In the absence of large income effects, a neoclassical model of labor supply predicts a positive wage elasticity of hours. However, Camerer et al. (1997) collected data on the daily labor supply of New York City cab drivers who, unlike most workers in modern economies, are free to choose their own hours, and found a strongly negative elasticity of hours with respect to their closest analog of a wage, realized earnings per hour. In Camerer et al.'s dataset, realized earnings per hour (which they call the "wage") is uncorrelated across days but positively serially correlated within a day, so that high earnings early in a day signal higher earnings later that day, and a neoclassical model predicts a positive elasticity even though realized earnings per hour is not precisely a wage. If instead realized earnings per hour is serially uncorrelated within a day, as Farber (2005) shows is roughly true in his dataset (see, however, our analysis in Section IIC), then a driver with high early earnings experiences a small change in income but no change in expected wage, and a neoclassical model predicts an elasticity near zero.

This paper developed a practical split-window (SW) algorithm to estimate land surface temperature (LST) from Thermal Infrared Sensor (TIRS) aboard Landsat 8. The coefficients of the SW algorithm were determined based on atmospheric water vapor sub-ranges, which were obtained through a modified split-window covariance-variance ratio method. The channel emissivities were acquired from newly released global land cover products at 30 m and from a fraction of the vegetation cover calculated from visible and near-infrared images aboard Landsat 8. Simulation results showed that the new algorithm can obtain LST with an accuracy of better than 1.0 K. The model consistency to the noise of the brightness temperature, emissivity and water vapor was conducted, which indicated the robustness of the new algorithm in LST retrieval. Furthermore, based on comparisons, the new algorithm performed better than the existing algorithms in retrieving LST from TIRS data. Finally, the SW algorithm was proven to be reliable through application in different regions. To further confirm the credibility of the SW algorithm, the LST will be validated in the future.

China has high rates of antibiotic abuse and antibiotic resistance but the causes are still a matter for debate. Strong physician financial incentives to prescribe are likely to be an important cause. However, patient demand (or physician beliefs about patient demand) is often cited and may also play a role. We use an audit study to examine the effect of removing financial incentives, and to try to separate out the effects of patient demand. We implement a number of different experimental treatments designed to try to rule out other possible explanations for our findings. Together, our results suggest that financial incentives are the main driver of antibiotic abuse in China, at least in the young and healthy population we draw on in our study.

This paper reconsiders whether cabdrivers' labor supply decisions reflect reference-dependent preferences. Following Botond Koszegi and Matthew Rabin (2006), we construct a model with targets for hours as well as income, both determined by rational expectations. Estimating using Henry S. Farber's (2005Farber's ( , 2008 data, we show that the reference-dependent model can reconcile his 2005 finding that drivers' stopping probabilities are significantly related to hours but not income with the negative wage elasticity of hours found by Colin Camerer et al. (1997( ) and Farber (2005( , 2008. The model yields sensible estimates that avoid Farber's (2008) criticism that drivers' income targets are too unstable to allow a useful reference-dependent model of labor supply.Abstract: This paper reconsiders whether cabdrivers' labor supply decisions reflect reference-dependent preferences. Following Botond Kıszegi and Matthew Rabin (2006), we construct a model with targets for hours as well as income, both determined by rational expectations. Estimating using Henry S. Farber's (2005Farber's ( , 2008 data, we show that the reference-dependent model can reconcile his 2005 finding that drivers' stopping probabilities are significantly related to hours but not income with the negative wage elasticity of hours found by Colin Camerer et al. (1997( ) and Farber (2005( , 2008. The model yields sensible estimates that avoid Farber's (2008) criticism that drivers' income targets are too unstable to allow a useful reference-dependent model of labor supply.