We evaluate the environmental and economic effects of Beijing's driving restrictions. Based on daily data from multiple monitoring stations, air pollution falls 19% during every-other-day and 8% during one-day-per-week restrictions. Based on hourly viewership data, the number of television viewers during the restrictions increases 1.7 to 2.3% for workers with discretionary work time but is unaffected for workers without, consistent with the restrictions' higher per-day commute costs reducing daily labor. Causal effects are identified from both time-series and spatial variation in air quality and intra-day variation in viewership. We provide possible reasons for the policy's success, including evidence of high compliance based on parking garage entrance records. Our results contrast with previous findings of no pollution reductions from driving restrictions and provide new evidence on commute costs and labor supply.

We provide nationwide causal estimates of air pollution's effect on short-run labour productivity for China's manufacturing sector from 1998 to 2007. Using thermal inversions as an instrument, we estimate a one $\mu $g/m3 decrease in PM2.5 increases productivity by 0.82% (elasticity of −0.44). Increased hiring attenuates the elasticity to −0.17. Using the differential effect of China's WTO accession on coastal versus inner regions, we estimate a pollution elasticity of 1.43 with respect to output. Simulating a dynamic general-equilibrium model yields an output elasticity of −0.28 with respect to PM2.5. An exogenous 1% decrease in PM2.5 nationwide increases GDP by 0.039%.

We evaluate the environmental and economic effects of Beijing's driving restrictions. Based on daily data from multiple monitoring stations, air pollution falls 19% during every-other-day and 8% during one-day-per-week restrictions. Based on hourly viewership data, the number of television viewers during the restrictions increases 1.7 to 2.3% for workers with discretionary work time but is unaffected for workers without, consistent with the restrictions' higher per-day commute costs reducing daily labor. Causal effects are identified from both time-series and spatial variation in air quality and intra-day variation in viewership. We provide possible reasons for the policy's success, including evidence of high compliance based on parking garage entrance records. Our results contrast with previous findings of no pollution reductions from driving restrictions and provide new evidence on commute costs and labor supply.

We provide comprehensive estimates of air pollution's effect on short-run labor productivity for manufacturing firms in China from 1998 to 2007. An emerging literature estimates air pollution's effects on labor productivity but only for small groups of workers of particular occupations or sets of firms to ensure causality. To provide more comprehensive estimates necessary for policy analysis, we estimate effects for all but some small firms (90% of manufacturing output in China) and capture all channels by which pollution influences productivity. We instrument for reverse causality between pollution and output using thermal inversions.Our causal estimates imply that a one g/m 3 decrease in PM2.5 (SO2) increases labor productivity by 0.0084% (0.0572%) with an elasticity of -0.45 (-0.86). Lowering PM2.5 (SO2) by 1% nationwide through methods other than reducing manufacturing output would generate productivity increases of CNY 57.6 (110.5) thousand annually for the average firm and CNY 9.2 (17.6) billion annually or 0.06% (0.12%) of GDP across all firms. Accounting for output's contribution to PM2.5 (SO2) emissions leads to a net elasticity of -0.23 (-0.75).Using air quality of a nearby city conditional on wind blowing toward a focal city as an alternative instrument, we find a one g/m 3 decrease in PM10 increases productivity by 0.41% with an elasticity of -0.43 using a subsample of larger cities. Improving air quality generates substantial output and productivity benefits and these should be considered in evaluating environmental regulations and in evaluating their effect on firm competitiveness. JEL Codes: D62; Q51; Q53; R11

Local telecommunications competition was an important goal of the 1996 Telecommunications