Weekend Op-Ed: Delhi driving restrictions actually work [so far]!

New semester, new blog-resolutions. We're back with a WWP...except that the analysis I'm talking about here hasn't actually made its way into a working paper yet. That said, the work is interesting and cool, and extremely policy-relevant, so it's worth taking a minute to discuss, I think.

For those of you not up on your India news, Delhi's air pollution is horrendous. Air pollution data suggest that Delhi's PM2.5 and PM10 concentrations are the worst in the world - the city has even less breathable air than notoriously dirty Beijing. Having spent some time in Delhi last January, I can add some of my own anecdata (my new favorite Fowlie-ism) as well: after three days of staying and moving around in the city, I was hacking up a lung trying to walk up three flights of stairs to our airbnb. I'm certainly not the fittest environmental economist around, but a few steps don't usually give me trouble. 

So I was glad to hear that Delhi has recently been undertaking some efforts to improve its air quality. I was less glad to hear the method for doing so: between January 1 and January 15, Delhi implemented a pilot driving restriction. Cars with license plates ending in odd numbers would be allowed to drive on odd-numbered dates only, while cars with license plates ending in even numbers would be allowed to drive on even-numbered dates only. This sounds good - cutting the number of cars on the road by about half should have a drastic effect on air quality, right? The problem is that Mexico City has had a similar rule in place for years -  Hoy No Circula - and rockstar professor Lucas Davis took a look at its effects in a 2008 paper, published in the Journal of Political Economy. Unfortunately (I thought) for the Indian regulation, Lucas finds that license-plate-based restrictions lead to no detectable effect on air quality across a range of pollutants.

Here's Lucas' graphical evidence for Nitrogen Dioxide. If the policy had worked, we would've expected a discontinuous jump downwards at the gray vertical line. He shows similar figures for CO, NOx, Ozone, and SO2. 

Lucas provides an interesting possible explanation for the lack of change: he has suggestive evidence that drivers responded to the regulation by buying additional vehicles - in the Delhi case, if I have a license plate ending in 1, but really value driving on even-numbered days, I might go out and get a car with a plate ending in 2 instead. In light of this evidence, I was less-than-optimistic about the Delhi case. 

So what actually happened in Delhi? New evidence from Michael Greenstone, Santosh Harish, Anant Sudarshan, and Rohini Pande suggests that the Delhi driving restriction pilot did have a meaningful effect on pollution levels - on the order of 10-13 percent! (A more detailed overview of what they did is available here). These authors use a difference-in-differences design, in which they compare Delhi to similar cities before and after the policy went into effect, doing something like this:

Effect = (Delhi - Others)_Post - (Delhi - Others)_Pre

Under the identifying assumption that Delhi and the chosen comparison cities were on parallel emissions trajectories before the program went into effect, this estimation strategy is nice because it removes common shocks in air pollution. The money figure from this analysis shows the dip in pollution in Delhi starkly:

It looks like, in its brief pilot, that Delhi was successful at reducing pollution using this policy. So why is the result so different than in Mexico City? Obviously, India and Mexico are very different contexts. It also seems like the channel Lucas highlighted, about vehicle owners purchasing more cars, is something that people would only do after being convinced that the policy would be permanent - so there might be additional adjustment that occurs that isn't picked up in a pilot like this. (Would you go out and buy a new car in response to someone telling you that over the next two weeks they're trying out a driving restriction? I don't think I have that kind of disposable income...) Also, the control group obviously matters a lot. I'd like to see (and expect to see, if this gets turned into an actual working paper) a further analysis of what's going on in the comparison cities over the same time period. The pollutants being measured are different - though I doubt that this actually affects much, given how highly correlated PM is with the pollutants measured in Lucas' paper. 

In general, I'm encouraged to see both that Delhi is taking active steps to attempt to reduce air pollution, and that these steps are being evaluated in credible ways. As the authors point out in their Op-Ed, and as I've tried to highlight above, we should be cautious about extrapolating the successes of this pilot to the long run - a congestion or emissions pricing scheme might be a more effective long-run approach to tackling air pollution.

I'd also like to briefly highlight the importance of making air pollution data available for these kinds of analysis. There's a cool new initiative online called OpenAQ that's trying to download administrative data from pollution  monitors and make this information publicly available - and they're not the only ones. Berkeley Earth is also providing some amazing data on Chinese air quality - and rumor has it they'll be adding more locations soon.  Understanding the implications of air quality on health, productivity, and welfare is increasingly important as developing country cities grow and house millions in dirty environments - the more data that's out there to aid in this effort, the better.

WWP: Blowin' in the wind

Berkeley ARE's resident econometrician (of FWER/FDR fame!), Michael Anderson, has a neat new NBER working paper out this (last) week. Titled "As the Wind Blows: The Effects of Long-Term Exposure to Air Pollution on Mortality", this paper tries to get at an important gap in the air pollution and health literature. We know a lot about the short-run effects of air pollution exposure: it's bad for you. But the long run is harder to pin down. Michael gets at this question using one of my all-time favorite things: a clever identification strategy. 

In order to get at the causal effect of long-term exposure to pollution, Michael exploits the Los Angeles (#beatLA) highway system. He performs a battery of tests to demonstrate that people who live upwind of highways look very similar on observables to people who live downwind of highways. He then uses angle to the highway as an instrument for time spent downwind (constructed using high-frequency wind data) to look at the effect of pollution on mortality among the elderly. He further shows that mobility is low in these Census tracts among the population of interest: the median 75-year-old has lived at her current address for 25 years. It's because of this that Michael can really get at the long-run here. 

Living next to this stuff is bad for your (long-run) health. Who knew? Photo stolen from these guys.

The cleverness doesn't stop there: there are two other really nice features of this paper. The first is the estimator: In conjunction with the 2SLS strategy, Michael actually uses a modified spatial fixed effects estimator (SFE), such that each observation i is demeaned relative to observations within a radius r along the dimension parallel to the highway - think of this as a highway-segment fixed effect. Instead of a regular fixed effects estimator, the SFE estimator's demeaning group changes for each observation. Running the estimation this way allows for demeaning independently along the highway and in terms of distance away from the highway. Secondly, this paper contains a nice set of falsification tests. Like other papers in this literature, Michael tests for (and finds no evidence of) effects on "placebo" health outcomes. He also shows that highway bearing has no effect on property values, which suggests that people aren't moving away to avoid these negative health consequences.

Michael's abstract describes the paper's results nicely:

“There is strong evidence that short-run fluctuations in air pollution negatively impact infant health and contemporaneous adult health, but there is less evidence on the causal link between long-term exposure to air pollution and increased adult mortality. This project estimates the impact of long-term exposure to air pollution on mortality by leveraging quasi- randomvariation inpollution levels generated by wind patterns near major highways. We combine geocoded data on the residence of every decedent in Los Angeles over three years, high-frequency wind data, and Census Short Form data. Using these data, we estimate the effect of downwind exposure to highway-generated pollutants on the age-specific mortality rate byusing bearing to the nearest major highway as an instrument for pollution exposure. We find that doubling the percentage of time spent downwind of a highway increases mortality among individuals 75 and older by 3.6 to 6.8 percent. These estimates are robust and economically significant.”

So, next time you're thinking about buying a house, it's probably worth adding yet another thing to the list of deal-breakers: distance to, and direction from, the nearest busy roadway.