Post-1500 Population Flows and the Long Run Determinants of Economic Growth and Inequality

We construct a matrix showing the share of the year 2000 population in every country that is descended from people in different source countries in the year 1500. Using this matrix, we analyze how post-1500 migration has influenced the level of GDP per capita and within-country income inequality in the world today. Indicators of early development such as early state history and the timing of transition to agriculture have much better predictive power for current GDP when one looks at the ancestors of the people who currently live in a country than when one considers the history on that country’s territory, without adjusting for migration. Measures of the ethnic or linguistic heterogeneity of a country’s current population do not predict income inequality as well as measures of the ethnic or linguistic heterogeneity of the current population’s ancestors. An even better predictor of current inequality in a country is the variance of early development history of the country’s inhabitants, with ethnic groups originating in regions having longer histories of agriculture and organized states tending to be at the upper end of a country’s income distribution. However, high within-country variance of early development also predicts higher income per capita, holding constant the average level of early development.

A Pseudolikelihood Approach for Simultaneous Analysis of Array Comparative Genomic Hybridizations (aCGH)

DNA sequence copy number has been shown to be associated with cancer development and progression. Array-based Comparative Genomic Hybridization (aCGH) is a recent development that seeks to identify the copy number ratio at large numbers of markers across the genome. Due to experimental and biological variations across chromosomes and across hybridizations, current methods are limited to analyses of single chromosomes. We propose a more powerful approach that borrows strength across chromosomes and across hybridizations. We assume a Gaussian mixture model, with a hidden Markov dependence structure, and with random effects to allow for intertumoral variation, as well as intratumoral clonal variation. For ease of computation, we base estimation on a pseudolikelihood function. The method produces quantitative assessments of the likelihood of genetic alterations at each clone, along with a graphical display for simple visual interpretation. We assess the characteristics of the method through simulation studies and through analysis of a brain tumor aCGH data set. We show that the pseudolikelihood approach is superior to existing methods both in detecting small regions of copy number alteration and in accurately classifying regions of change when intratumoral clonal variation is present