Last year’s recap was subtitled “Triumph of the Blowhards”. So far, my plea for the return of rational policy analysis (let alone facts) has failed to occur. But the struggle for sanity must continue.
January. Don’t reason from accounting identities. From More on the Trade Deficit and Economic Growth:
In an EconoFact post from Saturday, Michael Klein and I noted that usually for the US, the trade deficit grows during times of robust economic growth.
Here I provide an additional way of looking at the relationship between growth and trade deficits — namely scatterplots for the periods from recession trough to peak.
Figure 1: Annualized q/q real GDP growth, % against nominal trade deficit as share of nominal GDP, % for recession trough-to-peak samples. Source: GDP 2016Q3 3rd release, NBER, and author’s calculations.
Notice the clear correlation — as growth accelerates, trade deficits widen.
To understand how thinking in accounting terms can lead to misleading inferences, consider the statement in a Washington Post opinion piece, Peter Navarro and Wilbur Ross wrote:
Net exports are currently running at a negative $500 billion annually, a direct subtraction from growth.
While this is true in terms of accounting, it’s a misleading statement. Consider a firm with revenues of $3 million, and labor and materials costs of $1 million, and hence profits are $2 million. The $1 million in costs are a direct subtraction from $3 million in revenues, but if no labor and materials were purchased and costs $0 million, profits would not be $3 million, but zero.
February. The President feels unappreciated because he doesn’t credit for something that happens almost all the time. From The Media Fails to Report the Sun Rose Today:
President Trump is upset that the media missed this economic event:
“The media has not reported that the National Debt in my first month went down by $12 billion vs a $200 billion increase in Obama first mo”
I do not have the stats for Inauguration + 1 month data, but here is end-of-month data through January 2017 for total Federal debt.
Figure 1: Log first difference of monthly total Federal debt (blue), and red lines at January. Arrows at non-positive January entries. Source: Dallas Fed, and author’s calculations.
Notice that a reduction in debt in January happens more often than not. A regression of this series on a constant and a January dummy over the period 1947M01-2017M01 yields a January coefficient of -0.0036, t-stat of 5.6 (HAC robust standard errors). In words, gross Federal debt drops on average 4.3% (annualized) in January relative to average growth. . . .
March. Forecasting critiques from the least qualified. From Nowcasting with OMB Director Mick Mulvaney:
Since Mr. Mulvaney has been criticizing the numbers produced by the BLS , and scoring by CBO , I thought it of interest to see Mr. Mulvaney’s record on predictions. To make things easy on Mr. Mulvaney, I thought it would be more fair to evaluate his “nowcasting” abilities.
In July 2016, Mr. Mulvaney gave a speech to the John Birch Society in which he observed:
the Fed’s actions have “effectively devalued the dollar” and harmed economic growth.
It is difficult to assess the economic growth assessment without a counterfactual. However, we can easily observe what has happened to the dollar, as of July 2016, when Mr. Mulvaney provided his nowcast. . . .
[H]ere is the picture in inflation adjusted (“real”) terms:
Figure 2: Log real EUR/USD exchange rate (blue), value of USD against basket of major currencies (red), and against broad basket of currencies (teal), all normalized to 2007M12=0 (up is stronger dollar). Adjustment using CPI’s. NBER defined recession dates shaded gray. Dashed line at time of Mulvaney’s statement on dollar devalued. Source: Federal Reserve Board via FRED, NBER, and author’s calculations.
Note that at the time of Mr. Mulvaney’s “dollar devalued” statement, the dollar was actually fairly strong, as compared against the previous decade. So, if Mr. Mulvaney is not even aware of how the dollar fares at a given instant, in a world of readily available data, how are we to take his assessments of data validity (e.g., from BLS), or scoring (from CBO)?
On a separate note, is this statement a Freudian slip of some sort on the part of Mr. Mulvaney?
“I don’t believe the facts are correct”
You can’t make up this stuff.
April. Take ALEC predictions, invert them, and you have actual economic performance. From State Employment Trends: Some Selected States and ALEC Rankings :
Since 2011 — when Scott Walker and Sam Brownback came into power — California has powered far ahead of Wisconsin and Kansas. The newly released Rich States, Poor States, 2017 allows us to look at how four states, both low and high ranked by Arthur Laffer et al., fared, employmentwise.
Figure 1: Log nonfarm payroll employment for Wisconsin (red), Minnesota (blue), California (teal), Kansas (green) and the US (black), all seasonally adjusted, 2011M01=0. ALEC-Laffer State Economic Outlook rankings for 2016 and 2017. Vertical dashed line at beginning of terms for indicated governors. Source: BLS, Rich States, Poor States and author’s calculations.
Wisconsin was ranked 9th in RSPS 2016, and had what at best was lackluster employment growth over the subsequent year. California has been consistently ranked very low by ALEC, and yet has outperformed.
For formal statistical analyses relating ALEC rankings to economic outcomes, see this post.
May. One instance of “alternative statistics”. From Stephen Moore Is a Liar:
Or a statistical incompetent. Or both.
(I know, in the grand scheme of lying, this is small bore.)
I just heard Stephen Moore of Heritage Foundation saying we had a trillion dollar budget deficit, in a debate on CNN about the Paris Accord (1:42PM CST).
The FY 2017 budget deficit is $603 billion, according to the just released budget (see Table S-1). Add his serial lying to the statistical atrocity that is the ALEC-Laffer-Moore-Williams index, and all I can say is Mr. Moore should not be paid as a “CNN economic analyst”.
Update, 7:14 PM Pacific: I’d forgotten this tabulation of errors by Mr. Moore. The man has no shame.
Bonus: Rick Stryker writes that facts don’t matter.
In a similar fashion, does Moore’s mistake matter for the argument he was making? Moore’s point was that the Paris Accord obligated nations to spend 100 billion annually on climate “research” by 2020. So far, about 10 billion has been collected, with the US pledging 3 billion of the total. If those proportions hold, that means the US would be contributing 30 billion per year. And it won’t stop there. Moore knows that the number will climb, with the US contributing enormous amounts of money annually to a new international climate boondoggle organization, which will spend enormous amounts of US taxpayer money on useless studies, fly climate bureaucrats around to conferences (probably first class), agitate for new regulations, etc.
If we have that kind of money to throw around, why aren’t we spending it on Americans to serve their needs, especially when we have to borrow large sums every year? Whether that large sum is 600 billion or a trillion doesn’t really matter. We shouldn’t be doing it. I agree with Moore.
This is the quote of the year; it highlights the intellectual and moral rot at the heart of the Trump era.
June. From Pangloss in Wisconsin:
Wisconsin’s job growth over the past six years has been extraordinarily strong.
When last we met Mr. Riedl, he was explaining why fiscal policy could have no impact on GDP because, well, because. That does not augur well for his abilities an economic analysis, and indeed we can easily poke holes into the argument that Wisconsin’s doing just great!
… let’s compare what Wisconsin private nonfarm employment looks like against a counterfactual based upon the historical correlation between US and Wisconsin log employment (nUS and n respectively). To do this, let’s estimate an error correction model over the 1994M01-2010M12 period (so up until just before the Walker administrations):
(1) Δnt = -0.0036 – 0.036×nt-1 + 0.027×nUSt-1 – 0.178×ΔnUSt + one lag of first differences + ut
Adj-R2 = 0.65, SER = 0.0015, DW = 2.02, Obs.=204, sample 1994M01-2010M12. Bold face denotes significance at the 10% MSL, using HAC robust standard errors.
I then use this equation to dynamically forecast using ex post observations on national employment (this is sometimes termed an ex post historical simulation). I plot this forecast plus the 90% forecast interval against reported employment in Figure 2.
Figure 2: Wisconsin private nonfarm payroll employment, May release (black), and out-of-sample forecast (red), in 000’s, s.a. 90% forecast interval (gray). On log scale. Light green shading denotes data not yet benchmarked to QCEW data. Source: BLS and author’s calculations. See text.
Regression output is here. The methodology roughly follows that in Chinn (JPAM, 1991).
The results indicate that Wisconsin private employment has been lagging what one would have expected given historical correlations with US employment.
To paraphrase the President, again: “Sad”. …
July. When incoherence meets ignorance. Mr. Trump on Economics and Accounting:
“For many, many years, the United States has suffered through massive trade deficits. That’s why we have $20 trillion in debt. So we’ll be changing that.”
This statement was made during the meeting with the South Korean President on June 30th 
- The United States has experienced large trade deficits for years, starting with the Presidency of Ronald Reagan.
- Gross Federal debt at the end of 2017Q1 was $19.8 trillion.
- The US trade deficit is the excess of exports over imports. It is measured in a variety of ways. On a national income and product accounts (NIPA) basis, net exports is -$562.8 billion (Seasonally Adjusted at Annual Rates, SAAR) in 2017Q1.
- The gross national debt is the cumulated Federal budget balance, including interest accrued, and includes debt held by other agencies of the Federal government (e.g., Social Security trust funds).
- Net Federal government saving on a NIPA basis was -660.3 billion (SAAR) in 2017Q1.
- The budget balance and the trade balance (net exports) are:Budget balance ≡ taxes – government transfers – government spending
Trade balance ≡ exports – imports
- These two accounting identities are pertain to different sectors – one is the (Federal) government sector, the other the aggregate economy with respect to the rest-of-the-world.
The fact that the two deficits are unrelated in accounting terms does not mean that they are not somehow economically related. However, a causal mechanism has to be laid out in order to make the argument they are. Take a look at Figure 1.
Figure 1: Share of Federal government net saving (blue), and net exports (red), as share of nominal GDP. Source: BEA, 2017Q1 3rd release.
It’s clear the correlation, even with a lag, does not always fit in with President Trump’s story. And the correlation changes over time. To the extent they are related, I would say the causality more likely goes from budget balance to trade balance, rather than the reverse. For instance, higher government spending leads to a budget deficit, and at the same time higher imports due to higher consumption of imported goods, and less exports and higher imports due to an appreciated currency (Figure 2).
Figure 2: Share of Federal government net saving vs. net exports, as share of nominal GDP, 1967-85. Source: BEA, 2017Q1 3rd release.
Or, a positive shock to investment leads to higher income, a boom in tax revenues, so the budget balance improves. But higher economic activity draws in more imports (for consumption, investment purposes).
Figure 3: Share of Federal government net saving vs. net exports, as share of nominal GDP, 1986-07. Source: BEA, 2017Q1 3rd release.
Or something else, like the US growing faster than the rest-of-the-world.
Figure 4: Share of Federal government net saving vs. net exports, as share of nominal GDP, 2008-2017Q1. Source: BEA, 2017Q1 3rd release.
It could be that Mr. Trump meant to point to US net indebtedness with respect to the rest-of-the-world, as summarized by the US Net International Investment Position. However, this is not quite correct, as the NIIP (which includes equities and direct investment) does not match up with the cumulated current account deficit. External debt kind of does, but even that is not exact (and it’s not clear why cumulated current account balances should equal only the net position in debt).
So, I am forced to conclude (and here I’m going out on a limb(!)), Mr. Trump is (not for the first time) confused.
August. The strangest definition of dead weight loss ever – it’s like the person writing didn’t understand basic microeconomics. From What’s the Dead Weight Loss of a Consumption Tax When Externalities Are Present?:
Political Calculates takes on the issue of the Philadelphia soda tax issue, asking specifically “Who is really paying Philadelphia’s controversial soda tax? And how much, if at all, has it affected the economy of the City of Philadelphia?”
A little online tool to calculate tax incidence and dead weight loss (DWL) is provided, motivated by the following graph:
Source: Political Calculations.
Now, the funny thing is that the graph is appropriate when no externalities exist. But the reason for imposing the tax is to internalize the negative externalities associated with consuming sugary drinks. That means that, contrary to the graph provided by Political Calculations, the marginal social benefit and marginal private benefit (i.e., demand) curves do not overlap.
If the marginal social benefit of consuming a soda is less than that of private benefit, then one obtains the following graph (substitute “soda” for “alcohol” in the figure below).
The imposed tax is assumed to fully internalize the externality. The tax incidence calculations are correct, but the dead weight loss calculation offered by Political Calculations is wrong in this context. In fact, in this case, the DWL is exactly zero. Only by assuming away the externality is the DWL calculation correct.
It might be that Political Calculations believes there are no negative externalities associated with soda consumption; if so, he should mention it, given that’s a key reason the tax is there.
As an aside, Political Calculations provides an odd definition of DWL:
If a deadweight loss exists, it represents the amount of economic activity that has been directly lost because of the imposition of the tax, which tells us the degree to which the city’s economy may have shrunk as a result.
I don’t think I’ve ever seen such a definition of DWL. I think of DWL (in Political Calculation’s context) as the cumulation of excess social benefit over social cost, for all the foregone units of consumption arising from the tax; in his graph, he thinks it’s for units 501 to 700. So, it’s a welfare loss, expressed in dollar terms, under various assumptions regarding the nature of utility functions.
This discussion exercise illustrates the adage that a (very) little knowledge is a dangerous thing. At a minimum, bloggers should complete reading an entire intro micro textbook before writing about micro.
If you want to see Ironman mangle the use of economic statistics, see this post.
September. In Mr. Trump’s mind, Puerto Rico is eclipsed by NFL players kneeling, and (since September) just about anything else. From At Least Nero Fiddle:
From the Twitter feed of realDonaldTrump, commenting on the crisis in Puerto Rico this morning:
Such poor leadership ability by the Mayor of San Juan, and others in Puerto Rico, who are not able to get their workers to help. They want everything to be done for them when it should be a community effort. 10,000 Federal workers now on Island doing a fantastic job.
Mr. Trump has suggested the difficulty in delivering assistance to the 3.4 million American citizens in Puerto Rico is due to geography.
“We’ve gotten A-pluses on Texas and in Florida, and we will also on Puerto Rico,” Trump said at the White House. “But the difference is this is an island sitting in the middle of an ocean. It’s a big ocean, it’s a very big ocean. And we’re doing a really good job.”
Here is a relevant comparison, from WaPo:
After an earthquake shattered Haiti’s capital on Jan. 12, 2010, the U.S. military mobilized as if it were going to war.
Before dawn the next morning, an Army unit was airborne, on its way to seize control of the main airport in Port-au-Prince. Within two days, the Pentagon had 8,000 American troops en route. Within two weeks, 33 U.S. military ships and 22,000 troops had arrived. More than 300 military helicopters buzzed overhead, delivering millions of pounds of food and water.
No two disasters are alike. Each delivers customized violence that cannot be fully anticipated. But as criticism of the federal government’s initial response to the crisis in Puerto Rico continued to mount Thursday, the mission to Haiti — an island nation several hundred miles from the U.S. mainland — stands as an example of how quickly relief efforts can be mobilized.
By contrast, eight days after Hurricane Maria ripped across neighboring Puerto Rico, just 4,400 service members were participating in federal operations to assist the devastated island, an Army general told reporters Thursday. In addition, about 1,000 Coast Guard members were aiding the efforts. About 40 U.S. military helicopters were helping to deliver food and water to the 3.4 million residents of the U.S. territory, along with 10 Coast Guard helicopters.
Leaders of the humanitarian mission in Haiti said in interviews that they were dismayed by the relative lack of urgency and military muscle in the initial federal response to Puerto Rico’s catastrophe.
“I think it’s a fair ask why we’re not seeing a similar command and response,” said retired Lt. Gen. P.K. “Ken” Keen, the three-star general who commanded the U.S. military effort in Haiti, where 200,000 people died by some estimates. “The morning after, the president said we were going to respond in Port-au-Prince . . . robustly and immediately, and that gave the whole government clarity of purpose.”
Rajiv J. Shah, who led the U.S. Agency for International Development during the Haiti response, said he, too, was struggling to “understand the delays.”
“We were able to move more quickly in a foreign country, and with no warning because it was an earthquake, than a better-equipped agency was able to do in a domestic territory,” he said.
Here’s an assessment of Puerto Rico recovery effort, around a 100 days post-hurricane.
October. Why not base your entire CEA report on one unpublished study … From A Curiously Non-Quantitative Assessment of Deregulation Effects on Economic Growth
And a funny choice of citations.
The CEA released its first “report” under the leadership of Kevin Hassett. The report, entitled The Growth Potential of Deregulation, is summarized thusly:
Excessive regulation is a tax on the economy, costing the U.S. an average of 0.8 percent of GDP growth per year since 1980. This taxation by regulation has increased sharply in recent years, with approximately 500 new economically significant regulations created over the last eight years alone. Through a thorough review of the literature, the Council of Economic Advisers (CEA) finds that deregulation will stimulate U.S. GDP growth.
Interestingly, the report’s highlighted number is based on this paper:
Coffey et al. (2016) estimates that if we held fixed the number of industry relevant regulations at levels observed in 1980, the U.S. economy would have been about 25 percent larger (roughly $4 trillion) in 2012. According to the study, the cumulative effects of regulation have slowed economic growth in the United States by an average of 0.8 percent per year since 1980. This amounts to a loss of approximately $13,000 per capita.
In other words, the one quoted definitive number regarding output growth is drawn from an unpublished working paper. Now, endogenous growth models are not my specialty, but my impression is that the empirical evidence in favor of endogenous growth models is not overwhelming. The estimation approach is Bayesian, involving a nonlinear equation (as far as I can tell). My experience with estimating nonlinear equations is that they are sensitive to assumptions and starting points. It is interesting to note the several industries where under the counterfactual of no regulation, investment is lower than actual. Perhaps more interesting is the fact that only in 2008 does the actual level of GDP fall below the lower bound of the 90% confidence interval. In other words, 28 years after the simulation begins, output is significantly below predicted under the counterfactual. It’s troubling that the fall occurs in the year in which the economy suffers a major recession. This suggests that the deviation is due to model misspecification (i.e., the model cannot capture the dynamics of the recession) rather than necessarily regulation induces a deviation from predicted.
In any case, it is interesting that this is the source for essentially the only numerical prediction in the paper. The only other one is a regarding regulation in a cross country empirical framework (Djankov et al., 2006). Interestingly, they do not cite the fourth item that pops up when typing in the words “regulation economic growth” in google scholar: Jalilian et al. (2007). That paper concludes:
The results from both sets of modeling suggest a strong causal link between regulatory quality and economic growth and confirm that the standard of regulation matters for economic performance. The results are consistent with those of Olson et al. (1998) who found that productivity growth is strongly correlated with the quality of governance, and Kauffman et al. (2005) who found that the quality of governance has a positive effect on incomes.
In other words, the quality of regulatory framework might be as important, or even more important, than the quantity of regulation.
In any case, if I were to make a bold conclusion like “Excessive regulation is a tax on the economy, costing the U.S. an average of 0.8 percent of GDP growth per year since 1980”, I’d want just a tiny, tiny bit more empirical backing.
November. The (incredible) return of Bill Beach of the Heritage CDA. From Crimes against Economic Analysis:
Well, Sam Clovis is no longer under consideration to be the top scientist at USDA (a good thing given he has no scientific credentials). But Bill Beach is nominated to be Commissioner of Labor Statistics, i.e., heading BLS.
When last we heard of Bill Beach, he had just revised the fantastical forecasts of the Ryan (2011) plan. Recall:
Heritage (Center for Data Analysis) CDA re-adjusted the natural or structural rate of unemployment — and hence simulated unemployment — without having any reported impact on any of the other variables changing
In other words, Heritage’s CDA under Bill Beach’s leadership projected fantastical growth under the Ryan plan. As Macroeconomic Advisers put it:
- We don’t believe this finding, which was generated by manipulating an econometric model that would not otherwise have produced the result.
- That analysis implied other questionable results — some of them probably unintended — including over $1 trillion of net new borrowing from abroad over the coming decade and the construction of several million unoccupied houses.
- We consider the analysis both flawed and contrived, and are concerned it will create the false impression among some legislators that implementation of the Budget Resolution would entail no short-run macroeconomic pain.
Then when roundly criticized about the forecasted 2.8% natural rate of unemployment, CDA re-simulated the CDA model, and magically only the natural rate of unemployment changed, and not a single other one…
Since leaving Heritage at the beginning of 2013, he’s been at Mercatus, VP in charge of policy research.
Well, now he’ll be heading BLS. Here’s Dr. Beach’s bio.
December. This is Mnuchin’s secret study? Pathetic is not the word. Term Papers Due this Friday:
In my classes. And if this were one, I’d fail the author.
Full “report”. All 490 words of it (not including the words in the letterhead). It fits on one page.
More at Bloomberg.
Let’s hope for a better 2018 — one where facts are respected, and the technocratic economic institutions are no longer under assault from the ignorant and the craven.