that early interventions are significantly more cost-effective and successful than those attempted later in life. Research shows that preschoolers with low levels of cognitive development have lower school achievement and earn lower wages in adulthood (Currie and Thomas, 1999; Case and Paxson, 2006); and that early childhood education has substantial long-term impacts, ranging from adult earnings to retirement savings (Chetty and others, 2010). Moreover, lost opportunities during childhood cannot always be compensated for. Child malnutrition, for example, can generate life-long learning difficulties, poor health and lower productivity and earnings over a lifetime (Alderman and others, 2006 and Hoddinott and others, 2008). The weight of evidence seems to imply that high inequality of opportunity in a society would likely lead to lower incomes and growth among the less well-off. Research on the causal impact of inequality of opportunity on income growth at the lower end of the distribution is at a nascent stage. However, Skoufias and others (2014) finds some evidence to suggest a robust negative relationship between inequality of opportunity and income growth of the bottom 40% of the income distribution across provinces (changwats) in Thailand.

For policy makers grappling with the reality and perception of rising inequality in their countries, there is also a strong interest in identifying the link between circumstances at birth and _overall_ inequality of outcomes (such as income, wealth or employment status). Opportunity-equalizing investments in children are likely to yield dividends over time and across generations. These have the characteristics of what Heckman (2012) calls _“pre-distribution_ ”, which many societies find to be unambiguously desirable. However, the extent to which a society may find _ex post_ redistributive polices (such as tax/subsidies or affirmative action) palatable is likely to depend on the extent to which disadvantages at birth are held responsible for inequities in outcomes such as income or labor market status, _net_ of the effects of (differences in) effort and innate talent on those outcomes. Societies tend to be more ambivalent about inequality arising from differences in effort and innate talent; while inequalities due to differences in endowments (including parental characteristics) determined by the lottery of birth are more likely to be regarded as unfair, demanding policy attention of the sort that seeks to “level the playing field”.

Empirical work on measuring inequality of opportunities has proceeded along many different tracks, as developed by a number of researchers. One widely used approach, developed by Barros and others (2010), involves the Human Opportunity Index (HOI) that is used to measure inequality in access to basic goods and services among children, where the advantage or opportunity is defined as a binary variable. Another popular approach, by Bourguignon and others (2007) and Ferreira and Gignoux (2011), defines the opportunity in question as a continuous variable (such as income or wealth of individuals) and develops a measure known as Inequality of Economic Opportunity (IEO). In both these methodologies, the complete set of all circumstances at birth that could matter for inequality of opportunity is unlikely to be fully observable in data. Due to this handicap, the calculated scalar measure of inequality of opportunity is often interpreted as a _lower bound_ estimate. While useful to document the baseline level of inequality of opportunity, the lower bound estimate may lead to severe underestimation of the degree of equalization required to make the distribution of opportunities fair. This problem is particularly aggravated in the latter methodology as it is often applied to incomes of adults. Unlike in the case of children for whom individual agency is assumed to be minimal, an underestimate of inequality of opportunity in adult outcomes automatically implies an over estimation of inequality attributable to effort.

In this paper, we use panel data from the Russian Federation to address the lower-bound problem in the calculation of inequality of opportunities in wage earnings. We closely follow the methodology developed 3