Top Ten New York Times Bestselling Author
AI Summary. U.S. workers earn the most Big Macs annually (10,215), but Swiss workers lead on an hourly basis at 7 Big Macs per hour versus the U.S. at 6, reflecting longer American working hours rather than higher hourly wages.
Economist Staff The EconomistCore argument: American workers earn the equivalent of 10,215 Big Macs annually, topping global McWage rankings, but longer working hours reduce U.S. hourly purchasing power to six Big Macs per hour, behind Switzerland’s seven.
On an annual basis, America continues to top our McWages rankings. The average American worker earns enough to buy 10,215 Big Macs a year; Switzerland and Australia are in second and third place, respectively. But American working hours are supersized, too. On an hourly basis, Switzerland comes out on top: the average worker there earns the equivalent of seven Big Macs an hour, compared with America’s six. Australia ranks third, at five burgers for every hour worked.AI Summary. Corporate profit margins have expanded ~250 basis points over the past year, approaching all-time highs, as 23% profit growth far outpaced 8% growth in corporate value added. Labor's share of income is hitting new lows, confirming that margin expansion—not faster economic growth—is the primary driver of record profit levels.
Abiel Reinhart J.P. MorganCore argument: Corporate profit margins expanded nearly 250 bps over the past year and are approaching all-time highs, as domestic profit growth of 23% dwarfed the 8% rise in corporate value added, compressing labor’s share of income to record lows.
Nominal pre-tax corporate profits in the national income and product accounts (NIPA) were very robust in both 2Q (41% [annual rate]) and over the last year (23%). Excluding post-recession spikes, we haven’t seen a year this strong since the mid-2000s. Higher margins [were] the key driver [of profit growth], as 23% y/y domestic profit growth was far in excess of the 8% increase in corporate value added. Profit margins (pre-tax profits divided by value added) increased close to 250bp over the last year, and are approaching all-time highs, whereas the labor share is hitting new lows.AI Summary. Spending and wage growth have largely converged across income groups, with lower- and middle-income households posting after-tax wage growth of 5.2% and 4.2% year-over-year, narrowing a previously wide gap — though the top 5% of earners continue to outpace all others.
David Michael Tinsley, Joe Wadford, Liz Everett Krisberg, Vanessa Cook, et al. Bank of AmericaCore argument: The K-shaped spending and wage growth divide has largely closed since May, with income cohorts converging by July—except the top 5% of earners, who continue to outpace all other groups.
We have discussed the “K-shaped” divide between higher- and lower-income households’ spending and wage growth. But since May, our data has shown a significant narrowing in this gap. As of July, spending and wage growth have largely converged across income cohorts, with the exception of the top 5% of earners, who continue to outpace the rest. A similar dynamic was evident in discretionary spending. In our view, one factor behind the narrowing spending growth gap is stronger after-tax wage growth. For lower- and middle-income households, after-tax wage growth rose to 5.2% YoY and 4.2% YoY, respectively, in July.AI Summary. Guaranteed income transfers reduce total household earnings by more than the transfer amount, as other household members—particularly partners—work fewer hours and are less likely to advance in their jobs.
Elizabeth Rhodes, David Broockman, Eva Vivalt, Patrick Krause, et al. National Bureau of Economic ResearchCore argument: Guaranteed income transfers narrowed the gap between participant income and total household income by approximately $1,700 per year, a reduction driven by lower earnings among other household members rather than collective income gains.
Figure 4 summarizes treatment effects on the standardized family-level indices. The transfers’ effects reshaped the income and employment of other household members. The gap between participant income and total household income fell by about $1,700 per year (s.e. $800). The decline appears to reflect lower earnings among other household members. Effects on employment outcomes are consistent with this interpretation. Partner promotions and transitions to better jobs decrease significantly, but these effects are very small in magnitude. Partner hours and employment show more meaningful declines but are not significant in the unconditional analysis. Several other measures provide supporting evidence of negative effects on labor supply. Net transfers—the value given [to extended family] minus the value received—increased by roughly $135 per year. Estimates for household stability, decision-making, and the division of labor cluster near zero.AI Summary. Higher cognitive ability predicts both higher capital income and higher investment returns, with the return advantage reflecting skill rather than risk-taking, as high-ability individuals earn better risk-adjusted returns while holding lower-risk portfolios.
Spencer Bastani, Kristina Karlsson, Jonas Kolsrud and Daniel Waldenström Uppsala UniversityCore argument: Cognitive ability’s capital income gradient is 3x steeper than labor income gradient in log specifications, driven by higher saving rates.
We document three results. First, cognitive ability predicts capital income. Figure 2 plots mean log income and mean income rank against the nine cognitive ability scores reporting test performance on a 1–9 scale, with both series normalized to zero at the lowest score. In the log specification (Panel a), the capital income gradient is roughly three times steeper than the labor income gradient. In the rank specification (Panel b), the ordering reverses: the labor income gradient is steeper, because the heavy right tail of capital income compresses rank differences. Figure 3 provides a complementary perspective, plotting average cognitive ability across percentiles of the labor and capital income distributions. [The relationship between ability and rank flattens at the top of the labor distribution but strengthens at the top of the capital distribution.] Second, the capital-income gradient is only partially explained by labor income: a decomposition shows that ability is associated with higher saving rates and investment returns through channels beyond labor income. Third, the investment return channel is consistent with skill rather than risk compensation, as high-ability individuals earn higher risk-adjusted excess returns while holding portfolios with lower systematic risk.
Ed Comment:. I edited my claim that low skilled workers turn over frequently form my Harvard Q&A because I only had my experience to back it up (and I didn’t want to talk about Bain’s portfolio companies but this provides the evidence. Highlight this factoid (the reason for including the entry): 70% of low-skilled workers (who represent 17% of workers) leave their job within a year.
Research from FRBSL breaks American workforce into three types of workers in terms of quality. By their measure (though my descriptive words, only 57% of America workers are high quality, and 17% of American workers are effectively low marginal product workers, for example high likelihood of leaving a job within 3m
α (57% of workers)"...On average, α workers have the shortest unemployment spells, the highest likelihood of reaching 2 years of tenure on the same job, and the highest labor income while employed...."so high quality workers.
γ (17% of workers)"...In contrast,γ workers have the longest unemployment spells, a very high likelihood of leaving a job within the first quarter from its inception, a very low likelihood of reaching two years of tenure on a job, and the lowest income while employed...."so low quality, ~ low marginal product workers
β (26% of workers)"...Workers of type β are in between α’s and γ’s..."
What these groups look like by age, gender and educational attainment, "...Using the output of the k-means algorithm, we can assign each individual worker in the data to a particular type. We can then compute the average of some observable characteristics for different types of workers. We find that the average birth year is 1963 for α-workers, 1964 for β-workers and 1967 for γ-workers. These small age differences seem to rule out the possibility that the algorithm simply picks out differences in transition patters that are related to the life cycle of a worker.We find that the fraction of women is 48% for α-workers, 49% for β-workers, and 46% for γ-workers. Again, we take this evidence that the algorithm is not splitting workers based on gender. Lastly, the fraction of workers with some college education is 62% for α workers, 56% for β-workers, and 50% for γ-workers. Along this dimension, there are starker differences, and yet having some college education hardly predicts a worker’s type..."
Core findings, "....Different types of workers have very different patterns of labor market transitions. First, consider the distribution of job durations for different types. For α-workers, the fraction of job spells lasting less than 1 quarter is 14% and the fraction of job spells lasting more than 2 years is 44%. For β-workers, the fraction of job spells lasting less than 1 quarter is 20% and the fraction of job spells lasting more than 2 years is 33%. For γ-workers, the fraction of job spells lasting less than 1 quarter is 36%, and the fraction of job spells lasting more than 2 years is 12%. That is, α-workers are 50% more likely to remain in the same job for more than 2 years than β-workers and 400% more likely than γ-workers. Conversely, γ-workers are 100% more likely to leave a job within 1 quarter than β-workers, and 300% more likely than α-workers. Second, consider the distribution of unemployment durations for different types. For α workers, the fraction of unemployment spells lasting less than 1 quarter is 79% and the fraction of spells lasting more than 1 year is only 5%. For β-workers, the fraction of unemployment spells lasting less than 1 quarter is 39% and the fraction of spells lasting more than 1 year is 5%. For γ-workers, the fraction of unemployment spells lasting less than 1 quarter is 55% but the fraction of spells lasting more than 1 year is 13%. That is, α-workers typically have short unemployment spells and very rarely have long ones; β-workers are less likely to have short unemployment spells but they also have very few long ones; γ-workers are much more likely to experience long unemployment spells compared to αs and βs. The average time spent in unemployment is 3.5% for an α-worker, 9.6% for a β-worker, and 29.2% for a γ-worker. Overall, our classification of workers into types paints a clear picture. Workers of type α are likely to remain on a job for a long period of time and, when they do move into unemployment, they find a new job very quickly. Workers of type β are less likely to remain on a job for a long period of time and, when they move into unemployment, it takes them longer to find a new job. However, workers of type β are unlikely to be stuck into unemployment for more than a year. Workers of type γ are most likely to leave a job within two years. When they do become unemployed, they face a significant probability of becoming long-term unemployed..."