Now there is some controversy (surprise!) about the ISU judging system especially coming from the Japanese populace. Many of the critics say that the triple axel is scored too low given the difficulty of the jump, and its rare use in competition. Now it has to be said that the base value of the triple axel actually increased in 2008 from 7.5 to 8.2 compared to the next difficult jump (lutz) which has stayed at 6.0. It looks like there is a big philosophical divide with skating fans in how much weight a “super-move” should be attributed. Evgeni Plushenko, the silver medalist for the men’s competition, has complained in various media outlets that the men’s competition shouldn’t be won without the quad. Elvis Stojko, a big quad jumper himself, who covered the Olympics through Yahoo! also complained about the lack of big jumps. This is not to suggest that quads and triple axels should automatically win a competition, but skaters are discouraged to throw difficult jumps into their programs — hence, resulting in a down-spiral of skating (jumping) techniques. In other words, risk (or courage) is not reflected within the current ISU scoring system.

This inherent bias of the ISU system is elucidated especially with the initial combo jumps of Kim and Asada. While Asada threw in the triple axel + double toe combination into her program, Kim implemented the triple lutz + tripel toe. Most skaters agree (ask Sasha Cohen) that the former combo is much more difficult. However, the base value of the triple + triple is 0.5 points higher. This is because the triple + triple combo requires six rotations whereas the triple axel + double combination requires five and a half. A rebuttal to this argument (?) is that it’s harder to (say) do one double axel (2.5 revolutions) compared to three single loops (3 revolutions) so revolutions shouldn’t be the only factor attributing scores to these jumps (and it’s not). The rebuttal to this rebuttal is that the triple axel + triple toe combination is more comparable and (appropriately) scored higher (so no one should complain about the scoring).

There are two interdependent reasons for the scoring of the jumps to change: GOE and the uneven difficulty in combo jumps. As seen in the most recent ladies competition, Kim Yu-na scored massive bonus points for her jumps presumably due to her unparalleled speed of her passes. Kim’s jumps look significantly more fluid and part of the program because she doesn’t lose any momentum even for difficult triple + triple combos. But the subjective GOE scoring doesn’t fare well for tougher jumps because you would have to jump higher. Asada threw in triple axels (first since Ito Midori) with only small bonus points, presumably because it’s not as smooth (speedy passes) as Kim Yuna’s jumps. Consider the fact that Kim’s jumps are smoother, and let’s say she has the ability to throw triple axels. Why would Kim attempt one anyway due to higher difficulty in getting bonus points (sacrifice speed for height), a higher risk (biggest deduction relative to base value if missed) and a disproportionately higher difficulty of connecting combo jumps. The last point, I would argue again, that the reduced speed coming out of triple axels prevent many skaters to do triple axel + triple combinations.

So I propose a tinkering of the scoring for combo jumps. I propose that in combo jumps, the first jump should be multiplied by the number of rotations of that jump divided by three:

What this does is, it theoretically offsets (IMO) the disproportionate GOE scoring in the current system where a difficult jump is not sufficiently scored. It is different from simply changing the base score of triple axels and quads since I think the differences in the scores of singular jumps are well-cushioned. I choose the number three as the divisor because I consider that the threshold for the number of revolutions between rare and difficult jumps. One may choose the 3.5 as the divisor for the men. A divisor of anything less would force skaters to combo everything, and anything higher would make skaters not do combos except for the most difficult jumps. BTW this adds about (8.2*3.5/3 – 8.2=) 1.366… points to Asada’s scores pre-GOE, which would put it higher than any non-axel triple + triple combo.

What this multiplier does, is that it rewards skaters for pushing the limits (what Plushenko, Stojko and the entire country of Japan argues for). Furthermore, this ideally prompts skaters to not to “succumb” in becoming a rationalist, as opposed to continually improving the overall level of skating.

But even with (what I think) a proper offset to the scoring system, I don’t think Asada can achieve the level of skating of Kim Yu-na. I’d like to analogize the supposed rivalry between Asada and Kim with a couple of NBA players.

Jason Richardson (shown left) is a two time dunk champion with a couple (?) appearances to the all-star game. Known for his decent scoring skills, big size (for his position) and most of all by his big hops.

When Jason Richardson played for the Golden State Warriors, there was a few years where he played alongside Gilbert Arenas. Arenas is known for his incredible forward speed and scoring abilities with great ball-handling skills.

Both guys are incredibly athletic, very fun to watch and can take over a game when things are going good (scoring wise). Richardson with his high release point, can tower over other shooting guards. Arenas can cut through the best defense like butter. Yes, JRich is like Ma0-chan and Agent Zero is like Queen Yu-na.

The biggest difference between the two is speed. JRich has high hops and good lateral movement but those skills translate better in dunk contests. His forward speed is actually very slow so he struggles to get by defenders even when he has good ball handling skills. On the other hand, Arenas doesn’t have fancy dribbling or dunking skills. He always takes the shortest route to the basket and takes advantage of safe scoring opportunities (get fouled or layups). In high pressure situations (end of fourth quarter), the ball always went to Arenas instead of JRich because of Arenas’s reliability thanks to his speed and penchant for shots in close proximity to the basket. A crazy windmill dunk or a big fadeaway by JRich is still only two points.

Of course figure skating is a game that is sort of between a dunk contest and a basketball game. The windmill dunk does score higher than a normal one. Asada, like Richardson, has good lateral movement (complex step sequences) and a springy body (high altitude jumps with quick rotations). I believe it is key for Asada to improve her speed and conditioning (all her non-axel jumps get lower grades than Yu-na’s) to stray away a little from her strengths to compensate for her weaknesses. Watching videos of 15-year old Mao-chan you can already see some decline in her jumping abilities. The current version looks a little heavier and now with a more complex, tiring program, we’ve seen some mistakes that you don’t usually see in practice. A 23-year old Mao I don’t think can compete at the current (really high) level with “just a triple axel.” Just like when Richardson lost a step or two, he couldn’t turn into an efficient ballplayer like Arenas, I think it will be a difficult transition for Mao as well.

Last thought: Why do Asians do better in figure skating? Flexibility? Discipline? Money?

How about cuz figure skating is formulaic.

A metric based on emissions rates (CO2 output / year) is largely dependent on the emission rates it is based on. The current “consensus” seems to be 1990, but tuning this number can easily result in varied emission targets even with the same percentage reduction. This gives countries leeway in fudging numbers to their advantage. Metrics based on population (pdf) can provide moral elegance in CO2 emissions share — every human being has a right for its share of carbon. But practically, a pure population approach gives low population, low wealth countries in a vulnerable spot, while the share of the low population, high wealth countries get distorted (Posner and Sunstein 2008). The urgency of the problem is underwhelming for wealthy countries, which creates a divide in what is an egalitarian emissions distribution.

Due to this inherent bias in international talks leaning towards richer countries, many propose a green fund to flatten out this inequality (e.g. Stern Review), and governments have followed with this proposition. But still, metrics are deliberately chosen such that rich, more powerful countries can protect their economic interests. For example, the proportional carbon budget for the U.S. is higher based on cumulative carbon share compared to GDP or population (UCS 2007); because cumulative carbon share is high, they use the carbon emission rate relative to a recent year (1990 or 2005). This excuses the U.S. to forgo CO2 emissions in the past, even though carbon stays in the fast cycle for hundreds of years — a minimum bound for reduction efforts.

Developing countries on the other hand, have proposed stronger targets and metrics that are more favorable to them (like CO2 / GDP). China and India are seeking refuge from the emissions rate formula because they are fast growing, and it penalizes them of not having a saturated (i.e. high) level of CO2. A qualitative distinction between emission cut proposals of developed and developing countries can put developing countries into an advantage (although I think it should be split into three: wealthy, developing and poor).

In any case, these formulas need be complex to take into account bunch of the factors mentioned above (e.g. Frankel Proposal). However, by my understanding (please correct if wrong), cumulative emissions are taken lightly. Cumulative emissions are looked at post-hoc; meaning, the economics of the feasibility of emission cuts are taken into account first, and then cumulative CO2 reductions are calculated. Therefore, in international negotiations, the pie of emissions cuts get distributed with respect to financial constraints (not necessarily a bad thing).

When priorities are environment > carbon busting businesses, cumulative emissions should be the basis of negotiations since it directly affects climate change. CO2 staying in the atmosphere for a long time is a primary reason for this. If we presume that 450ppm is the threshold of CO2 needed to avert catastrophic effects, once the CO2 concentration goes above that number, you basically have to wait years for the CO2 to dilute. The CO2 emitted need to be of pre-industrial levels to lower the concentration, and that would be very difficult to obtain since the world emitted to 450ppm in the first place.

In addition, the distribution of global temperature increases due to CO2 and other greenhouse gases has a distribution with a huge upper tail due to much stronger positive feedbacks than negative ones (Roe and Baker 2007). The graph on the right is reminiscent of the model results. Let’s say 2 degree Celsius increase is the median at year 2100 given a CO2 emissions scenario. Then the mean would be higher than 2 degrees, with legitimate prospects of 4+ degrees warming. Every little bit of CO2 added has stronger and stronger effects on the climate.

Emission cuts per year metrics can hide these real effects on climate. I created a simple model of CO2 accumulation to illustrate this. This basically calculates accumulated CO2 given a reduction scheme and a constant CO2 dissipation.

where:

• ~ total CO2 accumulated till year n
• ~ initial total CO2 amount before starting reduction
• ~ fraction reduced relative to baseline CO2 emissions / year
• ~ baseline CO2 emissions / year (made constant)
• ~ lag, or number of years till CO2 dissipate
• ~ fraction of CO2 dissipating at year n-L

Let’s say CO2 emissions rates start and end (say 80% reduction) at the same amount. But even with these equivalent boundary conditions on emissions rates, the accumulated CO2 levels are path dependent. One could posit that since efficiency of green technologies get better and cheaper over time, we should increase the rate of reduction later to minimize the costs given that climate change won’t affect us for another 100 years. This is the exact same thing that the Aso Government of Japan argued not far long ago.

I chose semi-arbitrary numbers for the above graph. It is clear that the accumulated CO2 is higher for a path that starts to reduce slow. Since the CO2 is in the atmosphere for long, the speed of the reductions matters. In respect to the efficiency, it is probably true that the costs of green technologies decline, but I think it’s also true that the speed of overall efficiency improvements can be increased by acting early. This is (I think) because efficiency improvements are largely a function of mass deployment of green tech. (that replace carbon emitters), instead of innovations to reduce efficiency of green technologies even further.

Below is the graph where I varied with respect to the difference in accumulated CO2 in the “action” plan and the “delay” plan. This parameter is a proxy for the speed that CO2 dissipates. Higher the value of the parameter, the faster CO2 disappears like in other gases (e.g. water vapor).

The exact numbers are not really important, but we get a qualitative sense of how problematic “sticky” greenhouse gases tend to be.

Here is another thought experiment when we forego past CO2 emissions. Let’s say there is two countries in this world: U.S. and China. Then assume that U.S. industrialized early which produced massive amounts of CO2, way more than China. Then after international AGW talks, they try to reduce CO2, but China breaks away. U.S. still goes on and reduces it’s CO2 emissions, while China increases (both at the same pace/slope of 5% / year). Then assume at the talks, the CO2 emissions rate was 3:1. The equations for the emissions rate after the talks are the following:

We could say ooh! after 20 years, emissions rate for the U.S. will be under China, U.S.A! U.S.A! Especially good because each CO2 added is more harmful for recent years. But looking at the cumulative emissions, things look completely different:

The green line is the difference of the cumulative emissions between the U.S. and China. It is also the most important measure to determine effect towards climate change. When the U.S. rate meets the China rate, the U.S. hasn’t accounted for its past emissions. In equation form (since international talks):

This means that it takes 40 years to equalize U.S. and China cumulative emissions since the talks, and additional 20 years to equalize U.S. and China since the start of industrialization given the same reduction/emission pace (hence, equalization of climate change contribution).

Metrics based on cumulative CO2 are path independent (Zickfeld et al. 2009) and also a more appropriate measure for global warming contributions. This also means that the developed countries have a much bigger portion to reduce that may or may not be economically realistic. The unfortunate disconnect (environmental, economic) between developed and developing prompt other experts to explore other options aside from cap-and-trade (Cramton and Stoft 2010).

Refs:

1. Roe, G., & Baker, M. (2007). Why Is Climate Sensitivity So Unpredictable? Science, 318 (5850), 629-632 DOI: 10.1126/science.1144735
2. Zickfeld, K., Eby, M., Matthews, H., & Weaver, A. (2009). From the Cover: Setting cumulative emissions targets to reduce the risk of dangerous climate change Proceedings of the National Academy of Sciences, 106 (38), 16129-16134 DOI: 10.1073/pnas.0805800106
3. Peter Cramton and Steven Stoft. Price is a Better Climate Commitment. Paper provided by University of Maryland, Department of Economics – Peter Cramton in its series Papers of Peter Cramton with number 10pbcc. 2010.
4. Eric A. Posner, Cass R. Sunstein. Justice and Climate Change. Discussion Paper 08-04, Harvard Project on International Climate Agreements, Belfer Center for Science and International Affairs, Harvard Kennedy School. September 2008.
5. Union of Concerned Scientists. A Target for U.S. Emissions Reductions. 2007.

Japan Research Institute (JRI) ran a study and showed that in addition to the reasons stated above, the major cities are in overcapacity and cannot handle a higher number of “immigrants” from within the country. They showed that from 2000 onward to late 2007, there was a massive migration towards Tokyo to find work. This isn’t surprising because the job to job seeker ratio is much higher for bigger cities. But from late 2007 to now, there was a precipitous drop in migrants to Tokyo, while there was an increase in people residing overseas (especially the young population). What’s also interesting, is that many people move towards a bigger city in proximity to look for work; but these mid-sized cities are getting overwhelmed, and eventually, people end up in Tokyo.

I decided it would be interesting to plot this data (総務省) in animation form. With code from Aoki Shigenobu (青木繁伸), the following is the cumulative migration of each Prefecture zeroed at 1991 1990 (with a couple transformations to make it work). The darker colors represent higher migration, and the lighter colors as negative migration (hope the vid works!).

Pausing it at 2009, we could easily see that the Prefectures with big cities attract the most workers. Much of the northeastern bloc hasn’t attracted much at all, whereas the Kyushu go to Fukuoka. I list the Prefectures with the highest number of imports below:

# [1] Tokyo Kanagawa Saitama Chiba Aichi Fukuoka
# [7] Shiga Ibaraki Hyougo Tochigi Mie Okinawa
#[13] Miyagi Yamanashi Nara Gunma Kagawa Tottori
#[19] Okayama Nagano Toyama Tokushima Ishikawa Kouchi
#[25] Fukui Saga Ooita Shimane Shizuoka Kumamoto
#[31] Wakayama Miyazaki Gifu Kagoshima Ehime Yamagata
#[37] Yamaguchi Kyoto Iwate Hiroshima Niigata Akita
#[43] Fukushima Aomori Nagasaki Hokkaido Osaka

The first four Prefectures are from the same region, and the fifth is Aichi, another big population center. Curiously, Osaka ranked last.

I also plotted the trend in Japanese people living in a foreign country (外務省). I plotted this as a bar plot, while showing the marginal change in population for Tokyo and non-Tokyo big cities. Sorry the axes for the bar plot and line plot don’t match up properly (bad software!).

As was reported by JRI, we see a monotonically increasing population that is going overseas. Tokyo shows a massive export of its population in the 90s, but imports a massive amount subsequently. The other big cities have a vaguely similar trend, but the absolute amount of migrations pales in comparison to Tokyo. The 2009 foreign residents data is still missing, but it is expected to increase relative to 2008.

When people are leaving their native areas, it is because it’s more advantageous to live somewhere else. In this case, a population movement towards the big cities and subsequently(?) out of the country, it evidences less than optimal conditions. We saw the rise of a bunch of recruiting/employment agencies during the late 90s to accommodate an unstable workforce. Similarly, there will probably be a rise in agencies to deploy people to work overseas. Will the rising trend continue? Would it be ultimately be beneficial to socially diversify the population? Would the economy just shrink due to unattractive opportunities domestically? We will see.

Refs:

1. 日本総合研究所(Japan Research Institute) 調査部　ビジネス戦略研究センター。新たな成長地域を求める、わが国の人口流動 －人口流動の「停滞」を超えて 。2009年11月10日 。
2. 外務省。平成20年の海外在留邦人数調査統計。
3. 総務省。住民基本台帳人口移動報告 平成21年結果（統計表）－移動者数の状況－。

The structure of 2channel is characterized by its anonymity, the omnicompetence of its discussion topics, and its speed (both its interface and the ability to post news stories). Its anonymity is well known, as most people don’t even use pseudonyms to opine behind the walls as nanashi (名無し), or nameless. The anonymity helps broaden its influence in Japan for two reasons: the liking of privacy (or a dislike of publicity, depending on POV) and the lack of constructive online platforms independent of 2ch. The former is self-explanatory, while the latter is as such because there is a mismatch between the society’s communicativeness and its available infrastructure. 2channel can be partially looked at as a compromise between a serious online discussion and no online discussion; where 2ch anonymity is a compromise between an online presence and no online presence. Combine this with a constant forum to discuss news, a light website design (more dynamic and exciting) and the breadth of topics covered, 2ch has become a nationwide media presence.

Understanding the structure, and hence, the dynamics of 2ch interactions can potentially elucidate significant factors that has an effect on user and community behavior. To understand certain characteristics of 2ch interactions, Matsumura et al. (2005) collected and analyzed data on 2ch threads. This is no easy task because the forum is so goddamn large, but they managed to grab data on 5,748 threads (including 1,738,418 messages in total) classified into 30 categories. They explicitly defined eight measurable indices (variables) to characterize threads:

contents ~ Measures the average message size (jargons, emoticons, and ASCII arts are unaccounted) in a thread to know the depth of discussion

activity ~ Measures the number of messages in a thread to know the activity of discussions

interaction ~ Measures the proportion of messages replying to previous messages with ‘‘>’’ mark to know the tendency of interaction between people

speed ~ Measures the average number of messages posted per day to know the intensity of discussions

jargon ~ Measures the proportion of jargons specific to 2channel used in a thread to know the consciousness of people

ASCII art ~ Measures the proportion of ASCII arts specific to 2channel to know the consciousness of people

nameless ~ Measures the proportion of messages posted nameless in a thread to know anonymity of discussion

ABON ~ A coined word in 2channel with which administrators replace wrong messages such as disclosure of personal information, measures the proportion of messages including ‘‘ABON’’ in a thread to know the disturbance of 2channel

The data is standardized so that it is comparable across variables. I plot the data below so that the whiter areas are higher values and the redder areas are lower values of each metric with respect to each of the 30 categories.

Unfortunately, the categories weren’t described in the article so I can only be semi-sure of what each of the mean. For example, one of the categories is “Commentary,” which I assume to be the broadcast (実況) threads where users comment on ongoing (say) TV programs at real time. From this interpretation, it goes with intuition that the “speed” and “activity” of the threads are high. The “ABON” rates are high for the “Adult” category for reasons I won’t say .  The “ABON” rates are supposedly the biggest indicator of flaming, and it is high in “Society,” where they might discuss China and South Korea. Another interesting tidbit is that, “interaction” rates are high for the “Academic-science” category, whereas it is about average for “Academic-humanity.” The “activity” is also low for “Academic-science,” so it might hint that there exists a few people discussing issues in a constructive manner (humanities folks are incapable of such ).

Anyways, they did a factor analysis to study the relationships (correlations) between each variable, and validated their results by structural equation modeling (SEM). One of the things that they concluded was that anonymity (“nameless”) positively affected the activity of communication (“activity”) and negatively affected the depth of discussion (“contents”). They measure “nameless” by the proportion of pseudonyms used (vs. no pseudonym) and it stimulated postings, but degraded the discussion.

I plotted the “nameless” variable to the above variables for illustration.

Although this graph gives different quantitative results from the SEM, qualitatively, it is the same. We also see that the strength of the anonymity status in 2ch is very weak (not significant). Anonymity does help a little bit in the activity and lowers the depth of the discussion, but considering 2ch culture, it really is not a significant player.

Though this research did not give out any novel results, it does help to quantitatively lay out the heterogeneity in the discussion within 2ch. Some people tend to instinctively categorize 2ch as a flaming arena (part of its reputation deserved), but there is some decent, informative threads out there. Fairly recently, there was a pseudo-political debate show (太田総理) that proposed to limit internet usage to one hour (!) since the “internet” has debilitating effects on imagination, creativity, etc. It’s only an opinion of an archaic TV dude, but when a decent part of the population makes synonymous the “internet” and gloomy parts of 2ch, it makes it harder for a society to embrace a more mature, cultured online platform.

Refs:

1. Matsumura, N., Miura, A., Shibanai, Y., Ohsawa, Y., & Nishida, T. (2004). The dynamism of 2channel AI & SOCIETY, 19 (1), 84-92 DOI: 10.1007/s00146-004-0302-5

Since OECD collects data on self-employment, I decided to plot it below (OECD 2009).

Few of the countries show much higher self-employment rates like Turkey, Greece and Mexico. The actual numbers that the OECD gives can be debated, but in general, the smaller economic powers tend to have higher self-employment rates. This could be due to plenty of reasons, but one could conceive larger corporations having less influence on employment prospects.

However, what I really wanted to look at were entrepreneurship rates. Entrepreneurship is an important component of any economy. It provides competition and fills gaps that are not met by big businesses and governments. The Global Entrepreneurship Monitor (GEM) collects this data internationally. What differentiates GEM data from other entrepreneurial data is that they look for not only new businesses, but individuals that are planning to start a business (“Nascent Entrepreneurs”). This enables to grab a more comprehensive outlook on both entrepreneurial ability and intent (GEM 2009).

Below I showed the nascent entrepreneurs (defined as “…actively involved in setting up a business they will own or co-own; this business has not paid salaries, wages, or any other payments to the owners for more than three months”) for each country (GEM 2005).

The graph shows a differing view than the self-employment rates. Some countries (U.S., New Zealand, Iceland) had very low-self-employment rates but high entrepreneurship intent (and activity). On the opposite end, Japan, Sweden and Denmark had both low entrepreneurship and (mid-to-low) self-employment rates.

Looking at this in relative terms, I listed the ratio of self-employment rates to nascent entrepreneurship. This is only one data point, so not much can be said, but a partial reason for Japan having high values is that a relatively large part of the population is “dynastic” (Horioka 2001). Meaning, if one’s parents have their own business running, there is a high probability that the child would succeed its operations without going through the whole entrepreneurial thought process. There may be other effectively similar customs for Hungary, Brazil and China.

The GEM (2005) dataset had a number of entrepreneur related survey questions. All questions were answered yes or no, and the percentage of yes is collected for each country. In general, a higher yes percentage implies that the country has higher entrepreneurial spirit/intent/ability, etc. After standardizing the data into a z-distribution (to make comparable across questions), I decided to illustrate all of this data using a heat map. The redder spots implies relatively less approval, while the white spots indicate higher approval.

I listed the countries according to the same order as the number of nascent entrepreneurs above. Because we see a gradient from red to white, not surprisingly, low nascent entrepreneurship correlates with less approval of the questions asked. For example, NBSTAT05 asks, “In your country, those successful at starting a new business have a high level of status and respect?” where only 52% of Japanese and 35% of Hungarians answer yes (contrast to 61% for U.S.). Another question asks (SUSKIL05), “You have the knowledge, skill, and experience required to start a new business?” and Japan answers yes 13% of the time, whereas the U.S. answers yes 52% of the time. The code for the questions are included in the GEM (2005) dataset as a .pdf file.

In conclusion, low self-employment rates doesn’t necessary imply low entrepreneurship. Entrepreneurship rates and GDP growth are correlated; thus, it would be advisable to build a framework to maximize entrepreneurship as one of the wedges of economic recovery.

Refs:

1. OECD Factbook 2009. Self-Employment Stats.
2. GEM 2009 Global Report.
3. GEM 2005 MASTER DATA SET.
4. 池田信夫. 希望を捨てる勇気―停滞と成長の経済学. ダイヤモンド社 (2009/10/9).

Even though the recent bout of drug scandals haven’t included marijuana in the mix (aside from sumo), it’s also the drug that has the most compelling case for legalization. Let’s take a peek at the history of marijuana regulation in Japan.

Tradition

Not surprisingly pre-regulation, marijuana production was fairly common in Japan. Since the Jomon era, Japan has had a long tradition of integrating marijuana within culture, art and rituals. As one of the oldest crops, hemp had a robust set of uses, starting from basic material (clothes, ropes, strings, yarn, Japanese paper, oil), the hemp seeds were were used as food (shichimi, inari) and an important component for special occasions (rope for Yokozuna, ofuda at shrines, ropes that hang from shrines, burned hemp leaves at weddings). Even samurais supposedly used marijuana for inspiration. Hemp has been mentioned as a medicinal plant in pretty much all writings of drug and medicine (Chinese herb medicine, religious proceedings in Egypt, as various medicine in India), as its uses were later imported to Europe. In 1886, Japan listed hemp within their Pharmacopoeia, and frequently used it as medicine and painkiller.

Although also historically used in Buddhist rituals, marijuana is deeply integrated in Shinto rituals. To keep away demons and impurities, they made shide out of hemp plants. They also burned hemp leaves to create a holy fire — goma-daki. They made oonusa out of hemp leaves as well in the purification process. This process was also called, taima, the word for hemp/marijuana. There are also pics depicting geisha smoking what is believe to be marijuana (see above; hat tip: Marijuana Observer).

Ahh, maybe the gaijin sumo wrestlers wanted to keep away the demons by smoking pot instead of scattering salt! How’s that for the sumo association being ironical.

American Intrusion

Hemp used to be strongly linked to Japan both culturally and economically. The only thing that could break this bond was WWII and its subsequent regime change on Japan.

The U.S. also used hemp products fairly liberally in textile, medicine, oil and forestry industries. The following are the build-up of events that lead to demonizing and criminalizing of marijuana in Japan.

1. 1603, in Japan, tobacco was made illegal, repealed in 1639
2. around 1900, opiate addiction became known worldwide and a problem in the U.S.
3. early 1900s and beyond, medical advancements such as injections and morphine and other synthetic drugs made marijuana obsolete (as “natural” cures weren’t favorable back then)
4. early 1900s, Americans built much tension to Mexicans-americans
5. 1910, Mexican War spilled over to the U.S. border
6. 1913, CA passed first state anti-marijuana law where “locos” resided
7. 1914, Harrison Narcotics Tax Act, regulated and taxed cocaine and opiates
8. 1915, Utah, the first state to pass anti-marijuana law, which lead to a series of states banning it
9. 1929, start of the Great Depression
10. 1930, establishment of the Federal Bureau of Narcotics to execute the Harrison Act, where Harry Anslinger became the 1st commissioner
11. 1933, America repeals prohibition
12. Anslinger co-oped with William Randolf Hearst, an owner of a huge chain of newspapers. Also hated Mexicans and didn’t want hemp paper to be published. Also, lying about Mexicans sold papers
13. A series of anti-marijuana, anti-Mexican, anti-black propaganda come out. Latinos and black jazz musicians were a large part of the marijuana user group
14. 1935~1936, anti-marijuana movies come out: Marihuana: Assassin of Youth; Reefer Madnesss; Marihuana: The Devil’s Weed
15. The oil (plastic), synthetic fiber and forestry industries labor hard for a tax on marijuana
16. Anslinger brought anti-marijuana legislation directly to the White House, using Hearst’s gauntlet media and the specified use of the term marijuana instead of cannabis or hemp
17. The medical science community is completely skipped over in discussions, even though it was scientifically clear that marijuana wasn’t getting a fair treatment
18. 1937, Marijuana Tax Act passes
19. World War II.
20. Japanese Gov. distributed speed to soldiers and factory workers for encouragement
21. The Japanese attack on Pearl Harbor shut off foreign supplies of “manilla hemp” fiber from the Phillipines.
22. This gave rise to “Hemp for Victory” movement that temporarily stimulated the hemp industry (for military equipment) with collaboration from the Gov.
23. After War, Gov. quickly shuts down hemp industry
24. 1945 Potsdam declaration
25. 1948, Cannabis Control Act (bans the import, export, cultivation, sales and purchase of marijuana buds and leaves) in Japan still under command of General MacArthur, the reason? because U.S. thought it was natural to do so
26. Japan convinced the U.S. side to permit local gov. to give out licenses to use hemp for fabrics; 90% of commercial hemp is in Tochigi
27. 1952, speed banned in Japan
28. 1954, speed banned in U.S.
29. 1970 Comprehensive Drug Abuse Prevention and Control Act. Marijuana is classified as a Schedule 1 drug plus marijuana and hemp are not distinguished
30. two decades passes in Japan after the Cannabis Control Act w/o much fervor until hemp fields are found by protesters of the Vietnam War
31. 1985, Japan’s Supreme Court ruled that there were dangers in marijuana

In the U.S., anti-marijuana progressions were due to both economic and social factors, which speaks highly of the plasticity of hemp itself. But because of the economic and social double whammy on marijuana usage, the distinction between marijuana as material and marijuana as a drug became fuzzy. The only thing standing between criminalization was scientific opinion, but that was effectively muted out.

Similarly in Japan, rational discussions on marijuana were nil to none (except for the compromise on permits), and the citizens still today have a large stigma attached to anything categorized as mayaku (general word for “bad” drugs). Because the U.S. hadn’t banned speed by WWII, U.S. (hence, Japan) let go on regulations on speed for a few years. Usage of speed increased precipitously post-war, and Japan made regulations after markets were established (remember, the Gov. itself distributed it during the War). Now major players of yakuza control the black markets of speed (an expensive, profitable drug). Currently, speed is a much larger drug problem in Japan (speed usage is comparable to other countries, whereas marijuana usage is much lower). What needed to be regulated wasn’t regulated, but what didn’t need to be regulated was regulated.

While, alcohol and tobacco kills 2 million and 5 million respectively worldwide (marijuana = 0), and speed is a powerful synthetic drug similar to the effects of cocaine, marijuana’s positive medicinal, religious, economic effects along with debatable inverse relationships with “hard drug” usage rates and contributions to yakuza, marijuana has a compelling case for a nuanced legalization. Informed U.S. citizens get this, as marijuana legalization was the top query during Barack Obama’s internet townhall meeting because there is an obvious answer (not because internet users are dope). Japan still are spouting talking points that were discredited by science decades ago.

Refs:

1. Jack Herer. Emperor Wears No Clothes. Quick American Archives. January 1998.
2. 武田邦彦. 大麻ヒステリー. 光文社. (2009-06-17).
3. 大麻取締法変革センター (Taima Torishimari-Ho Henkaku Center | Cannabis Control Law Reform Center) (THC).

The feeling of loneliness (as with other feelings), defined as “a discrepancy between one’s desired and achieved level of social contact,” is difficult to interpret through different cultural lenses. Like in Cacioppo’s book, not only there are genetic differences and differences in their biological reactions for each individual when facing loneliness, there are differences in how people translate these feelings outward. If one uses the UCLA loneliness scale as a measure to quantify feelings of loneliness, the answer to the question, “How often do you feel completely alone?,” may differ because of heterogeneous assumptions in what is considered a “social contact” in Japan (or Finland or Germany or wherever). The United States, statistically, has the most friends per capita of any country, but they might have a slighter lower threshold of “friend,” resulting in a positive bias.

In Japan, or generally collectivist cultures, one could hypothesize that with the prevalence of highly structured social organizations, loneliness may not be much of a factor; conversely, idiocentric societies (like the U.S.) may promote loneliness due to a stronger preference for independence, competition and hedonism. Furthermore, collectivist cultures may increase loneliness with a lower sense of self especially if the fit and adaptation to the ingroup suffers; even within the ingroup, people in collectivist cultures may not feel as connected with their colleagues because relative to idiocentric cultures, it doesn’t require as much effort to fit in. Generally speaking, as societies become more complex (i.e. more industrialized), there exists more of these social organizations with lower barriers of entry and exit, and people become more independent (Triandis et al. 1988) — Japan is not an exception (one metric is to look at number of NGOs).

There are several indicators that Japan has higher levels of loneliness (all refs included in Schumaker et al. 1993):

1. excessive adaptation to the social environment can inhibit autonomy
2. Japanese undergraduates have higher levels of neuroticism
3. loneliness and life satisfaction is inversely related
4. low job satisfaction of employees
5. job satisfaction can be positively correlated with occupational self-direction and control
6. Japanese undergraduates are conforming, reserved, introverted, non-disclosing and more self-conscious, all factors lead to loneliness
7. low levels of religious belief and community involvement

As far as I know, there isn’t a convenient “loneliness indicator” that one can extract from an online database (aside from patches of research). But there is tons of research that shows loneliness is positively correlated with heart diseases. Even controlling for diet, weight and other physical attributes, increased rates of loneliness lead to developing heart diseases. Loneliness also leads to increased drinking, eating, smoking and less exercise, factors that all have negative effects to your heart. But in addition, what loneliness does is produce higher levels of cortisol, a hormone released in response to stress. Cortisol is actually used to protect from an incoming heart attack. But loneliness forces people to emit cortisol perpetually, so the body can’t distinguish the warning from a heart attack to loneliness. Loneliness is different from depression in that loneliness makes you try to get away from solitude, whereas depression makes you apathetic. The former requires more effort and can contribute to more wear and tear.

This is shown in a cross-cultural look at heart disease rates. The figure to the right shows heart disease rates for different cultures (Triandis et al. 1988). We see that even with people of the same race (Caucasian), growing up in different cultural backgrounds can have a huge effect on heart disease rates, and theoretically, loneliness. This set of data doesn’t control for diet so it may not be the perfect indicator for loneliness. Marmott and Syme (1976) actually did control for diet and body attributes to conclude that Japanese in Japan have significantly lower heart disease rates than Japanese Americans (although they didn’t control for Omega-3 intake, another predictor for heart diseases). The significant difference between Japanese and Japanese Americans is derived from “culture” but not necessary loneliness.

So both heart disease rates and happiness are both correlated with loneliness. So I decided to look at some data on heart disease rates versus levels of happiness. In theory, higher happiness should be relate well with lower heart disease rates. Happiness is also a proxy for “how good do you feel about yourself,” whereas heart disease rates is a proxy for “actual consequences from how you felt.” I used happiness data from OECD’s recent data and heart rate data from the WHO.

Take any happiness indicator, and you see that Japan has low heart rates relative to their well-being. Several inferences can be tentatively deemed from this:

1. even with low happiness, Japan is robust against effects on health (Japan still has the highest life expectancy)
2. it’s hard to detect Japan’s happiness (or loneliness) just from these data (Japan hiding feelings may underbias happiness stats)
3. all wealthy countries have low heart rates, Japan is just really unhappy for whatever reason; and wealth is largely related to happiness up to a point (Frey and Stutzer 2002)
4. loneliness is not a factor, but something else is depressing the happiness; therefore, this graph isn’t informative for studying loneliness
5. sushi is good for you
6. it may advantageous to have a loneliness personality

From a loneliness perspective, high loneliness (=> low happiness) hasn’t translated to debilitating effects on health (=> low heart disease rates). Of course this assumes these correlations are correct and longitudinal studies are required to look at this issue more carefully. Schumaker et al. (1993) has done a clinical study on Japanese and Australians to conclude that Japanese have higher loneliness (relative to Australians) but loneliness doesn’t correlated as strongly with happiness for the Japanese. So we might say that high loneliness isn’t the main factor that is affecting health (aside from high suicide rates!).

Refs:

1. Marmott M. and Syme S. L. 1976. ACCULTURATION AND CORONARY HEART DISEASE IN JAPANESE-AMERICANS. American Journal of Epidemiology Vol. 104, No. 3: 225-247.
2. Triandis, Harry C.; Bontempo, Robert; Villareal, Marcelo J.; Asai, Masaaki; Lucca, Nydia. Individualism and collectivism: Cross-cultural perspectives on self-ingroup relationships. Journal of Personality and Social Psychology. Vol 54(2), Feb 1988, 323-338.
3. Schumaker JF, Shea JD, Monfries MM, Groth-Marnat G.J Psychol. Loneliness and life satisfaction in Japan and Australia. 1993 Jan;127(1):65-71.
4. Bruno S. Frey, Alois Stutzer. Happiness and economics: how the economy and institutions affect well-being. Princeton University Press, 2002.