Author: andyeggers

Congressional stock trading is back in the news, so I recently had occasion to look back into the issue including my work with Jens Hainmueller about 10 years ago.

It reminded me of an intriguing anomaly in a 2004 paper by Alan Ziobrowski and coauthors (link). This paper was the first to claim that members of Congress enjoyed a systematic investing advantage. It looks at common stock transactions by members of the Senate in the period 1993-1998.

The first and most striking evidence of Congressional trading acumen in Ziobrowski et al (2004) is the figure below (Figure 1). The dark line shows the performance of a portfolio of stocks Senators sold, where the vertical line at zero indicates the day when they sold. In the year before the sale, these stocks beat the market by a whopping 25%; after the sale, they basically matched the market. Conversely, the stocks Senators bought were flat before the purchase but then beat the market by about 25% in the following year. This is stunning trading acumen.

But there is something odd about this figure. It is supposed to show cumulative abnormal returns, meaning the amount by which each portfolio outperforms the market in the year before and after the transaction (i.e. +/- 255 trading days). So it shouldn’t be surprising that the Buys series and the Sells series both start at 0. But it is surprising that they both end precisely at 28.6%. There is no reason to expect that.

On closer inspection, the Sells series looks like it is the same as the Buys series, just flipped and reversed. To see this, I rotated the figure by 180 degrees, colored it orange, and made it semi-transparent; then I overlaid it on the original, which I colored blue for contrast. Below is the result. You can see that the two series line up exactly.

Jens and I noticed this anomaly back when we were working on our papers (around 2010), but we didn’t do anything about it. Ziobrowski had already refused to share his data with us or with other researchers, so we couldn’t check what happened.

Jens and I ultimately published a paper arguing that the evidence of Congressional investing acumen in Ziobrowski et al (2004) and their 2011 follow-up (focusing on the House) is weaker than the authors claim, in part because they emphasize the strongest results among various possible specifications. I still think that’s a convincing critique of these papers.

I think it also would have been appropriate to highlight this anomaly, though at the time I was too timid to get into it. It’s hard to tell whether this was just an error in the presentation of the cumulative abnormal returns or something that affects the calendar-time portfolio analysis that is reported in the subsequent tables. Even if it was just this figure, I think it’s worth noticing because this figure is the most striking piece of evidence in the paper.

I’ve been trying to learn about climate change, mainly by listening to David Roberts’ Volts podcast. This has made me a lot less pessimistic (hopeful even!) about the prospects for big reductions in carbon emissions over the next few decades.

Like many others, I had viewed climate change through the lens of pollution and global collective action, where cutting carbon emissions required big sacrifices and thus would not occur without some combination of global cultural awakening and global enforcement, neither of which seemed likely. We would therefore likely keep polluting our way into climate misery and chaos.

What I’ve learned in listening to just a few episodes is that, due to changes in the cost of green energy production, my earlier perspective was incorrect and there is a lot of reason to be hopeful. The cost of generating electricity with wind and especially solar energy has been dropping very fast and is likely to continue dropping. Ongoing work is improving storage, which (along with the introduction of greener forms of energy that can be turned on and off) will allow us to make efficient use of this green energy. And as we roll out more of these technologies, their cost will continue dropping in a way that has never been the case with fossil fuels. The upshot is that, although government support is needed, a massive green transition can happen without much cultural change or international coordination at all. I find this to be hugely reassuring.

A key episode that shaped my views on this is the one about cost models produced by Doyne Farmer’s team, in which he argues that we will spend less money on energy the faster we achieve the green transition, so that greening the economy is not a difficult thing that we should swallow hard and do anyway, but is instead an efficient and cost-effective project. This is mainly because of “learning curves”, ie the tendency for costs to go down with expanded production of many technologies including wind and solar but not including most fossil fuel energy generation. Transitioning faster means getting more years of low-cost generation, so a faster transition gives us cheaper energy overall.

Another one is the episode on enhanced geothermal energy, which is speculative at this stage but can play two important roles in a green grid: it offers a green source of energy that can be turned on and off cheaply and on demand (unlike nuclear, wind, or solar), but it may also serve as a form of energy storage.

All of this makes me think about what the relevant issues in climate policy really are. Before paying attention to these technological developments, I thought it was really important that we tax or otherwise discourage carbon use. But given the speed at which alternatives are becoming cheaper, policy interventions that change the relative marginal cost of using one or the other seem less important. (Looking at the costs in the left panel of the figure below, from Farmer et al’s paper in Joule, what difference does it make if we move the fossil fuel lines up a little? The clean alternatives are already cheaper, or nearly so.)

I also thought that some form of binding international agreement limiting emissions would be necessary. But if the fundamentals are tilted so strongly toward green options, I’m not sure it’s that important. Maybe some pledges like we had in Paris help generate domestic political pressure for necessary public investments — that’s fine. But it no longer seems like big economies will need to make collective sacrifices to fight climate change changes.

So what are the policy issues? It seems to me that the key questions are around (1) R&D in remaining parts of the system, such as storage, (2) infrastructure investments that support the new forms of energy, such as transmission lines and electrification of cars, trains, buildings, etc and (3) management of economic/political impacts from the old system being made obsolete.

The good news is that these are more familiar forms of politics and policymaking than the ones I thought we had to engage in. Binding international emissions limits and global cultural change are tough to achieve, but researching, building, and mitigating are familiar and less heroic activities.

Matto Mildenberger‘s book Carbon Captured argues that a distinctive feature of climate politics is that carbon taxes and similar measures are opposed by a coalition of business and labor from polluting industries, which cuts across the usual left-right divide. I guess this is true of any regulation that seems to target an industry, but carbon taxes are perceived to target larger swathes of the economy and thus generate more sweeping coalitions in opposition. When we shift to politics around R&D and infrastructure investment, these measures don’t produce such well-defined losers. Coal companies and coal miners lose from a green transition, but this is not necessarily the case for manufacturing companies/workers whose factories use energy intensively and who would have been hurt by a carbon tax but now just get greener (and eventually much cheaper) energy.

So all of this is very heartening. I will keep learning and try to test these perspectives against some drearier ones.

Below is an updated version of some notes I made in 2016 on Samuel Decanio’s 2014 AJPS article “Democracy, the Market, and the Logic of Social Choice”.

Decanio’s paper intriguingly brings together important themes from causal inference and social choice in arguing that market mechanisms should be preferred to elections as means of identifying good ideas. In this post I will leave aside the important question of defining the domain of policy questions that is at issue; we may all agree that the market is better than elections at designing a car (Decanio’s example), but the controversy arises when we ask whether the argument applies to e.g. systems of health care provision. Instead I will focus on how and whether the market really is a better means of generating and refining ideas than politics/elections. My main point is that Decanio presents as a difference in kind what seems to me more to be a difference in scale. A consumer evaluating a product faces many of the same problems as a voter evaluating a party or policy. The consumer has some advantages because her evaluation may benefit from a larger data set, but the nature of her evaluation is the same.

First, a bit of exposition: The main theme is the comparison of the market with political choice. Decanio emphasizes that in political contexts parties produce exclusive goods: elections are procedures by which a single party is chosen to produce a bundle of goods (policy outcomes) for a period of time. The problem with this is that the citizen does not get to observe the counterfactual, i.e. what would have happened if another bundle of goods had been produced. He emphasizes a key difference from the market: in the market, firms produce goods and several are available for purchase having been already made: he says that this allows consumers to “evaluate the effects produced” by the scientific knowledge underlying the manufacture of the products “without ever having to understanding [sic] the knowledge responsible for producing such effects”. By contrast, Decanio asserts that in the case of selecting a party to implement a proposed policy, one needs to “accurately predict the effects of rival parties’ policy proposals before the party’s policies can be implemented and their effects observed. Rival parties’ policies cannot be implemented simultaneously, and voters cannot observe the effects of different parties’ policies until after they have made their electoral decisions, the new party is placed in power, and its policies are implemented.”

To me it seems that almost the whole question is one of scale. Decanio says things are easier in the market because firms don’t have exclusive rights over production. But at a large enough scale, a single party doesn’t have exclusive rights of production over policies: there are multiple polities (countries, states, municipalities) trying different things. It’s not that we cannot attempt to approximate an experiment in evaluating policies, it’s that our evaluations have fewer observations. From the perspective of the entity making the choice (the consumer, or voter), it is also a question of scale and aggregation: there may be multiple houses available whose attributes the consumer can compare, but each consumer basically chooses only one house (which is a bundle of different attributes -location, size, decor) and doesn’t know exactly what would have happened if she had chosen another, though she can visit her friends’ houses. In the political realm the choices apply to collectivities, so all of the voters in each electorate get assigned (and collectively get to choose) one bundle of policies; as with families in houses, they only get one outcome, but they can look at what other families chose and try to infer what would have happened if they chose something else. So I think Decanio’s focus on exclusivity, his characterization of the inferential problems facing voters as being so different from those of consumers in the market, is misguided.

Another way of putting this is that the problem of an electorate choosing among parties (i.e. bundles of policies) is not actually that different from the problem of an eater choosing among foods:

The food/diet case: You only get to choose one diet; you don’t know exactly what would have happened if you had chosen something else. You pay attention to how your weight and energy levels covary with different foods and diets, but it’s not easy to infer causal relationships: you’re eating a lot of different things, and there are many things other than diet affecting the same outcomes (e.g. exercise, sleep, stress, disease, relationships with others), and it’s not clear how long each possible effect is likely to take. You get to see what other people are eating, but it’s hard to infer effects because of other (i.e. non-diet) differences between yourself and others. You can try to learn about the effects of diet by looking at scientific studies conducted on observational data; occasionally you get to see a randomized experiment and try to learn from that.

The policy case: A collectivity (or a voter in that collectivity) only gets exposed to one policy bundle at a time, and doesn’t know what would have happened if it had chosen something else. The voter can pay attention to how he feels, or how things are generally in the collectivity, and how this covaries with policies that are attempted, but it’s not easy to infer causal relationships: there are many policies that matter for a given outcome (e.g. economic growth), and there are many things other than policies that affect the same outcomes (e.g. technology, culture, events in other polities), and it’s not clear how long each effect is likely to take. You get to see how policies differ in other collectivities, but it’s hard to infer effects because of other (i.e. non-policy) differences between one polity and another. You can look at scientific studies conducted on observational data; occasionally you get to see a randomized experiment and try to learn from that.

Decanio makes a lot of the price mechanism in markets and how it is so much more informative than electoral margins. To begin with, I think market share, revenue, or sales volume would be better indicators than price if we’re thinking about firms innovating on products, but the specific metric probably doesn’t matter to the argument. But fundamentally (and especially in a common-values setting) I don’t see much of a difference between a vector of vote shares in a PR system and a vector of market shares in a particular product market (e.g. cars). In both cases, the objects of choice (products/parties) differ on multiple dimensions, so it’s not straightforward to figure out why one is “selling” so much more than another, but the competitors will surely try to figure out why, as will the consumers/voters. Again there may be a difference of degree but I see no difference of kind.

Initially I was more convinced by Decanio’s argument that political competition is different from market competition because policy bundles are essentially promises about the future, which require a theory about the state of the world and how policies work, while consumer products are already made, and the effects of the technology can be observed (measured) by the consumer without any specialized knowledge. But on reflection I think the difference is overdrawn. Indeed the consumer can observe some differences among products and choose what she likes without needing a theory about how the firms’ products will end up looking, but this is not true of many important attributes of basically all consumer products (some more than others): reliability, performance, ease of use, suitability for the particular problems the consumer is going to face. The importance of these unverifiable elements is indicated by the willingness of producers to offer guarantees and servicing arrangements and by the existence of publications like Which and Consumer reports, the popularity of websites like Yelp, and the success of Amazon (which includes a huge number of reviews). Warrantees per se don’t exist in politics, but re-election incentives by default play a similar role.

When choosing among parties, voters have information about their track record, their promises, their ideology, their leadership. Some things they can evaluate at the time of “purchase”: what the parties have done in the past; what the leaders look like and say. Other things are harder to evaluate: competence; desire and ability to implement the policies proposed. The claim Decanio makes is that the effects of a firm’s technology are observable at time of purchase, but this is not true for parties; in fact some of the effects of the firm’s technology are not observable at time of purchase (e.g. durability) and some of the effects of the party’s technology are (arguably) observable (e.g. the skill of the leaders, the party’s record).

In conclusion, I think the inferential problem of choosing the right party to govern (i.e. bundle of policies, plus leaders) is very complicated; for a single voter it has all the same features of the problem of choosing the right diet, except that there are fewer observations and the scope for experimentation is smaller. Perhaps the important differences between decisions made by the market and by elections are not about what information the decision-makers can potentially have, i.e. Decanio’s focus. Rather, the important differences are that market decisions are individual and unanimous (i.e. no one is forced to enter into an agreement) while political decisions necessarily are not. This creates distributional issues (e.g. Buchanan & Tullock), and also incentive problems: why go through all the trouble of getting the information necessary to make a good choice when you are unlikely to be decisive?  My conclusion is that we should be less worried about the special difficulty of the problem of choice in politics (Decanio’s focus) than about the lack of incentive to solve it properly (as highlighted in classics by Olson and Downs).

(With apologies to readers who may have supposed that I became stranded along the Nakasendo never to be heard from again: below is the last entry, delayed by inattention and busy-ness.)

My last day on the Nakasendo was Thursday, Sept 29, 2016. This was my least eventful day. I walked southwest through early rain and clearing clouds from the western edge of Karuizawa to the Sakudaira shinkansen station. Along the way I passed through several charming post towns, and from some promotional billboards at resort hotels I got the idea that I was supposed to be able to see a beautiful mountain (Mt Asama) that was unfortunately deep in cloud cover.

The road on this day often followed a quite busy road, though at times it veered off to pass through charming post-towns. At one such point it also passed a homespun-looking Nakasendo museum (which I had read about elsewhere), complete with a miniature replica of the Nakasendo.

At one point I stopped to get a hair cut. I had a very fun conversation with the barber in which I struggled mightily to explain the expression “fire that before it quits”, the philosophy of hair care that I have adopted since signs of male pattern baldness became impossible to ignore.

As I got closer to Sakudaira the route became busier and busier. The most charming feature along this stretch was that the road passed several apple orchards with ludicrously large apples. Eventually I veered off to follow a parallel road that was not marked as Nakasendo in my guide book, but it was just as charming and showed signs of being on some people’s walking itinerary even if it wasn’t technically on mine.

Near to the Sakudaira station I stopped in front of an udon shop and checked the tabelog web site for recommendations. As it turned out, the udon shop in front of me was the top recommendation in the area, so I had a happy bowl of noodles before heading to the station and boarding the shinkansen for Kanazawa, where my trip in Japan continued.

I hope to return to Japan someday to do more of the Nakasendo trail, ideally with enough time to walk in the Iida Valley.  To Tokyo-based people with just a few days I would recommend doing the part I did, which gave a taste of history and nature while being highly accessible by public transport.

On day 2 I got going pretty early, definitely feeling sore from the previous day’s long hike. The staff at Kutsurogi no Sato seemed dismayed that I was leaving so early, without having visited the onsen next door — I think we all realized it was a bit silly for me to have visited by myself and made so little use of the facility.

I returned to the main road and headed up toward the mountains. The footpath over Usui Pass begins soon after Route 56 splits off from Route 18. The map in the guidebook shows the footpath leaving the road just before a water treatment plant, and thus skipping the big bend in Route 18, but I couldn’t find the path and just stayed on the road. (Soon after, I saw where it is supposed to intersect with Route 18, and it looked to me like the path is no longer maintained here — you should just stick to the road.) The screenshot from Google Street View shows where the footpath starts – there is a shelter, a signboard, and a path marker.

The path starts pretty steep and is pretty well maintained. (The wooden steps in the picture below are useful but will need to be maintained in the next few years.) I was pretty excited to walk on a forest path after a day of walking on asphalt. I can only imagine how much more exciting it is if you’ve been walking through the Tokyo outskirts for three or four days.

There are also some rocky stretches like the one below that could be a little treacherous in heavy rain, but contrary to how it might look in the picture the path is very clear in this section. It would be pretty impossible to get lost; there are basically no junctions. Still, to be safe it would be good to know the characters for Nakasendo (中山道).

One of the payoffs in this early section is a clear view back toward Takasaki in which you can see the Nakasendo running through the valley (see below). This view is already quite overgrown compared to photos I’ve seen of it on other blogs — make sure to visit soon before it is overgrown completely!

Along the way, there are many historical markers like the ones below. They describe historical events that took place along the path, religious statues, and the remains of tea rooms and other travelers’ accommodations that are still visible along the path.

The marker below explains that there was a tea house here (actually four, if my reading is correct) and that a stone wall remains (visible in the back of the photo). Plus some graves!

After the initial climb the path flattens out and follows a ridge through the forest. This was the nicest part of the whole trip for me.

Watch out for erosion! Again some maintenance will be necessary.

There is a stretch where the path is sunken and winding, as if in a bobsled run. In this section I heard rustling and movement in a stream bed below the path, which I thought might be bears. (I had seen signs about bears, and I knew that some walkers carried bear bells.) Eventually I caught sight of some large animals that I later figured out were not bears but wild boars, or i no shishi. After my initial sighting from a very safe distance I later heard some that were closer to the path; I got a pretty good glimpse (but not a good photo), then clapped my hands, which sent them darting away. At this point I was a little uneasy, as I was walking alone and I didn’t know anything about these animals. I was grateful that it wasn’t raining as I imagine it might get pretty muddy in these trough-paths.

As it started to rain slightly, I came across the stream below. I decided to take off my shoes and socks to wade through.

To my horror both ankles were bleeding! The blood had soaked through both socks, but I hadn’t noticed because they were covered by my pants. I had little cuts on each ankle in almost precisely the same spot. I thought back to some of the entries I had seen in the guestbook at a shelter earlier in the day, which referred to leeches. Alas somewhere along the path I had been bitten by “mountain leeches” or yamabiru. Very impressive creatures! I would recommend protecting yourself against these in some way — perhaps by wearing boots that lace up above the ankles or wearing gaiters.

As I continued the path came to more closely resemble a mountain road, and I encountered some interesting things left behind from when the road was actually used: an abandoned house (which I carefully entered), a road sign (I couldn’t figure out if it belonged here or was stashed here), and this beautifully rusted-out city bus:

Along this part of the path there was an intersection that as far as I recall was the only possible point of uncertainty during this stage of the trip. (See below.) The path I had come from was behind me, and two options lay in front of me. The path on the left is the Nakasendo trail, heading for Karuizawa; the one on the right heads up the mountain (to a historic battle site, if I recall correctly). So if you’re coming from Tokyo, you want to go left here; if coming from Karuizawa just go straight.

By this point in my journey it had started to rain more seriously. Just as I started to get soaked, the path turned into an asphalt road and I found myself in front of a shrine and a restaurant/gift shop. This collection of buildings is called Togemachi (literally, Pass Town) on Google Maps. I walked into the restaurant somewhat beleaguered and ordered some udon.

From here to the edge of Karuizawa (Ginza Dori) the Nakasendo path is apparently fairly difficult to follow. I was told by a guy working at another restaurant/gift shop in the same area that this was actually the hardest part to follow on the whole Nakasendo. He advised that I take the road (Route 123 on the map) instead. I set off to do that with my flimsy umbrella as the rain poured down. Eventually I was overtaken by a guy in a car who offered me a ride. I gratefully accepted. It took me a minute before I realized it was the same guy I had talked to about the path to Karuizawa; he had changed out of his work uniform so I didn’t recognize him at first. He dropped me off at Ginza Dori, where touristy shops line the route of the old Nakasendo. (See below.) This is a different scene from the towns along the Nakasendo from Takasaki to Yokokawa; Karuizawa is a tourist destination in its own right, not a run-down town whose only claim to significance is its status as a post town on a now-defunct trade route.

The more upscale feel intensified as I continued through Karuizawa, walking past expensive-looking private homes and resort hotels. My hotel for the night was on the other end of Karuizawa, in a ryokan called Yuusuge Onsen Ryokan. I arrived late afternoon, had a bath in their very modest onsen, and ate a big dinner in their dining room. Room and dinner shown below. If you’re looking to do this trip, I would not recommend this accommodation either. I saw it compellingly and dismissively described on a Japanese website as “Showa-poi”, meaning that it had the feel of the Showa era, which lasted from the 1920s to the 1980s. That in itself should not dissuade you, but it smelled of cigarettes in the lobby; the onsen itself was meager; and the window screens in my room were unable to keep out two mosquitos that terrorized me throughout the night. (I know there were two because the harassment didn’t stop until I had killed both.) All this would be fine if it were cheap but it wasn’t. It’s possible that everywhere else in Karuizawa is astronomically expensive (as it appears to be a pretty posh resort town) but I think I could have done better.

 

Takasaki is at the northwestern edge of the Kanto plain, where the Tokyo megalopolis turns into the mountains. I arrived by Shinkansen the evening before my walk would begin. It only takes an hour from Tokyo station, so I clearly could have gotten up early from Tokyo and arrived in Takasaki in time for a day of walking, but I decided to stay over because with jetlag it seemed like I might get more sleep this way. I checked into my hotel (the New Akagi business hotel; shabby and small room, which smelled like cigarette smoke — cheap but otherwise not recommended) and looked for a place for dinner. The place recommended on tabelog.com was closed that night, but I ended up finding a restaurant called IZANAI that served me an excellent dinner. (Not cheap and probably not easy for a non-Japanese speaker to order, but an excellent meal, pictured below.)

The next morning I set out early-ish along the route. My guide for the route, by the way, was this book that I bought from Amazon. There may be a KML or GPX file that one can download that captures the whole route, but I wasn’t able to find it. (My Japanese Google skills may not have been sufficient.) I used the book to create a partial route on Google Maps on my laptop, which I then could view on my phone along the route. (I’ll see about making that publicly available.) It would be challenging for someone who can’t read Japanese to use the book alone, as none of the place names are written in Roman characters, but I suppose if you were in a place and could find the corresponding map, you could look back and forth between google maps and the book to locate the route. Obviously someone should just make the whole route available as a GPX file — wouldn’t be that hard.

Anyway, my first day of walking was roughly 20 miles from Takasaki to Sakamoto, which is the last post-town before the path heads into the mountains and over the Usui Pass. This was actually more walking than I had planned; I think I had planned to take the Shin’etsu train from Takasaki to Annaka and walk from there, but I felt optimistic and wanted to walk right away. (And then of course how could I get on a train halfway!)

The walk out of Takasaki was basically pleasant: there is one stretch where the route takes you along Route 18 that looked kind of unappealing, so I walked on an asphalt path closer to the river (see photo below); I don’t think I missed much.

As I walked along the environment became more and more green and the mountains started looming larger; the setting becomes progressively less urban and more rural throughout the day. Also, as I got further from Takasaki the towns became quieter and more run down, but certainly not in an unpleasant way. I saw a lot of old people and a lot of shops that seemed unlikely to sell much; there were a lot of formerly grand-looking buildings that were deserted, decaying and falling apart. These towns are quite proud of their heritage as Nakasendo post towns; I frequently saw information billboards explaining the role of the post-town or describing events that happened nearby in connection to the road. This heritage seemed to be their strongest selling point to outsiders. There are also quite a few temples and shrines along the way, and I suppose that they catered in part to passing travelers.

Around mid-morning I found myself walking behind a man roughly in his 60s who was clearly doing the same walk I was. I eventually caught up to him and struck up a conversation. We ended up having a nice time walking together. Fujii-san is a retired chemical engineer who lives in Chiba and is walking the Nakasendo in stages; this was a day walk for him, with about three hours of train on either side. (From this point onward he planned to do the walk in two- or three-day trips, with his wife alongside — the walk from Nihonbashi in Tokyo to the mountains did not interest her, for understandable reasons!) We talked about a range of things, but eventually got around to talking about American politics; as we were walking, Donald Trump and Hillary Clinton were concluding their first debate, and during our lunch break on the steps of a temple we checked on his tablet to see how it had gone. Later in the day Fujii-san started to tire out, and he sent me ahead so he could go a bit slower to his train. We took the selfie below (and I learned the Japanese word for it: jidori) just before parting.

As I got further into the mountains and entered the post-town of Sakamoto I was surprised to see some monkeys in a garden. I leaned in to take the picture below (note monkey on the edge of the roof), after which the homeowner came out of her back door with some firecrackers to scare away the monkeys, who seemed to having a meal in her fruit trees.

In planning this trip I had some trouble figuring out where to stay at the end of the first day. I think there are some nice places in the mountains that are not near the route, and I thought about doing this and arranging a pickup in Sakamoto. There are a bunch of business hotels around Annaka, but that is not really far enough into the mountains to be interesting. (You could take the train back from Yokokawa to Annaka to stay overnight, but I don’t like to backtrack and get into the hassle of a train.) I ended up staying in a “cottage” at an establishment called Kutsurogi no Sato. At the bottom left of the picture below you can see the reception building for this set of cottages; there are six or seven of them scattered around behind it. It didn’t really make much sense to stay there by myself because the smallest cottage has four beds, and they are meant to be self-catering, but I was making arrangements kind of last-minute and I didn’t want to deal with the hassle of arranging a pick-up. I think it would make a lot of sense for a group of people doing this walk as a weekend outing from Tokyo to stay at this place. You do need to bring your food (there does not seem to be a restaurant in walking distance); with advance notice they can provide the materials for a BBQ. I brought some ready-made stuff from 7-11, which was not gourmet or anything, but it got the job done. The best part is that it’s close to the walk, next door to an onsen (though it was closed the day I stayed there), and has a beautiful view.

I recently spent 10 days in Japan, mostly to visit old friends in Tokyo and Kanazawa. (I also gave a talk at the University of Tokyo.) I took the opportunity to do some walking on the Nakasendo, which is one of the two roads established in the early 17th century to connect Tokyo (Edo) and Kyoto. I spent a lot of time before my trip trying to figure out how to fit this in, and I wanted to share some of my experiences here partly as an aid to anyone else who is thinking about doing a short trip on the Nakasendo. I was able to find some useful blogs describing journeys from Tokyo to Kyoto (or vice versa), such as this guy who did the whole thing in two weeks, this guy from New Zealand who did it over a more reasonable three weeks, and this guy who has great GPS data on the various stages. The insane do-it-in-two-weeks guy also has a short but useful post for people considering a shorter trip, basically saying that the Kiso Valley is the best part. In addition to telling my friends about my trip, I hope that these blog posts might help someone in the future to plan a short excursion to walk the Nakasendo around Karuizawa, which I think is another excellent trip for someone interested in walking and history who has a few days.

Initially I had thought about spending four days walking; I would arrive somewhere from Tokyo, walk for several days, and then continue on to Kanazawa. I developed a plan for going to the Kiso Valley, which everyone seems to agree is the most scenic part of the Nakasendo. (In fact, in some places I found the section involving Tsumago and Magome described as the Nakasendo, ignoring the rest of the route.) My plan roughly was to take a train to Kani one evening (via Nagoya), stay there overnight, walk to the Daikokuya Ryokan (described in the Walking Fool blog), then to Nakatsugawa, then to Minshuku Koiji in Okuwa (described favorably in the Walking Fool blog), and then maybe as far as Kiso-Fukushima before taking a train to Shiojiri and onward through Nagano to Kanazawa. As it turned out, I ended up with really only two and a half days for walking, so I started to explore an alternative approach that would require less transit time.

The plan I actually implemented was to take the train from Tokyo to Takasaki and walk from there to Sakudaira, stopping overnight in Sakamoto and west Karuizawa. This definitely minimized my transit hassle — basically it was shinkansen to Takasaki and shinkansen from Sakudaira to Kanazawa (with a change at Nagano). So it was perfect for my particular constraints. This also makes it a nice itinerary for an outing from Tokyo: you could walk from Annaka to Karuizawa in a very pleasant two days, with one overnight in Sakamoto, or add another overnight and train back from Sakudaira.

In the next posts I’ll describe my walk in more detail, but let me just quickly say a bit more about what I think would be a good weekend outing from Tokyo: Leave Tokyo by train early on day 1 to arrive in Takasaki; from Tokyo Station it takes about an hour. If you are up for 20 miles of walking in a day, walk from there; for an easier day, transfer to the Shin-etsu line to Annaka and walk from there. Stay at Kutsurogi no Sato in Sakamoto if you have a group of 3 or 4 people; otherwise you might want to arrange a pickup near Yokokawa station or train back to Annaka to stay overnight there. On day 2, walk through the mountains over the Usui Pass, arriving at Karuizawa Shinkansen station early afternoon for a train back to Tokyo (with time to do touristy things in Karuizawa or climb up Hanareyama if you’re up for it). Or, for a three day journey, continue on and stay somewhere between Naka-Karuizawa and Oiwake (I stayed at Yuusuge Onsen Ryokan but I wouldn’t recommend it) and walk to Sakudaira Shinkansen station the next day, for a train back to Tokyo. This itinerary does involve some ordinary road-side walking; it is not unremittingly picturesque. And I also prioritized transit simplicity over other goals. But it also involves some lovely paths in the mountains, lots of historical interest, and plenty of places you won’t see on the usual tourist itinerary.

The figure documents changes over time in ANES respondents’ answers to a question about how often they “can trust the government in Washington to do what is right”. Basically the story seems to be that there are two periods: a high-trust period before Vietnam War and Watergate and a low-trust period afterward: before the late 1960s almost two thirds of respondents said they trust the federal government “most of the time” (and about one in six said they trust the federal government “just about always”); from the mid-1970s we have about 2/3 saying they trust the government “some of the time” and 1/3 saying “most of the time”. Recent peaks and troughs (judging by the proportion saying “most of the time”) roughly track the business cycle, though the Iraq War probably contributed to the large drop between 2002 and 2006.

I remember learning in Gary King’s Gov 2001 class back in the fall of 2004 (!) that we should really only ask about the effect of one cause at a time. That is, having run our analysis we should not expect to be able to infer the effect of more than one variable from it. I remember not really seeing why this was the case: why can’t we run a regression and learn about the effect of more than one of the variables? I thought I would record here some of my current thoughts on this question.

Let me start by noting that we can infer the effects of more than one cause at a time given the right dataset. Suppose we have run a conjoint experiment, i.e. we have conducted a survey in which respondents are asked to compare or evaluate items (political candidates, products, reform proposals) whose attributes are jointly randomly varied. (So, one respondent is asked to choose her preferred candidate between a young female banker and a middle-aged female teacher; another is asked to choose between a young female lawyer and an old male banker; etc.) Given this data, we can run a regression of the outcome (the respondents’ choices) on the attributes they were shown, and the coefficients on the attributes will tell us the effect of each treatment averaged over the joint distribution of the other treatments. (We can also estimate interaction effects, but that is more in the spirit of “subgroup analysis” in designs that are focused on only one treatment.) The randomization of a given attribute makes it possible to straightforwardly measure the effect of that attribute; it shouldn’t be surprising that we can randomize more than one attribute at once and thus simultaneously measure more than one effect at once.

In some very lucky circumstances we could also use standard methods of design-based inference to simultaneously measure the effect of more than one treatment in observational data. You could have an RDD where treatment A depends on whether the forcing variable is above threshold Z1 and treatment B depends on whether the same forcing variable (or another forcing variable) is above threshold Z2; one could use this situation to measure the effect of both A and B, where each effect is conditional on the value of the other treatment at the relevant cutoff. As with the conjoint experiment, you could of course study these effects totally separately, though combining the analysis may make it more efficient.

Actually, we can simultaneously get unbiased estimates of the effects of multiple treatments in any situation where each treatment is a function of a set of covariates X (but not the other treatments) and we can do covariate adjustment for X. This could mean a regression that includes all of the treatments and X (with the proper functional form assumptions); we could also create matched groups for each combination of treatments and measure effects by averaging across groups. Where the treatments are conditionally independent of each other and determined only by observable covariates, then for each of the treatments we can say that the value of the treatment is independent of the potential outcomes (which Angrist & Pischke call the conditional independence assumption, or CIA) and thus the effects can all be estimated in one regression.

So clearly “one treatment at a time” is not an epistemological law. Why might it still be a good guide?

The key point is that although there are circumstances where the CIA is met for more than one treatment at a time (as illustrated above), these circumstances are not common. The more typical situation in observational studies is that the treatments affect each other in some way. When that is the case (for example when treatment T1 affects treatment T2), we must choose a treatment. Why? To estimate the effect of T2, we need to include T1 in the regression unless we have reason to think that T1 has no effect on the outcome (in which case we don’t really care about estimating the two treatment effects anyway). To estimate T1, however, we don’t want to include T2 in the regression because it is “post-treatment” and thus a “bad control” in Angrist & Pischke’s terms. Because T1 affects T2, estimating the effect of T1 precludes estimating the effect of T2; we must choose one of the two effects to estimate.

I think post-treatment bias is probably the context in which Gary King was talking about the “one treatment at a time” rule in 2004, because he was thinking about post-treatment bias around that time. (I’d like to go back and look at that work.) It occurs to me now that the causal ordering rationale for the “one treatment at a time” rule also has a useful connection to the literature on mediation effects by e.g. Kosuke Imai that I would like to flesh out at some point.

Let me restate all of this a bit differently (and a bit more succinctly): We can estimate the effects of a treatment whenever the conditional independence assumption (CIA) is met, but outside of conjoint experiments it is unusual for the CIA to be simultaneously met for more than one treatment. Fundamentally this is because the world is complicated and the treatments we’re interested in typically affect each other. In these situations we might be able to estimate the effects of the various treatments of interest. (It depends on the plausibility of the conditional independence assumption in each case.) But the design will need to be different for each treatment.

I’ve recently been talking about experiments in a graduate research design course at Oxford. I was a little surprised when students responded by saying, “Right I understand that experiments are great, but how do I pay for one? I’m a graduate student.”

The first response to someone asking this question is that I’m telling you about experiments not just because it would be nice if you did one, but also because thinking about experiments helps you understand the challenges we face in observational studies. For any causal research question, it’s always good to ask what the ideal experiment would be; in design-based observational studies, you follow that up by saying why the ideal experiment is impossible and why the design you’ve chosen is a good approximation.

But I also want to address the practical question: how can you do an experiment when you don’t have a huge grant?

The answer I gave in last week’s lecture is that you can cultivate a relationship with a party organization, or NGO, or government agency, and offer a free evaluation if they are willing to work with you on design. Of course this depends on some luck and possibly some connections, but it’s important to remember that you might be offering something that they would otherwise have to pay a lot of money for.

Afterward I asked Florian Foos, who did a lot of interesting experiments while he was at Oxford as a graduate student, and he offered these observations:

• Graduate students are usually poor in money, but rich in another resource: time. How much of what a senior researcher would usually pay for can you do yourself, or in collaboration with other students?
• Not all field experiments need to be expensive: Think for instance of the many audit experiments conducted with legislators by Butler/Broockman etc.
• If you have a great idea, but lack funding (and a bit of know-how), it might be worth asking a faculty member (this can, but need not be your supervisor) if they would be interested in collaborating on an experiment.
• Think about what type of outcome you are interested in: Some behavioural outcomes can be collected without expensive post-treatment surveys. Administrative data is usually free of charge or available from government agencies for a small charge.

Also, you could always fake the outcome data.