Macro Roundup

Key Finding, "..Do transitory changes in corporate and personal income taxes have persistent effects on output? And what are the channels?The effects of personal income taxes on output are large and significant during the first six quarters but return to zero within two years. In contrast, a corporate income tax cut generates a smaller stimulus on impact but has larger expansionary effects over the medium- and long-term, with peak effects occurring around eight years after the shock.Variable factor utilization is crucial to produce the short-run response of productivity. While the dynamic effects of personal income taxes are short-lived on productivity and negligible on R&D expenditure, changes in corporate income taxes are associated with a sustained, but temporary, rise in R&D spending and a persistent increase in aggregate productivity. R&D is key for the model’s ability to generate a persistent response of productivity and output following a temporary change in corporate income taxes. A standard New-Keynesian model would not deliver pro-cyclical productivity, thereby missing the estimated output responses to either shock...."
The Evidence

“…Using the approach outlined above, we now present our baseline set of empirical estimates about the longer-term effects of cuts to corporate and personal income taxes. We first focus on the response of the average tax rate, GDP and productivity, with the latter being a key and novel focus of our analysis. We will then extend our empirical evidence to examine investment, R&D and consumption to shed light on the most likely mechanism driving the GDP responses. Each further variable is added to the benchmark data vector Z one at the time to avoid a sharp increase in the number of parameters to be estimated. In Figure 1, we present our first set of main results. The figure contains two columns. On the left, we show the IRFs to a reduction in the average corporate tax rate. On the right, we show the results for a reduction in the average personal tax rate. The impact effect is normalized so that both shocks reduce their respective average tax rate by 1 percentage point in the first period. The solid red lines are the posterior medians and the shaded bands refer to 68% and 90% (Bayesian) credible intervals. Impulse response functions are computed using posterior draws of the coefficients A0 and B1. The solid blue lines come from the estimated structural model that will be presented, solved and estimated in Section 5. The first row in Figure 1 reveals that, following a shock to corporate and personal income taxes, the average tax rates decline temporarily. The change in the average corporate tax rate (first column) loses significance after 8 quarters and goes back to zero after around 20 quarters. The changes in the average personal income tax rate are somewhat less persistent, losing significance after 6 quarters and touching zero after around 16 quarters. Despite the different method (i.e. local projections vs. VAR), these results largely replicate the findings in Figures 2 and 3 of Mertens and Ravn (2013), where the results are plotted for the first 20 quarters. In short, the estimated tax cuts are rather transitory….”

The Mechanisms

“….The impulse responses in Figures 1 and 2 replicate the results in Mertens and Ravn (2013) over the first 20 quarters, which is the horizon at which most previous literature stops. On the other hand, we have shown that there are significant longer-term effects of corporate tax cuts that persist beyond the typical IRF horizons presented in earlier work. These persistent dynamics are not, however, evident for the effects of personal tax cuts. In the rest of the paper we investigate what may explain these findings. In this sub-section, we look at a number of additional variables that could offer insights on the transmission mechanism, especially at longer horizons. These are R&D expenditure, investment and consumption expenditure. The endogenous growth literature argues that R&D spending has the potential to generate persistent effects on both output and productivity. On the other hand, studies in the Real Business Cycle tradition emphasize the role of capital expenditure as an important propagation mechanism. Finally, given such longer-term output responses, we would also expect to see persistent effects on household expenditure for corporate tax changes, with short-lived effects from personal tax changes. The findings are reported in Figure 3. The first row shows the impulse responses of R&D expenditure to a corporate tax cut (left column) and to a personal tax cut (right column). The second and third rows show the dynamic effects on investment and consumption respectively. Red lines represent medians and 68% credible sets of the impulse response posterior distributions. Shaded areas refer to 90% central intervals. As discussed in Section 2, each variable is added one at the time to our baseline dataset to avoid a sharp increase in our already richly parameterized local projections….”

James Cloyne, Joseba Martinez, Haroon Mumtaz and Paolo Surico, “Short-Term Tax Cuts, Long-Term Stimulus,”National Bureau Of Economic Researchhttps://www.nber.org/papers/w30246

Jonathan Wai and Jeff Allen, "What Boots Talent Development? Examining Predictors Of Academic Growth In Secondary School Among Academically Advanced Youth Across 21 Years,"Gifted Child Quarterly, August 24, 2019, https://journals.sagepub.com/doi/full/10.1177/0016986219869042

Subgroups Of Interest

"...Although the total sample is predominantly White, subgroup sample sizes are still robust, ranging from N = 8,260 for students from non-English speaking homes to N = 20,895 for African Americans. From 1996 to 2017, significant im- provement in growth was observed for females, males, African Americans, and students with disabilities but not for Hispanic, low-income, and students from non-English speaking homes (Table 4). Relative to the total group (β = 0.035), the improve- ment in growth was less pronounced for African American students (β = 0.011). The improvement for males (β = 0.036) was similar to the improvement for females (β = 0.031); the improvement for students with disabilities was slightly lower (β = 0.028) than the total group improvement. Each subgroup showed more improvement during the later time period (2005-2017) relative to the earlier time period (1996- 2004). The largest differential was observed for Hispanic students and students with disabilities; the smallest differential was observed for students from non-English speaking homes and low-income student..."

Robert Armstrong, "Did QE Cause Inflation,"Financial Times, July 19, 2022, https://www.ft.com/content/2c3fc086-b543-4efe-b696-8c84aa919b69

Did QE cause inflation?

QE, bank lending and inflation Did quantitative easing contribute to inflation, and how much? Will quantitative tightening have the opposite effect?

Important questions: it is generally thought that low rates did contribute to inflation, and the Federal Reserve is hoping like hell that higher rates will slow it. The role of QE and QT, as we’ve noted before, is trickier. There is little consensus among practitioners, academics and Fed officials about what central bank bond buying does to the economy and how it does it. It would be a lot better if we had a clear idea of what was going on.

Benn Steil and Benjamin Della Rocca, economists at the Council on Foreign Relations, have argued in a recent post that QE had a very important, perhaps even determining, role in creating inflation. I suspect they’re wrong, but it’s an important argument to consider.

Here’s how the argument goes (picky financial details incoming!).

When, in the course of QE, the Fed buys a Treasury bond from investors, the transaction is completed through an intermediary — a bank. The proceeds from the sale become a customer deposit at that bank, a liability. At the same time, the bank is credited with a reserve deposit at the Fed for the same amount, an asset.

Because a bank will always have fewer reserve assets at the Fed than deposit liabilities, the addition of the same amount to each will push the bank’s reserve/deposit ratio up. QE improved the bank’s liquidity in that sense.

A bank that has more liquidity has an incentive to lend. They don’t have to lend more, but they have reason to, in order to optimise their balance sheet. This is what Steil and Della Roca call the “credit channel” of QE.

In 2020, the Fed bought many billions in Treasuries, but banks’ reserves fell (they call them “excess reserves”; more on this shortly). This, Steil and Della Rocca say, is evidence that “QE was working as hoped” — that is, it was encouraging lending. When lending goes up, they argue, excess reserves fall.

But the gap between the Fed’s holding of Treasuries and reserves grew wider and wider, allowing Steil and Della Rocca to predict that inflation was coming. Here is their chart:

I suspect that this argument is wrong for two reasons, one involving how banks behave, and one about financial plumbing.

I’ve been talking to bankers for several years, as an analyst and a reporter, and I have never heard of talk about the decision to lend or not in terms of having ample liquidity or being liquidity constrained. Instead, the effective constraint is loan demand — the availability of creditworthy borrowers who want money.

I asked my favourite bank analyst, Brian Foran of Autonomous Research, about this, and he confirmed my suspicions. “Ninety per cent of bank lending decisions are, do my customers want to borrow and are they in good shape to do so,” he told me. “I’ve never sat in a meeting with a bank CEO who said ‘I’ve got all these deposits and I have to figure out a way to lend them out’. They might say — I have a high loan to deposit ratio and I have to work that out over the long run,” for example by retaining more of the loans the bank makes rather than selling them on to the secondary market.

It could be that bankers either are unaware or hesitant to admit the role that liquidity plays in lending decisions. But there is another point, made to me by former Fed trader and the “Fed Guy” blogger Joseph Wang. Banks’ lending isn’t constrained by the volume of liquidity, but rather by its cost. If banks need cash they can always borrow it at some price. Reserves at the Fed, if they ever did matter, don’t matter now, because as Steil and Della Rocca point out in a footnote, the Fed eliminated all reserve requirements in March of 2020.

Now the financial plumbing point. Here is a version of the Steil and Della Rocca chart, with two other series added: total bank credit creation, and the balances in the Fed’s reverse repo programme (more on what that is momentarily).

Here’s the problem, though. Lending does not track reserves (blue line) at all. Now, as argued above, I don’t think there is much reason that it should. And if those two don’t track, the Steil/Della Rocca argument does not work, because it depends on the idea that higher reserves, driven up by QE, incentivise lending.

And there is another explanation, other than higher lending, for why banks’ reserves have come down. It was, again, explained to me by Joseph Wang. The reserves are being funnelled, somewhat circuitously, into the Fed’s growing reverse repo programme (yellow line).

Here is how that funnelling would take place (now we are getting really technical, so feel free to skip the next two paragraphs). The Fed uses the reverse repo programme to sop up excess liquidity in the banking system that would otherwise force the overnight rate below the Fed’s target. Market participants, mostly money-market funds, can give the Fed their cash and receive an interest-paying Treasury security in return. It’s a collateralised overnight loan to the Fed.

In recent years banks have had more deposits than they wanted, which caused problems with their capital requirements. So they have pushed clients towards money market funds. The money market funds have, in turn, put more and more money into the RRP. The way that transaction takes place is that the money market fund that wants to participate in the RRP makes (another) deposit at a bank, and then that same sum is taken out of the bank’s reserve account at the Fed, and placed in a Fed RRP account. The bank’s reserves at the Fed fall.

I’m not totally confident in any of this, and look forward to hearing what Steil and Della Rocca have to say in response. But it seems to me that if QE encourages lending, the mechanism is much more indirect than the one they suggest.

Zooming back out to why all this matters. Unhedged’s best guess is that QE works primarily by injecting/sopping up liquidity in financial markets, not by encouraging lending. So QT will have its effect by withdrawing liquidity from markets, making them more volatile, diminishing investor risk appetites and increasing demand for cash and risk-free assets. That, rather than weaker lending, is what we should prepare for as QT proceeds.

As this century progresses, though, it appears that China will be experiencing economic convergence with the US of another, less positive kind. The country’s working-age population of nearly a billion (defined here as those ages 15 through 64) has been essential to its economic rise, enabling it to become the workshop of the world and a vast consumer market. But according to population projections released last week by the United Nations, this cohort will start declining rapidly in the 2030s, and shrink by almost two-thirds by the end of the century. With the US working-age population projected to be about the same size in 2100 as it is now, China’s will go from more than four times larger to less than twice as big. Throw in Canada and Mexico, which aren’t exactly part of the same labor market as the US but do share a free-trade zone, and China’s working-age population is projected to be only 1.2 times bigger....The UN also offers a “low-fertility scenario” in which birth rates stabilize at lower levels in both China and the US. In it, China sees its working-age population drop by more than 80%, and North America’s surpasses it in 2097.

Justin Fox, "China's Economic Engine Is About to Start Shrinking,"Bloomberg, July 18, 2022, https://www.bloomberg.com/opinion/articles/2022-07-18/china-s-working-population-is-about-to-get-much-smaller

China's Economic Engine Is About to Start Shrinking

For a few decades now, China has been converging with the US economically. Depending how you measure it, its gross domestic product has either already passed that of its great global rival or is getting ever closer. Average incomes are still much lower in China, but by another key metric of living standards, life expectancy, China matched the US in the pandemic year of 2020.

As this century progresses, though, it appears that China will be experiencing economic convergence with the US of another, less positive kind. The country’s working-age population of nearly a billion (defined here as those ages 15 through 64) has been essential to its economic rise, enabling it to become the workshop of the world and a vast consumer market. But according to population projections released last week by the United Nations, this cohort will start declining rapidly in the 2030s, and shrink by almost two-thirds by the end of the century. With the US working-age population projected to be about the same size in 2100 as it is now, China’s will go from more than four times larger to less than twice as big. Throw in Canada and Mexico, which aren’t exactly part of the same labor market as the US but do share a free-trade zone, and China’s working-age population is projected to be only 1.2 times bigger.

Also, the UN population forecasts contain other information about future labor supply that may end up being much more important than how China and the US stack up. Africa is projected to be the big gainer, with a working-age population expected to nearly equal Asia’s by the end of the century. (For its continental/regional groupings, the UN puts Mexico in Latin America and defines “Northern America” as the US, Canada, Bermuda, Greenland and St. Pierre and Miquelon.)

By “decline” I don’t necessarily mean something akin to the fall of Rome. Japan remains an affluent, advanced economy despite its quarter century (so far) of working-age population decline. But its share of nominal global GDP has fallen to 5.1% in 2021 from 17.9% in 1994. All wealthy nations have ceded GDP share to make room for China and other emerging markets, but the US declined just to 23.9% from 26.1%, and the European Union share declined to 17.8% from 25.7%.

In 2021, China’s share of global GDP was 18.5% and its share of global working-age population was 19.2%. The latter percentage is projected to fall to 6.1% by century’s end. One way for Chinese leaders to prevent an equivalent decline in GDP would be to make reforms and investments that keep per-capita incomes rising faster than the global norm. But as my fellow Bloomberg Opinion columnist Hal Brands and Tufts University China scholar Michael Beckley argued in Foreign Policy last year, fear of reduced economic clout in the future could also elicit a less-productive, more externally aggressive response: “The most dangerous trajectory in world politics is a long rise followed by the prospect of a sharp decline.”

The US faces no such prospect, at least not for demographic reasons. One can even envisionit returning to population growth through a renewed embrace of immigration, a more supportive environment for parents or both. At a time of great pessimism among Americans, that’s an interesting prospect to contemplate.

“….Has the cyclical behavior of TFP changed through time to echo that of labor productivity? Table 3 repeats the productivity gap change regressions of Table 2 with the change in the TFP gap substituted as dependent variable for the change in the labor productivity gap. The main results of Table 2 are replicated here for the same three sample periods of 1950-85, 1986-2006, and 2007-2019, where the 2007-19 results repeat the recovery treatment of the right side of Table 2. The current-quarter TFP gap response to a change in the output gap is strongly positive — 0.71, 0.81, and 0.88 across the three intervals. The long-run response of TFP is significantly positive in all three periods, although substantially higher in the first (0.50) and third (0.66) intervals than in the middle interval (0.39). The long-run recession responses are insignificant in the first two periods but a highly significant -0.52 in the third period. The constant terms for all three periods are insignificant and close to zero….”

A Post-Sample Simulation to Detect a Productivity Growth Revival in the Pre- Pandemic Economy, 2017-19

"...For perspective on the magnitude and trajectory of 2017-19 productivity growth behavior, we first perform the same procedure on the productivity growth revival that we know occurred in the late 1990s. To match the three-year duration of the 2017-19 experience, we examine only the first three years of the late 1990s revival during 1996-98. Figure 5 illustrates how our procedure reveals a revival of productivity trend growth during this three-year interval. We re-estimate the change gap version of our labor productivity equation for the business sector from 1986 to 1995 (instead of 1986-2006 as in Table 2) and calculate the predicted growth rate of productivity during 1996 to 1998, holding constant throughout the 1996-98 interval the value of the Kalman productivity growth trend at its 1995:Q4 value of 2.13 percent per year. The difference between the actual and predicted growth rates of productivity measures the estimated increase in trend relative to the 1995:Q4 starting point. igure 5 extends from 1990:Q1 to 1998:Q4 and plots in red a four-quarter moving average of the actual growth rate of output and in green displays a similar average of the actual growth rate of productivity. The black line through 1995:Q4 is the four-quarter moving average of the predicted productivity gap change from the 1986-95 equation plus the Kalman trend that was used to calculate that gap, hence the predicted value for productivity change (gap plus trend). The continuation of the black line from 1996:Q1 to 1998:Q4 is the calculated prediction of the equation when the Kalman trend is fixed at its value of 2.13 percent for 1995:Q4. The fitted values do a relatively good job of predicting the change in productivity growth during the 1990-95 interval shown in Figure 5 to the left of the vertical bar; in particular the 1992-93 early recovery “hump” following the 1990-91 recession is tracked very closely. This was the episode christened at the time as “the jobless recovery” (Gordon, 1993).

"...We now turn to an interpretation of productivity behavior in the nine quarters between 2020:Q1 and 2022:Q1, the “pandemic economy.” This includes the period of the sharp lockdown of the economy in 2020:Q2, the rapid but partial opening up in 2020:Q3, and the subsequent recovery of the following six quarters. We are immediately confronted by a marked departure from the procyclical relationship between the productivity and output growth gaps that characterized the 2010-19 decade. As output collapsed in 2020:Q2, business- sector productivity growth moved in the opposite direction, rising at an annual rate of 6.6 percent in that quarter.The apparent countercyclical behavior of productivity in 2020:Q2 reflected not a sudden outburst of creative innovation, but rather in large part a shift in the mix of output and employment toward higher productivity sectors of the economy. While output and employment dropped everywhere, they declined at a much faster rate in the sectors where labor productivity and wages are relatively low. This shift in the industry mix reflects a relatively large decline in the employment of workers in low-paid industries where work involves close contact among employees, customers, or both, such as bricks-and-mortar retail trade and leisure/hospitality, and a relative increase in the employment of workers who could continue to work at home in relatively high-paid industries such as finance and information technology. The importance of this mix shift is highlighted by the enormous 9-to-1 difference in the level of productivity between the highest and lowest industries, as we see in Table 6 below….”

Robert Gordon and Hassan Sayed, "A New Interpretation Of Productivity Growth Dynamics In The Pre-Pandemic And Pandemic Era U.S. Economy, 1950-2022,"National Bureau Of Economic Research, July 2022, https://www.nber.org/papers/w30267

“…The first two columns of Table 1 provide the annual growth rates of labor productivity for both the total economy and the business sector, where total output is measured by the geometric average of GDP and gross domestic income (GDI), following the analysis of Nalewaik (2011), who showed that the average of GDP and GDI is more accurate than either one examined separately. This distinction turns out to be important for pandemic-era growth in 2020-22, when GDI grew considerably faster than GDP, implying that productivity growth rates based on the average of GDP and GDI, as in Table 1, outpaced by a substantial margin the BLS-published productivity growth rates calculated with GDP as the sole measure of output. Thus, the Table 1 business growth rate for 2020-22 of 2.11 percent contrasts with the BLS- published growth rate that is a much slower 1.15 percent. There is no published BLS equivalent of our data for the total economy in the first column, as our numbers are based on the average of aggregate GDP and GDI divided by an unpublished BLS series on hours in the total economy….As expected, the growth rate for the business sector in the second column is always more rapid than in the total economy, due to the relatively slow measured productivity growth for those portions of the economy excluded from the business sector, notably the government and nonprofit sectors (shown separately in the third column). The shortfall of productivity growth in the total economy compared to the business sector was similar in 2010-16 (0.24 percent) and in 2017-19 (0.41 percent). This difference was a much larger 0.70 percent during the dot.com era of 1996-2004. Thus, the slowdown from 1996-2004 to 2010-19 was substantially greater for the business sector than for the total economy. Recall that it is productivity growth in the total economy, not just the business sector, that matters for the growth of potential output and real income per capita, i.e., the standard of living….The contrast between the total economy and business sector was reversed in 2020-22. The revival from 2017-19 to 2020-22 was an impressive 0.81 percentage points in the total economy compared to 0.64 in the business sector. As a result, productivity growth in the non- business sector jumped from -0.18 to 1.16 percent, and this is the fastest productivity growth registered by the non-business sector since 1950-72. Why? We will return to this puzzle in a subsequent section which examines the widely differing pandemic-era productivity experiences of particular industries within the business sector. Shown in the fourth column of Table 1 are the growth rates for the manufacturing sector, which is notable because unlike the total economy or the business sector, it had virtually no slowdown from 1950-72 to 1973-95. During that interval of more than two decades, healthy productivity growth in the manufacturing sector contrasted with a disappointing record for the rest of the economy. The manufacturing sector also enjoyed a robust revival in excess of two percentage points during the dot.com interval. But then the tables were turned as the manufacturing sector suffered a stunning 4.93 percentage point growth slowdown from 1996- 2004 to 2010-19, much larger than the slowdown in either the total economy or the business sector. The unusually poor relative performance of the manufacturing sector after 2009 has not yet received much attention in the productivity growth literature. The 2020-22 bounce-back of manufacturing productivity growth is much greater than for the total economy or the business sector. Further insight into the performance of the business sector is provided in the fifth column, which lists growth in total factor productivity (TFP). The growth in TFP is usually smaller than that of labor productivity, differing by the normally positive contribution of capital deepening, with a minor additional contribution from improving labor quality. TFP growth was less than labor productivity growth in the business sector by 1.02 points in 1950-72 and a similar 1.00 points in 1972-95 but by a larger 1.50 points in 1996-2004, reflecting the marked rise in the growth rate of investment (i.e., capital deepening) in the dot.com era. Somewhat surprisingly, the TFP shortfall rose to an even larger 1.95 percent in 2005-2009 before dropping to 0.34 percent in 2010-16, 0.71 in 2017-19, and a similar 0.84 in 2020-22. The growth revival from 2010-16 to 2017-19 for TFP of 0.22 percent was substantially less than the revival of 0.59 percent for business-sector productivity, indicating that capital deepening contributed to the 2017-19 revival. The further revival of TFP growth from 2017-19 to 2020-22 of 0.51 percent was somewhat less than the 0.81 percent rebound of business-sector productivity growth. We return in Table 5 below to a detailed decomposition of the difference between business-sector labor productivity and TFP growth in 2010-16, 2017-19, and 2020-22….”
Pre-Pandemic Trends of Labor Productivity and TFP

"...The first step in our analysis is to estimate the underlying growth trends of labor productivity and TFP in the pre-pandemic economy. Our preferred method is the Kalman filter which removes correlations between any time series and the unemployment gap, defined as the difference between the unemployment rate and the “long-run NAIRU” estimated by the Congressional Budget Office. Thus, if a decline in productivity growth occurs at the same time as a rise in the unemployment gap, as during the 1981-82 recession, the Kalman procedure uses that correlation to eliminate the decline in productivity growth and instead show a smooth evolution of the productivity growth trend. In contrast, the commonly used Hodrick-Prescott (H-P) filter is univariate and smooths a series using only information on deviations from average growth of the series itself, without any “outside information” on the behavior of the unemployment gap. In practice the H-P filtered series for productivity growth still exhibits substantial fluctuations at business cycle frequencies....Our examination of trends in this section terminates the data in 2019 and leaves the interpretation of 2020-22 for later in the paper, where we deal with unique aspects of the evolution of productivity during the pandemic era. Figure 1 contrasts our Kalman filter for business sector productivity growth, shown in blue, with two alternative series. One is a simple 20-quarter moving average of one-quarter annualized changes, shown in yellow, and the other, plotted in green, is the H-P filtered series based on the same data (using the standard H-P quarterly smoothing parameter of 1600). Note that the H-P filter retains considerable cyclical sensitivity and appears to be a slightly smoothed replica of the 20-quarter moving average. For reference, shown on the right side of Figure 1 as a horizontal line to the right of the black vertical bar is the average annual growth rate of business-sector productivity for the nine quarters between 2020:Q1 an 2022:Q1...."

"... Table 1 introduces the behavior of productivity (Y/H) growth from 1950 to 2022. The rows divide this long span of seven decades into separate intervals that mark off different eras of productivity growth. First comes the rapid growth era of 1950:Q1-1972:Q4, followed by the slowdown interval of 1973:Q1-1995:Q4 that received so much attention during the 1970s and 1980s. Next comes the return of rapid growth during the “dot.com” era between 1996:Q1 and 2004:Q4, a revival usually attributed to the effects of high investment in the information technology revolution and the impact on TFP of personal computers, the internet, and search engines...The subsequent post-2004 interval is divided into four sub-periods. The first of these, 2005:Q1-2009:Q4, includes the last stages of the 2001-07 economic expansion, the 2007-09 recession, and the first two recovery quarters after the end of the recession. We treat 2010:Q1- 2016:Q4 as a distinct sub-period marked by unusually slow productivity growth and then break out the 12 quarters of 2017-19 that show a modest but noticeable revival. The last line of Table 1 includes the nine pandemic quarters 2020:Q1-2022:Q1, with a further acceleration beyond 2017-19 in every column of the table….”

Here is the key table

The Growth Rates of Labor Productivity and TFP by Major Sector

The paper begins by contrasting the dismal decade of 2010-19, when business sector productivity growth proceeded at only 1.1 percent per year, with the ebullient nine years 1996- 2004 when productivity growth reached 3.3 percent or the entire 1950-2009 postwar era with its productivity growth achievement of 2.5 percent. Could the slow growth of the 2010-19 decade imply another disappointing decade in the 2020’s or might the 2020’s become a decade of robust growth propelled by technological breakthroughs, robots, and artificial intelligence? The decade opened on an optimistic note with business-sector productivity reaching 4.1 percent during the four quarters of 2020. This paper provides a new interpretation of variations of productivity growth for the entire postwar era with particular emphasis on the 15 years since the start of the financial crisis recession of 2008-09. Our first insight is that short-term productivity changes during recessions and business expansions, and from one quarter to the next, do not reflect autonomous gyrations of productivity growth itself, the so-called “productivity shocks” that have played such a large role in the macroeconomics literature. Instead the behavior to be explained is the response of the change in hours of work to autonomous movements in output, which themselves are driven by demand-induced fluctuations in consumption, fixed investment, government spending, net exports, and particularly short-run changes in inventory accumulation. Output growth proceeds with sharp irregular quarter-to-quarter ups and downs during both expansions and recessions, with additional sustained downward movements during recessions. Hours respond partially and gradually to these output changes, and productivity changes are a residual, the definitional difference between output changes and the induced hours changes. Because of the gradual adjustment of hours, the residual productivity changes typically involve a large positive response in the current quarter followed by negative reactions in the next few subsequent quarters as hours complete their adjustment. If the sum of the positive initial productivity response and the negative lagged responses is significantly positive, then productivity growth is said to be procyclical.We concur with the previous literature that productivity growth was strongly procyclical during 1950-85; our coefficient of the response of productivity growth to output changes over the current quarter and four lagged quarters is a highly significant 0.28, similar to the one-third response embedded in the original formulation of Okun’s Law. With our focus on the gradual adjustment of hours, we make a distinction between the normal response of hours and an extra reaction to output changes that occurs in recessions and was particularly marked in2008-09. During that recession hours declined with a much higher elasticity to output changes than in other periods, which we interpret as “excess layoffs” due to the panic of business firms as output collapsed during and after the financial crisis of 2008. Since productivity change is a residual, equal to output change minus hours change, it behaved counter-cyclically and rose in the four quarters of 2009 by a massive 6.4 percent. This counter- cyclical episode when combined with non-recession data has led much of the previous literature to conclude that productivity growth was no longer procyclical after 1985. The excess layoffs of 2008-09 did not occur in isolation; eventually the lost jobs returned as workers were rehired in the post-recession recovery. We model the total recession and recovery effect as netting out to zero, so the cumulative extra rehiring addition of jobs in the recovery exactly offsets the estimated excess reduction of jobs during the recession. Because the excess adjustment of hours in 2008-09 was a special phenomenon, we divide up our regression analysis into three eras (1950-85, 1986-2006, 2007-19) rather than two divided at 1986. The set of current and lagged coefficients on our combined recession-recovery variable is highly significant for the 2007-19 interval but not for the earlier periods. As for the “normal” non- recession long-term response of productivity to output changes, when the current quarter response is combined with four lagged quarters, we conclude that procyclicality did not disappear after 1985. Our long-run sum of coefficients is 0.28 for 1950-85, 0.20 for 1986-2006, and 0.66 for 2007-19 (although the 0.20 for the middle interval is insignificant). Our novel recession/recovery treatment has six important implications that resolve several of the puzzles about productivity behavior that have emerged both in the pre-pandemic interval of 2007- 19 and in the pandemic era of 2020-22. First, the estimated coefficients for the 2008-09 recession/recovery effect imply that average annual productivity growth in the two years 2008-09 would have been -0.8 percent instead of the actual 3.2 percent if the excess layoff phenomenon had not occurred. Second, our estimated recession/recovery coefficients imply that a major explanation for slow productivity growth in 2010-16 was the extra post-recession rehiring that offset the excess hours reduction that had previously occurred in the recession. We estimate that without that post-recession rehiring spread out over the seven years 2010-16, the annual rate of productivity growth on average would have been almost twice as high — 1.7 percent per year rather than 0.9 percent per year. Third, the appearance in the productivity data of a growth revival from 2010-16 to 2017- 19 is more than explained by the winding down of post-recession recovery rehiring that had characterized the 2010-16 period. Fourth, when allowance is made for the recession/recovery effect, the regression coefficients indicate that productivity growth was strongly procyclical in 2007-19. Regarding the pandemic era, our fifth conclusion emerges when our estimated 2007-19 regression coefficients are applied in a post-sample simulation to actual data for 2020-22. We find that our coefficients are able to track almost exactly the marked acceleration of productivity growth during 2020, once adjustments are made for the shorter duration of the 2020 recession in comparison with the 2008-09 recession. Sixth, our post-recession rehiring treatment in the same simulations explains why productivity growth slowed so markedly from 4.1 percent at an annual rate in the four quarters of 2020 to only 0.6 percent in the following five quarters of 2020- 22.Further insight into productivity behavior in 2020-22 is provided by our new quarterly data base of productivity levels and changes for 17 separate industries in the private business sector extending from 2006:Q2 to 2022:Q1. The numerator of our industry indexes is real- value-added, that is, the business component of GDP. The aggregate growth rate of the industry indexes in 2020-22 is 1.3 percent, which is slower than the 2.1 percent growth rate of the business-sector data used elsewhere in the paper for which the numerator is the average of GDP and GDI (GDI grew considerably faster than GDP in 2020-22). Our first insight from the quarterly industry data is that published productivity indexes greatly exaggerate aggregate productivity growth in 2020:Q2 and understate it in 2020:Q3 as a result of the lockdown’s effect in causing sharp shift in the industry mix away from industries like restaurants and hotels with low levels of productivity toward work-from-home industries like information and financial services that have high levels of productivity. We illustrate this distortion by creating an alternative aggregate of the 17-industry data that holds constant industry output weights at the 2019 level. Because 2020-22 productivity growth across the 17 industries is so heterogeneous, we combine these industries into three groups: goods, work-from-home (WFH) services, and contact services. The three groups behave very differently. For the nine quarters of 2020-22 taken together, productivity growth in the goods group is near zero, in WFH services is a strongly positive 3.3 percent, while in contact services is a mediocre -2.6 percent. The average across the three groups is 1.3 percent. Comparing the four quarters of 2020 with the five quarters of 2021-22, productivity growth in the goods group declines from strongly positive to strongly negative, which is consistent with the hypothesis of excess layoffs followed by a gradual rehiring recovery. In WFH services productivity growth actually improved from 2020 to 2021-22, suggesting a more permanent phenomenon. In contact services productivity growth remained negative by about the same amount in both 2020 and 2021-22. Thus it appears that the apparent revival of productivity growth in 2020-22 was entirely caused by the outstanding performance of the WFH services. We cite A survey study showing that the WFH respondents assess their own productivity as substantially higher than their expectations, which may provide a comparison between productivity of WFH activity compared to the productivity of the same individuals in their previous office environments. We cite another study suggesting that pandemic-era GDP growth may be understated by neglecting the shift of residential capital from non-work to work activities and the large personal investment in technology hardware and communications software needed to make WFH effective. Where does this leave our assessment of the long-run trend in productivity growth? The paper has suggested that productivity growth was distorted by excess layoffs in 2008-09, shifting some productivity growth into that two-year period and moving it away from 2010-16 when rehiring cancelled the effects of the excess layoffs. The business-sector data displayed in Table 1 and used in our regressions and simulations records growth rates of 1.3 percent in 2005- 07, 3.2 percent in 2008-09, and 0.9 percent in 2010-16. The underlying trend of productivity growth is better represented by averaging across these three intervals, resulting in a 2005-16 growth rate of 1.4 percent. This is remarkably close to the 1.5 percent in the apparent revival interval of 2017-19 and also close to the 1.5 percent achieved in the long “slowdown” period of 1973-95. A cautionary note is that a suggestion of 1.5 percent for the long-term growth of productivity in the business sector translates into only 1.1 percent for the total economy, since productivity in the total economy grew an average of 0.4 percent slower than in the business sector during 1950-2019. This 1.1 percent suggestion is relevant for predictions of future potential output growth and is slower than the current CBO ten-year forecast of 1.4 percent total-economy productivity growth. Our conclusion is thus consistent with the long-term forecast of 1.2 percent for the total economy included in our previous long-term evaluationsEconomic commentary in mid-2022 is dominated by speculation about the imminence of a new recession. There is much puzzlement about the reality of negative GDP growth in the first quarter of 2022 and the likelihood of negative GDP growth again in the second quarter, juxtaposed with continued robust growth in payroll employment. Our paper resolves this puzzle in its post-recession recovery simulation which indicates that hours growth in 2021-22 is 2.5 percent faster at an annual rate in each quarter than otherwise, due to rehiring of workers to replace those who lost their jobs in the excess layoffs of 2020. The paper predicts also that continued rehiring will result in significant negative productivity growth in 2022:Q2 and likely in subsequent quarters, having the effect of further reducing the 2020-22 average productivity growth rate down from 2.1 percent closer to the 1.4 percent average of 2005-19. This assessment leaves no room for a pandemic-era revival in productivity growth as has been widely suggested. Instead, there appears to be a consistent growth rate of 1.4 percent (2005-19) roughly equal to 1.5 percent (1973-95), leaving the dot.com achievement of 3.3 percent as a historic outlier as it recedes further into the past.

Their Takeaway

The Mechanics Of 2020 Uptick

"...How do productivity changes in the three industry groups compare when the nine quarters of 2020-22 are compared with the previous ten years, 2010-19? This comparison is based on fixed output-weight (FYW) indexes for each group and is shown in Figure 10. During 2010-19 average productivity growth was relatively similar across the three groups, with respective growth rates of 0.6, 1.1, and 0.6 percent, implying an average of 0.8 percent annual growth for the FYW index of all three groups taken together. The experience in 2020-22 could not have been more different. Productivity growth in the goods group slowed slightly from 0.6 percent in 2010-19 to 0.4 percent in 2020-22. But productivity growth for WFH services soared from 1.1 percent in 2010-19 to 3.3 percent in 2020- 22. In contrast the performance of contact services slumped from 0.6 to -2.6 percent. Changes between the two intervals are plotted separately in the bottom section of Figure 10. Growth for the total of all three groups increased from 0.8 to 1.3 percent (recall again that the industry data are based on the GDP measure of productivity and exhibit slower growth in 2020-22 than the business-sector data used in Table 1 and our previous regression/simulation analysis based on the average of GDP and GDI in the numerator of productivity)...."

Key takeaway, "...The dismal decade of 2010-19 recorded the slowest productivity growth of any decade in U.S. history, only 1.1 percent per year in the business sector. Yet the pandemic appears to have created a resurgence in productivity growth with a 4.1 percent rate achieved in the four quarters of 2020. This paper provides a unified framework that explains productivity growth in both the pre-pandemic and pandemic-era U.S. economy. The key insight is that in their panicked reaction to the collapse of output in the 2008-09 recession, business firms overreacted with “excess layoffs,” adjusting hours to the output decline with a far higher elasticity than normal. Our regression analysis, which allows post-recession rehiring that gradually unwinds the excess layoffs, explains why productivity growth was countercyclical in 2009 and why it was so slow in 2010-16 as rehiring boosted hours growth. Post-sample simulations explain why productivity growth was so high in 2020 and why it fell to only 0.6 percent in the five quarters of 2021-22. The paper includes implications for the future long-term evolution of productivity growth in the business sector and total economy. A new data file on quarterly productivity levels and changes for 17 industries provides new perspectives for 2006-22 and particularly for the nine pandemic quarters of 2020-22. Positive pandemic-era productivity growth can be entirely explained by a surge in the performance of work-from-home service industries, while goods industries soared and then slumped, while contact services recorded strongly negative productivity growth throughout 2020-22...."

In the decade before and then through the early pandemic, the Fed was fighting to keep the economy growing and inflation from slipping below 2%. So it kept real short-term interest rates negative and bought bonds, which helped keep real bond yields below 1%. Low real rates bolstered stock and housing prices, and encouraged businesses to borrow; corporate debt climbed to a record relative to gross domestic product. (Households’ leverage plummeted after the financial crisis and hasn’t really recovered since.)The Bank for International Settlements recently evaluated what would happen if advanced economy central banks raised their policy rates 4.25 percentage points, as much as the Fed tightened in 2004-2006. Housing and equity prices would both end up 5% lower, adjusted for inflation, in 2025 than now, and private-sector debt service would consume an additional 3% of income. Federal budget math also gets ugly. In 2007 publicly held debt was 35% of GDP. A financial crisis, pandemic and two recessions later, it is 98%. But because bond yields have fallen from 5% then to 3% now, the cost of servicing that debt hasn’t increased.Furthermore, the Fed bought a lot of those bonds to hold down yields, remitting their interest back to Treasury. It paid for the bonds by issuing newly created electronic money, called reserves, to commercial banks, with an interest rate of close to zero. The Congressional Budget Office reckons a one-percentage-point increase in real rates will boost the annual deficit by $250 billion in 2026, or roughly 1% of GDP. Debt, already on an upward trajectory, will rise even faster.

Greg Ip, "Interest-Rate Pain From Higher Inflation Has Barely Begun,"Wall Street Journal, July 20, 2022, https://www.wsj.com/articles/interest-rate-pain-from-higher-inflation-has-barely-begun-11658327626

Interest-Rate Pain From Higher Inflation Has Barely Begun

Inflation hurts for many reasons, but one of the most important is that it usually means higher interest rates. Yet in the past year, while inflation has jumped 7 percentage points, the Fed’s short-term interest-rate target has gone up just 1.5 points and the 10-year Treasury note yield just 1.9 points.

The gap reflects a belief by investors that in a few years, inflation will relatively painlessly slide back to around 2%, the Federal Reserve’s target, allowing rates to return to the ultralow levels that prevailed before the Covid-19 pandemic. What if instead inflation remains stubborn and rates have to rise a lot more? That would spell trouble for an economy where asset values, private and public debt have risen on the assumption that rates will remain historically low.

Higher inflation boosts profits, incomes and asset values and thus neutralizes the effect of rising nominal rates. Thus, monetary policy becomes restrictive not through higher nominal rates, but higher real rates—nominal rates minus inflation.

Low real rates also became a justification for governments to worry less about deficits and debt, which President Biden’s team incorporated in their stimulus and budget plans. Mr. Biden’s budget projections put real short-term interest rates around zero over the coming decade, and the real bond yield at around 1%.

Those assumptions reflect a belief that the factors holding down real rates before the pandemic, such as sluggish economic growth, aging, a surplus of global savings, and investors’ desire for safety, will resume. Markets seem to agree: Inflation-indexed bonds put the real 10-year bond yield at just 0.5% and see inflation plummeting to around 2% in a year from 9.1% in June. Markets see the Fed raising its target interest rate two more percentage points, to between 3.5% and 3.75%, by next spring and then cutting it.

But if inflation proves more stubborn, nominal and real rates will likely have to go much higher than this benign outlook implies. This is the first tightening cycle since the early 1990s when inflation started out well above the Fed’s objective. To push it back down would, in theory, require substantially positive real rates. What this means for the Fed’s short-term interest-rate target is unclear because it depends on where underlying inflation ends up once current energy and supply disruptions dissipate. Still, popular rules of thumb inspired by the economist John Taylor prescribe a nominal rate target of 7%, according to a June Fed report, about double current expectations.

That would be a rude awakening for markets. The Bank for International Settlements recently evaluated what would happen if advanced economy central banks raised their policy rates 4.25 percentage points, as much as the Fed tightened in 2004-2006. Housing and equity prices would both end up 5% lower, adjusted for inflation, in 2025 than now, and private-sector debt service would consume an additional 3% of income.

Federal budget math also gets ugly. In 2007 publicly held debt was 35% of GDP. A financial crisis, pandemic and two recessions later, it is 98%. But because bond yields have fallen from 5% then to 3% now, the cost of servicing that debt hasn’t increased. Furthermore, the Fed bought a lot of those bonds to hold down yields, remitting their interest back to Treasury. It paid for the bonds by issuing newly created electronic money, called reserves, to commercial banks, with an interest rate of close to zero.

It’s been a long time since rising interest rates forced Congress and the president into painful decisions on which spending to cut or taxes to raise. Thanks to higher inflation, they may face those decisions again.

This paper reported on a randomized controlled trial that provided people experiencing poverty during the height of the COVID-19 pandemic with nothing, $500, or $2,000. The data reveal that participants spent the windfall of cash fairly quickly, with increased expenditures dwindling down the UCTs within a matter of weeks. Interestingly, the increase in expenditures did not translate to positive differences on the pre-registered financial, psychological, cognitive capacity, and health survey outcomes, neither between the Control and cash groups, nor between the two cash groups. If anything, the results show that relative to the Control group, cash groups reported worse financial, psychological, and health (but not cognitive capacity) outcomes for our prespecified analyses.We find that both cash transfer amounts increased expenditures in the short-term: for the two weeks following the cash receipt, the $500 group spent on average $26 more per day relative to Control, while the $2,000 group spent on average $82 more per day. Using a mixture of financial transaction data and self-reports, we find that the money was primarily used to fund “transfers” (a fairly broad category containing debit and credit card payments and digital payment vehicles such as Paypal and Zelle), bills, food/drink, shops, and transportation. We find no evidence that the treatment decreased the prevalence of bank fees such as overdraft fees, insufficient fund fees, late payment fees, or cash advances. In less than four weeks, there were no observable differences between any treatment groups on bank balances (savings) or transactions. These results suggest that the cash allowed participants to spend more money, improving objective financial outcomes for the few weeks immediately following the transfer and then dissipating thereafter. However, we find no evidence that the short-term increase in bank balances and spending improved self-reported survey outcomes in the short- or long-term. In fact, we find that at every post-treatment time point, both cash groups reported significantly worse outcomes than the Control group on the prespecified financial, psychological, and health survey index measures (and no differences on the cognitive capacity index measures). importantly, however, our analyses indicate that the effects of cash could have quite plausibly truly been null for all four indices and simply appeared negative for the financial, psychological, and health indices due to attrition. The finding that there are no positive effects of cash for any of the survey indices is robust to alternative index constructions, controlling for a host of covariates, only analyzing participants who answered all the surveys, and only analyzing participants who correctly reported how much cash they had received. Exploiting natural variation in UCT and survey timing, we further test and fail to find positive effects of cash on survey outcomes even just one to four days after cash receipt. We also find no differences between the $500 and $2,000 groups for any of the indices. However, it is important to note that once we account for attrition, it is likely more appropriate to conclude that the effect of cash was null on all four indices rather than negative on three and null on one. To the extent that there were negative effects on any of the survey items, they appear to primarily be concentrated among the subjectively-measured outcomes; the difference between cash and non-cash groups on objectively-measured survey outcomes more closely approximates a null. The lack of positive effects of cash on survey outcomes stand at odds with the predictions of both experts and laypeople, who predicted (i) substantial positive effects of receiving cash (0.16 to 0.65 SD, depending on the cash amount, outcome, and time period); and (ii) that these effects would be larger for participants receiving $2,000 than those receiving $500. We subsequently explored the mechanisms underlying our surprising findings. We test and largely rule out a range of plausible explanations: strategic distortion of responses, reference dependence, spending money in potentially harmful ways, expectations of UCT amounts, inference from UCT receipt, attrition, and social strife. Instead, the data seem most consistent with the notion that the windfall was insufficient to address participants’ needs, which in turn generated feelings of distress. We find that relative to the Control group, cash groups experienced greater stress in deciding how to spend money, were more likely to think about money, and were more aware of both their own needs and those of their friends and family—all of which (partly) mediated the effects of the treatment on the primary survey outcomes. While most existing literature in both low- and high-income countries has found positive effects of cash (publication bias notwithstanding), our study adds to the smaller body of work identifying more muted effects (see e.g. Banerjee et al. (2022); Berge, Bjorvatn and Tungodden (2015); Carvalho, Meier and Wang (2016); Evans and Moore (2011); Jacob et al. (Forthcoming); Pilkauskas et al. (2022) for a few examples). There are a number of differences between our study and those that have found positive effects that could explain the variability in conclusions, including the outcomes that were studied and how they were measured (Benjamin et al., 2012). That said, we believe that two particularly important moderators could be the geographic location (and, in particular, the fact that a dollar simply cannot buy as much in the US as it can in low-income countries) and the fact that the study was conducted during a time of incredible economic strain: the height of the COVID-19 pandemic(Peyton, Huber and Coppock, 2021; Meyer, Murphy and Sullivan, 2022). Both these moderators likely operated to limit the extent to which even the larger UCT could address the needs of a person living in poverty. Indeed, the only RCTs we are aware of that also administered UCTs in the US during the pandemic similarly found null effects (Jacob et al., Forthcoming; Pilkauskas et al., 2022). Our findings should be interpreted in light of several caveats. In particular, it is unclear to what extent our study is generalizable, given that—as just mentioned—it was conducted during a particularly challenging economic and public health crisis (the COVID-19 pandemic), and with a sample of low-income participants particularly affected by it. Nevertheless, we believe our findings raise important questions—and begin to provide some answers—on poverty alleviation and the basic functioning of cash transfers.

Ania Jaroszewicz, Jon Jachimowicz, Oliver Hauser and Julian Jamison, "How Effective Is (More) Money? Randomizing Unconditional Cash Transfer Amounts in the US,"Social Science Research Networks, July 18, 2022, https://papers.ssrn.com/sol3/papers.cfm

Core Findings, "We randomized over 5,000 US individuals in poverty to one of three conditions during the first year of the COVID-19 pandemic: receiving a one-time $500 unconditional cash transfer (UCT; half a month’s worth of total household income for the median participant; N = 1, 374), a $2,000 UCT (two months’ income; N = 699), or nothing (N = 3, 170). We measured the effects of the UCTs on participants’ financial well-being, psychological well- being, cognitive capacity, and physical health through surveys administered one week, six weeks, and 15 weeks after cash receipt. For 43% of our sample, we also observe bank account balances and financial transactions. While the cash transfers increased expenditures for a few weeks, we find no evidence that they had positive impacts on our pre-specified survey outcomes at any time point. We further find no significant differences between the $500 and $2,000 groups. These findings stand in stark contrast to the (incentivized) predictions of both experts and a nationally representative sample of laypeople, who—depending on the treatment group, outcome, and time period—estimated treatment effect sizes of +0.16 to +0.65 SDs. We test several explanations for these unexpected results, including via two survey experiments embedded in our trial. The data are most consistent with the notion that receiving some but not enough money made participants’ needs—and the gap between their resources and needs—more salient, which in turn generated feelings of distress."
The natural experiment

“…The basic design of our study is as follows. In the year following the start of the COVID-19 pandemic, we randomized 5,243 people living in poverty in the US to receive either (1) $0 (hereafter: “Control”; N = 3, 170), (2) a one-time UCT of $500 (roughly half a month’s worth of median total household income; N = 1, 374), or (3) a one-time UCT of $2,000 (two months’ worth of income; N = 699). All participants took a baseline survey before being randomized, and they were invited to take post-treatment surveys one week, six weeks, and 15 weeks after the cash transfer. These surveys measured the participants’ financial well-being (hereafter, simply “financial outcomes” for brevity; e.g., subjective financial stress, liquidity constraints, debt), psychological well-being (“psychological outcomes”; e.g., depression, anxiety, agency), cognitive capacity (e.g., a fluid intelligence measure, the extent to which the participant thought about money), and physical health (“health outcomes”; e.g., food security, nutrition, sleep quality). We summarize these four outcome categories as four separate indices, which together serve as our primary outcome variables. We also observe bank account balances and financial transactions during the trial period for the 43% of participants who opted into providing that data. These data allow us to measure when and how the cash transfers were spent, as well as how much money was saved and for how long…. We find that both cash transfer amounts increased expenditures in the short-term: for the two weeks following the cash receipt, the $500 group spent on average $26 more per day relative to Control, while the $2,000 group spent on average $82 more per day.Using a mixture of financial transaction data and self-reports, we find that the money was primarily used to fund “transfers” (a fairly broad category containing debit and credit card payments and digital payment vehicles such as Paypal and Zelle), bills, food/drink, shops, and transportation. We find no evidence that the treatment decreased the prevalence of bank fees such as overdraft fees, insufficient fund fees, late payment fees, or cash advances. In less than four weeks, there were no observable differences between any treatment groups on bank balances (savings) or transactions. These results suggest that the cash allowed participants to spend more money, improving objective financial outcomes for the few weeks immediately following the transferand then dissipating thereafter.. However, we find no evidence that the short-term increase in bank balances and spending improved self-reported survey outcomes in the short- or long-term. In fact, we find that at every post-treatment time point, both cash groups reported significantly worse outcomes than the Control group on the prespecified financial, psychological, and health survey index measures (and no differences on the cognitive capacity index measures).Importantly, however, our analyses indicate that the effects of cash could have quite plausibly truly been null for all four indices and simply appeared negative for the financial, psychological, and health indices due to attrition. The finding that there are no positive effects of cash for any of the survey indices is robust to alternative index constructions, controlling for a host of covariates, only analyzing participants who answered all the surveys, and only analyzing participants who correctly reported how much cash they had received. Exploiting natural variation in UCT and survey timing, we further test and fail to find positive effects of cash on survey outcomes even just one to four days after cash receipt. We also find no differences between the $500 and $2,000 groups for any of the indices…First, we find that, compared to the Control group, the cash groups thought about money and money-related concerns more in a series of hypothetical scenarios(Shah et al., 2018). Second, relative to the Control group, the cash groups reported that they would require more money to pay for everything their household needed over the next 30 days, suggesting a potential shift in how cash participants thought about their needs after they received the UCT. Third, when asked how they would spend a hypothetical $500 stimulus check, the cash groups named significantly more spending categories than the Control group, hinting at the possibility that the cash groups had a larger number of needs salient to them after (and likely due to) the receipt of cash. Fourth, as mentioned above, cash groups reported feeling more overwhelmed or burdened by the needs of people outside their household, a potential indicator that they felt an internal tension between wanting or being expected to help others and not feeling as though they had enough money to do so. Finally, cash groups reported feeling more stressed by the decision of how to spend their money (Chernev, B ̈ockenholt and Goodman, 2015; Iyengar and Lepper, 2000; Scheibehenne, Greifeneder and Todd, 2010; Sugden, 1985). Depending on the analyses, these variables either partially or fully statistically mediate the effect of cash on the survey outcomes. In sum, the data are most consistent with the explanation that the cash transfers were insufficient for the participants in our sample and this made their needs—and the gap between their (now available) resources and their (now apparent) needs—more salient to them, which in turn caused distress and a decline in survey outcomes….”

“…The labor force expanded at slower rates during the next two decades, by an annual average of 1.8% in the 1980s and 1.3% in the 1990s, owing to lower birthrates from the late 1960s through the 1970s. All told, the workforce grew by 18 million from 1980 to 1989 and by nearly 16 million from 1990 to 1999. At that point, expansion of the labor force fell sharply, to an average of 0.9% a year from 2000 to 2009, or by 13 million new workers during the decade, and 0.6% a year from 2010 to 2019, or 11.3 million new workers. Over the past two decades the workforce expanded by less than half the rate of the 1980s and by less than a third the rate of the 1970s, an important cause of slowing economic growth.The labor-force participation rate has also been declining in recent decades, thereby adding to the challenge of slowing population growth. The percentage of the adult population working or actively looking for work increased steadily from 59% in 1965 to 67% in the late 1990s, followed by a steady drop-off to 62% in 2022. With the current labor force at around 163 million workers, a 4% decline represents about six million jobs. Part of this decline reflects the aging of the population: The baby boomers are retiring. In addition, the labor-force participation rate among men has fallen from 88% in 1950 to 70% today, which mostly accounts for the overall drop in the participation rate. As men represent about 54% of the labor force, a decline of 18% is the equivalent of nine million or 10 million jobs in 2022. On the other hand, labor force participation for women increased from 35% in the 1950s to 60% by the late 1990s, neutralizing the fall-off among men. The increased participation among women in that era undoubtedly boosted the growth rate from the 1960s through the 1990s. Female participation has declined slightly in recent years, to 58%, so that this element in American economic growth appears to have run its course.These two factors account for the long-term slowdown in economic growth juxtaposed against a low unemployment rate. The growth of the labor force has gradually slowed over the past few decades, with obvious effects on overall economic growth. Today’s low unemployment rate obscures the substantial decline in workforce participation in recent years, as “dropouts” from the workforce aren’t included in the unemployment figures. If they were added, as some analysts think they should be, then the real unemployment rate wouldn’t be 3.6% but more like 9% or 10%....”

James Piereson, "Welcome to the Full-Employment Recession,"Wall Street Journal, July 14, 2022, https://www.wsj.com/articles/full-employment-recession-men-labor-participation-rate-baby-boomers-retire-11657827049

Welcome to the Full-Employment Recession

Is the U.S. economy in a recession? Real gross domestic product contracted at an annual rate of 1.6% in the first quarter of 2022, according to a recent estimate by the Bureau of Economic Analysis. The Federal Reserve Bank of Atlanta estimates that the economy contracted by a seasonally adjusted 1.2% in the second quarter. If those estimates turn out to be correct, the U.S. economy will have contracted for two consecutive quarters, the technical definition of a recession.

This recession—if that’s what it is—isn’t like other recessions. According to the latest employment report issued by the Bureau of Labor Statistics, the economy added 372,000 new jobs in June, with the unemployment rate remaining stable at 3.6%. Over the past 12 months, according to the same report, average hourly earnings increased by 5.1%, another sign of a tight labor market. What explains a full-employment recession? The answer lies in long-term developments in the U.S. labor force.

The economy grew by 5.7% in 2021, the largest annual gain since the 1980s, with an addition of some 6.1 million new jobs. But those gains were in the form of bounce-backs from unprecedented losses in 2020 because of the coronavirus pandemic. The economy contracted by 3.4% in 2020 and some nine million jobs disappeared. When the GDP changes from 2020 and 2021 are combined and averaged, the annual growth rate over these two years comes in at just above 2%, very close to the 2.3% rate in 2019, before the Covid lockdowns began.

Economic growth has been declining decade by decade since the 1960s, when annual real GDP advanced by an average of 4.5% before falling to 3.3% in the 1970s, 3.1% in the 1980s and 3.2% in the 1990s. Economic growth then slowed to 1.9% a year from 2000 to 2009, thanks in part to the deep recession and financial crisis of 2008, and to 2.2% from 2010 to 2019 as the economy gradually recovered from that crisis. A growth rate of around 2% a year, or less than half the rate of the 1960s, appears to be the “new normal” for the U.S. economy. Despite the robust labor market, the country remains stuck in a long cycle of slowing growth.

The Bureau of Labor Statistics has reported monthly since 1948 on the size of the civilian labor force in the U.S. This is a comprehensive measure of the labor force that includes all people over 16 who are either employed or seeking employment. The civilian labor force grew at uneven rates from 61.6 million in 1950 to 163 million in 2021, but growth has slowed substantially since the late 1990s.

During the 1950s, the workforce expanded at an average rate of 1.1% a year, with between 600,000 and a million workers added annually. It then accelerated during the 1960s as the workforce expanded at an average rate of 1.7% a year, with an addition of some 13 million new workers, a nearly 20% increase during the decade. The annual expansion of the workforce peaked during the 1970s with an average gain of 2.7% a year as the baby boom generation entered the workforce, with substantial increases in female workers adding to the overall totals. During that decade the labor force expanded by more than 24 million workers, or nearly 30%.

The labor force expanded at slower rates during the next two decades, by an annual average of 1.8% in the 1980s and 1.3% in the 1990s, owing to lower birthrates from the late 1960s through the 1970s. All told, the workforce grew by 18 million from 1980 to 1989 and by nearly 16 million from 1990 to 1999. At that point, expansion of the labor force fell sharply, to an average of 0.9% a year from 2000 to 2009, or by 13 million new workers during the decade, and 0.6% a year from 2010 to 2019, or 11.3 million new workers. Over the past two decades the workforce expanded by less than half the rate of the 1980s and by less than a third the rate of the 1970s, an important cause of slowing economic growth.

The labor-force participation rate has also been declining in recent decades, thereby adding to the challenge of slowing population growth. The percentage of the adult population working or actively looking for work increased steadily from 59% in 1965 to 67% in the late 1990s, followed by a steady drop-off to 62% in 2022. With the current labor force at around 163 million workers, a 4% decline represents about six million jobs.

Part of this decline reflects the aging of the population: The baby boomers are retiring. In addition, the labor-force participation rate among men has fallen from 88% in 1950 to 70% today, which mostly accounts for the overall drop in the participation rate. As men represent about 54% of the labor force, a decline of 18% is the equivalent of nine million or 10 million jobs in 2022.

On the other hand, labor force participation for women increased from 35% in the 1950s to 60% by the late 1990s, neutralizing the fall-off among men. The increased participation among women in that era undoubtedly boosted the growth rate from the 1960s through the 1990s. Female participation has declined slightly in recent years, to 58%, so that this element in American economic growth appears to have run its course.

These two factors account for the long-term slowdown in economic growth juxtaposed against a low unemployment rate. The growth of the labor force has gradually slowed over the past few decades, with obvious effects on overall economic growth. Today’s low unemployment rate obscures the substantial decline in workforce participation in recent years, as “dropouts” from the workforce aren’t included in the unemployment figures. If they were added, as some analysts think they should be, then the real unemployment rate wouldn’t be 3.6% but more like 9% or 10%.

There are no obvious solutions to this problem. Rising wages should draw more workers into the labor market. A rational immigration policy might bring skilled workers from abroad into the labor force. New information technologies may improve worker productivity, but no such payoffs are yet visible in those statistics. Lacking real solutions, Americans may have to adjust to a future of slowing economic growth, with all that implies for intergenerational progress, American strength abroad and political conflict at home.

It has become increasingly clear that the GOP, not the Democrats, is destined, by class arithmetic, not racial demography, to emerge as the hegemonic party of the 2020s; if indeed not beyond. For the statistical analysis, we obtain county-level presidential vote shares from MIT Election Lab for 2000-2020. We compute change in GOP vote share to control for entrenched partisanship which confounds the vote share levels. We then adjust change in GOP vote share for national trends by deducting the population-weighted means of change in GOP vote share by election cycle. In order to extract socioeconomic information from the cross-section, we then compute Spearman’s correlation coefficients for a number of features. Table 1 displays our estimates. The following heatmap shows the same in graphical form.The most significant correlate is the college graduation rate, a good summary measure of the class composition of a county. The correlation coefficient for the college graduation rate was roughly -0.3 in 2004 and 2008, fell to -0.1 in 2012, before rising to -0.5 in 2016. In 2020, it tempered, but the hemorrhaging continued: r = -0.2. The same pattern, but more attenuated, is visible in the coefficient of per capita income. This is the basic pattern of fluctuating but secular class-partisan realignment with first-order effects for party competition in the United States. The coefficient of population and population growth shows that the GOP coalition has become more rural over the past decade. The 2016 cycle marked a major shift. It was the only year for which population growth, net migration, and net international migration were significantly correlated with the adjusted change in GOP vote share. Finally, the coefficients for overdose death rates and suicide rates share the same pattern: significantly positive for 2004-2016 and insignificant for 2020. This suggests that Biden was able temper some of the anger in “flyover country” over deteriorating social conditions. At the heart of the emerging GOP coalition is the working class — people without college degrees and modest incomes who live in places that have few foreigners, that are losing people to the elite-dominated cities, and where social conditions have deteriorated most markedly. This is now the mass electoral base of the party.

Anuser Farooqui, "The Class Arithmetic,"Policy Tensor, July 25, 2022, https://policytensor.substack.com/p/the-class-arithmetic

The Class Arithmetic

It took years after the shock of 2016 for Democrats to confront the fact that the GOP had taken over the mantle of the working-class party. Many still refuse to face the facts. First, the empirical pattern of class-partisan realignment was treated as incredulous. Then, once the pattern was conceded to be robust, it was blamed on a presumably racially-resentful white working class (always a handy stereotype in elite discourse). Finally, as working-class “people of color,” above all, Hispanics, began to abandon the elite party for the party of the working class, panic has broken out in the ranks.

It has become increasingly clear that the GOP, not the Democrats, is destined, by class arithmetic, not racial demography, to emerge as the hegemonic party of the 2020s; if indeed not beyond. The changing class composition of their coalitions is transforming both parties. Democrats are increasingly unable to contain the ideological excesses of professional class Millennials — Democrats to the last man. The GOP, on the other hand, has comprehensively failed to contain the working class revolt. The future of American democracy hangs in the balance.

After a brief review of the electoral patterns over the past few presidential cycles, we’ll try to map the shifting GOP coalition. For the statistical analysis, we obtain county-level presidential vote shares from MIT Election Lab for 2000-2020. We compute change in GOP vote share to control for entrenched partisanship which confounds the vote share levels. We then adjust change in GOP vote share for national trends by deducting the population-weighted means of change in GOP vote share by election cycle. In order to extract socioeconomic information from the cross-section, we then compute Spearman’s correlation coefficients for a number of features.

Table 1 displays our estimates. The following heatmap shows the same in graphical form. The most significant correlate is the college graduation rate, a good summary measure of the class composition of a county. The correlation coefficient for the college graduation rate was roughly -0.3 in 2004 and 2008, fell to -0.1 in 2012, before rising to -0.5 in 2016. In 2020, it tempered, but the hemorrhaging continued: r = -0.2. The same pattern, but more attenuated, is visible in the coefficient of per capita income. This is the basic pattern of fluctuating but secular class-partisan realignment with first-order effects for party competition in the United States.

At the heart of the GOP’s coalition of elite interests is what we may call the deregulators. These are largely business interests whose main beef is with the regulatory-administrative state. This is not libertarian ideology — although libertarians are largely sympathetic. This is pure business interest. They just want the damn regulators off their backs. This is the hegemonic bloc of the GOP. There is no appetite among GOP donors for any expansion of regulations. If you have any such ideas, you’re not welcome within the GOP. Firms that are open to some regulation live in the Democratic party; firms that are opposed live in the GOP. This is largely a question of the nature of the industry — roughly, the “E” in ESG. Dirty industries — above all, oil and mining interests — are a natural GOP constituency, just as globally-oriented banks and tech firms with low national environmental footprint are natural Democrats.

Although it is more pronounced in the GOP, resistance to tax hikes is universal among firms. For instance, President Biden’s ambitious legislative agenda was killed by corporate democrats opposed to any hikes in corporate taxes. The diagnostic feature of corporate GOP is not taxation but deregulation. In that sense, this is still very much the party of Reagan.

The second mainstay of the GOP are the traditionalists. Think evangelicals, anti-abortion ideologues etc. Since their ideological convictions are totally incongruent with professional class ideology, they’re essentially captive GOP voters who have no electoral choice. The only impact of this bloc comes from motivation and turnout. For instance, the Court’s decision on Roe v. Wade is likely to generate enthusiasm and turnout in this bloc, just as much as among pro-abortion liberals.

These mainstays of the GOP coalition — the deregulators and the traditionalists — provide continuity amidst change. The real action is elsewhere. The GOP is now caught in an all-out war between free tradersand economic nationalists. The free traders — “Rocky Balboa” — were the heart of the neoliberal coalition in both parties. In both, they’ve lost ground to economic nationalists.

Economic nationalists are not a new feature of the American political landscape. Indeed, the GOP was created by Midwestern industrial interests to take on the coalition of New England mercantile interests and Southern planters who favored free trade with Britain. Their main goal was less to free the slaves and more to build a great tariff wall. This tariff wall, the tallest in the world, survived into midcentury; only eroding slowly as globally-oriented banks and multinational firms drove global economic disarmament. The high point of the free traders’ influence was the Clinton presidency, that presided over Nafta and China’s admission in the World Trade Organization.

While the tide has turned against the free traders on account of the rise of China in both parties, this process is more pronounced in the GOP. This is because the globally-oriented business interests who bankroll the Democrats exercise a veto against a rollback of globalization, while the domestically-oriented business interests who bankroll the GOP are, at worst, indifferent.

The war is being fought, as usual, by a shifting coalition of political entrepreneurs and intellectuals-for-hire, both on the payroll of business interests. For instance, Peter Thiel and Robert Mercer have bankrolled the Ohio senate campaign of J. D. Vance, one of the leading figures of the new economic nationalism.

Because of redundancy in check writers — there are six hundred billionaires in America and many thousands of loaded firms — the struggle over economic policy is where the changing class coalition meets elite politics. In this Republican game, the working class emerges as a kingmaker. For it is precisely the working class sympathy for economic nationalism that has shuffled the deck. Indeed, the economic nationalists see their protectionist formula as a solution to the crisis of the working class. The idea is that working class fortunes can be restored by the traditional American medicine of protectionism and corporate welfare.

The basic prognosis is clear. Because of changing class arithmetic and because the traditionalists and deregulators have no dog in the fight, the economic nationalists now have the upper hand in the struggle over GOP policy. China is not going anywhere and working class anger is very far from getting tempered. So this process is unlikely to go into reverse any time soon. Before long, we expect the GOP to emerge as the party of economic nationalism.

At first sight, the GOP has embraced denialism on global heating. But the real agenda is militarized adaptationism. Ie, we’re going to build an impenetrable border to the south to prevent climate refugees from escaping the heat and instability in Central America by moving north. And we’ll harden our institutions to deal with the direct effects of global heating — sea walls, air conditioning, etc. But is militarized adaptationism really consistent with the emerging GOP coalition? There are three reasons to think not.

The first is that, since economic nationalism is unlikely to relieve working class stress, some kind of investment-jobs programme will increasingly become necessary. The best available option is and will remain investing in the energy transition; especially because red states are likely to benefit the most.

Second, clean energy technology is emerging as the central arena of geoeconomic competition. Can America really be first if China is making all the solar panels for the world? So, a logic intrinsic to economic nationalism will drive a reversal in GOP climate policy.

Third, and finally, the main blockage towards rational climate policy is the power of the oil interests. They’re unlikely to bend. But balancing them, within the GOP coalition, are the mining and perhaps some clean energy interests (committed deregulators who are invested in clean tech). The demand for metals and materials of the energy transition are so large that, eventually, the size of their checkbooks will rival those of the oil interests.

If I am right about this dynamic — and the case is certainly not open-and-shut — the GOP will eventually develop a more or less coherent agenda. Professional class expertise, that largely abandoned the GOP under Trump, will then migrate back, first in a trickle and then in a flood. Ironically, the GOP will therefore achieve the professional class-working class coalition so desperately desired by Bernie Sanders.

The transformation of the GOP under the tectonic forces of class politics is an event of some historical significance. It is possible to see the emergence of a new party system dominated by the GOP. A lot is riding on the outcome of the war over GOP policy. Economic nationalism could easily get coupled to chauvinism. In that scenario, the US will become more like Modi’s India. The oil interests could hold the line, ensuring that clean energy and affiliated interests all end up in the Democratic coalition — and locked out of power. If that happens, militarized adaptationism will become US climate policy.

The outcomes of the struggles over the policy agenda of the GOP are far from certain. What is certain is that this is a necessarily open-ended process that will be determined by the balance of forces. Interested outside parties may be able to intervene productively. Indeed, turncoat Democrats could very well tip the balance. Given the dearth of professional class expertise in the GOP, turncoat professionals could make a crucial difference. And the earlier they shift their party allegiance, the more likely they are to make a difference.

The upshot is that the Democrats are cooked. The future of the American polity will be decided in the GOP. If you’re a professional class expert who hopes to make a difference, you may want to hold your nose and jump ship at this point.

In the just-released New York Times-Sienna poll, Democrats have a 21 point lead in the generic Congressional ballot amongcollege-educated whitevoters. Shockingly, white college Democratic support in this poll is actually higher than support among all nonwhite voters. This is remarkable and has much to do with anemic Hispanic support for Democrats, who favor Democrats over Republicans by a scant 3 points. More broadly, the lack of Democratic support among working class (noncollege) voters is striking. Democrats lose among all working class voters by 11 points, but carry the college-educated by 23 points. This is less a class gap than a yawning chasm.The crosstabs provided by Echelon allow for a comparison of strong progressives’ basic political views with those of Hispanic and working class voters. Here are some examples:1. America is not the greatest country in the world vs. America is the greatest country in the world. By 66 percent to 28 percent, strong progressives say America is not the greatest country in the world. By 70-23, Hispanics say the reverse and working class voters as a whole concur by 69-23. 2. Racism is built into our society, including into its policies and institutions vs. Racism comes from individuals who hold racist views, not from our society and institutions. Strong progressives are very, very sure of America’s systemic racism, endorsing the first statement by an amazing 94-6 margin. But Hispanics disagree, endorsing the second statement that racism comes from individuals by 58-36, as do working class voters by 57-33..6. Hard work and determination are no guarantee of success for most people vs. Most people who want to get ahead can make it if they're willing to work hard. Strong progressives don’t evidence much faith in upper mobility, endorsing the first statement on the questionable efficacy of hard work by 88-12. Hispanic voters, on the other hand, embrace the view that hard-working people are likely to get ahead by 55-39, as do working class voters by 55-40.

Ruy Teixeira, "Working Class and Hispanic Voters Are Losing Interest in the Party of Abortion, Gun Control and the January 6th Hearings,"The Liberal Patriot, July 14, 2022, https://theliberalpatriot.substack.com/p/working-class-and-hispanic-voters

Working Class and Hispanic Voters Are Losing Interest in the Party of Abortion, Gun Control and the January 6th Hearings

Democrats are betting on a small set of issues to mitigate their losses this November. Inflation may have just hit a 40 year high (9.1 percent) with concomitant recession riskbut Democrats believe that campaigning against the Supreme Court’s overturning of Roe v. Wade, arguing for more gun control in the wake of recent mass shootings and highlighting Trump’s anti-democratic malfeasance through the January 6th hearings can turn the tide in their favor.

It is true that recently the polls have tightened a bit in the Democrats’ favor (though some of this could be the eagerness of motivated Democrats to be polled). And there is general agreement that Democrats’ chances of holding the Senate are much better than their chances of holding the House.

Recent data indicate that success for the abortion-gun control-January 6th strategy, to the extent it is working (and might work in the future) is attributable to those votersfor whom these issues loom large and are less likely to be influenced by current economic problems. Such voters are disproportionately likely to be college-educated whites and it is here that Democrats have been demonstrating unusual strength.

In the just-released New York Times-Sienna poll, Democrats have a 21 point lead in the generic Congressional ballot among these voters. Shockingly, white college Democratic support in this poll is actually higher than support among all nonwhite voters. This is remarkable and has much to do with anemic Hispanic support for Democrats, who favor Democrats over Republicans by a scant 3 points.

More broadly, the lack of Democratic support among working class (noncollege) voters is striking. Democrats lose among all working class voters by 11 points, but carry the college-educated by 23 points. This is less a class gap than a yawning chasm.

It is difficult to avoid the conclusion that Democrats’ emphasis on social and democracy issues, while catnip to some socially liberal, educated voters, leaves many working class and Hispanic voters cold. Their concerns are more mundane and economically-driven. This is despite the fact that many of these voters are in favor of moderate abortion rights and gun control and disapprove of the January 6th events. But these issues are just not salient for them in the way they are for the Democrats’ educated and most fervent supporters.

Recent data from Echelon Insights provide an interesting window on this contrast. Their analysis breaks down the electorate into four quadrants (conservative, populist, libertarian and liberal) and further breaks out a “strong progressive” subset of the liberals who are highly liberal on most issues and also happen to be very highly-educated (and more likely to be white). They are about 10 percent of voters and bear some similarity in size, demographics and inclinations to the “progressive activists” group broken out in the More in Common study—a group with tremendous weight in current Democratic party politics who are described as "deeply concerned with issues concerning equity, fairness, and America's direction today. They tend to be more secular, cosmopolitan, and highly engaged with social media"

The crosstabs provided by Echelon allow for a comparison of strong progressives’ basic political views with those of Hispanic and working class voters. Here are some examples:

1. America is not the greatest country in the world vs. America is the greatest country in the world. By 66 percent to 28 percent, strong progressives say America is not the greatest country in the world. By 70-23, Hispanics say the reverse and working class voters as a whole concur by 69-23.

2. Racism is built into our society, including into its policies and institutions vs. Racism comes from individuals who hold racist views, not from our society and institutions. Strong progressives are very, very sure of America’s systemic racism, endorsing the first statement by an amazing 94-6 margin. But Hispanics disagree, endorsing the second statement that racism comes from individuals by 58-36, as do working class voters by 57-33.

3. The government should deal with illegal immigration by making it easier to immigrate to the US legally vs. The government should deal with illegal immigration by increasing border security and enforcement. Strong progressives have no doubts on this one, favoring easier immigration by 97-2. Hispanics, however, are split down the middle with 44 percent favoring increased border security and enforcement and 47 percent opting for easier immigration. Working class voters go farther, endorsing more border security and enforcement by 58-32.

4. Transgender athletes should be able to play on sports teams that match their current gender identity vs. Transgender athletes should only be allowed to play on sports teams that match their birth gender. Strong progressives overwhelmingly endorse allowing athletes to play on the sports team that matches their gender identity by 66-19. But Hispanic voters by 64-22 say athletes should only play on teams that match their birth gender; working class voters are almost identical at 63-22.

5. We need to reallocate funding from police departments to social services vs. We need to fully fund the budget for police departments. Strong progressives want to reallocate police funding by 87-12. In contrast, Hispanic voters want full funding of the police by 50-41 and working class voters are even stronger on full funding by 59-31.

6. Hard work and determination are no guarantee of success for most people vs. Most people who want to get ahead can make it if they're willing to work hard. Strong progressives don’t evidence much faith in upper mobility, endorsing the first statement on the questionable efficacy of hard work by 88-12. Hispanic voters, on the other hand, embrace the view that hard-working people are likely to get ahead by 55-39, as do working class voters by 55-40.

Strong progressives clearly live in a different world than Hispanic and working class voters. In strong progressive world, views on abortion, gun control and January 6th fit neatly into an overarching set of sociocultural beliefs that are highly salient to them and increasingly drive the Democratic party’s priorities and rhetoric. Hispanic and working class voters lack this overarching set of beliefs—in fact, don’t share many of them—and are much more concerned with the basics of their material lives. It should thus be no surprise that these voters are increasingly losing interest in the party of abortion, gun control and January 6th. As Josh Kraushaar notes:

Democrats are becoming the party of upscale voters concerned more about issues like gun control and abortion rights.

Republicans are quietly building a multiracial coalition of working-class voters, with inflation as an accelerant.

Exaggerated? Maybe. But consider this: between the 2012 and 2020 elections—which Democrats won by similar popular vote margins—Democrats’ advantage among nonwhite working class voters decreased by 19 margin points (Catalist data). Over the same period, Democrats’ performance among white college-educated voters improved by 16 margin points. So perhaps we’re just on trend.

I was worried enough by Pradhan and Goodhart’sargument to estimate the impact of demographic headwinds. I used a panel regression (with country and year fixed-effects) on the Jorda et al. dataset to estimate that a 1 percentage point increase in the dependency ratio reduces the growth rate of real output by 0.085%. If the US dependency ratio converged to that of Japan’s, I estimated that trend US growth would fall by 1.2% per annum. That’s a very fucking large number. It is the same order of magnitude as the decline in real growth rates between ‘the golden age’ of the New Deal era and the disappointments of the Neoliberal era. This led me to examine country-specific estimates of the impact of aging on real growth based on population projections. We obtain age-specific population projections from the UN. We obtain growth rates for the advanced economies from the Jorda et al. dataset. We estimate the impact of four “support ratios”: 15-64/65+, 20-64/65+, 20-69/70+, 25-69/70+, and 25-64/65+. We fit panel regression models using linearmodels PanelOLS. We have 1,224 country-year observations. We admit country and year fixed-effects to isolate the causal effect of demography on real growth, as is standard practice. We choose our predictor based on within R-squared. We find that the demographic signal is strongest in 20-69/70+ ratio (within r2 = 0.193). Our estimate of the elasticity of real growth to our selected demographic signal suggests that each additional percentage point in the 20-69/70+ ratio predicts 0.6% higher real growth. Put another way, if the ratio of the working age population to the aged falls by 1 percentage point, we expect real growth to fall by 0.6% per annum. For the “headquarter” advanced economies, we find very significant demographic headwinds. (The thickness of the curves corresponds to the difference between the low and high UN projections.) We expect real US trend growth to fall from nearly 2% per annum to under 1% per annum by the 2040s. Germany faces even worse demographic headwinds. We project that real German trend growth will fall from 1% per annum to 0% per annum by the 2040s. Finally, since the UN expects Japan to remain the “frontrunner” in aging, we project that Japanese trend growth will fall into negative territory by the 2040s. Can the fast-growing nations come to the rescue? Not really. We find that the Brics face significant demographic headwinds themselves. These headwinds are more pronounced in China due to its one-child policy. But they affect all major developing nations. For the United States, our central estimates suggest that trend growth will fall from 1.9% in 2022, to 1.1% in 2030, 0.7% in 2040, and 0.6% in 2050. So, the demographic headwinds promised by Pradhan and Goodhart may already be working in the background. They are set to intensify for decades to come.Demographic headwinds thus emerge as a major challenge for the foreseeable future. Indeed, they threaten to upend political economies worldwide and combine with the climate crisis to yield tremendous instability. I will have more to say on the political economy implications after I have had time to digest these striking numbers.

Anuser Farooqui, "A Quantitative Estimate of the Effect of the Demographic Reversal on Growth,"Policy Tensor, July 12, 2022, https://policytensor.substack.com/p/a-quantitative-estimate-of-the-effect

A Quantitative Estimate of the Effect of the Demographic Reversal on Growth

It began with this thread. I was worried enough by Pradhan and Goodhart’s argument to estimate the impact of demographic headwinds.

I used a panel regression (with country and year fixed-effects) on the Jorda et al. dataset to estimate that a 1 percentage point increase in the dependency ratio reduces the growth rate of real output by 0.085%. If the US dependency ratio converged to that of Japan’s, I estimated that trend US growth would fall by 1.2% per annum.

That’s a very fucking large number. It is the same order of magnitude as the decline in real growth rates between ‘the golden age’ of the New Deal era and the disappointments of the Neoliberal era.

This led me to examine country-specific estimates of the impact of aging on real growth based on population projections. We obtain age-specific population projections from the UN. We obtain growth rates for the advanced economies from the Jorda et al. dataset. We estimate the impact of four “support ratios”: 15-64/65+, 20-64/65+, 20-69/70+, 25-69/70+, and 25-64/65+. We fit panel regression models using linearmodels PanelOLS. We have 1,224 country-year observations. We admit country and year fixed-effects to isolate the causal effect of demography on real growth, as is standard practice. We choose our predictor based on within R-squared. We find that the demographic signal is strongest in 20-69/70+ ratio (within r2 = 0.193). Our estimate of the elasticity of real growth to our selected demographic signal suggests that each additional percentage point in the 20-69/70+ ratio predicts 0.6% higher real growth. Put another way, if the ratio of the working age population to the aged falls by 1 percentage point, we expect real growth to fall by 0.6% per annum.

We find that the three variants of the UN’s population projections don’t matter much. Our population-weighted estimate of global real growth falls from above 1.2% per annum to 0.2% per annum by the 2040s, regardless of the variant we use.

For the United States, our central estimates suggest that trend growth will fall from 1.9% in 2022, to 1.1% in 2030, 0.7% in 2040, and 0.6% in 2050. So, the demographic headwinds promised by Pradhan and Goodhart may already be working in the background. They are set to intensify for decades to come.

The global population grew by less than 1 per cent a year for the first time since the aftermath of the second world war in 2020 and 2021 with Europe’s total population actually falling during the coronavirus pandemic, according to a UN report.The populations of 61 countries are forecast to decrease by at least 1 per cent between 2022 and 2050, and the associated low fertility rates will also combine with better healthcare to accelerate the ageing of societies. Despite the slowing growth, the global population is still poised this year to reach the milestone of 8bn people, while next year India is projected to surpass China as the most populous country. The world population is expected to peak in the 2080s at 10.4bn and will then begin to fall — the first decline to be forecast in the annual UN report.Europe’s population shrank by 744,000 in 2020 and by 1.4mn last year — the largest fall of any continent since records began in 1950, reflecting a surge in deaths, a fall in births and lower net migration linked to the pandemic. Two-thirds of global citizens live in a country where the fertility rate is less than 2.1 births per woman, roughly the level required for populations to remain stable if mortality rates are low. In Asia, Japan’s population has been shrinking since 2010, South Korea’s fell in 2020, and China’s is forecast to do the same this year. China’s population is forecast to decline by about 6mn annually in the mid-2040s and by 12mn a year by the late 2050s — the world’s largest-ever drop. Africa overtook Asia in 2020 to become the main source of population growth. The UN reports that more than half of the projected increase up to 2050 will be concentrated in just eight countries, mostly in Africa, with the rapid growth threatening their development goals. By mid-century Nigeria is projected to be as populous as the US, closing the current 121mn gap between the countries.

Valentina Romei and Alan Smith, "Global population growth hits lowest rate since 1950,"Financial Times, July 11, 2022, https://www.ft.com/content/6b131d91-1834-4243-bb8b-dc49060b1450

Global population growth hits lowest rate since 1950

The global population grew by less than 1 per cent a year for the first time since the aftermath of the second world war in 2020 and 2021 with Europe’s total population actually falling during the coronavirus pandemic, according to a UN report.

The populations of 61 countries are forecast to decrease by at least 1 per cent between 2022 and 2050, and the associated low fertility rates will also combine with better healthcare to accelerate the ageing of societies.

As the figures were released in the UN’s World Population Prospects report, António Guterres, UN secretary-general, focused on the benefits of healthcare rather than declining fertility and hailed the “advancements in health that have extended lifespans and dramatically reduced maternal and child mortality rates”.

However, the rising proportion of older people in many countries is predicted to hit economic growth and public finances and is already posing growing political challenges.

Despite the slowing growth, the global population is still poised this year to reach the milestone of 8bn people, while next year India is projected to surpass China as the most populous country. The world population is expected to peak in the 2080s at 10.4bn and will then begin to fall — the first decline to be forecast in the annual UN report.

Europe’s population shrank by 744,000 in 2020 and by 1.4mn last year — the largest fall of any continent since records began in 1950, reflecting a surge in deaths, a fall in births and lower net migration linked to the pandemic.

Joshua Wilde, research scientist at Germany’s Max Planck Institute for Demographic Research, said that if fertility rates dropped to a rate at which the population declined, “it’s actually great because you have a higher fraction of the population in working-age groups”. But “in the long run”, he pointed out, “all those workers who are providing a boost to income per capita are going to retire and they will need pensions, they will need healthcare”.

Europe’s population is expected to continue to contract until 2100, with Germany and other countries joining a trend already established in eastern and southern European countries such as Poland and Italy.

Two-thirds of global citizens live in a country where the fertility rate is less than 2.1 births per woman, roughly the level required for populations to remain stable if mortality rates are low.

In countries with a falling population “unless you get a productivity miracle, overall economic growth will fall”, said Charles Goodhart, emeritus professor at the London School of Economics and co-author of The Great Demographic Reversal.

In Asia, Japan’s population has been shrinking since 2010, South Korea’s fell in 2020, and China’s is forecast to do the same this year. China’s population is forecast to decline by about 6mn annually in the mid-2040s and by 12mn a year by the late 2050s — the world’s largest-ever drop.

“If you look at a map of the world of countries that are going to decrease in population size, it basically starts in central Europe and goes east all the way to Japan across Russia and China,” said Wilmoth.

More production “could and should” move to Africa, said Goodhart, “because the alternative, of mass emigration into other countries where the population is falling, is politically not going to be viable”.

“It is primarily the ageing and shrinking of the working-age population that affects a country’s economic development,” said Martina Lizarazo López at Bertelsmann Stiftung, a German-based think-tank.

Increased productivity, automation and longer working lives can help reduce the impact of an ageing population, experts said.

Globally more than 1bn people will be aged over 65 in 2030 with 210mn aged over 80, about double the numbers in 2010. Older people already account for about a quarter of the population in many countries including Japan, Italy and Germany.

We use high-quality administrative data from the state of Minnesota to analyze the labor market effects of two large increases in the minimum wage from Minneapolis and Saint Paul. Our analysis proceeds in three steps. Leveraging recent advances in synthetic difference-in- differences approaches, we estimate counterfactual outcomes in the absence of the minimum wage using variation at the zip code within Minnesota or at the city level from the rest of the country. Using variation from the cross sections of establishments and workers within the Twin Cities, we estimate the labor market effects of differential exposure to the minimum wage increase. Finally, using a model of establishment dynamics, we attempt to reconcile the results from the time series with those from the cross section and discuss how our estimates are informative for deeper determinants of establishments’ labor demand.We reach several substantial conclusions. The minimum wage increase is associated with wage gains in most low-wage industries that accord well with estimates of the direct effect of the minimum wage on establishments’ labor costs. The time series analysis shows that in the Twin Cities the minimum wage increase is associated with an average jobs decline of roughly 2 percent. The great majority of job losses appear in the restaurant industry, which experiences a jobs decline of roughly 30 percent. The analysis using variation from the cross section leads to estimates of job losses that are half as large as the estimates from the time series. Our cross-sectional estimates are consistent with an elasticity of labor demand at the establishment level equal to −1, for establishments that operate in competitive labor markets and can adjust flexibly their inputs.Table 3 presents results for the low-wage industries identified in Section 2 and separately for restaurants. Entries are multiplied by 100 and equal the log point change in outcomes in 2020(4) due to the minimum wage increase, 100 · gT.The third column presents results for total hours. Estimated effects for hours are generally similar to estimated effects for jobs, with three exceptions. First, in both cities, arts, entertainment, and recreation experience smaller changes in hours than in jobs. Second, in Saint Paul, we cannot detect a statistically significant change in hours for other services, whereas we found an increase for other services’ jobs. Finally, in Saint Paul, we find a decline of 35 log points for retail hours, whereas we did not detect a statistically significant decline for retail jobs. The last column of the table presents results for worker earnings. Given the modest wage gains for all industries and the significant negative effects on employment for some industries, it is not surprising that in both cities we fail to detect a statistically significant increase in earnings in any industry except for other services. We detect statistically significant declines in earnings for retail in Saint Paul; arts, entertainment, and recreation in Saint Paul; accommodation and food services in both cities and separately for restaurants

The Natural Experiment

"...We present new evidence from two minimum wage policies instituted recently in Minneapolis and Saint Paul that bears on this debate. In 2018, Minneapolis implemented a policy that aims to raise the minimum wage to 15 dollars per hour for all workers by 2024. Saint Paul introduced a similar policy in 2018 for implementation in 2020. The changes in the minimum wage are large by historical standards. By 2020, the minimum wage in Minneapolis increased by 38 percent relative to the statewide minimum wage that would have prevailed in the absence of the increase. Just in the first year of the policy change, the corresponding increase in Saint Paul is 19 percent..."

Bottom line

“…First, we use time series variation to compare outcomes in the Twin Cities with those of appropriate control cities within Minnesota or the rest of the country. We find wage gains in most low-wage industries in Minneapolis, with the magnitude of the gains overlapping with our estimate of the direct effect of the minimum wage increase on establishments’ labor costs. We do not detect significant employment changes for most industries. The minimum wage increase, however, is associated with a roughly 30 percent decline in employment for the restaurant industry in both Minneapolis and Saint Paul. Second, using the differential exposure of establishments and workers to the minimum wage within the Twin Cities, we find employment effects that are half as large as those from the time series. Our cross-sectional estimates are consistent with an elasticity of establishment labor demand of −1. Finally, we quantify a model of establishment dynamics to reconcile estimates from the cross section with those from the time series. We argue that they plausibly reflect lower and upper bounds of employment losses, because the losses from the time series may be confounded by factors contemporaneous with the minimum wage and the losses from the cross section may omit equilibrium effects such as changes in the entry rate of establishments.. We demonstrate that establishments with larger exposure to the minimum wage experienced larger increases in their wage and larger declines in their jobs, hours, and wage bill. The ratio of employment to wage changes induced by exposure to the minimum wage policy is −1.Reassuringly for our research design, which attempts to difference out common factors at the zip code and industry level, we find that the response of all variables to exposure to the minimum wage is remarkably stable between 2019 and 2020. A concern about the interpretation of the results that use variation across establishments within an industry and zip code is that workers may have reallocated from exposed to non-exposed establishments or found jobs outside of their zip code. For this reason, we also analyze the cross section of workers whose jobs we can track everywhere within Minnesota. Similar to establishments, workers who are more exposed to the minimum wage experience significantly larger employment and earnings losses. To summarize our various estimates for the jobs effects, the time series analysis shows that the minimum wage increase was associated with an average jobs decline of roughly 2 percent in the Twin Cities. The great majority of job losses appear in the restaurant industry, which experienced a jobs decline of roughly 30 percent. The analysis using variation from the cross section leads to estimates of job losses around half as large as the estimates from the time series. For most low-wage industries, we fail to detect statistically significant effects on employment.The confidence intervals we estimate for these industries overlap with estimates found in papers that examine effects on low-wage workers across industries (Allegretto, Dube, and Reich, 2011; Cengiz, Dube, Lindner, and Zip- perer, 2019; Dube and Lindner, 2021; Neumark and Shirley, 2021). By contrast, our estimated employment elasticity with respect to the minimum wage for restaurants is more negative than the typical estimate found in the literature. For example, the comprehensive analysis of estimates for low-wage workers and low-wage industries in Neumark and Shirley (2021) reveals that roughly 80 percent of estimates are negative, with the average reported elasticity across studies being around −0.15. Our own analysis of published estimates for the restaurant industry finds an average elasticity of −0.1. This includes studies from the evaluation of city-wide minimum wage increases (Dube, Naidu, and Reich, 2007; Jardim, Long, Plotnick, Van Inwegen, Vigdor, and Wething, 2022). Our estimated jobs elasticity with respect to the minimum wage for restaurants is between −0.4 and −0.8. The employment impacts we document might be larger than those in the literature because the policy change we examine is larger. The 38 percent increase in the Minneapolis minimum wage by 2020 is significantly larger than the policy changes classified as “large minimum wage increases” in Clemens and Strain (2021), which range between 20 and 25 percent. The authors estimate a −0.5 elasticity of low-skill employment with respect to the minimum wage for large policy changes and a non-negative elasticity for small policy changes. An interpretation of our results is that the jobs elasticity is more negative when both a higher minimum wage and a recession are in effect. For example, the Seattle study by Jardim, Long, Plotnick, Van Inwegen, Vigdor, and Wething (2022) estimates an elasticity of −0.3 for restaurants. However, Seattle was booming during the implementation of their large minimum wage increase, whereas the Twin Cities were hit by a recession. The acceleration of job losses during the pandemic recession is consistent with earlier studies such as Addison, Blackburn, and Cotti (2013), who document the larger sensitivity during recessions, and more recent work such as Clemens and Wither (2019), who find an employment elasticity with respect to the minimum wage of −1 when analyzing the increase in federal minimum wage during the Great Recession....These changes in the minimum wage of the Twin Cities are large relative to other minimum wage changes (even relative to the ones classified as large in the analysis of Clemens and Strain, 2021). Using the average minimum wage applicable to small and large firms, we find that in 2020 the minimum wage in Minneapolis increased by 38 percent relative to implementing the statewide minimum wage. In 2020, the minimum wage in Saint Paul increased by 19 percent relative to the statewide minimum wage. Further, in both cities the minimum wage will be indexed to inflation once the target level of 15 dollars per hour is reached. Thus, to the extent that firms and workers do not perceive further increases in the statewide minimum wage, the minimum wage increase for operating within the Twin Cities is permanent...."

Labor Market Effects from the Time Series

“….Table 3 presents results for the low-wage industries identified in Section 2 and separately for restaurants….”

“….Entries are multiplied by 100 and equal the log point change in outcomes in 2020(4) due to the minimum wage increase, 100 · gT. The columns present different outcome variables. For example, the first row shows that the increase in the minimum wage in Minneapolis caused an 8.8 log points (roughly 9 percent) increase in the wage and a 9.4 log points (also roughly 9 percent) decrease in the number of retail jobs. Each entry in parentheses is the p-value associated with the estimated treatment effect, which is the probability of obtaining a treatment effect as extreme as the point estimate under the null hypothesis that the treatment effect is zero. Continuing the example, the placebo method produces a p-value of 0 for the wage and 12.8 percent for jobs, and thus we conclude that the wage effect is precisely estimated and that the jobs effect is not very precisely estimated and cannot be statistically distinguished from zero at conventional levels of significance. In Minneapolis, we estimate wage increases with low p-values for retail; administrative and support services; other services; and restaurants. Among industries with statistically significant increases, we document increases that range between 4 and 13 log points. For all other industries, we find either statistically insignificant wage changes or small declines. In Saint Paul, we estimate a wage increase with low p-values for retail and for accommodation and food services (and for limited-service restaurants within that industry). The wage increases in Saint Paul are generally smaller and more imprecisely estimated than the wage increases in Minneapolis. We find this result intuitive, because Saint Paul announced the minimum wage increase in 2018 but did not implement it until 2020, so there are only four quarters of data under the new minimum wage policy. We find the wage increases in Minneapolis reasonable. The difference between the minimum wage in Minneapolis and the one in the control cities is 38 percent. However, many workers are not close to the minimum wage, even in low-wage industries, and thus the estimated effects of the minimum wage increase on the wage are expected to be smaller than the change in the minimum wage. Holding worker hours constant at their 2017 level, the average establishment in Minneapolis would have experienced an 8 percent increase in its labor cost between 2017 and 2020. Weighted with employment, the average labor cost increase is 5 percent. The direct effect of the minimum wage on labor cost falls comfortably in the range of wage gains we estimate. Turning to the second column, we find negative jobs effects for accommodation and food in Minneapolis. Within accommodation and food, we find a 41 log points jobs decline for full- service restaurants and a 32 log points jobs decline for limited-service restaurants. We find jobs declines of 9 log points for retail and 13 log points for arts, entertainment, and recreation, but these declines are not statistically significant at conventional levels. We also document large jobs declines for restaurants in Saint Paul, which range between 39 and 57 log points. Additionally, we find a decline of 18 log points for arts, entertainment, and recreation that is statistically significant at a 6 percent level. Another difference with Minneapolis is that in Saint Paul, we find a 15 log points increase in jobs for other services. The third column presents results for total hours. Estimated effects for hours are generally similar to estimated effects for jobs, with three exceptions. First, in both cities, arts, entertainment, and recreation experience smaller changes in hours than in jobs. Second, in Saint Paul, we cannot detect a statistically significant change in hours for other services, whereas we found an increase for other services’ jobs. Finally, in Saint Paul, we find a decline of 35 log points for retail hours, whereas we did not detect a statistically significant decline for retail jobs. The last column of the table presents results for worker earnings. Given the modest wage gains for all industries and the significant negative effects on employment for some industries, it is not surprising that in both cities we fail to detect a statistically significant increase in earnings in any industry except for other services. We detect statistically significant declines in earnings for retail in Saint Paul; arts, entertainment, and recreation in Saint Paul; accommodation and food services in both cities and separately for restaurants….”

Loukas Karabarbounis, Jeremy Lise and Anusha Nath, "Minimum Wages and Labor Markets in the Twin Cities,"National Bureau Of Economic Research, July 2022, https://www.nber.org/papers/w30239