- Alex Eble
Search for EdWorkingPapers here by author, title, or keywords.
Books shape how children learn about society and social norms, in part through the representation of different characters. To better understand the messages children encounter in books, we introduce new artificial intelligence methods for systematically converting images into data. We apply these image tools, along with established text analysis methods, to measure the representation of race, gender, and age in children’s books commonly found in US schools and homes over the last century. We find that more characters with darker skin color appear over time, but "mainstream" award-winning books, which are twice as likely to be checked out from libraries, persistently depict more lighter-skinned characters even after conditioning on perceived race. Across all books, children are depicted with lighter skin than adults. Over time, females are increasingly present but are more represented in images than in text, suggesting greater symbolic inclusion in pictures than substantive inclusion in stories. Relative to their growing share of the US population, Black and Latinx people are underrepresented in the mainstream collection; males, particularly White males, are persistently overrepresented. Our data provide a view into the "black box" of education through children’s books in US schools and homes, highlighting what has changed and what has endured.
The quality of college education is hard for students and employers to observe. Knowing this, in the last 40 years over 1,000 colleges in the US and China alone have changed their names to signal higher quality. We study how these changes affect college choice and labor market performance of college graduates. Using administrative data, we show that colleges which change their names enroll higher-aptitude students and the effects persist over time. These effects are larger for attractive but misleading name changes, and larger among students with less information about the college. In a large resume audit study of the labor market for recent graduates, we find a small, insignificant premium for applicants listing new college names in most jobs, but a penalty in low-pay, low-status jobs. To better understand these results, we analyze scraped online text data, survey data, and other administrative data. These show that while many college applicants lack important information about college quality, employers can see that college name changes lead to an increase in graduate aptitude. Our study demonstrates that signals designed to change perception can have real, lasting impacts on market outcomes.
Aspirations shape important future-oriented behaviors, including educational investment. Higher family aspirations for children predict better educational outcomes in multiple developing countries. Unfortunately, aspirations sometimes outstrip people's ability to pursue them. We study the relationship between family aspirations for children and later child educational outcomes in an extremely poor context. We observe caregivers' educational and career aspirations for thousands of rural Gambian children about to begin schooling. While higher aspirations predict subsequent educational investment and, three years later, better child performance on reading/math tests, these gains are small in terms of skills learned, and high-aspirations children remain far from achieving literacy/numeracy. In contrast, a bundled supply-side intervention generated large literacy/numeracy gains in these areas. Since unobserved correlates of aspirations and educational outcomes likely bias our estimates upwards, the true aspirations-learning relationship may be even smaller. We conclude higher aspirations alone are insufficient to achieve literacy/numeracy in this, and perhaps similar contexts.
We study the transmission of beliefs about gender differences in math ability from adults to children and how this affects girls’ academic performance relative to boys. We exploit randomly assigned variation in the proportion of a child’s middle school classmates whose parents believe boys are innately better than girls at learning math. An increase in exposure to peers whose parents report this belief increases a child’s likelihood of believing it, with similar effects for boys and girls and greater effects from peers of the same gender. This exposure also affects children’s perceived difficulty of math, aspirations, and math performance, generating gains for boys and losses for girls.
Despite large schooling and learning gains in many developing countries, children in highly deprived areas are often unlikely to achieve even basic literacy and numeracy. We study how much of this problem can be resolved using a multi-pronged intervention combining several distinct interventions known to be effective in isolation. We conducted a cluster-randomized trial in The Gambia evaluating a literacy and numeracy intervention designed for primary-aged children in remote parts of poor countries. The intervention combines para teachers delivering after-school supplementary classes, scripted lesson plans, and frequent monitoring focusing on improving teacher practice (coaching). A similar intervention previously demonstrated large learning gains in a cluster-randomized trial in rural India. After three academic years, Gambian children receiving the intervention scored 46 percentage points (3.2 SD) better on a combined literacy and numeracy test than control children. This intervention holds great promise to address low learning levels in other poor, remote settings.
Children routinely benefit from being assigned a teacher who shares an identity with them, such as gender or ethnicity. We study how student beliefs impact teacher-student gender match effects, and how this varies across subjects with different societal beliefs about differential ability by gender. A simple model of belief formation predicts that match effects will be larger for students who believe they are of low ability, and be greater in subjects with more salient societal beliefs. We test these using data from Chinese middle schools, exploiting random assignment of students to teachers. In China, many people believe boys are innately better than girls at math. We find that being assigned a female math teacher helps low-perceived-ability girls and slightly harms low-perceived-ability boys, with no effects for other children. In English and Chinese – subjects with less salient societal beliefs – these patterns persist but diminish. This yields policy implications for the assignment of teachers to students.