Is the American Dream Really Dead?

2024-03-27 06:48
文章标签 american dream really dead

本文主要是介绍Is the American Dream Really Dead?,希望对大家解决编程问题提供一定的参考价值,需要的开发者们随着小编来一起学习吧!


Our latest Freakonomics Radio episode is called “Is the American Dream Really Dead?” (You can subscribe to the podcast at iTunes or elsewhere, get the RSS feed, or listen via the media player above.)

Just a few decades ago, more than 90 percent of 30-year-olds earned more than their parents had earned at the same age. Now it’s only about 50 percent. What happened — and what can be done about it?

Below is a transcript of the episode, modified for your reading pleasure. For more information on the people and ideas in the episode, see the links at the bottom of this post. And you’ll find credits for the music in the episode noted within the transcript.

*      *      *

[MUSIC: Joe Smith and the Spicy Pickles, “Dark Elixir” (from High-Fidelity)]

Let’s start today with a pop quiz. Here we go: in 1970, what percentage of 30-year-olds in America earned more money than their parents had earned at that age? Adjusted for inflation, of course. That’s question No. 1. And question No. 2: what percentage of American 30-year-olds today earn more than their parents earned at age 30? I’ll give you a second to think it over.

All right, you ready for the answer? The percentage of American 30-year-olds in 1970 who were earning more than their parents had earned at 30? Ninety-two percent. Isn’t that amazing? That, in a nutshell, is what we call the American Dream. And what’s the percentage now? It’s somewhere around 50 percent. Which has led some people to say this:

Donald TRUMP: Sadly, the American Dream is dead!

Donald Trump’s view of the American Dream – and his promise to revive it – had a lot to do with his getting elected president. According to Gallup polls, before the election more than 50 percent of Americans saw our economic conditions worsening.  And, in case you’re wondering, it’s not just cranky old people. A poll from the Harvard Institute of Politics found that nearly fifty percent of millennials think the American Dream is “dead.” We went out on the streets of New York ourselves to ask people if they thought the American dream was real, and achievable.

[MUSIC: Texas Gypsies, “Guitar Twins” (from Café Du Swing)]

VOICES ON THE STREET:

MALE 1: Absolutely it’s real. Especially standing here in Battery Park you look at different people from all across nations that come to America to realize the American dream.

FEMALE 1: I think that if you really work hard then you can do whatever you want in America. It might be a little difficult at first but you can still do it.

MALE 2: I don’t think the American dream is achievable. I think it’s a motivator, to try to achieve it.

FEMALE 2: The American Dream is something of a mythology for a way in which to advance and have a good life under what is essentially not just a capitalist system but a country founded on exploitation.

FEMALE 3 : You put in some work, you put in some sweat, and you can definitely make the American Dream happen.

MALE 3: Well, there’s a lot of cynicism now over the American dream. I am a product of it. My family, our families are refugees, came to this country 30 years ago. Had nothing. Was able to send all their kids to college, was able to have a house, was able to give a better future for myself and their children than they ever would have had back in Vietnam.

A lot of the conversations we have these days about the American Dream are in political terms, or theoretical terms. Today on Freakonomics Radio: the actual, unvarnished economics of the American Dream. Which we will define, for the sake of today’s conversation, as this:

[MUSIC: Tony Flynn, “Star Spangled Banner”]

Raj CHETTY: If you’re born into a low-income family, do you really have a shot at rising up no matter what your background is?

And we’ll discuss whether the American Dream is really dead – or maybe if it’s just moved a bit … north.

CHETTY: You’re twice as likely to realize the American Dream if you’re growing up in Canada rather than the U.S.

*      *      *

[MUSIC: Luke Brindley, “American Dream”]

James Truslow Adams, born in 1878 to a wealthy New York family, became a financier and, later, an author. He won a Pulitzer Prize for a history of New England; and later he wrote a book called The Epic of America. Even though it was written during the Great Depression, Adams took a fundamentally bullish view of the United States.

His book was hugely popular, and as best as we can tell, it introduced the phrase “The American Dream.” Adams defined this as “that dream of a land in which life should be better and richer and fuller for everyone, with opportunity for each according to ability or achievement.”  The phrase caught on, and not just a little bit. Especially among our presidents:

Barack OBAMA: The bedrock of our economic success is the American Dream.

Richard NIXON: The American Dream does not come to those who fall asleep.  

George W. BUSH: So every citizen has access to the American Dream

Ronald REAGAN:They have lived the American Dream

Bill CLINTON: The American Dream will succeed or fail in the 21st Century.

Donald TRUMP: Sadly, the American Dream is dead!

Raj CHETTY: The reason my parents came to this country was in search of the American Dream.

That’s Raj Chetty.

CHETTY: I was born in New Delhi, India, and came to the United States when I was 9 years old, and grew up mostly in the Midwest.

Chetty is now an economist at Stanford:

CHETTY: I study issues of inequality and opportunity and how we can use economic policy to improve people’s outcomes.

Chetty was one of the scholars behind the research I cited earlier, about the massive drop in the share of 30-year-old Americans earning more than their  parents did. In fact, he is behind a lot of the most important research on income inequality, mobility, and the fragile state of the American Dream. His work is highly regarded by the people who give awards – he has won a MacArthur “Genius” Award and the John Bates Clark Medal. Politicians admire him as well.

Senator Jeff SESSIONS: Dr. Chetty, thank you for your participation.

Senator Bernie SANDERS: Dr. Chetty, what do you think?

That was Senator Bernie Sanders and, before him, then-Senator Jeff Sessions, when Chetty testified at a Senate hearing on income mobility and inequality. Chetty is a favorite of Democrat Hillary Clinton.

Hillary CLINTON: Some really interesting work being done by Professor Raj Chetty and his colleagues.

As well as Republican Paul Ryan.

Paul RYAN: Economists — you know, if you talk to Raj Chetty or others — they’ll tell you this is social capital.

Chetty is the policymakers’ policymaker. The economists’ economist. Which means he tries to be, above all, empirical. Not ideological or political.

CHETTY: One of my missions is to try and inject more evidence into these important policy debates because I think we’re making huge investment decisions with very little knowledge about exactly what is going to work.

Stephen J. DUBNER: Do you vote? Are you a political participant?

CHETTY: I’m independent. And so I’ve thought hard about this. I think it’s very difficult to keep yourself objective, which is very important to me. I mean it’s important to me that I have some findings that I think are more supportive of policies that Democrats are pushing, and there are some findings that are more supportive of policies that Republicans are pushing.

DUBNER: Some academics I know whose work gets cited for political purposes have told me that the work is inevitably cherry-picked or cream-skimmed to suit the politician’s position. 

CHETTY: I think while the big-picture focus might be chosen based on political views, there are lots of details that matter greatly, and I think science can be very useful there, in addition to perhaps guiding which areas we focus on — affordable housing versus tax cuts versus other things.

For all his influence, Chetty is only 37 years old.

CHETTY: I was actually the last person in my family to publish a paper. My parents are both in academics, and I have two older sisters who are in bioscience.

Chetty went to Harvard as an undergrad but he didn’t spend spent much time undergradding: he got his Ph.D. at 23.

CHETTY: Basically I did a six-year Ph.D. and didn’t go to college, in the sense that starting my sophomore year, I actually didn’t take any undergraduate classes.

He taught at Berkeley, then Harvard, and in 2015, moved to Stanford.

DUBNER: You are hardly the first economist from Harvard to go to Stanford in the last few years. There’s been quite a little exodus.

CHETTY: Recently, as the field of economics is shifting towards big data and increasing use of modern statistical techniques, like machine learning, to think about economic questions, Stanford has tremendous strength in those areas and other fields. And of course we all know that the birthplace of much of modern computing is here in Silicon Valley at Stanford.

DUBNER: Now, economists in particular, but social scientists more broadly, have in the past few years especially, just been being gobbled up by tech firms. Because they, too, have discovered that big data is potentially exciting and a number of academic economists, many of whom I’m sure you know well, are moonlighting or sidelining with tech firms, Uber and Facebook and on and on. What about you? Was that an appeal for you to be out there and are you doing any consulting, advising work on the side with these private firms? Or are you strictly an academic economist?

CHETTY: Yeah, that is a very important trend. I myself am not doing any work with those firms directly, but what I am interested in is working with the data from firms like Facebook and Twitter, for instance, to think about social and economic policy questions. So, to give you a concrete example, I’m starting a project with my colleague Matt Jackson here at Stanford, and others at Facebook, where we’re exploring the role of social networks in inequality, and trying to understand essentially whether you can network yourself out of poverty. Social scientists have been interested in that sort of question for a very long time, but we just haven’t had the data to really investigate that question precisely from an empirical point of view. And the Facebook data, of course, are game-changing in that respect.

[MUSIC: Mokhov, “Mysterious Dream” (from Revel Revival)]

A lot of Chetty’s research falls under the banner of something called the Equality of Opportunity Project. That is a group of economists – and other social scientists – who are trying to find the most effective, and efficient, ways to address chronic poverty. Which, Chetty argues, is really important. Because the economy that for so many years facilitated the American Dream for so many millions is no longer reliably doing so.

CHETTY: While modern technology and economic growth is changing the world in tremendous ways — I mean, we can now do things with our cell phones that we never would have imagined 10 years ago — I think unless we think carefully about social policy, doesn’t necessarily end up benefiting everyone. There are many people for whom progress over the last 30 years hasn’t really had a tremendous impact on their lives in terms of better opportunities for their kids or better health outcomes and so forth.

Chetty admits the American Dream worked out great for his immigrant family.

CHETTY: And so partly with that personal motivation, partly out of scientific interest, I wanted to think about whether the American Dream truly is alive and well, and what the determinants of the American Dream are.

Okay, so how do you do that? How do you measure the state of the American Dream? And, more important, how do you identify the determinants that enable one family, or one kid, to shoot up out of poverty while others are left behind? Well, if you’re an economist, you do that with … data. Lots and lots of data.

CHETTY: And this specific angle we took is by using the large data that we have now from administrative tax and Social Security records where we’re able to see for the full population what income distributions look like for kids and for parents. And so you can basically ask, taking say, all of the kids born in America in the 1980s, “What fraction of the kids born to low-income families actually make it to the top of the income distribution? How much intergenerational mobility is there in America?” In the U.S., if you take, say, the set of children who are born to families in the bottom quintile of the income distribution, in the bottom fifth, about seven-and-a-half percent of those kids make it to the top fifth of the income distribution. 

DUBNER: And that number in isolation doesn’t sound off the bat so bad.

CHETTY: Yeah, that’s right. Exactly. Seven-and-a-half percent, is that a big number, is that a small number? It’s hard to judge in isolation. So to give some context for that, I think Stephen, it’s useful to start first by thinking about comparisons across countries. So if you look at that number in other countries where we have comparable data, like the United Kingdom, for instance. In the U.K., that number is nine percent. A little bit higher, but not all that much higher. If you go to a place like Canada or Denmark, the number is 13 percent, or 13 and a half percent. That’s quite a bit higher. And it’s useful, in thinking about these numbers, is 13 percent a big number? Well, you have to remember, of course, that no matter what you do, you can’t have more than 20 percent of people in the top 20 percent, right? So the maximum value this statistic can take, I think, is plausibly 20 percent. To put it more precisely, if you lived in a society where your parents played no role at all in determining your outcomes, we’d expect one-fifth of kids to rise from the bottom 20 percent to the top 20 percent. And so relative to that benchmark, that upper bound, if you will, the 13 and a half percent rate in Canada and the seven-and-a-half percent rate in the U.S., that’s a really big difference. It’s almost like you’re twice as likely to realize the American dream of moving up if you’re growing up in Canada rather than the U.S, right?

DUBNER: Or perhaps more precisely, we should just call it the Canadian Dream instead of the American Dream, if they’re twice as good. Well, but what we want to know then is why, right? What makes that more possible in Canada? So that’s what your research is really about, yes, is about identifying the factors that move that needle?

CHETTY: Exactly. Exactly. So that I think is kind of useful as background, but there are lots of differences between Canada and the U.S. The first of which is that Canada has less inequality than the U.S. There’s less distance between the 20th percentile and the 80th percentile in Canada, relative to America.

DUBNER: Which means — so are you making a social point or is this a statistical point? In other words, it’s easier to move because it’s a smaller jump.

CHETTY: That’s exactly right. From a statistical point of view, one view you could have is maybe the reason you see higher upward mobility in Canada is not really so much that it is actually easier to move up in Canada, but it’s easier to make that move because it’s a shorter distance in a sense. So that’s one example of why I think these cross-country comparisons, while they can be motivating, in and of themselves it’s inevitably going to be very difficult to say for sure what you can learn from comparing Canada to the U.S. But I think before we even get to the issue of why is the U.S. is different from Canada, it turns out the story even in America itself is much more nuanced. Within America, there are actually a number of places that truly are lands of opportunity, places where kids achieve the American Dream at high rates. In some places, like in Salt Lake City, Utah, or in the Bay Area, something like 13 percent of kids are making it from the bottom fifth to the top fifth. Turns out in the center of the country, like in Iowa, for example, in many areas of Iowa you see more than 15 or 16 percent of kids making it from the bottom fifth to the top fifth. So higher than the numbers we see in the data for Canada and for Scandinavian countries. But at the other end of the spectrum, you take places like Atlanta, Georgia; or Charlotte, North Carolina; or much of the southeast of the U.S., and you have rates of upward mobility below four-and-a-half percent. Lower than any country for which we currently have data.

DUBNER: So that shouldn’t really, I guess, surprise us if you know a little bit about the makeup and history of the United States and the fact that we are states that have different policies, different populations and then obviously counties and cities that really differ a lot. 

CHETTY: Yeah, I think that’s right. But I think that line of thinking would lead you to think that most of this variation is regional. But what’s perhaps more surprising is that as we zoom in more finely we continue to find almost as much variation. So kids growing up in San Francisco, for example, have about twice the chance of climbing from the bottom to top as kids just across the Bay Bridge in Oakland.

[MUSIC: Pearl Django; “Eleventh Hour” (from Eleven)]

Coming up on Freakonomics Radio: the preliminary results of an incredibly ambitious government program to address poverty.

CHETTY: What you ended up finding was frankly I think somewhat disappointing.

All right then: how do you engineer the possibility of the American Dream?

CHETTY: We’ve identified five factors that seem to be particularly strongly correlated with these differences.

*      *      *

[MUSIC: Lucy Bland, “Plumb” (from Down to Sea Level)]

The Stanford economist Raj Chetty has been working with large data sets to try to understand why so many Americans are no longer living the American Dream. When it comes to economic opportunity, Chetty and his colleagues found huge regional and even local differences throughout the U.S.

As he told us, kids growing up in San Francisco have about twice the chance of living the American Dream as kids from just across the bridge, in Oakland. Why? One easy explanation would be that the people in those different areas are just different – they have different abilities, different cultures, different job opportunities. And that certainly has some explanatory power. But Chetty and his colleagues found the story isn’t that simple.

CHETTY: A lot of this variation is driven by differences in childhood environment, as opposed to differences in conditions in the labor market or the types of jobs that are available or unemployment rates —things that affect you in adulthood.

How do the data explain that it’s the childhood environment making the difference?

CHETTY: So we’re going to start by thinking about families who move when their child is exactly 9 years old, alright? And why age nine? That happens to be the earliest age we can examine in currently available data. 

DUBNER: And that’s not just because you were nine years old when you moved from India to Milwaukee, is it?

CHETTY: Not, not quite.

DUBNER: Just a coincidence? Okay.

CHETTY: Interesting coincidence. And so when we look at these nine-year-olds who move, we find that they end up roughly halfway between the kids who grew up in Oakland from birth and the kids who grew up in San Francisco from birth. So they’re earning roughly $35,000 when we track them forward 21 years and measure their own incomes when they’re age 30. So that’s for the kids who move when they’re exactly nine, right? So now let’s replicate that for kids who move when they’re ten, eleven, twelve, and so forth and so on. And what you end up seeing in the data is a very clear declining pattern where the later you make that move, from Oakland to San Francisco, the less of the gain you get. And in fact, if you move after you’re 21 or 22 or so, you get absolutely no gain at all, and if you move in your early 20s or when you’re 30, the relationship is completely flat. There’s no further gain from moving. So this sort of analysis leads us to the conclusion that first of all, where you grow up matters. It’s not just that kids who live in Oakland are somehow different from the kids who live in San Francisco. Second, you see that neighborhood environment matters because of childhood factors and not factors in adulthood, right? 

[MUSIC: Planes on Paper, “Iron Boat” (from The Ruins)]

This is hardly a new idea – that growing up in a poor neighborhood isn’t the best launching ground for economic success. This idea, in fact, led the Clinton Administration to experiment in the mid-1990s with a program called Moving to Opportunity.

CHETTY: They took about 5,000 families, across five large cities in the U.S. including New York, Chicago, and Los Angeles.

In New York for instance …

CHETTY: So, for instance, in New York, many of the families were living in the Martin Luther King Towers, which is a very high-poverty, large public-housing project in New York. They took these families and they randomly assigned them to one of three groups.

The first was a control group. They stayed put in the Martin Luther King Towers.

CHETTY:  And then there were two treatment groups, one of which was called the Standard Section 8 Housing Voucher Group.

This group could use the vouchers to move wherever they wanted.

CHETTY: So many families, for example, moved to a place in the mid-Bronx called Soundview, which is about six miles away from the MLK Towers. So not in the super-high-concentrated-poverty public-housing project, but not in a dramatically different neighborhood either.

Families in the third group were also given a housing voucher.

CHETTY: However, with an additional restriction — which was that you could only use this voucher to rent a house or apartment in a place with a poverty rate below ten percent. So basically trying to encourage families to move into more mixed-income areas.

Hence the program’s name: Moving to Opportunity, or MTO. The people behind the program suspected, or at least hoped, that families who removed themselves from concentrated poverty would end up having better outcomes themselves. For both the adults already in the labor market and their kids, who’d be coming into the labor market. So what happened?

CHETTY: What you ended up finding was frankly I think somewhat disappointing. So you didn’t see any differences in employment rates or average levels of earnings.

There were some positive health effects – lower obesity and better mental health, for instance. But the MTO experiment was largely considered a failure.

CHETTY: I think it left the field in kind of a difficult spot, because people still instinctively felt and anecdotally felt like, “Of course it’s got to matter where you grow up.” But this gold-standard scientific experiment is telling us that it doesn’t matter for economic outcomes.

That indeed was the consensus among researchers who analyzed the MTO data. But several years later, Chetty and his colleague Nathaniel Hendren wound up taking another look at the data. And they saw a different picture. They saw a rather large benefit among some people who had participated in MTO. Why?

CHETTY: Our hypothesis was that earlier studies of MTO had looked at impacts on adults and children who were older at the point of the move.

Remember, the Moving to Opportunity experiment was conducted in the mid-1990s. The studies that found disappointing results were published roughly ten years later.

CHETTY: And, of course, the children who were very young at the point the experiment was implemented — say, kids who were 2 or 3 years old — 10 years after the experiment was implemented, they were still only 12 and so obviously, you couldn’t measure their earnings at that point because they weren’t working yet. So these earlier studies for that reason mainly focused on adults and older youth, and they didn’t find much of an impact. But to us, in light of our findings on the importance of childhood exposure, that actually made sense. We thought, “Well, in our data, it looks like in order to really see an effect of moving to a better neighborhood, you need many years of exposure to that better neighborhood.”

And that’s what led Chetty and his colleagues to re-examine the MTO data. They added a layer of IRS data, in order to measure the longer-term earnings for the kids who were young when they moved.

CHETTY: And, quite remarkably, and I still vividly remember seeing this when we were studying this at the IRS, looking at the data — when you look at children who moved when they were young, you see extremely clearly that they are doing dramatically better today as adults. They are earning 30 percent more, they are 27 percent more likely to go to college, they’re 30 percent less likely to become single parents. And that, in our view, just kind of completely changed everything and I think has changed people’s perceptions of MTO.

Okay, so young kids who move out of a high-poverty neighborhood do much better later on. What, exactly, does this signify? What’s going on in the poor neighborhoods to depress income mobility and what’s going on in the better neighborhoods to increase it? Answering those questions has become a big part of Raj Chetty’s work. He and his colleagues  have come up with five significant explanations.

CHETTY: The first is residential segregation. Cities that are more segregated by income and by race tend to have much lower levels of upward mobility. So if you look at a city like Atlanta it’s an incredibly segregated city. Now cities that look like that, in terms of residential structure, we find systematically tend to have very low rates of upward mobility. In contrast, if you look at a place like the Bay Area, at least in the 1980s and 1990s, and this, Stephen, I think is changing quite a bit over time, especially here in Silicon Valley as prices are rising, but in the 1980s and 1990s, the Bay Area was relatively integrated, at least compared to Atlanta. Where you had neighborhoods of San Francisco with both middle- and high-income people, you had people of different ethnicities living near each other, and those kinds of cities tend to have much higher rates of upward mobility.

The second factor:

CHETTY: Income inequality. You have more people in the middle class, you also tend to have higher levels of upward mobility. This relationship is what Alan Krueger, based on cross-country data, termed the Great Gatsby Curve. The idea that there’s a link between inequality in any one generation and rates of intergenerational mobility. Why is this link interesting if one can interpret it causally? It suggests that as we have growing inequality over time, as we do in the U.S., we might be concerned about that not just because we’re worried about equitable distribution, but also because we’re worried that it might erode children’s chances of achieving the American Dream. And so, again, we don’t know exactly what the mechanism is and whether this is really a causal effect — inequality causing changes in upward mobility—but there does seem to be some link between these two factors.

The third factor they identified relates to family.

CHETTY: It turns out that the single strongest correlation we find in the data is with measures of family structure, such as the fraction of single parents living in an area. We find that places with more single parents have significantly lower levels of upward mobility. Now in interpreting this correlation, it’s very important to note that it’s not purely driven by the fact that growing up in a one-parent family leads to worse outcomes for children. And the way you can see that is if we look at the subset of kids who grow up in a two-parent household, we see that for that subset of children, even for them, growing up in neighborhood with a lot of single parents is associated with lower levels of upward mobility. So it’s not literally about whether your own parents are married or not — again, it’s picking up some community-level factor where growing up in a place that has a lot of single parents — maybe there’s more family instability or it’s correlated with some third factor that is leading to higher rates of single parenthood. For whatever reason, that seems to be strongly associated with lower levels of upward mobility.

The fourth factor:

CHETTY: Social capital. And so the idea of social capital — I think of it in relation to the old adage that it takes a village to raise a child. Will someone else in your community help you out when you need help? So as an example, Salt Lake City with the Mormon Church is thought to be the quintessential example of a city with a lot of social capital. And correspondingly in our data, seems to exhibit a lot of social mobility. Now this concept of social capital, as you might know, was popularized in a very well-known book by Bob Putnam called Bowling Alone.

Indeed, we put out an episode not long ago, called “Trust Me,” with Bob Putnam, who teaches public policy at Harvard. Years ago, he was looking at the decline of civic life in America.

Bob PUTNAM: We were becoming more and more isolated. Or as a friend suggested to me once, “You mean we’re bowling alone?”

CHETTY: And the reason for the title of that book is social capital is notoriously difficult to measure. And Bob had the creative idea of using the number of bowling alleys in an area, and in particular whether people are bowling alone, as a proxy for social capital.

PUTNAM:The core idea of social capital is so simple that I’m almost embarrassed to say it. It is that social networks have value. There’s a huge amount of work on how social networks help us find jobs.

CHETTY: So I was amazed to find, I remember actually discussing this with Bob in his office at Harvard, that the number of bowling alleys is actually very highly correlated with the rates of upward mobility in our own data.

DUBNER: Were you skeptical when you first looked at that?

CHETTY: Yeah, I was surprised, certainly. I also thought, Wow, Bob really had some foresight in thinking about bowling alleys. But I mention that here because it illustrates a caveat to all of these relationships because these are all correlations rather than causal effects, right? And so it would be surprising if the policy implication to draw from this is that we should build more bowling alleys to increase upward mobility in the United States. And so I think that’s a very important caveat to keep in mind. I mention that because the fifth factor is a bit of an exception to that. The fifth factor is school quality.

CHETTY: We find that places with better public schools, as you might expect intuitively, have much higher rates of upward mobility. And on that dimension, there’s a lot of very good evidence showing that improving the quality of schools  can really, meaningfully, affect rates of intergenerational mobility. So I would treat school quality a little bit differently from the other four factors, where we see strong correlations, but are not yet sure exactly what the causal mechanisms are.

DUBNER: Now each of the factors that you’ve discussed, even I could think of some potential policy ideas to improve them. Do you think much about that, or are you content at this point to do the research that allows policymakers to have those ideas and make those moves?

CHETTY: Absolutely, we want to take the next step to think about what this means for policy, what the causal mechanisms are, what the levers are, that we can push to change some of these factors. So that I think is a good segue now to come back to the Moving to Opportunity experiment, which I see as a way to potentially tackle segregation. One concrete way in which you might try to integrate a city is by giving low-income families housing assistance, to be able to rent houses in more mixed-income neighborhoods. Thereby mechanically reducing segregation. 

DUBNER: Now, I could hear you talking about this, extolling the virtues, the latent virtues, that you ultimately unearthed of a program like Moving to Opportunity, where the government spends a bunch of money to relocate families, and I could think, “Oh, you’re just another big-government-spending advocate.” On the other hand, I know that you have thought quite a bit about the money that is spent in the U.S. on a variety of affordable housing programs, I believe a total of roughly $45 billion. So I’m curious as an economist how you would assess the efficiency of typical or historical housing spending in the U.S., and compare that to the ROI on something like Moving to Opportunity.

CHETTY: I certainly recognize that in a time when we have a government that’s already spending quite a bit on initiatives like this, the answer can’t simply be to just spend more on these problems. I think the power of these data and what we need to be doing is spending money in smarter ways. And so this is a good example of a program, where we’re spending $45 billion on various forms of affordable housing, but we’re not spending that money in the most efficient possible way in order to achieve outcomes like reduced poverty in the long-run. So let me give you a couple of examples on dimensions in which we can, I think, make improvements. First , the optimal age at which to help families move is when their kids are born or when their kids are very young. In practice, we do almost exactly the opposite. We put families on waiting lists when they have kids, and those waiting lists sometimes take many, many years, particularly in the most depressed cities where we really would like to be moving families out of concentrated poverty. And so what ends up happening is that families only get the opportunity to move exactly when their kids are older, which is exactly backwards, right, in terms of what you’d like to be accomplishing here. So that’s a tweak that would not increase program costs but I think would dramatically increase impact. Another example is that the vast majority of housing vouchers are currently being used in very high-poverty, low-opportunity areas. And that is problematic, because we find that it’s really critical to move to these higher-opportunity, low-poverty areas in order to see beneficial outcomes. And so we’re working with HUD and a large group of public-housing authorities to figure out how, again, without spending more money, how we might be able to reform the program so we can get more families that get these vouchers to move to neighborhoods that are going to better serve their kids in the long-run. A further important aspect to think about in the context of cost is that my sense is that the government will actually recover much of the money we invest in programs like this because we see that these children who are earning 30 percent more as adults — they of course are paying more in income taxes themselves as they have higher earnings. And so we calculate that the extra income taxes that they pay actually more than offsets the incremental cost of a program like Moving to Opportunity. So it’s actually a, we think, a budget-saving program in many ways. 

DUBNER: Your work has been cited by politicians certainly across the aisle — by Paul Ryan, you’ve personally tutored Hillary Clinton in mobility issues and perhaps others, you’ve advised the Obama Administration and advised Jeb Bush — I’m really curious to know how, well, I was going to ask you how it feels to have that policy pull, I don’t know if you actually have pull, but at least you’re in the room and you are looked to as an authority who really understands or can explain cause and effect in addressing these issues that policymakers deal with all the time, often not in an evidence-based manner. So could you just talk about that — what those conversations are like, if you feel they’re fruitful, if you feel your research is considered seriously and perhaps even acted upon?

CHETTY: Yes. I am quite encouraged by how interested policymakers are in this type of evidence, and I think there is a genuine interest, often on both sides of the aisle, in trying to do better things with the money that we’re spending. I think when you can come into a room and say, “I’m not saying we should spend an extra $30 billion on affordable housing, I’m saying we should take the money we’re already spending and maybe tweak it in certain ways and enact certain reforms, that based on the evidence will actually deliver better outcomes that we all want to achieve.” I think that can really be impactful. My sense is by the way, a lot of the political influence that matters here is not just at the national level, but at the local level, given the nature of the problem. Mayors can do a lot. We noticed mayors are talking about things differently. Our hope is ultimately the evidence that we’re accumulating and a number of other researchers will ultimately influence policy.

[MUSIC: Joe Hedges “I Can Try” (from: Alchemy)]

Since our interview with Raj Chetty, he has met for an hour-and-a-half with Ben Carson, the presumptive Secretary of Housing and Urban Development. As Chetty described it in an e-mail:  “He and his staff were eager to hear about how the data could help us make better use of the dollars HUD is spending to achieve better outcomes for low-income children.”

We asked Chetty if he’d consider serving in this Administration himself. “I would not have considered serving in either a Trump or Clinton administration, largely because I’d like to continue focusing on research to identify the best policy solutions at this point. Perhaps down the road I’d reconsider.” Chetty also wrote this: “I hope that the new administration will take an evidence-based approach to making policy decisions, for instance by making smart investments in childhood education, affordable housing, and other programs that can create opportunity in effective ways.”

If you want to look at some of the research by Raj Chetty and his colleagues on the Equality of Opportunity Project, I suggest you spend some time on their website. You can look it up: Equality of Opportunity Project. And next time on Freakonomics Radio, we’ll expand this conversation about the state of the American Dream.

One argument we’ve all heard is that the U.S. was too willing to let its manufacturing jobs go to China and elsewhere. Economists were for the most part sanguine; they told us not to worry, that the upsides of global trade would cancel out the downsides of that job loss. What do they say now?

David AUTOR: I’m much less sanguine about it than I used to be. I think if we had realized how traumatic the pace of change would have been, we would have at a minimum had much better policies in place to assist workers in communities that suffered these very severe and immediate consequences and we might have tried to moderate the pace at which it occurred.

The true story of Chinese trade and American job loss – that’s next time, on Freakonomics Radio.

*      *      *

Freakonomics Radio is produced by WNYC Studios and Dubner Productions. This episode was produced by Greg Rosalsky.  Our staff also includes Shelley LewisChristopher WerthStephanie TamMerritt JacobEliza LambertAlison HockenberryEmma MorgensternHarry Huggins, and Brian Gutierrez, and we had help on this episode from Andrew Dunn and Noam Osband. You can subscribe to Freakonomics Radio on iTunes or wherever else you get your podcasts.


这篇关于Is the American Dream Really Dead?的文章就介绍到这儿,希望我们推荐的文章对编程师们有所帮助!



http://www.chinasem.cn/article/851248

相关文章

【ACdream】ACdream原创群赛(18)のAK's dream

这次的群赛AK的不少,7题的也很多啊。。Orrrrrrrz。。。。 暂时只写出7题。。。 A:1196 模拟。。 /** this code is made by poursoul* Problem: 1196* Verdict: Accepted* Submission Date: 2014-09-06 19:12:44* Time: 0MS* Memo

2018CCPC网络赛 C - Dream

题意 给定一个P,现在可以以任意方式重载‘+’和‘*’两个符号,使得满足下列等式 其中定义如下 需要注意的是,重载过后的'*'需要满足以下要求: 也就是说,重载‘*’之后通过计算得到的(k=1,2,3,...,p-1)的取值对于集合{1,2,3,...,p-1}是一个一一映射的关系 ​​​​最后输出2*p行,按照自己重载的‘+’和‘*’,输出: 思路 由于可以以任意方

October——Just believe youself,you really did good job

写这个月英语总结博客时候翻看了一下自己为知上面的记录,从10月1号开始看一直到最后才发现自己的英语学习有那么多的记录,而且很多日报感觉就像是一篇小小的博客,每天的日报都有太多的文字记录,每天都会有新的感悟,每天都会有很多收获。     每天的英语学习主要是能量英语,喜欢听aj老师讲话,喜欢听main text,喜欢vocabulary,更喜欢mini story     学

Luma Dream Machine 更新推出1.5版本

现在,Dream Machine具有更高质量的文本到视频、更智能地理解提示词、自定义文本渲染以及改进的图像生成视频! 喜好儿网 Luma Dream Machine 是由 Luma AI 开发的一款先进的 AI 视频生成模型,旨在通过文本和图#像快速生成高质量、逼真的视频内容。 该模型具有以下主要特点和功能: 高效生成能力:Dream Machine 能够在 120 秒内生成包含 12

You think you use SharePoint but you really don't 你认为你使用了SharePoint,但是实际上不是

You think you use SharePoint but you really don't  你认为你使用了SharePoint,但是实际上不是         Thousands of organizations have implemented SharePoint but fail to exploit the most obvious of SharePoint's many

“Dream Machine“震撼登场!免费推出的AI电影级巨制在网络上引爆热潮

"巅峰初现!视频AI新星‘梦幻制造者’华美登场! 在视频生成技术的赛道上,Luma AI昨日骄傲地揭开了其旗舰创新——'梦幻制造者'(Dream Machine)的神秘面纱,凭借无与伦比的文本到视频及图像到视频转换技术,轻松实现令人惊叹的电影级画质,将创意的边界推向极致。 更令人振奋的是,'梦幻制造者'的API服务面向全球用户免费开放,仅需简单的谷歌账号注册,即可在官方网站上启动这台梦想引擎,

dead--栈队列

创建链表 分别用头插法和尾插法创建并输出带附加头结点的单链表。 头插法是指每个新元素都插入到链表的最前面,即头结点和链表第一个元素之间; 尾插法指的是每个新元素都插入到链表的最后面。 输入描述 输入:一组整数,以EOF为结束。 输出描述 输出:分别创建好的两个链表的元素。 用例输入 1  -4 5 8 -34 0 9 36 -1 77 用例输出 1  -4 5 8 -34 0 9 36 -1

Java面试题:什么是死锁?如何手写一个死锁(Dead Lock)

要想实现一个死锁,首先要明白什么是死锁,我们看一下死锁的定义: 死锁是指两个或两个以上的进程在执行过程中,由于竞争资源或者由于彼此通信而造成的一种阻塞的现象,若无外力作用,它们都将无法推进下去。此时称系统处于死锁状态或系统产生了死锁,这些永远在互相等待的进程称为死锁进程。--百度 用通俗的话来说就是张三跟李四下饭馆吃饺子,张三拿着醋,李四拿着蒜瓣,张三说李四你给我吃点蒜,李四说,那不行,

视频生成模型 Dream Machine 开放试用;微软将停止 Copilot GPTs丨 RTE 开发者日报 Vol.224

开发者朋友们大家好: 这里是 「RTE 开发者日报」 ,每天和大家一起看新闻、聊八卦。我们的社区编辑团队会整理分享 RTE(Real-Time Engagement) 领域内「有话题的 新闻 」、「有态度的 观点 」、「有意思的 数据 」、「有思考的 文章 」、「有看点的 会议 」,但内容仅代表编辑的个人观点,欢迎大家留言、跟帖、讨论。 本期编辑:@CY,@JLT,@鲍勃 01 有话题的

uva10057 A mid-summer night's dream.

开始题目理解错了,wa了几次。 第二个数字是指input里面满足要求的所有数字的个数,我理解成了最小的那个数字的个数。。。。 整体比较简单,就是找中位数 #include<cstdio>#include<cstring>#include<algorithm>#include<cmath>#define MAX 70000using namespace std;int N,s[