U.S. Urban-Centered Mega-Regions

National Geographic reports an excellent job with a new version of an idea I included in my text starting with version 2.0. This is that much of macroeconomics is about where you live.

So check out this article entitled “Four Million Commutes Reveal New U.S. ‘Megaregions’”. I do think they need a better editor: it’s not the megaregions that are new, but rather the use of four million commutes to map that out.

Anyway, they produced this map:

Each line on here is someone’s commute. The shading is chosen to indicate the hubs for commuting that have evolved because this is an emergent process. Those are based on an algorithm rather than personal preferences. Interestingly, it determined something that most Cedar City and St. George residents know, but that seems lost on SUU administrators and Utah state officials: we’re in the Las Vegas megaregion, not the Salt Lake City one. If SUU feels like an afterthought across the state, now you know why.

This is based on an academic article entitled “An Economic Geography of the United States: From Commutes to Megaregions” that appeared on PLOS|one. That’s not required, but even so it has lots of maps that even an uninterested reader might find interesting.

Why Is Macro So Hard? Voters Sometimes Get What They Want

The news this week is that President-Elect Donald Trump has convinced executives at Carrier to not move a production facility from Indiana to Mexico.

The backstory to this is that the business had bottom line reasons for wanting to move to Mexico, and government officials (with the explicit backing of the currently powerless Trump) bought them off with tax dollars.*

Here’s Larry Summers view:

Some of the worst abuses of power are not those that leaders inflict on their people. They are the acts that the people demand from their leaders.

This is similar to this H.L. Mencken quote from just over a century ago:

Democracy is the theory that the common people know what they want, and deserve to get it good and hard.

If that seems like a micro-offense, please recognize that those were different times.

Hat tip to Greg Mankiw for noting Summers’ turn of phrase, and to Don Boudreaux for repeating this Mencken quote many times through the years.

P.S. A couple of days after posting this, Tim Worstall posted a similar quote:

Populism: the unpardonable sin of offering the populace what they appear to want rather than what they ought to.

* On the negative side, in the short-run, we’re all investors in Carrier whether we want to be or not. In the long-run, this may solidify the dangerous precedent of corporate executives holding out for government handouts. On the positive side, it’s still early … perhaps Trump will just do this once to establish credibility that obviates it’s future need.

An Example of Bizarro Journalism About Cuba

After I wrote this, Tim Worstall linked to a supportive article about Castro.

The only data charted in it is GDP of Cuba, versus two comparables: The Dominican Republic, and Jamaica.

The chart is used to support the position that Castro did OK.

Except the variables charted are not corrected for either inflation or population growth.

That's kind of like asserting that Castro was great because he taxed away nominal wealth (with an inflation tax) but let people have babies.

Putting the Cart Before the Horse

Macroeconomics is generally not an experimental science. It’s observational.

One of the advantages of an experimental science is that you can control causes to isolate their effects. You can come up with different stories about what the causes are, but in principal you can confirm whether those stories are supported by the data. In some sense, you can go “fishing”.

In an observational science, you have to get your story about causality straight first. By that I mean what effects you expect to see and what you should not see. Then you can go and check your data.

Unfortunately, we’re bombarded with GDP data, but not with stories about the causality that generates it. This leaves a lot of room for getting things wrong.

Which brings us to Trump’s nominee for Secretary of Commerce: Wilbur Ross. Here’s his theory (taken from a Wall Street Journal editorial entitled “Trump’s Money Men”):

… Mr. Ross wrote, “It’s Econ 101 that GDP equals the sum of domestic economic activity plus ‘net exports,’ i.e., exports minus imports. Therefore, when we run massive and chronic trade deficits, it weakens our economy.”

Who taught him that? Imports are subtracted in GDP calculations to avoid overstating domestic production, not because they make us poorer. …

The causality Ross presumes is pretty clear: “… trade deficits … weaken…”

This is weird: GDP (and trade deficits) are something we measure after they occur. They’re a description of what did happen, not an explanation of how it happened.

It’s more correct to say that a trade deficit might be a symptom of a weak economy, rather than a cause.

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Ross is making a ridiculous logical mistake here, but one that is all too common in thinking about macroeconomics.

First off, GDP is what we count up after production and consumption happen. And as we’re counting, perhaps we divvy it up into different bins, including (gross) exports and (gross) imports.

• This is analogous to going to one of those old-fashioned machines where you put in a quarter and it dumps out a handful of, say, Skittles. That’s your macroeconomy.
• Then you count up your Skittles. That’s your measure of your economy’s GDP.
• Then you divide up the Skittles by color, and call the yellow ones exports. And then you announce that if you’d gotten more yellow Skittles you would have gotten more Skittles in total.

A child might make that mistake. An adult should not.

Secondly, there are interconnections within GDP besides Y = C + I + G + X. Let me introduce 4 alternative variables:

• DP►DC, this is Domestic Production that goes into Domestic Consumption
• DP►FC, this is Domestic Production that goes into Foreign Consumption
• DC◄DP, this is Domestic Consumption that comes from Domestic Production
• DC◄FP, this is Domestic Consumption that comes from Foreign Production

My notation is a little bit weird: do not think of the the ► as a >, or the ◄ as a <. But there is a method to my madness.

Note that DP►DC and DC◄DP have to end up with the same number (although you might make a measurement error here or there).

In terms of the conventional textbook items:

• C + I + G = DP►DC (or DC◄DP)
• Gross Exports = DP►FC
• Gross Imports = DC◄FP
• GDP = DP►DC + DP►FC – DC◄FP (or DC◄DP + DP►FC – DC◄FP)

The cool thing about this is that we can think of these new variables in this way. Remember the fable about getting a mule to move with a carrot and a stick? The stick is work, and the carrot is your reward:

• DP►DC, means that your carrot and stick are balanced
• DP►FC, means that you’re all stick
• DC◄FP, means that you’re all carrot

Now let’s think about some naive policy ideas.

Let’s export more! So we’re going to make DP►FC bigger. This means we have to both work more here, and somehow get foreigners to buy stuff they weren’t before. Maybe we could advertise to make the latter happen. But there’s only two ways to handle the former part: actually work harder (by using more stick), or divert some of our work by making DP►DC smaller to make DP►FC larger. Except that we can’t make DP►DC smaller without making DC◄DP smaller. If you think about it, this amounts to giving away our carrots. So there you have it: a proposal to increase exports either means more stick or less carrots.

Let’s import less! You can probably see where this is going. This means making DC◄FP smaller. One way to do that would be to make DC◄DP larger. That way we could keep the number of carrots the same, but just get less of them from foreigners. But again, it gets weird: we’re getting the same number of carrots, but because DC◄DP = DP►DC, we have to work harder. So there you have (part of) it: you get more imports with more stick and no extra carrots. You can work out on your own that you could also get less carrots with the same amount of stick.

I understand that these examples are not easy. But that’s the point: trade policy is not something that most people think about very clearly … including people we put in charge.

Fidel Castro Is Dead

King Fidel died the other day.

What? You didn’t know he was a king? What do you call someone that ruled since 1959 without reasonable elections until they chose to retire, then abdicates in favor of a younger family member (i.e., King Raúl)? Oh, and there’s a son-in-law in the mix to succeed him; we’ll call him Prince Alberto. Oh, and somehow he’s worth a fortune, just like a king.

People call King Fidel a revolutionary. As if kings could not lead revolutions … hmmm … what was that bit in English history about a minor nobleman William of Orange being the military leader on the winning side of the Glorious Revolution and becoming King William III of England?

I’m going to pull rank here. King Fidel took over a country. The way to measure a country is with GDP. So, if you’re not getting information from a macroeconomist about Cuba and Fidel … you pretty much shouldn’t bother.

The thing is, it appears to be a huge mission of many people in the developed world to present Fidel as something that he was not, and this mostly begins with ignoring the GDP data.

From a macroeconomic perspective, Fidel is one of the worst humanitarian disasters in human history.

Data is not hard to come by on this. For all its faults, there is no competition for assessing economic conditions at the individual level than real GDP per capita. More on that later.

In the U.S., we are somewhat unsure of how we feel about Obama because the economy has struggled to grow real GDP at 2.0 to 2.5% per year. Even Obama’s supporters will typically admit that the economy doesn’t feel that great because we need to take away 1.5% to 2.0% population growth from the U.S. figure. Yet, from 1959 to 1999 Cuba’s real GDP per capita grew at 0.3% per year. Even in rather weak times, the U.S. has pulled away from Cuba.

Going further though, Cuba’s growth has lagged behind most of the world. It’s closest analogs in the Americas of 1959 were Jamaica, Panama, and Ecuador. All three have pulled away from Cuba, in terms of real GDP per capita.

One of the tightest arguments we make in science is to compare matched pairs. Try to find the closest analog, and then compare their differences with the passage of time. This is how we know North Korea is so bad — because it used to be comparable to South Korea. The same goes for the old West and East Germany. For Cuba, the closest analog is Puerto Rico. Both were Spanish colonies for 400 years. Both were occupied and dominated by the U.S. for the next 60 years. And Puerto Rico has quadrupled its real per capita GDP since 1959. The data on all this is solid, accepted, and widely used.

Fair enough. But supporters extoll the availability of free and effective healthcare and education in Cuba.

Let’s be very clear about this. If you don’t start with real GDP per capita, it’s easier to find claims about healthcare and education to be more convincing. But, if you do start with weak real GDP performance, then it should be clear that any claim that healthcare and educaton have gotten better in Cuba must be accompanied by a statement that everything else is even worse than we thought. Have you ever heard a statement like that? I didn’t think so.

Oh, and it’s very easy to find articles detailing how Cuba’s healthcare and education systems in 1959 were … already pretty good by international standards. Go ahead, dig into that on your own. Cuba only looks good on these counts if you forget the “compared to what” aspect.

Don’t believe me? Here’s a personal viewpoint from George Borjas. He’s a professor at Harvard, one of the top labor economists in the world, and a refugee from Cuba.

Why Is Macro So Hard: Putting the (Political) Cart Before the (Political) Horse

Yes, there’s an element to this news story that’s jokey, and there clearly was some lack of editorial oversight here. But it shows how mood affiliation leads to policy activity, with or without deep background thought.

Anyway, Canada has 2 main parties that are each a little to the left of the their American counterparts. And it has a big third party, the NDP (New Democratic Party), which is even further left. In America it would be Sanders-esque. It tends to be strongest in the West, and here’s what happened in the Yukon Territory. They released these by accident:

The key word here is “will”, the only verb shared by both slogans. That word implies future activity. It doesn’t seem to matter what the specifics are though.

There’s no goal in these images. There’s no objective. What there is … is a predisposition towards busybodyness.

In macroeconomics this can be a problem. We have 200 or so countries around the world, and the record of those that interfere with their macoeconomies is not very good. That makes busybodies dangerous.

Via Munger writing at Kids Prefer Cheese.

Why Is Macro So Hard? Activity Enslavement

Charles Coonradt:

In the absence of clearly defined goals, we are forced to concentrate on activity and ultimately become enslaved by it.

I don’t have a source for this. I got the quote from my colleague Greg Powell, who used it in a brown bag about motivating student engagement through questions.†

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But I like the quote because it applies to another problem we have in understanding macroeconomics. Bryan Caplan dubbed this the “activist’s fallacy”:

Something must be done; this is something; therefore, this must be done.

I have lost the cite, but Lynne Kiesling paraphrased it as:

Don’t mistake activity for accomplishment.

When we think about macroeconomics — and certainly politicians talk an awful lot about it — what are their goals? No recessions on their watch? Low unemployment rate (how low)? Low inflation rate? Increased spending? Higher real GDP growth rate?

Consider the stimulus package of 2009. It was chock full of activity. But what was the goal? If it was stimulating the economy … it doesn’t seem like it did much (although I’m aware that we don’t have a control for this experiment).

† Coonradt, Charles A. with Lee Nelson. The Game of Work, Shadow Mountain, 1991, p. 10.

Touche

Adam Davidson skewers Navarro, the author of Trump’s economic plan:

The first hint of how Navarro’s ideas might play out in a Trump Administration came in the most comprehensive economic document so far released by the campaign. Navarro, along with the Trump supporter and private-equity investor Wilbur Ross, wrote “Scoring the Trump Economic Plan: Trade, Regulatory, & Energy Policy Impacts.” …

The document … presents no concrete prescription to correct the trade imbalance it abhors. One key sentence: “Trump proposes eliminating America’s $500 billion trade deficit through a combination of increased exports and reduced imports.” This is a tautology, equivalent to saying one plans to weigh less in the future through a combination of losing weight and not gaining weight. [emphasis added]

That’s not a plan. That’s a wish.

Via Greg Mankiw.

Trump’s Economic Proposals Continue to Make No Sense

A headline that “Trump makes no sense” would not surprise many people.

But in this case, it’s the more official economics pronouncements of the Trump campaign that have problems:

Trump proposes eliminating America’s $500 billion trade deficit through a combination of increased exports and reduced imports. [pg 18]

Note that this isn’t a policy. Instead it’s a description of one way the numbers might change if the trade deficit were reduced to zero.

What’s worse is that this non-explanation of how things will work is then used to argue that this is a way to increase real GDP growth:

To illustrate this, suppose the US had been able to completely eliminate its roughly $500 billion 2015 trade deficit through a combination of increased exports and decreased imports rather than simply closing its borders to trade. This would have resulted in a onetime gain of 3.38 real GDP points and a real GDP growth rate that year of 5.97%.

This is why economists talk do so much math, and talk about theory. It’s in our math that we make sure that all the connections are there that should be. And it’s our theory that suggests what those connections ought to be.

The big missing piece here is that, with flexible exchange rates, our current account deficit is balanced by our capital account surplus. Trump is presuming that through “negotiation” he can move one down without thinking about the other. I think it’s dubious to begin with that any trade negotiation between governments is even a large part of international transactions made by millions of people and firms. But anyway, will the capital account just tag along? What if it’s actually the capital account surplus that’s causing the current account deficit (a position I’ve taught in my macro classes for going on 30 years)?

This is sad. In this election cycle we seem to have gone past potential policymakers making up hopeful but plausible stories about their proposals, to potential policymakers just making the whole thing up. This is not politics as usual; it’s way beyond that.

N.B. I’m not the only macroeconomist that was floored when I saw the quotes in the news (see Marginal Revolution, Greg Mankiw’s Blog, or Paul Krugman’s column).

Infrastructure Investment

Politicians want to sell you infrastructure investment. Don’t buy it.

What is infrastructure anyway? It’s basically big capital projects that are kinda’ sorta’ public goods: bridges, roads, airports, and so on.

There’s a notion that in the U.S. our infrastructure is “crumbling”. Maybe. There is no very good way to measure this. One thing we do is talk about how old our infrastructure is. But that misses the “compared to what’ question you should always ask: how do we know a specific age is “old”?

The biggest name in urban economics is Ed Glaeser from Harvard (no one regards Glaeser as a supporter of political conservatives). He’s just published a piece in City Journal entitled “If You Build It …” that’s getting a lot of talk.

Why do politicians always tout infrastructure?

The progressive romance with infrastructure spending is based on three beliefs. First is that it supercharges economic growth. …  [Second], by putting people to work building needed things, infrastructure spending is an ideal government tool for fighting unemployment during recessions. [Third] Infrastructure should also be a national responsibility …

None of this is right. …

First, let’s The thing is, no one spending their own money would do stuff like this.

In 2009, [Glaeser] calculated a rough cost-benefit calculation for a (fictional) high-speed rail link between Houston and Dallas and found that costs outweighed benefits by an order of magnitude.

Note that when a scientist says “an order of magnitude” they mean something is multiplied by the base of log. Typically this is ten for casual use of order of magnitude, but e is probably a reasonable lower bound. Anyway, he’s saying the costs are several times the benefits. That’s not off by a little bit; it’s more like offering to pay $10 or $20 for every gallon of gas. And, if you’re not up on this stuff, Dallas to Houston is one of the corridors across the country that the infrastructure people are always proposing for high speed rail. So it’s not like he picked that example out of a hat.

And who are we trying to help with infrastructure spending anyway?

The relatively simple technology of infrastructure construction of the 1930s meant that the unskilled unemployed could easily be put to work building roads. Among the iconic images of the Great Depression are scores of men wielding shovels and picks. That isn’t how roads and bridges are built anymore, though. Big infrastructure requires fancy equipment and skilled engineers, who aren’t likely to be unemployed. The most at-risk Americans, if they’re working at all, usually toil in fast-food restaurants, where the average worker makes $22,000 a year. They’re typically not trained to labor on complex civil-construction projects. Subsidizing Big Mac consumption would be a more effective way to provide jobs for the temporarily unemployed than subsidizing airport renovation.

 

I love that last sentence. That really gets to the heart of what progressives ought to be telling Americans. But if they did, we’d recognize how silly their ideas actually are.

It turns out that the Council of Economic Advisors has a number they’ve estimated for how much it costs the government to create a “job-year” (that’s one job lasting one year). It’s $92K. What that means is that if you can’t create a job that pays more than that per year, you shouldn’t bother. The thing is, income distribution data shows us that only 15-20% of jobs pay that much. So government is really only capable of economically creating jobs for the rich, but keeps telling us how good it is at creating jobs for the poor. Not so.

On the third point, I love this turn of phrase:

The most pressing problem with federal infrastructure spending is that it is hard to keep it from going to the wrong places. We seem to have spent more in the places that already had short commutes and less in the places with the most need. Federal transportation spending follows highway-apportionment formulas that have long favored places with lots of land but not so many people. …

… Low-density areas are remarkably well-endowed with senators per capita, of course, and they unsurprisingly get a disproportionate share of spending from any nationwide program. [emphasis added]

In the end, Glaeser recommends basic economics: use newer technology (like EZ Pass) to get users to pay for the infrastructure they actually use.

The “Pig In the Python”

I’ve been talking in macro classes for several years about how a lot of the weakness in our economy is due to baby boomers starting to retire in large numbers.

Check out this piece from Marketplace. You can either read the transcript or click through and listen to the podcast. The topic is Trump’s claims about what his proposals will do for economic growth. But I’m posting about it here for a small detail. One of the interviewees describes the baby boom as a “pig in the python” that’s been working it’s way through our economic system.

You’ve probably all seen pictures of snakes after a big meal. ‘Nuff said. But the metaphor is that many national labor statistics look the same way.

OMG: Thoughtfulness on Significant Digits

The Summer Olympics are running right now.

And in swimming there are a lot of ties.

How is it possible that there are ties when we can measure time accurately to 3 to 6 digits?

Here’s why: because in 0.001 seconds a swimmer travels about 2 millimeters, but the construction tolerance for differences in pool lane lengths is plus or minus 30 millimeters (due to expansion and contraction from heat and cold).

FINA (which governs international swimming) recognized 40 years ago that improvements in timekeeping simply is not differentiating swimmers better. Here’s the link, although it’s not required.

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Why can’t we do this in macroeconomics?

It’s not official, but the popular site usdebtclock.org certainly tries to give people the impression it’s counting off each dollar of national debt. JustFacts.org asserts that on August 4, 2016, our national debt was $19,379,566,441,022. They get this from TreasuryDirect, which sounds like a government site but isn’t.

These numbers are not credible. I would be OK with a figure of $19T, but even that is dicey. We simply do not have the ability to measure things that accurately.

How about GDP? The BEA asserts it was $18,437,600,000,000 in 2016 II. That 600 is 600 million. So they are claiming they have this down — in a country of about 300 million people — to an average of within 2 dollars for each of us? Heck, I don’t know my income in a quarter within 2 dollars. Ever. And I am mostly paid in regular paychecks.

How about economic growth? The most recent figure is 1.2% for that same quarter. Could you feel it if it was 1.3%? Or 1.1%? I have asserted for years in principles that we have trouble feeling GDP growth rate differences of sharper than 1% (and unemployment rate differences of sharper than 0.5%, which is actually consistent with Okun’s Rule).

Why can’t we get bureaucrats be as forthcoming as swimming officials?

Regulation, Distrust, and Growth

Countries with high levels of distrust have more regulation.

Note that this is not distrust in markets leads to more regulation of markets. Rather, it is distrust in general leads to more regulation of markets.

But disturbingly, while poor policy choices cause weak growth, it also appears that weak growth may foster additional bad policies — like more regulation of markets.

Here’s something to worry about: distrust leads to more regulation which leads to lower growth which promotes more bad choices and may increase distrust.

The scary thing is this sounds like a lot of countries around the globe. Think South America.

The really scary thing is that measures of trust in the U.S. have been on the decline for over a generation.

Check out this updated post (and at least browse the links) by Alex Tabarrok.

Stolper on Wages vs. Production

Americans (and others in developed countries) get paid a lot. We also hear a lot of naive people remarking that this makes it difficult for us to compete with workers in other countries who are paid less.

That sort of remark is often nonsense. The reason is that it neglects the “compared to what?” questions we should always ask.

Instead, what’s important is the ratio of wages to production in two places. The lower that is, the more likely a location is to attract productive firms.†

Here’s a quote of what Wolfgang Stolper said in 1960:‡

African labour is the worst paid and most expensive in the world.

It was the worst paid because their wage was so low, but it was expensive because they produced less than what they were paid.

† Of course, smart students will recognize while potential employers will be attracted to places where the wage/production ratio is lower, potential employees will be going in the opposite direction. That’s what economics is all about, because that pair of tendencies is going to tend to make the wage/production ratio the same everywhere. That process of smoothing out differences is called arbitrage, and it’s what makes equilibrium a useful concept.

‡ You also hear many people remark about macroeconomics something like “why didn’t anyone see this before”. One of the things you need to learn about macroeconomics is that most of these things have been seen before, but people prefer to ignore them in favor of magical thinking.

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The source article is really about trade rather than macroeconomics, so I’ve marked off this section because it’s less important for this class.

There is a backstory in the source article. It’s about a textile plant in Nigeria that couldn’t compete without tariff protection.

This is from a longer article in The Economist entitled “An Inconvenient Iota of Truth”. It’s about the Stolper-Samuelsom Theorem (yes, that Samuelson). That theorem explains why, say, oil-rich countries have such lousy economies outside the oil industry. The reason is that when the price of the output in the oil sector goes up, that in turn shifts demand outward for inputs to the oil sector, increasing their price too, but also shifting demand for inputs to other sectors inward, reducing their price. In short, if you live in Saudi Arabia, and don’t work in the oil industry, you’re hurt every time the price of oil goes up. More generally, the input that’s getting scarcer is the one being hurt.

This can also be extended to skilled and unskilled labor in developed countries like the U.S. Unskilled labor is scarce in the U.S. And th story of the last century or so is that worldwide economic growth increases the price of the outputs of our skilled labor. Following the Stolper-Samuelson result, this means that unsklled workers are hurt because people around the world want the exports of our skilled workers. This gives some motivation for the politics of protecting unskilled workers from trade with other countries.

Coming full circle, protecting unskilled workers in developed countries is the flip-side of protecting skilled workers in developing countries. That was Stolper’s point about Nigeria. They couldn’t develop skilled labor for the future without protecting inefficient industries (where they might work) in the present.

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Via Greg Mankiw’s Blog.

Productivity Down for Third Straight Quarter

I don’t worry too much about occasional downs in macroeconomic statistics, as long as they are mostly ups. Three in a row gets my attention though.

Productivity probably should be the key macroeconomic statistic. After all, the increased productivity of individual workers is the only reason that anyone can afford to pay them more in real terms.

The thing is, productivity isn’t the key statistic because we can’t measure it directly. Instead we measure it as a residual. We track output growth first, then we subtract out how much of that has to do with growth in employment and hours. From that we subtract out how much has to do with growth in amount and utilization of capital. What’s left is improvements in productivity (usually attributed to improved technology). So, productivity is a little dicey.

Nonetheless, government statisticians are careful about their data and methods, so while we may treat a productivity growth number as imprecise, we think that evens out over several consecutive measurements.

So, why on earth would productivity be slowing down? Determining causality here is problematic. What we do in practice is run through some plausible explanations.

First, could technology be going backwards? This seems unlikely. Just look around. Some would argue that the technological improvements we are making are just not that important (it’s summer of 2016, so the big new thing is Pokémon Go), and maybe that’s true. Personally, I’m always leery of just-so stories about how the present is horrible and the past was so much better. Nonetheless, there is a serious macroeconomic research looking into this possibility (like this).

Second, are our machines not working properly? The statistic to look at here is capacity utilization. This shows roughly what percentage of available hours the available machinery is working, so higher is better. This has been on a downward trend for decades, and we’re not sure that’s a bad thing. It does mean that we should probably focus on the last decade or so though. Now, the way this relates to productivity is that if capacity utilization is higher, productivity might be lower because we’re pushing the more marginal machines into production. This does not seem to be the case: capacity utilization has been falling for about a year. Maybe we pushed our machines too hard a year or two ago, and we’re seeing the consequences now … but that’s stretching a bit, don’t you think?

Third, are our people not working properly? Maybe productivity, averaged across all of us, is faltering because some of us just stink at working.

Macroeconomics is a craft that one practices. No one ever really knows anything for certain. But the idea with a craft is that you practice to get better at noticing the details and taking care of them. And for my money, that last one is the explanation that’s holding water for me.

Our labor force is near, or at, full employment, with our unemployment rate hovering below 5%. Firms say that have lots of jobs (you have to dig into the JOLTS data a bit to find this) but have trouble finding people with the skills needed to fill them (job openings are going up faster than job hires). Taken together, these tend to suggest that firms are scraping the bottom of the barrel with their new hires. A third factor to add is that, demographically, the baby boomers are starting to retire in large numbers. They take their experience with them, and this is hurting some firms.

The question that needs to be addressed then, is whether what we’re experiencing is 1) merely the tempoarily heavy replacement of experienced workers with inexperienced ones (if a demographic change that’s going to take a couple of decades to play itself out is best described as temporary), or 2) solid evidence that a larger than normal chunk of the population simply isn’t worth employing. Productivity data is pretty volatile, but a quick graph I produced on the BLS website shows that productivity has been in negative territory quite a bit during the Obama expansion. For as long as I can remember, society has been complaining about an underclass of people who don’t fit into the working world. I used to regard that as just grousing, but now I wonder if that group is finally large enough to make an observable difference.

IT Companies Are Hiring Lots of Economists

This is according to Bloomberg BusinessWeek, in a June 13-26 article entitled “Algorithms Aren’t Just for Coders”.

I found it online here at PressReader. I think the long-term viability of that link is sketchy. I got to it by entering the following into Google

“algorithms aren’t just for coders” bloomberg businessweek

If you still can’t find it, let me know; I downloaded a copy.

More On Economists In Silicon Valley

This article entitled “Economists Adding Up At Amazon.com, Microsoft, Google” appeared on the website Investors Business Daily.

Why Is Macro So Hard? Repeating the Bad News but Not the Good

Humans are weird. We like to hear bad news, particularly if it’s about others, or if we can use it to gain sympathy for ourselves without actually getting hurt.

In macroeconomics, this means that you will be exposed to less news stories about the economy when it is doing well, and more when it is doing poorly.

We are currently towards the end of a weak expansion, maybe around a peak. This is prime time for bad news plus the same bad news.

Three months back, the news was that forecasting models were indicating that the economy was that the economy might be peaking soon (we discussed this here and here). Now the news is that real GDP growth for the first quarter of 2016 is going to come in around zero (the linked article is required reading). If you think about it, they mean the same thing.

The closely watched Atlanta Fed GDPNow model now shows first-quarter growth tracking at 0.1 percent, compared to a 0.4 percent estimate earlier in the week.

Do note that I’m not saying that both of these items are not news. Instead, what I am saying is that “evidence that we’re peaking and evidence that we peaked” is covered more than “evidence that we’re not peaking and evidence that we did not peak”. But, given the fact that the economy tends to be in expansion about three times as much as it’s in contraction, we should actually hear more about the latter.

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One problem with cyclical industries is that they overproduce and then have to pull back production and draw down their inventories. Some are of the view that the first quarter was a pull back, and that this is a good sign for the second quarter:

The JPMorgan economists, however, say there may be light at the end of the tunnel.

"While 1Q is adding up to be a clear disappointment relative to expectations from a few weeks ago, it now looks like the inventory correction was largely completed by the start of the second quarter, which is a favorable development for growth in that period. We now think that real inventories increased $54bn saar in 1Q, a rate that is likely to be sustainable moving forward," wrote JPMorgan economist Daniel Silver, in a note.

Journalists Are Often Lousy Economists

Most of what people learn about the economy comes from journalists. Fair enough. But things get stickier when it’s about economics rather than the economy.

For students moving on to careers in FIRE, reading what journalists write is essential, but parsing whether or not the journalists theories make sense is even more so.

Tim Worstall spotted this one in one of Britain’s largest newspapers (not required, but fairly easy reading). It starts out with a discussion of the poor decision of newly rich investors from India getting burned buying worn out old companies from their former colonial parent.

But, towards the end, it turns into a clueless analysis of the trilemma (you know, the thing that China is having so much trouble with this past year):

In contrast to my early years as a financial journalist, when sterling crises were two a penny [note: sterling is a pet name the British have for their currency], nobody much cares about the current account deficit these days. Yet news last week that it reached a jaw-dropping 7pc in the final quarter of last year was enough to make even the most sanguine of observers sit up and take notice.

I’m going to continue with the quote, but here’s an aside about the paragraph coming next. I have touched on this in class: this sort of claim is like saying you’re amazing because you set a new world record just this morning for the number of breakfasts you’ve eaten in your lifetime.

It’s a profoundly alarming spectacle, but both the UK budget and the current account deficits seem get markedly worse with each passing, post war, economic cycle. These latest ones are by far the deepest yet.

That they are in any way tolerable is I suppose down to the much more sophisticated nature of global capital markets, which makes funding them a lot easier than it was. But this in turn may make the country even more vulnerable to a sudden stop, or to any loss of international confidence in the economy’s underlying solvency. Eventually there will be a shock, triggered possibly by Brexit, which will manifest itself in a deep devaluation and possibly a consequent, precipitous rise in interest rates.

A current account deficit of such magnitude would normally be an indicator of an economy which is seriously overheating, sucking in imports for lack of available domestic capacity. Yet inflation is at virtually zero …

Worstall correctly points out that the reason the U.K. used to have sterling crises all the time was from bad management of the trilemma.

[It’s] because we don’t have either fixed currency rates nor dirty floats. You can manage two of three, just about: currency rates, interest rates and trade balances. You cannot manage three of three. For the third is the tool that must be used to manage the other two.

But if you’re not trying to manage currency rates then you can leave the trade balance alone.

In class, and in this blog, with respect to China I described the trilemma as comprising exchange rates, monetary policy, and capital flows. These are just different aspects of the “currency rates, interest rates and trade balances” listed above.

The U.K. learned the hard way that you can’t control all three. The Chinese are still learning.

And do not forget that new government officials tend to forget lessons like this, and have to relearn them periodically.

Economists Get Real About Measuring Inequality

Inequality is exceptionally hard to measure: you need lots of data, on lots of different people, in lots of different circumstances, and then a bunch of high level tools to figure it out.

Politicians, bureaucrats, and advocates claims about inequality should probably be dismissed out of hand as not credible.

Having said that, it doesn’t take a rocket scientist to recognize that there’s some.

There’s four big easy ideas, right at the top of the list, that should be grasped by every student at this level.

1)

Income and wealth are not the same. Income is a flow, wealth is a stock. Economists know a lot more about income, because it’s in everyone’s paychecks, and the government keeps tabs on most of us. Economists know a lot less about wealth because it’s harder to value (for example, what your car is actually worth isn’t known until someone buys it from you).

But, what you know about your neighbors is mostly about wealth. You don’t see their paychecks. You don’t know all their sources of income. But their wealth is sitting right there in front of you.

So, we worry about what is mostly wealth inequality, and then politicians, bureaucrats and advocates do bait and switch and tell us they can fix that by going after income. And they do.

2)

What we really want to be more equal is consumption not income (both are flows). We could get a lot less income inequality if we went and punished people for working long hours … you know … go watch more TV or we’ll get the IRS after you. But we don’t do that because it’s dumb. We could also get less income inequality by making sure the poor worked more … you know … no TV for you until you put in more hours. Somehow our society seems to think this one is a little better, but I don’t always agree with it.

Think about it, who bugs you more, the rich person who eats in fancy restaurants, or the rich person who eats at home? It’s not their income that bugs us, it’s what they do with it.

This means we need to be concerned about consumption inequality. So, politicians, bureaucrats, and advocates … focus instead on income. Learn to ask them why. The answers aren’t pretty.

3)

A lot of inequality is between young and old. Some of the poorest zip codes in the country are located in … college towns (because of the low income of most students). Do note that I do not want to diminish the areas of real poverty, I just want to point out that the data isn’t what we think it is. And, a lot of the rich live in coastal Florida, south Texas, Arizona, and southern California. Hmmm … the places that attract retirees.

But the old vote. So the response of politicians, bureaucrats, and advocates has been to shelter a lot of their income (which is often lower because the house is paid off and the kids are done with college anyway) from redistribution. One of the biggest sources of consumption for the old is … Medicare.

BTW: our fiscal problems with Medicare are much larger than those with Social Security (even though that gets more press). The reason is that Medicare is open ended during any given month for senior, while their Social Security checks have known and stable values.

4)

We already do a lot to reduce inequality. We can debate about whether that’s enough or not, but we can’t deny that we’re trying.

This means that if we want to assess whether inequality is getting better or worse, we have to do it after taxes have been collected, and redistributed at transfer payments.

That’s sensible. The only reason to not do that would be that you really don’t want to know the answer.

So what do our politicians, bureaucrats, and advocates do? Base almost all of their discussion and policy prescriptions on pre-tax, pre-transfer, income.

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All of that was a preface. There’s a new paper out that does a better job at this. It’s entitled “U.S. Inequality, Fiscal Progressivity, and Work Disincentives: An Intragenerational Accounting ”, and it’s by Alan J. Auerbach Laurence J. Kotlikoff Darryl R. Koehler. It's part of the NBER Working Paper Series: a lot of papers by top economists appear their first before they come out in a formal journal publication.

These guys are not conservatives/Republicans. A more accessible discussion appeared in The New Republic (a liberal/progressive magazine). It’s entitled “We’ve Been Measuring Income Inequality All Wrong” and it’s required.

Let me note that none of this is surprising to economists. It’s just a big job, and holy cow am I glad someone else did the work so that I just have to read it.

Here’s their big findings.

1)

First, spending inequality—what we should really care about—is far smaller than wealth inequality. This is true no matter the age cohort you consider.

… The poorest are able to spend far more than their wealth would imply …

The fact that spending inequality is dramatically smaller than wealth inequality results from our highly progressive fiscal system, as well as the fact that labor income is distributed more equally than wealth.

2)

… U.S. fiscal policy acts as a serious disincentive to work longer hours or harder for more pay.

3)

Our standard means of judging whether a household is rich or poor is based on current income. But this classification can produce huge mistakes.

… Nearly a third of the people we identified as middle income are being mis-classified as either richer or poorer.

And what are their policy recommendations?

Raising taxes and benefits as Democrats advocate will, unless existing tax and benefit systems are properly reformed, come at the cost of even larger work disincentives. Lowering taxes as Republicans advocate—presumably funding this with benefit cuts—will improve work incentives but may exacerbate spending inequality unless the benefit cuts disproportionately hit the rich.

Just to be Fair - Let's Beat Up Trump's Proposals Too

Unlike the other candidates, Trump’s proposals are backed up with very few specifics. In some sense, serious critics have very little tinder to start their fires.

By far the biggest of Trump’s proposals is a series of tax reforms that amount to very large tax cuts.

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A problem of sorts with Republican proposals over the last few decades has been a ridiculous sales pitch — supporters are sold a story that tax cuts will “pay for themselves” when even conservative economists can’t support any conclusion even close to that. All of these proposals (or actions in the case of Bush II’s tax cuts) amount to back door plans to increase government borrowing to offset reduced tax revenue.

Yes, there is something called a Laffer curve which says that cutting rates when they are already low will reduce tax revenue. That makes sense. It also says that if cutting rates when they are already high will increase tax revenue. That also makes sense: it’s because the tax rate is a discouragement, and cutting it creates more taxable economic activity. That’s the theory. The empirical evidence is that almost all tax rates are way below the level where behavior would switch. There are some exceptions, usually involving taxes on one-time purchases or sales of assets. But for most taxes most of the time, you should assume that cutting rates will cut revenue. Unfortunately, Republican politicians have bought wholesale into the part of the argument they like (that cutting tax rates raises tax revenue) and ignored the evidence that this result is just not common at all.

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What Republicans should pay more attention to is something called Ricardian equivalence (this is related to a newer result in finance known as the Modigliani-Miller theorem). The evidence is that how governments finance themselves — either through taxes or borrowing — is Ricardian equivalent. This means it doesn’t matter, and also the corrolary that the size of the deficit doesn’t matter.

When people first hear of Ricardian equivalence the think it sounds crazy. Let me explain why it’s not. The way the government pays for the stuff it provides is either with taxes or with borrowing. What Ricardian equivalence says that when evaluating a program, how it’s paid for doesn’t make any difference to its effectiveness. When you put it that way, it makes a lot of sense.

A useful analogy is buying vegetables or illegal drugs. Let’s presume the first is good for you, and the second one is bad. For the government, tax revenue is their income, and deficits are their borrowing. So the analogous choice for a person is whether to pay for something out of cash you received as income, or to charge it. Would you ever tell someone that vegetables are less good for you if you charge them? Or would you tell someone that illegal drugs are less harmful if you pay for them with cash? Of course not. If that analogy works for you, then it shouldn’t make a difference whether a government program is financed by tax revenues or deficit borrowing.

That’s actually a useful thing if you’re predisposed to liking smaller government. Because it says that all you really ought to think about is not how big or how small the government budget, deficit, debt, or taxes are … but just whether the program the money is being spent for is a good one or not. I think a lot of the public would benefit from thinking that way.

But honestly, I don’t think anyone involved in government likes smaller government. So, while Republicans say they want smaller government, when they’re in power they seem to be pretty good at making it bigger. For example, a Republican President and Republican-dominated Congress passed passed Medicare Part D (providing subsidization for seniors to afford pharmaceuticals). That’s a fairly liberal/progressive program to be added by the people who claim they want smaller government. Oh … and in case you had any doubts … they were paying for wars in Iraq and Afghanistan at the same time.

So Republicans cover their tracks by saying they want to cut taxes. And for a lot of people, reducing the pain of government taxes is hard to differentiate from reducing the size of government.

Except that Ricardian equivalence means that to be in favor of cutting taxes without cutting spending is to also be in favor of more borrowing.

To go back to the analogy, the Democrats are saying “more vegetables for everyone, all the time, and the government will pay for it”. And the Republicans are saying “more vegetables for everyone, but just when we’re in power, and government will pay for it” … “oh … and … um … the vegetables will be better for you if we pay for them with a credit card”. Then the whole Laffer curve bit amounts to justifying the credit card purchases because you get points you can use to buy even more vegetables (when, really, we’re all supposed to know that the points are a way to take away the sting of spending your money, and not a worthwhile financial objective in and of themselves).

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Lastly, I think everyone should always, everywhere, be in favor of simplifying the tax code. Republicans tend to push this more than Democrats. That’s a good thing, but it’s hard to quantify how useful it is.

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This is all important because when the Republicans make concrete proposals, they’re usually about tax rates, and when Republicans are criticized the basis is often the amount of borrowing the government does. Ricardian equivalence should be part of that conversation, and it is at some levels, but not in common public discussion.

So, what can we say about Trump’s proposals?

Here’s a piece from The New York Times entitled "Analysts Question Viability of Deep Tax Cuts Proposed by Republican Candidates" from which I drew this chart:

Most of the article is about the size of the projected cuts to tax revenue: “By most estimates of the outside groups, the costliest plan is Mr. Trump’s.”

Do note that The Times does make the Ricardian equivalence argument, but it doesn’t use that name:

“I believe by cutting taxes and simplifying the tax code, we will grow our economy and create more taxpayers rather than more taxes,” Senator Marco Rubio of Florida has said.

Tax policy groups agree generally, but only if the revenue losses are offset by budget savings that avoid piling up more debt that would be counterproductive to spurring the economy.

“The candidates need to present real specifics for how they would address our record levels of debt,” said Maya MacGuineas, the president of the bipartisan Committee for a Responsible Federal Budget.

“Massive tax cuts and few specifics for what spending to reduce will only make the challenges much worse,” she added. “And miraculous growth projections and ‘waste, fraud and abuse’ are just not credible solutions.”

Now, here’s a much larger analysis of all Trump’s proposals from the Committee for a Responsible Federal Budget, a bipartisan think tank. It’s pretty detailed, but if you parse it out, by far the biggest economic proposal of Trump’s it the tax cuts. So it’s fair to stay relatively focused on that.

Trump has also argued that he’d like to balance the government’s budget. Think about that: 1) the government budget is already in deficit (so it’s partially financed by borrowing), and 2) Trump wants to cut taxes (which will require even more borrowing), so 3) he’s implicitly stating that he wants to cut spending. Now that we’ve gotten to the heart of the matter, the useful question is by how much?

But … wait for it … Trump has also said there’s some categories of spending that he won’t cut. He wants more spending for veterans and immigration, so no cuts there. But he doesn’t want to cut anything for seniors, which takes Social Cecurity and Medicare off the table. And he won’t cut defense either. Here’s the chart:

The result is on the right: Trump is arguing for cutting 60-80% of everything else.

Of course, he can’t say that, because no one would vote for that.

And, I actually doubt that he means it. What politicians do is throw out policy proposals and hope that no one actually does the analysis I’ve just reported here.

Now, obviously, economic growth could make all of this possible. But the estimates are that Trump’s proposals would collectively require sustained real GDP growth rates of about 11% per year. Compare that with Sanders’ estimates assuming sustained real GDP growth rates of 5.3% per year to pay for his proposals.

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Paul Krugman, the Nobel Prize winning economist, who writes a column for The New York Times, is a solid Democrat/Progressive on the leftish side of their spectrum.

He’s written about how he’s conflicted about the Democrats proposals because they’re so disconnected from reality. (He says worse things about the Republicans) but his own Democrats are making him feel conflicted.

Anyway, he has a great metaphor about this. He says that Clinton is proposing to give voters a unicorn, and Sanders is proposing to give them a magic unicorn.

You probably shouldn’t believe either one of them. And you probably shouldn’t prefer the magic unicorn because … you’re never going to get any unicorn at all. And … oh my gosh … if Sanders’ numbers are supported by a math mistake, then it’s more like the Sanders is offering a unicorn that he says is magical because the unicorn already appeared to him and told him to say that.

The Ricardian equivalence argument implies that choosing between the Republicans is more innocuous: along the lines of each candidate is offering a differently colored unicorn. No doubt, Trump’s unicorn would be aggressively colored in bold hues of red and blue stripes, with white stars. And when Trump (or others) starts making outlandish claims about cutting taxes and balancing the budget and not cutting most spending … then their unicorn is far more magical than Sanders.

Towards a Measure of National Wealth

I’ve emphasized in class that we’d get better macroeconomic decision-making if spent more time looking at national wealth and less time looking at GDP.

Do note that this is not because we don’t have data on national wealth. But we do have is not as accurate as GDP, and no where near as commonly used.

Someone in class mentioned that Josh Price had talked about measuring national wealth in class. I talked to Josh, and he was referring to a paper entitled “Sustainability and the Measurement of Wealth” by Arrow, Dasgupta, Goulder, Mumfor, and Oleson.

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First a note about Ken Arrow. He’s huge in economics. He won a Nobel Prize. You usually win these when you are old for work you did in your 30’s. Arrow won his over 40 years ago for work he did in the 1950’s. This was mostly about how to generalize the idea of equilibrium from supply and demand, including the first proof that perfect competition is Pareto optimal (which is the basis for doing so much supply and demand in principles of micro).

Just because Arrow wrote it doesn’t make it great. But it’s a pretty good sign.

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The idea of the paper is that economic growth is sustainable (forever) if national wealth per capita is persistently increasing.

They look at 5 countries. Here’s what they find:

… Our results show that the United States, China, India and Brazil are currently meeting the sustainability criterion, although Brazil meets the requirement by a narrow margin. Venezuela fails to meet this requirement as a result of substantial depletion of natural capital and negative estimated TFP growth. In the United States and India, investments in human capital prove to be very important contributors to increases in per capita wealth; in China, investments in reproducible capital dominate. Accounting for improvements in health dramatically affects the estimates of changes in per capita wealth. We estimate the value of health capital to be more than twice as large as all other forms of capital combined.

Now, this isn’t weird to an econmist, but it might be something you haven’t thought about before. The biggest component of national wealth is the value of citizens themselves. Educate them and they’re worth more. Keep them healthy and they’re worth more. And young peoplle are worth more than old people because so much of their productivity is in the future rather than the past.

For example, I personally might earn the equivalent of $4,000,000 (in constant 2016 dollars) over my lifetime. So, in some sense, if we went back to 1985 we could have said that I was a piece of capital with a net present value to society of $4,000,000 (the value of my leisure might be half of that more added on top). Now compare that to the other capital I have: a house worth about $250,000, a $30K car, a retirement account, and some other stuff that’s just not as significant. In short, if we just count up the physical stuff we see, then we’re missing most of the wealth.

What this means is that the most important things that countries do is keep their populations alive for a long time, and keep them productive for a long time.

And if we want to find the wealthy countries around the globe, we need to look for the ones that are capable of nurtuing citizens through a lifetime of productive work, and putting them in a position where they can work when they are old (but don’t have to if they don’t want to).

Some Growth Isn’t Just Economic

Macroeconomists stress that real GDP is correlated with a lot of desirable non-economic outcomes.

I feel that sometimes non-economists don’t believe us when we say that.

Here’s an example from North and South Korea. After World War II this was the poorest place on Earth (or not far off of that).* Since then South Korea has displayed phenomenal growth, arguably the longest sustained high growth period of any country. On the other hand, North Korea has struggled, and even had periods of widespread famine.

There’s now been a formal study of a tidbit of data that economists have been talking about for a couple of decades: you can tell that the South is richer because its people are 1-3 inchest taller than those from North Korea. This is interesting because the Korean peninsula has a relatively homogenous population, with little historical colonization. So the only way to reasonably explain that difference is with better diet coming from a richer economy.

* Interestingly, South Korea was the poorer of the two. Most of the limited industriatlization before 1950 had taken place in the North.

Watch This Space

It’s Tuesday morning. The interesting data news this morning is that the initial data on (nominal) retail sales shows a drop in February, but also that the revision of (nominal) retail sales for January also shows a large drop.

Real retail sales is one of the “big four” coincident indicators used to evaluate whether or not the economy has peaked.

To convert those nominal retail sales into real ones, we will have to wait for the release of the CPI for February. That’s at 6:30 tomorrow morning.

Lots of people will be watching that because recession indicators are being pulled down strongly by industrial production. This has been offset by good numbers in employment, real income … and real retail sales. If the initial numbers for that last one have fallen enough, then maybe we do need to be thinking that the economy has peaked.

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March 16, 2016 @ 1:15

Here’s the updates.

Real (Retail) Sales fell in January, but grew ever so slightly last month. Industrial production had a good month in January. Put it all together, and it looks less like a peak than it did 2 months ago when we looked at this.

Keep in mind that these data series do continue to get revised. And, the NBER Business Cycle Dating Committee would wait until all those revisions are done to go and look several months back and pick out where the turning point occurred. What they’re doing here is mimicking that process.

Roughly, that is to add up all the current green ones (0.17 + 0.02 + 0.43 = 0.62), and all the consequtive pink ones within a row (just the –0.49 now), to get 0.13. The threshold I was looking for to declare a peak was –2.00 or below.

Extrapolation, Sanders, Proposed Minimum Wage Increases, and the Challenger Disaster

You all know what extrapolation is: you measure some effect in the range of data that you have, and you presume that it will continue to operate in a range of data that you don’t have.

All of us extrapolate, but we also know that we should be careful because it can get us into trouble. For example, if we extrapolated Stephen Curry’s shooting, we might claim in a few years he’ll be hitting mostly 80 footers. He’s good, but not that good.

Anyway, this has come up in a couple of contexts in lecture, so I figured I’d lay them out for you.

One of those is Galbraith’s support of Sanders. It’s basically that Sanders is projecting bigger results because he’s making bigger proposals. That’s an extrapolation because Sanders is making proposals of a size about which we have very little experience. Just because it’s an extrapolation doesn’t make it wrong, but it should signal you to be extra careful.

The same thing has also happened with the large minimum wage increases that have been proposed nationwide. There is some empirical evidence that labor demand is inelastic with respect to the minimum wage (see below). This makes the typical theoretical complaint that minimum wage increases cause job losses into a weak counterargument. The thing is, all the evidence showing this is drawn from modest increases in the minumum wage of something like 10%. It’s an extrapolation to presume that if we increase the minimum wage by 100% — which is the amount under discussion in many places — that we will get the same behavior.

I mention the Challenger Disaster because it’s the best known example of extrapolation gone wrong. Challenger was the space shuttle that blew up after launch in 1986. It blew up because it was launched on a cold day, some rubber o-rings shrank in the cold, and hot combustion gases blew threw the resulting gaps until they burned through other critical parts.

The thing is, rubber parts shrinking in the cold is to be expected, so the engineers had been measuring o-ring damage versus launch temperature for a while. The measurements of all flights before the crash are the points on the right. The temperature for the Challenger launch is the gray bar on the left. Managers extrapolated the large number of launches with no damage at all to assert that it was safe to launch on such a cold day. To do this, they had to dismiss as outliers the four points in the middle showing that on the coldest extent launches they always observed damage, and further that it was negatively associated with temperature.

This chart is part of the discussion of poor decision-making due to bad graphics in Edward Tufte’s (no relation) treatment of the Challenger disaster in his book Visual Explanations, pages 38 – 53.

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About that minimum wage evidence. Check out this chart. Menzie Chen posted this at EconBrowser. It shows all published estimates of the elasticity of labor with respect to wage changes. This is called a funnel graph: points toward the top are more accuate than those below them.

Most of the estimates are negative: increasing wages decreases labor demand. That’s the sensible theory you hear in micro. But note that quite a few of them are positive too: that’s the more newfangled idea that labor markets are a bit goofy, and minimum wage increases might actually be beneficial. The red line seems like a reasonable guess at the true effect: an elasticity of –0.2. Values that close to zero mean that labor demand is about as responsive to wage increases as smokers are to cigarette price increases — not very. If you work out the math it means that a 5% job loss would be associated with a 25% increase in the minimum wage (that the other 95% would get). These results tend to support the Democratic position strongly. Having said that, I’d be pretty leery of any increase larger than the 10% or so that the studies were based on: if that turns out OK, we can repeat it.

Figure 2 from Doucouliagos, Hristos, and Tom D. Stanley. “Publication Selection Bias in Minimum-Wage Research? A Meta-Regression Analysis.” British Journal of Industrial Relations 47.2 (2009): 406-428. [ungated working paper version], with red line drawn in at elasticity = -0.2.

The Sanders Saga Continues

Here’s the story so far:

• Sanders, running as a Democrat, has proposed sweeping changes on the campaign trail. Proposals that are quite far to the left of Obama, and arguably further left than any presidential hopeful since Wallace in the 1940’s.
• A letter supporting Sanders was signed by 170 economists.
• Friedman wrote a paper providing economic support for Sanders’ claims.
• This does not attract much attention since the claims sound too good to be true. Sanders has been making claims like that for a while, and he didn’t attract much attention before Christmas.
• Four former chairs of the Council of Economic Advisors (Krueger, Goolsbee, Romer, and Tyson) denounced all the above.
• Galbraith fired back with a letter noting some flaws in that argument, and emphasizing that Sanders’ proposals get big results from big changes.

Now we have a paper from Christina Romer and David Romer. She’s the Romer listed above. They’re both macroeconomists at Berkeley. David Romer wrote the first level Ph.D. macroeconomics text that just about everyone uses (including me when I taught that class from 2008 to 2011 as a guest at another university).

It’s scathing on the majority of issues, and presents them at an accessible level. More on that later (even though this is what the media will focus on).

But they were very diplomatic about a technical issue. They can’t explain a result that Friedman got. But they’re willing to speculate. And their speculation is that he made a conceptual mistake in his economics that led to math errors.

That’s a big deal: presidential candidate makes economic proposals that sound too good to be true supported by economist that can’t do the economics right.

Even better, the mistake is at an advanced undergraduate level, and related to issues covered in your handbook. They relate to growth vs. level effects, and permanent vs. transitory effects of macroeconomic shocks.

An example from retirement planning may be helpful.

When investing, everyone tries to get an edge: either a better return from the same risk, or the same return from less risk. If you can do that, you can permanently outperform your competitors. Not surprisingly, an investment edge that yields permanent performance improvements is very hard (if not impossible) to find. Instead, most of the time if you get an edge it’s transitory: you beat the market for a bit, but someone mimics what you’re doing until that edge is arbitraged away.

As to growth and level effects, which would you rather have gifted to you: access to a better return on your investments, or a lump-sum gift? Most people would prefer the access to a better return, because with compounding eventually you’ll make more money. That’s a growth effect because it helps your investment grow. A lump-sum gift, like an inheritance, is a level effect: it increases the level of your investment … but just once.

The way that it works almost any time someone gets an investment edge is that they get a transitory growth effect, that makes permanent improvements to the level of their investment. Basically, good luck makes your investment bigger, but you can’t plan on it. The whole point of learning about efficient markets in your finance classes is that permanent growth effects are very hard to come by, and if you could develop one it would require a lot of input on your part each period to sustain.

That whole argument carries over to macroeconomics. We think most shocks have transitory effects on growth rates, so to maintain a growth effect you need to somehow continue providing beneficial shocks to the economy. If you do that, you could get a bigger level effect every period.

So what do Romer and Romer find in Friedman? They can’t explain some of his more outlandish assumptions about growth. Here’s what they suspect: Friedman presumed that a temporary shock to growth rates had a permenent effect on them, leading to estimates of ongoing growth and level effects. In the investment example, this is like assuming that one lucky stock pick in turn makes all your stock picks lucky … forever … and your investment nest egg pulls away rapidly and permanently from your competitors. The implication is that Friedman’s work is no better than a fairy tale.

We have a conjecture about how Friedman may have incorrectly found such large effects. Suppose  one  is considering  a  permanent  increase  in  government spending  of 1%  of GDP,  and suppose one assumes that government spending raises output one-for-one. Then one might be tempted to think that the program would raise output growth each year by a percentage point, and so raise the level of output after a decade by about 10%.

To the public, this sounds like jargon. To a macroeconomist, this sounds like “made a mistake on Tufte’s ECON 3020 Exam 3 that he’ll take off full credit for”.

I remarked above that the surface issues of Romer and Romer are more accessible to the general public. Here’s their summary of what they find (their original had emphasis that does not come through a cut and paste operation):

Unfortunately,  careful  examination  of  Friedman’s  work  confirms  the  old  adage,  “if something seems too good to be true, it probably is.” We identify three fundamental problems in Friedman’s analysis.
•    First, all the effects of Senator Sanders’s policies that he identifies are assumed
to  come  through  their  impact  on  demand.  However,  his  estimates  of  those
demand effects are far too large to be credible—even given Friedman’s
own assumptions.
•    Second, in assuming that demand stimulus can raise output 37% over the next
10  years  relative  to  the  Congressional  Budget  Office’s  baseline  forecast,
Friedman is implicitly assuming that the U.S. economy is (and will continue to
be for a long time) dramatically below its productive capacity. However, while
some  output  gap  likely  still  exists,  the  plausible  range  for  the  output
gap  is  much  too  small  to  accommodate  demand  effects nearly as
large as Friedman finds. As a result, capacity constraints would likely lead
to  inflation  and  the  Federal  Reserve  raising  interest  rates  long  before  such
high growth rates were realized.
•    Third,  a  realistic  examination of  the  impact of  the  Sanders  policies  on
the economy’s productive capacity suggests those effects are likely to
be small at best, and possibly even negative.

I encourage you to, but won’t require you to, read the Romer and Romer paper. It’s fairly accessible, and has lots of clear thinking about the data, different viewpoints, and how economists assess policy.

Testing, Forecasting, Positives, and Negatives

There was some weakness on the exam answers, mostly on the background for thinking about false positives and false negatives. I’m going to start at the beginning and walk you through it.

First, there has to be some thing we’re measuring. In macro, this is most often real GDP. I’ve written in a bunch of other places (like the handbook, and this semester’s quodlibet) why we measure real GDP.

Second, we need to have some reason for forecasting that series. If we know it’s value in the future, there’s no need to forecast other than curiosity. The adjective we use for knowing a series’ future values is deterministic. Parts of real GDP might be deterministic, but the whole thing is not, so we want to forecast it. Alternatively, if no forecast is very good, then we might give up. GDP isn’t too hard to forecast, but it’s hard to forecast well: not because it requires a lot of skill, but rather just because it's fairly unpredictable (that's part of the reason no one believes the numbers coming out of China — they're too predictable to be real).

Third, fitting is a little different than forecasting. With both, we’re trying to predict one thing (that we can’t figure out well) from something else. With fitting, we’re using a variable we have right now to predict values for the thing we can’t figure out either at the same time or in the past. This is what we do with a coincident indicator like industrial production: we get new values of it first and we use them to fit what we think real GDP is going to be before we actually get its measurements. Forecasting is doing something similar, but now we’re trying to use a variable we have now to figure out some other variable we don't have yet. This is what we use leading indicators for.

Fourth, testing is a little different from forecasting. When we test for something, we’re looking for a yes or no answer. This is usually along the lines of asking if this thing is different than these other things. And that difference has to be worthwhile or we wouldn’t bother doing the test. In macroeconomics, we’re interested in whether we continue to be in an expansion (or have switched) or continue to be in a contraction (or have switched).

Note that it’s one thing (forecasting) to say that you have data on a leading indicator, and you expect it to be able to help explain the future behavior of a variable you’re interested in. But, it’s another thing (testing) to say that if your indicator has peaked, does that mean the variable you’re really interested in will peak too.

We do a lot of this very naturally when we care for a sick child at home. The first thing we’re interested in is the well-being of the child. The second thing is that we have some reason to forecast how the child feels, because this can tell us about their well-being. Third, we have coincident and leading indicators for well-being, like how much the child eats and does the child have sniffles. And fourth, sometimes we do a test to assess how the child is doing, by taking their temperature: most people will keep a child home from school if they have a fever even if they say they feel OK.

There’s an interesting notion here that you may not have thought about before: no one knows what temperature you should be when you’re sick! What we know is the temperature you should be when you’re not sick.

When we put the implication of that in words it sounds confusing: we test whether you’re sick by assessing whether you’re not sick. When taking a child’s temperature, it’s either right, or it’s not. When it’s not, we declare that the child is sick.

Statisticians are pretty careful about the language they use for this. Most people aren’t very good at following their lead.

At home, we might say to ourselves that a child seems sick. It sounds technical, but some might say that we’re hypothesizing that the child is sick. So we take their temperature.

A statistician will be careful to state a null hypothesis and an alternative hypothesis. A test differentiates between those two. What’s important about choosing a null hypothesis is not that it be true (we may never know that) or even plausible. Instead, what’s important is that we know how the data will behave if it is true.

In the case of a child, sometimes we may never really know for sure whether they are sick or not. But, whether we know that or not, we know that when they are not sick their temperature will be around 98.6° F.

So the null hypothesis is that the child is not sick, and the alternative is that they are sick. We take their temperature, and if it is close to 98.6° F we don’t reject the null hypothesis that the child is not sick. If their temperature is far enough away from 98.6° F we reject the null that the child is not sick.

I put all those "nots" in italics for a reason. Since we’re testing the null that they’re not sick, it’s called a negative if we can’t reject it. If we can reject then it’s called a positive.

But is the negative or positive correct? We may never know. We don’t usually use the modifiers for true, but we do use them for false, and I’ll use both here to make a point. So, our test might deliver a negative to us, but we have to figure out whether it’s a (true) negative or a false negative: either the child is not sick or they have some illness which doesn’t cause fever . Alternatively, the test might deliver a positive to us, but we have to figure out whether it’s a (true) positive or a false positive: either we reject the null that the child is not sick or their temperature is off for some other reason.

Now I’m ready to add the fifth thing we need: an ability to explain how the data is going to behave if the null hypothesis is true (whether or not we can ever know that for sure). We take kid's temperatures because there's a fair presumption that if the kid is OK their temperature will be normal.

OK, are you with me? Let’s work backwards through what I just explained. If you get a false positive or false negative, it’s only because you did a test. But you’d only do the test if you can explain how the data will behave if some particular thing is true. And the test is only worthwhile if something is different that can be differentiated by it. Ideally this helps you make forecasts about a variable you’re interested in, and maybe that helps you figure out something even more important. One more thing: you're doing tests all the time whether or not you think about them that way (just like parents who listen for sniffles and coughs around the house) ... but your thinking about those tests may be clearer once you realize they're all around you.

What were some of the exam answers that motivated me to do this post then?

Some students just confused getting a positive or negative from a test, with data being positively or negatively correlated. Industrial production is negatively correlated with the unemployment rate, and it does lead it so it might form a good basis for a test. But if industrial production fell, and unemployment rose, I'd call that a positive (result from my test) because it's probably not dumb luck that I found something.

Some students said false positive and false negatives are just errors. They're actually much more than that, because we've chosen to do the test, and we never would have gotten to the error if we hadn't taken that action.

Further, those errors are always in relation to a null hypothesis, so you have to know what that was to figure out what could go wrong. Sending a sick kid to school is unfortunate, but it's not an error unless you took their temperature, found out it was normal, and acted on that.

False positives and negatives are also not just a matter of incorrectly reporting of something that should be obvious; instead they're mistakes we make about interpreting something that's murky to begin with. But they're really not even a misinterpretation: we'd be better off acknowledging that mistakes are going to be made even when we do everything right. Caregivers aren't always sure how sick kids are, and kids fake being sick too.

It's also not enough for the data to, say, go up and then down. You have to be thinking about actually doing something with those movements. You're probably not a good caregiver if you keep taking a child's temperature and then not doing anything with the information when you see they're running a fever.

Finally, while it can be about symptoms you've already observed, the point of doing the test is that you usually haven't observed them yet. In my house I used to drive my wife crazy when my kids were sick: I didn't bug them to much with the thermometer if it was already clear to me they were pretty sick — I'd just be bugging them, and it wouldn't tell me much I didn't know already. This is why hospitals take patients temperatures continuously in many cases, and do almost nothing with that information most of the time. They monitor it as a matter or routine, but it's just one component of what they do.

A lot of this probably seems like common sense. It is; I'm just being specific about the wording and the implications.

Except that when we get to macroeconomics, a lot of people don't practice this common sense.
Historians, politicians, bureaucrats, pundits, and many economists tell us that the stock market is like the thermometer. Fair enough. What evidence do the provide to support that position? Well ... because it seems to have been right once in 1929. It's been right other times (I can confirm that), but if you think about it, it's really weird that you can't actually name any of those other times. You probably don't know anyone who can either.

Then the news media trumpets what our stock market thermometer is doing ... every day, and more often if you'd like to pay attention. Investors may have reason to pay attention to that, but there are also people around who've bought into following the stock market and have been waiting a long time to get their 1929 signal. How on Earth are so many people so certain that stock market behavior caused the 2007-9 recession? Heck, economists aren't even sure it contributed to causing the recession, and we have all the data and computers and know-how and stuff. It's so bad that if we admit we looked and didn't find much, the public tunes us out.

And almost no one admits that business cycles might be inherently difficult to predict, that the data is uncertain, and that it's a big world with a lot of transactions to worry about. It's messy! Every family has stories about how hard it is to figure their kids out, how they tested out different approaches and got contradictory results, and society accepts that. Few people accept that business cycles are hard to figure out, or that our tests might not work very well.

But we plug away at it. Macroeconomic outcomes are important for personal well-being. We can measure them fairly well with real GDP. We can forecast that with some accuracy. But the economy changes from well to sick and back once in a while. It's even pretty easy to forecast real GDP if we assume it will stay expanding, or stay contracting if we're in a recession ... it's the timing of the switches from the one to the other that are tough to figure out. And we're testing all the time, whether it's explicit or implicit, but the tests aren't very good.

In some sense, getting false positives and false negatives is a good sign. It means we care enough to be trying to figure things out. And we can't get false ones at all without running the tests and getting some true results too. Caregivers are doing their job when they take a kid's temperature once in a while, and we cut them some slack about how they interpret the results. We need to take the same approach to macroeconomics.

More China Weirdness

Lack of transparency is making it very hard to figure out what is going on in China’s economy. Here’s two items for today.

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First up is what hedge fund advisors think. I’m not usually one to put much stock in financial advisors’ views of macroeconomic events. But, with China, I have a strong presumption that they know stuff that isn’t being priced efficiently by markets. Here’s Kyle Bass’s investment letter for paying customers via ValueWalk:

… Banking system losses – which could exceed 400% of the US banking losses incurred during the subprime crisis – are starting to accelerate.

Our research suggests that China does not have the financial arsenal to continue on without restructuring many of its banks and undergoing a large devaluation of its currency. It is normal for economies and markets to experience cycles, and a near-term downturn that works to correct the current economic imbalance does not qualitatively change China’s longer-term growth outlook and transition to a service economy. … What we are witnessing is the resetting of the largest macro imbalance the world has ever seen.

His reasoning is that people are too focused on the relatively recent depreciation of China’s currency versus the dollar. What they are missing is that many other large countries depreciated against China first:

… This fixation misses the point that many other manufacturing economies and currencies, including those belonging to Japan, Europe, Russia, and several Southeast Asian countries, have gained significant price advantages at China’s expense.

And he thinks Chinese authorities have done the wrong thing already:

A dramatic devaluation of the renminbi is warranted to regain export competitiveness; however, the Chinese authorities have errantly fought against this so far, spending around $1 trillion to defend their currency.

So, yes, that’s just one investment advisor. But his view is ridiculously pessimistic.

FWIW: No one is quite sure, but there are rumors to the effect that the official Chinese government statement that came out this week suggesting that there would be punishment for media that didn’t support the government’s positions was targeted at outlets that intended to republish Bass’s letter. And there are rumors that a journalist was arrrested Friday for publishing it.

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Next up is Balding’s World. He argues that everyone knows that China is running a trade surplus … except for people like him who are looking for it and can’t find it any more.

This is a big deal because trade surpluses add to GDP. China’s reported trade surplus for 2015 was 79% of China’s GDP growth for that year. So, China is reporting both a 7% GDP growth rate (that no one thinks is that big), and that 5.6% of that came from a trade surplus (that Balding can’t find any more).

China has generally had tight capital controls: it’s tough to get investment money in or out. But no one is rationalizing any more that investors have been fleeing China for a while. So how do they get the money out? In particular, how do Chinese citizens who have money get it out of China (to a place that’s safer for a year or a decade)?

Balding argues that they are overpaying people outside the country for imports brought into the country, with some side deal to come pick up the overpayments at a later date.

How can you tell? By the difference in reported flows of funds for exports and imports.

The export data makes sense: customs is reporting them at $2.27T, while the government’s foreign exchnge office (SAFE) is reporting them at $2.14T, and banks are reporting them at $2.37T for the past year. So, there’s some leeway there, but there all within 10% of the same number. Given that everyone, everywhere, has always smuggled a bit, this is probably to be expected.

But the import data no longer makes sense. Those same three sources are reporting them at $1.68T, $1.57T, and $2.55T. Look carefully at the scale of those differences: banks are sending payments out of China for imports that are 50% more than can be accounted for — and to the tune of nearly a trillion dollars. You read that correctly: the discrepancy is $870,000,000,000 to $980,000,000,000.

Subtract those overpayments from the reported trade surplus and you get … a trade deficit … and a reasonable possibility that China’s economy is already contracting.

Keep in mind that China has already spent about $1T out of its $3-4T pile of foreign exchange to defend the renminbi.* This missing trillion is not counted in that other spent trillion.

* Foreign exchange can be confusing. Defending a currency means a government is trying keep its value up. The way you do this is by using foreign exchange you already have to buy more of your own currency. But, what’s foreign exchange? That’s money the government already owns which is denominated in some other currency (or it might be gold too). And how do you “buy more”? You do this by paying a better price (in terms of foreign currency) than others will for your own currency. In short, Limiting the explanation to China and just one trading partner (for simplicity), this means China spent decades sending goods to America and getting paid with dollars, but now people are trying so hard to get renminbi out of China that its exchange rate is falling, and China is buying peoples’ renminbi (and shredding them) with dollars it’s no longer accumulating, and doing so at a rate that can’t be sustained for long.