Wages and inflation

Marty Feldstein has a very interesting opinion piece on Project Syndicate. His main point is that micro distortions from social programs (and taxes, labor laws, regulations etc.) are leading many people not to work, and is well stated.

An introductory paragraph poses a puzzle to me, however,

Consider this: Average hourly earnings in May were 2.3% higher than in May 2014; but, since the beginning of this year, hourly earnings are up 3.3%, and in May alone rose at a 3.8% rate – a clear sign of full employment. The acceleration began in 2013 as labor markets started to tighten. Average compensation per hour rose just 1.1% from 2012 to 2013, but then increased at a 2.6% rate from 2013 to 2014, and at 3.3% in the first quarter of 2015.

These wage increases will soon show up in higher price inflation. 

This is a common story I hear. However I hear another story too -- the puzzle that the share of capital seems to have increased, and that real wages have not kept up with productivity.

So, maybe we should cheer -- rising real wages means wages finally catch up with productivity, and do not signal inflation. The long-delayed "middle class" (real) wage rise is here.

I'd be curious to hear opinions, better informed than mine, about how to tell the two stories apart.  

Arezki, Ramey, and Sheng on news shocks

I attended the NBER EFG (economic fluctuations and growth) meeting a few weeks ago, and saw a very nice paper by Rabah Arezki, Valerie Ramey, and Liugang Sheng, "News Shocks in Open Economies: Evidence from Giant Oil Discoveries" (There were a lot of nice papers, but this one is more bloggable.)

They look at what happens to economies that discover they have a lot of oil.

An oil discovery is a well identified "news shock."

Standard productivity shocks are a bit nebulous, and alter two things at once: they give greater productivity and hence incentive to work today and also news about more income in the future.

An oil discovery is well publicized. It incentivizes a small investment in oil drilling, but mostly is pure news of an income flow in the future. It does not affect overall labor productivity or other changes to preferences or technology.

Rabah,Valerie, and Liugang then construct a straightforward macro model of such an event.

Utility comes from consumption and work. The production function has an oil sector and non-oil sector. There are adjustment costs to investment and to reallocation of capital between oil and non-oil sectors. The consumption good is tradeable, and the economy sells oil internationally to get it as well as to produce it.

They compute impulse-response functions to big oil discoveries, and compare the model dynamics to the response functions. It's a nice fit and an intuitive story. After the shock hits, during the period of investment, the current account declines -- borrow money, buy oil investment goods and also borrow to finance higher consumption now. GDP is basically flat, as oil investment is a small fraction of the economy. Savings also declines. Consumption goes up right away, and then stays up in permanent income fashion. (You have to look closely at the green line, because the vertical scale is too small.) Investment rises, to build those oil wells.

Employment declines, as there is a wealth effect encouraging leisure but no higher productivity of labor to encourage work. This is why productivity shocks, emphasizing a temporarily higher marginal product of labor, are important in real business cycle models.

Once oil comes on line, the current account changes sign, as the economy exports oil and pays back debt. GDP, including the oil, rises. Consumption stays were it was, by permanent income logic. And investment returns to zero.

Valerie, presenting the paper, was a bit discouraged. This "news shock" doesn't generate a pattern that looks like standard recessions, because GDP and employment go in the opposite direction.

I am much more encouraged. Here are macroeconomies behaving exactly as they should, in response to a shock where for once we really know what the shock is. And in response to a shock with a nice dynamic pattern, which we also really understand.

My comment was something to the effect of "this paper is much more important than you think. You match the dynamic response of economies to this large and very well identified shock with a standard, transparent and intuitive neoclassical model. Here's a list of some of the ingredients you didn't need: Sticky prices, sticky wages, money, monetary policy, (i.e. interest rates that respond via a policy rule to output and inflation or zero bounds that stop them from doing so), home bias, segmented financial markets, credit constraints, liquidity constraints, hand-to-mouth consumers, financial intermediation, liquidity spirals, fire sales, leverage, sudden stops, hot money, collateral constraints, incomplete markets, idiosyncratic risks, strange preferences including habits, nonexpected utility, ambiguity aversion, and so forth, behavioral biases, nonexpected utility, or rare disasters. If those ingredients are really there, they ought to matter for explaining the response to your shocks too. After all, there is only one economic structure, which is hit by many shocks. So your paper calls into question just how many of those ingredients are really there at all."

Thomas Philippon, whose previous paper had a pretty masterful collection of a lot of those ingredients, quickly pointed out my overstatement. One needs not need every ingredient to understand every shock. Constraint variables are inequalities. A positive news shock may not cause credit constraints etc. to bind, while a negative shock may reveal them.

Good point. And really, the proof is in the pudding. If those ingredients are not necessary, then I should produce a model without them that produces events like 2008. But we've been debating the ingredients and shock necessary to explain 1932 for 82 years, so that approach, though correct, might take a while.

In the meantime, we can still cheer successful simple models and well identified shocks on the few occasions that they appear and fit data so nicely. Note to graduate students, this paper is a really nice example to follow for its integration of clear theory and excellent empirical work.

Bachmann, Berg and Sims on inflation as stimulus

Rüdiger Bachmann, Tim Berg, and Eric Sims have an interesting article, "Inflation Expectations and Readiness to Spend: Cross-Sectional Evidence" in the American Economic Journal: Economic Policy.

Many macroeconomists have advocated deliberate, expected inflation to "stimulate" the economy while interest rates are stuck at the lower bound. The idea is that higher expected inflation amounts to a lower real interest rate. This lower rate encourages people to spend today rather than to save, which, the story goes, will raise today's level of output and employment.

As usual in macroeconomics, measuring this effect is hard. There are few zero-bound observations, fewer still with substantial variation in expected inflation.  And as always in macro it's hard to tell causation from correlation, supply from demand, because from despite of any small inflation-output correlation we see.

This paper is an interesting part of the movement that uses microeconomic observations to illuminate such macroeconomic questions, and also a very interesting use of survey data. Bachman, Berg, and Sims look at survey data from the University of Michigan. This survey asks about spending plans and inflation expectations. Thus, looking across people at a given moment in time, Bachman, Berg, and Sims ask whether people who think there is going to be a lot more inflation are also people who are planning to spend a lot more. (Whether more "spending" causes more GDP is separate question.)

The answer is... No. Not at all. There is just no correlation between people's expectations of inflation and their plans to spend money.

In a sense that's not too surprising. The intertemporal substitution relation -- expected consumption growth = elasticity times expected real interest rate -- has been very unreliable in macro and micro data for decades. That hasn't stopped it from being the center of much macroeconomics and the article of faith in policy prescriptions for stimulus. But fresh reminders of its instability are welcome.

At first blush, this just seems great. Finally, micro data are illuminating macro questions.


It's cleaner than the  Hagedorn, Manovskii and Mitman paper I blogged last week, because many of the aggregation issues are absent. There, I complained that employment in one state might be  gained by business moving from another, which would not be an available channel for the whole economy. Here, if we know that people who expect more inflation spend more, it's an easier jump that if we all expect more inflation we all want to spend more. This aggregation problem is usually one of the biggest stumbling blocks for the project to measure macro effects from micro data.

Now, for a little whining. This isn't really criticism as I don't know how to do any better. But it does make for a very well-done example in which to ponder the limitations of the micro evidence on macro questions methodology.

Here are Table 1 and 2, the "baseline specification."

It's a probit regression. The left hand variable is whether a person answered yes or no to the question,

Q1: “About the big things people buy for their homes—such as furniture, a refrigerator, stove, television, and things like that. Generally speaking, do you think now is a good or a bad time for people to buy major household items?” 

The main right hand variable, ("Inflation expectations (1Y)") is the answer to the question,

Q2: “By about what percent do you expect future prices to go (up/down) on the average, during the next 12 months?”

The main fact is that the top row of numbers are all essentially zero, decently well measured, and nonetheless statistically insignificant. Where it is significant, in the zero-bound years, it's negative -- higher inflation expectations are associated with plans to spend less, not more!

So far, so good. But what are all those other numbers in the table? Well, these are "controls," extra right hand variables in the regression.

What in the world are they doing there? The fact is not "people with higher inflation expectations don't plan to spend any less." The fact is that "people with higher inflation expectations, holding constant their expected financial situation and income, their expected change in nominal interest rate and aggregate business conditions, ..., a long vector of aggregate variables, and then the whole Table 2 of demographic variables, don't plan to spend any less." Hmm.

The long list of "controls" brings back memories of all the regression horror stories I was taught in graduate school (thank you Tom Rothenberg).

Left shoe sales = a + b price + c right shoe sales + error. 

Wage = a + b education + c industry + error. 

(In case the latter isn't obvious: including industry helps a lot to "explain" wages and raise R2. But the point of education is to let you change industries from fast food to computers, so you absolutely do not want to "control" for industry!)

What are all the controls doing here? Could we not at least start with OLS, a clean digestible fact, or a graph so that poor bloggers have something to brighten up posts?

I asked the correspondent who sent me the paper (thanks) who opined that the referees probably made the authors do it, and out of a reasonable concern. Maybe the correlation between inflation expectations and spending plans across people does not measure the causal effect, what if we change inflation and leave other things constant?  It could well be that the correlation of expectations across people is zero, reflecting other forces at work, but if we raise everyone's inflation expectations, then we would raise everyone's spending.

Most simply, just because we put inflation expectations on the right hand side of a regression and spending on the left, does not mean that changes in inflation expectations across people cause their spending plans to change.

Demographic controls seem reasonable. Suppose the fact was that women all expected higher inflation and planned to spend a lot, while men expected low inflation and did not plan to spend a lot. One would not want to use that correlation to measure how increasing expected inflation for all of us would affect our spending. Such a demographic correlation is much more likely a result of other causes affecting both variables (inflation expectations and spending). This really remains the deep issue of micro to macro implications: Does a correlation across people tell us what happens if something affects all of us?

But if demographic controls changed the result a lot over OLS, one would be very suspicious. A correlation that survives controls is a lot more persuasive than a correlation that only emerges with controls. It's much nicer to say there is a raw correlation, and verify that it is not the result of differences between demographic groups, than to say the correlation is only measured after demographic controls. Because no set of controls is perfect. (The implicit assumption "my controls perfectly capture all the reverse causation or all third variable influences" pervades regression analysis.)

Many of the controls are macro variables. There are almost as many controls here as time data points. Year dummies would have removed all the time-series variation and left us the pure cross section a lot more simply.

The first set of controls for other expectations strikes me as the most fishy. Why would we measure the effect of a change in expected inflation holding constant expected unemployment? The whole point of the macro experiment is to raise both expected inflation and to lower expected unemployment.

This is the hard nut of all regression analysis: why does the right hand variable vary? People spend a lot of effort on the left hand variable, but that's actually less important. What caused the variation in your data? We don't have randomized experiments. Why is it that households have such widely (insanely!) varying expectations of inflation? Until we know that, it's really going to be hard to tell whether their similarly widely varying spending plans are because of higher inflation expectations, or because inflation and spending plans are both results of some third cause.

The paper isn't much help on this issue. At least I wish they (or much of any regression work) at least asked the question. They don't even really discuss the "controls" in this way; why expected inflation varies, and then control for determinants of expected inflation that are correlated with determinants of spending.

The discussion of the control variables sounds a lot like the habit of assuming everything on the right is a "cause," and fishing for R2, like left shoes in the right shoe equation, and industry in the wage equations.

With respect to the coefficients on the economic control variables, we obtain for the most part plausible and significant estimates,... the expected financial situation of the household and its real income, the expected business conditions (idiosyncratic and aggregate), the current financial situation, and the current real household income all have significantly positive effects on the reported spending readiness. In addition, a positive judgement of US economic policy also affects spending dispositions positively. Moreover, an expected increase in future nominal interest rates makes people want to spend more today,  while higher economic uncertainty in the form of stock market volatility, inflation volatility and higher unemployment rates (both current and expected) decrease the probability that people find buying conditions favorable ...

But enough whining. My point is that micro, regression-based analysis has its limitations too. This seemed like a good example on which to remind graduate student readers of common regression pitfalls: Always ask what caused the variation in the right hand variable. Use minimal controls, not the kitchen sink. Make sure the partial effects of your regression (move x holding z constant) make sense. And so on.

But I don't think I could have done better, as making sense of why people's expectations are as widely dispersed as they are seems a big challenge.

It's still a powerful observation, and I trust it's there in the OLS with minimal controls. People who expect more inflation do not plan to spend more. If you think raising all our expected inflation will make us all spend more, you have some creative explaining to do.

Update: Eric responds:

On your point about all the control variables . . . we did (more or less) what you suggest in the blog post. If you look at Table 3, we drop all of the idiosyncratic control variables in one specification and get essentially the same results; also in Table 3 we do the version with time fixed effects instead of aggregate controls. If you go to the online appendix, in Table 8 we show raw correlations between expected inflation and buying attitudes. We also split the raw correlation by a large number of different demographics. In Figure 7 we show plots of time-varying raw correlations between expected inflation and spending attitudes -- it is the analog of Figure 6 in the main paper which plots a time-varying marginal effect based on the probit estimation. Basically this all shows exactly what you ask for in the blog post -- the correlation/coefficient between expected inflation and buying attitudes does not depend on the controls.

I admit not reading all the way through or the online appendix. They also confirm that the early drafts started with raw correlations. There is an interesting writing (and editing and refereeing) conundrum, should a paper start with the "main" result, or should one start with suggestive robust facts and correlations and then address objections with a more sophisticated model. It's not an easy question -- Most papers drag you through 10 tables of motivation and summary statistics and suggestive correlations before getting to the point, and I really admire that this paper had the main result on Table 1.  OTOH, by going the other way around busy bloggers miss the interesting correlations in online appendix Table 8!