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Introduction: David Backus writes: This is from my area of work, macroeconomics. The suggestion here is that the economy is growing slowly because consumers aren’t spending money. But how do we know it’s not the reverse: that consumers are spending less because the economy isn’t doing well. As a teacher, I can tell you that it’s almost impossible to get students to understand that the first statement isn’t obviously true. What I’d call the demand-side story (more spending leads to more output) is everywhere, including this piece, from the usually reliable David Leonhardt. This whole situation reminds me of the story of the village whose inhabitants support themselves by taking in each others’ laundry. I guess we’re rich enough in the U.S. that we can stay afloat for a few decades just buying things from each other? Regarding the causal question, I’d like to move away from the idea of “Does A causes B or does B cause A” and toward a more intervention-based framework (Rubin’s model for


Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 The suggestion here is that the economy is growing slowly because consumers aren’t spending money. [sent-2, score-1.241]

2 But how do we know it’s not the reverse: that consumers are spending less because the economy isn’t doing well. [sent-3, score-1.012]

3 As a teacher, I can tell you that it’s almost impossible to get students to understand that the first statement isn’t obviously true. [sent-4, score-0.137]

4 What I’d call the demand-side story (more spending leads to more output) is everywhere, including this piece, from the usually reliable David Leonhardt. [sent-5, score-0.544]

5 This whole situation reminds me of the story of the village whose inhabitants support themselves by taking in each others’ laundry. [sent-6, score-0.295]

6 that we can stay afloat for a few decades just buying things from each other? [sent-9, score-0.289]

7 Regarding the causal question, I’d like to move away from the idea of “Does A causes B or does B cause A” and toward a more intervention-based framework (Rubin’s model for causal inference) in which we consider effects of potential actions. [sent-10, score-0.762]

8 Considering the example above, a focus on interventions clarifies some of the causal questions. [sent-12, score-0.576]

9 For example, if you want to talk about the effect of consumers spending less, you have to consider what interventions you have in mind that would cause consumers to spend more. [sent-13, score-1.882]

10 One such intervention is the famous helicopter drop but there are others, I assume. [sent-14, score-0.157]

11 Conversely, if you want to talk about the poor economy affecting spending, you have to consider what interventions you have in mind to make the economy go better. [sent-15, score-1.224]

12 In that sense, instrumental variables are a fundamental way to think of just about all causal questions of this sort. [sent-16, score-0.377]

13 You start with variables A and B (for example, consumer spending and economic growth). [sent-17, score-0.564]

14 Instead of picturing A causing B or B causing A, you consider various treatments that can affect both A and B . [sent-18, score-0.684]

15 As I never tire of saying, my knowledge of macroeconomics hasn’t developed since I took econ class in 11th grade. [sent-20, score-0.357]


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Introduction: David Backus writes: This is from my area of work, macroeconomics. The suggestion here is that the economy is growing slowly because consumers aren’t spending money. But how do we know it’s not the reverse: that consumers are spending less because the economy isn’t doing well. As a teacher, I can tell you that it’s almost impossible to get students to understand that the first statement isn’t obviously true. What I’d call the demand-side story (more spending leads to more output) is everywhere, including this piece, from the usually reliable David Leonhardt. This whole situation reminds me of the story of the village whose inhabitants support themselves by taking in each others’ laundry. I guess we’re rich enough in the U.S. that we can stay afloat for a few decades just buying things from each other? Regarding the causal question, I’d like to move away from the idea of “Does A causes B or does B cause A” and toward a more intervention-based framework (Rubin’s model for

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Introduction: Economist David Backus writes : A casual reader of economic news can’t help but get the impression that the way to get the economy moving is to have people spend more — consume more, in the language of macroeconomics. Seems obvious, doesn’t it? At the risk of making the obvious complicated, I’d say it’s not so obvious. It’s also not obvious that consumption has gone down since the crisis, or that saving has gone up. So what’s going on with the labor market? I’ll get to the rest of the explanation, but first some background. The other day, I posted posted this remark from Backus: This is from my area of work, macroeconomics. The suggestion here is that the economy is growing slowly because consumers aren’t spending money. But how do we know it’s not the reverse: that consumers are spending less because the economy isn’t doing well. As a teacher, I can tell you that it’s almost impossible to get students to understand that the first statement isn’t obviously true

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Introduction: Hank Aaron at the Brookings Institution, who knows a lot more about policy than I do, had some interesting comments on the recent New York Times article about problems with the Dartmouth health care atlas. which I discussed a few hours ago . Aaron writes that much of the criticism in that newspaper article was off-base, but that there are real difficulties in translating the Dartmouth results (finding little relation between spending and quality of care) to cost savings in the real world. Aaron writes: The Dartmouth research, showing huge variation in the use of various medical procedures and large variations in per patient spending under Medicare, has been a revelation and a useful one. There is no way to explain such variation on medical grounds and it is problematic. But readers, including my former colleague Orszag, have taken an oversimplistic view of what the numbers mean and what to do about them. There are three really big problems with the common interpreta

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Introduction: People keep pointing me to this excellent news article by David Brown, about a scientist who was convicted of data manipulation: In all, 330 patients were randomly assigned to get either interferon gamma-1b or placebo injections. Disease progression or death occurred in 46 percent of those on the drug and 52 percent of those on placebo. That was not a significant difference, statistically speaking. When only survival was considered, however, the drug looked better: 10 percent of people getting the drug died, compared with 17 percent of those on placebo. However, that difference wasn’t “statistically significant,” either. Specifically, the so-called P value — a mathematical measure of the strength of the evidence that there’s a true difference between a treatment and placebo — was 0.08. . . . Technically, the study was a bust, although the results leaned toward a benefit from interferon gamma-1b. Was there a group of patients in which the results tipped? Harkonen asked the statis

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