Privately-operated minibuses provide the majority of urban transit in developing-country cities yet are often viewed as a hindrance to development, not least because of often-long wait times passengers face for buses to fill up and depart. To quantify the efficiency of these seemingly-chaotic networks, I build the first model of privatized shared transit, which features increasing returns in the form of shorter waits on busier routes and a key role for bus supply as "insurance'" against demand spikes. Minibus-sector market power inhibits realization of the former but facilitates internalization of the latter. I then estimate the model with newly-collected data on minibus arrivals and passenger queues in Cape Town as well as stated preferences for exogenously-varied commute attributes. Limited minibus market power in Cape Town allows demand and thereby wait times to reach an almost-efficient scale, suggesting that appropriately-regulated privatized minibuses can efficiently provide the public good of transit.
International trade creates winners and losers, yet surprisingly little is known about policies that help workers escape declining sectors. We study how government-subsidized retraining in Germany can help workers smooth sectoral shocks. Using administrative data, we show that workers switch sectors and retrain in response to Chinese import competition. We introduce retraining into a dynamic model of labor mobility within a quantitative trade framework, where workers simultaneously choose their sector and whether to retrain; we find retraining lowers the costs of moving between sectors. In counterfactual experiments, retraining compresses the distributional effects of trade shocks while leaving aggregate gains unchanged.
Demand-side policies which relax borrowing constraints for homebuyers, with the aim of increasing homeownership, are increasingly common. We propose a new approach to evaluating ex-ante how such policies would change individuals’ opportunity sets according to their income, location and parental background. We estimate the maximum deposit each non-homeowner could plausibly raise based on their observable characteristics using stochastic frontier analysis, and map this onto local house price distributions to calculate the share of local properties that become newly affordable under the policy. We apply this method to assess the 2013 ‘Help to Buy’ schemes introduced in the UK. Affordability gains under these schemes were concentrated among higher-income individuals, with those living in London and the South East seeing larger increases in maximum affordable price but smaller increases in the share of local properties they could afford. Our approach can be adapted to evaluate the likely distributional impacts of new schemes and alternative forms of support.
We propose a theory-inspired measure of the accessibility to a city’s central work location: the size of the surrounding area from which it can be reached within a specific time. Using publicly available optimal-routing software, we compute these ”accessibility zones” for the 100 largest cities in the US and Europe, separately for cars and public transit commutes. Compared with European cities, US cities are half as accessible via public transit and twice as accessible via cars. Car accessibility zones are always larger than public transit zones, so that US cities are accessible from larger areas than European cities. However, population density within the most accessible zones is relatively low in the US, and European cities provide more residents quicker access to their city centers. Moreover, greater car orientation is associated with less green space, more congestion, and worse health and pollution externalities.