Modeling Economic Mobility: a Machine Learning Approach


May 2023


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This thesis constructs and evaluates models of increasing complexity that aim to predict a child’s income rank in adulthood based on the characteristics of the neighborhood in which they grew up. Each model was trained on newly-released public data with estimates of outcomes for children based on their Census tract. A set of simple decision tree models provided substantial performance in the prediction task, even with highly restricted depth. These decision trees, and a basic neural network model, were each restricted by an inability to handle missing features in the input samples. To handle this restriction, a joint neural network statistical model was created that utilizes learned distributions for each feature to provide estimates of each missing value to the neural network, prior to prediction. The joint neural network statistical model achieved a lower error rate than the more basic models. Assessing the features selected by the decision tree learning algorithm and comparing the relative importance of features in the neural network through sensitivity analysis demonstrated that each model relied on similar characteristics.



Computer Science