positive national-origin identification from respondents in the general population. (Also see Smith, 1983; for research regarding the identification of Mexican-Americans, see Hernandez et al., 1973.) Mauve ~_Am~'~ ~c~ Dependent Variables Two dependent variables are analyzed: labor force participation and unemployment. For both variables, the statistics reported are for those in the civilian labor force; those enlisted in the military are counted as out of the labor force. Because school-leaving and employ- ment decisions are interdependent, I treat labor force and school status as simultaneous events. Therefore, for both sophomores and seniors, the two dependent variables each have four categories. For labor force participation, the four categories for sophomores are participating in the labor force and enrolled in high school; partici- pating in the labor force and not enrolled in high school; out of the labor force and enrolled in school; out of the labor force and not enrolled in school. The variable is defined similarly for seniors with the exception that the relevant school-continuation decision is used, i.e., enrollment in postsecondary education rather than enrolled versus not enrolled in high school. The unemployment variable is defined in analogous fashion for both cohorts, i.e., among those participating in the labor force, respondents were distinguished as employed versus unemployed and enrolled in school versus not enrolled. Independent Variables Corresponding to the discussion in the literature review section, the independent variables are divided into two groups: general and specific. Among the general predictors of labor force and school enrollment status are family socioeconomic background, scholastic performance, demographic variables, and a measure of past labor force involvement.

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419 For both sophomores and seniors, I measured family socioeconomic background with a composite variable derived from a number of measures of parental background and family resources. 2 To assess the effects of scholastic performance on school retention and employment propensity, I also included among the general predictors of labor force and school enrollment status two measures of scholastic achievement: self-reported grades and a standardized-test composite. As measures of scholastic achievement, grades and test scores differ in that grades do not vary across schools, while test scores vary both within and between schools. Three demographic variables are also included as general predictors sex, age, and marital status. Respondents' sex is measured by a dummy variable coded 1 = male and 0 = female. Because younger respondents are expected to be less likely to participate in the labor force and more likely to be enrolled in school, I also included a measure of the respondent's age, coded in years, in the models discussed below. Marital status was included as a demographic variable to test the hypothesis that the increased financial responsibilities that accompany marriage are likely to force respondents into the labor force. Finally, to assess the effects of past labor force experience on youths' labor force and enrollment status (see Stevenson, 1978a), I included a dummy variable measured in the base-year survey of past work experience. Consistent with the discussion above, I also included six variables that are likely to affect Hispanics disproportionately as predictors: respondent's, father's, and mother's length of U.S. residence (measured in years); a dummy variable for whether the respondent is bilingual; proficiency in the non-English language; and proficiency in English. (See appendix for coding details.) Regarding the language measures, I considered respondents bilingual if a language other than English was given in response to at least one of three questions: mother tongue of respondent (first language spoken), second mother tongue (other language spoken before schooling), respondent's usual language. These criteria clearly distinguish those students who have never used a language other than English from those who have had at least some natural exposure to another language. Note that this is unlike the criteria used in the Bilingual Education Act (as amended in 1974) to define children of limited English proficiency in that it does not hinge on students' level of English proficiency or nativity (see O'Malley, 1981:Ch. 2~. My definition also excludes respondents with only indirect contact with languages other than English, such as those who studied a language in school as an academic subject. 2Replacing the socioeconomc status composite with measures of father's and mother's education and family income does not change the substantive results reported here. The summary measure was used because of the large numbers of missing values on parental education (15 to 20 percent) and family income (12 to 18 percent).

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420 The non-English language proficiency scale used in the survey is based on the student's self-assessed ability to understand, speak, read, and write in the non-English language. 3 These questions are contained in a separate language questionnaire and are only asked of students who indicated some exposure to a non-English language. Finally, English proficiency is measured by performance on a standardized vocabulary test. Note that using vocabulary-test per- formance as an indicator of English proficiency builds in a correlation with the standardized-test composite that is used as a measure of the student's scholastic achievement. Although it would have been preferable to have independent measures of a student's English proficiency and scholastic ability, I chose this specification because the alternative self-reported measure of English proficiency (based on a set of items parallel to the proficiency in other language items) showed very little variance. The fact that the measure of English proficiency is correlated with the composite test measuring scholastic achievement is not of itself disturbing. Indeed, it is difficult to imagine any measure of English proficiency that is uncorrelated with these tests of scholastic achievement since the tests are written in the English language and purport to measure knowledge and skills that are largely taught in the schools through the English language. In addition, my experience in past research (Nielsen and Fernandez, 1982; Fernandez and Nielsen, 1984) and in the preliminary stages of these analyses has shown that the pattern of results is the same if one uses the vocabulary test as a measure of English proficiency and the mathematics test as a measure of scholastic achievement, or the vocabulary test with the composite test (i.e., reading, vocabulary, and mathematics) as a measure of scholastic achievement, as I have done here. DESCRIPTIVE ANALYSES Sophomores For High School and Beyond, sophomores were interviewed in 1980 and two years later, regardless of whether they were still in high school. Table 1 presents high school dropout rates, by sex and population subgroup, for the sophomores. 4 3Self-reported measures of language practices have been found to be highly reliable and valid (see Fishman, 1969; Fishman and Cooper, 1969; Fishman and Terry, 1969~. Fishman and Terry (1969) attribute these qualities to the fact that respondents are forced to perform a global assessment of their linguistic behavior. Many objective measures capture more fragmentary aspects of language usage and have correspondingly lower validity. 4The standard errors reported in the descriptive analyses have been corrected for the effects of sample design.

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451 to have a mean of 50 and a standard deviation of 10. For sophomores, the composite was computed by taking the mean of the non-missing test scores. This procedure was slightly modified for seniors because they were administered two vocabulary tests. Items from the two vocabulary tests were combined before the vocabulary test was standardidized (see Jones et al., 1983:Section 6.9~. The test composite was then computed by taking the mean of the standardized non-missing reading, vocabulary, and mathematics test scores. Regarding the demographic variables (i.e., age, sex, and marital status), age and sex were measured by base-year items. However, because marital status was not measured directly in the base-year survey for seniors, a question from the follow-up survey was used: "What was your marital status the first week of February 1982?" Responses were recoded so that 1 = ever married (i.e., married, divorced, separated, widowed) and 0 = never married. Because sophomores were not asked their marital status directly in either the base-year or follow-up surveys, the following question from the follow-up survey was used to distinguish respondents who had been married from those who had not been married. Respondents were presented a question worded "At what age do you expect to ... ," which was completed with a number of items, including "Get Married?" Among the response categories for this question is "Have already done this." Respondents who chose this response to the "Get Married" item were coded in parallel fashion to the seniors, i.e., 1 = ever married, versus 0 = never married for those who did not choose this response. Both sophomores and seniors were asked, "Did you do any work for pay last week, not counting work around the house?" Responses of "yes" and "no" were offered and are coded here as one and zero, respectively. Regarding parent's length of U.S. residence, students were asked in the base-year survey how much of their mother's and father's lives have been spent in the United States. Each variable had five response cate- gories: (1) about 1-5 years; (2) about 6-10 years; (3) about 11-20 years; (4) more than 20 years, but not all; and (5) all or almost all. Categories (1) through (3) were recoded to the midpoint (3, 8, and 15.5 years, respectively). Categories (4) and (5) presented more of a prob- lem because they implicitly refer to the parent's age, for which High School and Beyond does not have a measure. The values for these two categories were imputed by using the modal age of mother's childbearing (25) and adding the student's modal age (15 for sophomores and 17 for seniors) and assigning that to the fifth ("All or almost all") cate- gory. Therefore, the value imputed for sophomores is 40 and for seniors, 42. The midpoint of the fourth category then became defined as 29 years for sophomores and 31 years for seniors. This procedure was repeated for father's length of residence, but three years were added to account for a typical three-year difference in age between husbands and wives. Thus, the fourth and fifth categories for father's length of residence were recoded to 43 and 30.5, respectively, for sophomores, and 45 and 32.5 for seniors. Students were also asked to report how much of their lives had been spent in the United States. The response categories were (1) about 1-5 years; (2) about 6-10 years; (3) more than 10 years, but not all; and

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452 (4) all or almost all. Since available data included the student's age, all the categories were well defined and recoded as follows: (1) 3 years; (2) 8 years; (3) (10 + student's age)/2; and (4) student's age. If the student's age was not available, it was imputed for use in the student length-of-residence variable as the modal age--for sophomores 15 and for seniors 17. This was done for only a few cases. Language questions were administered through a separate question- naire to all respondents (i.e., not just Hispanics) who passed a filter of five questions that asked about the respondent's mother tongue and languages presently spoken at home. Those students who reported a language other than English in response to one of the five questions regarding language background were asked to choose on a four-point scale how well they understood, spoke, read, and wrote the non-English language. The response categories are "Not at All," "Not Very Well," "Pretty Well," and "Very Well" and were coded from zero to four. Exploratory factor analysis of the survey's pretest data showed that the four items clearly load on one factor, with each of the indicators contributing equally (see Fernandez, 1980~. The composite index was formed by taking the mean of the four items. Note that the coding is positive, ranging from a low of zero (indicating no proficiency in the other language) to a high of three (indicating high proficiency). Those students who did not pass the language background filter (i.e., were monolinguals) were assigned a zero on the scale for proficiency in non-English language. When combined with the dummy variable for language background, this coding has the effect of creating a spline for the proficiency-in-other-language scale. English proficiency is measured by the student's performance on the base-year standardized vocabulary test. To simplify across-cohort comparisons, the scores used are based on the subset of test items that were identical in the sophomore and senior test batteries. The test is standardized to a mean of 50 and a standard deviation of 10.

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453 REFERENCES AND BIBLIOGRAPHY Albert, Martin L., and Loraine K. Obler 1978 The Bilingual Brain: Neuropsychological and Neurolinguistic Aspects of Bilingualism. New York: Academic Press. Ashford, J., and R. Snowden 1970 Multivariate probit analysis. Biometrics 26:535-546. Aspira 1976 Social Factors in Educational Attainment among Puerto Ricans in U.S. Metropolitan Areas, 1970. Puerto Ricans and Education Report No. 1. New York: Aspira of America. Bach, Robert L. 1980 The new Cuban immigrants: their background and prospects. Monthly Labor Review 103(October):39-46. Barrera, Mario 1979 Race and Class in the Southwest. South Bend, Tnd.: - University of Notre Dame Press. Barth, Fredrick, ed. 1969 Ethnic Groups and Boundaries. Boston, Mass.: Little, Brown. Blau, Peter M., and Otis Dudley Duncan 1967 The American Occupational Structure. New York: John Wiley & Sons. Borjas, George J. 1982 The earnings of male Hispanic immigrants in the United States. Industrial and Labor Relations Review 35, no. 3 - (April):343-353. Borus, Michael E., ed. 1983 Tomorrow's Workers. Lexington, Mass.: D.C. Heath. Borus, Michael E., J.E. Crowley, R.W. Rumberger, R. Santos, and David Shapiro 1980 Pathways to the Future: A Longitudinal Study of Young Americans. Youth Knowledge Development Report 2.7. Washington, D.C.: U.S. Department of Labor. Bowles, Samuel, and Herbert Gintis 1977 Schooling in Capitalist America. New York: Basic Books. Briggs, Vernon M., Walter Fogel, and Fred Schmidt 1977 The Chicano Worker. Austin: University of Texas Press. Bureau of the Census 1979a Coverage of the Hispanic population of the United States in the 1970 census: a methodological analysis. Current Population Reports. Series P. 23, no. 82. Washington, D.C Bureau of the Census. 1979b Persons of Spanish origin in the United States: March 1978. Current Population Reports. Series P. 20, no. 339. Washington, D.C.: Bureau of the Census. 1982 Population profile of the United States: 1981. Current Population Reports. Series P. 20, no. 374. Washington, D.C.: Bureau of the Census. Carliner, Geoffrey 1976 Returns to education for blacks, anglos and five Spanish groups. Journal of Human Resources 11:172-184. _ -

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