Donna K. Ginther

Family Structure and Children's Educational Outcomes: Blended Families, Stylized Facts, and Descriptive Regressions

This article adds to the growing literature describing correlations between children’s educational outcomes and family structure. Popular discussions have focused on the distinction between two-parent families and single-parent families. This article shows that educational outcomes for both types of children in blended families—stepchildren and their half-siblings who are the joint children of both parents—are similar to each other and substantially worse than outcomes for children reared in traditional nuclear families. We conclude that as a description of the data, the crucial distinction is between children reared in traditional nuclear families (i.e., families in which all children are the joint children of both parents) and children reared in other family structures (e.g., single-parent families or blended families). We then turn from “stylized facts” (i.e., simple correlations) that control only for family structure to “descriptive regressions” that control for other variables such as family income. When controls for other variables are introduced, the relationship between family structure and children’s educational outcomes weakens substantially and is often statistically insignificant.

RACE, ETHNICITY, AND NIH RESEARCH AWARDS

NIH research project grants from 2000 to 2006 show evidence of racial/ethnic disparities in the probability of receiving an award. We investigated the association between a U.S. National Institutes of Health (NIH) R01 applicant’s self-identified race or ethnicity and the probability of receiving an award by using data from the NIH IMPAC II grant database, the Thomson Reuters Web of Science, and other sources. Although proposals with strong priority scores were equally likely to be funded regardless of race, we find that Asians are 4 percentage points and black or African-American applicants are 13 percentage points less likely to receive NIH investigator-initiated research funding compared with whites. After controlling for the applicant’s educational background, country of origin, training, previous research awards, publication record, and employer characteristics, we find that black applicants remain 10 percentage points less likely than whites to be awarded NIH research funding. Our results suggest some leverage points for policy intervention.

Women in Academic Science A Changing Landscape

Much has been written in the past two decades about women in academic science careers, but this literature is contradictory. Many analyses have revealed a level playing field, with men and women faring equally, whereas other analyses have suggested numerous areas in which the playing field is not level. The only widely-agreed-upon conclusion is that women are underrepresented in college majors, graduate school programs, and the professoriate in those fields that are the most mathematically intensive, such as geoscience, engineering, economics, mathematics/computer science, and the physical sciences. In other scientific fields (psychology, life science, social science), women are found in much higher percentages. In this monograph, we undertake extensive life-course analyses comparing the trajectories of women and men in math-intensive fields with those of their counterparts in non-math-intensive fields in which women are close to parity with or even exceed the number of men. We begin by examining early-childhood differences in spatial processing and follow this through quantitative performance in middle childhood and adolescence, including high school coursework. We then focus on the transition of the sexes from high school to college major, then to graduate school, and, finally, to careers in academic science. The results of our myriad analyses reveal that early sex differences in spatial and mathematical reasoning need not stem from biological bases, that the gap between average female and male math ability is narrowing (suggesting strong environmental influences), and that sex differences in math ability at the right tail show variation over time and across nationalities, ethnicities, and other factors, indicating that the ratio of males to females at the right tail can and does change. We find that gender differences in attitudes toward and expectations about math careers and ability (controlling for actual ability) are evident by kindergarten and increase thereafter, leading to lower female propensities to major in math-intensive subjects in college but higher female propensities to major in non-math-intensive sciences, with overall science, technology, engineering, and mathematics (STEM) majors at 50% female for more than a decade. Post-college, although men with majors in math-intensive subjects have historically chosen and completed PhDs in these fields more often than women, the gap has recently narrowed by two thirds; among non-math-intensive STEM majors, women are more likely than men to go into health and other people-related occupations instead of pursuing PhDs. Importantly, of those who obtain doctorates in math-intensive fields, men and women entering the professoriate have equivalent access to tenure-track academic jobs in science, and they persist and are remunerated at comparable rates—with some caveats that we discuss. The transition from graduate programs to assistant professorships shows more pipeline leakage in the fields in which women are already very prevalent (psychology, life science, social science) than in the math-intensive fields in which they are underrepresented but in which the number of females holding assistant professorships is at least commensurate with (if not greater than) that of males. That is, invitations to interview for tenure-track positions in math-intensive fields—as well as actual employment offers—reveal that female PhD applicants fare at least as well as their male counterparts in math-intensive fields. Along these same lines, our analyses reveal that manuscript reviewing and grant funding are gender neutral: Male and female authors and principal investigators are equally likely to have their manuscripts accepted by journal editors and their grants funded, with only very occasional exceptions. There are no compelling sex differences in hours worked or average citations per publication, but there is an overall male advantage in productivity. We attempt to reconcile these results amid the disparate claims made regarding their causes, examining sex differences in citations, hours worked, and interests. We conclude by suggesting that although in the past, gender discrimination was an important cause of women’s underrepresentation in scientific academic careers, this claim has continued to be invoked after it has ceased being a valid cause of women’s underrepresentation in math-intensive fields. Consequently, current barriers to women’s full participation in mathematically intensive academic science fields are rooted in pre-college factors and the subsequent likelihood of majoring in these fields, and future research should focus on these barriers rather than misdirecting attention toward historical barriers that no longer account for women’s underrepresentation in academic science.

Neighborhood Attributes as Determinants of Children's Outcomes: How Robust Are the Relationships?

Estimates of neighborhood effects on childrens outcomes vary widely among the studies that seek to identify their existence and magnitude, reflecting substantial variation in data and model specification. Here, we review that literature, and ask if the disparity in estimates of neighborhood effects may reflect the differences among studies in the specification of family characteristics, and hence omitted variables bias. We report a systematic set of robustness results for three youth outcomes (high school graduation, the number of years of completed schooling, and teen nonmarital childbearing) using data on about 2,600 children from the Panel Study of Income Dynamics. We observe these children over a period of at least 21 years and have included an extensive set of neighborhood variables for these individuals measured over the entire school-age period. We measure the relationship of these neighborhood variables to the three outcomes, moving from basic models containing no individual and family characteristic variables to models containing an extensive set of individual and family statistical controls. We conclude that the reliability of estimates of these impacts may be an artifact of the degree to which family background is characterized in model specification. Confidence that reported neighborhood effects reveal true relationships requires statistical controls for the full range of family and individual background that may also influence childrens attainments; not all variables with coefficients showing asterisks have significant effects.

Women in Economics: Moving Up or Falling Off the Academic Career Ladder?

The percentage of economics doctorates awarded to women has increased over the past twenty years. This article considers whether women Ph.D. economists have increased their representation in academia, particularly at higher tenured ranks. Our study draws upon several empirical approaches and multiple data sets for the 1990s. We find that when compared with other academic disciplines, women in economics are less likely to get tenure and take longer to achieve it. Although gender differences in productivity and the effect of children on promotion partly explain womens lesser chances of receiving tenure in economics, a significant portion of the gender promotion gap remains unexplained by observable characteristics.

Gender differences in salary and promotion for faculty in the humanities, 1977–95

This study uses data from the Survey of Doctorate Recipients to evaluate gender differences in salaries and promotion for academics in the humanities. Differences in employment outcomes by gender are evaluated using three methods: the Oaxaca decomposition is used to examine salary differentials, and binary choice models and duration analysis are used to estimate the probability of promotion to tenure. Over time, gender salary differences can largely be explained by academic rank. Substantial gender differences in promotion to tenure exist after controlling for productivity and demographic characteristics. However, the authors observe a slight decline in the gender promotion gap for the most recent cohort evaluated. On the basis of this evidence, the authors conclude that gender discrimination for academics in the humanities tends to operate through differences in promotion, which in turn affects wages.

Are race, ethnicity, and medical school affiliation associated with NIH R01 type 1 award probability for physician investigators?

Purpose To analyze the relationship among National Institutes of Health (NIH) R01 Type 1 applicant degree, institution type, and race/ethnicity, and application award probability. Method The authors used 2000–2006 data from the NIH IMPAC II grants database and other sources to determine which individual and institutional characteristics of applicants may affect the probability of applications being awarded funding. They used descriptive statistics and probit models to estimate correlations between race/ethnicity, degree (MD or PhD), and institution type (medical school or other institution), and application award probability, controlling for a large set of observable characteristics. Results Applications from medical schools were significantly more likely than those from other institutions to receive funding, as were applications from MDs versus PhDs. Overall, applications from blacks and Asians were less likely than those from whites to be awarded funding; however, among applications from MDs at medical schools, there was no difference in funding probability between whites and Asians, and the difference between blacks and whites decreased to 7.8%. The inclusion of human subjects significantly decreased the likelihood of receiving funding. Conclusions Compared with applications from whites, applications from blacks have a lower probability of being awarded R01 Type 1 funding, regardless of the investigator’s degree. However, funding probability is increased for applications with MD investigators and for those from medical schools. To some degree, these advantages combine so that applications from black MDs at medical schools have the smallest difference in funding probability compared with those from whites.

Is MIT an Exception? Gender Pay Differences in Academic Science

This study uses data from the Survey of Doctorate Recipients to evaluate gender differences in salaries for academic scientists. Over time gender salary differences can partly be explained by differences in observable characteristics for faculty at the assistant and associate ranks. Substantial gender salary differences for full professors are not explained by observable characteristics. Between 1973 and 1997, very little has changed in terms of gender salary and promotion differences for academics in science. After evaluating potential explanations, the author concludes that gender discrimination similar to that observed at MIT accounts for unexplained gender disparities.

Does Science Discriminate Against Women? Evidence from Academia, 1973-1997

This study uses data from the Survey of Doctorate Recipients to evaluate differences in employment outcomes for academic scientists by gender. A decomposition of estimated salary differences shows that over time, gender salary differences can partly be explained by differences in observable characteristics for faculty at the assistant and associate ranks. Substantial gender salary differences for full professors are not explained by observable characteristics. Probit and duration model estimates indicate gender differences in the probability of promotion, making it less likely for women to be promoted to tenure. Between 1973 and 1997, very little changed in terms of gender salary and promotion differences for academics in science. After evaluating potential explanations, the author concludes that gender discrimination similar to that observed at the Massachusetts Institute of Technology accounts for unexplained gender disparities.

ALTERNATIVE ESTIMATES OF THE EFFECT OF SCHOOLING ON EARNINGS

This paper examines how assumptions imposed on the data influence estimates of schoolings effect on earnings. The paper models schooling decisions as treatment effects and imposes assumptions about schooling selection to estimate bounds on the treatment effect. The study begins by using the worst-case bounds derived by Manski (1989, 1990, 1994, 1995) and adds assumptions from the Roy model of schooling self-selection to narrow the bounds on the schooling treatment effect. The bounds are narrowed further by using family structure, college proximity, and school-quality characteristics as exclusion restrictions. The selection problem requires the researcher to make explicit assumptions to estimate the effect of schooling on earnings. This paper demonstrates that different selection assumptions yield very different results.