Macro Roundup

Nathan Wilmers and Clem Aeppli, "Consolidated Advantage: New Organizational Dynamics of Wage Inequality," Washington Center For Equitable Growth, March 2021https://equitablegrowth.org/working-papers/consolidated-advantage-new-organizational-dynamics-of-wage-inequality/

Findings explained (see two charts), “…Figure 2 shows the main results from these models. While in the one-way models both between workplace and between-occupation variance increase over time (see Figure A.8 in the appendix), in the two-way model they remain fairly steady across all years. Neither rising variance in occupation premiums nor rising variance in workplace premiums alone explains much of the increasing inequality trend. The residual variance component, a combination of workplace-specific occupation premiums and within-job inequality, does not change over time. Instead, it is the covariance that increases sharply from 1999 to 2017. The increase in covariance accounts for almost two-thirds of the total growth in inequality, whereas the small increases in variance in workplace premiums and occupation premiums account for only about 12 percent and 23 percent, respectively. Figure 3 shows that this increase in covariance is due to a rise in correlation between workplace fixed effects and occupation fixed effects, which doubles from 0.14 to 0.28 between 1999 and 2017. Workplace and occupation have become increasingly consolidate…”

Here is the dataset, “…restricted-use microdata from 1999-2017 from the Occupational Employment Statistics Survey (OES), collected by the Bureau of Labor Statistics (BLS). The OES is fielded to provide official annual estimates of occupational wages and employment levels (BLS 2008). The OES surveys around 400,000 establishments per year, sampled from the population of private and public sector workplaces.3 The survey is administered to employers, managers and human resource managers. Each respondent is asked to report all occupations employed in their establishment and to indicate the number of employees in each occupation who receive pay in each of 12 pay intervals.The data include no information about individual worker characteristicsbeyond pay, occupation and establishment information. The OES is thus useful for studying occupation- and workplace-related pay changes, but we cannot adjust for individual characteristics like education, race or gender. This limitation of the data means that the occupation and workplace premiums we estimate should be interpreted as broadly summarizing pay effects due to skill, education, and other characteristics correlated with occupation and workplace…”

In practical terms this means, "...A privatizing, low-paying mental health clinic may substitute lower-paid social workers in for psychologists...An assembly job, low in the ranking of occupation or skill, can either be similarly low in the workplace premium ranking, at a low-paying supplier, or benefit from employment at a large, unionized corporation, with pay premiums even for low-level employees..... As workers are increasingly employed in jobs matching high or low premiums across workplace and occupation, fewer workers are in jobs defined by mismatched and offsetting premiums. It is this consolidated inequality, rather than rising variance in pay premiums associated with occupation or with workplace alone, that is the dominant source of rising inequality over the last two decades...."

Core finding, “…The two main sources of inequality in the US labor market—occupation and workplace—have increasingly consolidated. Workers benefiting from employment at a high-paying workplace are increasingly those who already benefit from membership in a high-paying occupation. Drawing on occupation-by-workplace data, we show that two-thirds of the rise in wage inequality since 1999 can be accounted for not by occupation or workplace inequality alone, but by their increased consolidation. This consolidation is not attributable to firm turnover or to how occupations have shifted across a fixed set of high paying firms (as in outsourcing). Instead, consolidation has resulted from new bases of workplace pay premiums. Workplace premiums associated with teams of professionals have increased, while premiums for previously high-paid blue-collar workers have been cut. Yet the largest source of consolidation is bifurcation in the social sector, whereby some previously low-paying but high-professional share workplaces, like hospitals and schools, have deskilled their jobs, while others have raised pay. Broadly, the results demonstrate an understudied way that organizations affect wage inequality: not by directly increasing variability in workplace or occupation premiums, but by consolidating these two sources of inequality….”

What this looks like in terms of categorization, “… we divide workplaces into different groups according to their starting level of occupation and workplace premiums. We define starting groups of workplaces by crossing 3 categories from each of the workplace premiums and average occupation premiums. The crossed tertiles define sets of workplaces that track substantive differences in industry and occupation composition. For the cell lowest in workplace pay premiums and in average occupation premiums, the most common industries are accommodation, food service and retail. These workplaces employ cooks, food service workers and sales clerks. In contrast, establishments with low average occupation premiums and high workplace premiums commonly employ production, transportation and administrative support workers. These establishments are typically in manufacturing, warehousing and transportation industries. Among high occupation premium establishments, those with low workplace premiums are predominantly healthcare and social services, professional and technical services and education. These establishments employ managerial and professional employees like doctors, social workers and teachers. However, they pay relatively little. In contrast, workplaces in the high occupation and high workplace premium cell are in professional and technical industries, healthcare and finance….”

Their data seems consistent with the superstar firms hypothesis, where employees at the high wage firms make more than their peers at low wage firms. Paper argues/shows that over the past 20 (1999-2017) years high paying jobs are increasingly concentrated in high paying workplaces, and low paying jobs are increasingly concentrated at low paying workforces"....Specifically, we argue that recent increases in wage inequality are accounted for by increased correlation between occupation pay premiums and workplace pay premiums. High-paying workplaces once employed low-skill workers in circumstances ranging from unionized manufacturing assembly jobs to maintenance and food service positions at large corporate headquarters. Moreover, previously many members of high-paying occupations—like doctors, teachers, and psychiatrists—worked in low-paying service workplaces. In this paper, we show that these cases of offsetting mismatch between occupation and workplace premiums have become more rare...."

New report from Nathan Wilmers and the Washington Center for Equitable Growth (using restricted BLS data) provides evidence (implicitly) that higher skilled workers are increasingly working to provide services to other higher skilled workers, "... organizations have consolidated occupation and workplace inequalities.... we show how workplace sources of premiums for blue collar workers and penalties for pink collar service organizations have been supplanted by higher pay for already-highly paid teams of managers and professionals. The net result is that it is professionals and managers, not blue collar workers, who now benefit from workplace premiums..."

Mankiw speaks! Argues (model based) that people who brush off the potential for crowding out of capital by the increase in government debt are mistaken. They argue that large deficits could be/are particularly damaging when firms have market power because of a the wedge market power introduces btw interest rates and the marginal product of capital (he assumes market power exists for purpose of this paper). “In thisenvironment, the real interest rate earned by savers is below the net marginal product of capital.For a plausible calibration of the expanded Solow model, the wedge between them can be more than 4 percentage points. The calibration also suggests that the U.S. economy is dynamically efficient. When market power is introduced into the Diamond model, government Ponzi schemes can have different implications for welfare than they do under competition. Even if a perpetual rollover of government debt and accumulating interest is feasible, the crowding out of capital may still reduce steady-state welfare…..”

The basic dynamic, "... If the real interest rate is below the growth rate, indicating dynamic inefficiency, the government can run a permanent primary deficit: it can give a transfer to every generation and roll over the resulting government debt forever. The crowding out that results from the debt issuance increases welfare because the economy has more capital than is optimal. This close connection between dynamic efficiency and Ponzi schemes breaks down in an economy with market power.We show that if market power pushes the real interest rate below the growth rate, the government can again run a Ponzi scheme. However, such a Ponzi scheme, though feasible, may not be a free lunch. Even a successful Ponzi scheme may reduce welfare in the steady state....If typical prices are marked up 20 percent over marginal cost, as some of the literature suggests, the real interest rate is more than 4 percentage points below the social return on capital. Correcting for the effects of market power yields an estimate of the social return of more than 8 percent.Because this estimated social return on capital is much higher than the economy’s growth rate, it indicates that the U.S. economy is dynamically efficient...."
The drivers, "....The impact of a government Ponzi scheme on welfare depends on two effects. The first we call the aggregate effect. Because government debt crowds out capital, it depresses aggregate consumption if the net marginal product of capital exceeds the growth rate and increases aggregate consumption in the opposite case.... The second we call the generational effect: the introduction of government debt improves the allocation of aggregate consumption between the young and the old. This effect is similar to the Pareto improvement that can result from pay-as-you-go social security when the growth rate exceeds the interest rate.... In a competitive economy, the aggregate and generational effects of debt work in the same direction. In an economy with market power, when the economy’s growth rate is greater than the real interest rate but less than the net marginal product of capital, the two effects conflict. The relative size of the two effects depends on parameters. For example, the magnitude of the generational effect depends on the intertemporal elasticity of substitution in consumption. If that elasticity is small, the generational effect is small, and the aggregate effect dominates. More generally, these findings show that, in the presence of market power, the real interest rate is a poor guide by itself for judging the impact of increased government indebtedness...."
Conclusion, "....This paper examines the optimal accumulation of capital and the effects of government debt in neoclassical growth models in which firms have market power and therefore charge prices above marginal cost. In this environment, the real interest rate earned by savers is less than the net marginal product of capital.We establish a new method for evaluating dynamic efficiency that can be applied in such economies. A plausible calibration suggests that the wedge between the real interest rate and the marginal product of capital is more than 4 percentage points and that the U.S. economy is dynamically efficient. In addition, government Ponzi schemes can have different implications for welfare than they do under competition. Even if the government can sustain a perpetual rollover of debt and accumulating interest, the policy may nonetheless reduce welfare by depressing steady-state capital and aggregate consumption. These findings suggest that even with low interest rates, as have been observed recently, fiscal policymakers should still be concerned about the crowding-out effects of government debt...."

Laurence Ball and Gregory Mankiw, "Market Power In Neoclassical Growth Models," National Bureau Of Economic Research, March 2021, https://www.nber.org/papers/w28538

Case that the "Trump Bump" was overstated relative the Euro Area and Obama's performance

Core findings, “…Five facts stand out. First, per capita GDP growth was only mildly stronger under Trump than under Obama or in the euro area (+0.2 percentage points on a yearly basis) - and the euro area is not usually considered a star performer during the Trump years. Second, in terms of gross fixed capital formation (investment before depreciation), Trump did worse than the euro area and only mildly better than Obama. Note that boosting private investment was perhaps the key argument made by Trump in support of his tax reform which favoured the very rich by reducing the taxation of profits. Third, private final consumption did worse under Trump than under Obama.Unfortunately, data on income distribution after taxes and transfers, which would be useful, inter alia, to understand if this difference can be attributed to redistribution favouring households with a lower propensity to consume, are not yet available. Fourth, the labour market did better under Obama and in the euro area than under Trump. The unemployment numbers - both the levels and relative to the labour force - are particularly striking in this respect.2 Fifth, and perhaps most interestingly, the last cell of each panel shows that compared to the euro area, Obama did better than Trump in all variables...."

Methodology, "…We take a purely macroeconomic perspective and a simple (perhaps simplistic) methodology. We compare the performance of macroeconomic variables such as GDP growth, gross fixed capital formation, private consumption, employment, and unemploymentover the first three years of the Trump administration (2017-2019; for obvious reasons we leave out 2020) and the last three years of the Obama administration (2014-2016).We also make the same comparison relative to the euro area - not usually considered a star performer over that period - during the first three Trump years.Finally, we compare the first difference with the second difference - a simple difference-in-differences exercise. The results are displayed in Table 1, which is organised as follows. The first row under each heading (GDP growth, gross fixed capital formation, etc.) displays the US both under Trump (first column) and under Obama (second column), while the second row displays the euro area over the same time periods. The difference between Trump and Obama is displayed in bold in the third column, and the difference between Trump and the euro area is displayed in bold in the third row. The last cell on the right-hand side of the third row, in italics, displays a difference-in-difference: (Trump less euro area) - (Obama less euro area). This is meant to partial out worldwide effects that might influence the difference between Trump and Obama. All the data come from the latest issue of the OECD Economic Outlook database…”