Edward Conard

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Macro Roundup


Thursday, May 6, 2021

AI Summary. The share of the U.S. wage bill paid to managers more than doubled from 15% to 32% over six decades, driven by employment growth and rising relative wages in management rather than top executive pay. Over the same period the age of peak earnings shifted from the late 30s to the mid-50s.

David J. Deming National Bureau of Economic Research
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Btw 1960-2020, total wage bill paid to managers in the US more than doubled from 15% to 32%, also leading to an uptick in age of peak earnings.

Is decision-making work claiming a growing share of U.S. wages?

Core argument: The share of the U.S. wage bill paid to managers more than doubled from 15% in 1960 to 32% in 2020, driven by employment growth and rising relative wages in management rather than top-end executive pay.

Between 1960 and 2020, the total wage bill paid to managers in the U.S. more than doubled from 15% to 32%, reflecting a significant shift towards decision-intensive occupations. This trend is not driven by top-end executive pay but by increased employment growth and rising relative wages in management roles, particularly in the private sector and high-growth industries. Concurrently, the age of peak earnings has shifted from the late 30s to the mid-50s, as experience in decision-making roles accumulates over longer periods. The share of jobs requiring decision-making rose from 6% in 1960 to 34% in 2018, indicating an economy-wide demand for skills like problem-solving and adaptability. This shift underscores the growing value of human capital in enhancing productivity and adapting to technological advancements, with implications for wage policies and educational investments.

Takeaways by Macro Roundup® AI

  1. The share of the U.S. wage bill paid to managers more than doubled from 15% in 1960 to 32% in 2020, driven by employment growth and rising relative wages in management rather than top-end executive pay.
  2. The share of U.S. jobs requiring decision-making rose from 6% in 1960 to 34% in 2018.
  3. The age of peak earnings shifted from the late 30s to the mid-50s as experience in decision-making roles accumulates over longer periods.

Tyler CowenComment:“Career earnings growth in the U.S. more than doubled between 1960 and 2017, and the age of peak earnings increased from the late 30s to the mid-50s. I show that a substantial share of this shift is explained by increased employment in decision-intensive occupations, which have longer and more gradual periods of earnings growth…Experience takes longer to accumulate in high variance, non-routine jobs.”

His takeaways from the data, “…The model yields insights about the likely impact of machine learning and artificial intelligence, which are fundamentally prediction technologies…These technologies will be more effective substitutes for human labor as data quality increases, and their impact is potentially larger in occupations where work experience has previously been more important.This paper connects to a growing literature on the economics of data, which emphasizes the value of data as an input into prediction that reduces firm uncertainty but has diminishing returns…This paper also contributes to our understanding of how human capital increases earnings.,,,a standard approach assumes that human capital augments effective units of labor in a production function, so that workers with more schooling or higher ability can produce more output per hour. This paper is related to an older literature showing that human capital can increase allocative efficiency, especially in agriculture, where educated farmers are more likely to adopt new technologies….The model nests the standard productive human capital model as a special case, and integrates productive and allocative human capital together into a unified framework. Finally, it explains how behavioral decision-making errors affect worker productivity and connects to the idea….that human capital improves one’s ability to adapt to change…The third contribution of this paper is to the macroeconomics literature on educational investment and life-cycle wage growth. It is well-known that educated workers have steeper age-earnings profiles, yet the role of occupations is less well understood…The model provides a microfoundation for the learning-by-doing (LBD) model in macroeconomics and helps explain why on-the-job learning and thus wage growth might differ across occupations and countries…Jobs that allow for more worker decision-making facilitate human capital accumulation on the job, which has important implications for wage subsidy policies and for optimal taxation over the life-cycle…Machines increasingly replace people in routine job tasks. The remaining tasks require workers to make open-ended decisions and to have “soft” skills such as problem-solving, critical thinking and adaptability. This paper documents growing demand for decision making and explores the consequences for life-cycle earnings. Career earnings growth in the U.S. more than doubled between 1960 and 2017, and the age of peak earnings increased from the late 30s to the mid-50s. I show that a substantial share of this shift is explained by increased employment in decision-intensive occupations, which have longer and more gradual periods of earnings growth. To understand these patterns, I develop a model that nests decision-making in a standard human capital framework. Workers predict the output of uncertain, context-dependent actions. Experience reduces prediction error, improving a worker’s ability to adapt using data from similar decisions they have made in the past. Experience takes longer to accumulate in high variance, non-routine jobs. I test the predictions of the model using data from the three waves of the NLS. Life-cycle wage growth in decision-intensive occupations has increased over time, and it has increased relatively more for highly-skilled workers.

David J. Deming, "The Growing Importance of Decision-Making on the Job," National Bureau Of Economic Research, April 2021, https://www.nber.org/papers/w28733

The Evidence:

His primary takeaway, “…Career earnings growth in the U.S. more than doubled between 1960 and 2017, andthe age of peak earnings increased from the late 30s to the mid-50s. I show that a substantial share of this shift is explained by increased employment in decision-intensive occupations,which have longer and more gradual periods of earnings growth. To understand these patterns, I develop a model that nests decision-making in a standard human capital framework. Workers predict the output of uncertain, context-dependent actions. Experience reduces prediction error, improving a worker’s ability to adapt using data from similar decisions they have made in the past. Experience takes longer to accumulate in high variance, non-routine jobs…In 1960, earnings of full-time workers peaked in the late 30s, compared to the mid-50s today, and the magnitude of life-cycle earnings growth has more than doubled over this period…

How this has impact wages, “…Figure 5 shows how life-cycle earnings growth in the U.S. has changed over the past sixty years….I group respondents in the 1960, 1980 and 2000 Censuses and the 2007-2009 and 2016-2018 ACS into five-year age bins and estimate log earnings differences relative to the age 20-24 group, controlling for year fixed effects. Age-earnings profiles in the U.S. in 1960 are similar to what Lagakos et al. (2018) find for Mexico over the 1990-2010 period. Earnings growth peaks at about 40 percent when workers are in their late thirties and early forties. This peak is followed by a gradual decline of about 10 percentage points over the next twenty years of age. The 1980 profile is shifted upward, with a peak around 50 percent, but is otherwise similar to 1960. By 2000, U.S. age earnings profiles had shifted outward dramatically, with a higher peak that occurs much later in the life-cycle. In 2000, full-time workers age 50-54 earned about 90 percent more than full-time workers age 20-24. In 2017, earnings growth peaked at nearly 100 percent and workers age 55-59 were earning substantially more than workers in their 30s….”

He first documents those three important facts, “…The rising importance of decision-making can be seen in the rapid growth of management jobs in the U.S. over the last half-century. Figure 1 shows that the total wage bill paid to management and management-related occupations in the U.S. has more than doubled since 1960, from 15 percent to 32 percent. This trend incorporates both increasing employment growth and rising relative wages,is more pronounced in the private sector and in high-growth industries, and is not driven by growth in top-end executive pay…. Figure 2 shows the rising importance of decision-making across the entire U.S. economy, using job vacancy data to directly measure employer skill demands. I combine data from Atalay et al. (2020) and from Burning Glass Technologies (BGT), covering the 1960-2000 and 2007-2019 periods respectively, and I use the actual text of job ads to form a consistent definition of decision-making over time.4 To ensure representativeness, I weight the job ad data by the actual distribution of occupations in each year.5Figure 2 shows that the share of all jobs requiring decision-making increased from 6 percent in 1960 to 34 percent in 2018, with nearly half of the increase occurring just since 2007.The grey lines show the same trend but controlling for occupation fixed effects, which diminishes the impact only slightly, implying that most of the shift toward decision-making is occurring within rather than between occupations. Excluding management occupations diminishes the growth in Figure 2 only slightly, suggesting that growing demand for worker decision-making is an economy-wide phenomenon….”

Important Research from David Demings (we will likely cite this) suggests supervision is a limiting constraint. He highlights that the employment share of jobs that require decision making (like mangers) has increased significantly since 1960. Using managers as an example the share of all wages paid to managers has increased by more than 2x, from 15 percent to 33 percent driven by the private sector (this is not driven by executive comp). During that same ~ period the share of jobs requiring decision making increased from 6% in 1960 to 34% in 2018. This has lead to an uptick in the age of peak earnings as management requires experience.

Ed: To your last point, aside from the lucky risk-takers, i.e., the top 0.1%, I believe the top 5 or 10% are growing more skillful in part because of better (technical) training such as computer programming and engineering) and partially because the jobs pay more for the same amount of training and IQ because they are managing things that are more valuable (3x GDP per captia or more if the top is growing more productive, which I believe).

Steve: it’s tax data, the Statistics of Income (SOI) series “…a nationally representative sample of individual income tax returns collected by the Internal Revenue Service….”

Ben Comment:Thanks!So 2 different estimates of wage growth from 2 different data sets come within 8 percentage points of each other? Nothing to reconcile. It’s the same.

Steve Comment:it’s tax data, the Statistics of Income (SOI) series “…a nationally representative sample of individual income tax returns collected by the Internal Revenue Service….”which they supplement with Census data. They have a table builder that allows you use the data behind their figures.https://www.cbo.gov/publication/56575It’s from the most recent version of the CBO’ recurring Distribution Of Household Income (From October of last year) for 2017.https://www.cbo.gov/system/files/2020-10/56575-Household-Income.pdf

Ben Comment:“Does anyone know what data set this CBO graph comes from? My guess is they’re using tax data so they can get at the top percentages. (This is just for my own edification) I’m not really sure there’s anything to reconcile - both Deming and the CBO seem to saying the same thing, aren’t they? Also, I want to make sure we’re being careful. Deming says career earning’s growth doubled - not that career earnings doubled (I mistyped that in my email response). So the growth rate of wages is faster for workers today than it was in the ‘60s. But look at real output growth (graph attached). It’s much higher today than it was in the ‘60s and ‘70s.Really, we’d need to see the slopes of the lines in the CBO graph and compare them to the slopes of the same wage percentiles in preceding decades. Anyway, Figure 5 in Deming is the log(wage_group) ~ alpha + beta_group * I. The graph shows the coefficients for the different groups. If you look at 55-59, it’s coefficient is almost exactly 1 (it’s a little higher but let’s be conservative), so let’s call it 1. Subtracting the groups, you get: log(w_ 55-59) - log(w_20-24) = 1 (the coefficient from the group) => log(w_ 55-59/ w_20-24) = 1 => w_ 55-59 / w_20-24 = e. To change from log points to percentage changes subtract 1 => (w_ 55-59 - w_20-24) / w_20-24 = 1.72. So over these 35 years in a career (pretty close to 1980 - 2017), Deming’s says people can expect their wages to grow 72%. CBO says 80%. These don’t look different to me. Finally, I don’t the BGT is going to capture very high income earners. They’re most likely looking at the 81-99 %-tile earners. So, I think you’re right but I think the CBO graph and the Deming graph are telling us the same story.”

Ed Comment:To your last point, aside from the lucky risk-takers, i.e., the top 0.1%, I believe the top 5 or 10% are growing more skillful in part because of better (technical) training such as computer programming and engineering) and partially because the jobs pay more for the same amount of training and IQ because they are managing things that are more valuable (3x GDP per captia or more if the top is growing more productive, which I believe).Below them I think the decision-making/talent jobs are expanding faster than the quality (and training) of the jobholders, i.e., the quality of people and their training is not increasing, so the quality of the person in the job is declining as the share of those typed of jobs expands. So i wouldn't expect them to have incomes growing faster than gdp/capita. Only the same job with the same quality person with the same training should be growing 3x faster, if training doesn’t do much for a 70the precitile person. That is. I don’t think a history major does much for someone other than credential their IQ. Ultimately we have to reconcile to this CBO analysis, although this is household not workerhttps://www.cbo.gov/system/files/2020-10/56575-Household-Income.pdf(the top grows faster pretax and transfers):

Ben Comment:“Just finished reading the paper. Interesting read. I think a lot of what it is doing is documenting things we sort of knew or believed. It’s an important task, but I’m not sure how much real insight he’s bringing to the table. The high level take away seems to be “managers make relatively more money today than they used to” but Deming has translated “manager” into “decision maker.” Also, I want to be a little careful if/when we use this in the future. He’s classifying things as decision making instead of supervising other workers. From a job persepective, I think there’s relatively little difference but I think we want to be accurate when we quote/cite/reference this work. My thoughts in the order I thought of them: First, Ed says "with nearly half of the increase occurring just since 2007. “ The most important thing in 2007 (in this paper, at least) is the switch to burning glass technologies (he calls it BG or BGT in the paper) data. I think the nature of BGT data drives some of these results, described below. We should be very very careful about dating a big change to exactly the same year we see the author change data sets. Second, I don’t know that the BG data captures what people say it captures. I think there are, likely, a lot of very low skill, very low decision making jobs that are not posted online. For example, my guess is that this data set almost entirely ignore kitchen staff, fast food workers, delivery people, etc. If that’s the case, I’m not sure how to interpret the employment-share of managers since we would be undercounting total employment. Third, looking at figure 7, it looks to me like the biggest differences in wage growth for low decision and high decision jobs is in 1980. That’s not exactly the story he’s selling. Fourth, figure 2 shows the weighted average share of job vacancies requiring decision making. But I am afraid this is biased going back to point 2 (above). If the main data source is online job posting, I think it goes without saying there would be a huge jump in the skill-level (or decision making) required in those jobs. If I were looking for a bus boy, I don’t think I’d post ads on Indeed. Maybe I’m way off base here but I think this potential bias really hurst the paper’s credibility. Fifth, Figure 1 says managers’ pay has been increasing relative to non-managers'. I think this is a well known fact. I’m not sure what else to get from this. Sixth, Figure 5 is a little bit of a mystery to me. I look at that graph and think about how many people went to law school, business school, master’s programs or PhDs in the 1960s vs. today. Many of the people we think of as “high skill” people are still in school for longer than previously. People who don’t start their career until 25, which I think is relatively common these days, don’t even show up util the second bucket. If these are high skilled people just finishing their master’s in computer science, of course they’re showing huge wage growth! I’m not sure how you could account for the different talent and/or educational distributions within each age-group/year pair but that seems meaningful to me. Seventh, did we need a paper to tell us the smartest workers become the managers? I think that’s one of his key implications. I guess it’s good to document these things but that’s pretty intuitive, right?Eighth, Should we be surprised that career earnings more than doubled when per capita real GDP is up 3x over the same 1960-2017 period? I’m more surprised it’s not up higher, truthfully. “

Monday, May 10, 2021

AI Summary. China's productivity is about 30% of that of leading economies, with business services productivity at 17% of the global frontier, partly reflecting high barriers to entry. Total factor productivity growth slowed from 2.8% a year in the decade before the global financial crisis to 0.7% afterward.

Kenneth Kang International Monetary Fund
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China’s economy only 30% as productive as advanced economies, with aggregate TFP growth slowing from 2.8% 1999-2008 to 0.7% in 2009-2018.

How far does China's productivity lag leading economies?

Core argument: China's economy remains only 30% as productive as the world's leading economies, with business services productivity at just 17% of the global frontier, partly due to high entry barriers.

China's economy, while having experienced significant growth, remains only 30% as productive as the world's leading economies. This productivity gap is particularly pronounced in the services sector, where business services productivity is just 17% of the global frontier, partly due to high entry barriers. Aggregate Total Factor Productivity (TFP) growth has decelerated sharply, from 2.8% in the decade before the global financial crisis to 0.7% between 2009 and 2018. This slowdown has contributed to a decline in GDP growth, which fell below 7% for the first time since 1991 during 2015-18. Despite some signs of improving labor productivity and TFP growth in 2017, these metrics remain significantly lower than pre-crisis levels. The need for institutional reform is emphasized as a critical step towards enhancing resource allocation efficiency and addressing investment misallocation, which could otherwise hinder China's economic potential.

Takeaways by Macro Roundup® AI

  1. China's economy remains only 30% as productive as the world's leading economies, with business services productivity at just 17% of the global frontier, partly due to high entry barriers.
  2. Aggregate total factor productivity growth in China decelerated from 2.8% in the decade before the global financial crisis to 0.7% over 2009–2018.
  3. China's GDP growth fell below 7% during 2015–18 for the first time since 1991, and the staff report emphasizes institutional reform to improve resource allocation and address investment misallocation.

Kenneth Kang and Sanjaya Panth, "Staff Report People's Republic Of China," International Monetary Fund, January 8, 2021, https://www.imf.org/en/Publications/CR/Issues/2021/01/06/Peoples-Republic-of-China-2020-Article-IV-Consultation-Press-Release-Staff-Report-and-49992

“….China has seen remarkable growth over the last decades, but with average sectoral productivity at about one third of the global frontier, there is ample opportunity for more. Productivity gaps are especially large in the services sector—for example, business services productivity stands at only 17 percent of the frontier level, owing in part to high entry barriers…”

Loren Brandt John Litwack Elitza Mileva Luhang Wang Yifan Zhang Luan Zhao, " China’s Productivity Slowdown and Future Growth Potential," World Bank, June 2020, https://openknowledge.worldbank.org/bitstream/handle/10986/33993/Chinas-Productivity-Slowdown-and-Future-Growth-Potential.pdf

World Bank, “…China has experienced a marked slowdown in growth in output per worker since the global financial crisis. In 2015-18, average GDP growth fell below 7 percent for the first time since 1991, to a large extent due to slowing growth in total factor productivity (TFP). Aggregate TFP growth slowed from 2.8 percent in the 10 years before the global financial crisis to 0.7 percent in 2009-18. In 2017, signs of improving labor productivity and TFP growth emerged but both remain significantly lower than their pre-crisis levels. …High investment growth was in part driven by the large fiscal stimulus packages introduced first in in response to the 2009 global recession and later to cushion the 2015-16 growth slowdown (see below). In contrast, TFP growth decreased sharply to 0.7 percent a year in the same period. Other studies also estimate that aggregate TFP has declined in recent years (Wei et al., 2017; Wu, 2017). The TFP growth slowdown is explored further in the following sections…”

Ed, reviewed the IMF report. The report confirmed (see first chart) the WSJ claim, “…China’s economy is only 30% as productive as the world’s best-performing economies …”however it’s data didn’t support, “…The IMF estimates that annual productivity growth averaged just 0.6% between 2012 and 2017, a sharp decline from an average of 3.5% in the previous five years…”However Ben noted a World Bank report that ~ mirrored the WSJ report“…Aggregate TFP growth slowed from 2.8 percent in the 10 years before the global financial crisis to 0.7 percent in 2009-18…” (See second chart)

Michael Pettis, “China’s Economy Needs Institutional Reform Rather Than Additional Capital Deepening," China Financial Markets, July 24, 2021, https://carnegieendowment.org/chinafinancialmarkets/82362

Our friend Mike Pettis commented on the World Bank report when it was published:"...Except to the extent thatthe phrase “improving the efficiency of resource allocation” is carrying an extraordinarily heavy load, I think this is likely to be the wrong approach and will lead mainly to more investment misallocation in the country. What China really needs is a transformation of its institutions in a direction that some might argue is very different from the direction it is currently following...."

Ed Comment:Surprising. Mark important and add to data base. What accounts for the 3x (=1/0.30) difference in our productivity vs China—capital per worker, human capital per worker (i.e. education), or TFP? Perhaps they can close much of the gap with our productivity by investing more capital per worker. That would be relatively easy. So would increasing levels of education. It might be hard to close a TFP gap if they are only growing TFP 0.6% a year (about the same rate as us). What do your 2 china buddies say about this.

Steve Comment:If you are interested Nick Lardy book from a few years ago The State Strikes Back does a great job flushing out the dynamics driving what you are describing. Under Xi they have tacked away from private market driven growth and are currently shrinking the role for private firms (so raising the role of SOE) and the market in the overall economy (Jack Ma’s disappearance is a good illustration), Lardy forecast that was going to slow productivity growth which the World Bank and IMF are now picking up. The men who run China understand that market economies diffuse power in a way that would threaten the CCP control, and it’s a good bet they will always optimize towards maintaining the CCP monopoly on power despite the tradeoffs, slower productivity growth for example. The shift under Xi shows it’s in fact a deliberate choice. Now that is likely enough for their middle term goals (for example in practical terms the PLAN likely has the USN beat today in terms of the distribution of relevant resources) as this chart of military spending inppP terms implies:

Ben Comment:The report also mentions that some of the service industries with the highest productivity gap are the least competitive: "Productivity gaps are especially large in the services sector—for example, business services productivity stands at only 17 percent of the frontier level, owing in part to high entry barriers.” It will also come as no surprise that the state operated enterprises (SOEs) are less productive than private firms. The graph I’m attaching says, while SOEs are declining in relative importance, they’re still important users of capital and credit. My interpretation is that SOEs are not allowing private firms to reach the scale they’d need to become big drivers of productivity and productivity growth. There also seems to be a geographic mobility issue: "Urbanization has helped rural workers to find work in economically active regions, where household incomes are twice as high as in rural areas. A more holistic reform to further improve the hukou system, preserve migrant worker land ownership rights, improve the efficiency of rural land markets, and increase spending on public services and social safety nets, would facilitate labor market mobility and raise growth.”Taken together - it looks to me like China’s productivity lag is unlikely to disappear because the reforms necessary (SOE reform, opening markets, mobility, etc) are unlikely to be popular among the control-obsessed Chinese government. I think will take a lot more than just capital deepening. The World Bank Report graph Steve pulled out shows that the Chinese are adding tons of capital per worker but not very much human capital and very little TFP. The increased capital is unlikely to be that productive if, as the IMF claims, it’s going into poorly run SOEs. So it seems like the Chinese are behind in all three: capital per worker, human capital per worker (i.e. education), and TFP, and they’re struggling to close the gap because they have institutional and structural issues that would make catch up hard even if capital per worker and human capital (education) investment increase.