The past thirty years have seen a dramatic decline in the rate of income convergence across states and in population flows to high-income places. These changes coincide with a disproportionate increase in housing prices in high-income places, a divergence in the skill-specific returns to moving to high-income places, and a redirection of low-skill migration away from high-income places. We develop a model in which rising housing prices in high-income areas deter low-skill migration and slow income convergence. Using a new panel measure of housing supply regulations, we demonstrate the importance of this channel in the data.

Can protests cause political change, or are they merely symptoms of underlying shifts in policy preferences? We address this question by studying the Tea Party movement in the United States, which rose to prominence through coordinated rallies across the country on Tax Day, April 15, 2009. We exploit variation in rainfall on the day of these rallies as an exogenous source of variation in attendance. We show that good weather at this initial, coordinating event had significant consequences for the subsequent local strength of the movement, increased public support for Tea Party positions, and led to more Republican votes in the 2010 midterm elections. Policy making was also affected, as incumbents responded to large protests in their district by voting more conservatively in Congress. Our estimates suggest significant multiplier effects: an additional protester increased the number of Republican votes by a factor well above 1. Together our results show that protests can build political movements that ultimately affect policy making and that they do so by influencing political views rather than solely through the revelation of existing political preferences.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Using a novel database of 82.5 million online job postings, we show that employer skill requirements fell as the labor market improved from 2010 to 2014. We find that a 1 percentage point reduction in the local unemployment rate is associated with a roughly 0.27 percentage point reduction in the fraction of jobs requiring at least a bachelor's degree and a roughly 0.23 percentage point reduction in the fraction requiring five or more years of experience. This pattern is established using multiple measures of labor availability, is bolstered by similar trends along heretofore unmeasured dimensions of skill, and even occurs within firm-job title pairs. We further confirm the causal effect of labor market tightening on skill requirements using a natural experiment based on the fracking boom in the United States as an exogenous shock to the local labor supply in tradable, non-fracking industries. These industries are not plausibly affected by local demand shocks or natural gas extraction technology, but still show fewer skill requirements in response to tighter labor markets. Our results imply this labor market-induced downskilling reversed much of the cyclical increase in education and experience requirements that occurred during the Great Recession. Terms of use: Documents inJEL classifications: D22, E24, J23, J24, J63.

Can data from mobile phones be used to observe economic shocks and their consequences at multiple scales? Here we present novel methods to detect mass layoffs, identify individuals affected by them and predict changes in aggregate unemployment rates using call detail records (CDRs) from mobile phones. Using the closure of a large manufacturing plant as a case study, we first describe a structural break model to correctly detect the date of a mass layoff and estimate its size. We then use a Bayesian classification model to identify affected individuals by observing changes in calling behaviour following the plant's closure. For these affected individuals, we observe significant declines in social behaviour and mobility following job loss. Using the features identified at the micro level, we show that the same changes in these calling behaviours, aggregated at the regional level, can improve forecasts of macro unemployment rates. These methods and results highlight promise of new data resources to measure microeconomic behaviour and improve estimates of critical economic indicators.