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From High School to Teaching:Many Steps, Who Makes It?

EMILIANA VEGAS, RICHARD J. MURNANE,AND JOHN B. WILLETTHarvard University

In this paper, we focus on the roles that race, ethnicity, and academic skills play inpredicting whether high school students persist along each of the various steps of thepath into teaching. We show that the challenge of creating a racially and ethnicallydiverse teaching force is not primarily one of influencing the occupational decisionsof minority college graduates. Instead, the critical challenge is to increase the highschool graduation, college enrollment, and college graduation rates of minority youth.We use a sequence of four samples originating in the sophomore cohort of HighSchool and Beyond (1992). We explore high school sophomores’ career transitionsalong each step of the path into teaching—high school graduation, entry into college,college graduation, and entry into teaching.

Over the next 10 years, the nation’s teaching force will change dramaticallyas almost two million new teachers replace the large number of teacherswho will retire or change occupations. What will the teaching force looklike in the years ahead? Will it be racially and ethnically diverse? Will itconsist of academically talented college graduates? Insights into the answersto these questions come from analyzing which high school students fromthe 1980s completed the long path into teaching. Steps along this pathinclude high school graduation, college entry, college graduation, and,finally, entry into teaching.

In this paper, we focus on the roles that race, ethnicity, and academicskills play in predicting whether high school students persist along each ofthe various steps of the path into teaching. Our paper is not intended tomodel the process of decision making in which potential teachers engage.Instead of modeling factors that influence individuals’ decisions, our aim isto provide descriptive evidence on the steps that form the path into teachingand to draw attention to the steps where minority group members are mostlikely to fall off the path into teaching.

We focus on the role of race and ethnicity in predicting who becomes ateacher because we believe that in our increasingly heterogeneous society,children need to observe that adults from different ethnic backgrounds can

Teachers College Record Volume 103, Number 3, June 2001, pp. 427–449Copyright © by Teachers College, Columbia University0161-4681

effectively hold leadership positions in our society. Moreover, particularlyfor students of color, effective teachers of diverse racial and ethnic back-grounds may provide the inspiration and role models needed to succeed inschool.1

We focus on the impact of academic skills on entry into teaching becauseteaching well is a complex, cognitive challenge requiring the ability tothink and reason clearly. The positive correlation between teachers’ scoreson standardized tests and the test scores or test score gains of their studentsfound in a number of studies support the notion that standardized testscores are an indicator of relevant academic skills for teachers ~Colemanet al. 1966; Hanushek 1972; Summers and Wolfe, 1977; Ferguson 1990;Strauss and Sawyer 1986!. This is why we believe it is important to attractacademically talented college graduates to teaching. At the same time, wedo not equate the test scores of potential teachers with their teachingeffectiveness. Effective teaching requires many skills not measured by scoreson multiple choice tests of reading and mathematics.

Most national studies of the characteristics of entrants into teaching havefocused on the occupational decisions of college graduates ~for example,Vance and Schlechty 1982; Murnane et al. 1991; Hanushek and Pace 1995!.2

These studies show that college graduates who enter teaching tend to havelower academic skills ~as measured by test scores! than graduates enteringother professions. However, the occupational decisions of college graduatesare their last in a series of steps that determines who becomes a teacher. Inthis paper, we examine the role that race0ethnicity and academic skills playin predicting students’ success in completing each of these steps.

In their study of teacher supply and demand in Texas, Kirby, Berends,and Naftel ~1999! found that in Texas, the major hurdles for minorities toenter the teaching profession were graduation from high school and enroll-ment in college. As in the case of Texas, our paper provides evidence thatthe challenge of creating a racially and ethnically diverse teaching force forour nation is not primarily one of influencing the occupational decisions ofBlack, Hispanic, and Native American college graduates. Instead, the crit-ical challenge is to increase the high school graduation, college enroll-ment, and college graduation rates of Black, Hispanic, and Native Americanyouth. We also show that low academic skills in high school are a criticalfactor predicting why relatively low percentages of Black, Hispanic, andNative American students complete both high school and college.

The paper is organized as follows. In Section I, we discuss our dataand empirical strategy. In Section II, we present the findings of our analy-sis of who succeeds at each step of the path into teaching, specifically:~Step 1! Who graduates from high school?, ~Step 2! Who enters college?,~Step 3! Who obtains a bachelor’s degree?, and ~Step 4! Who enters teach-ing? Finally, in Section III, we discuss our findings.

428 Teachers College Record

RESEARCH DESIGN

DATASET AND SAMPLE

We use a sequence of four samples originating in the sophomore cohort ofHigh School and Beyond ~HSB! ~1992!, a longitudinal survey that interviewedparticipants in 1980, 1982, 1984, 1986, and 1992. The last round of inter-views in 1992 provides information on the initial career decisions of indi-viduals in the survey.

To answer the question “Who graduates from high school?” we use asample that includes all sophomores in 1980 included in the HSB ~1992!who participated in the 1992 follow-up survey. To explore “Who enterscollege?” we use a second sample, which includes only individuals in theoriginal sample who completed high school. To answer the question “Whoobtains a B.A.?” we use a third sample, which includes only individuals inthe second sample who entered college. The fourth and last sample includesonly individuals in the third sample who graduated from college and, as aresult, were potential teacher entrants. We use this last sample to answerthe question “Who enters teaching?” To facilitate the reader’s understand-ing, Figure 1 presents a flowchart describing our sample structure andproviding the sizes of each successive sample.

Our analyses are conducted separately for each sample and, conse-quently, our findings are generalizable only to the sample in question.3 For

Figure 1. Flowchart of sample structure.

From High School to Teaching 429

example, our findings about who graduates from high school are condi-tional on having been a sophomore in high school and thus are general-izable to that population. Similarly, our results regarding who enters collegeare conditional on having completed high school and thus are generaliz-able only to the population of high school graduates. Analogously, ourfindings on who obtains a bachelor’s degree are generalizable only to thepopulation of college entrants. Last, our findings regarding who enters theteaching profession are generalizable only to the population of collegegraduates.

MEASURES

We use four dichotomous outcome variables, one for each of the key stepsalong the path into teaching. These outcome variables represent ~1! grad-uation from high school, ~2! entry into college, ~3! graduation from col-lege, and ~4! entry into teaching.

The predictors of interest include indicators of racial and ethnic back-ground and of tenth-grade academic skills. In all our analyses, we alsocontrol for gender and for important interactions among our predictors.The indicators of racial and ethnic background are a set of dummy vari-ables representing African American, Asian American, Hispanic, and NativeAmerican. The omitted group is White, non-Hispanic ~to which, for sim-plicity, we will refer as simply White!. For ease of exposition, we present anddiscuss the findings regarding the minority groups that constitute the major-ity of the sample—Whites, African Americans, and Hispanics. We choosenot to control for socioeconomic status, given that we are more interestedin describing the characteristics of individuals at each step of the path intoteaching than in modeling the factors that influence their decisions.

Our measure of tenth-grade academic skills is a composite of the scoresthat 1980 high school sophomores obtained on a battery of tests in science,math, reading, vocabulary, and writing. For each individual, we created acomposite index of scores on the five tests using principal componentsanalysis ~see Appendix 2!. A detailed description of our measures is pre-sented in Appendix 1, along with relevant descriptive statistics.

STATISTICAL ANALYSES

In our analyses, we explore high school sophomores’ career transitionsalong each step of the path into teaching. We analyze who graduates fromhigh school, who enters college, who graduates from college, and, finally,who becomes a teacher. At each step of the path into teaching, we ask~1! whether the conditional probability ~conditional on having achieved theprior step! of moving to the next step differs among individuals of different


racial or ethnic origin, ~2! whether the conditional probability of moving tothe next step is predicted by tenth-grade academic skills, and ~3! whetherindividuals of different racial or ethnic origin but with the same level oftenth-grade academic skills have different conditional probabilities of complet-ing each step of the path into teaching.

To answer the first question, we present a table displaying the percent-age of each racial0ethnic group by gender that achieved each step.

To answer the second question, we use logistic regression analyses toexamine whether succeeding in each step along the path into teaching ispredicted by tenth-grade academic skills, controlling for gender and race0ethnicity. As part of this analysis, we explore whether the role of academicskills in predicting success at each step varies by gender and by racial0ethnic group.

To answer the third question, we use the fitted logistic regression modelsto construct plots displaying the probability of completing each step forsubstantively interesting “prototypical” people. Given that teaching is aprimarily female profession, we present these prototypical fitted plots of theprobability of completing each step of the path into teaching for females byrace0ethnic group. However, we note one instance where the fitted prob-abilities are significantly different for males—the fitted probability of enter-ing teaching conditional on having graduated from college. Each prototypicalplot extends between the 10th and 90th percentiles of test scores for femalesof each racial or ethnic group.4 By examining these prototypical fittedplots, we can assess the extent to which differences by race and ethnicity inthe probabilities of completing each step of the path into teaching can beexplained by differences in tenth-grade academic skills.

FINDINGS

Our initial sample consisted of 11,816 high school sophomores in 1980. Ofthese, 62 percent were White, 21 percent were Hispanic, 13 percent wereAfrican American, 3 percent were Asian American, and 2 percent were ofNative American descent. A total of 434 individuals, or 3.7 percent of theoriginal sample, became teachers in the period between 1986 and 1992.The percentages by racial0ethnic group vary considerably. The highest per-centage is for African American sophomores, 4.4 percent of whom becameteachers. The percentage of White high school sophomores who becameteachers is the same as the percentage of Native American sophomores whodid so, 3.7 percent. The percentages of Hispanic sophomores and AsianAmerican sophomores who became teachers are 3.3 and 2.9, respectively.These percentages, while interesting, mask enormous differences in thepercentages of sophomores of each racial0ethnic group who succeed ateach of the steps of the path into teaching. Understanding these differ-


ences is critical to targeting interventions aimed at increasing the numberof students from particular racial0ethnic groups who enter teaching. It is tothese individual steps that we now turn.

WHO GRADUATES FROM HIGH SCHOOL?

The first column of Table 1 presents the percentages of high school soph-omores in the sample who graduated from high school, by gender, race,and ethnicity. It shows that females have slightly higher rates of high schoolgraduation than males. It also shows that Native American, Black, andHispanic high school sophomores are less likely to obtain high schooldiplomas than are White and Asian American sophomores.

To what extent are the differences in high school graduation rates byrace0ethnicity explained by differences in academic skills as measured bytenth-grade test scores? The prototypical fitted curves in Figure 2 illustratethe contents of our final fitted logistic regression models ~which are pre-sented in Appendix 3, Table A3.1!. Each of the curves in Figure 2 presentsthe fitted relationship between tenth-grade test scores and probability ofhigh school graduation for females of a particular racial0ethnic group. Thepositive slopes of all the curves in Figure 2 illustrate that among students inevery racial0ethnic group, those with higher tenth-grade test scores weremore likely to graduate from high school.

If differences in high school graduation rates by race0ethnicity wereexplained entirely by differences in average tenth-grade test scores, therewould be only one curve in Figure 2. The vertical distances between thecurves corresponding to different racial0ethnic groups illustrate that thereare differences in high school graduation rates by racial0ethnic group forhigh school sophomores with the same tenth-grade test scores. The curvesfor White, African American, and Hispanic students in Figure 2 are quite

Table 1. Percentages of Males and Females Completing Each Stepof the Path into Teaching, by Race/Ethnicity

Step 1: HSsophomoresgraduatingfrom HS

Step 2: HSgraduatesenteringcollege

Step 3: Collegeentrants

graduatingfrom college

Step 4: Collegegraduatesenteringteaching

Males Females Males Females Males Females Males Females

African American 78.5 82.6 66.7 68.6 40.3 37.7 10.6 28.7Asian American 91.4 94.4 80.6 78.4 67.9 67.5 2.6 8.6Hispanic 79.6 80.7 53.8 58.4 39.8 42.3 10.4 24.1Native American 77.2 81.7 44.3 52.9 35.9 26.7 7.1 58.3White 85.2 89.4 67.0 64.4 56.0 54.5 5.4 17.5


Figure 2. Fitted probability of the high school graduation of females as afunction of tenth-grade academic skills, by race0ethnicity.


close, illustrating that students from these groups who had the same tenth-grade test scores had about the same probability of graduating from highschool.

Yet as we know from Table 1, Black and Hispanic students were less likelyto graduate from high school than were Whites. Much of the explanationfor these differences lies in heterogeneity in tenth-grade test scores. Black,Hispanic, and Native American children in our society are much morelikely to grow up in low-income families than are White, non-Hispanicchildren. They are also much more likely to attend low-quality schools thanare White youth. One critical way in which these handicaps manifest them-selves is in weak academic skills—skills that are of great importance inacquiring educational credentials.

Each solid circle in Figure 2 illustrates the predicted high school grad-uation rate for a female with the average tenth-grade test score for femalesin her racial0ethnic group. The fact that the circles for African Americanand Hispanic students lie to the left of those for White students illustratesthat the average tenth-grade test scores of the former groups are consid-erably lower than those of the latter groups. The dashed lines connectingthe solid circles to the vertical axis illustrate that the lower high schoolgraduation rates of Black students and Hispanic students ~as shown inTable 1 and by the vertical ranking of the horizontal lines in Figure 2! canbe explained by differences in tenth-grade test scores. In fact, throughoutmost of the test score range, African American females were more likely tograduate from high school than were White females with the same test scores.

Differences in the ranges of tenth-grade academic skills by race andethnicity are worth highlighting. White high school sophomores tend tohave higher tenth-grade academic skills than Hispanics and African Amer-icans. This implies that if we were to choose an African American or His-panic person at random from the population, the probability that he or sheis a high school graduate will be lower than if the person were White orAsian American because of the existing differences in ranges of tenth-gradeacademic skills by race0ethnicity. But if we were to select two individualswith the same tenth-grade academic skills level but different race and eth-nicity, the probability that the person is a high school graduate is slightlyhigher for African Americans and Hispanics than for Whites. Thus, tenth-grade academic skills account for most of the variation by race0ethnicity inthe probability of high school graduation.

WHO ENTERS COLLEGE?

Patterns of college entry are different from those of high school gradua-tion. Column 2 of Table 1 shows that among Black, Hispanic, and NativeAmerican high school graduates, females are more likely to enter college


than males. In contrast, among high school graduates of White or AsianAmerican backgrounds, males are more likely to enter college.

As in the case of high school graduation, there are considerable differ-ences in the probability of college entry by race and ethnicity. Asian Amer-ican high school graduates have the highest rate of college entry ~80.6percent for males and 78.4 percent for females!. But African American highschool graduates have the second-highest rate, with about 69 percent femaleand 67 percent male of high school graduates in this race group enteringcollege. About 64 percent of White female and 67 percent of White malehigh school graduates in the sample entered college. As in the case of highschool graduation, college entry rates for Hispanic and Native Americanhigh school graduates lag behind those of Whites and Asian Americans.

Figure 3 presents prototypical fitted curves of the probability of enteringcollege for female high school graduates in the three majority race andethnic groups by tenth-grade academic skills ~see Appendix 3, Table A3.2for the fitted models!. The positive slopes of the curves in Figure 3 illustratethat, in all racial0ethnic groups, those high school graduates with strongacademic skills, as measured by tenth-grade test scores, were more likely toenroll in college than graduates with lower academic skills.

Figure 3 also shows that, among female high school graduates with thesame tenth-grade academic skills, African Americans have the highest esti-mated probability of entering college. Next in line are Hispanic students,then White students.

How important are tenth-grade academic skills in explaining differencesin college enrollment rates by racial0ethnic group? Figure 3 illustrates thatthe answer varies by racial0ethnic group. If the average tenth-grade testscore of African American and Hispanic female high school graduates wereas high as the average tenth grade score of White female graduates, theirpredicted college enrollment rate would be just as high—in fact, slightlyhigher. The figure illustrates that most African American and Hispanichigh school graduates with solid academic skills do enroll in college. Theproblem is that many minority high school graduates have relatively weakacademic skills, as measured by tenth-grade test scores.

WHO OBTAINS A BACHELOR’S DEGREE?

Among students who enroll in college, Asian Americans have the highestgraduation rate, followed by Whites. The graduation rates of Hispanic andBlack students are considerably lower, and that of Native Americans is byfar the lowest ~see column 3 of Table 1!.

How much of these differences can be explained by differences in thequality of students’ preparation for college, as measured by tenth-gradeacademic skills? Figure 4 illustrates the answer to this question ~see Appen-


Figure 3. Fitted probability of the college entry of females as a function oftenth-grade academic skills, by race0ethnicity.


Figure 4. Fitted probability of the college graduation of females as a func-tion of tenth-grade academic skills, by race0ethnicity.


dix 3, Table A3.3 for the fitted models!. In all majority racial0ethnic groups,college students with relatively high tenth-grade test scores were morelikely to graduate than students with low tenth-grade scores. The closenessof the curves illustrates that the graduation rates of White, Black, andHispanic college students with the same tenth-grade academic skills arevery similar. This pattern suggests that increasing the quality of Black andHispanic students’ preparation for college would significantly reduce thegap between their college graduation rates and that of White college students.

WHO ENTERS TEACHING?

The last column of Table 1 shows the percentages of college graduates bygender in each racial0ethnic group who entered teaching. Female collegegraduates of all racial and ethnic groups were more likely to enter teachingthan their male counterparts. Among college graduates in the sample,Native Americans, African Americans, and Hispanics have the highest per-centages of teacher entry ~58 percent, 29 percent, and 24 percent respec-tively for females; and 7.1 percent, 10.6 percent, and 10.4 percent respectivelyfor males!. About 18 percent of White female college graduates in thesample entered teaching, whereas only about 5 percent of White malecollege graduates did so. The group with the lowest rate of teacher entry inthe sample is Asian American college graduates, with 9 percent of femalesand 3 percent of males in the sample having chosen to become teachers.5

The prototypical fitted plots in panel ~a! of Figure 5 ~see Appendix 3,Table A3.4 for the fitted models! illustrate that for every majority racial0ethnic group, female college graduates with strong academic skills as mea-sured by tenth-grade test scores were less likely to become teachers thanwere graduates with weaker academic skills. This pattern, which has beenreported in other studies ~for example, Murnane et al. 1991; Hanushek andPace 1995!, is not difficult to explain. Teaching is a relatively low status, low-paying occupation, and one in which there are few financial rewards forexcellence. Academically talented college graduates are drawn to occupa-tions with greater prestige and compensation.

In fact, what is of greater surprise than the downward slopes of the fittedcurves in panel ~a! of Figure 5 is the relative flatness of the curves for Whitesand African Americans. The flatness indicates that for these groups, there isnot a large difference between the probability that academically talented col-lege graduates enter teaching and the probability that less talented graduatesbecome teachers. For example, the predicted probability of becoming a teacherfor a White female college graduate whose tenth-grade test score was at the90th percentile for her group is 0.15, compared to 0.12 for a White femalecollege graduate with a score at the corresponding 10th percentile. The cor-responding figures for African American female college graduates are 0.19


Figure 5. Fitted probability of the teacher entry of females and males as afunction of tenth-grade academic skills, by race0ethnicity.


and 0.16. These patterns indicate that in the late 1980s and early 1990s it wasnot the case that among African American and White female college gradu-ates, only those with weak academic skills became teachers.

In contrast, this claim appears to be correct for Hispanic female collegegraduates. Only one in ten Hispanic female college graduates with testscores at the 90th percentile for their group is predicted to enter teachingcompared to one in four Hispanic female graduates with test scores at the10th percentile for the group. This is a disturbing pattern. Hispanics arethe most rapidly growing group in the U.S. student population. If thecountry is to have a teaching force demographically similar to its studentpopulation, schools will need to hire a great many Hispanic teachers in theyears ahead. These new teachers must not be the least academically tal-ented Hispanic college graduates.

Panel ~a! of Figure 5 helps clarify the patterns of teacher entry by racedisplayed in Table 1. Comparing females with the same tenth-grade aca-demic skills, African American college graduates were more likely to becometeachers than were White college graduates. This may reflect barriers thatBlack females face in gaining entry into other professions. The significantvertical distances between the curves illustrate that differences in the prob-ability that college graduates of different races and ethnic backgroundsenter teaching do not stem primarily from differences in average tenth-grade test scores. In fact, for every group except Hispanics, test scores playonly a modest role in explaining differences by race and ethnicity in femalecollege graduates’ probability of becoming teachers.

Though not surprising, it is noteworthy that the fitted probability ofentering teaching for male college graduates are significantly lower thanthose for female college graduates at all levels of tenth-grade academicskills, as illustrated in panel ~b! of Figure 5. In addition, the slopes of theprototypical fitted curves for African American male college graduates areslightly upward sloping. This suggests that, in contrast to our findings forfemales, males of this racial0ethnic group with high tenth-grade academicskills have a slightly higher estimated probability of entering teaching thanmales with low tenth-grade academic skills. However, this is not the caseamong Hispanic male college graduates, for whom the prototypical fittedcurve has a flat slope, indicating that there are no differences in the fittedprobability of entering teaching by tenth-grade academic skills for males ofthis ethnic group.

DISCUSSION

In this paper, we have shown that there are important differences amongracial0ethnic groups in the rates of success at each step along the path intoteaching. The early steps in the path into teaching—high school gradua-


tion, college entry, and college graduation—are critical in explaining racialand ethnic differences in who becomes a teacher. Weak academic skillsrelative to those of Whites and Asian Americans play a key role in limitingBlack students’ and Hispanic students’ opportunities of succeeding at eachof these early steps.

Our analyses suggest that improving the academic preparation of Afri-can American and Hispanic high school students would close the gapsbetween their success rates relative to those of White students in achievinghigh school graduation, college enrollment, and college graduation. Rais-ing the average academic skills of Native American high school studentswould also contribute markedly to the educational attainment of NativeAmerican students. However, unlike the case for Black students and His-panic students, raising the average academic preparation of Native Ameri-can high school students to that of White high school students would noteliminate the gaps between these groups’ high school graduation, collegeenrollment, and college graduation rates. Native American students appearto face particular difficulties in acquiring the educational credentials thatare increasingly valued in today’s labor market ~Gottschalk 1997! and arenecessary steps along the path into teaching and other professions.

In summary, this paper has shown that focusing only on the occupa-tional decisions of college graduates misses critical obstacles to developinga racially and ethnically diverse, academically talented teaching force. Increas-ing the number of Black and Hispanic students who graduate from highschool, enroll in college, and graduate from college is the key to increasingthe representation of these groups in the teaching force. In fact, Blackcollege graduates are much more likely to enter teaching than are Whitecollege graduates, and this is just as true for graduates with very strongacademic skills as it is for graduates with weaker academic skills. The prob-lem is that there are not enough Black college graduates and Hispaniccollege graduates. Only 21 percent of African American, 17 percent ofHispanic, and 12 percent of Native American high school sophomores in1980 graduated from college by 1992, compared to 50 percent of AsianAmericans and 32 percent of Whites. Weak academic skills are an impor-tant part of the explanation of these gaps, especially for African Americansand Hispanics.

We began this paper by asking what the nation’s teaching force will looklike in the years ahead. In particular, we asked whether it will be a raciallydiverse, academically talented teaching force. We have shown that the answerto these questions will depend to a great extent on the nation’s success inimproving the quality of education provided in the nation’s elementary andsecondary schools. A strikingly high percentage of American students withstrong academic skills graduate from high school, enroll in college, andgraduate from college. The problem is that too many students, especially


African American, Hispanic, and Native American students, reach tenthgrade without strong academic skills. Progress in solving this problem is thekey to creating a racially and ethnically diverse, academically talented teach-ing force in the future.

APPENDIX 1: DESCRIPTION OF VARIABLES

Table A1.1. Variable Definitions

Variable Name Definition

HSGRAD a dichotomous variable that takes on the value 1 if the individualis a high school graduate and 0 otherwise.

ENTCOL a dichotomous variable that takes on the value 1 if the individualentered a 2- or 4-year college and 0 otherwise.

BAGRAD a dichotomous variable that takes on the value 1 if the individualis a 4-year college graduate and 0 otherwise.

TEACHER a dichotomous variable that takes on the value 1 if the individualever declared to be a teacher as his0her occupation and 0otherwise.

HISPANIC a dichotomous variable that takes on the value 1 if the individualis of Hispanic origin and 0 otherwise.

NATIVE a dichotomous variable that takes on the value 1 if the individualis of Native American origin and 0 otherwise.

ASIAN a dichotomous variable that takes on the value 1 if the individualis of Asian American origin and 0 otherwise.

BLACK a dichotomous variable that takes on the value 1 if the individualis of African American origin and 0 otherwise.

WHITE a dichotomous variable that takes on the value 1 if the individualis of White origin and 0 otherwise.

IRT FACTORSCORE

a continuous variable that contains a composite of theindividual’s scores on a battery of IRT tests in 1980, includingreading, writing, vocabulary, math, and science. See Appendix 2for details on how this composite was created.

Table A1.2. Summary Statistics

Variable Mean Std. Dev. Min Max

HSGRAD 0.862ENTCOL 0.560BAGRAD 0.289TEACHER 0.038HISPANIC 0.199INDIAN 0.019ASIAN 0.026BLACK 0.117WHITE 0.640IRT FACTOR SCORE 0.004 1.928 24.298 4.980


APPENDIX 2: PRINCIPAL COMPONENTS ANALYSISOF TENTH-GRADE ACADEMIC SKILLS

The results of a principal components analysis of the five IRT tests—vocab-ulary, reading, math, science, and writing—are presented in Table A2.1.

Table A2.1. Principal Components Analysis

Component Eigenvalue Variable Eigenvector

1 3.72 IRT vocabulary 0.452 0.40 IRT reading 0.463 0.36 IRT math 0.454 0.27 IRT science 0.455 0.26 IRT writing 0.43

In the second column, we display the eigenvalues for each measure. Thefirst principal component has a much higher eigenvalue than the otherfour ~3.72! and accounts for 74 percent of the total variance. A screen plotof eigenvalues against the component numbers indicates that the first com-ponent is the most important, suggesting that the five scores can be effec-tively composited into a single indicator.

Examination of the eigenvectors ~or loadings! of each test of the firstprincipal component shows that the five tests contribute almost equally. Toscore high on this component, an individual would have to score high in allfive IRT tests. This suggests that this principal component measures overallstudent achievement.


APPENDIX 3: FITTED LOGISTIC REGRESSION MODELSAT EACH STEP OF THE PATH INTO TEACHING

Table A3.1. Fitted Logistic Regression Analyses of the Probability ofHigh School Graduation by Gender, Race/Ethnicity,and Tenth-Grade Academic Skills ~n 5 10584!

Model

A B C

Intercept 2.165* 2.393* 2.377*~0.048! ~0.062! ~0.061!

Male 20.290* 20.442* 20.442*~0.057! ~0.076! ~0.076!

Hispanic 20.526* 20.017 20.001~0.068! ~0.102! ~0.101!

Native American 20.568* 20.339 20.323~0.185! ~0.259! ~0.245

Asian American 0.522; 0.435 0.655*~0.235! ~0.253! ~0.245!

African American 20.498* 0.108 0.247*~0.084 ~0.138! ~0.092!

Standardized IRT Score 1.180* 1.138*~0.064! ~0.060!

Std. IRT * Male 20.176; 20.172;~0.071! ~0.071!

Std. IRT * Hispanic 20.194; 20.156~0.089! ~0.086!

Std. IRT * Native American 20.527; 20.489;~0.227! ~0.226!

Std. IRT * Asian American 20.462~0.244!

Std. IRT * African American 20.159~0.115!

Pseudo-R2 0.014 0.119 0.119

Notes: ~1! Standard errors in parentheses with p , 0.05 5 ;, p , 0.01 5 *. ~2! GeneralLinear Hypothesis tests were conducted of the joint effects of race, the jointeffect of standardized IRT score and its interaction with male, the joint effect ofstandardized IRT score and its interaction with each race group, and the jointeffects of all the interactions of standardized IRT score with each race group. Inall of these tests, the null hypothesis that the coefficients are zero in the populationcan be rejected at the 5 percent level.


Table A3.2. Fitted Logistic Regression Analyses of the Probabilityof Entering College by Gender, Race/Ethnicity,and Tenth-Grade Academic Skills ~n 5 9125!

Model

A B C

Intercept 0.547* 0.354* 0.358*~0.034! ~0.037! ~0.037!

Male 0.017* 20.056* 20.057*~0.043! ~0.048! ~0.048!

Hispanic 20.426* 0.264* 0.268*~0.055! ~0.069! ~0.064!

Native American 20.987* 20.358 20.331~0.164! ~0.196! ~0.184!

Asian American 0.528* 0.758* 0.765*~0.147! ~0.165! ~0.163!

African American 20.005* 0.795* 0.863*~0.070! ~0.099! ~0.081!


Std. IRT * Male 0.077; 0.077;~0.056! ~0.056!

Std. IRT * Hispanic 20.042; 20.156~0.076! ~0.086!




Pseudo-R2 0.009 0.147 0.147

Notes: ~1! Standard errors in parentheses with p , 0.05 5 ;, p , 0.01 5 *. ~2! GeneralLinear Hypothesis tests were conducted of the joint effects of race, the jointeffect of standardized IRT score and its interaction with male, the joint effect ofstandardized IRT score and its interaction with each race group, and the jointeffects of all the interactions of standardized IRT score with each race group.The null hypothesis that the coefficients are zero in the population can be rejectedat the 5 percent level in all the tests except for the test of the joint effects of allthe interactions between standardized IRT score and race0ethnicity. In this test,we cannot reject the null hypothesis that all the coefficients are simultaneouslyzero in the population.


Table A3.3. Fitted Logistic Regression Analyses of the Probability ofObtaining a B.A. Degree by Gender, Race/Ethnicity,and Tenth-Grade Academic Skills ~n 5 5924!

Model

A B C

Intercept 0.148* 20.296* 20.295*~0.040! ~0.050! ~0.048!

Male 0.052* 0.005* 0.005*~0.053! ~0.061! ~0.061!

Hispanic 20.513* 20.063; 20.065*~0.072! ~0.081! ~0.079!

Native American 21.079* 20.663; 20.658;~0.261! ~0.285! ~0.276!

Asian American 0.550* 0.750* 0.760*~0.153! ~0.171! ~0.164!



Std. IRT * Male 20.157; 20.156;~0.068! ~0.067!

Std. IRT * Hispanic 20.004; 20.156~0.094! ~0.086!




Pseudo-R2 0.016 0.096 0.096



Table A3.4. Fitted Logistic Regression Analyses of the Probabilityof Becoming a Teacher by Gender, Race/Ethnicity,and Tenth-Grade Academic Skills ~n 5 3037!

Model

A B C

Intercept 21.834* 21.689* 21.760*~0.082! ~0.112! ~0.109!

Male 21.140* 21.416* 21.404*~0.140! ~0.194! ~0.193!

Hispanic 0.258* 0.244* 0.313*~0.170! ~0.189! ~0.187!

Asian American 20.871; 21.825; 20.872;~0.426! ~0.831! ~0.426!



Std. IRT * Male 0.402; 0.395;~0.176! ~0.174!

Std. IRT * Hispanic 20.259; 20.348~0.212! ~0.207!



Pseudo-R2 0.044 0.052 0.049


Notes

1 For a discussion of many issues related to the teaching profession in the United States,see Quality counts 2000: Who should each? prepared by Education Week and The Pew Chari-table Trusts.

2 Patricia Albjerg Graham’s ~1987! paper is an exception to the typical pattern of studiesin that it does emphasize the importance of the early steps of the path into teaching.


3 The HSB survey oversampled certain populations, such as Hispanics. Weights are includedin each year of the survey to allow researchers to generalize back to the original population ofhigh school sophomores. Because our population of reference changes at each step of thepath into teaching, we have conducted all our analyses using unweighted data.

4 The 10th and 90th percentile scores are better indicators of the range of a racial orethnic group’s scores than the corresponding minima and maxima, as they are more robust tothe presence of outliers.

5 Because of the small number of Native American college graduates in the sample, wehave excluded this group from our logistic regression analyses.

References

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EMILIANA VEGAS is an advanced doctoral student at the Harvard Grad-uate School of Education. She has a master’s of public policy from DukeUniversity. Her research interests include the economics of education and,in particular, teacher labor markets. Her dissertation research focuses onteacher labor markets in Latin America.

RICHARD J. MURNANE, the Thompson Professor of Education and Soci-ety at the Harvard Graduate School of Education, is an economist whoseresearch focuses on the relationships between education and the economy,


teacher labor markets, the determinants of children’s achievement, andstrategies for making schools more effective. In 1991, he co-authored WhoWill Teach? His most recent book, co-authored with MIT professor FrankLevy, is titled Teaching the New Basic Skills ~1996!.

JOHN B. WILLETT is a professor of applied courses on data analysis andstatistical methods at the Harvard Graduate School of Education. His researchfocuses on the effective application of quantitative methodology to substan-tive problems in education and the social sciences, including the improve-ment of research design. He has a particular interest in methods for analyzingthe timing and occurrence of events and methods for the measurement oflearning and development.


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