India experiences some of the highest air pollution levels globally, with 13 of the 20 most polluted cities in the world. In this paper, we estimate the relationship between air pollution policies in India and mortality for people of all ages and all causes. We estimate the relationship between mortality and two major air pollution regulations, the Supreme Court Action Plan (SCAP) and the Catalytic Converter (CC) policy. Although data for mortality in India have improved over time, the annual average mortality for many districts is volatile, with many outliers and missing values. After addressing these measurement issues in a difference-in-differences setup, we do not find evidence that the policies were effective in significantly reducing mortality. In an effort to understand the potential benefits of reducing pollution levels in India, we investigate the association of different pollution types with mortality. This analysis relies upon relatively recent satellite data on PM_2.5 levels in India. We examine this relationship for India for the first time, using a fixed effects model in an attempt to address issues of endogeneity and measurement error. We find that PM_2.5 levels are positively associated with mortality, with a 10% increase in pollution conditionally associated with a 2.0% increase in the mortality rate.

印度是全球20个空气污染最严重的城市中的13个，全球空气污染水平最高。在本文中，我们估算了印度的空气污染政策与所有年龄段和所有原因的人的死亡率之间的关系。我们估算了死亡率与两项主要的空气污染法规，最高法院行动计划（SCAP）和催化转化器（CC）政策之间的关系。尽管印度的死亡率数据随着时间的推移而有所改善，但许多地区的年平均死亡率是不稳定的，有许多离群值和缺失值。在差异差异设置中解决了这些衡量问题之后，我们找不到证据表明该政策可以有效降低死亡率。为了了解降低印度污染水平的潜在好处，我们调查了不同污染类型与死亡率的关系。该分析依赖于PM上相对较新的卫星数据在印度_2.5级。我们首次使用固定效应模型来检验印度的这种关系，以试图解决内生性和测量误差问题。我们发现，PM _2.5水平与死亡率呈正相关，有条件的污染增加10％与死亡率增加2.0％相关。