The Measurement of Educational Inequality: Achievement and Opportunity

This paper proposes two related measures of educational inequality: one for educational achievement and another for educational opportunity. The former is the simple variance (or standard deviation) of test scores. It is selected after careful consideration of two measurement issues that have typically been overlooked in the literature: the implications of the standardization of test scores for inequality indices, and the possible sample selection biases arising from the PISA sampling frame. The measure of inequality of educational opportunity is given by the share of the variance in test scores which is explained by pre-determined circumstances. Both measures are computed for the 57 countries in which PISA surveys were conducted in 2006. Inequality of opportunity accounts for up to 35% of all disparities in educational achievement. It is greater in (most of) continental Europe and Latin America than in Asia, Scandinavia and North America. It is uncorrelated with average educational achievement and only weakly negatively correlated with per capita GDP. It correlates negatively with the share of spending in primary schooling, and positively with tracking in secondary schools

Multidimensional and Persistent Poverty: Methodological Approaches to Measurement Issues

Multidimensional deprivation and persistent poverty are important research areas within the poverty measurement literature. Still, both encompass measurement issues for which methodological solutions are yet to be analysed. The thesis that I present here analyses three specific measurement issues, identified as relevant within these research areas, and proposes methodological approaches to tackle each of them. First, it evaluates the effect of different demographic population structures on societal multidimensional deprivation incidence comparisons. The results of this evaluation demonstrate that societal multidimensional comparisons reflect not only differences in relative deprivation but also differences in the demographic composition of the societies to be compared. These differences in the demographic structure of the population, thus, confound societal multidimensional deprivation comparisons. To tackle this comparability problem, the application of direct and indirect standardisation methods is proposed and analysed in this context. Second, it studies the effect of differences in need, exhibited across individuals from different demographic population subgroups or households of different sizes and compositions, on multidimensional deprivation incidence profiles. To address differences in needs and enhance individual or household comparability, I propose a family of multidimensional deprivation indices that describes how much deprivation two demographically heterogeneous units with different needs must exhibit to be catalogued as equivalently deprived. The obtained empirical results demonstrate that neglecting differences in needs yields biased multidimensional deprivation incidence profiles. The results also shed light on the ability of my proposed family of measures to capture these differences in need effectively. Third, this thesis analyses the reliability of persistent poverty measures in the presence of survey non-response. The obtained empirical results indicate that persistent poverty measures based on balanced panel estimates that do not account for the relationship between survey non-response and the socioeconomic status of the household provide a significantly biased picture of the intertemporal phenomenon. The methodologies that I present in this thesis are meant foremost to be easy to implement and understand by policymakers. As such, they are proposed as methodological tools to improve the measurement and analysis of poverty in the policy context

A Survey of Neural Trees

Neural networks (NNs) and decision trees (DTs) are both popular models of machine learning, yet coming with mutually exclusive advantages and limitations. To bring the best of the two worlds, a variety of approaches are proposed to integrate NNs and DTs explicitly or implicitly. In this survey, these approaches are organized in a school which we term as neural trees (NTs). This survey aims to present a comprehensive review of NTs and attempts to identify how they enhance the model interpretability. We first propose a thorough taxonomy of NTs that expresses the gradual integration and co-evolution of NNs and DTs. Afterward, we analyze NTs in terms of their interpretability and performance, and suggest possible solutions to the remaining challenges. Finally, this survey concludes with a discussion about other considerations like conditional computation and promising directions towards this field. A list of papers reviewed in this survey, along with their corresponding codes, is available at: https://github.com/zju-vipa/awesome-neural-treesComment: 35 pages, 7 figures and 1 tabl