Equality of opportunity: Definitions and testable conditions, with an application to income in France

We offer a model of equality of opportunity that encompasses different conceptions expressed in the public debate. In addition to circumstances whose effect on outcome should be compensated and eort which represents a legitimate source of inequality, we introduce a third factor, luck, that captures the non-responsibility factors whose impact on outcome should be even-handed for equality of opportunity to be satisfied. Then, we analyse how the various definitions of equality of opportunity can be empirically identified, given data limitations and provide testable conditions. Definitions and conditions resort to standard stochastic dominance tools. Lastly, we develop an empirical analysis of equality of opportunity for income acquisition in France over the period 1979-2000 which reveals that the degree of inequality of opportunity tends to decrease and that the risk of social lotteries appears very similar across the different groups of social origin.Equality of opportunity, Income distribution, Luck, Stochastic dominance.

Equality of Opportunity: Definitions and testable conditions, with an application to income in France

We offer a model of equality of opportunity that encompasses different conceptions expressed in the public debate. In addition to circumstances whose effect on outcome should be compensated and effort which represents a legitimate source of inequality, we introduce a third factor, luck, that captures the non-responsibility factors whose impact on outcome should be even-handed for equality of opportunity to be satisfied. Then, we analyse how the various definitions of equality of opportunity can be empirically identified, given data limitations and provide testable conditions. Definitions and conditions resort to standard stochastic dominance tools. Lastly, we develop an empirical analysis of equality of opportunity for income acquisition in France over the period 1979-2000 which reveals that the degree of inequality of opportunity tends to decrease and that the risk of social lotteries appears very similar across the different groups of social origin.Equality of Opportunity, Income distribution, Luck, Stochastic dominance

Equality of opportunity and luck: Denitions and testable conditions, with an application to income in France

We oer a model of equality of opportunity that encompasses dierent conceptions expressed in the public and philosophical debates. In addition to circumstances whose eect on outcome should be compensated and eort which represents a legitimate source of inequality, we introduce a third factor, luck, that captures the random factors whose impact on outcome should be even-handed for equality of opportunity to be satised. Then, we analyse how the various denitions of equality of opportunity can be empirically identied, given data limitations and provide testable conditions. Denitions and conditions resort to standard stochastic dominance tools. Lastly, we develop an empirical analysis of equality of opportunity for income acquisition in France over the period 1979-2000 which reveals that the degree of inequality of opportunity tends to decrease and that the degree of risk of income distributions, conditional on social origin, appears very similar across all groups of social origins.Equality of opportunity, Luck, Stochastic dominance, Income distribution.

Robust Inference for Inverse Stochastic Dominance

The notion of inverse stochastic dominance is gaining increasing support in risk, inequality, and welfare analysis as a relevant criterion for ranking distributions, which is alternative to the standard stochastic dominance approach. Its implementation rests on comparisons of two distributions\u2019 quantile functions, or of their multiple partial integrals, at fixed population proportions. This article develops a novel statistical inference model for inverse stochastic dominance that is based on the influence function approach. The proposed method allows model-free evaluations that are limitedly affected by contamination in the data. Asymptotic normality of the estimators allows to derive tests for the restrictions implied by various forms of inverse stochastic dominance. Monte Carlo experiments and an application promote the qualities of the influence function estimator when compared with alternative dominance criteria

Stocks, Bonds and the Investment Horizon: A Spatial Dominance Approach

Financial advisors typically recommend that a long-term investor should hold a higher percentage of his wealth in stocks than a short-term investor. However, part of the academic literature disagrees with this advice. We use a spatial dominance test which is suited for comparing alternative investments when their distributions are time-varying. Using daily data for the US from 1965 to 2008, we test for dominance of cumulative returns series for stocks versus bonds at different investment horizons from one to ten years. We find that bonds second order spatially dominate stocks for one and two year horizons. For horizons of nine years or longer, we find evidence that stocks dominate bonds. When different portfolios of stocks and bonds are compared, we find that for long investment horizons, only those portfolios with a sufficiently high proportion of stocks are efficient in the sense of spatial dominance.Investment decisions, Investment horizon, Stochastic dominance.

Elitism and Stochastic Dominance

Stochastic dominance has been typically used with a special emphasis on risk and in-equality reduction something captured by the concavity of the utility function in the expected utility model. We claim that the applicability of the stochastic dominance ap-proach goes far beyond risk and inequality measurement provided suitable adaptations be made. We apply in the paper the stochastic dominance approach to the measurement of elitism which may be considered the opposite of egalitarianism. While the usual stochastic dominance quasi-orderings attach more value to more equal and more effi-cient distributions, our criteria ensure that, the more unequal and the more efficient the distribution, the higher it is ranked. Two instances are provided by (i) comparisons of scientific performance across institutions like universities or departments, and (ii) com-parisons of affluence as opposed to poverty between countries.Decumulative Distribution Functions, Stochastic Dominance, Regressive Transfers, Elitism, Scientific Performance, Affluence

Elitism and Stochastic Dominance

Stochastic dominance has typically been used with a special emphasis on risk and inequality reduction something captured by the concavity of the utility function in the expected utility model. We claim that the applicability of the stochastic dominance approach goes far beyond risk and inequality measurement provided suitable adpations be made. We apply in the paper the stochastic dominance approach to the measurment of elitism which may be considered the opposite of egalitarianism. While the usual stochastic dominance quasi-orderings attach more value to more equal and more efficient distributions, our criteria ensure that the more unequal and the more the efficient the distribution, the higher it is ranked. two instances are provided by (i) comparisons of scientific performance across institutions like universities or departments and (ii) comparisons of affluence as opposed to poverty across countries.Decumulative distribution functions; Stochastic dominance; Regressive transfers; Elitism; Scientific Performance; Affluence

Beyond Optimal Forecasting

forecasting,forecast loss functions,stochastic dominance.

A stochastic dominance approach to program evaluation with an application to child nutritional status in arid and semi-arid Kenya

Food Consumption/Nutrition/Food Safety,