09:14
TED2009

Nate Silver: Does racism affect how you vote?

ネイト・シルバー:人種は投票に影響を及ぼすか?

Filmed:

ネイト・シルバーが政治における人種についての物議をかもす疑問に答えます。果たしてオバマの人種は一部の地域で彼の得票数を削ぐことになったのか? 統計と俗説のぶつかり合うこの魅惑的な講演は、街づくりがいかに寛容さを促進させうるかという驚くべき洞察で締めくくられます。

- Statistician
Math whiz and baseball fan Nate Silver was mainly known for predicting outcomes in fantasy ballgames -- until his technique hit a home run calling the outcome of the 2008 election primaries. Full bio

I want to talk about the election.
選挙について話そうと思います
00:15
For the first time in the United States, a predominantly white group of voters
多数の白人有権者が アフリカ系アメリカ人の
00:18
voted for an African-American candidate for President.
大統領候補に投票したというのは 初めてのことでした
00:21
And in fact Barack Obama did quite well.
実際オバマはかなりよくやりました
00:24
He won 375 electoral votes.
選挙人投票で375票を獲得し
00:26
And he won about 70 million popular votes
そして一般票では約7000万票という
00:28
more than any other presidential candidate --
人種や政党にかかわらず
00:31
of any race, of any party -- in history.
史上最多の得票をしたのです
00:33
If you compare how Obama did against how John Kerry had done four years earlier --
オバマの状況を4年前のジョン・ケリーと比べるなら
00:36
Democrats really like seeing this transition here,
この推移に見てとれるように
00:40
where almost every state becomes bluer, becomes more democratic --
ほとんどの州がより青く より民主党寄りになっています
00:43
even states Obama lost, like out west,
西部のようなオバマが落とした州でさえそうです
00:47
those states became more blue.
南部でも北東部でもほとんど全ての州で
00:49
In the south, in the northeast, almost everywhere
青くなっています
00:51
but with a couple of exceptions here and there.
そこここにある いくつかの例外を除いて
00:54
One exception is in Massachusetts.
例外の一つはマサチューセッツ州ですが
00:57
That was John Kerry's home state.
ジョン・ケリーの本拠地なので
00:59
No big surprise, Obama couldn't do better than Kerry there.
オバマが下でも驚くことはありません
01:01
Or in Arizona, which is John McCain's home,
それにマケインの本拠のアリゾナ州でも
01:03
Obama didn't have much improvement.
オバマは票を増やせませんでした
01:05
But there is also this part of the country, kind of in the middle region here.
他方で この国の中央に位置する
01:07
This kind of Arkansas, Tennessee, Oklahoma, West Virginia region.
アーカンソー テネシー オクラホマ ウェストヴァージニアの辺りも駄目でした
01:09
Now if you look at '96, Bill Clinton --
96年のビル・クリントンは
01:13
the last Democrat to actually win -- how he did in '96,
民主党が前回勝利した時ですが
01:15
you see real big differences in this part of the country right here,
まさにこの地域で 実に大きな違いが見られます
01:18
the kind of Appalachians, Ozarks, highlands region, as I call it:
アパラチアやオザークといった 高地地域です
01:21
20 or 30 point swings
96年のクリントンと 08年のオバマの間で
01:25
from how Bill Clinton did in '96 to how Obama did
20から30ポイントも
01:27
in 2008.
下がりました
01:29
Yes Bill Clinton was from Arkansas, but these are very, very profound differences.
確かにクリントンはアーカンソー出身でしたが それにしても大きな変化です
01:31
So, when we think about parts of the country like Arkansas, you know.
アーカンソーのような地域について考えるとき
01:36
There is a book written called, "What's the Matter with Kansas?"
「カンザスはどうなっているのか?」という本がありますが
01:38
But really the question here -- Obama did relatively well in Kansas.
オバマはカンザスでは割合上手くやっています
01:41
He lost badly but every Democrat does.
負けましたが 民主党はここではいつも負けています
01:44
He lost no worse than most people do.
他の人ほど ひどくは負けませんでした
01:46
But yeah, what's the matter with Arkansas?
それにしてもアーカンソーはどうなっているのか?
01:48
(Laughter)
(笑)
01:52
And when we think of Arkansas we tend to have pretty negative connotations.
アーカンソーについてはネガティブなイメージがあります
01:53
We think of a bunch of rednecks, quote, unquote, with guns.
銃を持った多数のいわゆる「頑固な南部人」
01:56
And we think people like this probably don't want to vote
彼らが投票しないだろうとは想像できます
01:59
for people who look like this and are named Barack Obama.
こんな見た目をした バラク・オバマなどという名前の人間には
02:02
We think it's a matter of race. And is this fair?
人種問題です これはフェアな見方でしょうか?
02:05
Are we kind of stigmatizing people from Arkansas, and this part of the country?
アーカンソーあたりの人に対する偏見でしょうか?
02:08
And the answer is: it is at least partially fair.
少なくともある部分フェアな見方です
02:11
We know that race was a factor, and the reason why we know that
人種という要因が確かにあるとわかるのは
02:14
is because we asked those people.
彼らに尋ねたからです
02:16
Actually we didn't ask them, but when they conducted
直接尋ねた訳ではありませんが
02:18
exit polls in every state,
50州中の37州で行われた
02:20
in 37 states, out of the 50,
出口調査で
02:22
they asked a question, that was pretty direct, about race.
人種について直接的な質問がありました
02:24
They asked this question.
こう質問したのです
02:27
In deciding your vote for President today, was the race
“今日の大統領選の投票先を決めるにあたって
02:29
of the candidate a factor?
候補者の人種はその要因になりましたか?”
02:31
We're looking for people that said, "Yes, race was a factor;
この質問に対し “はい 人種は要因であるのみならず
02:33
moreover it was an important factor, in my decision,"
決める上で重要な点でした”と答えた人や
02:36
and people who voted for John McCain
恐らく他の要因との兼ね合いか
02:38
as a result of that factor,
この要因自体のために 結果として
02:41
maybe in combination with other factors, and maybe alone.
マケインに投票した人は どれほどいたのか?
02:43
We're looking for this behavior among white voters
そのような振る舞いは 白人投票者の間で
02:45
or, really, non-black voters.
あるいは黒人以外の投票者の間で どれくらいあったのか?
02:48
So you see big differences in different parts
ご覧のように 結果は地域によって
02:51
of the country on this question.
大きな違いが見られます
02:53
In Louisiana, about one in five white voters
ルイジアナでは約5人に1人の白人投票者が
02:55
said, "Yes, one of the big reasons why I voted against Barack Obama
“はい 私がオバマに投票しなかった大きな理由は
02:58
is because he was an African-American."
アフリカ系アメリカ人だからです”と答えています
03:01
If those people had voted for Obama,
仮にそれらの人々の半分でも
03:03
even half of them, Obama would have won Louisiana safely.
オバマに投票していたなら 彼はルイジアナで難なく勝てたでしょう
03:05
Same is true with, I think, all of these states you see on the top of the list.
このリストの上位の州すべてにそう言えます
03:09
Meanwhile, California, New York, we can say, "Oh we're enlightened"
一方カリフォルニアやニューヨークの人々は “我々は啓蒙されている”
03:11
but you know, certainly a much lower incidence of this
と言うでしょうが
03:15
admitted, I suppose,
人種に基づく投票というのは
03:17
manifestation of racially-based voting.
確かにずっと少なくなっています
03:19
Here is the same data on a map.
これは同じデータを地図で表したものです
03:22
You kind of see the relationship between
この図で赤くなっているのは
03:24
the redder states of where more people responded and said,
“オバマの人種は 私には問題でした”
03:26
"Yes, Barack Obama's race was a problem for me."
と答えた人の多かった州です
03:28
You see, comparing the map to '96, you see an overlap here.
96年と比較した地図と この地図の間には共通点が見られます
03:31
This really seems to explain
これはまさにオバマが
03:34
why Barack Obama did worse
何故この地域でひどい結果だったのかを
03:36
in this one part of the country.
説明しているように見えます
03:38
So we have to ask why.
我々は問うべきでしょう
03:40
Is racism predictable in some way?
人種差別は何らかの方法で予測できるのか?
03:42
Is there something driving this?
人種差別を引き起こすものが何かあるのか?
03:44
Is it just about some weird stuff that goes on in Arkansas
それは我々が理解していないアーカンソーや
03:46
that we don't understand, and Kentucky?
ケンタッキーの特異性にすぎないのか?
03:48
Or are there more systematic factors at work?
それとも何か構造的な要因が働いているのか?
03:50
And so we can look at a bunch of different variables.
見る事のできる変数は他にもたくさんあります
03:52
These are things that economists and political scientists look at all the time --
経済学者や政治科学者が常々観察してきた
03:54
things like income, and religion, education.
収入や宗教や教育といった要因は
03:57
Which of these seem to drive
我々が11月4日に行った
04:00
this manifestation of racism
この国家的大実験における
04:02
in this big national experiment we had on November 4th?
人種差別の表出を引き起こしたものなのでしょうか?
04:04
And there are a couple of these that have
実際予測を可能にするような
04:07
strong predictive relationships,
強い相関を持つ要素が2つあります
04:09
one of which is education,
教育がその一つです
04:11
where you see the states with the fewest years of schooling
成人あたりの修学期間の短い州が
04:14
per adult are in red,
赤く表示されています
04:16
and you see this part of the country, the kind of Appalachians region,
ご覧の通りアパラチア地域は教育水準が低くなっています
04:18
is less educated. It's just a fact.
これは単に事実です
04:21
And you see the relationship there
ここに人種差別に基づく投票傾向との
04:23
with the racially-based voting patterns.
関連を見る事ができます
04:25
The other variable that's important is
もう一つの重要な変数は
04:28
the type of neighborhood that you live in.
近隣住民のタイプです
04:30
States that are more rural --
より田舎の州では
04:33
even to some extent of the states like New Hampshire and Maine --
ニューハンプシャーやメインでさえ
04:35
they exhibit a little bit of
人種差別によるオバマへの
04:37
this racially-based voting against Barack Obama.
反対投票が見受けられます
04:39
So it's the combination of these two things: it's education
ですからこの2つの要素の組み合わせなのです
04:42
and the type of neighbors that you have,
教育と近隣住民のタイプです
04:44
which we'll talk about more in a moment.
これについてはすぐ後で話しますが
04:46
And the thing about states like Arkansas and Tennessee
アーカンソーやテネシーのような州の問題は
04:48
is that they're both very rural,
非常に田舎であり
04:50
and they are educationally impoverished.
かつ教育的にも貧しいという事です
04:52
So yes, racism is predictable.
だから人種差別は予測可能なのです
04:56
These things, among maybe other variables,
恐らく他にも要因があるでしょうが
04:58
but these things seem to predict it.
これは人種差別を予見しているように見えます
05:00
We're going to drill down a little bit more now,
この「総合的社会調査」というのを
05:02
into something called the General Social Survey.
もう少し掘り下げてみましょう
05:04
This is conducted by the University of Chicago
これはシカゴ大学によって一年おきに
05:06
every other year.
行われているもので
05:08
And they ask a series of really interesting questions.
彼らは実に興味深い一連の質問をしています
05:10
In 2000 they had particularly interesting questions
2000年には人種差別的意見に関する
05:12
about racial attitudes.
特に興味深い質問があります
05:14
One simple question they asked is,
一例として挙げると
05:16
"Does anyone of the opposite race live in your neighborhood?"
“近隣にあなたと異なるの人種の人は誰か住んでいますか?”
05:18
We can see in different types of communities that the results are quite different.
コミュニティのタイプによって結果が大きく異なるのがわかります
05:22
In cites, about 80 percent of people
都市部では約80%の人が
05:25
have someone whom they consider a neighbor of another race,
近隣に別の人種が住んでいると答えています
05:28
but in rural communities, only about 30 percent.
しかし田舎のコミュニティでは僅か30%です
05:31
Probably because if you live on a farm, you might not have a lot of neighbors, period.
農場暮らしなら隣人は少ないというだけかもしれません
05:34
But nevertheless, you're not having a lot of interaction with people
しかし いずれにしても 自分と違った人との交流は
05:37
who are unlike you.
多くないのです
05:40
So what we're going to do now is take the white people in the survey
そこで この調査から白人を抽出し
05:42
and split them between those who have black neighbors --
黒人や別の人種の隣人がいる人と
05:45
or, really, some neighbor of another race --
白人の隣人しかいない人とに
05:48
and people who have only white neighbors.
分けてみることにしましょう
05:50
And we see in some variables
政治的意見に関しては
05:53
in terms of political attitudes, not a lot of difference.
大きな違いは見られません
05:55
This was eight years ago, some people were more Republican back then.
これは8年前で 当時は共和党寄りの人が多くいました
05:57
But you see Democrats versus Republican,
しかし民主党支持か共和党支持かは
06:00
not a big difference based on who your neighbors are.
隣人がどうであるかによって さほど変わりません
06:02
And even some questions about race -- for example
人種に関する質問でさえそうです 例えば
06:05
affirmative action, which is kind of a political question,
マイノリティ優遇措置という
06:07
a policy question about race, if you will --
人種に関する政策の問題についても
06:09
not much difference here.
ここに大きな違いはありません
06:11
Affirmative action is not very popular frankly, with white voters, period.
マイノリティ優遇措置は白人投票者に人気がない点で共通していますが
06:13
But people with black neighbors and people with mono-racial neighborhoods
黒人の隣人がいる人といない人との間に
06:16
feel no differently about it really.
この問題に対する感覚の違いは全くありません
06:19
But if you probe a bit deeper and get a bit more personal if you will,
しかしもう少し深く 個人的なことを聞いてみるとどうでしょう
06:22
"Do you favor a law banning interracial marriage?"
“異人種間の結婚は法的に禁止すべきか?”
06:26
There is a big difference.
この場合大きな違いが見られます
06:28
People who don't have neighbors of a different race
異なる人種の隣人がいない人は
06:30
are about twice as likely
いる人と比べ2倍近くが
06:32
to oppose interracial marriage as people who do.
異人種間の結婚に否定的です
06:34
Just based on who lives in your immediate neighborhood around you.
単に周囲に住む隣人によって変わるのです
06:37
And likewise they asked, not in 2000, but in the same survey in 1996,
2000年版ではなく1996年版ですが 同じ調査でこんな質問をしています
06:40
"Would you not vote for a qualified black president?"
“優れた大統領候補なら黒人でも投票しますか?”
06:44
You see people without neighbors who are African-American who
隣人にアフリカ系アメリカ人がいない人は
06:48
were much more likely to say, "That would give me a problem."
“それは私には問題だ”と答える傾向が強くなっています
06:50
So it's really not even about urban versus rural.
だからこれは都会か田舎かという話ではなく
06:53
It's about who you live with.
誰が周りに暮らしているかということなのです
06:55
Racism is predictable. And it's predicted by
人種差別は予測可能であり それは
06:57
interaction or lack thereof with people unlike you, people of other races.
異人種との交流の有無によって予見できるのです
06:59
So if you want to address it,
だからこの問題に取り組みたいなら
07:03
the goal is to facilitate interaction with people of other races.
人種間の交流促進を目指すべきでしょう
07:05
I have a couple of very obvious, I suppose,
それを行うための
07:08
ideas for maybe how to do that.
ごく自明なアイデアがいくつかあります
07:10
I'm a big fan of cities.
私は都会が大好きです
07:13
Especially if we have cites that are diverse and sustainable,
特にその都市が持続可能で多様であり
07:15
and can support people of different ethnicities and different income groups.
異なる民族性や収入帯の集団を支えられるなら素晴らしいです
07:18
I think cities facilitate more of the kind of networking,
都会は田舎よりも日常的に
07:21
the kind of casual interaction than you might have on a daily basis.
人の繋がりや気軽な交流を促進すると思います
07:24
But also not everyone wants to live in a city, certainly not a city like New York.
しかし誰もがニューヨークのような都会で暮らしたいわけではありません
07:27
So we can think more about things like street grids.
そこで考えるべきなのは道路網のようなものについてです
07:30
This is the neighborhood where I grew up in East Lansing, Michigan.
これは私が育ったミシガン州イーストランシングの町並みです
07:33
It's a traditional Midwestern community, which means you have real grid.
伝統的な中西部の田舎で 道が格子状になっています
07:35
You have real neighborhoods and real trees, and real streets you can walk on.
本当のご近所や 木や 歩くことが出来る道路があり
07:38
And you interact a lot with your neighbors --
そして近所にいる 好きな人や 知らない人たちと
07:41
people you like, people you might not know.
交流することになります
07:44
And as a result it's a very tolerant community,
結果としてそれはとても寛容なコミュニティとなり
07:46
which is different, I think, than something like this,
次のような所とは異なると私は考えます
07:49
which is in Schaumburg, Illinois,
このイリノイ州のシャンバーグでは
07:51
where every little set of houses has their own cul-de-sac
袋小路になった住宅群や ドライブスルーのスターバックスが
07:53
and drive-through Starbucks and stuff like that.
このように詰め込まれています
07:56
I think that actually this type of urban design,
私はこのタイプの都市計画が
07:58
which became more prevalent in the 1970s and 1980s --
1970年代から80年代に流行したことと
08:01
I think there is a relationship between that and the country becoming
レーガン政権下で国が保守化したことの間には
08:04
more conservative under Ronald Reagan.
関連があると考えています
08:07
But also here is another idea we have --
他にもアイデアがあります
08:09
is an intercollegiate exchange program
ニューヨークと外国とで学生を交換する
08:12
where you have students going from New York abroad.
交換留学制度がありますが
08:14
But frankly there are enough differences within the country now
率直に言って米国内だけでも十分な差異があります
08:17
where maybe you can take a bunch of kids from NYU,
たとえばニューヨーク大学の学生を
08:19
have them go study for a semester at the University of Arkansas,
一学期間アーカンソー大学で学ばせ
08:22
and vice versa. Do it at the high school level.
逆も同様にするとか それを高校の段階でやるのはどうでしょう
08:24
Literally there are people who might be in school in Arkansas or Tennessee
実際アーカンソーやテネシーの学校にいる人は
08:27
and might never interact in a positive affirmative way
恐らく別の州や別の人種の人たちと
08:30
with someone from another part of the country, or of another racial group.
積極的に交流する機会を持たないでしょう
08:33
I think part of the education variable we talked about before
先程話した教育という要因の一部は
08:37
is the networking experience you get when you go to college
大学に行くことで得られる
08:40
where you do get a mix of people that you might not interact with otherwise.
他では交流する事がなかった人達と混じり合う経験のためだと思います
08:42
But the point is, this is all good news,
重要なのはこれが全て良いニュースだという事です
08:46
because when something is predictable,
なぜなら 何かが予測可能であるなら
08:48
it is what I call designable.
それはデザイン可能でもあるからです
08:51
You can start thinking about solutions to solving that problem,
問題の解決策を考える事ができます
08:53
even if the problem is pernicious and as intractable as racism.
たとえその問題が人種差別のように悪質で扱い難いものであっても
08:55
If we understand the root causes of the behavior
その行動の根本原因や
08:58
and where it manifests itself and where it doesn't,
どういう時に起きるかがわかっているなら
09:00
we can start to design solutions to it.
その解決策をデザインする事ができるのです
09:02
So that's all I have to say. Thank you very much.
これが私の言うべき全てです ありがとうございました
09:05
(Applause)
(拍手)
09:07
Translated by Nao Yokoyama
Reviewed by Yasushi Aoki

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About the Speaker:

Nate Silver - Statistician
Math whiz and baseball fan Nate Silver was mainly known for predicting outcomes in fantasy ballgames -- until his technique hit a home run calling the outcome of the 2008 election primaries.

Why you should listen

In the 2008 election season's closing weeks, throngs of wonks and laypeople alike were glued to FiveThirtyEight.com, a habitforming political blog. Red and blue bar charts crowded the scrollbars as the pulse of exit polls crept along the site's latest projections. It seemed almost miraculous: In a year of acute turns of favor, the site's owner and mouthpiece, Nate Silver (who blogged anonymously as "Poblano" until outing himself on May 30, 2008, as a baseball numberhead), managed to predict the winners of every U.S. Senate contest -- and the general Presidential election.

Besides being just-damn-fascinating, Silver's analysis is a decidedly contrarian gauntlet thrown before an unrepentant, spectacle-driven media. The up-and-coming pundit, who cut his teeth forecasting the performance of Major League Baseball players, has a fairly direct explanation of why most projections fail: "Polls are cherry-picked based on their brand name or shock value rather than their track record of accuracy."

Silver's considerable smarts are already helping local campaigns build constituencies and strategize. He is the author of The Signal and the Noise: Why So Many Predictions Fail - but Some Don't

More profile about the speaker
Nate Silver | Speaker | TED.com