Jean-Yves Duclos

Bio: Jean-Yves Duclos is an academic researcher from Laval University. The author has contributed to research in topics: Poverty & Stochastic dominance. The author has an hindex of 30, co-authored 206 publications receiving 5396 citations. Previous affiliations of Jean-Yves Duclos include Institute for the Study of Labor & CIRANO.

Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality

Abstract: We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of poverty, welfare and inequality. This includes estimators of most of the poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can be used to determine whether poverty, inequality or social welfare is greater in one distribution than in another for general classes of indices. We also derive the sampling distribution of the maximal poverty lines (or income censoring thresholds) up to which we may confidently assert that poverty or social welfare is greater in one distribution than in another. The sampling distribution of convenient estimators for dual approaches to the measurement of poverty is also established. The statistical results are established for deterministic or stochastic poverty lines as well as for paired or independent samples of incomes. Our results are briefly illustrated using data for 6 countries drawn from the Luxembourg Income Study data bases.

Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality

Abstract: We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of poverty, welfare, and inequality. This includes estimators of most of the poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can be used to determine whether poverty, inequality, or social welfare is greater in one distribution than in another for general classes of indices and for ranges of possible poverty lines. We also derive the sampling distribution of the maximal poverty lines up to which we may confidently assert that poverty is greater in one distribution than in another. The sampling distribution of convenient dual estimators for the measurement of poverty is also established. The statistical results are established for deterministic or stochastic poverty lines as well as for paired or independent samples of incomes. Our results are briefly illustrated using data for four countries drawn from the Luxembourg Income Study data bases.

Polarization: Concepts, measurement, estimation

Abstract: distributions can be described using density functions. The main theorem uniquely characterizes a class of polarization measures that fits into what we call the “identityalienation” framework, and simultanously satisfies a set of axioms. Second, we provide sample estimators of population polarization indices that can be used to compare polarization across time or entities. Distribution-free statistical inference results are also used in order to ensure that the orderings of polarization across entities are not simply due to sampling noise. An illustration of the use of these tools using data from 21 countries shows that polarization and inequality orderings can often differ in practice.

Robust multidimensional poverty comparisons

Abstract: We demonstrate how to make poverty comparisons using multidimensional indicators of well-being, showing in particular how to check whether the comparisons are robust to aggregation procedures and to the choice of multidimensional poverty lines. In contrast to earlier work, our methodology applies equally well to what can be defined as ”union”, ”intersection” or ”intermediate” approaches to dealing with multidimensional indicators of well-being. To make this procedure of some practical usefulness, the paper also derives the sampling distribution of various multidimensional poverty estimators, including estimators of the ”critical” poverty frontiers outside which multidimensional poverty comparisons can no longer be deemed ethically robust. The results are illustrated using data from a number of developing countries.

Poverty and Equity: Measurement, Policy, and Estimation with DAD

Abstract: This series will publish volumes that go beyond the traditional concepts of consumption, income or wealth and will offer a broad, inclusive view of inequality and well-being. Specific areas of interest will include Capabilities and Inequalities, Discrimination and Segregation in the Labor Market, Equality of Opportunities, Globalization and Inequality, Human Development and the Quality of Life, Income and Social Mobility, Inequality and Development, Inequality and Happiness, Inequality and Malnutrition, Income and Social Mobility, Inequality in Consumption and Time Use, Inequalities in Health and Education, Multidimensional Inequality and Poverty Measurement, Polarization among Children and Elderly People, Social Policy and the Welfare State, and Wealth Distribution.

Economic growth and income inequality

Abstract: We investigate whether income inequality affects subsequent growth in a cross-country sample for 1965-90, using the models of Barro (1997), Bleaney and Nishiyama (2002) and Sachs and Warner (1997), with negative results. We then investigate the evolution of income inequality over the same period and its correlation with growth. The dominating feature is inequality convergence across countries. This convergence has been significantly faster amongst developed countries. Growth does not appear to influence the evolution of inequality over time. Outline

Developments in the Measurement of Subjective Well-Being

Abstract: For good reasons, economists have had a long-standing preference for studying peoples' revealed preferences; that is, looking at individuals' actual choices and decisions rather than their stated intentions or subjective reports of likes and dislikes. Yet people often make choices that bear a mixed relationship to their own happiness. A large literature from behavioral economics and psychology finds that people often make inconsistent choices, fail to learn from experience, exhibit reluctance to trade, base their own satisfaction on how their situation compares with the satisfaction of others and depart from the standard model of the rational economic agent in other ways. If people display bounded rationality when it comes to maximizing utility, then their choices do not necessarily reflect their "true" preferences, and an exclusive reliance on choices to infer what people desire loses some of its appeal. Direct reports of subjective well-being may have a useful role in the measure

Counting and Multidimensional Poverty Measurement

Abstract: This paper proposes a new methodology for multidimensional poverty measurement consisting of an identification method ρk that extends the traditional intersection and union approaches, and a class of poverty measures Mα. Our identification step employs two forms of cutoff: one within each dimension to determine whether a person is deprived in that dimension, and a second across dimensions that identifies the poor by ‘counting’ the dimensions in which a person is deprived. The aggregation step employs the FGT measures, appropriately adjusted to account for multidimensionality. The axioms are presented as joint restrictions on identification and the measures, and the methodology satisfies a range of desirable properties including decomposability. The identification method is particularly well suited for use with ordinal data, as is the first of our measures, the adjusted headcount ratio. We present some dominance results and an interpretation of the adjusted headcount ratio as a measure of unfreedom. Examples from the US and Indonesia illustrate our methodology.