Identifying the roots of inequality of opportunity in South Korea by application of algorithmic approaches

This study identifies the roots of inequality of opportunity in South Korea by applying algorithmic approaches to survey data. In contrast to extant studies, we identify the roots of inequality of opportunity by estimating the importance of variables, interpreting the estimated results, and analyzing the importance of individual variables, instead of measuring inequality of opportunity. We apply a decision tree classification algorithm, light gradient boosting machine, and SHapley Additive exPlanations to estimate the importance of the studied variables and interpret the estimated results. According to the estimated results, the region where the individuals grew up, their gender, and their father’s job during their childhood were the main factors contributing to inequality of opportunity. This study proves that the considerable regional disparity and social environment perpetuate gender inequality in South Korean society. It argues that an individual’s socio-economic achievements are strongly influenced by their father’s background, thus, outweighing other family background-related factors. Individuals receive unequal opportunities owing to a combination of region, father’s background, and their own gender, thereby, affecting their socioeconomic achievements. If these factors remain influential from birth to adulthood, removing the conditions that structure them would be one way to achieve equality of opportunity.

Making the most of world talent for science? The Nobel Prize and Fields Medal experience

Opportunities in science largely affect the accumulation of scientific knowledge and, therefore, technological change. However, there is little evidence of how much of people’s talent is actually wasted. Here we focus on scientists with the highest performance, the recipients of the Nobel Prize and Fields Medal. We found that the average age of scientists at the time of the breakthrough was higher for researchers from less developed countries. Moreover, individual opportunities in the world were extremely unequal by country of birth, gender significantly conditioned any participation in research, and the probability of becoming a top researcher more than doubled for individuals with parents belonging to the most favoured occupational categories. Thus, inequality of opportunity in science at the highest level was higher than in sports excellence (Olympic medals) and educational attainment. These findings would not be so negative if opportunities in science at the highest level had increased over time. Contrary to the expectations, our results show that opportunities in science, in contrast with humanities, have stagnated.

Genetic advantage and equality of opportunity in education: Two definitions and an empirical application

The literature of Equality of Opportunity (EOp) has long acknowledged the existence of 'talents', 'innate ability' or 'genetic ability'. Nonetheless, attempts to explicitly incorporate a measure of innate ability in the quantification of EOp have been rare. On the other hand, the literature of social-science genetics has found credible genetic-based components of EOp, without an explicit quantification of overall EOp. In addition, there exists prevalent disagreement within both kinds of literature on whether innate ability should be perceived as a fair or unfair source of advantage. This paper proposes to quantify EOp while explicitly including a genetic-based measure of innate ability. It proposes two formal definitions of EOp that draw on both stances regarding the compensation of innate ability. Novel testable implications are derived. The educational attainment polygenic index is used as a measure of innate ability while correcting for genetic nurture and accounting for the correlation between genes and other circumstances. An empirical application in the US Health and Retirement Study finds that the share of inequality of opportunity is 26% under the view that genetic differences are unfair sources of advantage and 21% otherwise. A comparative analysis over cohorts reveals that the trend in EOp depends on the definition adopted; if genetic advantage is a fair source of inequality then EOp has improved; the opposite holds if one considers genetic advantage an unfair source of inequality. These results highlight the importance of accounting for genetic differences in the EOp framework.

Layers of Inequality: Unequal Opportunities and Skin Color in Mexico

We document the contribution of skin color toward quantifying inequality of opportunity over a proxy indicator of wealth. Our Ferreira–Gignoux estimates of inequality of opportunity as a share of total wealth inequality show that once parental wealth is included as a circumstance variable, the share of inequality of opportunity rises above 40%, overall and for every age cohort. By contrast, the contribution of skin tone to total inequality of opportunity remains minor throughout.

Lower and upper bound estimates of inequality of opportunity for emerging economies

Equality of opportunity is an important normative ideal of distributive justice. In spite of its wide acceptance and economic relevance, standard estimation approaches suffer from data limitations that can lead to both downward and upward biased estimates of inequality of opportunity. These shortcomings may be particularly pronounced for emerging economies in which comprehensive household survey data of sufficient sample size is often unavailable. In this paper, we assess the extent of upward and downward bias in inequality of opportunity estimates for a set of twelve emerging economies. Our findings suggest strongly downward biased estimates of inequality of opportunity in these countries. To the contrary, there is little scope for upward bias. By bounding inequality of opportunity from above, we address recent critiques that worry about the prevalence of downward biased estimates and the ensuing possibility to downplay the normative significance of inequality.