emerging trends and recruitment initiatives of the stem teaching workforce: an analysis of teacher...

8/12/2019 Emerging Trends and Recruitment Initiatives of the STEM Teaching Workforce: An Analysis of Teacher Quality, Motivators of Entry, and Gender Effects

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STEMTEACHING WORKFORCE TRENDS 1

Emerging Trends and Recruitment Initiatives of the STEM Teaching Workforce:

An Analysis of Teacher Quality, Motivators of Entry, and Gender Effects

Andrea Dykyj, Alicia Haelen, and Victoria Hess

New York University


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I. Introduction

The expansion of careers in science and technology has garnered the attention of the

industry leaders and governmental agencies committed to meeting these workforce demands.

United States job growth in Science, Technology, Engineering and Science (STEM) fields is

projected to far outpace job growth in non-STEM fields over course of the next decade (National

Science Foundation, 2012). Simultaneously, there is a recognized shortage of professionals who

are qualified to fulfill these employment needs. To better prepare students for employment in

STEM fields, policymakers have placed an increased emphasis on developing primary and

secondary-level (K-12) education initiatives in math and science, with a particular focus on

attracting highly qualified STEM graduates to teaching. In this paper, we look to identify the

effects of graduate, time, and policy characteristics on the likelihood of the nations trained

scientists and engineers entering the teaching profession as STEM teachers.

Our analysis focuses on broader employment and workforce trends, analyzing the impact

of policy initiatives on changes in STEM professionals choosing career paths in teaching.

Utilizing STEM professional data, we aim to identify whether professionals who are highly

qualified in content-related knowledge are moving into the STEM teaching field at a greater rate,

given recent policy trends. In our analysis we seek to answer the following three research

questions:

1) Has there been a change in the proportion of content-knowledgeable


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in key ways. Finally, we posit that the reasons for entering the STEM teaching profession differ

by gender in systematic ways that can be mitigated by target policies.

To test our hypotheses, we employ education and employment data captured in the

Scientists and Engineers Statistical Data System (SESTAT). In line with our first hypothesis, our

results indicate a steady growth in the STEM teaching workforce, with an increasing number of

credentialed and content-knowledgeable individuals entering the field. In addressing our second

hypothesis, we find that common determinants of career change, as measured by SESTAT , were

not positively associated with professionals switching careers into STEM teaching. Lastly, we

found empirical evidence to support our third hypothesis regarding systematic differences in

workforce composition by gender; specifically, we find highly qualified male career changes are

less likely to enter STEM teaching than female career changers.

II. Policy and Background

The current job market underscores the need for qualified STEM professionals and,

consequentially, the need for an increase preparedness of students to take on these opportunities.

The Bureau of Labor Statistics projects that Science and Engineering occupations will grow by

20.6 percent from 2008 to 2018, more than double the projected 10.1 percent growth for all

occupations during that same time period (Ibid, 2012). The drive for major improvements within

STEM education, however, is not a recent movement. Since the 1980s, US policymakers have

recognized a need for improvement in K-12 math and science education when A Nation at Risk


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In 2005, Secretary of Education Margaret Spellings stated that many states had improved their

teaching force, with a majority of teachers meeting the HQT requirements.

A substantial move towards STEM-focused educational policies came in 2009, when

President Barack Obama launched Educate to Innovate, an initiative specifically targeting

improvement in math and science education. As a concerted effort between the Federal

Government, non-profits, corporations and science societies, Educate to Innovate seeks to

drastically improve American studentsperformance in math and science. The policy includes a

large federal investment in STEM education; initiatives aimed at the broadening of the STEM

talent pool; and the 100kin10plan, which aims to prepare 100,000 new and effective STEM

teachers over the next 10 years. 100Kin10 specifically recognizes the importance highly

qualified teachers in student outcomes and the need to recruit and develop highly qualified

STEM teachers (WhiteHouse.gov, 2013).

The focus of Educate to Innovate on teacher quality is supported by research that

continues to highlight the importance of teacher quality in student outcomes. Darling-Hammond

(1999) found that policies focused on teacher quality are related to improvements in student

performance. Specific research on the role of mathematics content knowledge in the classroom

also highlights the importance of teacher quality and content knowledge in student performance.

Ball, Thames, and Phelps (2008) examined the unique demands of mathematics education, and

the impact of teachers with common content knowledge versus a more advanced, expert-like

knowledge of ones subject area While the teachers with common content knowledge were able


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conversation within STEM departments regarding the possibilities offered by a career as a

STEM teacher, a pervading sense of apathy among faculty towards the recruitment and

education of STEM teachers, and the narrow scope of recruitment nets for STEM teacher

education programsfailing to reach these key audiences (Ibid, 2012).

Prior studies have identified other unique observable qualities in the STEM-talented

individuals who do decide to teachdifferentiating these individuals from their peers who go on

to pursue other careers. In their study of pre-service STEM teachers in Australia, Watt,

Richardson, and Pietsch (2007) examined the characteristics and motivations of prospective

STEM teachers. The authors found significant demographic trends in gender representation

(specifically, the majority of prospective mathematics teachers being male, but the majority of

science candidates being female) and career switcher backgrounds of students (where 90

percent of the candidates reported having come from working in other STEM-related

occupations). The authors also identified trends in motivation including choosing to teach for

the intrinsic value[s] of the field, shap[ing]the future of students, and making a meaningful

social contribution (Ibid, 2007).

Curtis (2012) also explored the motivational factors inspiring individuals to pursue

careers as mathematics teachers. In analyzing the motivational factors reported by teachers,

Curtis found strong correlations between responses indicating a desire to work with young

people and the likelihood of individuals to stay or leave the field. Other high frequency

responses included a passion for mathematics and a desire to help meet the high need for math


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more likely to have worked in other careers before settling on teaching initially deterred by the

social stigma associated with teaching being traditionally female-dominated field.

Similar to Cushman, Drudy et al. (2005) also found that many males were attracted to the

teaching profession out of the same sense of altruism and desire to work with children expressed

by females. Upon further examination, however, the authors also observed differences in the

nature of the respective motives of males and females in deciding to become teachers. On

average, Drudy et. al. found that males were influenced by broader external factors like the

favorable job conditions of teaching, and the worthwhile nature of the job, while females

were drawn to teaching by factors specific to the field itself, such as the mechanics of teaching,

or working with children. Given the broader focus of male motivation in choosing to teach,

teaching is a much more readily substitutable career for males, making males less likely to enter

the field, and more likely to leave.

In her study of second-career teachers in Chicago, Chambers (2010) found that many

respondents had considered teaching in the past, but had been deterred from entering the field by

reasons including financial pressures, discouragement from family, and the fear of the harsh

realities of teaching (for example, school violence, classroom management issues, and the lack

of administrative support). Many respondents also believed that their previous experiences were

beneficial to students, allowing them to share skills, perspectives, and innovative approaches to

teaching with their students, rooted in real-world applications of subject matter.


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To study the relationship between degree holder attributes and workforce composition we

use employment and education data captured in SESTAT. The SESTAT population consists of

individuals who have received a bachelors degree or higher in a science, engineering, or health

or related field from a postsecondary institution within the United States and who are either

working or have been trained in a science and engineering field. To our advantage, this broad

spectrum of disciplines includes technology and mathematics-related fields.

To efficiently capture information on this extensive population, the data are integrated

from three component surveys: the Survey of Doctorate Recipients (SDR), the National Survey

of College Graduates (NSCG) and the National Survey of Recent College Graduates (NSRCG).

Each survey slightly varies in the target populations, questionnaires, and sampling methodology;

together, however, they provide a comprehensive profile of the nations scientist and engineers.

The SESTAT component surveys have been administered every two to three years beginning in

the 1970s, each applying the same survey reference date to ensure measurement consistency.

Since data became publically available in 1993, SESTAT has contained on average 100,000

records per round; to illustrate the vast growth of this subpopulation, weighted counts indicate

11.6 million individuals in 1993 and 26.9 million in 2010 constitute the nations SESTAT

population.

While these data provide a unique lens through which to analyze career paths of the

SESTAT population, they come with few substantial shortcomings. First, as Stephan and Levin

(2005) found to be problematic SESTAT only examines patterns of retention for survey eligible


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b. Study Sample and Descriptive StatisticsSince no state in the U.S. requires primary and secondary school teachers to acquire

doctorate degrees, we limit our analysis to individuals having earned no higher than a masters

degree; thus we only use data from the NSCG and NSRCG. The NSRCG includes a cross-

sectional probability-based sample design covering individuals who received a bachelors or

masters degree in a science or engineering field within about three years of the survey reference

period. The NSCG is unique in that it is a longitudinal study of persons identified as having at

least a bachelor's degree in any degree field, whereas only those trained and/or working in a

science of engineering field are eligible for SESTAT. Therefore, it is possible ones highest

degree is not in a science or engineering field; however, since we can assume they were trained

in one of these fields at some point during their education, and are interested in the outcomes

relating to content knowledgeableness, we retain all degrees regardless of field of study.

We limit our analyses to four cross-sections of data measured from the most recent

survey rounds: 2003, 2006, 2008, and 2010. These years provide for more rich, consistently

measured and reported data than prior rounds. In this sample includes individuals having earned

a degree between 1950 and 2006 and who were employed in the workforce at the time of

measurement. Descriptive statistics of our sample are provided in Tables 1a through 1d in the

appendix. While some individuals may be followed across multiple survey rounds in the NSCG,

and thus included in our sample more than once, we do not follow them overtime. Instead, with

the application of population weights we study the representativeness of these cases in the


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We are also interested in how the composition of the STEM teaching workforce has

changed over time. Table 1b shows prevalence of workforce entry, aggregated by the decade of

degree receipt; here we see STEM teachers have occupied about 8 percent of the overall

SESTAT population since the 1960s. Meanwhile, the percent of STEM majors from the 1960s

becoming STEM teachers (73.2 percent) is about 16 percent greater than in the 1970s (56.9

percent); this proportion drops further for degree earners in the 1980s (54.3 percent), rises in the

1990s (58 percent), but reaches is lowest proportion of teachers (we are exclude degrees earned

in the 1950s because of the small sample size) in the 2000s (52.8 percent). Meanwhile, we see a

steady growth in the percent of masters degree recipients over the decades becoming STEM

teachers. The variability in the proportions of STEM majors and mastersdegree recipients may

be consequential of the substantial increase in the number of STEM degrees measured in the data

from decade to decade. To that end, our parametric model will serve to quantify systematic

trends in the deviations of quality through time.

Moreover and possibly consequentially, STEM teachers indicated their occupation was

related to their field of study more often than other professionals (90.6 versus 78.2 percent).

However, this difference may not be a reliable comparative measure of relatedness across our

two categories of professions because other professional may have been trained in a STEM

field and employed in an outside field, or trained in a non-STEM field and employed in the

STEM field. We assume, nonetheless, that STEM teachers were trained in STEM-related fields.

Furthermore the proportion of overall career changers who entered STEM teaching versus


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variable of interest is a measure of employment status, indicating being employed as a primary or

secondary school STEM teacher. The preliminary outcome measure is an indicator for whether

an individual majored in a STEM related field (in ones highest degree received), and is used as

an initial indicator of workforce quality. We operationalize this measure in part because subject

major is considered a relevant factor, with school administrators often making a substantial effort

to attract math and science teachers who are trained in their subjects (Angrist and Guryan, 2008).

Our second quality indicator identifies the level of ones highest degree as a masters versus a

bachelors degree. Both indicators are aggregates of broader major1and degree type variables.

Further, an interaction between these two indicators was derived to measure the marginal effect

of having both of these qualifications.

Although operationalizing these measures as proxies for teacher quality are generally

weak and have not been found to influence educational outcomes with any consistency

(Hanushek1986 & 1997), our data do not include more plausible teacher-specific measures, such

as teacher test scores or the quality of teachers' undergraduate institution (Ehrenberg and Brewer

1994). Nonetheless, the specific academic subjects of interest in our study are those whose

underlying disciplines are associated with a considerable range of occupational options. That is,

those trained in a STEM field, and further those with masters degrees, are likely to have more

opportunities outside of teaching than for those trained in other fields; while those who become

STEM teachers, specifically, are presumed to be fitted and knowledgeable in both teaching and

their discipline


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Since this data source is not specific to teacher training and the teacher workforce, a

considerable limitation we face is that we do not observe why degree holders pursuing teaching

did not become teachers. We are also unable to observe trends in satisfaction with job conditions

in addition to school characteristics and the geographic local of the degree holder. One way we

overcome these shortcomings is by assessing heterogeneity across career changers and their

chosen occupations. Here, we operationalized indicators of the most important factors

influencing decisions to work in an area outside of his or her field of study measured in the data.

These reasons include: pay or promotion opportunities, working conditions (hours, use of

equipment, working environment), job location, change in career or professional interests,

family-related reasons (children, spouses job moved), and a job in ones highest degree field

was not available.

Notably, these reasons are only applicable to those indicating their occupation is not

related to their field, which we acknowledge as another limitation in that it prevents us from

measuring why STEM teachers who anticipated entering the field did so. In addition, the term

related is subjective and therefore could be a source of measurement bias. Nonetheless, we find

this information useful in measuring characteristics of career changers with respect to whether

or not reasons for entering the STEM teaching profession differ substantially from entering

another field. The analysis concludes with a focus on differential effects of gender on the

likelihood of becoming a STEM teacher. To that end, we include interactions between gender

(where a value of one indicates male) and the reason indicators as well as with indicators of


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variable, we use logit regressions to predict the odds of a degree holder becoming a STEM

teacher given a relevant characteristic. In the case of a binary response variable, the assumptions

of linear regression are not valid; for instance, the relationship between the dependent and

independent variables is nonlinear and the error terms are not normally distributed. The logit

model instead estimates the non-linear relationship between predictors and outcomes where the

logit estimator represents the marginal odds of an event occurring (Y = 1), or how many more

times likely it is to occur, given a 1 unit increase in an explanatory variable and with all others

held constant. Our initial hypothesis stems from the notion that the composition of STEM

teachers has changed over the time to include more content-knowledgeable professionals. To test

this presumption, we specify STEM teaching as a function of quality and time indicators. The

first model, shown below, predicts the odds of a degree holder becoming a STEM teacher as a

function of STEM major status. This status serves as a proxy of content knowledgeability,

where those who majored in a STEM field are considered more effective in teaching a STEM

discipline than those who majored in other fields. We expect the estimate of STEM major to be

positively related to becoming a STEM teacher.

= + STEM_majorit+ i+ it

The outcome of interest, yit is the predicted probability of degree holder i becoming a STEM

teacher at time t; is equivalent to the log of the odds of yittaking on a value of 1. The


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The second specification controls for having a masters degree, versus a bachelors

degree, along with selected degree holder characteristics presumably related to both an

individuals major and career outcome. These attributes include: race, gender, age, and whether

or not the degree holder has children. Specification II can be expressed as follows:

= + STEM_majorit+Masters + Xit+ i+ it,

where is the estimated effect of having a masters degree, Xit represents the vector of

characteristics for respondent iat time tand is the estimated effect of characteristic i.Our next two specifications attempt to identify general trends in the STEM teaching

workforce growth over time and the growth in quality over time. First, in Specification III we

include decade indicators of highest degree receipt to determine if and how the time period an

individual received his or her degree is related to both majoring in a STEM field and becoming a

STEM teacher. We then add degree decade interactions with indicators of majoring in a STEMfield and having a masters degree; this specification allows us to identify the extent to which

content knowledge and average level of education of STEM teachers differs within and across

decades. Again, we hypothesize that recent initiatives in STEM education training, coupled with

an increasing number of states requiring masters degrees, have caused more qualified degree

holders to enter the STEM teaching workforce today than in years past. Beyond that, major and

degree level are expected to reflect variations of quality over time and thus provide insight on the

successes or failures of targeted program initiatives. Specification IV can be expressed as


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masters degree decrease to 1.36, suggesting time-of-degree imposed a downward bias on the

effect of having a STEM degree and an upward bias on masters degree.

Similarly, when controlling for interactions between quality indicators with time of

degree receipt, we find the prior estimates of having a STEM major and having a masters degree

on STEM teaching were slightly bias downward. Specifically, the odds of individuals having

either majored in STEM or earned a masters degree is becoming less negative compared to odds

of similar graduates in the 2000s. We see in Specification IV the odds of becoming a STEM

teacher given one majored in STEM increases to 2.29 when controlling for quality in a given

decade. While there is little difference, and a slight decrease in the odds of STEM majors in the

1970s and 1980s entering the profession (1.76 and 1.74, respectively) compared to outside

majors, STEM majors in the 1990s entered with a substantially larger odds of 2.06. We find a

similar trend, though to a lesser degree for the effect of having a masters degree. The odds of

becoming a STEM teacher for masters degree recipients was about 1.15 greater than for

bachelor degree recipients in each decade from 1950s to 1980s. The odds for masters degree

recipients in 1990s entering STEM teaching; however, is 4.39 greater than that of their bachelor

degree receiving counterparts.

Another way to interpret these results is to assess the quality of graduates entering STEM

teaching as compared to the 2000s. When doing so, we find the odds of STEM majors becoming

STEM teachers increases at an increasing rate, nearing closer to the odds of STEM teaching for

STEM degree earners in 2000s; a similar pattern holds true for master degree recipients While


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Specification V assesses the incentives for changing careers into STEM teaching;

specifically, whether reasons for entering STEM teaching differ from reasons for entering

another profession. This specification includes the SESTAT survey options for reasons that an

individual chose an occupation that is not related to their major field of study. The options

provided in the SESTAT survey include pay or promotion opportunities, working conditions, job

location, change in career or professional interests, family-related reasons, job in ones highest

degree field was not available, and otherunspecified reasons.

The inclusion of these variables in Specification V does not reveal bias in the findings

related to majoring in STEM or masters degreereceipt in earlier specifications. The results do

however show that all of the reasons assessed are associated with negative odds compared to

other unspecified reasons for changing careers. Of the reasons included in the survey, the

lowest odds of individuals changing careers into STEM teaching are associated with family

related reasons and location: placing 0.24 and 0.25 odds, respectively, for becoming STEM

teachers. Based on these results, we conclude that individuals are switching into teaching for

reasons not collected by this data set. These reasons could include a sense of idealism or a desire

to work with young people, reasons that have previously been cited as incentives to go into

teaching but are not provided as options within these surveys.

c. Variation in workforce quality by genderThe incorporation of the male gender interactions in Specifications VI and VII further

diminishes bias and increases precision in the estimates of teacher workforce quality for the


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compared to females who possess these qualifications. In contrast to the trends observed in the

broader population, the probability of becoming a STEM teacher is lower for males who major in

STEM than it is for females who major in STEM: for every one female STEM major going into

STEM teaching, there are only 0.72 males. The impact of receiving a masters degree, however,

increases the odds of becoming a STEM teacher for males to a greater effect than the impact of

receiving a masters degree for females: for every female masters degree receipt, there are 1.70

male masters degree recipients. While content-knowledgeable males are less likely to pursue

careers as STEM teachers compared to content-knowledgeable females, these odds significantly

increase (in favor of males) among those who receive a masters degree.

In addition to examining differential trends in teacher workforce quality across content-

knowledgeable and credentialed males and females, Specifications VI and VII also examine the

circumstantial and motivational factors influencing males and females who change careers to go

into teaching, including having children, location, working conditions, pay, job availability, and

family-related reasons (for example, the relocation of a spouses job). Of these factors, favorable

working conditions, family-related reasons, and a lack of relevant alternative job options were

found to influence males to a greater degree than females to become STEM teachers. We find

4.52 males for every female become STEM teachers due to favorable working conditions, 5.03

males for every female become STEM teachers due to family-related factors, and 4.71 males for

every female become STEM teachers due to the lack of relevant alternative job options. Having

children however was the one factor found to impact females to a greater extent than males in


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who have majored in STEM fields are less likely to less likely to become STEM teachers than

females who majored in STEM, though among masters degree recipients, males are more likely

to become STEM teachers than females. The incorporation of male interactions also revealed

substantial differences across factors motivating males and females to switch careers into STEM

teaching. Specifically, male career changers are more likely to enter STEM teaching for

professional, career substantiating reasons, such as working conditions and if no other suitable

job in their field is available.

Concerns regarding the composition and development of the STEM teaching workforce, in

addition to the factors that motivate individuals to enter the field remain in question. To that end,

there are many directions our research can be extended upon to examine trends and inform

policy. For instance, research is warranted on the types of individuals becoming STEM teachers

across STEM disciplines. Specifically, research should examine whether individuals majoring in

subjects more aligned with standard K-12 STEM subjects, such as math or biology, are more

likely to become teachers when compared to majors less aligned with such subjects, including

engineering and computer sciences. Future research on the dynamics of prior occupations among

career changes would also help in identifying indicators for those more likely to switch into

STEM teaching. While the current emphasis is on attracting STEM majors into teaching, further

analysis should focus on retention rates among content-knowledgeable and credentialed STEM

teachers. Undoubtedly, each of the aforementioned research suggestions would more informative

with the inclusion of state and district information such as teacher recruitment mandates and


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STEMTEACHING WORKFORCE TRENDS A-1VII. Appendix

Table 1a. Study Sample Descriptive Statistics: Overall and by Profession

STEM Teaching Other Professions Overall

Variable meanstandard

deviationmean

standard

deviationmean

standard

deviation

K-12 STEM Teachers 1.000 0.000 0.000 0.000 0.083 0.276STEM Major 0.553 0.497 0.389 0.488 0.403 0.491

Masters Degree 0.384 0.486 0.330 0.470 0.335 0.472Male 0.605 0.489 0.540 0.498 0.545 0.498

White 0.786 0.410 0.755 0.430 0.757 0.429

Asian 0.086 0.281 0.102 0.303 0.101 0.301Other Race 0.128 0.334 0.143 0.351 0.142 0.349

Age 43.418 11.407 43.416 11.550 43.416 11.539

Degree related to field 0.906 0.292 0.782 0.413 0.792 0.406Degree decade

1950s 0.000 0.021 0.001 0.035 0.001 0.0341960s 0.014 0.119 0.018 0.133 0.018 0.1311970s 0.104 0.306 0.109 0.311 0.108 0.311

1980s 0.199 0.399 0.213 0.410 0.212 0.409

1990s 0.273 0.445 0.276 0.447 0.276 0.447

2000s 0.409 0.492 0.383 0.486 0.385 0.487

N 17,434 204,840 222,274Note: Percentages are weighted, total frequencies are unweighted.

Table 1b. Study Sample Descriptive Statistics: Proportions of STEM teachers with

STEM Majors and Master Degrees by Decade of Degree Attainment


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STEMTEACHING WORKFORCE TRENDS A-2

Table 1c. Most Important Influential Factors for Working in an area outside the

field of Highest Degree

Entered STEM Teaching Entered Other Professions

meanstandard

deviationmean

standard

deviation

Pay, promotion 0.300 0.458 0.277 0.448

Work conditions 0.107 0.309 0.111 0.315Job location 0.039 0.193 0.065 0.247

Career change 0.254 0.435 0.199 0.399Family related reasons 0.066 0.249 0.118 0.323

Jobs not available 0.167 0.373 0.155 0.362

Other reason 0.067 0.250 0.074 0.262

N 1,732 34,554Note: Percentages are weighted, total frequencies are unweighted.

Table 1d. Most Important Influential Factors by Gender for Working as a STEM

Teacher when STEM teaching is outside the field of Highest Degree

Female Male

meanstandarddeviation

meanstandarddeviation

P ti 0 265 0 442 0 315 0 465


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Table 2. Summary of Logit Regression Predictions of the Log Odds of becoming a K-12 STEM Teacher

Independent Variable I II III IV V VI VII

STEM Major (1=yes) 0.6624*** 0.7283*** 0.7492*** 0.8275*** 0.7642*** 0.7648*** 1.3922***(0.032) (0.038) (0.037) (0.068) (0.069) (0.069) (0.067)

Masters Degree (0=Bachelors) 0.3790*** 0.3044*** 0.4153*** 0.3006*** 0.2991*** 0.4374***

(0.034) (0.038) (0.076) (0.076) (0.076) (0.078)

STEM Major Masters Degree 0.1022 0.1561** 0.1550** 0.1973***

(0.067) (0.067) (0.067) (0.067)

Male 0.0705** 0.0776** 0.0819** 0.0730** 0.1314*** 0.8798***

(0.036) (0.036) (0.036) (0.037) (0.051) (0.078)

Asian -0.4035*** -0.3999*** -0.4285*** -0.4289*** -0.4307*** -0.4274***

(0.052) (0.052) (0.054) (0.054) (0.054) (0.053)

Other Race -0.1300*** -0.1508*** -0.1529*** -0.1504*** -0.1520*** -0.1503***

(0.043) (0.043) (0.043) (0.043) (0.043) (0.044)

Age -0.0021 0.0094*** 0.0096*** 0.0091*** 0.0091*** 0.0096***

(0.001) (0.003) (0.003) (0.003) (0.003) (0.003)

Has Children (1=yes) 0.0342 0.0437 0.0328 0.0254 0.1427*** 0.1459***

(0.031) (0.032) (0.032) (0.032) (0.048) (0.049)

Degree in 1950s -1.4988*** -2.0896** -1.9902* -1.9936* -2.1495**

(0.500) (1.021) (1.028) (1.029) (1.033)

Degree in 1960s -0.7196*** -0.6269*** -0.5678*** -0.5766*** -0.7418***

(0.129) (0.211) (0.210) (0.210) (0.216)

Degree in 1970s -0.3900*** -0.1373 -0.0617 -0.0637 -0.1998

(0.088) (0.129) (0.128) (0.128) (0.132)Degree in 1980s -0.3400*** -0.0820 -0.0550 -0.0632 -0.1207

(0.062) (0.108) (0.108) (0.108) (0.110)

Degree in 1990s -0.2126*** -0.0512 -0.0318 -0.0418 -0.0766

(0.044) (0.085) (0.085) (0.085) (0.084)


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Gender InteractionsSTEM major male -1.2026***

(0.068)

Masters Degree male -0.3480***(0.070)

Has Children male -0.1969*** -0.2001***

(0.063) (0.064)

Pay male 0.2034 -0.0118

(0.162) (0.164)

Working conditions male 0.7480** 0.6292*

(0.326) (0.324)

Location male 0.5356 0.4752

(0.369) (0.372)

Change in career male 0.3978** 0.2598

(0.185) (0.187)Family-related male 0.7831*** 0.7366***

(0.271) (0.272)

Job not available male 0.6750*** 0.6698***

(0.173) (0.175)

Survey year = 2003 -0.0184 -0.0267 0.0578 0.0580 0.0486 0.0499 0.0475

(0.046) (0.046) (0.048) (0.048) (0.048) (0.048) (0.048)

Survey year = 2006 -0.2293*** -0.2350*** -0.1824*** -0.1816*** -0.1789*** -0.1777*** -0.1806***

(0.047) (0.047) (0.047) (0.047) (0.047) (0.047) (0.047)

Survey year = 2008 -0.2292*** -0.2374*** -0.2069*** -0.2074*** -0.2134*** -0.2123*** -0.2109***

(0.047) (0.047) (0.047) (0.047) (0.047) (0.047) (0.047)Constant -2.6001*** -2.6730*** -3.0178*** -3.1271*** -2.9353*** -2.9693*** -3.3172***

(0.050) (0.078) (0.104) (0.108) (0.110) (0.109) (0.115)

Observations 222,274 222,274 222,274 222,274 222,274 222,274 222,274

Pseudo R-squared 0.0160 0.0218 0.0232 0.0241 0.0346 0.0356 0.0462Standard errors in parentheses; *** p

emerging trends and recruitment initiatives of the stem teaching workforce: an analysis of teacher...

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