Our paper relies on stock price reactions to colour words, in order to provide new dictionaries of positive and negative words in a finance context. We extend the machine learning algorithm of Taddy (2013), adding a cross-validation layer to avoid over-fitting. In head-to-head comparisons, our dictionaries outperform the standard bag-of-words approach (Loughran and McDonald, 2011) when predicting stock price movements out-of-sample. By comparing their composition, word-by-word, our method refines and expands the sentiment dictionaries in the literature. The breadth of our dictionaries and their ability to disambiguate words using bigrams both help to colour finance discourse better.

This paper provides novel evidence that female politicians increase the proportion of US government procurement contracts allocated to women-owned firms. For identification, we use a regression discontinuity design on a sample of mixed-gender elections in the US House of Representatives. The effect grows over a female representative's tenure and concentrates in female representatives who are on powerful congressional committees. Changes in the pool of and behavior by government contractors cannot explain the result. The more gender-balanced representation in government contracting is not associated with economic costs.

We estimate the willingness-to-pay to bypass a loan-to-value (LTV) cap. Our identification relies on exogenous variation in debt exempt from the LTV regulation that can only be used as a substitute for a personal mortgage. Our baseline estimate reveals that homebuyers pay 7.3 Swedish Kroner (SEK) to avoid 1 SEK of equity down payment. The supply of debt not part of the LTV calculation increased by approximately 50% within 2 years after the LTV regulation. Financially weaker households drive the results.