Job gains exceeded output growth in 2022, bringing GDP per worker back down to its trend level after being well above for an extended period. Employment is consequently set to grow slower than output going forward, as it typically does. Breaking down the GDP per worker by industry, though, shows a significant divergence between the services and goods-producing sectors. Productivity in the services sector was modestly above its pre-pandemic path at the end of last year, suggesting room for relatively strong employment growth, with the gap particularly large in the health care, professional and business services, and leisure and hospitality sectors. Productivity in goods-producing industries, though, was depressed, implying that payroll growth is set to lag that sector’s GDP growth.
Output by Industry
The first release of GDP data from the Bureau of Economic Analysis looks at output from the demand-side perspective, with output evaluated in terms of expenditures by households, businesses, governments and the rest of the world. Two months later, another set of data is published that breaks down GDP by industry, giving a supply-side view of the economy’s performance. To understand these data, remember that GDP is a value-added concept and is not the same as gross output. The sale of a loaf of bread reflects the gross output of bread, with value-added contributions from both the goods side (manufacturing and agricultural) and services side (retail, wholesale, transportation, and financial, among others.) An individual industry’s GDP is a measure of how that sector’s labor and capital inputs, along with the organization of production, have added value to inputs from other domestic industries and imports.
The chart below divides real GDP into the goods-producing sector (mining, construction, and manufacturing) and private services (wholesale, retail, transportation, warehousing, health care, financial, leisure and hospitality, information, and professional and business services), with the values indexed so that 2019:Q4=100. Before the pandemic, both services and goods-producing sectors grew in line with the whole economy, with both rising at around a 2.5 percent annual rate from 2014-19, while the public sector grew at a 1 percent rate over the same time frame.
The Services Sector Grew Much Faster than the Rest of the Economy during the Pandemic
The chart shows that growth since the start of the pandemic has been essentially limited to the services sector, with flat contribution from the public sector and a decline in output in the goods-producing sector. From 2019:Q4 through 2022:Q4, GDP rose at 1.7 percent annual rate, with the private services sector GDP rising at 2.6 percent rate, the goods-producing output falling at a 1.4 percent rate, and the public sector output rising at a 0.2 percent rate.
The relative strength of the services sector might seem surprising given that there was a large shift in consumer spending to goods and away from services during the pandemic. But consider the how much of the value added behind any goods purchase is supplied by the shipping, wholesale, and retail sectors. This is even more the case when the good is imported instead of produced domestically.
Productivity by Industry
Productivity, measured here by GDP per worker, can be used to evaluate how well employment tracked output during the pandemic period. Industries initially responded to the economy shutting down in the second quarter of 2020 by reducing their workforce and these jobs did not all come back when the economy staged a solid recovery in third quarter. (The focus here is on payrolls, but note that average hours worked increased when the economy rebounded, meaning that output per hour did not rise as much as output per worker in 2020-21. This wedge disappeared over the course of 2022.)
Productivity was flat for the whole economy in 2021 as both payrolls and the economy grew at fast rates. Specifically, output rose 6 percent over the four quarters and payrolls rose 5 percent over the same period. By sector, services output was up 8 percent versus payrolls up 6 percent (higher productivity), goods-producing output was up 1 percent versus payrolls increasing 3 percent (lower), and government output was up 1 percent versus payrolls up 2 percent (lower).
In 2022, overall productivity finally fell as GDP growth slowed to 1 percent while payrolls rose 3 percent. By sector, services GDP grew 2 percent versus payrolls up 4 percent, goods-producing output fell 3 percent versus payrolls up 4 percent, and government output and payrolls both grew 1 percent.
With 2022’s retreat, overall productivity at the end of last year was near or modestly above its trend path, with a positive gap in services offsetting a large negative gap for the goods-producing sector. In services, the gap was largely due to health care, professional and business, and leisure and hospitality services industries, while the depressed productivity for the goods-producing sectors was broad based, with low readings for mining, construction, and manufacturing.
Output per Worker in the Goods-Producing Sector Was Unusually Low at the End of 2022
Payrolls and Output
The combination in 2022 of a steep slowdown of GDP growth and continued robust employment growth brought the level of productivity back down to near its trend path. While GDP-by-industry data are not yet available for 2023:Q1, initial releases have overall payroll growth exceeding output growth, bringing the productivity index in the chart above down by less than half a point. Going forward, this productivity reading suggests that payrolls are poised to go back to growing slower than output, in line with productivity trending up over time.
The industry breakdown shows how this story differs across industries. Services employment has some room to grow above output particularly in the health care, professional and business services, and leisure and hospitality sectors. The outlook is less sanguine for the goods-producing sector, with its productivity well below trend at the end of last year. Firms have hired aggressively even in the face of that sector’s declining GDP. Because of the resulting low level of productivity, employment gains in the goods sector could be quite slow relative to any recovery in its output.
Thomas Klitgaard is an economic research advisor in International Studies in the Federal Reserve Bank of New York’s Research and Statistics Group.
Ethan Nourbash is a research analyst in the Federal Reserve Bank of New York’s Research and Statistics Group.
How to cite this post:
Thomas Klitgaard and Ethan Nourbash, “Assessing the Outlook for Employment across Industries,” Federal Reserve Bank of New York Liberty Street Economics, May 10, 2023, https://libertystreeteconomics.newyorkfed.org/2023/05/assessing-the-outlook-for-employment-across-industries/.
The views expressed in this post are those of the author(s) and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the author(s).
Pay-to-use blockchains will never achieve mass adoption
Blockchain projects should learn from Google and Facebook by monetizing their users without directly asking for their money.
Blockchain projects should learn from Google and Facebook by monetizing their users without directly asking for their money.
Pay-to-use blockchains are done.
Not for us, of course — the nerdy crypto crowd. We’re perfectly happy to open wallets, engrave seed phrases on steel cards we bury in the ground, find exchanges we haven’t been blocked from yet, wrap some assets to leverage yield, and become OpSec professionals while we pray to the blockchain gods that the North Koreans aren’t online right now.
We’re fine with this. Years of experience have dulled the pain.
But the mass adoption we all hoped for? It relies on the 99% of people who have zero appetite for such trauma.
If permissionless blockchains are to become the backbone of our online experiences, three major changes need to happen:
- They need to become free.
- They need to become frictionless.
- They need to become familiar.
“Free” means free for the user, “frictionless” means as easy as opening an app or playing a video game, and “familiar” means we need to stop asking regular people to change their behavior to meet the limitations of our tech. We need to meet them where they already are.
Right now, we are zero for three. In fact, we’re so far away from where we need to be that we’re not even trying to address these problems seriously — we’re busy making small, incremental improvements to dysfunctional tech rather than addressing the root of the dysfunction itself.
Free to use
Layer-1 blockchains have been designed, built and funded by people who figure that their value is in directly monetizing the user.
This is a fallacy.
Google serves you ads. It monetizes you indirectly. Facebook monetizes your data, but it doesn’t charge you to use its platform. Apple’s store takes a 30% cut from developers and publishers, not from you.
In all cases, you’re paying — but not with cash.
Google is visited 85 billion times a month. If it monetized directly, charging just one-tenth of one cent to visit its homepage, it could theoretically pull in $85 million every single month.
It doesn’t, as the pool of people who want to pay for that experience with cash is infinitesimally small compared with those who are fine with Google serving them ads and keeping it free.
We are used to being monetized indirectly. But current blockchain protocols monetize us directly, asking us to pay gas fees for each transaction.
One of the most exciting premises of Web3 is that it creates the possibility for aligned incentives between creators and consumers. Countless nonfungible token (NFT) creators have found ways to grow communities around such incentives — but layer-1 blockchain builders just keep doing the same thing, over and over again.
And no matter how small their fees get, thanks to incremental reductions from the likes of Solana or the myriad layer 2s out there, it’s still a fee that most people won’t pay.
Frictionless and simple
We are not very loyal to our apps. Around 77% of daily active users abandon Android apps within three days. Estimates suggest that 25% of all downloaded apps are abandoned within minutes due to poor onboarding.
Andrew Chen, a partner at Andreessen Horowitz investing in games, metaverse and consumer tech, shared the following graph. He suggested that “the best way to bend the retention curve is to target the first few days of usage, and in particular the first visit.”
Compare the onboarding process of a poorly designed app to onboarding to crypto. It may be bad, but it’s not even the same sport. Crypto is the most user-unfriendly technology ever hawked to the public. To those who struggle with tech, it’s the digital equivalent of being punched repeatedly in the face.
By Mike Tyson.
In his heyday.
And over time, crypto has not become much friendlier. You, dear reader, are enjoying a specialist publication. You’re probably a degen with a liquidity position on Uniswap and a Milady in cold storage. But even the words in that sentence make no sense to a normal person.
So, blockchain has to change. It has to become a frictionless experience, a background technology, like everything else we use — from the internet to our phones to our TVs.
We don’t care how they work. We just care that they work.
Familiar and fun
Lastly, and perhaps my single biggest critique of the crypto industry, is how utterly nonchalant we have come about asking billions of people to do things they don’t really want to do.
Crypto has not been good at creating decentralized social media alternatives to Facebook. It has not been good at creating unique gaming experiences. It has not been good at replacing traditional supplier-user Web2 models with aligned-incentive Web3 models.
It has been good at monkey pictures, scams, arguing on Twitter and speculative trading.
This is not to say that crypto is of no use. It absolutely is. The economic models that crypto enables will eventually be seen as a defining shift in power structures and personal autonomy, if we stop replicating the financial system and inequality that made crypto necessary in the first place.
But only if we make it as easy to use as opening an app or clearing a level in a game. Because that’s what people actually do, in real life.
This is all silly, impossible and just wishful thinking — right?
None of this is impossible.
We’ve just been conditioned to believe it is, as a few people have become very, very (very) rich by promoting pay-to-use foundational blockchains that have niche appeal, at best.
Ethereum is a wonderful innovation that will continue to serve as the foundation for decentralized finance precisely because it is secure, decentralized and slow-moving. But it’s not going to revolutionize gaming, as gamers will not pay gas fees. Period.
Solana is great for NFTs, maybe even for stablecoins. It won’t work for smart cities or the Internet of Things.
It’s time for the blockchain industry to acknowledge that our path toward becoming a foundation for consumer tech is blocked by these fundamental truths:
- People don’t want to pay for what should be free.
- They don’t want to do difficult things that should be easy.
- And they don’t want to change their behavior to fit our vision of the world.
The sooner we build protocols and applications that accept these realities, the sooner we silence the critics and change the world.
This article is for general information purposes and is not intended to be and should not be taken as legal or investment advice. The views, thoughts and opinions expressed here are the author’s alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.bitcoin ethereum blockchain crypto etf crypto
Generative AI’s growing impact on businesses
Over recent years, artificial intelligence (AI) has gained considerable traction. And on the back of the resultant excitement, price-earnings (P/E) ratios…
Over recent years, artificial intelligence (AI) has gained considerable traction. And on the back of the resultant excitement, price-earnings (P/E) ratios for stocks even remotely related have soared. Is the excitement premature?
McKinsey recently published an article titled The State of AI in 2023: Generative AI’s Breakout year, draws on the results of six years of consistent surveying and reveals some compelling findings. My takeaway is that service providers are buying the chips and working furiously to offer AI-enhanced solutions, but corporate customers are still some way off embedding those solutions in their own workflows. There exists a lack of understanding, necessitating more education.
The highest-performing organisations however, as showcased in the research, are already adopting a comprehensive approach to AI, emphasising not just its potential but also the requisite strategies to harness its full value.
Irrespective of the industry, and of whether they are service organisations or manufacturers, the most successful industry leaders strategically chart significant AI opportunities across their operational domains. McKinsey’s findings suggest that despite the buzz surrounding the innovations in generative AI (gen AI), a substantial portion of potential business value originates from AI solutions that don’t even involve gen AI. This reflects a disciplined and value-focused (cost) perspective adopted by even top-tier companies.
One of the critical takeaways from McKinsey’s research is the integration of AI in strategic planning and capability building. For instance, in areas like technology and data management, leading firms emphasise the functionalities essential for capturing the value AI promises. They are capitalising on large language models’ (LLM) prowess to analyse company and industry-specific data. Moreover, these companies are diligently assessing the merits of using prevailing AI services, termed by McKinsey as the “taker” approach. In parallel, many are working on refining their AI models, a strategy McKinsey labels the “shaper” approach, where firms train these models using proprietary data to build a competitive edge.
But the number of organisations doing so are relatively few (Figure 1.)
Figure 1. Gen AI is mostly used in marketing, sales, product and service development
Nevertheless, the latest McKinsey global survey reveals the burgeoning influence of gen AI tools is unmistakably evident. A mere year after their debut, a striking one-third of respondents disclosed that their companies consistently integrate gen AI in specific business functions. The implications of AI stretch far beyond its technological aspects, capturing the strategic focus of top-tier leadership. McKinsey quotes, “Nearly one-quarter of surveyed C-suite executives say they are personally using gen AI tools for work,” signalling the mainstreaming of AI in executive deliberations.
In other words, however, a common finding is individuals are using gen AI personally, but their organisation have yet to formally incorporate it into daily processes and workflows. This, despite the “three-quarters of all respondents expect[ing] gen AI to cause significant or disruptive change in the nature of their industry’s competition in the next three years.”
As an aside, AI’s disruptive impact is expected to vary by industry.
McKinsey notes, “Industries relying most heavily on knowledge work are likely to see more disruption—and potentially reap more value. While our estimates suggest that tech companies, unsurprisingly, are poised to see the highest impact from gen AI—adding value equivalent to as much as 9 per cent of global industry revenue—knowledge-based industries such as banking (up to 5 per cent), pharmaceuticals and medical products (also up to 5 per cent), and education (up to 4 per cent) could experience significant effects as well. By contrast, manufacturing-based industries, such as aerospace, automotive, and advanced electronics, could experience less disruptive effects. This stands in contrast to the impact of previous technology waves that affected manufacturing the most and is due to gen AI’s strengths in language-based activities, as opposed to those requiring physical labour.”
Moreover, the journey with AI isn’t devoid of challenges. McKinsey’s findings highlight a significant area of concern: risk management related to gen AI. Many organisations appear unprepared to address gen AI-associated risks, with under half of the respondents indicating measures to mitigate what they perceive as the most pressing risk – inaccuracy.
Drawing from McKinsey’s comprehensive survey, it’s evident that while the realm of AI, particularly gen AI, presents immense potential, it’s a domain still in its very early stages. Many organisations are on the brink of leveraging its power, but there’s still a considerable journey ahead in terms of risk management, strategic adoption, and capability building. As the landscape continues to evolve, McKinsey’s research offers a crucial ‘Give Way’ sign in the roadmap for businesses to navigate the AI frontier.
And that means there is every possibility the boom in AI-related stocks is a bubble. Stock market investors are notoriously impatient and if the benefits (measured in dollars) aren’t coming through investors will recalibrate their expectations. There is every possibility AI is as transformative for the world as promised, but the stock market’s journey is likely to be rocky, inevitably rewinding premature expectations ahead of more sober assessments. Think, ‘fits and starts’.
As a result, investors should have ample opportunity to invest in the transformative impact of AI at reasonable prices again and shouldn’t feel compelled to pay bubble-like prices amid a fear of missing out.stocks
Lights Out for Stocks and Bonds? Not So Fast.
The stock market suddenly has the look of a wounded prize fighter. And the bond market is bordering on being dysfunctional. In a word, the market is…
The stock market suddenly has the look of a wounded prize fighter. And the bond market is bordering on being dysfunctional. In a word, the market is disoriented. Disorientation leads to mistakes.
Don't be fooled. From an investment standpoint, this is one of those periods where those who stay vigilant and pay attention to developments will be in better shape than those who remain confused by circumstances.
As I noted last week: "The relationship between interest rates and stocks is about to be tested, perhaps in a big way. Observe the tightening of the volatility bands (Bollinger Bands) around the New York Stock Exchange Advance Decline line ($NYAD) and the major indexes. This type of technical development reliably predicts big moves. The real arbiter may be the US Treasury bond market. And the place where a lot of the action may take place once bonds decide what to do next may be the large-cap tech stocks. Think QQQ."
Bond Yields Trade Outside Normal Megatrend Boundaries
Big things are happening in the bond market, which could have lasting effects on stocks and the US economy.
I've been expecting a big move in bond yields, noting recently that yields on the 10-Year US Treasury Yield Index ($TNX) were "on the verge of breaking above long-term resistance," while adding that if such a move took place, it "would likely be meaningful for all markets; stocks, commodities, and currencies."
Well, it happened; after the FOMC meeting and Powell's post-mortem (uh, press conference), TNX blew out all expectations and broke above the 4.4% yield area in a big way, marking their highest point since 2007. It was such a big move that it may be an intermediate-term top. At one point in overnight trading on September 21, 2023, TNX hit the 4.5% level. But the current selling in bonds is way overdone, which means that at least a temporary drop in yields is on the cards.
Here's what I mean. The price chart above portrays the relationship between TNX and its 200-day moving average and its corresponding Bollinger Bands. As I noted in my recent video on Bollinger Bands, this is a crucial indicator for pointing out trends that have gone too far and are ripe for a reversal.
In this case, TNX blew out above the upper Bollinger Band, which is two standard deviations above the 200-day moving average. That move is the magnitude of a Category 5 hurricane on steroids and amphetamines. It's also unlikely to remain in place for long unless the market is completely broken.
The price chart suggests we may see a similar situation to what we saw in October 2022 when TNX made a similar move before delivering a nifty fall in yields, which also marked the bottom for stocks.
Meanwhile, as described below, the S&P 500 ($SPX) is reaching oversold levels not seen since the October 2022 and the March 2023 market bottoms.
Oil Holds Up Better Than QQQ For Now
A great way to regroup after a tough trading period is to first look for areas of the market that are exhibiting relative strength. Currently, the oil sector fits the bill. Second, it pays to look for beaten-up sectors where recoveries are happening the fastest. At this point, it's still early for that part of the equation to develop, as too many traders are still shell-shocked.
Starting with a look at West Texas Intermediate Crude ($WTIC), prices are holding above $90 as the supply for diesel and fuel is well below the five-year average. And yes, U.S. oil supplies continue to tighten while the weekly rig count falls.
The NYSE Oil Index ($XOI), home to the big oil companies such as Chevron Texaco (CVX), had a mild reaction to the heavy selling we saw in the rest of the market. XOI looks set to test its 50-day simple moving average in what looks to be a short-term pullback.
Chevron's shares barely budged earlier in the week despite an ongoing, albeit short-lived strike by natural gas workers at its Australian facilities. That's a strong showing of relative strength. You can see that short sellers are trying to knock the stock down (falling Accumulation/Distribution line), but buyers are not budging as the On Balance Volume (OBV) line is holding steady.
On the other hand, the very popular trading vehicle the Invesco QQQ Trust (QQQ) broke below the key support level offered by the $370 price point and its 20 and 50-day simple moving averages. This is an area that I highlighted here last week as being critical support. It now faces a test of the support area at $355. A break below that would likely take QQQ and the rest of the market lower.
An encouraging development is that the RSI for QQQ is nearing 30, which means it's oversold. Let's see what happens next. You can also see a similar pattern in the ADI/OBV indicators to what's evident in CVX above, which suggests that when the shorts get squeezed, it could be an impressive move up.
And for frequent updates on the technicals for the big stocks in QQQ, click here.
The Market's Breadth Breaks Down and Heads to Oversold Territory
The NYSE Advance Decline line ($NYAD) finally broke below its 20 and 50-day simple moving averages and is headed toward an oversold reading on the RSI, which is approaching the 30 area.
The Nasdaq 100 Index ($NDX) followed and is not testing the 14500–14750 support area. ADI is falling, but OBV is holding up, which means we will likely see a clash between short sellers and buyers at some point in the future.
The S&P 500 ($SPX) is in deeper trouble as it has broken below the key support at 4350 and its 20 and 50-day moving averages. On the other hand, SPX closed below its lower Bollinger Band on September 22, 2023, and is nearing an oversold level on RSI. Still, the selling pressure was solid as ADI and OBV broke down.
VIX Remains Below 20
The Cboe Volatility Index ($VIX) is still below the 20 area but is rising. A move above 20 would be very negative.
When VIX rises, stocks tend to fall as it signifies that traders are buying puts. Rising put volume is a sign that market makers are selling stock index futures in order to hedge their put sales to the public. A fall in VIX is bullish as it means less put option buying, and it eventually leads to call buying, which causes market makers to hedge by buying stock index futures, raising the odds of higher stock prices.
Liquidity is Tightening Some
Liquidity is tightening. The Secured Overnight Financing Rate (SOFR) is an approximate sign of the market's liquidity. It remains near its recent high in response to the Fed's move and the rise in bond yields. A move below 5 would be bullish. A move above 5.5% would signal that monetary conditions are tightening beyond the Fed's intentions. That would be very bearish.
To get the latest information on options trading, check out Options Trading for Dummies, now in its 4th Edition—Get Your Copy Now! Now also available in Audible audiobook format!
In The Money Options
Joe Duarte is a former money manager, an active trader, and a widely recognized independent stock market analyst since 1987. He is author of eight investment books, including the best-selling Trading Options for Dummies, rated a TOP Options Book for 2018 by Benzinga.com and now in its third edition, plus The Everything Investing in Your 20s and 30s Book and six other trading books.
To receive Joe's exclusive stock, option and ETF recommendations, in your mailbox every week visit https://joeduarteinthemoneyoptions.com/secure/order_email.asp.bonds sp 500 nasdaq stocks fomc fed us treasury etf currencies testing interest rates commodities oil
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