Carolina Milesi

Early childhood health, reproduction of economic inequalities and the persistence of health and mortality differentials ☆

The persistence of adult health and mortality socioeconomic inequalities and the equally stubborn reproduction of social class inequalities are salient features in modern societies that puzzle researchers in seemingly unconnected research fields. Neither can be satisfactorily explained with standard theoretical frameworks. In the domain of health and mortality, it is unclear if and to what an extent adult health and mortality disparities across socioeconomic status (SES) are the product of attributes of the positions themselves, the partial result of health conditions established earlier in life that influence both adult health and economic success, or the outcome of the reverse impact of health status on SES. In the domain of social stratification, the transmission of inequalities across generations has been remarkably resistant to satisfactory explanations. Although the literature on social stratification is by and large silent about the role played by early health status in shaping adult socioeconomic opportunities, new research on human capital formation suggests this is a serious error of omission. In this paper we propose to investigate the connections between these two domains. We use data from male respondents of the 1958 British Cohort to estimate (a) the influence of early health conditions on adult SES and (b) the contribution of early health status to observed adult health differentials. The model incorporates early conditions as determinants of traits that enhance (inhibit) social mobility and also conventional and unconventional factors that affect adult health and socioeconomic status. Our findings reveal that early childhood health plays a small, but non-trivial role as a determinant of adult SES and the adult socioeconomic gradient in health. These findings enrich current explanations of SES inequalities and of adult health and mortality disparities.

Effects of Class Size and Instruction on Kindergarten Achievement.

Although experimental results indicate that smaller classes promote higher achievement in early elementary school, the broader literature on class-size effects is inconclusive. This seeming contradiction raises questions about the generalizability of experimental evidence, an issue that this article addresses by examining the effects of class size on achievement in kindergarten with data from a nationwide survey, the Early Childhood Longitudinal Study–Kindergarten Class of 1998–99. To distinguish class-level from individual-level effects, this analysis utilizes hierarchical linear models. In response to concerns about selectivity, teacher fixed-effects models are also estimated. In an effort to understand the inconsistent findings of the past, the authors examine classroom conditions that may affect the link between class size and academic achievement, and also consider whether class size has different effects for different groups of students. The authors find no evidence of class-size effects on student achievement in either reading or mathematics, and results indicate that class size is equally insignificant for students from different race/ethnic, economic, and academic backgrounds. Teacher fixed-effects analyses also yield null findings for class size. Instructional activities offer significant boosts to achievement, but the effects of instruction do not differ between small and large classes. The authors discuss why the small class size advantage evidenced by experimental research might not generalize to nonexperimental, naturally occurring settings throughout the nation.

Charting the Impact of Federal Spending for Education Research A Bibliometric Approach

Impact evaluation plays a critical role in determining whether federally funded research programs in science, technology, engineering, and mathematics are wise investments. This paper develops quantitative methods for program evaluation and applies this approach to a flagship National Science Foundation–funded education research program, Research and Evaluation on Education in Science and Engineering (REESE). Results of three different bibliometric analyses all point to the same conclusion: REESE is an interdisciplinary research program that attracts highly productive investigators who exhibit an additional increase in their productivity rate as a result of receiving REESE funding. Limitations of the bibliometric approach are discussed, and directions are provided for the future of impact evaluations of research programs intended to serve the public good.

Engagement, persistence, and gender in computer science: Results of a smartphone ESM study

While the underrepresentation of women in the fast-growing STEM field of computer science (CS) has been much studied, no consensus exists on the key factors influencing this widening gender gap. Possible suspects include gender differences in aptitude, interest, and academic environment. Our study contributes to this literature by applying student engagement research to study the experiences of college students studying CS, to assess the degree to which differences in men and womens engagement may help account for gender inequity in the field. Specifically, we use the Experience Sampling Method (ESM) to evaluate in real-time the engagement of college students during varied activities and environments. Over the course of a full week in fall semester and a full week in spring semester, 165 students majoring in CS at two Research I universities were “beeped” several times a day via a smartphone app prompting them to fill out a short questionnaire including open-ended and scaled items. These responses were paired with administrative and over 2 years of transcript data provided by their institutions. We used mean comparisons and logistic regression analysis to compare enrollment and persistence patterns among CS men and women. Results suggest that despite the obstacles associated with womens underrepresentation in computer science, women are more likely to continue taking computer science courses when they felt challenged and skilled in their initial computer science classes. We discuss implications for further research.