We study how investor sentiment affects stock prices around the world. Relying on households’ Google search behavior, we construct a weekly measure of sentiment for 38 countries during 2004–2014. We validate the sentiment index in tests using sports outcomes and show that the sentiment measure is a contrarian predictor of country-level market returns. Furthermore, we document an important role of global sentiment in stock markets.

We find that people revise their beliefs about climate change upward when experiencing warmer than usual temperatures in their area. Using international data, we show that attention to climate change, as proxied by Google search volume, increases when the local temperature is abnormally high. In financial markets, stocks of carbon-intensive firms underperform firms with low carbon emissions in abnormally warm weather. Retail investors (not institutional investors) sell carbon-intensive firms in such weather, and return patterns are unlikely to be driven by changes in fundamentals. Our study sheds light on peoples’ collective beliefs and actions about global warming.

Using a unique setting in China, where the geographic distance between collective firms and local governments is highly persistent because of legal restrictions on land ownership and mobility, we investigate the role of government involvement in small firms. In our analysis of survey responses, we find that weaker government involvement, measured by greater distance from government, is associated with higher firm autonomy and reduced taxes, protectionism, and anticompetitive behavior. In our analysis of firm-level financial data, we find that distant firms have better operating performance, higher growth, and higher entry rates. We find similar results around exogenous government office relocations.

We develop a model to analyze information aggregation and learning in housing markets. Households enter a neighborhood by buying houses and consuming each other’s final goods. In the presence of pervasive informational frictions, housing prices serve as important signals to households and capital producers about the neighborhood’s economic strength. Our model provides a novel amplification mechanism in which noise from housing markets propagates throughout the local economy via learning because of the complementarity in households’ decisions, distorting migration into the neighborhood and the supply of capital and labor. We provide consistent evidence based on the recent U.S. housing cycle.