Macro Roundup

Jordan Nickerson and David Solomon, “Car Seats as Contraception,” Social Science Research Network, September 22, 2020, https://papers.ssrn.com/sol3/papers.cfm

Bottomline, “…First, when conditioning on women that give birth to a second child, we examine the lifetime probability of having a third child. Each additional year that the two eldest children are required to be in car seats results in a 0.60 percentage point reduction in the lifetime probability of having a third child. Second, we embed our panel data estimations into dynamic simulations which incorporate potential offsetting effects. We estimate that switching from an eight-year-old mandate to a four-year-old mandate would result in the average woman having 0.0076 more children. In 2017, we estimate that car seat laws lead to a permanent reduction of approximately 8,000 births, and have prevented 145,000 births over our sample period, with 60% of this effect being since 2008. By contrast, if current laws had applied over the whole sample, we estimate there would have been a further 350,000 fewer births. When comparing simulated birth decreases with predicted changes in births from our base regressions (which only measure contemporaneous effects), we estimate that approximately 71% of the contemporaneous effect is permanent….”

Here they explain their mechanism, “…These laws can significantly raise the cost of having children for women who already have two young children, and are considering a third. While the particular type of mandated restraining device varies, many cars cannot easily accommodate three child seats in the back row of seats, as would be needed if both front seats are occupied by adults. This especially increases the cost of a third child for many families, by necessitating the purchase of a larger car. The most practical options, like minivans, have additional problems of being expensive and unfashionable. We exploit the specific nature of costs associated with a third child, along with considerable variation in mandated ages over states and years, to identify the impact of state car seat laws on fertility. Using census data on the number and age of children for each woman surveyed,we estimate the effect of these laws on birthrates from 1973 to 2017. We find that when a woman has two children below the car seat age, her chances of giving birth that year decline by 0.73 percentage points. This represents a large decline, as the probability of giving birth for a woman age 18-35 with two children already is 9.36% in our sample…”

But as the same time the associated costs of having to buy a bigger car has lowered fertility“…Using census data and state-year variation in laws,we estimate that when women have two children of ages requiring mandated car seats, they have a lower annual probability of giving birth by 0.73 percentage points. Consistent with a causal channel, this effect is limited to third child births, is concentrated in households with access to a car, and is larger when a male is present (when both front seats are likely to be occupied)…. . Simultaneously, they led to a permanent reduction of approximately 8,000 births in the same year, and 145,000 fewer births since 1980, with 90% of this decline being since 2000….”

Unintended consequences, car seat laws. They did limit child deaths, paper estimates, “…these laws prevented…57 car crash fatalities of children nationwide in 2017…”but their cumulative impact was likely limited, “…To give context, it is worth noting how many children’s car crash fatalities there are in the first place in the U.S. In 1978, the number of fatalities for children age 0-8 was 2,392. By 2017, this had declined 73% to 654 total. However, this is almost exactly the same as the 72% decline in fatalities for children ages 9-12 over the same period from 975 to 273 (who were largely unaffected by these laws). In other words, back of the envelope approximations are consistent with the small numbers we document….”

Benjamin Jones and Lawrence Summers, “A Calculation of the Social Returns To Innovation,” National Bureau of Economic Research, September 2020, https://www.nber.org/papers/w27863

On capital deepening, “…Finally, note that incorporating capital investment doesn’t diminish the society wide gains. Rather it acts to spread the gains over a broader set of investments, beyond R&D. The social returns to capital deepening thus appear much larger than the equilibrium private rate of return to capital investment would suggest. To the extent that embodiment is important, R&D investment and capital investment collectively unlock large social returns. From a policy point of view, supporting R&D and capital deepening together would may then be important to attain the high social returns from innovative investments….”

Key to their formula, “.. The cost, in the denominator, is the ratio of innovation investment expenditure (𝑥) to GDP (𝑦). The benefit, in the numerator, is the growth rate (𝑔) that results, discounted to the present at the discount rate (𝑟).We are suppressing time in this expression to emphasize that the ratio of innovation investment expenditure to GDP and the growth rate of income are approximately constant over time..Namely, we can think of the cost benefit ratio as the cost of one year’s innovation (𝑥/𝑦) producing a stream of output gains that are𝑔percent higher. The present value of this permanent output gain is𝑔/𝑟.”

Here they explain their core logic, “We present new calculations for the social returns to innovation investment, building on core features of the innovation and growth literatures. Our measures emphasize the advantages of examining the path of GDP, which acts to aggregate and net out complicated spillovers involved in the innovation process. The approach offers a seemingly quite general means of estimating the average social returns. Moreover, the simplicity of the method allows us to transparently examine the influence of other, potentially key features that are not typically addressed in studies of the social returns to R&D. These features include embodied vs. disembodied technological progress, diffusion rates, learning-by-doing, productivity mismeasurement, health benefits, cross-country spillovers, and other dimensions for assessing the social returns….Taking this approach seriously, the average returns to innovative investments are determined by linking the aggregate cost of innovation investments to the aggregate production increase that results. Intuitively, by looking at the net value-added gains in the GDP path, one can implicitly net out the spillover margins. By looking at total innovation investment, one includes both research successes and failures. A simple social returns calculation can proceed as follows. Let income per capita be y, innovation investment per capita be x, and the discount rate be r. If a year's worth of innovation investments creates a g percent increase in productivity, then the ratio of benefits to costs is: p=(g/r)/(x/y) The key idea here, as in endogenous growth theory, is that, by investing a GDP share𝑥/𝑦in innovation today (i.e., once), we permanently raise productivity in the economy by𝑔percent, the present value of which is𝑔/𝑟.* Notably, this approach suggests that the average social returns to innovation may be enormous. For example, if we take an R&D investment orientation, with the R&D share of GDP at its usual level in the U.S.,𝑥/𝑦 ≈ 2.7%, and let these investments drive productivity growth, then we have𝑔 ≈ 1.8%. 1 Standard discount rates then imply that 1$ of R&D investment today on average creates over $10 of economy-wide benefits in today’s dollars.2 This return is extremely large, but it follows from the basic mechanics of growth, as understood in advanced economies. That is, a permanent gain in living standards from a seemingly small investment in innovation will, by the above logic, tend to suggest enormous returns….”

“…Overall, we find that the average social returns to innovation investments appear very large. If formal R&D and new venture creation drive the bulk of productivity gains, then the social returns to these investments appear enormous. If a much broader set of investments, including capital embodiment, are needed to fulfill these productivity gains, then the social returns to these broader activities still appear large. Even under very conservative assumptions, it is difficult to find an average return below $4 per $1 spent. Accounting for health benefits, inflation bias, or international spillovers can bring the social returns to over $20 per $1 spent, with internal rates of return approaching 100%.We further consider how these average returns may relate to the marginal return of additional investment in innovation. Using various perspectives, motivated by the micro and macro literatures on innovation, there are good reasons to believe that the marginal returns are also high. The implication is that policies to support further innovation investment are broadly well motivated. Innovation investments can credibly raise economic growth rates and extend lives, paying for their costs many times over. And because the social returns exceed the private returns, public policy has a central role, and opportunity, in unleashing these gains….”

Looking at the US alone they calculate, “…Overall, it appears that a conservative estimate of theaverage social gains is about $5 in benefit per $1 invested. Considering reasonable amounts of inflation bias or health benefits can easily push the average benefit to $10 or even $20 per $1 invested. These gains are just in terms of the U.S. economy. Incorporating international spillovers extends the benefits further. In sum, analyzing the average returns form a wide variety of perspectives suggests that the social returns are remarkably high….”

New Summers tries to look at social return to innovation (including trying to account for research failures) and finds massive positive spillovers the internal rate approach 100%.

Andy Puzder on the impact of the high pressure economy, attached is an excel sheet (of our own calculations) of the following series showing the last three years of Obama’s second term (December 2013-December 2016), versus Trump’s prior to pandemic (January 2017-January 2020)

Job Openings Total Nonfarm (Seasonally Adjusted)

Unemployment Level (Seasonally Adjusted)

Ratio Of Job Openings To Unemployment

Working Age Population 25-54

Working Age Population 55-64

Working Age Population 25-64

All Employees Total (Nonfarm, Seasonally Adjusted)

Ratio Employment Growth To Population Growth

Average Hourly Earnings (All Employees, Total Private)

Labor Force Participation (Seasonally Adjusted)

Real Private Fixed Investment (Billions 2012$ Seasonally Adjusted)

Real GDP (Billions 2012$, Seasonally Adjusted Annual Rate)

Ratio Private Fixed Investment To Real GDP

Real Private Fixed Investment As a Percentage Of Real GDP

Total Construction Spending Residential (Millions, Seasonally Adjusted)

Total Construction Spending Commercial (Millions, Seasonally Adjusted)

University Of Michigan Consumer Sentiment (December 2013 =100)

Real Net Fixed Investment: Non Residential Consumption Of Fixed Capital (Billions 2009$)

Puzder job openings never exceeded number of people employed for 8 years of Obama

“…For the eight years of the Obama-Biden administration,there wasn’t a single month when job openings exceeded the number of people unemployed. Zero.In fact, when Obama and Biden left office in January 2017, there were 1.9 million more people unemployed than there were job openings. So there was an abundance of people looking for work, but too few jobs opportunities open to them….”

Trump’s term (pre pandemic)“…A little more than a year later, in March 2018, that situation had reversed. Following Trump’s regulatory overhaul and passage of the Tax Cuts and Jobs Act,employment openings exceeded the number of people unemployed for the first time on records going back to 2000.Astoundingly, that remained the case for 24 consecutive months — until the pandemic — with over 1 million more job openings than people unemployed for 17 of those months….”

Andy Puzder, "Unpacking Joe Biden’s lies about the Trump job-creation miracle,"New York Post, October 1, 2020, https://nypost.com/2020/10/01/unpacking-joe-bidens-lies-about-the-trump-job-creation-miracle/

2009-2017 comp, “…By contrast, under the Obama-Biden administration, people fled the labor force, discouraged by limited opportunities and stagnant wages. During Biden’s eight years in office, the labor force participation rate — the share of people working or actively looking for work — dropped from 65.7 to 62.8 percent. During Trump’s first three pre-pandemic years, it rose back up to 63.4 percent, as people rejoined the labor force….”

Impact on LFP and wage growth, “….This unprecedented competition for workers had extremely positive consequences. It lowered the unemployment rate, which consistently hit 50-year lows, and as a result, employers began competing for workers and raising wages. In August 2018, year-over-year wage growth exceeded 3 percent for the first time in nearly a decade and stayed at or above 3 percent for 20 consecutive months until the pandemic. By comparison, under Team Obama-Biden there wasn’t a single post-recession month when wage growth exceeded 3 percent. Again, zero. With wages rising, people started coming out of the woodwork to fill those abundant good-paying job openings. In the fourth quarter of 2019, 74.2 percent of workers who took jobs came from outside the labor force rather than the ranks of the unemployed. That was the highest percentage since 1990, when the government began reporting such data…”

Population Data: “Education Data At Your Fingertips,” Education Data Explorer, Urban Institute, Accessed October 2, 2020, https://educationdata.urban.org/data-explorer/

Funding Data: Matthew Chingos and Kristin Blagg, “How Has Educational Funding Changed Over Time?” Urban Institute, Accessed October 2, 2020, https://apps.urban.org/features/education-funding-trends/

Just one note, neither Hawaii and DC had state level poor/nonpoor. To check my work I used a simplifying assumption of 1:1. The numbers are small and therefore wont overly impact the work (but slightly contribute to any ever the small difference vs Urban’s USA numbers.) See crosscheck at bottom.

State governments spent $327,700,785,688$67,753,564,064on the poor and$259,947,221,624on the non-poor.

The Federal government spent $54,900,691,984 in total,$13,602,028,879was spent on the poor and$41,298,663,105was spent on non-poor.

That breaks down as follows.

In total Federal/State educational spending was$382,601,477,672.

Despite that difference in per person funding as there are so many more non-poor students than poor students -10,233,557 poor students versus 43,429,294 non-poor students-Federal/State spending on non-poor is greater than spending on poor to the tune of $219,890,291,786.So total non-poor spending of $301,245,884,729 and poor spending of 81,355,592,943 even though the we spend “more” on the poor compared to their non-poor peers.

On a per student basis Federal/State spending was $7,950 onpoor studentsand $6,936 onnon-poor studentsa Δ of$1,013, that implies a total excess spending of almost$10.4 Billion. ($1,013*10,233,557 poor students = $10,370,857,792.) The $10.4B breaks as follows, the states contribute $6,500,330,026and the Fed’s $3,870,527,766.