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Using bank and credit card transaction data from a large sample of U.S. households, Rebecca Diamond of Stanford and Enrico Moretti of the University of California, Berkeley create local price indexes to estimate how households’ standard of living varies across cities. The authors find large geographic differences in the cost of living, particularly for low-income families (measured using nominal income), for whom living costs in the most expensive city (San Jose, CA) are 49% higher than the median city, Cleveland, and 99% higher than the least expensive (Natchez, MS). Standard of living (expenditures adjusted for local prices) follows the same pattern: low-income families in the least affordable city consume 74% less than those in the most affordable. Furthermore, the authors find that low-income households reduce their consumption less than high-income households in response to higher prices, likely because they are already paying mostly for necessities; as a result, low-income households in expensive cities face high rates of financial distress (including negative savings and overdraft fees). Diamond and Moretti also compare geographical variation in consumption by level of education. For college-educated households, they find that higher costs of living and taxes are offset by higher wages, and consumption varies little geographically. For high school graduates and dropouts, however, wages adjust less to meet local prices and consumption is more sensitive to local affordability. Consequently, the authors say, consumption inequality in a given city increases with the cost of living.
In recent decades, pay for the typical worker has fallen increasingly behind average labor productivity in both the U.S. and Canada. Does higher productivity no longer push up compensation? Or are wages held back by other factors unrelated to productivity? Anna Stansbury of MIT and Jacob Greenspon and Lawrence Summers of Harvard find that, in the U.S., marginal increases in productivity growth still raise pay if all else is held equal: a 1 percentage point increase in productivity growth is associated with 0.6-0.8 percentage points faster average compensation growth. This linkage is weaker in Canada, with a 1 percentage point increase in productivity leading to a 0.3-0.7 percentage point increase in pay growth. (The authors suggest that wages in Canada’s smaller, more open economy are more sensitive to trends abroad rather than domestic productivity alone.) These results suggest that economic policies that encourage higher productivity, like increased investment in technology, “should be expected to increase the pay of typical workers… even as there may at the same time be other variables reducing the relative growth in incomes [for low- and middle-income workers].”
Economic theory suggests that fiscal stimulus is effective under conditions of high unemployment and excess capacity. However, little is known about the effects of fiscal stimulus during a pandemic recession with widespread shelter in place policies. Exploiting high-frequency data on government defense spending, stay-at-home orders, consumption, mobility, and employment, Alan J. Auerbach from the University of California, Berkeley. and co-authors show that the effects of government spending on employment were stronger during the peak of the pandemic recession than in normal times, but only in cities that were not subject to strong stay-at-home orders. However, government spending had no effect on consumption—even in unrestricted locations. The lack of a consumption response implies that the larger employment effects are because supply is more responsive to demand-side stimulus in the presence of slack and not because income declines led to higher marginal propensities to consume out of additional income. The authors conclude that “the nature of economic downturns is potentially important for the effectiveness of government spending in stimulating aggregate demand”.
“As the data have continued to evolve, I think I would attach much more likelihood to the view that this is not a question of demand at pre-COVID levels and supply taking a while to reach back up to that pre-COVID capacity. Rather we have sustained higher demand, and this is not really solely — maybe not even primarily — a bottleneck story anymore. It’s a question that we’ve got to increase productive capacity to meet that sustained higher level of demand and that takes time,” says Randal Quarles, Member, Federal Reserve Board.
“That is exactly what monetary policy is designed to prevent: a sustained period of inflation from that imbalance possibly unanchoring inflation expectations if that were to last for a long period of time. Therefore, we should respond more quickly to constrain that demand and allow supply and demand to come together over a longer period of time and with less inflation over the process.”
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