Results for “tgs” 132 found
Martin Wolf reviews TGS
In today’s FT, here is his final paragraph:
I like this book: it starts from provocative theses and ends with a plea for investment in science. I do not agree with all of it, far from it. But it is good to remember that there are far bigger economic stories than the failure of finance or the appeal of austerity. In the long run, our future depends on good ideas. These may not be ours to determine. But they remain ours to influence.
TGS for Britain
Britons will be worse off in 2015 than they were in 2002 as the nation grapples with a severe squeeze on living standards, the Institute for Fiscal Studies said on Wednesday.
Slow income growth before the downturn, the depth of the recession and the sluggishness of the recovery have combined to create easily the longest period of stagnation in real incomes since records began in 1961.
That is from the FT, via Michael Rosenwald.
*Race Against the Machine* and TGS, a comparison
Race Against the Machine is compared to TGS in this forthcoming Economist article, and see this earlier piece. Short bit:
Erik Brynjolfsson, an economist, and Andrew McAfee, a technology expert, argue in their new e-book, “Race Against the Machine”, that too much innovation is the bane of struggling workers. Progress in information and communication technology (ICT) may be occurring too fast for labour markets to keep up.
I agree a version of this is happening (though I wouldn’t use the phrase “too much”) and I don’t see my analysis as so different from theirs. Possibly the three of us could agree on these propositions:
1. IT has seen rapid innovation since the 1990s, and it has led to great gains, but not so much for ordinary workers.
2. Had innovation gains been much greater in non-IT sectors, median living standards would have gone up much more.
I’m not sure if Erik and Andrew would agree with my:
3. Innovation gains in non-IT sectors have been much slower than usual (by post 1870 standards) in the post 1973 period.
I would make a few other points which I suspect they would not agree with, or would wish to reframe:
4. IT productivity was highest in 1995-1998 and those were splendid years for wages and the labor market. That is one reason why I focus on the “glass half empty problem” (low non-IT innovation post 1973), rather than the “too much IT innovation for labor’s good” argument. I do think the “too much innovation for labor’s good” argument explains why the productivity statistics from 2001-2004 didn’t translate into significant real wage gains and that is an important phenomenon. But it’s at the side of my argument rather than central to it. It’s central to their argument.
5. The S&P 500 has been flat, in real terms, for well over a decade. I thus see a truly gloomy productivity picture post 1997-98 or so, weighing IT successes against other problems and slowdowns. (I see this as one issue for their account, since they are asserting good times for capital in recent years.) More generally, the productivity picture for the U.S. between 1995-1998 is quite good and IT-based, and 1973-1995 is poorly understood, highly mixed, but overall still quite inadequate with the 1970s and early 1990s having been worse than most people realize. Overall I see the bad productivity years as bad years for the workers, not vice versa. I also see the broader technological stagnation as setting in well before the IT boom of the 1990s.
6. The biggest employment problems, namely those of late, have come when output is low and/or falling, not rising. That is another reason why my agreement with some of their major propositions comes at the side of my argument rather than being central to it. I understand how the TGS argument fits into the cyclical story of 2007-2011 (excess confidence and overextension, Minsky moment, AD contraction, AS problems slow down the recovery), but I don’t understand how the Race Against the Machine story interacts with the cycle, or if it is even supposed to.
I had a long chat with Erik earlier in the week and found large areas of agreement on many matters (without wishing to speak for him in any formal sense, don’t attribute any of this to him!). We also have quite similar predictions about the future, and this blog post is solely about the past. These “side arguments” I am referring to are already in the process of becoming more central and they will shape our future. The story of a largely stagnant median will become progressively less important relative to the story of labor market polarization, and I suspect that over time Erik and I (I didn’t get to chat with Andrew as much) will agree increasingly.
Addendum: Arnold Kling comments.
More Russ Roberts on TGS
Russ has written a reply to my response, read his whole piece, using his numbers I will put a few follow-up responses under the fold…
1. Male median wage data (down since 1969) suggest divorce is not the main issue; in any case divorce is an economic and psychological catastrophe for many people, and defending living standards by invoking the effects of divorce in the data strikes me as actually more pessimistic than my view. I suspect Russ’s own cultural values are in accord with this perspective. Russ’s postulated effect also does not explain 1998-2011 median wage stagnation very well.
3. The key question is the net bias of statistics, not the bias for consumer durables alone. Our real economic performance on a lot of services — a huge and growing part of the economy — is extremely weak. As durables get cheaper, the biases in measuring their quality become less important.
4. I don’t see that Russ has made an actual counter to my argument here.
6. In successful periods growth shows up in the major mainstream economic statistics, including the median. If it doesn’t, at the very least we should conclude that growth is considerably slower than usual.
9. There is no measured median income progress since 1997 and very little since 1973; that’s not just a cyclical phenomenon. The supposedly good years of the noughties now look like a bubble, not the reality.
On panel data, I read the Pew Report which Russ cites. Over a more than thirty year time period, only 63 percent of children had incomes exceeding those of their parents, and that comparison includes some pre-TGS, quite high-growth years. I don’t find that number impressive at all. In any case the key question is a comparative one, and while the study has not been done, it is highly likely one would find much stronger cross-generational measures of progress for earlier generations.
On reconciling the per capita gdp and median stories, the concept of rent-seeking — most of all through the service sectors and finance and government — will suffice. I know that Russ already agrees with the finance side of this story, maybe the government side too and who knows, perhaps education and medicine as well?
Russ Roberts asks for a response on TGS, plus some all-purpose responses to queries
Read the whole post by Russ, but here is one excerpt:
So my challenge to Tyler is to tell me what he thinks the stagnation in median income signifies. Has there been a change in the returns to education or creativity? Or is it mostly a statistical artifact? Whichever answer he gives, I would like to see him reconcile it with the panel data–the surveys of economic information that follow the same people over time.
I will put the rest under the fold…Russ makes points about household size and immigration and there are brief mentions of CPI bias and rising benefits. A few responses:
1. I discuss household size in the footnotes to TGS. Adjusting for it doesn’t make a huge difference and furthermore the rapid-median-income-growth 1960s were a time when household size was falling quite rapidly. I blogged some of the details here.
2. Immigration doesn’t seem to shift the median enough to create an illusion of stagnation, I blogged the numbers and details here.
3. CPI bias has likely fallen over time, which will make the true median income growth differential over time even greater than the numbers indicate. Furthermore CPI measures are getting better over time and doing more to adjust for quality biases; that’s further bad news. Most of all, a lot of CPI bias is offset by ‘wasteful spending on health care, education, defense, and government yet all counted in gdp” bias.
4. Russ doesn’t mention the internet but it’s getting more monetized — and thus more counted in gdp — all the time. The consumer surplus of the unpriced parts, once you eliminate double-counting, probably isn’t much more than two percent of income. Not “two percent growth a year” but two percent period. I could see it being three or four percent, for sure, but that still won’t overturn the basic slowdown.
5. Rising household debt and abysmal job creation since 2000 suggest to me that the quantity data are in line with the incomes data. Around 1999-2000, stagnation suddenly becomes much worse. The only good years since then are the bubble years, whereas across 1973-1998 there are some truly good economic years (partially offset by some very bad ones).
6. 1995-1998 are a poster child for what a non-stagnating period should look like in terms of wages and median income. Lots and lots of years since 1973 don’t look anything like that period. When the growth is real, it shows up in all of the standard numbers and no mystery variables or invocations of biased measurements are needed. I find this comparison illuminating.
7. I discuss benefits in the book, for the time being I’ll note a) cradle-to-grave private sector jobs, with union-based pension benefits are rarer than they used to be, b) fewer people get health care through their jobs than used to be the case, c) most of the benefits are health insurance but don’t fixate on the size of the expenditure, rather consider that health progress has been slowing down, and d) last year health insurance costs rose by nine percent and no way should that be interpreted as equivalent to an increase in real income, rather it is a sign of system failure. That all said, the text of TGS still leaves room open for a world where virtually all of the benefits of economic growth accrue to the elderly. Such a world still will have a lot of TGS properties.
8. Consumption data often selectively focus on the commodities which have become much cheaper (e.g., flat-screen TVs) and ignore the growth in debt, which now must be paid back.
9. The 2000-2011 case for stagnation is stronger and clearer than the 1973-2011 and there also has been more growth along the latter and longer period of time, plus numbers are easier to interpret across shorter time stretches. I will ask Russ if he at least can buy into TGS for the last eleven to twelve years.
I don’t see panel data as offering a significantly different story from the above but if Russ tosses me a specific citation I will consider it.
On the Conover critique of income stagnation, Karl Smith is devastating. On all the general issues, Arnold Kling comments.
Going back to the Russ excerpt above, I don’t think we should reify median income statistics or give them a final ontological meaning; they are tools. The slow growth in the measured median, or zero growth since that late 90s, strongly suggests that something is seriously wrong with the real economy. That slowdown seems robust to the standard attempts to explain it away.
I don’t dismiss Arnold Kling’s factor price equalization hypothesis, but still the question remains why we haven’t kept leapfrogging ahead of our competitors, as we had done in earlier decades. We’ve become much more of a sitting duck and that will make Samuelson-Stolper effects stronger if you are the world leader on the technological frontier.
On Russ’s other query, there has been an ongoing change in the returns to education. Note the recent study that over the last decade only Ph.ds, MBAs, JDs, and MDs have seen real income gains; even individuals with a Masters degree are getting whacked. One way of reading those numbers is that the workers with lower educational credentials are getting less “manna from heaven” in the form of new innovations cascading into their laps. On top of that, there is more rent-seeking in the economy and many jobs require stronger cognitive skills than in the past.
TGS for anarchists
John Mauldin writes (pdf):
Few would argue that a healthy economy can grow without the private sector leading the way. The real per capita “Private Sector GDP” is another powerful measure that is easy to calculate. It nets out government spending—federal, state, and local. Very like our Structural GDP, Private Sector GDP is bottom-bouncing, 11% below the 2007 peak, 6% below the 2000–2003 plateau, and has reverted to roughly match 1998 levels. Figure 1 illustrates the situation. Absent debt-financed consumption, we have gone nowhere since the late 1990s.
There are some good diagrams at the link. For the pointer I thank Shiraz Allidina.
Small steps toward a much better world (TGS is over)
Terrafugia, Inc., the Woburn, Mass., company developing a flying car or “roadable aircraft” called the Transition, says it received special exemptions from the National Highway Traffic Safety Administration.
The exemptions, which are particular to vehicles that fly and drive on roads, will allow the company to begin delivering the Transition when it is ready late next year. They allow the Transition to use plastic windows instead of standard automotive safety glass, and tires that aren’t normally allowed on multi-purpose vehicles.
The company says laminated safety glass used on cars for decades would add too much weight and could fracture in a way that would obscure the pilot’s view through the windshield. Lightweight polycarbonate windshields used in aircraft are designed in part to withstand impacts with birds, which are generally more of a hazard to pilots than drivers.
The article is here and for the pointer I thank Alex.
The new preface from TGS
It is added to the print version and is available on-line from Reuters, to coincide with the publication of the physical book, which is now in stores. Excerpt:
The original publication of The Great Stagnation was in eBook form only, and I meant for that to reflect an argument of the book itself: The contemporary world has plenty of innovations, but most of them do not benefit the average household. After all, the average household does not own an eReader. It’s not even clear whether the average household buys and reads books. So I viewed the exclusive electronic publication, somewhat impishly, as an act of self-reference to the underlying problem itself. It was therefore a bit amusing when some critics suggested that the new medium of the eBook itself refuted the book’s stagnation theory—quite the contrary.
Corin Wagen defends Leviticus (from my email)
In your recent conversation with Misha Saul, you and Misha discussed your joint dislike for Leviticus. I can’t say that I find Leviticus a page-turner, but the book that’s done the most to help me understand why it’s important and what role it plays in the movement of the narrative is L Michael Morales’s book Who Shall Ascend The Mountain Of The Lord? (Amazon). A number of folks I’ve talked to have found this book very helpful. (Disclaimer: Morales is a Protestant, as is D. A. Carson (the editor), so the biases are apparent.)
Briefly, his argument is that Leviticus serves to resolve the narrative tension introduced by the ending of Exodus. Exodus 40:34–35: “Then the cloud covered the tent of meeting, and the glory of the Lord filled the tabernacle. And Moses was not able to enter the tent of meeting because the cloud settled on it, and the glory of the Lord filled the tabernacle.” The tension introduced by Genesis 3 is that God and man can no longer co-exist because of sin. Moses is able to ascend Sinai, speak with God, and bring the people his laws, but even after building the tabernacle and the ark, even Moses is unable to reside in the presence of God—let alone the people who cannot even touch Sinai!
The rules of Leviticus presents the conditions to resolve this tension and allow the people access to God—protected by the rules that God gives them. In particular the book has a chiastic structure centered around Leviticus 16 (Yom Kippur) where the high priest himself is able to enter the Holy of Holies. There’s other points about how the structure of the tabernacle and later the temple mirrors Eden, etc. “Interesting throughout,” as they say.
Info Finance
Excellent post by Vitalik on prediction markets and the broader category of what he calls info finance:
Now, we get to the important part: predicting the election is just the first app. The broader concept is that you can use finance as a way to align incentives in order to provide viewers with valuable information.
…Similar to the concept of correct-by-construction in software engineering, info finance is a discipline where you (i) start from a fact that you want to know, and then (ii) deliberately design a market to optimally elicit that information from market participants.
One example of this is prediction markets: you want to know a specific fact that will take place in the future, and so you set up a market for people to bet on that fact. Another example is decision markets: you want to know whether decision A or decision B will produce a better outcome according to some metric M. To achieve this, you set up conditional markets: you ask people to bet on (i) which decision will be chosen, (ii) value of M if decision A is chosen, otherwise zero, (iii) value of M if decision B is chosen, otherwise zero. Given these three variables, you can figure out if the market thinks decision A or decision B is more bullish for the value of M. 
Info finance as a three-sided market: bettors make predictions, readers read predictions. The market outputs predictions about the future as a public good (because that’s what it was designed to do).
Importantly, Vitalik notes that AI agents can make decision and prediction markets more liquid at much lower cost.
One technology that I expect will turbocharge info finance in the next decade is AI (whether LLMs or some future technology). This is because many of the most interesting applications of info finance are on “micro” questions: millions of mini-markets for decisions that individually have relatively low consequence. In practice, markets with low volume often do not work effectively: it does not make sense for a sophisticated participant to spend the time to make a detailed analysis just for the sake of a few hundred dollars of profit, and many have even argued that without subsidies such markets won’t work at all because on all but the most large and sensational questions, there are not enough naive traders for sophisticated traders to take profit from. AI changes that equation completely, and means that we could potentially get reasonably high-quality info elicited even on markets with $10 of volume. Even if subsidies are required, the size of the subsidy per question becomes extremely affordable.
Incentives matter, for childbirth too
That is the topic of my latest Bloomberg column, here is one bit:
There is in fact a pronounced “baby bump” in December. The numbers show that induced deliveries and scheduled Caesarian section deliveries are higher than average toward the very end of the year.
Why? In the US, there are significant tax advantages to having a child. If you are a single parent with an adjusted gross income below $112,500, an extra child brings you a $3,600 child tax credit per year.
So — speaking strictly about the tax implications, of course — a New Year’s Eve baby is better than New Year’s baby: You can claim that little bundle of joy as a dependent for the entire year, even though they were only there for a day of it. Yet further benefits could come from state-level earned income tax credit and child tax credit programs.
You might argue that the parents, not the kids, gain the most from these tax benefits. You might also ask if there are some costs to these newly born children. In fact, the study shows that these children have lower birthweights. Further research shows that the accelerated births had noticeable impacts on the children, again finding lower birthweights.
The good news, however, is that those same kids have accelerated weight gains over the course of subsequent examinations. The further good news is that those children reach early development milestones at a faster pace than average. That may reflect the extra income the parents have, since higher income and other positive parental features do predict better developmental outcomes for the kids.
Don’t wait until April!
Response from Devin Pope, on religious attendance
All of this is from Devin Pope, in response to Lyman Stone (and myself). Here was my original post on the paper, concerning the degree of religious attendance. I won’t double indent, but here is Devin and Devin alone:
“I’m super grateful for Lyman’s willingness to engage with my recent research on measuring religious worship attendance using cellphone data. Lyman and I have been able to go back and forth a bit on Twitter/X, but I thought it might be useful to send a review of this to you Tyler.
For starters, I appreciate that Lyman and I agree on a lot of stuff about the paper. He has been very kind by sharing that he agrees that many parts of my paper are interesting and “very cool work”. Where we disagree is about whether the cellphone data can provide a useful estimate for population-wide estimates of worship attendance. Specifically, Lyman’s concerns are that due to people leaving their cellphones at home when they go to church and due to questionable cellphone coverage that might exist within church buildings, the results could be super biased. He sums up his critiques well with the following: “Exactly how big these effects are is anyone’s guess. But I really think you should consider just saying, `This isn’t a valid way of estimating aggregate religious behavior. But it’s a great way to look at some unique patterns of behavior among the religious!’ Don’t make a bold claim with a bunch of caveats, just make the claim you actually have really great data for!” This a very reasonable critique and I’m grateful for him making it.
My first response to Lyman’s concerns is: we agree! I try to be super careful in how the paper is written to discuss these exact concerns that Lyman raises. Even the last line of the abstract indicates, “While cellphone data has limitations, this paper provides a unique way of understanding worship attendance and its correlates.”
Here is where we differ though… To my knowledge, there have been just 2 approaches used to estimate the number of Americans who go to worship services weekly (say, 75% of the time): Surveys that ask people “do you go to religious services weekly?” and my paper using cell phone data. It is a very hard question to answer. Time-use surveys, counting cars in parking lots, and other methods don’t allow for estimating the number of people who are frequent religious attenders because of their repeated cross-sectional designs.
There are definitely limitations with the cellphone data (I’ve had about 100 people tell me that I’m not doing a good job tracking Orthodox Jews!). I know that these issues exist. But survey data has its own issues. Social desirability bias and other issues could lead to widely incorrect estimates of the number of people who frequently attend services (and surveys are going to have a hard time sampling Orthodox Jews too!). Given the difficulty of measuring some of these questions, I think that a new method – even with limitations – is useful.
At the end of the day, one has to think hard about the degree of bias of various methods and think about how much weight to put on each. The degree of bias is also where Lyman and I disagree. In my paper, I document that the cell phone data do not do a great job of predicting the number of people who go to NBA basketball games and the number of people who go to AMC theaters. I both undercount overall attendance and don’t predict differences across NBA stadiums well at all.
The reason why Lyman is able to complain about those results so vociferously is because I’m trying to be super honest and include those results in the paper! And I don’t try to hide them. On page 2 of the paper I note: “Not all data checks are perfect. For example, I undercount the number of people who go to an AMC theater or attend NBA basketball games and provide a discussion of these mispredictions.”
There are many other data checks that look really quite good. For example, here is a Table from the paper that compares cellphone visits as predicted by the cellphone data with actual visits using data from various companies:
The cellphone predictions in the above table tend to do a decent job predicting many population-wide estimates of attendance to a variety of locations. The one large miss is AMC theaters where we undercount attendance by 30%. Now about half of that undercount is because the data are missing a chunk of AMC theaters (this is not due to a cellphone pinging issue, but due to a data construction issue). But even if one were to make that correction, we undercount theater attendance by 15%.
Lyman argues that one should be especially worried about undercounting worship attendance due to people leaving their phones at home. I agree that this is a huge concern that is specific to religious worship and doesn’t apply in the same way for trips to Walmart. I run and report results from a Prolific Survey (N=5k) that finds that 87% of people who attend worship regularly indicate that they “always” or “almost always” take their phone to services with them. So definitely some people are leaving their phones at home, but this survey can help guide our thinking about how large that bias might be. Are Prolific participants representative of the US as a whole? Certainly not. There is additional bias that one should think about in that regard.
Overall, my view is that estimating population-wide estimates for how many people attend religious services weekly is super hard and cellphone data has limitations. My view is that other methods (surveys) also have substantial limitations. I do not think the cellphone data limitations are as large as Lyman thinks they are and stand by the last line of the abstract that once again states, “While cellphone data has limitations, this paper provides a unique way of understanding worship attendance and its correlates.”
All of that was Devin Pope!
Zimbabwe launches new gold-backed currency
Zimbabwe has introduced a new gold-backed currency called ZiG – the name stands for “Zimbabwe Gold”.
It is the latest attempt to stabilise an economy that has lurched from crisis to crisis for the past 25 years.
Unveiling the new notes, central bank governor John Mushayavanhu said the ZiG would be structured, and set at a market-determined exchange rate.
The ZiG replaces a Zimbabwean dollar, the RTGS, that had lost three-quarters of its value so far this year.
Annual inflation in March reached 55% – a seven-month high.
Zimbabweans have 21 days to exchange old, inflation-hit notes for the new currency.
However, the US dollar, which accounts for 85% of transactions, will remain legal tender and most people are likely to continue to prefer this…
He committed to ensuring that the amount of local currency in circulation was backed by equivalent value in precious minerals – mainly gold – or foreign exchange, in order to prevent the currency losing value like its predecessors.
Here is the full story, file under “less than fully credible.” That said, I do think that many of the important monetary innovations of the future are likely to come in Africa.
Will Rinehart on YIMBY and Sure (from my email)
I won’t double indent, everything that follows is from Will and not from me:
“…you put up the post “MR commentator ‘Sure’ on YIMBY” and I wanted to send an email because I’m not sure I agree with the comment, given Rosen-Roback and some recent research in urban economics.
Sure writes that “what people want from their housing is overwhelmingly a short commute and low density,” which is only half right. People want amenities, including a short commute and space, but more importantly, they want good schools and a mix of local consumption goods.
One of the most important amenities for a school is its school district. Basically, any survey of home buyers ranks school districts at the very top of demands, and they show a willingness to give up space in order to be in better schools.
Then, there’s the broad notion of local consumption. Sparked by Miyauchi, Nakajima, and Redding (2021), urban economics is shifting to include smartphone data in order to understand the consumption side of agglomeration better. It is an area we know little about because data was so hard to collect.
Combining smartphone data with economic census data, the authors show that non-commuting trips are frequent, more localized than commuting trips, and are strongly related to the availability of nontraded services. From here, the authors augmented a standard model to incorporate travel to work and this hyper local travel. Their findings are powerful. Consumption access makes a sizable contribution relative to workplace access in explaining the observed variation in residents and land prices across locations.
So when Sure asks,
Suppose they do [liberalize housing], who is going to move in [to Arlington and Alexandria]? The guys who are buying in Chantilly because they want space? Or the guys crowded into a apartment building in NE DC who work in Foggy Bottom?I submit it will be the latter.
I think that’s probably wrong. The people moving into those homes in the suburbs will not want space but good schools first and foremost. So it very well could be people from Chantilly move to Arlington, but I would suspect that Arlington will get more people because they generally have better schools than Alexandria and others. Thus, the amenity of interest would be education not space.
Sure is right that “If we liberalize zoning everywhere (i.e. the YIMBY dream) then we should expect a net movement from the areas where people say they don’t want to live to the areas where they say they want to live.” But they misstep in thinking that “on net that means out of the urban core and into something less dense.” In the open-city Rosen-Roback model, generally speaking, liberalization of housing would mean people head into the urban core and into the suburbs.
In total, Sure seriously overweights commuting time and housing space, and underweights education as an amenity and local consumption.”
Google searches for inequality (from my email)
From Jared Sleeper:
I was surprised to see this magnitude of effect. Google searches for “inequality” are twice as high during the academic year as they are during the summer. They double in September vs. August and then collapse by 50% in June vs. May.
