Thesis: This thesis covers several topics on the distribution of income and wealth. In the first chapter, we develop a new methodology to exploit tabulations of income and wealth such as the one published by tax authorities. In it, we define generalized Pareto curves as the curve of inverted Pareto coefficients b(p), where b(p) is the ratio between average income or wealth above rank p and the p-th quantile Q(p) (i.e. b(p)=E[X|X>Q(p)]/Q(p)). We use them to characterize entire distributions, including places like the top where power laws are a good description, and places further down where they are not. We develop a method to flexibly recover the entire distribution based on tabulated income or wealth data which produces smooth and realistic shapes of generalized Pareto curves.In the second chapter, we present a new approach to combine survey data with tax tabulations to correct for the underrepresentation of the rich at the top. It endogenously determines a "merging point'' between the datasets before modifying weights along the entire distribution and replacing new observations beyond the survey's original support. We provide simulations of the method and applications to real data. The former demonstrate that our method improves the accuracy and precision of distributional estimates, even under extreme assumptions, and in comparison to other survey correction methods using external data. The empirical applications show that not only can income inequality levels change, but also trends.In the third chapter, we estimate the distribution of national income in thirty-eight European countries between 1980 and 2017 by combining surveys, tax data and national accounts. We develop a unified methodology combining machine learning, nonlinear survey calibration and extreme value theory in order to produce estimates of pre-tax and post-tax income inequality, comparable across countries and consistent with macroeconomic growth rates. We find that inequality has increased in a majority of European countries, especially between 1980 and 2000. The European top 1% grew more than two times faster than the bottom 50% and captured 18% of regional income growth.In the fourth chapter, I decompose the dynamics of the wealth distribution using a simple dynamic stochastic model that separates the effects of consumption, labor income, rates of return, growth, demographics and inheritance. Based on two results of stochastic calculus, I show that this model is nonparametrically identified and can be estimated using only repeated cross-sections of the data. I estimate it using distributional national accounts for the United States since 1962. I find that, out of the 15pp. increase in the top 1% wealth share observed since 1980, about 7pp. can be attributed to rising labor income inequality, 6pp. to rising returns on wealth (mostly in the form of capital gains), and 2pp. to lower growth. Under current parameters, the top 1% wealth share would reach its steady-state value of roughly 45% by the 2040s, a level similar to that of the beginning of the 20th century. I then use the model to analyze the effect of progressive wealth taxation at the top of the distribution.