TED演讲之说什么 你存钱的能力跟你用的语言有关?(5)
Interestingly enough, when you start to crank the data,
有意思的是，当你开始收集数据时，
these pockets of futureless language speakers all around the world turn out to be, by and large, some of the world’s best savers.
你就会发现这些说没有将来时态的语言的国家，很大程度上，恰恰是最喜欢储蓄的国家。
Just to give you a hint of that, let’s look back at that OECD graph that we were talking about.
为了给你们一点提示，请看看刚才提到的OECD储蓄比例图。
What you see is that these bars are systematically taller and systematically shifted to the left
你看到相比那些语言中区别将来时态的国家，
compared to these bars which are the members of the OECD that speak futured languages.
没有区分时态的国家的储蓄率更高并且总体上更靠左边。
What is the average difference here?
平均的差值有多大？
Five percentage points of your GDP saved per year.
约占各国每年GDP的5%。
Over 25 years that has huge long-run effects on the wealth of your nation.
这种差别持续了25年，对国家总体财富有着长远的影响。
Now while these findings are suggestive, countries can be different in so many different ways
现在虽然这些发现能说明问题，但是比较的国家之间在很多很多方面都有差异，
that it’s very, very difficult sometimes to account for all of these possible differences.
有时候很难将这些差异归结为某个原因。
What I’m going to show you, though, is something that I’ve been engaging in for a year,
我将想你们展示，我过去一年收集到的，
which is trying to gather all of the largest datasets that we have access to as economists,
作为经济学家能够得到的所有的大数据集，
and I’m going to try and strip away all of those possible differences, hoping to get this relationship to break.
并且我正在尝试将国家之间可能的差异性消除，希望可以为（我理论提到的）这段关系提供证据。
And just in summary, no matter how far I push this, I can’t get it to break.
总体而言，无论我目前如何尝试，我都无法将这种关联消除。
Let me show you how far you can do that.
让我告诉你们，你们能做到什么程度。
One way to imagine that is I gather large datasets from around the world.
一种可能的验证方式就是收集了全球范围的（经济）数据信息。
So for example, there is the Survey of Health, and Retirement in Europe.
例如，欧洲的健康、老龄化及退休情况统计。
From this dataset you actually learn that retired European families are extremely patient with survey takers.
通过这个数据采集工作你会发现欧洲退休老人对于调查人员是极度有耐心的。
演讲简介：
经济学家能从语言学家那里学到什么？行为经济学家基思·陈介绍了他的研究中发现的奇妙关联：没有区分将来时态的语言——“明天下雨”而不是“下明天的雨”——跟储蓄比例有很强的相关性。