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Tuesday, October 26, 2021

The Tax Burden on Corporations

Kyle Pomerleau American Enterprise Institute

Date Posted:: October 26, 2021
Is Database:: Database

The US corporate tax burden, currently at a combined rate of 25.8%, is slightly below the OECD average of 26%. Proposed increases to 28% or 26.5% would raise the US rate to 32.3% or 30.9%, positioning it among the highest in the OECD, highlighting potential competitiveness concerns.

The US corporate tax burden, when compared to OECD peers, reveals significant insights under current law and proposed changes. The US combined statutory corporate tax rate is 25.8%, slightly below the OECD average of 26%. However, proposed increases to 28% or 26.5% would elevate the US rate to 32.3% or 30.9%, respectively, placing it among the highest in the OECD. The US marginal effective tax rate (METR) on corporate investment is 18.3%, exceeding the OECD average of 15.5%, due to less favorable depreciation allowances. The average effective tax rate (AETR) in the US is 23.4%, closely aligning with the OECD average of 22.9%. These metrics highlight the competitive challenges and potential impacts of proposed tax reforms on US corporations relative to global counterparts.

Takeaways by Macro Roundup^® AI

The US combined statutory corporate tax rate is 25.8%, slightly below the OECD average of 26%, but proposed increases to 28% or 26.5% would raise the combined US rate to 32.3% or 30.9%, among the highest in the OECD.
The US marginal effective tax rate on corporate investment is 18.3%, above the OECD average of 15.5%, attributed to less favorable depreciation allowances.
The US average effective corporate tax rate is 23.4%, close to the OECD average of 22.9%.

Kyle Pomerleau on American corporations tax burden relative to peer economies, "...This report compares the tax burden on corporations in the United States under current law to the corporate tax burdens of 36 OECD member nations.7 It also considers two leading proposals to reform US corporate income taxation and several alternative policies. Each option is evaluated using three metrics: the statutory corporate income tax rate, the corporate marginal effective tax rate (METR), and the corporate average effective tax rate (AETR)...."

Overall snapshot:

The Statutory Corporate Income Tax Rate, "...Currently, the average value (weighted by gross domestic product) of the combined statutory corporate income tax rate in the OECD is 26 percent. (All references to averages in this report denote gross domestic product-weighted averages.) Combined statutory tax rates range from 9 percent in Hungary to 35 percent in Colombia. Approximately half of OECD countries (19 of 37 member nations surveyed) have combined corporate tax rates between 20 and 25 percent. Only six countries have statutory corporate tax rates below 20 percent. The United States’ combined statutory corporate tax rate of 25.8 percent is 0.2 percentage points below the OECD average and lower than the rate for one-third of OECD members. If the US federal corporate income tax rate is increased to 28 percent, as in Biden’s proposal, the United States would have the second highest combined statutory corporate tax rate in the OECD, at 32.3 percent. The House proposal, which would raise the federal tax rate to 26.5 percent, would increase the United States’ combined statutory corporate tax rate to 30.9 percent, which would be among the highest in the OECD, but still below Portugal and Colombia (Figure 2)..."

The Tax Burden on Corporations: Extended Excerpt Image 1

The Marginal Effective Tax Rate,"...METRs on corporate investment are lower than statutory tax rates in the OECD. The average METR in the OECD is 15.5 percent. Given the large differences in corporate tax bases, the range of marginal tax rates is greater than the range of statutory corporate tax rates. METRs range from -33.5 percent in Portugal to 23.9 percent in Colombia (Figure 3).The METR on corporate investment in the United States under current law is 18.3 percent, 2.8 percentage points higher than the OECD average. The relatively high METR in the United States is driven by less generous depreciation allowances than in other OECD countries. Recall that the calculations assume that the slated expiration of 100 percent bonus depreciation has occurred and that the slated introduction of amortization of R&D costs has taken effect. Those assumptions raise the tax burden on equipment and R&D..."

The Average Effective Tax Rate, "....The average effective tax rate on corporate investment among OECD nations is 22.9 percent (Figure 4). AETRs range from 7.9 percent in Hungary to 31.4 percent in Colombia. Similar to the distribution of statutory corporate tax rates, most countries’ AETRs fall in a tight range. Twenty-seven of 37 OECD nations have AETRs that fall between 15 and 25 percent. Only six countries have AETRs above 25 percent, and only four countries (Belgium, Hungary, Ireland, and Lithuania) have AETRs below 15 percent. Under current law, the AETR in the United States is 23.4 percent. This is in line with the OECD average of 22.9 percent...."

Kyle Pomerleau, "The Tax Burden on Corporations," American Enterprise Institute, October 2021, https://www.aei.org/wp-content/uploads/2021/10/The-tax-burden-on-corporations-A-comparison-of-Organisation-for-Economic-Co-operation-and-Development-countries-and-proposals-to-reform-the-US-tax-system.pdf

Carol Corrado, Jonathan Haskel and Cecilia Jona-Lasinio

Carol Corrado Intelligence

Date Posted:: October 26, 2021
Is Database:: Database

AI investments have not translated into sustained TFP growth post-financial crisis, despite significant unmeasured investment in AI-related intangible assets. @CarolCorrado

Despite significant unmeasured investment in AI-related intangible assets, such as design and training, there is little evidence of a 'J-curve' effect on total factor productivity (TFP) growth. The anticipated upward 'swoosh' in TFP growth, which would indicate long-lasting returns from these investments, is not evident. Data from the US and EU show that while intangible investment has increased, particularly in the US, it has not translated into sustained TFP growth post-financial crisis. The high depreciation rates of intangibles mean that any positive impact on TFP growth appears quickly but is insufficient to counteract the slowdown observed. This suggests that current AI investments may not be significantly mismeasured or that we are still early in the AI investment cycle to detect substantial effects.

Takeaways by Macro Roundup^® AI

Corrado, Haskel and Jona-Lasinio find little evidence of a "J-curve" in total factor productivity growth from unmeasured investment in AI-related intangibles such as design and training.
US and EU data show intangible investment rising, particularly in the US, without translating into sustained TFP growth after the financial crisis.
High depreciation rates of intangibles mean any positive effect on TFP growth appears quickly but is insufficient to offset the observed slowdown, suggesting either limited mismeasurement of AI investment or an early stage of the AI investment cycle.

Carol Corrado on the potential impact of AI on productivity, basically fails to find support for the J curve camp, key quote, "... Our main finding is that, on our data at least, there is indeed plenty of unmeasured investment but little sign of a ‘J-curve’ effect on TFP growth. The upward ‘swoosh’ of the effects of investments whose returns are long-lasting just is not there..."
Core of paper, "...We have set out a framework to illustrate this effect and used a cross-country-industry-year data set for the US and European economies to examine it. To look for unmeasured investment, we have used the CHS approach, which brings into national accounts unmeasured investment in intangible assets which are likely complementary to artificial intelligence, such as design, training, and business process re-engineering. We have also seen that at least some artificial intelligence investment is likely in software investment and is thus already counted. Thus we have harmonized the deflation of software and hardware investment to facilitate comparison across different countries. At least on these data, we do not find much support for the J-curve view. There is, indeed, plenty of unmeasured investment, but the trend in such investment does not seem to be sufficient to give an effect. In particular, the high depreciation rates mean that the missing capital payments which bias TFP growth up follow quickly after a burst of intangible investment, meaning that the upward-sloping part of the J curve, at least on our data, appears very quickly, too quickly to account for a sustained TFP growth slowdown following the financial crisis. That said, we are in the early stages of measuring AI, and since much of it is taking place within firms on their own account, the detection of such investment is extremely difficult..."

The evidence

"...Figure 8 shows shares of tangible and intangible investment across countries from 1997 to 2017. As the graph shows, intangible investment is generally trended upwards and the US invests considerably more intangible investment then the EU. This immediately suggested accounting for intangible investment might potentially be important...."

"...As we have discussed, national accounts do indeed measure intangible investment. Figure 9 plots national accounts intangible investment and the additional investment suggested by CHS set out in Table 2. As the graph shows, the non-national accounts intangible share is higher than the national accounts intangible share, reflecting the fact that expenditure on items such as training and design are large. However, the trend is towards relatively more intensive national accounts intangibles measurement..."

"...Finally, Figure 10 shows growth in real investment for tangibles, intangibles, and software. Notice that, particularly in the US, software spending grows particularly strongly at the end of the period. If artificial intelligence is included in such spending this is suggestive. The position seems much more volatile in Europe..."

"...To start our examination of total factor productivity,Figure 11 shows growth accounting for the EU and US for the years 1997-2007 and 2010-17 (we omit the recession years of 2008 and 2009). As the top panel shows, labour productivity and TFP were growing at a healthy rate in the run up to the financial crisis. The contributions of both tangible and intangible capital deepening were higher in the US than in Europe, and their contributions exceeded those of labour composition. In the period following the financial crisis, the situation changed. As is well known, labour productivity growth slowed substantially by around 0.5 percentage points per annum (pppa) in Europe and 2pppa in the US. US TFP growth also slowed: if anything European TFP growth was around the same. Intangible capital deepening slowed very strongly in the US, and somewhat in Europe...."

"... Figure 12 shows the biases to measured TFP growth for the EU and the US respectively: these are the terms on the right of equation (4), second line. A positive number indicates that measured TFP growth is too big. Recall that the ‘J curve’ hypothesis is that measured TFP growth after the financial crisis is too small and then rises, suggesting that after the financial crisis the bias line should dip and then perhaps rise. At least for the EU, there is not much support for this hypothesis. Note first that the biases are small, at most around 0.2 per cent per annum, but less than this for most of the sample. On average, in the EU the bias is slightly positive but seems to show no particular trend. Turning to the US, there is a hint of a J-curve effect after the mid-2000s. The bias, which was almost 0.4 per cent per annum fell steadily to around -0.2 per cent per annum in 2011, with the spike in the financial crisis years which presumably reflects mismeasured utilization and the like. Since then, the bias has been moving back towards being positive. This then suggests that the pre-crisis TFP growth slow-down, which has been noted by a number of authors, may be somewhat overstated. That said, the effects do not appear to be all that large...."

"...What is the intuition behind this apparent finding that the positive ‘swoosh’ of the ‘J’ appears quite quickly?We can get some insight into this from Figure 13, which shows the different components of the mismeasurement on the right-hand side of equation (4). These are the capitalization effect, the errors-in-shares effect, and the mismeasured capital payments effect. What is notable is that the mismeasured capital payments effect comes in very strongly and quite quickly. The intuition here would seem to be the following. As set out in equation (3), intangibles depreciate quite quickly. That means that a burst in intangible investment rapidly builds up the intangible stock, and the rental price on that stock is relatively large (the per-period rental price of capital has to be large for capital that depreciates to compensate the capital owner for renting out an asset whose value will fall quickly). As a result, the missing rental payments effect comes in very quickly following even unmeasured intangible investment. That means that the second half of the ‘J’ appears rather quickly after the initial dip. All this suggests that investments in artificial intelligence will have a more substantial ‘J effect’ the more they are mismeasured and the lower their depreciation rate. Now, it might be that current AI investments are not substantially mismeasured for the simple reason that we might still be too early in the AI investment cycle. As the earlier graphs showed, the ‘second wave’ of AI might be comparatively recent and so it might be too early to pick up the mismeasurement involved. It might also be that we have mismeasured depreciation rates. At the moment, in our system AI-related investments are given the high depreciation rates the literature has found are appropriate to intangibles. But it may, of course, be the case that artificial intelligence investment has a much lower depreciation rate...."

Carol Corrado, Jonathan Haskel and Cecilia Jona-Lasinio, "Artificial intelligence and productivity: an intangible assets approach," Oxford Review Of Economic Policy, 2021, https://academic.oup.com/oxrep/article/37/3/435/6374681

Wednesday, October 27, 2021

The Bernie Sanders Experiment for America’s Children

James Freeman Wall Street Journal

Date Posted:: October 27, 2021
Is Database:: Database

Research suggests that some pre-K programs may be counterproductive to child development due to stress, raising concerns about the economic and developmental implications of expanding federal pre-K programs.

Max Eden argues that some pre-K programs may be counterproductive to child development due to stress, as studies indicate higher levels of stress hormones in child-care environments compared to home settings, potentially impacting brain architecture. The Brookings Institution found sparse evidence on the long-term academic benefits of scaled-up pre-K programs, with some studies showing null or negative impacts. A Vanderbilt study revealed that while pre-K participants initially outperformed peers, they were surpassed by kindergarten and 3rd grade. The Quebec Family Policy, a similar universal child care model, showed negative outcomes in parenting practices and children's behavior, suggesting potential long-term harm. These findings raise concerns about the economic and developmental implications of expanding federal pre-K programs, urging policymakers to consider the substantial costs to future generations.

Takeaways by Macro Roundup^® AI

A Vanderbilt study found that pre-K participants initially outperformed peers but were surpassed by them by kindergarten and 3rd grade.
The Brookings Institution found sparse evidence on the long-term academic benefits of scaled-up pre-K programs, with some studies showing null or negative impacts.
Max Eden argues that some pre-K programs may be counterproductive to child development, citing studies indicating higher stress hormone levels in child-care settings than at home and the Quebec Family Policy's negative outcomes in parenting practices and children's behavior.

“..In February of this year Max Eden wrote for the Manhattan Institute that for some children pre-K programs are not even useless—they may cause genuine harm given how young the kids are when they are moved out of the household and into a new setting:These findings are consistent with—and likely partly explained by—recent advances in our understanding of neuroscience and child development. Studies suggest that many children exhibit higher levels of stress hormones—colloquially termed “toxic stress”—in child-care environments than they do at home, which could leave a lasting physical impact on their brain architecture…”

James Freeman, "The Bernie Sanders Experiment for America’s Children,"Wall Street Journal, October 26, 2021, https://www.wsj.com/articles/the-bernie-sanders-experiment-for-americas-children-11635280881

The Bernie Sanders Experiment for America’s Children

The story of the Covid era is largely about a political class imposing one burden after another on the young for the theoretical benefit of the old, with little effort to conduct serious calculations of risks and costs. Kids who were never in great danger from the virus have been forced into isolation and quarantine, forced to suffer lost educational and cultural opportunities, and forced to accept an expanded federal debt burden that will haunt them through their taxpaying lives. Many children remain masked all day thanks to edicts from politicos with hardly a care for the long-term consequences to mental health or development.

Now we have Sen. Bernie Sanders (socialist, Vt.), who has been an elected official for nearly 40 years, preparing to add to the federal debt burden once again but claiming that his massive new program is for the children. Mr. Sanders is on the brink of achieving his dream of a federal universal pre-kindergarten program. No doubt it will be costly for taxpayers, but parents should not assume it will benefit their kids.

Like everything else in the pending reconciliation bill, the highly consequential details remain hidden from the public. Reporting on the back-room negotiations for the Democrats’ multitrillion-dollar plan, the Journal’s Andrew Duehren, Kristina Peterson and Natalie Andrews write that the next draft “is still set to include several party priorities, such as universal prekindergarten.” They add that according to a source, House Speaker Nancy Pelosi (D., Calif.) told House Democrats on Tuesday that Congress was on “the verge of something major.”

Let’s hope that sometime before enactment, Americans are able to learn what exactly that something is, given the enormity of the interventions Democrats are attempting into American life.

As for the pre-K plan, a New York Times report accurately described it as part of “a progressive dream for the nation’s education system.” Expanding government is the point. When critics point out disappointing results from various pre-K programs, advocates simply clarify that what they are building is “high-quality pre-K,” as if universal government programs are synonymous with high quality.

In 2017 the Brookings Institution assembled a task force of interdisciplinary scientists from various leading research universities to assess state-funded pre-K programs. The Brookings task force reported:

Convincing evidence on the longer-term impacts of scaled-up pre-k programs on academic outcomes and school progress is sparse, precluding broad conclusions. The evidence that does exist often shows that pre-k-induced improvements in learning are detectable during elementary school, but studies also reveal null or negative longer-term impacts for some programs.

In 2018 Early Childhood Research Quarterly published a paper by Vanderbilt researchers: “This study of the Tennessee Voluntary Pre-K Program (VPK) is the first randomized control trial of a state pre-k program.” The Vanderbilt team reported that while pre-K students in the program initially surpassed kids who didn’t go to pre-K across a battery of achievement tests, this advantage didn’t hold:

During the kindergarten year and thereafter, the control children caught up with the pre-k participants on those tests and generally surpassed them. Similar results appeared on the 3rd grade state achievement tests for the full randomized sample - pre-k participants did not perform as well as the control children.

In February of this year Max Eden wrote for the Manhattan Institute that for some children pre-K programs are not even useless—they may cause genuine harm given how young the kids are when they are moved out of the household and into a new setting:

These findings are consistent with—and likely partly explained by—recent advances in our understanding of neuroscience and child development. Studies suggest that many children exhibit higher levels of stress hormones—colloquially termed “toxic stress”—in child-care environments than they do at home, which could leave a lasting physical impact on their brain architecture.
Such concerns may not necessarily deter Mr. Sanders given his ideological commitment to central planning. Pre-K is just one part of a larger plan for the federal government to be involved in child care from the earliest ages. But voters may want to urge their elected representatives to pause before initiating this Sanders revolution.

Mr. Eden writes:

The most representative and rigorous research on early childhood interventions does not suggest that additional investment would yield great benefits. Indeed, it provides good reason to believe that the opposite is true: additional investment may come at a substantial cost to the next generation.

The Quebec Family Policy provides the closest analogue to what we might expect if America were to make a similar commitment to universal child care. For children in two-parent households, the results were rather grim: deterioration in parenting practices, deterioration in children’s behavior, and long-term harm to their health and life satisfaction.

American tech firms are repatriating billions in offshore profits

Economist Staff The Economist

Date Posted:: October 27, 2021
Is Database:: Database

Domestic profit share of 20 large American tech firms surged from 40% in 2019 to 56% in 2020, driven largely by repatriation efforts.

The domestic profit share of 20 large American tech firms surged from 40% in 2019 to 56% in 2020, driven largely by repatriation efforts. This shift is attributed to the Tax Cuts and Jobs Act (TCJA) of 2017, which introduced incentives like the FDII [foreign-derived intangible income] deduction and penalties such as the Gilti [global intangible low-taxed income] levy. Notably, Microsoft, Apple, Alphabet, and Facebook repatriated $49.3bn. While the pandemic and supply disruptions were unlikely to cause such a significant increase, the TCJA's provisions are now effectively encouraging firms to book more profits domestically. Overall, domestic profits for all publicly traded American multinationals rose from 51% to 56% between 2019 and 2020. This trend suggests a potential increase in tax revenue as more profits are reported in the US.

Takeaways by Macro Roundup^® AI

The domestic profit share of 20 large American tech firms rose from 40% in 2019 to 56% in 2020, a shift attributed largely to repatriation of offshore profits.
Microsoft, Apple, Alphabet and Facebook repatriated $49.3bn, a shift attributed to the 2017 Tax Cuts and Jobs Act's foreign-derived intangible income deduction and its global intangible low-taxed income levy.
Domestic profits for all publicly traded American multinationals rose from 51% to 56% between 2019 and 2020, suggesting tax revenue could rise as more profits are booked in the United States.

"... Looking at 20 American technology companies, Mr Sullivan calculates that in 2017 the firms had booked as much as two-thirds of their profits overseas. Recently, their domestic profits as a share of total profits rose sharply, from 40% in 2019 to 56% in 2020.Pandemic and supply disruptions notwithstanding, actual business activity is unlikely to explain this big a jump. Four tech titans—Microsoft, Apple, Alphabet and Facebook—brought home a whopping $49.3bn. Some firms recorded a far higher share of their profits overseas than in previous years. As the pandemic raged, ten Big Pharma firms booked just 11% of their global profits at home, notes Mr Sullivan, down from a quarter in 2019. Yet overall, for all publicly traded American multinationals, domestic profits as a share of worldwide profits rose from 51% to 56% between 2019 and 2020.The likely explanation, writes Mr Sullivan, is that the TCJA’s international provisions are at last taking effect. The act employed both carrot and stick. The carrot was a tax deduction for foreign-derived intangible income (FDII), including earnings on intellectual property and other assets that are not physical in nature. The stick was its “Gilti” levy on what it called global intangible low-taxed income booked in Ireland, Caribbean islands and elsewhere...."

Economist Staff, "American tech firms are repatriating billions in offshore profits,"The Economist, September 15, 2021, https://www.economist.com/graphic-detail/2021/09/15/american-tech-firms-are-repatriating-billions-in-offshore-profits

American tech firms are repatriating billions in offshore profits

American tech firms are repatriating billions in offshore profits: Extended Excerpt Image 1

“COMPANIES AREN’T going to be able to hide their income in places like the Cayman Islands and Bermuda,” declared President Joe Biden this spring as the US Treasury detailed its tax plan aimed at helping fund $2trn-worth of infrastructure spending. Mr Biden wants to let loose a barrage of legislation to stamp out profit-shifting, following on from Donald Trump’s Tax Cuts and Jobs Act (TCJA) of 2017, which many tax experts argue got watered down with loopholes and exemptions.

Shifting income is the practice by which multinationals artificially divorce their reported profits from where they actually do business. A tech giant, for example, may park its intellectual property in Bermuda, then charge its subsidiary in a higher-tax country such as France or America for using the IP. Profit is thus magicked away to the tax haven.

Martin Sullivan, an American economist, has long been exposing such schemes. He is known for combing the footnotes of large multinationals’ financial reports to divine what taxes are paid where. Now his Tax Notes newsletter has some good news. A great repatriation of profits by American tech companies is in full swing. That should mean higher tax revenue.

American tech firms are repatriating billions in offshore profits: Extended Excerpt Image 2

Looking at 20 American technology companies, Mr Sullivan calculates that in 2017 the firms had booked as much as two-thirds of their profits overseas. Recently, their domestic profits as a share of total profits rose sharply, from 40% in 2019 to 56% in 2020. Pandemic and supply disruptions notwithstanding, actual business activity is unlikely to explain this big a jump. Four tech titans—Microsoft, Apple, Alphabet and Facebook—brought home a whopping $49.3bn.

Some firms recorded a far higher share of their profits overseas than in previous years. As the pandemic raged, ten Big Pharma firms booked just 11% of their global profits at home, notes Mr Sullivan, down from a quarter in 2019. Yet overall, for all publicly traded American multinationals, domestic profits as a share of worldwide profits rose from 51% to 56% between 2019 and 2020.

The likely explanation, writes Mr Sullivan, is that the TCJA’s international provisions are at last taking effect. The act employed both carrot and stick. The carrot was a tax deduction for foreign-derived intangible income (FDII), including earnings on intellectual property and other assets that are not physical in nature. The stick was its “Gilti” levy on what it called global intangible low-taxed income booked in Ireland, Caribbean islands and elsewhere.

Despite Gilti and other elements, the act had been seen as weak because of other provisions that alleviated tax for corporations. But Mr Biden may now have to acknowledge the TCJA had some good effects. He still wants to go further. He plans to raise the Gilti tax rate from 10.5% to 21% and repeal the FDII deduction. The measure with the biggest potential impact would be a 15% “minimum book tax” on big firms that report high profits but have little taxable income. That might further lift the share of profits being booked in America.

IT and Urban Polarization

Jan Eeckhout Federal Reserve Bank Of Cleveland

Date Posted:: October 27, 2021
Is Database:: Database

IT investment is higher in high-wage cities, driving job & wage polarization. From 1990 to 2015, a 21.4% increase in local price index correlated with a $107.43 rise in average IT budget per worker. @JanEeckhout

IT investment is significantly higher in high-wage, expensive cities, driving a polarization of occupations across geography. From 1990 to 2015, a 21.4% increase in local price index correlated with a $107.43 rise in average IT budget per worker, a 3.67% increase. This investment shift has led to a 40% greater decline in routine cognitive jobs in expensive areas compared to the average, with a corresponding 20% wider wage gap between routine and non-routine cognitive jobs. The fall in IT prices accounts for 50% of the rising wage gap and 28% of the employment shift from routine to non-routine cognitive jobs. This trend underscores the role of IT in exacerbating job and wage polarization, particularly in costly urban centers.

Takeaways by Macro Roundup^® AI

From 1990 to 2015, a 21.4% higher local price index was associated with a $107.43 higher average IT budget per worker, a 3.67% increase, with IT investment concentrated in high-wage, expensive cities.
Expensive areas saw a 40% greater decline in routine cognitive jobs than the average, alongside a 20% wider wage gap between routine and non-routine cognitive occupations.
Falling IT prices account for 50% of the rising wage gap between routine and non-routine cognitive jobs and 28% of the employment shift from routine to non-routine cognitive work.

Jan Eeckhout, Christoph Hedtrich and Roberto Pinheiro, "IT and Urban Polarization," Federal Reserve Bank Of Cleveland, September 2021, https://www.clevelandfed.org/en/newsroom-and-events/publications/working-papers/2021-working-papers/wp-2118-it-and-urban-polarization.aspx

"...Fact 2. Routine Cognitive Occupations Decline Faster in Expensive Cities. We now turn to the second result: high cost locations feature a decline in the share of workers in routine occupations, whose tasks can presumably be automated after the introduction of new technology. We use 1980 as the pre-technology period in order to construct the control variables and compare it to the occupational composition in the period 1990-2015. Our focus on such a long span of time is motivated by the fact that in the model, we compare steady-state predictions and ignore short-term dynamics. Furthermore, the national trend shows a decline in the share of routine cognitive jobs starting in the late 1980s. Table 3 presents the results of linear regressions of the change in the MSA’s share of routine cognitive occupations between 1990 and 2015. Specification (1) indicates that a one standard deviation increase in the local price index (an increase of 21.4 percent in the local price index) is associated with a 0.8 percentage point larger drop in the routine cognitive share over 1990-2015. Thus, the most expensive places have about a 4.5 percentage point larger drop in the routine cognitive share relative to the cheapest locations. This is one quarter lower than the average routine-cognitive share of 23 percent in 2015….”

"...A counterfactual exercise where we simulate a fall in IT prices by 65 percent - corresponding to a similar change in the data between 1990 and 2015 - explains both the fall in employment in routine cognitive jobs and the rise in non-routine cognitive jobs. Quantitatively, the exercise explains about 28 percent of the change in employment shares in cognitive occupations...."

"...the employment share of routine cognitive occupations falls substantially more in expensive locations, about 40 percent more relative to the average. Similarly, the wage gap between routine and non-routine cognitive jobs widens even more in expensive locations, about 20 percent more compared to the average. Overall the results indicate a strong role for IT in the displacement of routine cognitive employment and a rise in non-routine cognitive employment and the accompanying polarization of earnings across jobs and cities..."

Evidence"...Fact 1. Stronger IT Adoption in Expensive Cities. Figure 2 visualizes the positive correlation between local rental prices and the average IT budget per worker. Mere inspection shows that the magnitude of the change in IT spending as the rent index changes is sizable. Furthermore,Table 1 shows the results for MSA-level linear regression models of the log of the average IT budget per worker, adjusted for plant employment interacted with three-digit SIC industries, following Beaudry et al. (2010) and Doms and Lewis (2006). The regression results provide support for the hypothesis that IT expenditure per worker is increasing in the cost of housing. The elasticity is highly significant and its value barely changes under different regression specifications. The MSA’s rental price index in 1980 helps to explain the variation in IT budget per worker across MSAs, even after controlling for the presence of natural amenities, housing supply elasticity, and industry composition.9 In specification (1), a one standard deviation increase in the local price index (an increase of 21.4 percent in the 1980 local price index) is associated with an increase of $107.43 in the MSA’s average IT budget per worker. This magnitude corresponds to an increase of 3.67 percent in the average IT budget per worker. Specification (2) finds no statistically significant correlation between the MSA’s share of routine cognitive jobs in 1980 and the average IT budget per worker in 2015. Specification (3) finds a statistically significant correlation between the area’s ratio of college equivalents to non-college equivalents and the average IT budget per worker in 2015, constructed as suggested by Beaudry et al. (2010). However, as we include all controls presented in specifications (1)-(3) together in specification (4), the area’s ratio of college equivalents to non-college equivalents loses statistical significance. Differently, the impact of local rent prices shows just minor change in statistical significance between specifications (1) and (4). Finally, specification (5) controls for the MSA’s average degree of offshorability of local jobs in 1980 - using the task offshorability index presented by Autor and Dorn (2013). We find again that the impact of local housing prices is robust to the addition of the controls...."

Bottomline, "We show that differential IT investment across cities has been a key driver of job and wage polarization since the 1980s. Using a novel data set, we establish two stylized facts:IT investment is highest in firms in large and expensive cities, and the decline in routine cognitive occupations is most prevalent in large and expensive cities. To explain these facts, we propose a model mechanism where the substitution of routine workers by IT leads to higher IT adoption in large cities due to a higher cost of living and higher wages. We estimate the spatial equilibrium model to trace out the effects of IT on the labor market between 1990 and 2015. We find that the fall in IT prices explains 50 percent of the rising wage gap between routine and non-routine cognitive jobs. The decline in IT prices also accounts for 28 percent of the shift in employment away from routine cognitive towards non-routine cognitive jobs. Moreover, our estimates show that the impact of IT is uneven across space. Expensive locations have seen a stronger displacement of routine cognitive jobs and a larger widening of the wage gap between routine and non-routine cognitive jobs….Figure 2 visualizes the positive correlation between local rental prices and the average IT budget per worker. Mere inspection shows that the magnitude of the change in IT spending as the rent index changes is sizable….”

Ed Comment:shows high wages lead to investment.

Reconnecting Americans to the Benefits of Work

Scott Winship Joint Economic Committee

Date Posted:: October 27, 2021
Is Database:: Database

Decline in prime age male labor force participation unlikely due to declining compensation, as median hourly compensation rose 12% from 1973-2019.

The decline in Labor Force Participation (LFP) among prime-age males is unlikely due to declining compensation, as data from 1973 to 2019 shows. While median hourly wages for prime-age men rose 5%, median hourly compensation, which includes non-wage benefits, increased by 12%. At the 10th percentile, wages rose 3%, but compensation rose 10%. Median annual earnings increased by 6%, and when non-wage compensation is included, the rise is 14%. Even under counterfactual scenarios accounting for non-working men, annual compensation still rose by 11%. The decline in LFP is more pronounced among men without a college degree, who are more likely to live in rural areas. This suggests that factors other than compensation, such as cultural or educational disparities, may be driving the decline in workforce participation.

Takeaways by Macro Roundup^® AI

Median hourly compensation for prime-age men rose 12% from 1973 to 2019, versus a 5% rise in median hourly wages, suggesting declining compensation is unlikely to explain falling labor force participation.
At the 10th percentile, hourly wages for prime-age men rose 3% while hourly compensation including non-wage benefits rose 10%, and median annual earnings rose 6% versus 14% including non-wage compensation, from 1973 to 2019.
The decline in prime-age male labor force participation is more pronounced among men without a college degree, who are more likely to live in rural areas, suggesting cultural or educational factors rather than pay.

Reconnecting Americans to the Benefits of Work: Extended Excerpt Image 1

“…Another issue missed by wage trends is that over time, non-wage compensation became a greater share of pay. Non-wage compensation includes employer contributions to employees’ health and other insurance, contributions to retirement savings, and the payroll taxes they pay toward federal and state social insurance programs. These contributions were 13 percent of compensation in 1973 but 19 percent in 2019.51Figure 10 shows the same percentiles as in Figure 9, but this time wages at each percentile are adjusted upward by the same factor to account for non-wage compensation growth.52 This factor adjustment still likely underestimates the growth in compensation, particularly for the lower deciles for whom non-wage compensation often makes up a larger share of total compensation.53 Broader measures of compensation show more growth than a simple accounting of only wages. While median hourly wages among prime-age men rose 5 percent from 1973 to 2019, median hourly compensation rose 12 percent. At the 10thpercentile, wages rose 3 percent and compensation rose 10 percent during the same time period. Instead of falling, the 30th percentile of hourly compensation rose by 4 percent, and the 70th and 90th percentiles rose by 33 percent and 52 percent….”

“…Rather than showing trends in hourly wages,Figure 11 displays the trend in median annual earnings. Line 1 shows the trend in annual wage and salary income among those who have no self-employment earnings. From 1973 to 2019, the increase in the annual median was 6 percent, compared with a 5 percent rise in the median hourly wage of prime-age men. Adding nonwage compensation (Line 2) leads to a 14 percent increase (compared with 12 percent for hourly compensation).55 It turns out that adding the self-employed (and their earnings) to this sample does not change that conclusion: median annual compensation (Line 3) rises 13 percent.56 At the 10th percentile (not shown), annual compensation (including the earnings of the self-employed) rose 4 percent, compared with 10 percent for the 10th percentile of hourly compensation among employees. In this case, lower annual compensation compared to the hourly measure is likely because more workers at the 10th percentile do not work consistently throughout the year or work fewer hours overall. A final criticism of the trends shown in this section is that we cannot observe the compensation of non-working prime-age males who have dropped out of the labor force. It may be that the only reason compensation seems to have risen is because would-be workers with low compensation are more likely to drop out of the data. In this telling, demand for less-skilled workers may have fallen, but the charts above fail to show it because they only look at men who continue to work. Line 4 of Figure 11 attempts to address this criticism by displaying a counterfactual trend. Respondents in the Current Population Survey data who did not work in the previous year are asked why they did not work. The possible answers include inability to find work, being sick or disabled, taking care of home or family, going to school, retirement, being in the Armed Forces, or “other.” Imagine that nonworking men who were disabled, sick, retired, or said they were nonworking for ‘other’ reasons not listed did not become a larger group between 1973 and 2019 relative to workers. Further, imagine that all these additional men who would have been working would have been below-median workers had they held down jobs. Finally, imagine that in every year all men with no earnings who said they could not find work had worked at below-median compensation. Line 4 in Figure 11 attempts to say what the trend in prime-age male compensation would have been under those counterfactual circumstances. According to this counterfactual trend, the annual compensation of prime-age men still would have risen by 11 percent (instead of 13 percent).57 Note too that annual compensation estimates include men who worked part of the year before leaving the workforce, some with no intention of coming back anytime soon. For such men, their annual compensation is a poor indicator of what they command in the labor market, and if such men grow more common in the data over time, it will tend to pull the compensation trends downward. If pay growth is stronger for men with stable connections to the workforce, then the counterfactual of annual compensation may also conceal the growth in pay among below median workers with consistent labor force connections. Given this, the growth in annual compensation over time is very likely understated for men consistently participating in the workforce….”

"... Figure 12 walks through each of these adjustments to the productivity and compensation data, updating work by labor economist James Sherk.59 The two bolded lines below (light green for adjusted productivity and lightest blue for adjusted compensation) use the same implicit price deflator to show how wages and productivity remain closely associated with one another. The top green line shows growth in net hourly productivity for all workers including the self employed. Proponents of the pay-productivity gap often present just the top line and the bottom dark blue line showing average hourly compensation of production and nonsupervisory workers. By using a broader measure of workers (medium blue line) and using more accurately comparable measures of inflation, the pay-productivity gap all but disappears. The medium blue line is adjusted using the Personal Consumption Expenditures (PCE) index which approximates inflation for the things people regularly purchase. The lightest blue line is adjusted using the implicit price deflator (IPD) a better measure of price changes for the things Americans actually produce and is more directly comparable to measures of the associated changes in productivity. Comparing the lightest blue line to the light green line, more accurately aligns net hourly productivity with average hourly compensation using the same implicit price deflator and a similar universe of workers….”

Christina King, Scott Winship and Adam Michel, "Reconnecting Americans to the Benefits of Work," Joint Economic Committee, October 2021, https://www.jec.senate.gov/public/_cache/files/5ac0a254-ff00-4a18-baf0-bdfedb9bb154/connections-to-work.pdf

New Winship on declining LFP, finds that pay hasn't declined, nor has pay lagged productivity (though median pay has lagged productivity growth) implies decline in LFP is driven by other factors (cultural, transfers though he doesn’t attempt to quantify those drivers) Key chart, “…Rather than showing trends in hourly wages,Figure 11 displays the trend in median annual earnings. Line 1 shows the trend in annual wage and salary income among those who have no self-employment earnings. From 1973 to 2019, the increase in the annual median was 6 percent, compared with a 5 percent rise in the median hourly wage of prime-age men. Adding nonwage compensation (Line 2) leads to a 14 percent increase (compared with 12 percent for hourly compensation). It turns out that adding the self-employed (and their earnings) to this sample does not change that conclusion: median annual compensation (Line 3) rises 13 percent.56 At the 10th percentile (not shown), annual compensation (including the earnings of the self-employed) rose 4 percent, compared with 10 percent for the 10th percentile of hourly compensation among employees. In this case, lower annual compensation compared to the hourly measure is likely because more workers at the 10th percentile do not work consistently throughout the year or work fewer hours overall. A final criticism of the trends shown in this section is that we cannot observe the compensation of non-working prime-age males who have dropped out of the labor force. It may be that the only reason compensation seems to have risen is because would-be workers with low compensation are more likely to drop out of the data. In this telling, demand for less-skilled workers may have fallen, but the charts above fail to show it because they only look at men who continue to work. Line 4 of Figure 11 attempts to address this criticism by displaying a counterfactual trend. Respondents in the Current Population Survey data who did not work in the previous year are asked why they did not work. The possible answers include inability to find work, being sick or disabled, taking care of home or family, going to school, retirement, being in the Armed Forces, or “other.” Imagine that nonworking men who were disabled, sick, retired, or said they were nonworking for ‘other’ reasons not listed did not become a larger group between 1973 and 2019 relative to workers. Further, imagine that all these additional men who would have been working would have been below-median workers had they held down jobs. Finally, imagine that in every year all men with no earnings who said they could not find work had worked at below-median compensation. Line 4 in Figure 11 attempts to say what the trend in prime-age male compensation would have been under those counterfactual circumstances. According to this counterfactual trend, the annual compensation of prime-age men still would have risen by 11 percent (instead of 13 percent).

"... Prime-age men’s LFPR peaked at over 97 percent in 1955, slowly declined to 90.5 percent in 2008, and then dropped to 88 percent by 2014, as indicated in Figure 1. It inched up from there, but pre-pandemic it was only 89 percent in 2019. With the onset of the pandemic-induced recession in 2020, prime-age male labor force participation fell below 88 percent in April 2020, a record low. The pandemic may have worsened decades-long trends in declining workforce attachment, especially among lower-income prime-age workers....A large majority of the out of work force prime-age male population, 82 percent, does not have a bachelor’s degree. In the last twenty years, inactivity rose the most among men without a college degree and among those who previously earned low wages.30 These men are disproportionately likely to live in rural localities—particularly in the Southeast....

"...Figure 3 shows that labor force participation rises with education, and the participation trends for men and for women follow the same broad pattern as in Figure 1, regardless of education level. However, the decline in male labor force participation and rise in female participation vary by level of schooling..."

"... Figure 8 displays median wage trends for prime-age men at five different levels of educational attainment.The chart indicates that wages were lower in 2019 than in 1973 among men who lacked a four-year college degree—down 13 percent among those lacking a high school diploma, down 16 percent among those with a diploma but no other schooling, and down 12 percent among those with some college but no bachelor’s degree. Between 1973 and 2019 wages rose 15 percent among prime-age men with a bachelor’s degree but no graduate degree, and they rose 43 percent among those
with a graduate degree.... However, analyzing wages by educational attainment ignores the fact that, as Figure 2 shows, the workforce is growing more educated over time which changes the composition of the education groupings. For instance, looking at men without a high school diploma means assessing the wages of the least educated 30 percent of men in 1973 but the least-educated 10 percent of men in 2019. The group became much more disadvantaged over time, so all else equal, its pay would have fallen even if the pay of the bottom 30 percent did not. Similarly, in 1973, 19 percent of men were in one of the top two groups, but nearly 38 percent were in 2020. Therefore, the men in those top groups were less “elite” in 2020 than 47 years earlier.A better way to assess wage trends for less- and more-advantaged workers is to look at wages at fixed points of the wage distribution. The median wage earner is the one in the middle of the distribution. The earner at the 10th percentile is the one with higher wages than 10 percent of workers but lower wages than 90 percent of workers. Figure 9 shows wage trends for prime-age men at different percentiles. As indicated above, the median male worker saw a wage increase of 5 percent between 1973 and 2019. Below the median, the 10th percentile of wages rose 3 percent, and the 30th percentile fell 3 percent. Above the median, wages grew by 24 percent at the 70th percentile and by 42 percent at the 90th….”

Ed Comment:The last slide shows what a bunch of liars the left are. They claimed and continue to claim that productivity grew without wage growth.

Telemigration and Development: On the Offshorability of Teleworkable Jobs

Richard Baldwin University of Chicago

Date Posted:: October 27, 2021
Is Database:: Database

Telemigration impact remains modest: US service imports from India/China/Brazil/Canada under $11bn (2019). Data contradicts offshore service job exodus concerns. Key metric shows limited displacement effect.

The potential for offshoring teleworkable jobs from the US is significant, with estimates suggesting that 29-38m jobs, or 22-29% of the labor force, could be offshorable. However, the current impact remains modest. In 2019, US imports of Other Business Services (OBS) from countries like India, China, Brazil, and Canada were valued at $11bn or less, resulting in a limited number of telemigrant jobs: 2.38m in India, 360,000 in China, 400,000 in Brazil, and 190,000 in Canada. Despite an 18% increase in US OBS imports, job growth remains small. The development potential for emerging economies is constrained by the small baseline of OBS trade and the lack of large responses to modest trade cost declines.

Takeaways by Macro Roundup^® AI

An estimated 29–38m US jobs, or 22–29% of the labor force, are potentially offshorable as teleworkable work.
US imports of Other Business Services from India, China, Brazil, and Canada were each valued at $11bn or less in 2019, supporting 2.38m telemigrant jobs in India, 400,000 in Brazil, 360,000 in China, and 190,000 in Canada.
Despite an 18% rise in US Other Business Services imports, telemigrant job growth remained small, and development potential for emerging economies is constrained by the small baseline of such trade and the lack of large responses to modest trade cost declines.

Telemigration and Development: On the Offshorability of Teleworkable Jobs: Extended Excerpt Image 1

Lit review of scale estimates, "...The inner product of the low scenario vector and the jobs vector yields the answer that about29 million jobs are offshorable, which was about 22% of the labour force in 2004.The inner product of the high scenario vector with the jobs vector gives the answer that about 38 million jobs might be offshorable (about 29% of all jobs). Blinderrefined the first vector, which describes the offshorability of each occupation, in subsequent research (Blinder 2009; Blinder and Krueger 2013). Some researchers came up with broadly similar results, while others found much lower figures. McKinsey Global Institute (2005) looked at eight representative sectors in various high-wage economies and estimated that about 11% of the jobs were offshorable to developing countries. Bardhan and Kroll (2003) found that about 11% of all US jobs were offshorable, but they limited themselves to occupations where some offshoring was already occurring.Van Welsum and Vickery (2005) gauged that 20% of total US employment was offshorable. Jensen and Kletzer (2010) used a very different approach that leveraged location data inside the US. Geographically concentrated service sectors were more tradable, they suggested, because service consumers tend to be dispersed in proportion to the population. Their work implied that 38% of US workers are in tradable, and therefore offshorable, occupations. In brief, the estimates were never negligible, but some were less than half those of Blinder’s original estimate...."

These jobs are English and soft skill intensive"...As shown in Figure 2.3, jobs that can be done remotely are also occupations that typically place greater importance on oral expression and written expression. Figure 2.4 shows that teleworkable jobs also typically place greater importance on persuasion and social perceptiveness.Both sales engineers and mathematical technicians can telework, but they are at opposite extremes in terms of the importance of persuasion (4.25 versus 1.66 out of 5). To the extent that social and cultural contexts vary across countries, this makes it less likely that a public relations specialist or a sales engineer located in Hanoi is a perfect substitute for one located in Seattle..."

Telemigration and Development: On the Offshorability of Teleworkable Jobs: Extended Excerpt Image 2

Gravity estimates of bilateral frictions for trade in services, "...Table 4.1 shows our results for total services. The first 5 columns are for the 1995-2006 period (corresponding to the original HMR period). The signs and magnitudes of the point estimates are all reasonable in light of their prior findings. Like HMR, we find that distance has a large, negative coefficient that is more negative than what is typically found for goods trade. Given that the totalservices data are such a grab-bag of phenomena - everything from airline tickets and online game playing to pipeline fees and call centres - it is hard to characterize all the roles bilateral distance may play. It may seem counterintuitive that distance should matter more for trade in services, which is less connected to the transportation of physical objects. Total services, however, includes many flows for which face-to-face interaction may be important for setting up, maintaining, or operating the international exchange. For instance, distribution services, tourism, transportation, and financial services are all major services categories and likely require managers and specialists to move among facilities. The coefficients on time-zone differences, shared language, colonial link, and shared legal origin are all significant in the full sample. The coefficients vary substantially across estimation samples defined by different sets of countries. This could reflect substantial heterogeneity within the “total services” set of activities. Trade in services within the EU, for instance, likely has a very different composition than trade among the G20 nations.8 Beyond such compositional effects, measurement error and other data features may also vary across the samples. Finally, the relevant elasticities may not be global constants.9 The sixth column of Table 4.1 reports estimates for more recent years (2005-2019) for the set of nations where both are in the EU and/or the OECD. The estimated effects of distance, shared legal origin, and time-zone are smaller, and the distance trend becomes insignificant, but the effect of shared language more than doubles. The coefficient on the dummy for a colonial link also increases in magnitude. We refrain from overinterpreting these differences in coefficients, since the nature of trade in services shifted considerably between 2005 and 2019, let alone between the 1990s and the later period….”

Telemigration and Development: On the Offshorability of Teleworkable Jobs: Extended Excerpt Image 3

Modest impact,"...The first line of Table 4.4 reports the value of US OBS imports in 2019 from four partners: China, India, Brazil, and Canada. The United States is the largest destination for Brazilian, Canadian, and Indian exports of OBS. It is China's second largest destination after Hong Kong. The figures are all very modest: about $11 billion from India and Canada and even less from China and Brazil. Thus, any export growth will be starting from low initial values. Consolidated across all EU members, China and India do export more OBS to the EU, but these values are again modest. These small flows explain why even seemingly large changes in the impact of distance on OBS flows will have rather modest effects on jobs. We convert the import values to a baseline of foreign jobs using a proxy for the relevant foreign wage: the average hourly wage of an IT worker. US workers in computer and mathematical occupations average $41.51 per hour (Dingel and Neiman 2020). Canadian wages are about threequarters of that, while those in Brazil, China and India are notably lower.10The resulting numbers of telemigrant jobs suggested by the calculation are 190,000 in Canada, 400,000 in Brazil, 360,000 in China, and 2.38 million in India. As a result, the jobs increases associated with an 18% increase in US OBS imports are very modest. For India, the number is 420,000 more telemigrant jobs. The figure is less than 70,000 for the three other economies. The job increases associated with increased exports to the EU are similarly modest...."

Richard Baldwin and Jonathan Dingel, "Telemigration and Development: On the Offshorability of Teleworkable Jobs," University Of Chicago, October 2021, https://papers.ssrn.com/sol3/papers.cfm

See their table 4.4,"...The first line of Table 4.4 reports the value of US OBS imports in 2019 from four partners: China, India, Brazil, and Canada. The United States is the largest destination for Brazilian, Canadian, and Indian exports of OBS. It is China's second largest destination after Hong Kong. The figures are all very modest: about $11 billion from India and Canada and even less from China and Brazil. The resulting numbers of telemigrant jobs suggested by the calculation are 190,000 in Canada, 400,000 in Brazil, 360,000 in China, and 2.38 million in India. As a result, the jobs increases associated with an 18% increase in US OBS imports are very modest. For India, the number is 420,000 more telemigrant jobs. The figure is less than 70,000 for the three other economies. The job increases associated with increased exports to the EU are similarly modest...."

Richard Baldwin and Jonathan Dingel start to try to think about how offshoring will work going forward in terms of moving higher skilled jobs from high wage to low wage countries, "...The starting point is to recognize that many jobs that can be performed remotely require soft skills that make domestic and foreign workers imperfect substitutes. The equilibrium number of telemigrants, therefore, depends on the number of potential foreign suppliers of these tasks and the bilateral frictions that impede trade in these services..."
Ultimately they suspect the impact will be small (on the recipient country)"...Our quantification is based on two simple ingredients. The first is interpreting trade in certain services as payments to telemigrants so that the gravity equation for trade flows can be used to describe how the volume of transactions will respond to lower trade costs. We updated gravity regressions estimated by Head, Mayer, and Ries (2009) to ballpark these magnitudes. The second ingredient is to assess the development potential of this trade by crudely translating trade flows into numbers of jobs using average wages and the approximation that payments for these services are mainly payments to workers. This is clearly not true for some types of services trade - fees for using oil pipelines, for instance - but it seems sensible for categories like Other Business Services, which contains many of the activities that would be transformed by remote work going global. Our quantification exercise yields a simple message: the number of offshored jobs is unlikely to be transformative when it comes to the development paths of most emerging economies. This conclusion stems from a point of fact and a point of economic logic. First, the current baseline of trade in Other Business Services is very small. Multiplying these flows by a factor of two or three would not translate to many jobs in emerging markets. Second, quantitative trade models that assume constant elasticities cannot generate very large responses to modest declines in trade costs. There are no “tipping points” in canonical structural gravity models...."

Ed Comment:I’m not sure I buy it. 2 years ago, you came to the office every day. now you don’t. It just seems like once we get used to people working remotely, there see like there area an awful lot of high-skilled jobs that can be done remotely. I think people around the world and the US are gradually going to make the effort to learn how to do those jobs. And employers will do the same. I think it’s a concern for high-skilled American workers. let’s hope the demand grows faster than the substitution rate.

Ed Comment:Zero incremental transportation cost to offering intelligence once the bandwidth is installed.

Steve Comment:It has to be true. We’ve already seen it with high skilled service jobs that provide a commodity like service, basically website design. My dad’s cousin is a radiologist, maybe 15 years ago his practice got a big contract to do the radiology for the University of Pennsylvania health systems. He went out and hired a bunch of radiologist in Israel and the images were all sent there digitally, he’s been paying the guys 1/3 of the cost of an American radiologist and they do the review and send to the attending doc. The doctors there are happy with the gig because they make more than they would working for the Israeli health service.

The US corporate tax burden, currently at a combined rate of 25.8%, is slightly below the OECD average of 26%. Proposed increases to 28% or 26.5% would raise the US rate to 32.3% or 30.9%, positioning it among the highest in the OECD, highlighting potential competitiveness concerns.

How does the US corporate tax burden compare with OECD peers?

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AI investments have not translated into sustained TFP growth post-financial crisis, despite significant unmeasured investment in AI-related intangible assets. @CarolCorrado

Is unmeasured investment in AI intangibles boosting productivity growth?

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Research suggests that some pre-K programs may be counterproductive to child development due to stress, raising concerns about the economic and developmental implications of expanding federal pre-K programs.

Do universal pre-K programs deliver lasting benefits for children?

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Domestic profit share of 20 large American tech firms surged from 40% in 2019 to 56% in 2020, driven largely by repatriation efforts.

Are tax law changes pushing tech firms to book profits at home?

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IT investment is higher in high-wage cities, driving job & wage polarization. From 1990 to 2015, a 21.4% increase in local price index correlated with a $107.43 rise in average IT budget per worker. @JanEeckhout

Does falling technology prices widen job and wage gaps across cities?

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Decline in prime age male labor force participation unlikely due to declining compensation, as median hourly compensation rose 12% from 1973-2019.

Does falling pay explain declining labor force participation among prime-age men?

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Telemigration impact remains modest: US service imports from India/China/Brazil/Canada under $11bn (2019). Data contradicts offshore service job exodus concerns. Key metric shows limited displacement effect.

How many US jobs can be performed remotely from abroad?

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