Recession Time? Don’t Act Surprised

Treasury Secretary Yellen does not see any indicator of an imminent recession.  She isn’t looking.  The normal economic tailwinds have calmed and, as predicted, Biden's economic policies are a significant headwind.

A recession is sometimes defined as a reduction in the number employed nationally for a couple of months.  Other times it is defined as a reduction in real GDP for two quarters or more.

When it comes to predicting events like this, my recursive approach is to first understand where the general trends are heading.  In technical terms, is the economy’s “steady state” above or below where we are now, and how much?  If the trends are strong up, small perturbations around that trend will not make a recession.  If the trends are flat, then even a small negative shock will create a recession by one or more of the definitions.  Which definition will be triggered can be assessed by contrasting employment trends with productivity trends.

Four important trends are worth considering: organic productivity growth, organic population growth, recovery from the pandemic recession, and new public policies affecting productivity,  population, or employment.

Organic trends

Given that recessions are defined in absolute rather than per capita terms, population growth is normally an economic tailwind.  However, annual adult population has fallen from a bit above one percent 1980-2018 to about 0.4 percent.  Illegal immigration is a wild card here because we do not know how many are immigrating, what fraction are adults, and whether and how those adults will be economically engaged.  With that caveat, we now are in a situation where even a small negative shock that would not have caused a recession in the one-percent population growth era will now.

Recovery from the pandemic was also a tailwind.  It someday will continue to lift employment, but at the moment it looks like employment has recovered as much as it can given the serious health problems encountered during the pandemic, including but not limited to self-destructive substance abuse habits that are not complementary with productive employment.  Some of these people will show up on payrolls but how reliably they show up for work is another question.  Diabetes, liver disease and heart disease have gotten out of control since 2020.

Workers lost skills and capital laid idle during the pandemic.  These are recovering, although their recovery will not be fully recognized in the growth data.  GDP and productivity levels were exaggerated during the pandemic as many goods were unavailable or low quality in ways not captured by the national accountants.  For example, public school teachers stayed home from school but the national accountants assumed that they were as productive as ever merely because they continued to get paid.  As they get back to traditional teaching, this will not be officially recognized as economic progress for the same reason the pandemic regress was never acknowledged. 

Crime has gotten bad, especially in big cities where productivity is normally the highest.  Consumers and businesses are avoiding big cities, which is a cost (“excess burden”) beyond the crime statistics because the whole point of the avoidance behaviors is to keep from being one of those statistics.

Fitzgerald, Hassett, and I predicted in 2020 that Biden’s economic agenda would reduce the levels of full-time equivalent employment per capita by 3.1 percent and real gdp per capita by 8.5 percent.  If that level effect were spread over five years, that would be 0.6 percent per year and 1.7 percent per year, respectively, as shown in the Table as an addendum panel.  That by itself makes a recession likely in one of those five years.

Regulatory Policy

Our analysis of Biden’s agenda distinguished regulation from capital taxation from labor taxation.  His regulatory agenda seems to be going ahead as we expected.  The good news is that Biden’s nomination of David Weil to the Department of Labor was rejected by the Senate and Biden was slow to fully mismanage the National Labor Relations Board.  But we did not anticipate that Biden’s DOL would disrupt labor markets as much as it did with its mask mandates.  Sticking with our original estimate, it looks that Biden’s regulatory agenda is reducing employment by 0.2 percent per year (of five years) and real GDP by 0.7 percent per year below the organic trends.  See the Table’s top panel.

Of particular concern over the next few months is the reliability of the electric grid and air travel.  Snafus of this type are already built into our regulatory analysis but these examples put more texture on the economic reasoning that links the marginal regulations with poor economic performance.

Capital Taxation: Inflation Sneaks In

Biden’s Build Back Better bill would implement much of the capital taxation we envisioned in 2020.  The good news is that the bill has not yet passed, and passage of its capital tax elements are not imminent in some other form.  The bad news is that inflation is taxing businesses without any Congressional action (recall Feldstein and more recently Hassett on the effect of inflation on the cost of capital), while it appears that Biden will let temporary provisions in the 2017 TCJA expire.  With capital taxation during the Biden administration increasing about half of what we expected, it would reduce real GDP by about 0.4 percent per year over five years.

Speaking of inflation, higher Fed Funds rates are already showing up in mortgage rates.  In effect, the Federal Reserve is introducing a tax (or cutting a subsidy) on structures investment, which is likely to send at least that sector into a recession.  Socially responsible (a.k.a., woke) investing is also skewing the allocation of capital.

Combining capital taxation and regulation, the headwinds in the Biden economy are 0.25 percent per year for employment and 1.1 percent per year for real GDP.

Labor Taxation: Direction Unclear

Labor taxation is an interesting wild card here.  Marginal tax rates on work were cut sharply when the $300 weekly unemployment bonus expired last summer.  That effect has played out already.  But I expect that Congressional Democrats, and even some Republicans, will expand unemployment benefits if anything resembling a recession were occurring.  That could easily and quickly reduce employment by one percent, if not more.  On the other hand, various federal health insurance subsidies are about to expire.  If they do (without resurrection), that will encourage work.

Bottom Line

Overall, a recession is highly likely with so many headwinds and so few tailwinds.  A recession is more likely by the GDP definition than the employment definition.  The depth of the recession depends on how much Congress destabilizes things by further adding to the already large federal portfolio of programs for the unemployed and poor and further adding to tax burdens.

Are Regulations "Job Killing"?

The traditional models of regulations and growth treat regulation as an adverse productivity shock (more inputs for the same output) in order to help the environment, fairness, or some other social good.  But a productivity shock has opposing income and substitution effects on labor supply.  Arguably a regulation that works as a productivity shock has no aggregate effect on jobs.

Reminded how Gary Becker many times told me that "somebody benefits," I do not endorse the productivity-shock model of regulation, at least as relates to the Federal regulations added and removed over the past 20 years.  In economics jargon, the "rectangle" created in a market by regulation is not entirely wasted: some of it is a transfer to special interests and therefore not an income effect in the aggregate.  This kind of regulation is more like an excise tax with the revenue paid to special interests.  Excises taxes unambiguously reduce aggregate equilibrium employment.

As the CEA showed in the 2019 and 2020 Economic Reports of the President, many of the regulations removed by the Trump Administration were more like the excise tax than like a productivity shock.  That's why special interests fought back so hard (and a couple of times, they won).  

[Even a productivity shock reduces employment in the short run to the extent that it reduces the productivity of investment.  i.e, that's another way that the "job killing" can occur.  There is another interesting case in which the rectangle from regulation is a transfer from Americans to foreigners in which case the employment reduction is outside the country.]

The sign of the effect on the employment of the regulated industry is also ambiguous.  If the regulation is a transfer from consumers to producers, with no adverse productivity effect, the regulation will reduce industry employment because that transfer is achieved by restraining supply.  But that frees up resources for other industries, which is why the aggregate employment effect can be nil.

To the extent that regulation reduces productivity in the regulated industry, we need Marshall's Laws of Derived Demand to sign the effect on industry employment.

One application provided in Chicago Price Theory is the regulation of illegal drugs.  Their demand is price inelastic in the sense that drug prohibition reduces consumption (although see here for a tragic exception) while it increases what consumers spend on drugs.  The price elasticity of demand is an important part of Marshall's Laws.  For illegal drugs, the result is more people working to (or serving prison time for) grow, manufacture and distribute illegal drugs because law enforcement reduces their "efficiency."  But those people are coming out of other activities, which is why the aggregate employment effect can still be nil. 

The Economic Cost of Shutting Down "Non-essential" Businesses

THE ESTIMATES BELOW WERE MADE CIRCA MARCH 20, 2020.  Updated estimates are available here, complete with additional cost categories and references and based on important new data.

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We are currently fighting a war against the COVID-19 virus.  The war presents an obvious and massive tradeoff between “guns” – activities whose primary purpose is war production – and “butter,” which refers to the normal activities of households and businesses.  Without any improvement in our techniques for fighting the war, the sacrifices by households and businesses will be staggering and historically unprecedented.

This document enumerates and quantifies the sacrifices using two novel methods.  The results suggest that negative 50 percent is an optimistic projection for the annualized growth rate of U.S. GDP in 2020 Q2 if the nonessential businesses were not allowed to operate during that quarter.  GDP losses, while massive, nonetheless understate the true costs of the sacrifices that households and businesses are making, which I estimate to total almost $10,000 per household per quarter.  This is why better techniques for fighting the war are incredibly valuable.

Government officials around the world have ordered businesses shut and families to stay in their homes except for essential activities.  My purpose here is to enumerate and quantify the real economic costs of fighting the war in this way.  This document estimates the opportunity costs of lockdown relative to a normally functioning economy, which is the relevant comparison for the purposes of policy decisions such as medical innovation (or, possibly, a statistical sampling effort) that could end the lockdown earlier. 

To be clear, the cost-enumeration exercise can be consistent with a conclusion that the war is worth fighting – that depends on quantifying the benefits, which are surely significant given the value that people place on health and longevity.  Although this document does not address the question of whether a lockdown is better than no policy response, it offers some of the essential ingredients for such an analysis.  No policy response would itself involve economic contraction during the period of time in which no treatment or vaccine is available.

The lockdown method of fighting the war on the virus directly affects how people allocate their time.  That includes what they do, where they do it, and with whom.  Because normal time allocation includes elements of saving and capital accumulation, such as learning skills, the economic effects of the war are felt into the future as stocks of physical and human capital are reduced.  For enumeration purposes, I distinguish market production activities from all other activities, especially because market production is counted in conventionally-measured GDP whereas leisure activities are not.  Although I refer to the non-market activities as “leisure,” they include religious gatherings and forms of effort such as student effort in school and effort put toward housework.  Both market production and leisure activities are significantly affected by lockdown.

A.  Net Costs Associated with Market Production

Lockdown reduces the amount and effectiveness with which people work.  The effects of this can be measured on either the production side of the national accounts, as the value of goods and services not produced, or the income side as reductions in total incomes.  Either approach yields the same result, up to measurement error.[1]  However, the incidence – the distribution of impact across industries, occupations, and income groups – is different from the production and income perspectives.

Momentarily putting aside costs associated with leisure activities, the lockdown can be analogized with a change in the number of holidays and weekends (“nonwork days”).  A well-studied, albeit obscure, element of national income accounting is the adjustment for the fact that the number of nonwork days normally varies from year to year.[2]  A normal year has about 251 working days and about 114 nonworking days.[3]  The national accountants have found that adding a nonwork day to the year reduces the year’s real GDP by about 0.1 percent and have been applying this estimate to both the production and real income accounts.[4]  Adding a nonwork day to a quarter would therefore reduce the quarter’s unadjusted real GDP by about 0.4 percent.

Extrapolating from this finding, removing all of the working days from a quarter is 62 or 63 times this, or 25 percent.[5]  In other words, if seasonally-adjusted GDP 2020-Q2 would have been $5.5 trillion at a quarterly rate (see Table 1), then changing all of that quarter’s working days to the functional equivalent of a weekend or holiday would reduce the quarter’s GDP to $4.2 trillion.[6]  Applying the same approach to 2020-Q1, with a lockdown occurring for one-eighth of the quarter, 2020-Q1 real GDP (in 2020-Q2 prices) would be $5.4 trillion.  The quarter-over-quarter growth rate of seasonally-adjusted real GDP would, expressed at annual rates, therefore be -10 percent in Q1 and -63 percent in Q2.[7]  The Q2 growth rate would be less negative to the extent that a lockdown was in place for only part of the quarter or for part of the country.

[Table 1 contains POINT ESTIMATES, not worst-case scenarios.  If these costs are to be netted against health benefits, then those benefits should be POINT ESTIMATES too.  Worst-case scenario health benefits should be compared with worst-case scenario costs, which far exceed what I provided].

Lockdown is not exactly the functional equivalent of changing workdays to weekends or holidays.  On one hand, a segment of the workforce will engage in telework during lockdown that they would not perform on a normal weekend or holiday.  Other segments or regions will be exempt from shutdown.  This by itself suggests that the $4.2 trillion estimate is too pessimistic.  On the other hand, much of the normal weekend activity such as restaurants, entertainment, and religious activities is not occurring during lockdown.  This by itself suggests that the $4.2 trillion estimate is too optimistic.

A second method uses the production side alone.  Labor is reduced by the number of “non-essential” employees, which has been about 30 percent during Federal shutdowns.[8]  In some of the industries, real capital will continue to be used, albeit by fewer employees.  Other industries will not use their capital, although it may be repurposed, such as a hotel being used as a hospital ward.  To be conservative, I assume that few industries increase their labor-capital ratio.[9]  The reduction in capital input is therefore somewhere between 0 and 30 percent; I assume 15 percent.  History has repeatedly shown that labor is more important in the production process than capital, so that by the second method real GDP is reduced 26 percent.[10]

The estimates above assume no black markets.  But, as seen with border patrol and the war on drugs, any government regulation attempting to block valuable gains from trade will result in black market activity.  Businesses will also work the gray area, lobbying and distorting their operations to have more activities declared “essential.”

Black-market activity is far less productive than legitimate activity, which is why it does not come close to replacing the “non-essential” sales that were banned.  But it still has value, which is why the best welfare effects of shutdown may be less pessimistic than analysis assuming zero black market.[11]  I assume that black markets replace 25 percent of the gains from trade, based on studies of illegal drugs.[12]  However, value generated in black markets is typically not measured as part of GDP.  Indeed, black markets compete with legitimate markets for the factors of production and by this channel would reduce measured real GDP even more than would occur without black markets (Fleming, Roman and Farrell 2000).

Table 1 shows only averages, but the distribution of costs is unequal.  Revealed preference -- that fact that the demand for social insurance increases in these situations -- suggests that the inequality itself is a cost large enough that people are willing to tolerate even further increases in the average costs (i.e., further decreases in GDP) in order to mitigate the costs for those disproportionately affected.

Although the national income accounts were designed on the basis of the principles of welfare economics, GDP growth is not exactly a benefit and GDP reduction is not exactly a welfare cost because valuable activities and assets such as home production, elements of human capital accumulation, and environmental quality are not yet recognized in the official national accounts (Hartwick 1990, Nordhaus and Kokkelenberg 1999, Jorgenson 2010).  However, as discussed further below, the GDP losses cited above prove to reasonably approximate more comprehensive welfare losses.

B.  Net Costs Associated with Nonmarket Activities

The nonmarket/home sector is affected by lockdown through two basic channels, as shown in Table 1.  The first channel is discussed above: the nonmarket sector has additional labor that has been forced out of the market sector.  The second channel is that the nonmarket sector becomes less productive, both for the nonmarket time that normally exists as well as the additional nonmarket time coming from the market sector, because even in their nonwork activities people are restricted in terms of where they go and how they associate with others.  The percentage change in the value created in the nonmarket sector combines the two channels and is approximately the sum of the (positive) percentage change in labor input and the (negative) percentage change of nonmarket productivity.[13]

An important example of the second channel is the time allocation of children and young adults who would normally be enrolled in school and now spend their time at home.  Their learning from normal face-to-face interactions with teachers and fellow students is not fully reflected in GDP, but is nonetheless valuable.  In other cases, as with religious gatherings, entertainment, and tourism, lockdowns reduce the value of these activities by limiting how people can congregate and the market inputs that can be used as part of the leisure activity.

Because the national accounts are based on the principles of welfare economics, GDP would ideally capture value created or destroyed in both the market and nonmarket sectors.  Measurement challenges have so far limited the scope of conventional GDP measures to the market sector.  Conventional GDP measures therefore miss the value of additional nonmarket time added by the shutdown (the first channel) as well as the reduced productivity of nonmarket time (the second channel).  This section provides estimates of the two, which can be added to the GDP losses from Section I.B to arrive at a welfare loss of shutdown as compared to normal economic activity.

To estimate the nonmarket value of added labor, I use the short run of the neoclassical growth model, which is essentially a labor supply and demand framework.  The average nonmarket value of time is below the after-tax real wage that would normally prevail, but above the marginal value of time with a shutdown, which I estimate to be 49 percent of the former.[14]   With a 48 percent marginal tax rate (inclusive of implicit taxes on labor income), the total nonmarket value of the extra time is about $7 billion per day (see Table 1), or about 30 percent of the reduction in real GDP.  Simply put, about two-thirds of the $22 billion daily GDP loss is a welfare loss, even without considering any productivity change in the nonmarket sector.

Full-time schooling, where there are normally about 73 million children and young adults enrolled, is the part of the non-market sector’s productivity loss that is easiest to quantify.  Their time and efforts, which are known as “foregone earnings” and not counted in conventional GDP measures, are combined with direct schooling costs such as the education industry’s payroll and capital expenses because the students, their parents, or their community value the results of schooling.  The direct costs were $370 billion in 2018.  Various studies, such as Breton’s (2013) estimate that foregone earnings are about 102 percent of the direct costs, which would be $377 billion in 2018, or about $4.5 per hour that the average student was in school.  Assuming that some schooling will still occur during lockdown, I take the loss of student output attributable to their time and effort to be half, or about $2.25 per hour that they would have been in school.

Learning does not stop at graduation.  Post-graduation workers learn on the job, which shifts the composition of their compensation toward skill acquisition and away from the cash and other fringe benefits that are part of conventionally measured GDP (Rosen 1972).  Although the market sector may be the physical location of this learning, I count the foregone earnings as “nonmarket” because it is usually unmeasured.  I estimate the value of foregone earnings using the cross-section age-earnings profile and the average of two estimates of the age-training profile (Mulligan 1998).  During a shutdown, this learning does not occur for 30 percent of the workforce, although (as with market production) I assume that about one-third of its value is replaced with nonmarket activity.  The net opportunity cost associated with on-the-job training (OJT) is therefore about $107 billion at an annual rate, as shown in Table 1.

The normal population has even more adults not in the labor force than full-time students, not to mention all of the time that workers normally spend outside of work.  If a shutdown also reduced the hourly value of their time by $2.25 for 2000 hours per year for those out of the labor force and for 500 hours per year for those who work, that would be a loss of $767 billion at an annual rate.

C.  The Incidence of the Net Costs

The massive costs of shutting down “non-essential” activities are not shared equally among Americans.  Some workers are still able to draw a normal salary even while their industry is inactive.  Others work in industries such as parts of healthcare that are booming as a result of the pandemic.  Because the aggregate reduction in the value of what is produced must equal the aggregate reduction in total income, the costs of lockdown will fall disproportionately on the remainder of the population that are not in these circumstances.

Public programs are being created and expanded with the intention of helping some of those who are disproportionately bearing the costs.  Redistribution policy may help distribute the aggregate costs more fairly, but in no way can it reduce the aggregate cost.  Even while these policies assist those who are not working because of the pandemic, they do not replace the work and production that the workers would have been doing.  Instead, redistribution itself has its own aggregate costs, for example, by reducing incentives to work and incentives of workers to shift into industries that need them most (Mulligan 2012).

D.  Bibliography

Barro, Robert J., José F. Ursúa, and Joanna Weng. "The Coronavirus and the Great Influenza Pandemic: Lessons from the “Spanish Flu” for the Coronavirus’s Potential Effects on Mortality and Economic Activity." NBER working paper, no. 26866 (March 2020).

Breton, Theodore R. "Were Mankiw, Romer, and Weil right? A reconciliation of the micro and macro effects of schooling on income." Macroeconomic Dynamics 17 (2013): 1023–1054.

Christian, Michael S., and others. Human capital accounting in the United States: 1994 to 2006. BEA, 2010.

Council of Economic Advisers. Mitigating the Impact of Pandemic Influenza through Vaccine Innovation. Executive Office of the President, September 2019.

DiMasi, Joseph A., Henry G. Grabowski, and Ronald W. Hansen. "Innovation in the pharmaceutical industry: new estimates of R&D costs." Journal of health economics 47 (2016): 20–33.

Eichenbaum, Martin S., Sergio Rebelo, and Mathias Trabandt. "The Macroeconomics of Epidemics." googlegroups.com. March 23, 2020. https://fb8280a8-a-62cb3a1a-s-sites.googlegroups.com/site/mathiastrabandt/home/downloads/EichenbaumRebeloTrabandt_EpidemicsMacro.pdf?attachauth=ANoY7coRODwA_z1gJoSLHsTEXF0IQcWOQbFP5bfX9cHSypuO2NuwJPyq7T2A4p2wdppOl0jWVgmAzN4-16-irT7vZqkwvBs8_8PNb3hs0ZJmZtr.

European Central Bank. "The impact of the number of working days on euro area GDP in 2004." Monthly Bulletin, June 2004: 51-63.

Fleming, Matthew H., John Roman, and Graham Farrell. "The shadow economy." Journal of International Affairs, 2000: 387–409.

Hartwick, John M. "Natural resources, national accounting and economic depreciation." Journal of Public Economics 43 (1990): 291–304.

Jaffe, Sonia, Robert Minton, Casey B. Mulligan, and Kevin M. Murphy. Chicago Price Theory. Princeton University Press (ChicagoPriceTheory.com), 2019.

Jorgenson, Dale W. "A new architecture for the US national accounts." Review of Income and Wealth 55 (2009): 1–42.

Jorgenson, Dale W. "Human capital and the national accounts." Survey of Current Business 90 (2010): 54–56.

Katz, Eric. "Agencies Paid Federal Employees $3.7 Billion Not to Work During Recent Shutdowns." govexec.com. September 17, 2019. https://www.govexec.com/pay-benefits/2019/09/agencies-paid-federal-employees-37-billion-not-work-during-recent-shutdowns/159936/.

Mulligan, Casey B. The Redistribution Recession. New York: Oxford University Press (redistributionrecession.com), 2012.

Nordhaus, William D., and Edward C. Kokkelenberg. Nature’s Numbers. National Academy Press Washington, DC, 1999.

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[1] Adding the opportunity costs of production to the reductions in incomes would be double counting.

[2] Leap years also create variation in the total number of days.

[3] A year has 52 weeks plus an additional one or two days, for a total of between 104 and 106 weekend days.  There are ten Federal holidays.

[4] European Central Bank (2004).  Note that 0.1 percent is much less than 1/251; the estimate implies that the average nonworking day has two-thirds the GDP of a working day.

[5] The exact number of working days that 2020 Q2 would have normally had is irrelevant for this calculation because the national accountants adjust for its deviation from 251/4.

[6] This is the sum of Table 1’s first row with its addendum row.

[7] This assumes a baseline annual growth rate of 2 percent and applies it one for one.  The formula for the annualized growth rate in Q2 is . 

[8] Katz (2019).  The non-essential share has not yet been measured for government-mandated private-sector shutdowns, which is why the holiday-weekend method should receive more weight.  Given that I obtain almost the same result for the two methods, their weighting is not critical to the analysis.

[9] Using their labor is different than keeping the labor on the payroll.  An unused worker still on the payroll does not contribute to current output.

[10] Barro, Ursúa and Weng (2020) estimate that, if the COVID-19 epidemic were a scaled version of the 1918 Spanish Flu, real GDP would fall less than eight percent.  The authors note that the current pandemic is unique in that “countries have been pursuing a policy of lowering real GDP,” which are the lockdowns that are the subject of this document.  Looking at the current pandemic, Eichenbaum, Rebelo, and Trabandt (2020) estimate that aggregate consumption and GDP (their model has no investment) will fall up to 20 percent.

[11] But government regulators can hardly defend their shutdown regulations on the grounds that they will not be obeyed!  Moreover, black markets add another social cost by eroding respect for law and order.

[12] The war on drugs increases the retail price (and unit cost to suppliers) by a roughly a factor of four and reduces the quantity consumed by a factor of two (Jaffe, et al. 2019, Figure 12-1).  Illegal drug sellers have had many years to accumulate organizational and other capital that lockdown violators did not.  On the other side, drug war enforcers have had many years that lockdown enforcers have not.

[13] The approximation is exact when the changes are measured in logarithms rather than percentages.

[14] This assumes a wage elasticity of labor supply of 1/2 (0.49 = 0.7²).

Judge the Federal Dept of Labor by Intentions, not Results

According to the Department of Labor, it is now adding 4.2 million workers and their employers to those required to obey detailed federal regulations on weekly pay and work hours. The stated intention is to help women and other relatively low-income employees.

Most of those 4.2 million workers are earning significantly above the straight-time minimum wage rate. For them, economic theory and evidence (e.g., Prof. Stephen Trejo, now of the University of Texas, wrote his dissertation on this at the University of Chicago) suggest that their straight-time wage rate will be lower than it would have been if the DOL had not changed the rules. This will make their income more cyclical -- making them poorer when their incomes are low and richer when their incomes are high -- with little effect on their average pay or hours.

Trejo's results suggest that there will be also some adjustment of employment and work schedules. Because the new regulations apply only to workers earning below about $48K per year, they create incentives to reallocate work from low-income workers to higher income workers. Another way of increasing income inequality!

This is a new cost for employers that disproportionately hire low-income workers, and ultimately for the customers that buy their products. This will cause some of those industries to shrink, perhaps leaving low-income people with less employment as well as less income while they are employed. It is possible that high-income workers benefit, as their industries are not harmed as much by the new rules. i.e., yet another way of increasing income inequality!

The new rules are about cash wages, and not fringe benefits, so another effect is fewer fringe benefits to help pay for the extra cash wages. The consolation for those of the affected workers that lose their health insurance: Obamacare! (No consolation for taxpayers, who will have to help pay for that problem).

You may have noticed that GDP per capita is hardly growing -- at a mere 1.3 percent per year over the past 3 years. An important reason for this is all of the new federal interference in how business is done. Obamacare already heavily distorts the workweek, especially for workers with incomes below $48K, and now these new regulations are adding to it. More and more, work schedules are being chosen to satisfy federal rules and less for creating value in the marketplace.

My promise: If you like your weak economy, you can keep it! Especially if you are a low-income worker.

Some fallacies never die

Posted over a year ago as Modern Wage Economics: Too Subtle for the Blogosphere?, but some fallacies never die:

Our economy uses a lot less labor than it did 10 years ago, and for good reason people are interested in the relative importance of supply and demand factors for explaining what happened to the quantity of labor.

Naturally, a supply-demand decomposition exercise is enhanced by looking at both the quantity and price of labor, also known as the wage rate. That's why my book on the recession starts off with various indicators of wage rates and their dynamics (see chapter 2 beginning on page 9).

Three or four decades of labor economics research are of great assistance in this exercise. That research tells us that the price of labor from an employer's point of view is often significantly different than cash earnings per hour, and that a reduction in labor supply could be associated with reduced cash earnings even while it was increasing employer costs:

1. A reduction in labor supply could reduce the quality of labor, with workers putting in less effort, or doing less to maintain their skills, or become less attached to the labor market.  This tends to reduce cash earnings per hour because each hour is less productive.  These have been major factors in the analysis of women's wages, where most economist believe that women's hourly earnings increased as a consequence of supplying more (see Becker 1985, Goldin and Katz 2002, Mulligan and Rubinstein 2008, and many others). See also some of the literature on unemployment insurance such as Ljungqvist and Sargent's paper on European unemployment.
2. A reduction in labor supply or demand could increase the average quality of labor through a composition bias.  See p. 17ff of my book and the references cited therein.
3. Because of fringe benefits, cash hourly earnings are not the same as employer cost.  As employer health insurance expenditure has been growing over time, the growth of cash hourly earnings has substantially under-estimated the growth of employer cost.

The Incidence of Supply and Demand Impulses.

Labor economists have also long studied the incidence of supply and demand impulses: that is, the effects of supply and demand factors on both wage rates and the quantity of labor. The consensus is that: (a) labor demand is more wage elastic than labor supply and (b) labor demand is even more wage elastic in the long run than it is in the short run.

Suppose that the reduction in the quantity of labor were 50% due to demand factors and 50% due to supply factors, and that we had overcome all of the measurement issues cited above. Result (a) means that wages would fall in the short run, because supply shifts translate more into labor quantity than into wage rates while, in comparison, demand shifts translate more into wage rates than labor quantity. In this example, it would be wrong to conclude from reduced wage rates than supply is less important than demand for explaining the change in the quantity of labor.

To put it another way, if we found that wage rates (properly measured) were constant, but didn't know the relative contribution of demand and supply factors to the quantity change, result (a) tells us that the majority of the labor quantity change was due to supply factors. With a labor supply elasticity of 0.5 and labor demand elasticity of -3 (reasonably conservative short run estimates), the constant wage rate result means that 86 percent of the quantity change was due to supply factors and only 14 percent due to demand factors. In the long run, labor demand is even more wage elastic, and the share attributable to labor supply is even closer to 100%.

To put it yet another way, if it were true that labor demand explained the majority of the change in labor quantity, then employer costs (properly measured) would have fallen dramatically.

Despite all of these lessons from labor economics, blogosphere economists attempt to dissect the hourly cash earnings data to perhaps find a small and probably ill-timed reduction and jump to the conclusion that labor supply shifts have made a trivial contribution to the change in labor quantity.

The ACA and wages

The ACA reduces hourly employer cost in at least 3 ways:

(1) employer penalty. I doubt the national accountants will count this as employee comp, so it will lower measured employer cost even if it raises marg prod of labor

(2) productivity. the aca changes the allocation of factors to sectors and the allocation of spending to sectors. my best estimate is that it lowers productivity one percent

(3) for large segments of the population, quasi-fixed costs of employment are amortized over fewer hours. ie, part-time jobs pay less per hour than full-time jobs do.

Trevor and I have a paper with two of these effects. "wedges, wages and productivity under the ACA"

A paper with the third is almost ready for NBER wp. Trevor also looks at the productivity losses from inducing employers to keep FT employment below 50.

Far more important than any of this is what the ACA does to AFTER-TAX wages: sends them to zero in too many cases. I'd like to see the empirical labor economists try to take the log of that!

Robots and Property Values

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As robots begin to move goods and people from place to place, urban land might become more valuable.

Amazon.com has announced that it is testing package delivery by drones — small, unmanned helicopters that would bring a purchase from Amazon’s fulfillment center to the customer’s front porch. Driverless cars are being developed to help move goods and people from place to place.

“Location, location, location” is the saying in real estate: a property’s value is determined primarily by its location. An apartment in central Illinois might be worth 20 times as much in Manhattan, because a Manhattan apartment gives its resident access to many more goods, activities and high-paying jobs.

This is not to say that urban living is always the best, or that all urban properties are created equal. Locations involve trade-offs, and rural areas offer amenities that big cities cannot. But for centuries, real estate markets have shown that people and businesses are willing to pay more for urban properties.

As technology helps with moving goods and people more cheaply, it might seem that urban real estate would give up some of its price premium because distance becomes less of an obstacle to economic transactions. Wouldn’t a driverless car cause some workers to sell their Manhattan apartments and commute to their jobs from more spacious homes in the suburbs or even rural New York State?

But don’t forget that many people and businesses currently avoid urban areas because of the monthly expense of owning or renting urban property. New technologies might allow them to use urban properties on a part-time basis, or use less urban property to accomplish the same tasks, which would make urban property more valuable.

A restaurant may need less refrigeration and storage space because it takes multiple food deliveries per day. Grocery stores may save on shelf space by having a greater fraction of their items delivered directly to customers without being shelved in the store. Households may opt for less storage space or parking, for example — and more room for people — when they can get items and transportation cheaply and on time.

For every Manhattan resident who leaves his apartment for the suburbs, there could be many others for whom technology induces them to use a Manhattan property on a part-time basis.

New technologies are more likely to emerge in urban areas, because that’s where the innovators expect to find the most customers. Amazon said that it planned to start its drone service in urban areas, and I wouldn’t be surprised if the first commercial uses of driverless cars were in big cities like San Francisco or Los Angeles.

Thus, while cities already give their residents access to more goods and services, technology may further shift that advantage and thereby increase urban property values.

Robotic-Task Economics

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The forces of supply and demand suggest that the machines of the future will continue to be significantly different from people.

Anthropomorphic robots are prominent in science fiction. Novels like Mary Shelley’s “Frankenstein,” and films like “E.T.,” “Star Wars” and “The Terminator” feature machines with one head, two eyes and many other features of humans. But those robots were created by authors to entertain audiences, and not by investors to produce some other kind of economic value.

At first glance, it would seem that the state of technological progress is all that limits the creation of machines closely resembling humans. Especially after the recent recession, people are concerned that technological progress is moving at a pace that will soon permit machines to put wide swaths of the human population out of work, and not just displace workers from one industry to another.

I disagree. Powerful economic forces will push the machines of the future to be different from people, and to complement workers rather than mimic what they do.

Machines are expensive to design and manufacture, and most of the people directing the creation of machines have an eye on the rate of return: the economic value of a finished machine as compared to the costs of creating it. The more profitable investments will, by definition, be in machines with a higher rate of return.

The earth is already occupied by quite a number of people, and a machine like Frankenstein or C-3PO might find itself with seven billion competitors.

Take my example last week of baby-sitter robots. A baby-sitting machine with a high rate of return might, as one commenter suggested, be one designed to help children during fires and other disasters. Or a machine to care for children at night. These are examples of child care tasks with less competition from people than the ordinary baby-sitting tasks.

Human help is, of course, not free, and economizing on the cost of a baby sitter or an automobile driver is a reward for creating a machine to do those tasks. But the amount of the reward is commensurate with the amount of wages that people earn in the task.

Machines that drive human workers into unemployment, rather than into another industry where the human workers will be productive, will serve only to drive down workers’ wages and thereby drive down the machine’s value. Machines that, instead, help people to be more productive will find it much harder to saturate their own market.

Rather than finding an intelligent and tireless robot in your office chair, expect the machines of the future to help workers, not harm them.

Nanny Robots and Population Growth

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As any parent can attest, caring for young children is time-intensive. As a result, child care is one of the largest segments of the economy, at least when the nonmarket household sector is included.

Many parents treasure their children and feel the benefits outweigh the time and costs of having children. Many other adults decide not to have their own children, and the time costs are sometimes a factor in that decision. (To be sure, the influences are complex; a study by Satoshi Kanazawa of the London School of Economics, which suggests that women with higher IQs are less likely to have children, made waves in the blogosphere in recent days.)

The time costs of child care are also a factor limiting teenage pregnancy. Teenagers are encouraged to complete high school and higher levels of schooling, and students’ parents, teachers and counselors – if not the teenagers themselves – understand that teenage motherhood takes time away from schoolwork and thereby makes academic success less likely.

The world would be very different if children did not need so much time. More people, perhaps especially teenagers, would have children if children did not require so much time and attention, especially from their mothers. People who already have children despite the cost might have more of them if they expected each child to require less time.

As the time costs of children limit population growth, the population would be likely to grow more rapidly if those costs were somehow reduced, whether you think that such growth is a good thing or a bad one.

If each child required less parental time, you might expect parents – especially mothers – to use their time on other things, like work more outside the home, pursue their own schooling or leisure activities. But it is possible that people would spend more of their lives caring for children and less time on those other things because they would be having more children.

Wealthy people have already had some of these opportunities, because they can afford numerous baby sitters, nurses and tutors. But technological progress may one day reduce child-care costs for the general population.

Because robots and other machines take on a number of tasks formerly done by people – even playing chess – and are expected to do others like drive cars, perhaps we should expect that robots will some day take care of children, too.

People today may believe that it would be inhuman or immoral to leave young children at home alone with a robot or to drop them off at a day care center staffed by machines. But economic and technological changes of the past have already transformed child-rearing attitudes and practices: take test tube babies, working mothers, screen time, fast food or children with their own telephones.

There is little need to worry that machines will take over all aspects of child rearing. People will always have a comparative advantage over machines, even if machines could in principle be better at just about anything. For the same economic reason that the world can produce more by assigning some tasks to unskilled people and other tasks to talented people, people will be doing tasks that are difficult for machines relative to other tasks.

But perhaps robots will make parenting easier and thus more popular.

Modern Wage Economics: Too Subtle for the Blogosphere?

Our economy uses a lot less labor than it did 10 years ago, and for good reason people are interested in the relative importance of supply and demand factors for explaining what happened to the quantity of labor.

Naturally, a supply-demand decomposition exercise is enhanced by looking at both the quantity and price of labor, also known as the wage rate. That's why my book on the recession starts off with various indicators of wage rates and their dynamics (see chapter 2 beginning on page 9).

Three or four decades of labor economics research are of great assistance in this exercise. That research tells us that the price of labor from an employer's point of view is often significantly different than cash earnings per hour, and that a reduction in labor supply could be associated with reduced cash earnings even while it was increasing employer costs:

1. A reduction in labor supply could reduce the quality of labor, with workers putting in less effort, or doing less to maintain their skills, or become less attached to the labor market.  This tends to reduce cash earnings per hour because each hour is less productive.  These have been major factors in the analysis of women's wages, where most economist believe that women's hourly earnings increased as a consequence of supplying more (see Becker 1985, Goldin and Katz 2002, Mulligan and Rubinstein 2008, and many others). See also some of the literature on unemployment insurance such as Ljungqvist and Sargent's paper on European unemployment.
2. A reduction in labor supply or demand could increase the average quality of labor through a composition bias.  See p. 17ff of my book and the references cited therein.
3. Because of fringe benefits, cash hourly earnings are not the same as employer cost.  As employer health insurance expenditure has been growing over time, the growth of cash hourly earnings has substantially under-estimated the growth of employer cost.

The Incidence of Supply and Demand Impulses.

Labor economists have also long studied the incidence of supply and demand impulses: that is, the effects of supply and demand factors on both wage rates and the quantity of labor. The consensus is that: (a) labor demand is more wage elastic than labor supply and (b) labor demand is even more wage elastic in the long run than it is in the short run.

Suppose that the reduction in the quantity of labor were 50% due to demand factors and 50% due to supply factors, and that we had overcome all of the measurement issues cited above. Result (a) means that wages would fall in the short run, because supply shifts translate more into labor quantity than into wage rates while, in comparison, demand shifts translate more into wage rates than labor quantity. In this example, it would be wrong to conclude from reduced wage rates than supply is less important than demand for explaining the change in the quantity of labor.

To put it another way, if we found that wage rates (properly measured) were constant, but didn't know the relative contribution of demand and supply factors to the quantity change, result (a) tells us that the majority of the labor quantity change was due to supply factors. With a labor supply elasticity of 0.5 and labor demand elasticity of -3 (reasonably conservative short run estimates), the constant wage rate result means that 86 percent of the quantity change was due to supply factors and only 14 percent due to demand factors. In the long run, labor demand is even more wage elastic, and the share attributable to labor supply is even closer to 100%.

To put it yet another way, if it were true that labor demand explained the majority of the change in labor quantity, then employer costs (properly measured) would have fallen dramatically.

Despite all of these lessons from labor economics, blogosphere economists attempt to dissect the hourly cash earnings data to perhaps find a small and probably ill-timed reduction and jump to the conclusion that labor supply shifts have made a trivial contribution to the change in labor quantity.

The Future of Driving

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Driverless vehicles would be a windfall for households and businesses that acquire them but would probably increase traffic and nationwide fuel usage.

Google and other innovators are working on vehicles that someday might drive themselves with little or no attention from human passengers. The vehicles of the future will have fast, observant computers that automatically communicate position and road conditions with other vehicles on the road.

Driverless vehicles are expected to help children, the blind, the elderly and others who currently cannot safely drive themselves. Helped by their huge amounts of data and computing power, driverless cars are also purported to reduce traffic congestion and nationwide fuel consumption by driving smarter.

But smarter driving will lead to more driving, because smarter driving reduces the cost per mile of vehicle usage. The end result of additional driving could be more traffic and more aggregate fuel consumption.

These days, a driver has three main costs of the trip to consider: fuel consumption, vehicle wear and tear, and time and attention devoted to driving that could be for something else. (Drivers also need to consider other costs of vehicle ownership, such as the purchase price and the cost of insuring the vehicle.)

Fuel and wear and tear cost roughly 50 cents a mile, which is why employers reimburse employees for job-related personal vehicle usage at about that rate. At an average speed of 30 miles an hour (including stops, traffic conditions and so on), each mile takes two minutes of driver time. For those who value their time at more than $15 an hour, the time cost of the trip exceeds the combined fuel and wear and tear costs.

Research has shown that cutting travel costs through reduced gas prices causes people to drive more, for example by eschewing carpools and public transportation. A driverless car should also cause people to use their vehicles for more miles, because they could use their time in the car to sleep, work, watch television, read a book and do other things they might normally do at home.

Households and business may also begin to use vehicles with no human passengers or drivers in order to move goods from one place to another and, by economizing on the human driver costs, they may want to move more goods than they do today.

As people take on additional activities in their personal vehicles, they may also demand larger vehicles that necessarily require more fuel per mile.

Before driverless cars are adopted, a number of hurdles must be cleared. Some refinements in vehicle technology need to be resolved; insurance companies and state regulators must also figure out liability issues.

Even if driverless vehicles led to more congestion and more aggregate fuel consumption, driverless vehicles would be a welcome technological advance, because the billions of hours that people already devote to driving could be put to alternative uses.

But expect new driving technologies to increase the number of vehicles on the road.

Hidden Costs of the Minimum Wage

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The current federal minimum wage of $7.25 an hour is increasingly creating economic damage that needs to be considered with the benefits it might offer the poor.

Democrats are now proposing to increase the federal minimum wage to $9 an hour. News organizations have repeatedly noted that economists do not agree on the employment effects of historical minimum-wage changes (the more recent federal changes in 2007, 2008 and 2009 have not yet been studied enough for us to agree or disagree on results specific to those episodes) and do not agree on whether minimum wage increases confer benefits on the poor.

That doesn’t mean that we economists disagree on every aspect of the minimum wage. We agree that minimum wages do some economic damage, although reasonable economists sometimes believe that the damage can be offset and even outweighed by benefits.

More important, we agree that the extent of that damage increases with the gap between the minimum wage and the market wage that would prevail without the minimum. A $10 minimum wage does less damage in an economy in which market wages would have been $9 than it would in an economy in which market wages would have been $2.

Moreover, elevating the wage $2 above the market does more than twice the damage of elevating the wage $1 above the market. (Employers can more easily adjust to the first dollar by asking employees to take more responsibility or taking steps to reduce turnover, steps that get progressively harder.) That’s why economists who favor small minimum wage increases do not call for, say, a $100 minimum wage, because at that point the damage would far outweigh the benefits.

Market wages normally tend to increase over time with inflation and as workers become more productive. As long as the minimum wage is a fixed dollar amount, the tendency for market wages to increase over time means that economic damage from the minimum wage is shrinking. That’s one reason that economists who see benefits of minimum wages would like to see minimum wages indexed to inflation, allowing the minimum wage to increase automatically as the economic damages fell.

But these are not normal times. The least-skilled workers are seeing their wages fall over time, largely because they are out of work and failing to acquire the skills that come with working. Moreover, the new health care regulations going into effect in January are expected to reduce cash wages, as many employers of low-skill workers are hit with per-employee fines of about $3,000 per employee per year, as the law mandates new fringe benefits for other employers and low-skill workers have to compete with others for the part-time jobs that are a popular loophole in the new legislation. (The minimum wage law restricts flexibility on cash wages, by establishing a floor, but makes no rule on fringe benefits.)

To keep constant the damage from the federal minimum wage, the federal minimum wage needs not an increase but an automatic reduction over the next couple of years in order for it to stay in parallel with market wages.

Measuring Employer Confidence

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The labor market effects of employer confidence are probably real but have been exaggerated.

American employers have hired more than 230 million times since the recession began, but need to hire even more for employment per person to return to what it was five years ago.

One explanation for this state of affairs, according to the Mitt Romney campaign, has been a supposed lack of confidence among employers who are worried about taxes and new regulations, especially those associated with the Affordable Care Act (often referred to as Obamacare) as it is fully put in effect.

It is easy to find a businessman worried about government intervention, but the prevalence of such worries does not by itself tell us whether a lack of employer confidence depresses employment 10 percent, 1 percent or 0.1 percent.

Presumably if we have two employers who can hire workers at $20 an hour for the same task, the more confident of the two will hire more people than the less confident one, all else being the same. To put it another way, the less confident employer will require a greater immediate profit from his next $20-an-hour hire than the more confident employer, to compensate the less confident employer for the extra tax and regulatory costs associated with the hire that he anticipates in the future.

To the degree that an erosion of employer confidence has depressed the entire labor market, we should see the immediate profit from employees grow over time, as employers perceive more tax and regulatory costs on top of the cash and fringes they already owe their employees.

The gap between labor productivity and inflation-adjusted employee compensation is one way that economists measure the immediate profit from hiring, because the immediate benefits of hiring come from the production of the workers and the immediate costs include the employees’ compensation. (This measure is also sometimes called the “inverse of real unit labor cost.”)

The chart below shows indexes of labor productivity (red) and inflation-adjusted hourly employee compensation (black) since the recession began. Both indexes are measured after taxes and employee subsidies according to single marginal tax-rate series, so their gap reflects changes in the immediate profit from hiring. (For more information on these measures, see my previous post and my recent book on the labor market since 2007.)

About the time the Affordable Care Act was debated, after-tax productivity began to recover, and more quickly than after-tax real wages did. By the end of 2011, wages had still not recovered as much as productivity had.

(Another issue potentially depressing wages more than productivity is the anticipation of a new-employee tax credit, like the one proposed by President Obama in September 2011 and seriously discussed as early as 2009 and analyzed on this blog.)

Something prevented wages from keeping up with productivity, and perhaps eroding employer confidence is part of the story. But even if employer confidence were the whole story, it still explains less of the labor-market depression than do taxes on employees and subsidies for people who do not work. In early 2010, for example, after-tax real wages had been depressed more than 11 percent, of which only two percentage points were because of a failure of wages to keep up with productivity.

As I explained in a previous blog post, subsidies for people not working are the primary reason that wages fell so much after taxes and subsidies.

It would be incorrect to attribute all of the deviation between productivity and wages to employer confidence. A change in the composition of demand in the direction of less labor-intensive industries would have a similar effect, even if employer confidence had remained constant. New Keynesian economists also blame some of the deviation on a failure of employers to adjust their prices fully to their production costs.

Moreover, it is possible that employer fears of future regulatory and tax costs would not depress employment even while they depressed wages to the degree that employees eagerly anticipate benefits they would receive from those regulations and taxes.

While traces of eroded employer confidence are seen in labor market outcomes, it also appears that eroded employer confidence explains less than one-quarter of the overall depression of the labor market.