Education and Its Distributional Impacts on Living Standards

This paper investigates the determinants of living standards (measured by per capita consumption expenditure) at the household level, addressing heterogeneity in returns to education and endogeneity of educational status. The estimation results obtained through an instrumental variables quantile regression suggest that the endogeneity of education matters in determining the causal effect of education on living standards, while no evidence of the heterogeneity in the rate of returns to education is found. However, the results also provide evidence that impacts of other determinants vary significantly over the outcome (expenditure) distribution, and consequently a simulation based on the results shows that poverty alleviation impacts of education differs substantially between the instrumental variables quantile regression and standard instrumental variables regression results. The comparison of the two indicates the possibility that the impact on poverty reduction is likely to be overestimated in the standard instrumental variable regression.poverty, heterogeneous returns to education, instrumental variables quantile regression

Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology

Propensity score methods were proposed by Rosenbaum and Rubin [Biometrika 70 (1983) 41--55] as central tools to help assess the causal effects of interventions. Since their introduction more than two decades ago, they have found wide application in a variety of areas, including medical research, economics, epidemiology and education, especially in those situations where randomized experiments are either difficult to perform, or raise ethical questions, or would require extensive delays before answers could be obtained. In the past few years, the number of published applications using propensity score methods to evaluate medical and epidemiological interventions has increased dramatically. Nevertheless, thus far, we believe that there have been few applications of propensity score methods to evaluate marketing interventions (e.g., advertising, promotions), where the tradition is to use generally inappropriate techniques, which focus on the prediction of an outcome from background characteristics and an indicator for the intervention using statistical tools such as least-squares regression, data mining, and so on. With these techniques, an estimated parameter in the model is used to estimate some global ``causal'' effect. This practice can generate grossly incorrect answers that can be self-perpetuating: polishing the Ferraris rather than the Jeeps ``causes'' them to continue to win more races than the Jeeps $\Leftrightarrow$ visiting the high-prescribing doctors rather than the low-prescribing doctors ``causes'' them to continue to write more prescriptions. This presentation will take ``causality'' seriously, not just as a casual concept implying some predictive association in a data set, and will illustrate why propensity score methods are generally superior in practice to the standard predictive approaches for estimating causal effects.Comment: Published at http://dx.doi.org/10.1214/088342306000000259 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Quality of schooling and inequality of opportunity in health

This paper explores the role of quality of schooling as a source of inequality of opportunity in health. Substantiating earlier literature that links differences in education to health disparities, the paper uses variation in quality of schooling to test for inequality of opportunity in health. Analysis of the 1958 NCDS cohort exploits the variation in type and quality of schools generated by the comprehensive schooling reforms in England and Wales. The analysis provides evidence of a statistically significant and economically sizable association between some dimensions of quality of education and a range of health and health-related outcomes. For some outcomes the association persists, over and above the effects of measured ability, social development, academic qualifications and adult socioeconomic status and lifestyle

The Importance of Being Clustered: Uncluttering the Trends of Statistics from 1970 to 2015

In this paper we retrace the recent history of statistics by analyzing all the papers published in five prestigious statistical journals since 1970, namely: Annals of Statistics, Biometrika, Journal of the American Statistical Association, Journal of the Royal Statistical Society, series B and Statistical Science. The aim is to construct a kind of "taxonomy" of the statistical papers by organizing and by clustering them in main themes. In this sense being identified in a cluster means being important enough to be uncluttered in the vast and interconnected world of the statistical research. Since the main statistical research topics naturally born, evolve or die during time, we will also develop a dynamic clustering strategy, where a group in a time period is allowed to migrate or to merge into different groups in the following one. Results show that statistics is a very dynamic and evolving science, stimulated by the rise of new research questions and types of data

When international organizations bargain: evidence from the global environment facility

Who gets what in bargaining between states and international organizations (IOs)? Although distributional conflict is unavoidable in international cooperation, previous research provides few empirical insights into the determinants of bargaining outcomes. We test a simple bargaining model of cooperation between states and IOs. We expect that nonegalitarian international organizations, such as the World Bank, secure more gains from bargaining with economically weak than with economically powerful states. For egalitarian international organizations, such as most United Nations (UN) agencies, the state’s economic power should be less important. We test these hypotheses against a novel data set on funding shares for 2,255 projects implemented under the auspices of the Global Environment Facility, from1991 to 2011. The data allow us to directly measure bargaining outcomes. The results highlight the importance of accounting for the interactive effects of international organization and state characteristics

Lexical representation explains cortical entrainment during speech comprehension

Results from a recent neuroimaging study on spoken sentence comprehension have been interpreted as evidence for cortical entrainment to hierarchical syntactic structure. We present a simple computational model that predicts the power spectra from this study, even though the model's linguistic knowledge is restricted to the lexical level, and word-level representations are not combined into higher-level units (phrases or sentences). Hence, the cortical entrainment results can also be explained from the lexical properties of the stimuli, without recourse to hierarchical syntax.Comment: Submitted for publicatio

Estimating individual treatment effect: generalization bounds and algorithms

There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education. In particular, individual-level causal inference has important applications such as precision medicine. We give a new theoretical analysis and family of algorithms for predicting individual treatment effect (ITE) from observational data, under the assumption known as strong ignorability. The algorithms learn a "balanced" representation such that the induced treated and control distributions look similar. We give a novel, simple and intuitive generalization-error bound showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalization-error of that representation and the distance between the treated and control distributions induced by the representation. We use Integral Probability Metrics to measure distances between distributions, deriving explicit bounds for the Wasserstein and Maximum Mean Discrepancy (MMD) distances. Experiments on real and simulated data show the new algorithms match or outperform the state-of-the-art.Comment: Added name "TARNet" to refer to version with alpha = 0. Removed sup