1. Air Pollution Index
In order to examine the relationship between subway expansion and air quality, I choose the daily Air Pollution Index to represent the air quality condition. The Air Pollution Index is a simple number that shows the level of air pollution in this city or area, and is based on five atmospheric pollutants, sulfur dioxide (SO2), nitrogen dioxide (NO2), suspended particulates (PM10), carbon monoxide (CO), and ozone (O3) measured at the monitoring stations throughout each city. The pollutants that Chinese government taking into account in Chinese API are the first three. The mechanics of the API level is that, an individual score is assigned to each pollutant based on their levels, and the final API is the highest of the three pollutant scores (The standard of scoring each pollutant is showed in Table 1).
Table 1: Transformation from Pollutant Concentration to API
For Beijing, there are two types of API data that can be access through the government’s website, one is the aggregate daily API released by the Ministry of Environmental Protection (MEP) of China, another one is the station-level daily API released by Beijing Municipal Environmental Monitoring Center (MEMC).
The station-level API data is more preferred because of its objectivity. The aggregate API is a number combined from all monitoring stations in Beijing, and is more easily to be manipulated by the government in order to give the public a false impression that the air pollution problem in Beijing is not as bad as it appears. Well the station-level API is different numbers monitored by 27 different monitoring stations that are located all around Beijing. Since the monitors are located in different locations, the index they monitored would ...
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...lso verify this argument by the graphs generated from the raw data, showing the average API for treatment and control group during 60 weekdays before and after the subway opening.
Test of Parallel Trend Assumption
Prior to the basic model, we test the most important assumption for DD – the parallel trend assumption.
DD with Continuous Measure of Time
Different from the standard DD, we take one step further and replace the time indicator for subway opening with a continuous variable which takes the value of zero for all dates before subway opening, and continuous numbers of days for dates after the opening date. The interaction term in turn is replace with . Assume the the impact of subway expansion on ln(API) is linear in time, then captures the difference in the effect between two groups for one more day after the subway opens.
Heterogeneity in Treatment Group
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