(What’s Left of) Our Economy: Manufacturing Data Puzzles

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data, Federal Reserve, industrial production index, manufacturing, New York Fed, Philadelphia Fed, statistics, survivorship bias, {What's Left of) Our Economy

The last few months have provided a timely reminder that maybe some of the leading gauges of U.S.-based manufacturing’s strengths shouldn’t play such a leading role. Or at least maybe they shouldn’t if you believe (as you should) that reports that try to measure actual changes in economic indicators in various parts of the economy are more important than reports that try to measure how companies in those parts of the economy say that they’re faring.

In this case, I’m talking about the differences between the results of the Federal Reserve’s monthly industrial production index (an actual change report), and those of two series of monthly soundings on the status of their region’s manufacturing done by the New York and Philadelphia branches of the Federal Reserve system (which are “sentiment” surveys).

Although the manufacturing data contained in the industrial production index show that domestic industry has now moved past a brief production downturn in late spring, and has been growing solidly since, you wouldn’t know it from the two regional Fed results. Even stranger, the New York Fed survey results didn’t correspond with very well with the previous months’ actual manufacturing output numbers, either.

Specifically, the Fed’s industrial production index reported that the nation’s manufacturing output adjusted for inflation rose by 0.46 percent in April, but then dropped by 0.39 percent in May and 0.58 percent in June.

Manufacturers in the New York area assessed their current business conditions in April as positive by an impressive 24.6 score. Their views darkened significantly in May (down to -11.6), as manufacturing production nationally fell. But in June, when the U.S.-based manufacturers output fell even faster, New York manufacturers’ sentiment improved almost back to a neutral zero score.

Their counterparts in the Philadelphia matched up better with the national results. They were nearly as positive as the New Yorkers about business in April, as domestic manufacturing as a whole grew (producing a score of +17.6), and became more negative during contractionary May (with their score falling to a barely positive +2.6). But unlike the New Yorkers, their negativism worsened further (producing a -3.3 score) as the national manufacturing slump deepened during June.

But both sentiment surveys have veered considerably off-base since then.

In July, August, and September, domestic manufacturing production rebounded month-to-month in real terms by 0.60 percent, 0.38 percent, and 0.43 percent, respectively. (All these results could still be revised.)

But although New York Fed respondents boosted their ratings of general business conditions by 12 points in July, to a +11.1 reading, in August this assessment nosedived by 42 points, to -31.3. September saw a big (30 point) rebound, but at -1.5 remained in the red.

During the strong nation-wide manufacturing growth of July, Philadelphia area manufacturers lowered their business conditions views by 12 points to a -12.3 score. But when after-inflation U.S. manufacturing output kept growing in August (though by a somewhat slower pace), their ratings zoomed back up by 18.5 points to +6.2. And when nation-wide manufacturing growth picked up slightly in September, the Philadelphia companies’ drove their business conditions grade back down by 16.1 points, to -9.9.

As known by RealityChek regulars, I’ve previously identified a serious flaw in these sentiment surveys that’s called “survivorship bias.” That is, because they only look into the views of manufacturers that exist at that moment, they can miss setbacks suffered because of closures of all kinds, especially over time. In other words, to use an extreme example, if the U.S. manufacturing base had been reduced to a single company in a given month, and that company was in a good mood that month, a national sentiment survey would report that U.S.-based industry was doing just swimingly.

It’s hard, however, to figure out how survivorship bias would produce similar distortions in present circumstances.

But other reasons for the divergeance between the sentiment surveys and the actual data reports don’t readily come to mind, either. For example, it’s certainly possible that manufacturing conditions in the New York and Philadelphia areas these days differ significantly from those in the United States as a whole. Yet that explanation seems far-fetched given the thick web of links among domestic manufacturers in all regions (specifically, so often selling to and buying from customers and suppliers all over the country). It’s also strange how the New York and Philadelphia results differ not only from those of the entire economy, but from each other in some instances.

Maybe the real answer is that the latest New York and Philadelphia results are just short-term blips, and that over time they’ll start converging with the national results? We may not know that until more time passes.

What we do know, though, is that in October, the former’s results sank further in October (by 8 points, to a -9.1 read) and the latter’s edged up by 1.2 points, but was still -8.7. So let’s see how consistent they are with the Fed industrial production numbers for that month, which are slated to come out on November 16. Mark your calendars!

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Im-Politic: The Myth that Violent American Crime is Mainly a Red State Problem

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cities, crime, data, Democrats, election 2022, GOP, Im-Politic, midterms, murders, Republicans, statistics, Third Way

If you’ve been following the national debate about crime during this midterm election year, you’ve probably read one of the Democrats’ main efforts to deny responsibility for surging numbers of murders and other violent lawlessness in particular, and indeed to pin the blame on Republicans. That’s the finding that the vast majority of states in which the murder problem is worst have long been dominated politically by the GOP.

Trouble is, it’s a claim that’s as false as it’s easily demolished – for the simple reason that most U.S. states are pretty big and, above all, diverse political units, and that crime rates can vary dramatically among them. And that’s precisely what was accidentally overlooked or politically ignored by researchers at Third Way, “a national think tank that champions modern center-left ideas” along with the Democrats’ defenders throughout the Mainstream Media (see, e.g., here and here).

Specifically, when authors Kylie Murdock and Jim Kessler argued that “8 of the 10 states with the highest murder rates in 2020 voted for the Republican presidential nominee in every election this century,” what they didn’t mention is that in most of these states, the numbers are high mainly because of pervasive violence in cities with Democratic mayors.

For 2020, the year emphasized in the Third Way report, that case doesn’t hold for South Carolina, and it’s weak for Arkansas (although interestingly, Democratic-led Little Rock, the state’s capital and biggest city, accounted for 18.18 percent of Arkansas’ murders despite containing only 6.57 of its inhabitants). And there’s not enough detailed data for Alabama to make judgements either way. But according to the official data I’ve combed through from the U.S. Census (for population), the FBI (for numbers of state murders), and various state governments (for numbers of city murders), it emphatically does hold for:

>Mississippi. It sits atop Third Way’s list of murder leaders, but would surely be much further down if not for Jackson. Despite containing only 5.49 percent of Mississippi’s population in 2020, its Democratic-led capital city accounted for 61.32 percent of its murders.

>Louisana. The Bayou State is second on Third Way’s list, but murders in Democratic-run New Orleans represented 28.98 percent of its 2020 murders, even though the Crescent City’s population was only 6.73 percent of the 2020 state total. Moreover, Louisiana’s second-biggest city, state capital Baton Rouge, is also headed by a Democratic mayor, and suffered 14.35 percent of the state’s 2020 murders, despite accounting for just 4.76 percent of all Louisianans.

All told, these 43.33 percent of Louisiana’s murders in 2020 took place in these two Democratic cities, which only accounted for 11.50 percent of the state’s population.

>Kentucky. Ranking third on Third Way’s list, the Bluegrass State’s murder totals have obviously been boosted by Democratic Louisville. The city was home to 13.87 percent of Kentucky-ans in 2020, yet was responsible for 55.99 percent of its murders that year.

>Missouri. The fourth state on Third Way’s list is another state whose murder totals have been distorted by two Democratc-led cities. St. Louis and Kansas City combined represented 11.82 percent of all Missourians in 2020, but 58.73 percent of the state’s murders that year took place within their limits.

>Tennessee. The Volunteer State, tenth on Third Way’s list, also contains two Democratic-run cities with outsized murder totals. Memphis and Nashville held 20.05 percent of the state’s population in 2020, but were the sites of 60.48 percent of their murders that year.

It’s true that big city totals also account for disproportionate shares of murders in many Democratic-run states. For example, in 2020, New York City contained 42.95 percent of all New York State’s 2020 residents. But the City experienced 57.82 percent of the state’s murders that year. (The gap widens further when you add in Democratic-led Buffalo, the state’s second largest city.)

More extreme is the situation in Illinois, where in 2020 Chicago was home to 21.22 percent of the Illinois-ans, but was the scene of 74.78 percent of the state’s murders.

But the obvious conclusion here isn’t the one drawn by Third Way – that Republican states have at least as big a violent crime problem as Democratic states. The obvious conclusion is that the nation’s crime problem is heavily concentrated in big cities, which are run by Democrats whether they’re in Red states or Blue states.

With the midterm elections just a few weeks away, the good news here is that voters seem to understand this reality, as they’ve consistently been giving Republicans higher marks on handling the crime issue than Democrats (see, e.g., here and here for two recent examples). Can Democrats turn this situation around? Time of course is running short. But their chances will be especially dim if they keep trying to blame-shift rather than offering credible solutions to violent crime.

Im-Politic: Anti-Pandemic Economy Clamps Could Be Strengthening Just as the Virus Threat is Weakening

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Andrew Jackson, Battle of New Orleans, Biden, CCP Virus, CDC, Centers for Disease Control and Prevention, coronavirus, COVID 19, data, hospitalizations, Im-Politic, Jobs, lockdowns, mortality, OurWorldinData.org, stay-at-home, vaccination, vaccine mandates, vaccine passports, vaccines, War of 1812, Washington Post, Wuhan virus

What a stunning and thoroughly depressing point the U.S. fight against the CCP Virus may be at. Governments at all levels, private businesses, and non-profit institutions of all kinds are imposing all sorts of vaccination mandates on employees that could result in significant layoffs for the recalcitrant (including those with natural virus immunity) and equally important damage to the economy. And at the same time, the most reliable data now show that the virus’ destructive impact – recently renewed by the highly infectious Delta variant – is easing once again, and for reasons that look completely unrelated to vaccination rates.

Not that the most reliable CCP Virus data are incredibly reliable. As I’ve previously written, there are some awfully dubious definitions of “Covid-related deaths” being used across the country, and major holes in the coverage achieved by the official record keepers. In addition, serious problems have been revealed even in the hospitalization numbers – which I’d considered the most accurate gauge of the virus’ effects on human health.

All the same, the proverbial statistical curve for both indicators is now bending down for the first time since Delta began dominating the American virus scene in mid-summer.

As often the case, my source for the death and hospitalization figures are the Washington Post‘s very user-friendly CCP Virus databases. For this post, I’m also using some hospitalization figures for the U.S. Centers for Disease Control and Prevention’s (CDC) website. Unless otherwise mentioned, the specific numbers here are changes in seven-day averages (7DA), which smooth out random fluctuations that tend to occur on a day-to-day basis.

Regarding mortality, the 7DA for daily reported covid-related deaths bottomed out on July 6 at 209 and it had plummeted by nearly 30 percent during the previous week. And through July 27, the 7DA stayed below 300. But by August 16, it hit 651 and thereafter began soaring rapidly.

By the 18^th, the 7DA average had jumped by nearly 32 percent week-on-week, and the rate of increase continued surging until it peaked on the 24^th at an appalling 77.90 percent. But thereafter, these increases dropped dramaticaly. A week later, they were down to just over 21 percent. That is, consistent with the “bend the curve” criteria, the problem kept worsening, but it was worsening much more slowly, which counts as welcome progress.

This encouraging development continued through September 9, by which time the 7DA was rising on a weekly basis by just 3.17 percent. In other words, it nearly stopped rising altogether. But this fall-off proved to be a head fake. Almost immediately, the weekly increases in the 7DA for covid-related mortality bounced back, and reached a discouraging 27.49 percent in less than a week (by the 15^th).

Yet another decline has followed, and this one has been considerably deeper. By September 21, the weekly 7DA increase was back below ten percent, and just four days later, hit zero for the first time since the second half of July.

Since then, and through yesterday, the 7DA has not only been decreasing on a weekly basis. It’s been decreasing faster and faster. Yesterday, the decline stood at 6.74 percent.

The hospitalization story has been somewhat different, and brighter, especially since early September. The 7DA for daily new hospital admissions for CCP Virus-related reasons bottomed out on June 25 at 1,824 and at that point, it was down on week by just under 5.20 percent.

By August 9, the situation had turned around completely – and then some. The 7DA had soared by 34 percent. Afterwards, however, came a consistent decline. By the 20^th, the weekly rate of increase in the 7DA had fallen to ten percent, and by September 1, the increases had stopped. The weekly 7DA registered its first weekly decline on September 6 (down two percent), and its first double-digit decrease on the 21^st (ten percent).

Since then through the 30^th, it’s fallen by ten percent or more twice, and the weekly decrease in the 7DA hasn’t dipped below seven percent.

Given the mushrooming of vaccine mandates and widespread claims – including by President Biden – that the nation is now facing a “pandemic of the unvaccinated,” you’d think that the above improvements stemmed overwhelmingly from increased vaccination rates. But the data – in this case, from the OurWorldinData.org website, provide no support for this conclusion.

Specifically, on August 24, when the 7DA of daily covid-related deaths was skyrocketing at that awful 77.90 percent weekly rate, 51 percent of Americans were fully vaccinated against the CCP Virus, and 9.1 percent were partly vaccinated. By yesterday, these figures were only 55 percent and 8.8 percent, respectively.

On August 9, when the 7DA for covid-related hospitalizations was growing by 34 percent week-on-week, half of Americans were fully vaccinated and 8.5 percent were partly vaccinated. Through yesterday, those numbers hadn’t changed dramatically, either.

Could mask-wearing be responsible? Trouble is, I haven’t seen any figures on how this practice has changed in recent months. (If you have, let me know.) As far as I’m concerned, the real reasons for this good CCP Virus news have to do with rising levels of natural immunity (especially important given Delta’s virulence), the distinct possibility that the CCP Virus is one of those pathogens whose lethality wanes as it mutates (an important Delta consideration, too), and the nation’s better treatment record – due to a combination of more experienced doctors and new therapeutics.

In early 1815, then-General Andrew Jackson led American forces to a great victory over the British in the Battle of New Orleans. But due to that era’s painfully slow communications, the triumph came about two weeks after the United States and Great Britain signed the treaty ending the War of 1812.  It makes me wonder how long the U.S. public and private sectors — which don’t have the communications excuse — will keep threatening the economy’s recovery with redoubled anti-virus measures just as the pandemic tide appears to be turning.   

Following Up: Time for a “Truth in Testimony Act” for Think Tanks

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business, civil society groups, Congress, corporations, data, exports, Following Up, globalization, idea laundering, imports, Jobs, labor unions, offshoring, statistics, think tanks, Trade, trade balances, transparency

So far, my work on the problems for our democracy caused by corporate- or other special interest-funded think tanks has emphasized that the media has a special responsibility – and ability — to help solve them. How? By making sure that whenever they quote staffers from these organizations as experts on this or that issue, they reveal who’s signing the tankers’ paychecks.

But another major segment of society also needs to play a role in preventing what I call think tank idea-laundering – posing as objective, academicky-type organizations in order to portray their staffs’ findings as the products of disinterested scholarly research rather than exercises in agenda-pushing. That segment is government.

Legislatures at the local, state, and federal levels should pass what might be called “Truth in Testifying Acts.” That is, whenever they invite input from think tanks in hearings they hold, or in public comment exercises they conduct, the law-making bodies should require these organizations to disclose all their funders with a financial stake in the subject being examined, or the decision that’s pending. As a result, the public or any other consumers of these analyses would have the information they need to judge how much credibility they feel the information deserves, and what kind of material has been deliberately exaggerated or spotlighted or downplayed or ignored altogether.

In fact, these requirements should be imposed on so-called civil society groups, foundations, labor unions, academic institutions, and business organizations, too. Sometimes their biases are obvious from their names, but only sometimes. Best to err therefore on the side of caution – and more disclosure.

Further, while we’re on the subject, I’d like to see something else added to these Truth in Testimony Acts, or follow-on legislation, which is especially relevant to the trade issues I follow so closely: requirements that business groups and their think tank fronts lay out comprehensively their own domestic and international operations and structures, and those of their major funders. They’re needed because representatives of these organizations have long gotten away with literal intellectual murder by presenting legislators with shamelessly cherry-picked data.

For example, when trade agreements and other trade policy decisions are being examined, it’s become standard operating procedure for witnesses in favor of greater liberalization to present figures on exports from the country as a whole, from individual states or Congressional districts (always of major concern to Senators and House members), or from whatever company or industry they represent. And typically, they’re allowed to ignore the import and trade balance sides of the equation. Talk about a total crock.

Similarly, these individuals and organizations are happy to report on how many workers they employ nationally, and in various states and localities, and how many of these jobs depend on exports at a given moment. But they have no interest in discussing how these trends have changed over time, or how many jobs and how much production they’ve sent overseas or have lost to imports, or how these situations have evolved, say, over the life of a certain trade deal.

The companies and industries justify this selectivity by contending that information on imports and offshoring is proprietary, and that keeping it confidential is crucial to their commercial success. That’s often true. But the Truth in Testimony Act should specify that if witnesses wish to keep close to their vest information on one side of the trade ledger (e.g., their firm’s imports), then they can’t brag about their performance on the other side (e.g., their firm’s exports). There’s simply no reason to allow these businesses to play, “Heads, We Win; Tails, You Lose.”

Nor need there be anything the slightest bit coercive about such requirements. If businesses and industries and their various representatives feel so strongly about the secrets to their success, they should be free to decline invites to appear before lawmakers.

Actually, I’d like to extend these requirements to the financial statements public companies need to file with the feds. As with their testimony, such businesses often include flattering trade-related information in quarterly and annual financial statements. If they’re not willing to give investors the full picture, they should need to drop the whole subject.

And why restrict such disclosures to public businesses? Companies of all kinds are required to report all sorts of information to Washington. Their submissions form the basis of much of the economic data that is made publicly available by the federal government. The shield of anonymity provided by the Census Bureau and other statistical agencies to prevent rivals from using the data to gain advantage is entirely reasonable from the standpoint of these businesses. But from a national standpoint, it makes no sense at all. Indeed, it puts policymakers and the public in the position of flying largely blind when it comes to evaluating the impact of trade policy decisions.

The same kind of problem is created by the narrow range of trade-related info that businesses are legally obligated to share. Why not force them to specify their job and production offshoring, the wages of their U.S. and overseas workers, their foreign and domestic procurement, the foreign and domestic content of their products, and similar statistics? And why not demand time series, so that long-term patterns can be identified?  BTW — content information has been required of auto-makers selling in the United States since the 1990s, so major precedent exists. 

The business secrets problem is easily solved: If all firms wishing the privilege of operating in the United States need to share the same information, no one company is put behind the eight-ball. And again, no coercion is involved. Companies would be perfectly free not to comply – and exit the world’s most lucrative market by far in the process. And what about the regulatory burden that would be placed on smaller firms? There’s a strong argument for exempting them, as larger firms dominate U.S. trade flows anyway.

Such a sweeping “Truth in Globalization Act” would probably be a heavier legislative lift than the “Truth in Testimony Act,” so I’d focus first on the former. But both are urgently needed to ensure the soundest possible U.S. policymaking process.  And how could anyone genuinely devoted to the national interest object?  

(What’s Left of) Our Economy: Some Real Fake News on Trump and Trade Data

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Canada, data, domestic exports, economic growth, exports, imports, imports for consumption, Jobs, manufacturing, Mexico, NAFTA, North American Free Trade Agreement, Public Citizen, re-exports, The Wall Street Journal, Trade, Trump, {What's Left of) Our Economy

Talk about a non-story. That some globalization cheerleaders have tried to blow up into a scandal. And all because the Trump administration seems to be interested in correcting important distortions in some commonly used U.S. trade data that presents a misleading picture of America’s exports, imports, and trade balances.

Here’s the situation. Last Sunday, The Wall Street Journal reported that  “The Trump administration is considering changing the way it calculates U.S. trade deficits, a shift that would make the country’s trade gap appear larger than it had in past years, according to people involved in the discussions.”

According to the Journal, “The leading idea under consideration would exclude from U.S. exports any goods first imported into the country, such as cars, and then transferred to a third country like Canada or Mexico unchanged….”

Continued the article, “Economists say that approach would inflate trade deficit numbers because it would typically count goods as imports when they come into the country but not count the same goods when they go back out, known as re-exports.”

So in other words, President Trump and his minions are thinking of artificially deflating the figures describing what the United States sells to the rest of the world, but not making a corresponding change on the import side that would reduce the amount of goods that the nation buys from its trade partners. The result would be a larger U.S. trade deficit, and added ammo for the administration’s claim that America’s trade policy needs major surgery. Talk about creating “alternative facts,” right?

That’s what the Journal‘s editorial board concluded. Charged these trade zealots, the Trump-ers’ “effort to recalculate U.S. trade flows to show larger deficits” is a “trick….borrowed from the political left” that “deserves to be hooted down as an attempt to manipulate statistics to assist bad economic policy [i.e., curbs on trade flows].”

But these allegations aren’t even close to the mark – that is, if you believe the Journal‘s own reporting. For as the original piece eventually reveals (based, as is the entire article, on anonymous sources), the president’s team is indeed mulling making those import data changes, too – which would involve switching the import measure “to ‘imports for consumption,’ a slightly narrower way of measuring imports that would make less of a difference in the overall balance. “

Which means that – weirdly – the Journal reporters decided not to tell those outraged economists that the supposed Trump administration exercise would make statistically valid symmetrical changes, or that these (of course nameless) economists received this info from the reporters and decided to ignore it in order to try to create the appearance of impropriety. It also means that Journal editorial writers either didn’t read their own publication’s coverage all the way through, or chose to ignore that decisive material. Either way, someone has just massively violated their profession’s ethics.

As for the change (reportedly) under consideration itself, it’s entirely justified because those re-exports that under the main system for presenting trade data are counted as real exports literally are not Made in America. As indicated above, they enter the U.S. economy from abroad and then are shipped overseas (or across the border to Canada or Mexico) in nearly all cases entirely or virtually unchanged.

This means that they add virtually nothing to American economic growth or employment – a major and entirely valid reason that exports are so beloved). And although, as some trade advocates claim, their transit into and through the United States creates logistical jobs (in transportation and,warehousing services), such logistical jobs would be created anyway if those goods were domestically produced (Unless you think that such products typically don’t need to be stored after production and then transported to customers, too?)

Moreover, the distortions resulting from sloppy methodology of the main exports numbers are anything but bupkis. Last year, for example, failing to strip out foreign-produced goods boosted total U.S. merchandise exports by 15.43 percent – or $224.33 billion. Relatively speaking, the impact on manufactures exports was even bigger – 17.48 percent, or $223.36 billion.

And the effects on America’s goods exports to Mexico and Canada, its partners in the controversial North American Free Trade Agreement (NAFTA), are especially noteworthy. Proper counting would reduce 2016 U.S. merchandise exports to the former by 23.19 percent and manufactures exports by 25.30 percent. The comparable numbers for Canada are 17.14 percent and 17.93 percent.

Moreover, since proper counting has little effect on import totals, either globally or for NAFTA trade, raising its profile would definitely show higher U.S. deficits. And the export gap has been growing steadily across the board.

Fittingly, this story can be closed on an absurd note, too. As indicated above, the U.S. government already compiles and reports (though in an unsatisfactorily low-profile way), export and import data that quantify exports actually produced in America, and imports actually consumed in America (although, as discussed in this solid Public Citizen analyses, the import numbers could still use some improvement). So a changeover to more accurate figures that reveal trade’s true impact on U.S. production and job creation looks to be pretty easy. Think we’ll be reading about that in The Wall Street Journal?

(What’s Left of) Our Economy: More Dubious Manufacturing Figures – from the Regional Feds

Tags

data, inflation-adjusted growth, Institute for Supply Management, ISM, manufacturing, Philadelphia Federal Reserve, statistics, survivorship bias, {What's Left of) Our Economy

I hope that RealityChek regulars remember my posts debunking the idea that a widely followed private sector gauge of manufacturing’s health has much to do with manufacturing’s health. As I explained, the Institute for Supply Management’s (ISM) monthly surveys of American industry suffer badly from “survivorship bias.”

In other words, they may accurately report on the performance of the nation’s manufacturing base at that moment. But because they only question companies still in existence in a given month, they provide no information on how that base has changed over time, and especially on the vital question of whether the base has shrunk or grown. As a result, I was able to show that in recent decades, the ISM’s findings that domestic manufacturing is in “expansion” mode have usually – and increasingly – clashed with the (more comprehensive) government data.

At the same time, the ISM is far from the only survey-based report on manufacturing that’s closely followed by students of the economy and of industry – including investors. Many of the Federal Reserve’s regional banks analyze manufacturing in their geographic districts in the same way, and one such series that often makes headlines comes from the Philadelphia Federal Reserve. I just looked over its latest release – from this morning – and it was so completely weird that I checked to see whether its findings have matched up or not with government statistics on manufacturing’s growth in the area it covers. And guess what? Its results could well be as off base as the ISM’s.

What set me off was the Philly Fed’s finding that manufacturing in its district – which includes the eastern three-fourths of Pennsylvania (pretty much everything up to Pittsburgh), southern New Jersey, and Delaware – had moved back into expansion mode in July. Nothing strange per se about that. What was utterly bizarre was the contention that this improvement took place even though new orders for this same manufacturing complex plunged deep into contraction territory, and the employment indicators performed almost as badly.

These aren’t the only measures tracked by Philly Fed economists (and their counterparts at other regional Fed banks), and much more positive readings for other indicators pushed the overall headline figure – which is a composite of all the data – into the black for July. But let’s leave aside whatever narrow technical issues this methodology raises and grant the Philly Fed’s view that such a mix represents “expansion” or “growth.” Let’s also leave aside the reliance of the ISM and Philly Fed and the like on manufacturers’ judgments on how their companies are performing – rather than on their actual performance.

That still leaves us with the question of how well this definition of expansion or growth tracks with U.S. government data on the actual production achieved by manufacturing in the district over time. These strike me at least as better measures since they focus (however imperfectly) on what’s measurably come out of a factory. And of course, without adequate output, higher profile gauges of manufacturing’s health, like employment, can’t possibly be expected to be satisfactory (Unless you’re OK with productivity stagnating – which seems to be the case recently.)

There are no output numbers for the Philly Fed’s district as such. But you can get a pretty good idea of the situation by looking up the manufacturing production statistics for the major towns and cities it contains, which are kept by the U.S. Commerce Department. At this level of specificity, such data only go up to 2014. But the contrast between them during the current economic recovery (which began in 2009), and the Philly Fed headlines over the 2009-20014 period, is striking.

Here’s a chart from the Philly Fed that shows those headlines:

As you can see, the brown “current activity” line doesn’t indicate terrific performance. But it stayed over zero (i.e., in expansion) for most of the relevant five years.

The Commerce Department keeps statistics on manufacturing production pre- and post-inflation for 18 of the “metropolitan areas” in the Philly Fed district. I looked at the former, since it yields the best sense of volumes, and therefore of the level of activity. And these figures show that manufacturing production rose in nine of them. Score one for the Philly Fed? If you’re generous.

But there are still two big problems. First, two of those increases, in tiny Gettysburg and Bloomsburg-Berwick, were minimal – i.e., much less than one half of one percent. In bigger Lancaster, manufacturing production expanded by a total of 2.10 percent in real terms. Harrisburg-Carlisle, in the middle, size-wise, between those two areas, fared better, with 3.95 percent after-inflation manufacturing growth. But these increases look pretty paltry over a five-year stretch.

By far the best performance in the Philly Fed’s district was turned in by the Trenton, New Jersey area, where constant dollar manufacturing output soared by more than 54 percent between 2009 and 2014. But its manufacturing sector is still peanuts, relatively speaking. Moreover, the real manufacturing declines that show up in the Commerce data were much bigger on average than the increases.

The second big problem is that there’s no adequate data – either pre- or post-inflation – for the Philadelphia-Camden (New Jersey)-Wilmington (Delaware) metropolitan area, which is by far the biggest in the Philly Fed district. Nonetheless, the few numbers that are provided suggest that its manufacturing sector has fallen on hard times. Specifically, between 2008 and 2012 (the only post-2005 numbers available), its manufacturing production shrank by 18.63 percent adjusting for inflation.

So it seems fair to conclude that, if you’re looking for a reasonably accurate portrait of those domestic American manufacturers who are still standing after decades of offshoring-happy trade policies, other officially created challenges, the economy’s inevitable ups and downs, and frequently changing product markets and technologies, by all means rely on the monthly ISM and the regional Fed surveys. If you’re interested in knowing about manufacturing recently aside from these survivors – including about whether their ranks have grown or shrunk – you’ll need to look someplace else.

(What’s Left of) Our Economy: What’s the Real Story with Manufacturing Pay?

Tags

benefits, Bureau of Labor Statistics, competitiveness, data, Employment Cost Index, manufacturing, manufacturing renaissance, National Association of Manufacturers, National Employment Law Project, wages, {What's Left of) Our Economy

Thanks to the Pittsburgh Post-Gazette’s Len Boselovic, I got dragged (willingly, to be sure) into a dust-up about manufacturing wages that recently broke out between the National Employment Law Project (NELP) and the National Association of Manufacturers.

Len did an excellent job of explaining how the former organization could come out with a report lamenting wage decline in manufacturing and the sector’s steady transformation into a low-wage employer, while the latter could respond by insisting that American industry “continues to be a pathway to the middle class.” As is often the case, it depends largely on which data you look at.

But it’s still worth elaborating on some points that Len was only able to touch on, especially since all data aren’t created equal. In the first place, the NELP report really should have looked at benefits as well as wages. They’ve been integral parts of compensation packages for workers throughout the economy for decades, and powerfully effect families’ living standards and financial health. I’m also still scratching my head as to why the NELP omitted white-collar manufacturing workers from its survey. They represent nearly half of all U.S. manufacturing employment.

But there’s also less to the NAM’s retort than meets the eye. As Len pointed out, the total compensation figures cited by its chief economist, Chad Moutray, aren’t adjusted for inflation. That’s a main reason I focus in my own analyses of manufacturing pay on wages – for which inflation-adjusted data is kept by the Bureau of Labor Statistics.

It’s also true that total compensation (as measured by the BLS’ Employment Cost Index), is up more during the current recovery for all manufacturing workers (by 11.91 percent) than for all private sector workers (by 11.14 percent). But in addition to being unadjusted for inflation, these ECI figures are only available going back to 2001. Therefore, they don’t permit any examinations or comparisons of longer-term trends – which for real wages, show multi-decade stagnation.

In addition, given how free many companies have felt to cut pensions in particular, it’s fair to ask how much longer manufacturing’s benefits package will remain so impressive. And finally, the NELP deserves credit for spotlighting the growing use by domestic manufacturers of temporary workers – whose wages are typically less than those of full-timers, who rarely receive any non-wage benefits at all, and who aren’t included in the manufacturing wage or total compensation data. (They’re considered instead as employees of job placement or temp firms). Obviously, if these workers were reclassified, manufacturing pay would be lower whatever measurement is used.

Bottom line? It’s still valid to claim that manufacturing’s rebound from an horrific recession has taken place largely on workers’ backs. Until domestic industry starts boosting production while raising, not cutting, real wages and overall compensation, talk of a manufacturing renaissance, or any regained competitiveness, will remain a grim joke.

(What’s Left of) Our Economy: If You’re Not Following Productivity Data, You Don’t Know About Manufacturing

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data, Labor Department, labor productivity, manufacturing, manufacturing renaissance, multi-factor productivity, Obama, offshoring, productivity, {What's Left of) Our Economy

Everyone who thinks they think seriously about economics touts the importance of productivity – and rightly so, since boosting this critical measure of efficiency is vital to boosting living standards over any meaningful period of time. But we’ve just gotten a sign that virtually no one walks the productivity walk. If they did, would the release of the latest Labor Department multi-factor productivity data last week have been so thoroughly ignored?

The department’s labor productivity statistics are pretty closely followed – no doubt because they come out quarterly and are therefore pretty up to date. The multi-factor productivity numbers only come out annually, so there’s a longer time lag. And the figures for specific parts of the economy, like manufacturing, are a full year behind that.

All the same, the multi-factor productivity statistics are much more important than their labor productivity counterparts for two main reasons. First, they take into account all the major inputs of production, like capital and technology and returns to scale – not just labor. Second, they’re not distorted by business offshoring activity the way the labor productivity data are.

And because the multi-factor statistics have been neglected, so have two important messages sent by the new figures: Manufacturing leads the nation in productivity growth by a long shot, and if these data are any indication (which they are), American industry is far from in renaissance mode.

The gap between manufacturing multi-factor productivity and total private sector productivity once again highlights the outsized importance of manufacturing to American economic performance and prosperity, and once again justifies treating its health as a high public policy priority. According to the new Labor Department data, from 1987 (when the statistics began to be kept) to 2012, multi-factor productivity in manufacturing rose by 34.81 percent. The same figure for the entire private sector? Just 28.55 percent – and since this includes manufacturing, the improvement for non-manufacturing industries was even smaller.

We won’t get the manufacturing data for 2013 until next year but the private sector gain – an historically unimpressive 0.7 percent – suggests that this picture won’t change much when they are released.

But those manufacturing renaissance claims are undercut big-time by these same multi-factor productivity figures. The Labor Department breaks down its performance by different time periods since 1987, and these data show that the growth of manufacturing’s multi-factor productivity has slowed significantly since the 1990s – and is now just about the rate it achieved in the decidedly non-renaissance-y late 1980s.

The numbers: From 1987 to 1990, multi-factor productivity grew by just 0.76 percent total – lagging the rest of the private sector considerably. From 1990 to 1995, this pace sped up to 5.75 percent, and the acceleration continued over the next five years, when manufacturing multi-factor productivity jumped to 9.17 percent. But from 2011 to 2012, manufacturing’s efficiency increased by only 0.62 percent according to this measure – again, more slowly than the 1.43 percent rise for total private sector multi-factor productivity.

Yes, the 1990s totals were no doubt inflated by that decade’s tech bubble – which means that much of the period’s manufacturing output was completely unjustified by economic fundamentals. But manufacturing’s multi-factor productivity was also growing much faster during the pre-bubble early 1990s – which included a short recession.

The big takeaways: If they really want to strengthen the U.S. economy, America’s leaders, businesses, and chattering classes need to work more effectively to strengthen domestic manufacturing – to make sure it enjoys a real renaissance, not the imaginary version proclaimed by the President and so many other cheerleaders.

(What’s Left of) Our Economy: Is Aerospace Really a Durable Goods Throwaway?

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aerospace, data, durable goods, industrial production index, manufacturing, {What's Left of) Our Economy

This morning’s data from the Census Bureau on new orders for durable goods provides a great illustration both of why it’s vital to be careful reading and interpreting statistics, and why it’s so unavoidably difficult to measure and interpret economic trends.

This monthly data – today’s preliminary report for July will be revised on September 3 (and then several times more over the next few years) – is viewed as a key gauge of domestic manufacturing’s health, but the headline monthly change figure includes sectors that most specialists believe distorts the picture: defense-related goods and aircraft.

Their importance was spotlighted again in this morning’s report. New orders for defense-related manufactures fell by 15.3 percent on a monthly basis – after rising by 4.1 percent in June and dropping by 24.0 percent in May. In other words, these data are really volatile. Moreover, they’re heavily affected by political decisions – the size and growth of the defense budget. As a result, it’s understandable why defense-related orders aren’t seen as genuinely representative of domestic industry’s chops or prospects.

The distortion looks way more extreme for civilian aircraft in particular. Orders for airliners skyrocketed by 318.0 percent in July month-to-month according to the advance figures – after increasing by 11.1 percent in June and decreasing by 2.9 percent in May. It’s obvious, then, that these data are awfully volatile, too. Further, as predicted in The Wall Street Journal yesterday, these incredible aerospace numbers resulted from one great month for a single company – Boeing.

Largely as a result, total new orders for durable goods jumped by a record 22.6 percent in July on month – after growing by only 2.7 percent in June and actually shrinking by 0.9 percent in May. Meanwhile, the results for durable goods orders stripping out aircraft and defense goods – the ”core capital expenditures” number widely considered to be the very best indicator of manufacturing’s health – fell by 0.5 percent in July, after improving by 5.4 percent in June and dropping by 1.4 percent in May.

Here’s the (big) complication, though. Although orders for civilian aircraft can indeed fluctuate wildly from month to month, aerospace isn’t exactly a cottage industry. The Federal Reserve’s industrial production index makes this clear – even though unlike the durable goods numbers, it adjusts for inflation.

According to the Fed, in July, manufacturing represented just under 71 percent of the nation’s total industrial output. (The production of mines and utilities makes up the rest.) That’s down from 76.6 percent ten years ago. Last month, the aerospace industry (which includes missiles as well as aircraft parts, both for military and non-military plans) made up 4.6 percent of the industrial production index. But since July, 2004, this percentage has grown from a little less than 3.2 percent.

So however volatile, aerospace is becoming a greater and greater part of the total American manufacturing scene. And every month this trend continues, removing it from the core capital goods data becomes a little less justified.

(What’s Left of) Our Economy: Words of Warning on Manufacturing Surveys

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data, manufacturing, manufacturing renaissance, surveys, {What's Left of) Our Economy

Today was a pretty big day for U.S. manufacturing data, with the release of an advance August survey by the respected research firm Markit.com, and of the Philadelphia Federal Reserve bank’s closely watched gauge of manufacturing activity in some of the middle Atlantic states. There’s lots more monthly manufacturing data to come in the next few days, so it’s important to understand that these types of reports in particular can present a seriously misleading picture of manufacturing’s health. They’re by no means completely worthless. But their results need to be taken with a big grain of salt.

Here’s why: The Markit reports – as well as the surveys released each month by various regional Federal Reserve banks and the monthly results of the Institute for Supply Management – present the results of what are called diffusion indices. That is to say, they show the percentage of the firms surveyed that report a better performance in whatever is being measured over a certain timeframe (e.g., overall business conditions, employment, new orders), a worse performance, or about the same performance.

The big problem with diffusion indices: They usually suffer what statisticians call “survivorship bias.” They only survey the performance of companies that are in existence during the particular time period being examined. Because they don’t, therefore, say anything about how the number of companies in existence has changed over time, much less about the actual production or employment levels of these companies, they create a picture of overall manufacturing activity in the location in question that’s at best highly incomplete.

Before I was even aware of the formal concept of survivorship bias, I learned about it in practice from talking with manufacturing executives who belonged to my former organization, the U.S. Business and Industry Council. Especially as the current recovery took hold, I would regularly ask them how their own companies were faring. The response I often heard? “Never (or rarely) better!”

So I then asked them why they remained in the Council, which has long warned of major competitive weaknesses in domestic industry. I expected most to emphasize continuing uncertainty about the future, and a reluctance to read too much into the present – especially considering that virtually none was adequately prepared for the Great Recession. And uncertainty was certainly mentioned.

Even more often, however, these business owners and managers explained that they were benefitting significantly from the demise of weaker domestic rivals. Because so many so many firms that were perfectly viable and competitive during normal times were suddenly drowned by the worst U.S. downturn since the Great Depression, the remaining companies were able to win the business formerly held by the losers once growth and even stability began to return.

As the USBIC members put it, they were taking advantage of pie that was growing much more slowly – if it was growing at all – because so much more of it was up for grabs than ever before. But they remained deeply concerned about the future overall size of the pie, which they correctly believed was crucial both to a return to genuine overall U.S. economic health and to national security.

A February article of mine on the Institute for Supply Management’s monthly manufacturing index showed how its survivorship bias was producing results often sharply at odds with the national manufacturing production figures put out by the Federal Reserve, which are far from perfect, but which do attempt to measure actual industrial output and its rise and fall. I haven’t tracked the relationship between the Markit and regional Fed manufacturing results on the one hand, and industrial production on the other, but it’s entirely reasonable to suppose that a notable gap exists there, too.

These diffusion index surveys can contain some useful information about the momentum of domestic manufacturing. And the regional Feds regularly ask manufacturers in their geographic districts interesting and important questions about specific policy and other challenges and opportunities they face. (This morning’s Philly Fed report included some fascinating information about how manufacturers in that region are reacting to Obamacare.) That’s why I follow and report on them. But understanding the true state of domestic manufacturing requires looking at a much wider range of material. Relying solely or even largely on diffusion index surveys is one of those tempting shortcuts that can lead you seriously astray.