2018年6月20日,星期三

采访Marianne Bertrand:不平等,性别规范,技能

道格拉斯克莱门特有一个“对玛丽安·贝特朗的采访,“标题是芝加哥大学经济学家关于玻璃天花板、日益加剧的不平等的影响和男孩问题”(明尼阿波利斯联邦储备银行,2018年6月19日在线)。下面的一些评论尤其让我印象深刻,但面试中还有更多。

已婚夫妇中男人应该比女人挣得多的性别规范
"The idea of the paper was to focus on the particular gender identity norm, which is the idea that men should earn more than their wives. It’s an interesting one to focus on because it’s a norm that may only have become binding today. It may not have been that relevant in the past because women were much less likely to have the potential to out-earn their husbands, and now they do. So the idea of the paper was to investigate the empirical relevance of this norm among households as well as its implications. ...
这是论文的第一项。让我们做一些非常简单的事情:看看夫妻之间的相对收入分配。如果这一标准很重要,我们应该看到,“极少”夫妻中妻子的收入高于丈夫。这正是我们在管理数据中发现的;这就是我们现在看到的图像。

然后,在某种意义上,从这幅图开始,我们试图找出这可能来自哪里。一种可能性是,那些妻子挣得比丈夫多的“失踪”夫妇可能永远不会形成,这意味着这是关于婚姻市场. ...的东西这幅图存在的另一个原因是,那些所谓的“失踪”夫妇不太稳定。所以它们是存在的,但是它们更容易崩溃。我们也在数据中找到了证据。观察那些妻子比丈夫挣得多的夫妇,我们发现婚姻更不稳定、更不幸福,而且有迹象表明这些夫妇更可能以离婚告终。”
关于消费类型和政治观点差异的思考
"We were talking about income inequality, and one of our colleagues said, basically, “Well, at the end of the day, who cares? Yes, maybe we’re growing apart economically, but on Sunday all we all do is watch TV. We are growing apart economically, but our lives may not be that different; they may, in fact, have converged.”
这是一个很有趣的观点。对于富人和穷人的生活是如何改变的,我们能说多少呢?让我们一起试着把所有的数据集,我们可以认为在最长的时间,我们可以谈谈是什么样子是丰富的,我们定义为最高四分位数的收入分配和贫困,我们定义为底部四分位数。几十年前是什么样子的?今天天气怎么样?...
“我想用你提到的例子,社会流动性。假设我们在同一家公司工作。你是我的老板。我是你的员工。你来自收入分配的顶端,而我来自收入分配的底部。我在公司晋升的能力可能取决于你和我的联系有多紧密,而相互之间的联系可能取决于我们在饮水机旁交谈的质量。我们周末也做了同样的事吗?我们看同样的节目吗?我们有相同的爱好,吃相同的食物吗?
“所以我们试着收集所有可能的数据集;例如,时间使用数据可以追溯到20世纪60年代。许多社会科学家使用的另一个数据集是“综合社会调查”,它告诉我们一些关于观点和观点的东西——关于堕胎、同性恋、种族问题、政府开支等问题的观点. ...我们能够接触到营销数据集,这是非常了不起的。在这个数据集中,我们可以看到媒体消费——人们看什么电视节目,他们看什么电影,他们读什么杂志。数据还显示了人们可能购买或不购买的数千种产品,以及人们可能购买或不拥有的数千个品牌。
"Then we built a metric of cultural distance between groups by income. There are many ways you could measure distance. We use a machine-learning algorithm and aggregate a number of methods that allow us to find the best model to predict someone’s income based on the brands or products they report consuming or the attitudes the person has. ...
"The main headline result of the paper is that most of the trend lines are flat. Our ability to predict someone’s income based on the consumption of particular goods and brands is essentially the same today as it was 25 years ago. There’s no trend in our ability to predict people’s income based on how they spend their time today, compared to close to 50 years ago. The only area where we see some slight evidence of divergence on income is with respect to social attitudes, where our ability to predict people’s income based on what they think, their views, is slightly better today than it was in the early 1970s. ...
"[N]ow we’ve done this exercise, as I said, for race, gender and urbanicity. When we first got these results on income, people said, especially in the context of the recent election, “Well, income is not the important one; it’s urban/rural. That’s the important divide in America.” We’ve also done it based on political attitudes, and the main result, which I just gave you for income—there’s no big trend—essentially applies to, at a first-level of approximation, everything that we have looked at.
"The one really large exception quantitatively is our ability to predict whether someone is liberal or conservative/Democrat or Republican based on their social attitudes. That has been increasing over time. So liberals and conservatives haven’t been diverging over time on TV consumption, brands or goods, but on social views they have been diverging a lot over time.
"The results were surprising to us. We went into this with in the back of our mind the discussion that’s happening right now [that Americans are increasingly divided along economic and other lines], and we really thought that we were going to see signs of that in the data.
"How do I rationalize the results? It’s not clear, but here’s one thought when it comes to products and brands. I think today we think you can easily see who is rich or poor because rich people own an iPhone and poor people don’t; but, then, 25 years ago, it was whether you owned a DVD player that separated rich and poor. There are waves of technological changes—the rich, the more educated are always going to be the early adopters of those—but there are constant waves of technological change."
在破碎的家庭中,认知技能的性别差异更大
"[T]he gender gap in noncognitive skills is particularly large in broken families. And that term can mean many different things. It’s low income, it’s absent fathers, it’s less education, it’s fewer parental inputs. ... If you have boys doing more poorly in broken families, that means that a lot of these boys become less marriageable. That means more single moms and more broken families in the future and hence, again, more boys growing up in conditions where they may not get the kind of parenting that could address whatever deficiencies they have in noncognitive skills. ... One argument we make in the paper is that boys may be born at greater risk of having noncognitive problems than girls. ... And if that’s true, then it’s particularly important to have stronger parenting for boys than girls in order to correct this deficit. But, again, that’s highly speculative."