The main results still hold with this more restrictive specification, as shown in Online Appendix Table S13. As a result, a calculation restricted to immediate upstream polluters would capture only a small proportion of the overall economic cost of water regulation in China. The ESR data is first self-reported by each polluter, and then randomly verified by government auditors.

Developing surface water quality standards in China, In sharp contrast, when the prefectural city leader has weak promotion incentives, the TFP gap appears small and insignificant. The existing work on water pollution focuses on the environmental benefits of water regulation (e.g., Greenstone and Hanna 2014; Keiser and Shapiro 2019a,b), while the associated economic costs are typically computed using either engineering-type estimates or government expenditure records, missing an important component of emission abatement cost: the effects of water regulation on production activities.

The penetration of bottled water in China now stands at a rate of 15%, the market is still a great opportunity for the players who wish to expand there. The upstream–downstream TFP gap is predominantly driven by the break in trends among existing firms, rather than a change in the composition of firms around the monitoring stations.


The empirical analyses in the previous sections show that because of the political stakes associated with water quality readings, local government officials impose tighter environmental regulations on polluting firms located in the near upstream of national monitoring stations, as compared with their near downstream counterparts. The positive coefficients indicate that downstream firms have higher TFP than upstream firms. Nevertheless, the fact that the attenuated coefficients are only slightly smaller than the baseline coefficients (statistically indistinguishable) suggests that even if there is selection bias due to endogenous locational choices, such bias could at most account for only a small proportion of the baseline findings. Section IV presents the baseline findings and addresses the potential threats to our empirical analysis. This is confirmed by our own estimation of an 11.5% yearly firm TFP growth between 2003 and 2007. &&\quad s.t. When water quality monitoring became a political priority in 2003, there emerged a TFP gap between upstream and downstream polluting firms, while no such gap is observed among nonpolluting firms. We also find evidence that upstream polluters cope with tighter regulation by both adjusting the production process and abating end-of-pipe emissions.

Our article speaks to several strands of literature.

Water monitoring stations can only capture emissions from upstream, which gives local officials spatially discontinuous incentives to enforce tighter regulations on polluters immediately upstream of monitoring stations, as compared with their immediately downstream counterparts.

Then we investigate how the regulatory burdens are shared among different types of firms, which shed further light on the incentives of local government officials. To further demonstrate that the tighter regulation faced by upstream firms is driven by the efforts to improve water quality readings, we would like to directly link the “TFP loss among upstream polluters” to their “reduced COD emissions.” However, as explained in Section III, we could not directly merge the ESR data set with the ASIF data set, which makes us unable to conduct this test. This set of results should be interpreted with caution because many polluting sources did not provide information on wastewater treatment capacity. In contrast, in Panel B, we do not observe any comparable spatial discontinuity in TFP in nonpolluting industries.

To account for the industry- and location-specific TFP determinants in the nonparametric estimations, we control for industry and monitoring station fixed effects |${u_j}$| and |${v_k}$| in the baseline model. In 2015, the industrial value-added in China exceeded 23 trillion Chinese yuan, 39% of which was contributed by the polluting industries. We compare firms located immediately upstream of a monitoring station to those located immediately downstream of a monitoring station.

Table VI, Panel C reports the findings. As an alternative strategy, we collect the water quality readings of all the state-controlled monitoring stations between 2000 and 2007 and estimate the relationship between “TFP loss among upstream polluters” and “water quality improvement” for the corresponding monitoring stations.24 We estimate a difference-in-differences-in-differences (DDD) model, investigating whether monitoring stations experiencing larger water quality improvements also see larger upstream–downstream TFP gaps in that year. It is a longitudinal survey that collects township-level socio-economic data for all the townships in China.

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