information on these variables is usually available in household survey data (Bourguignon, Ferreira, and Menéndez 2007; Checchi and Peragine 2010; Cogneau and Mesplé-Somps 2008; Ferreira and Gignoux 2008, 2011; Hassine 2012; Lefranc, Pistolesi, and Trannoy 2008; Pistolesi 2009; Singh 2012).

Nonetheless, recent analyses strive to incorporate other circumstances deemed relevant. A notable example is the inclusion of circumstances related to the childhood environment. In a recent paper, Hufe et al. (2017) argue that all measurable achievements and behaviors of children before the age of consent should be considered as the results of their circumstances.<sup>2</sup> Therefore, the children should not be responsible for any of their accomplishments before that age. Examples of circumstance variables related to childhood environment include school support from parents, playing with parents, perceived quantity of time with the mother, and so on. Using two data sets, the National Longitudinal Survey of Youth 1979 and the 1970 British Cohort Study that capture data on childhood environment, Hufe et al. (2017) find that, in the U.S. sample (the longitudinal survey), the share of income inequality attributable to circumstances rises from 27 percent to 43 percent, while, in the U.K. sample, it rises from 18 percent to 27 percent. Making a similar argument (but with no empirical application), Roemer (2017) posits that all achievements of children before the age of consent (14 or 16 years of age), such as birthweight, nutritional measures, health status, and test scores, should be attributed to nature and nurture and should therefore be categorized as circumstances.

Using microdata on Germany, Peichel and Ungerer (2016) analyze how inequality of opportunity estimates are affected if a partner or spouse’s circumstance or effort variables are factored in as if they were one’s own circumstances. They consider selected sets of circumstance variables (gender, year of birth, place of birth, and so on) and effort variables (work experience, working hours, education, industry) among individuals ages 25–55 in their analysis. Their results indicate that, including spouse’s variables can increase inequality of opportunity measures by more than 20 (35) percent of gross (net) earnings.

Across all these studies, a common trait is the extensive focus on the use of mostly genetic and social backgrounds (including childhood environment) as circumstance variables. This paper therefore examines whether, beyond genetic and social backgrounds, there are other circumstantial factors that may contribute to inequality of opportunity. Answering this question requires an understanding of various circumstantial factors that are relevant for inequality of opportunity analysis. Ramos and Van de gaer (2016) label factors that affect individual outcomes, say earnings, as luck and list four categories of luck: social background luck (factors related to the family or social background one happens to inherit), genetic luck (constituent characteristics of the individual), brute luck (situations in which the individual cannot alter the probability that an event

> 2 The authors define age of consent as the age at which a child is legally considered an adult, which they indicate may vary across countries.

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