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Essays on the econometric analysis of welfare.

机译：关于福利的计量经济学分析的论文。

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This thesis explores several econometric approaches to making empirical welfare comparisons. It is divided into three separate, but closely related chapters.;In the first chapter, we propose empirical likelihood-based inference for decomposable (additively separable) poverty measures which utilize relative poverty lines (i.e., poverty lines which are some fraction of either the mean or the median of the underlying income distribution). The primary advantage of using the empirical likelihood framework in this setting is that no estimates of scale parameters (i.e., standard errors) are required. Since estimates of poverty measures which utilize relative poverty lines involve a nuisance parameter (the poverty line), the asymptotic variance of these estimates are quite complex, as they depend on the underlying density function. Simulation evidence provided here suggests that the proposed methods may offer some improved performance over both asymptotic approximations and the bootstrap- T procedure. These methods are illustrated with an empirical example using Canadian household survey data on income.;In the second chapter, we consider methods of statistical inference for vectors of inequality and poverty measures. The use of vector measures recognizes the fact that there is often no single measure of either inequality or poverty which is completely satisfactory to researchers. For example, there is sometimes no clear choice of the "best" (scalar) measure of inequality or poverty. Alternatively, when measuring poverty, there may be some disagreement over which poverty line should be used. The uses of vector measures also allows us to approach the issues of multidimensional inequality and poverty. That is, we may consider vectors of measures which represent inequality and/or poverty in different dimensions (e.g., income, wealth, health, educational attainment, etc.). Specifically, we propose hypothesis tests for such measures using a general framework for testing inequality constraints. Our proposed method has the advantage of allowing us to obtain unambiguous welfare orderings between two different populations. We present some simulation evidence which suggests that such tests have good size and power properties. Finally, this approach is illustrated with an empirical example using Canadian household survey data on income and educational attainment.;In the final chapter of this thesis, we turn our attention from specific measures of welfare towards the more general approach of stochastic dominance. Specifically, we propose tests of bivariate stochastic dominance using a generalized framework for testing inequality constraints. Our proposed methods are illustrated with some Canadian household survey data on income and educational attainment.
机译：本文探讨了进行实证福利比较的几种计量经济学方法。它分为三个独立的但密切相关的章节。在第一章中，我们提出了基于经验似然性的可分解（可分开）贫困测度的推断，该测度利用了相对贫困线（即，贫困线是两个贫困线中的一部分）平均值或基础收入分配的中位数）。在这种情况下使用经验似然框架的主要优势在于，无需估算尺度参数（即标准误差）。由于利用相对贫困线的贫困测度估计值涉及一个令人讨厌的参数（贫困线），因此这些估计值的渐近方差非常复杂，因为它们取决于潜在的密度函数。这里提供的仿真证据表明，所提出的方法可能在渐近逼近和bootstrap-T程序上都提供了一些改进的性能。这些方法以加拿大家庭收入调查数据为例进行说明。在第二章中，我们考虑了不平等向量和贫困测度的统计推断方法。使用向量测度认识到这样的事实，即通常不存在对研究人员完全满意的不平等或贫困的单一测度。例如，有时无法明确选择不平等或贫困的“最佳”（标量）度量。或者，在衡量贫困时，可能在使用贫困线方面存在一些分歧。向量度量的使用还使我们能够处理多维不平等和贫困问题。也就是说，我们可以考虑在不同维度（例如，收入，财富，健康，教育程度等）代表不平等和/或贫困的衡量指标。具体而言，我们使用检验不平等约束的通用框架为此类措施提出了假设检验。我们提出的方法的优点是允许我们获得两个不同人群之间的明确福利顺序。我们提供了一些仿真证据，表明此类测试具有良好的尺寸和功率特性。最后，以加拿大家庭收入和教育程度调查数据为例，对这种方法进行了举例说明。在本论文的最后一章中，我们将注意力从福利的具体措施转向更为普遍的随机支配性方法。具体来说，我们提出了使用广义框架来测试不平等约束的双变量随机优势测试。加拿大的一些家庭收入和教育程度调查数据说明了我们提出的方法。

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