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"EconWeekly" - 5 new articles

  1. The (surprising?) resilience of inflation expectations in the eurozone
  2. Gasoline and Brent
  3. Don't dismiss the long-term unemployed
  4. Africa's unlikely rise*
  5. Latitude, fertility, and economic development
  6. More Recent Articles
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The (surprising?) resilience of inflation expectations in the eurozone

One way for central banks to gauge long-term inflation expectations is to look at the \(n\)-year-forward, \(m\)-year-ahead expected inflation rate. For instance, the two-year-forward, three-year-ahead expected inflation is the expected change of prices two years from now, over the following three years. As of December 2014, that would be the inflation rate expected in Dec. 2016, over the 2017-2019 period.

Central banks like these forward measures because they eliminate--or at least diminish--the influence of short-term inflation inflation expectations.

One data source for the construction of such forward measures are surveys of forecasters. The ECB Survey of Professional Forecasters asks participants for their inflation outlook over the next year, the next two years, and the next five years. Using those overlapping horizons one can infer the forward expected inflation rate from this equation:

$$(1+\pi _{0,5}^{e})^{5}=(1+\pi _{0,2}^{e})^{2} (1+\pi _{2,3}^{e})^{3}, $$ where \({\pi}_{n,m}^{e}\) is the annual rate of inflation expected \(n\) years from now, over the next \(m\) years. The forward inflation rate we're interested in is the second term on the right-hand side.

$$(1+\pi _{0,2}^{e})^{2}=(1+\pi _{0,1}^{e}) (1+\pi _{1,1}^{e}), $$ So $$(1+\pi _{0,5}^{e})^{5}=(1+\pi _{0,1}^{e}) (1+\pi _{1,1}^{e}) (1+\pi _{2,3}^{e})^{3} $$ If you estimate the forward expected inflation rates this way and plot the time series, this is what you get:

I am surprised to see that, despite much talk of deflation and falling inflation expectations, the inflation expectations that matter most (the long-run, forward rate, in green) is firmly glued slightly above 2%. The intermediate forward rate (i.e. one-year-forward, one-year-ahead) has declined moderately over the past year from 2% to 1.7%. The only inflation expectation that has dropped dramatically is the short-term rate (inflation expected over the next year), which is now close to 1%, down from 1.5% a year ago, and 1.9% two years ago.

This undermines the case for quantitative easing in Europe. Granted, the ECB might be looking at other measures of inflation expectations (i.e. inflation compensation from nominal and inflation-linked bonds, inflation swap rates, consumer surveys of inflation expectations, etc.) And the ECB might also be worried, besides inflation expectations, about credit growth, output growth, etc.

P.S. By the way, the formulas above were written thanks to the Mathjax tool for Blogger. With Mathjax added to your blog, you can type formulas as you would with LaTex. And if you are not fluent in LaTex, this website is helpful. You just type in your formulas, with the help of some buttons, and it produces the LaTex code for you.

Gasoline and Brent

Over two years ago Jim Hamilton posted that the relationship between the retail price of U.S. gasoline and the price of Brent, empirically, is given by

$$g_t = 0.839 + 0.02499 b_t + e_t, $$

an equation that comes from an OLS regression of weekly prices of gasoline on prices of Brent oil, from 2000 through June 2012. \(R^2=0.962\) and the standard errors of the constant and the slope coefficients are \(s_\alpha=0.013\) and \(s_\beta=0.00019\). (Gasoline and Brent, by the way, are cointegrated.)

Jim explains why the numbers are what one would expect, given that one barrel of oil holds 42 gallons, and average taxes on gasoline, refining costs, and markups.

Given the recent slump in oil prices, I was curious to see what would be the predicted and actual prices of gasoline for this week.

First, I update the data through the week of November 24* (the most recent for which FRED has data for both gasoline and Brent) and run the regression. The estimated coefficients are now

$$g_t = 0.8429 + 0.0249 b_t + e_t $$

with \(R^2=0.9646\) and standard errors \(s_\alpha=0.0129\) and \(s_\beta=0.00017\).

Here I show you the actual (red) and predicted prices (blue) of gasoline:

The most recent data point for Brent, for the week that ended on December 1, 2014, is $74.44, which produces a predicted gasoline price of $2.69 per gallon, down from the previous week's $2.82. The average price of Brent Tuesday, Wednesday and so far today has been about $70. If the average weekly price were to stay at $70 through next Monday, the predicted price of gasoline this week would be $2.59, marginally lower than last week's.

How about the year ahead? With the caveat that predicting oil prices is in that grey area between the brave and the foolish, the November panel of forecasters polled by Consensus Forecasts produced an average forecast for Brent of $91 for the end of November 2015. That's a whopping $17 increase from last week's price, and would put gasoline prices back above $3/gallon ($3.11, to be more precise).

*Note: the EIA's website provides slightly more up-to-date gasoline prices, but the series is not identical to the one on FRED--the two differ by about $0.085 lately.

Don't dismiss the long-term unemployed

The long-term unemployed matter. That might be the conclusion from a trio of blog posts by New York Fed researchers, over at Liberty Street Economics. (The authors are Rob Dent, Samuel Kapon, Fatih Karahan, Benjamin W. Pugsley, and Ayşegül Sahin.)

In the first part they compare the observable characteristics of four groups of potential workers: the short-term unemployed, the long-term unemployed, nonparticipants who say they want a job, and nonparticipants who say they don't to work. In particular, they compare the distributions of gender, age, race, education, occupation and industry across groups (see the charts below).

The authors conclude that "on the basis of these observable characteristics, we find that long-term unemployed workers are not less attached to the labor market than short-term unemployed workers" (emphasis mine).

The emphasis on observable characteristics is important. There might be other, unobserved characteristics that affect the supply or demand of labor, and so it would be incorrect to imply that labor force attachment, employability, etc. is the same across groups. Motivation and family responsibilities are two examples, off the top of my head, of such unobserved characteristics (unobserved to us, not to job seekers or employers). The authors do acknowledge this: "While there may be unobservable characteristics of long-term unemployed workers that make them less attached to the labor force..."

In the second part the authors find that the short-term unemployed are more likely to find a job than the long-term unemployed, both within one month and within one year. At the one-month horizon, the long-term unemployed and nonparticipants who want a job have almost the same odds of finding a job, but at the one-year horizon long-term unemployed are more likely to find a job than nonparticipants. Finally, they estimate the dropout rate (fraction who leave the labor force) by group, with the expected results.

In the third part, they find little evidence that long-term unemployed exert less pressure on wages than short-term unemployed.

My take from those blog posts is that the long-term unemployed are not like non-participants, and shouldn't be dismissed as just "dropouts by another name." They are probably somewhere between short-term unemployed and nonparticipants, in terms of likelihood of getting a job, and not starkly different from other groups in terms of demographic characteristics.

Africa's unlikely rise*

Africa had it good for ten years. Income per person adjusted for inflation, which declined in the 1980s and 1990s, rose almost 30% between 2000 and 2010. In Ethiopia and Angola real average income doubled—after zero growth in the previous twenty years. Glitzy shops and skyscrapers have replaced drab neighborhoods, and new millionaires have sprouted out of nowhere. A single decade has seen the continent go from basket case to basking in the narrative of “Africa Rising.”

Africa owes its growth spurt to a windfall of high commodity prices and low interest rates. China alone has changed the fortune of the entire continent, buying increasing amounts of natural resources, providing cheap loans, and stimulating foreign direct investment. But the manna is running out.

China won’t grow at 7% per year forever. The recent surges of credit, capital spending, and housing prices are simply unsustainable. Aging will soon catch up to the Red Dragon, after decades of one-child policy. Plain mean reversion, according to a new paper by Lant Pritchett and Larry Summers, will lower China’s and India’s average growth to 3% or 4%. Even a growth rate between 4% and 7%, as most forecasters project, would imply a sharp slowdown for African exports, compared with the roaring 2000s.

Africa has already felt the bite. Oil prices crashed in 2009 and, despite a partial rebound through 2011, they’ve been overall flat since then. A sluggish global recovery means that African oil production dwindled to 9.3 million barrels per day in 2013 from 10.7 million in 2010. The GSCI precious metals index has fallen 20% since 2011. The prices of cash crops such as cotton, coffee, and cocoa are all lower than four years ago. GDP growth, naturally, has suffered: sub-Saharan Africa’s has slowed to 5.1% this year from 6.9% in 2010, according to the International Monetary Fund.

Restarting the commodity boom doesn’t guarantee Africa’s success. In resource-led economies manufacturing tends to shrivel. Mining hogs capital. Foreign purchases of commodities push up the 
exchange rate, which  raises the foreign price of  goods and renders domestic firms uncompetitive. Labor goes under-utilized, since digging and drilling employ few bodies. The gains from mining wind up in the hands of a minority, failing to create a broad base for domestic demand.

Structural change in reverse

The normal engine of development is industrialization. Almost every large country that has become rich has, along the way, industrialized. That’s what happened in 19th-century England, early 20th-century North America, post-war peripheral Europe, and Asia-Pacific. It’s not an economic law that a country must industrialize to become rich, but history shows that other paths are seldom walked.

Industrialization drives development because labor moves from low-productivity jobs (typically in agriculture) to high-productivity ones. Even if output per hour is stagnant within sectors, simply shifting workers to the more productive uses raises total growth.

But Africa, it turns out, is de-industrializing. Manufacturing is now smaller as a share of GDP than in 1974 (Exhibit 1). Fewer than 8% of workers toil in a factory making goods, and the number is falling.In sub-Saharan Africa, between 1990 and 2005, workers seem to have shifted towards lower-productivity jobs. That retrogression lowered growth by 1.3 percentage points per year, according to a 2011 paper by Margaret McMillan and Dani Rodrik. Shifting workers to high productivity jobs, by contrast, lifted Asia’s rate of growth by 0.6 points.

Exhibit 1

Globalization  may be to blame. In Japan, Korea, and China, international trade fostered progress. Modern technologies were adopted; power lines and railroads were laid out; rural workers left the field for the factory.

In sub-Saharan Africa, however, globalization had the opposite effect. Dani Rodrik, a leading development economist, has argued that competition wiped out many African firms, while the rest had to downsize. Nigeria’s garment industry couldn’t compete with Chinese textiles; South Africa’s industry withered during the 1990s, just as Asia’s was rising.

Foreign competition may have even lowered productivity. Rodrik thinks some of the surviving factories went underground. As a result, many African goods are made in illicit sweatshops, which have little access to capital and produce low-tech goods.

Most of the redundant factory hands, however, found work in services, mostly informal. Almost 80% of working people in sub-Saharan Africa have “vulnerable” jobs—meaning, as defined by the International Labor Organization, that they’re self-employed or contribute to the family business. Raising human capital under those conditions is difficult.

The sad upshot is that, even in periods of strong global growth, like 2001–2007, Africa falls further and further behind the world’s productivity frontier. In 1997 Angola’s output per worker was about half of China’s, adjusted for purchasing power parity. Ten years later, despite stellar growth, the ratio was ten percentage points lower. In Mozambique, another fast-rising country, relative  productivity fell to 52% from 72%.

A second chance?

Can Africa rekindle industrialization? Many analysts will tell you that “institutional factors” get in the way. Red tape, legal insecurity, and poor infrastructure discourage investment. Every year African countries fill the bottom rungs of “ease of doing business” rankings. Pervasive corruption doesn’t help.

But that’s not what’s holding back Africa. Industrialization and ease-of-doing-business are, after all, a chicken-or-egg problem: countries don’t get more business friendly before they get richer. Plus, Asian manufacturing thrived amid lax law enforcement and rickety infrastructure.

Rodrik puts his finger on several items that may actually decide the fate of African industry. The first one is the exchange rate. Angolan goods might compete with Malaysian exports— if only the kwanza  was cheap enough. An undervalued currency is a subsidy on tradable-good industries, and offsets the difficulty of doing business. The right mix of fiscal and monetary policy would achieve a competitive exchange rate.

A second thing policymakers can do is unshackle the labor market. McMillan and Rodrik find evidence that countries with flexible labor markets experience productivity-raising development.
On the other hand, some hurdles to progress are beyond the policymakers’ reach. One handicap is the aforementioned curse of natural resources, which suck the life out of manufacturing. Another obstacle is that industrialization is getting harder for everyone.

In a recent article for the Milken Institute Review, Rodrik says that when manufacturing peaked in Britain and Germany it employed nearly a third of the labor force. Korea’s industry never topped a 30% share. India and Latin America have recently peaked at less than 20%. Most strikingly is that in countries that are hardly rich or industrialized, such as Vietnam, manufacturing is already losing ground!

As the global demand for goods declines relative to services, competition among the world’s factories will get stiffer. In those circumstances African producers will have a tough time carving market share away from Chinese or Mexican manufacturers. Besides, labor-saving technology means that factories need fewer and fewer workers.


Would services lead growth instead? Tradable services seem a nonstarter. Most exportable, high-productivity services are delivered by highly educated people, Rodrik notes. Services like programming, engineering, medical diagnosis, and finance call for advanced degrees, on which the continent is short. Africa is unlikely to be the next India, which is leaping from a rural to a services-based economy.

Many non-tradable services in Africa today—retail, personal services, transportation—can be performed by relatively unskilled people. The problem here is demand. For all the talk of soaring income per capita, 70% of sub-Saharan Africa still lives on less than $2 a day. The natural resource boon has blessed a thin minority, typically connected to mining, construction, government, or all of the above. Among the vast majority, few hold a formal, salaried job. Lacking steady, adequate earnings, consumers won’t spend much on services—or on anything else. 

What future, then, awaits Africa? Two scenarios are possible. One, the continent keeps growing, but falls short of an economic revolution. Industrialization doesn’t jell, and Africa’s lot improves through a mix of resource exports and a mild rise of overall productivity, while falling further behind the world’s economic frontier.

In the alternative scenario, Africa does have a growth miracle, by adopting a development model that we’ve never seen before. Somehow,  by using new technologies or providing yet-unimagined goods and services on which Africa holds still-unknown comparative advantages, the continent catapults itself to high-income status. It's a stretch of the imagination for me, but never underestimate human creativity.


McMillan, Margaret and Dani Rodrik (2011), “Globalization,structural change, and productivity growth,” NBER Working Paper #17143.

Pritchett, Lant, and Larry Summers (2014), “Asiaphoria meetregression to the mean,” NBER Working   Paper #20573.

Rodrik, Dani (2014), “Why an African growth miracle isunlikely,” The Milken Institute Review, Fourth Quarter 2014.

*This is an edited version of an article I wrote for the Dec/Jan issue of Morningstar magazine, my employer's publication.

Latitude, fertility, and economic development

Dietrich Vollrath comments on a paper by economists Holger Strulik and Carl-Johan Dalgaard, on the relationship between geographic latitude and economic development. I found it fascinating to follow the entire reasoning chain.

He starts with the striking observation that the relationship between latitude and development (measured here as population density or urbanization rate) has reversed over time. In year 2000, the further a country is from the equator, the richer it is; but in year 1500, it was the opposite: Mediterranean countries were wealthier than their northerly neighbors.

To explain this change, Dalgaard and Strulik use the Bergmann's Rule. In Vollrath's words:
Bergmann’s rule states that average body mass of organisms rises as they get farther from the equator. This holds for people as well as animals. People generally have higher body mass farther from the equator.
Bergmann's rule is due to biological reasons. Having a small surface area-to-mass ratio is good in cold climates, because it means you lose less body heat. So in high latitudes it's good to have large bodies (small body surface relative to weight), while near the equator it's the opposite.

Big bodies also need a lot of calories, and pregnant women need even more calories. Given a supply of food, women in higher latitudes were forced to have fewer babies, so populations were smaller. (All this is prior to the Industrial Revolution.) So in Southern Europe populations were larger, which also meant, "in almost any type of growth model you write down," more innovation. More innovation, voilà, means wealthier people.

Eventually, the argument goes, the northern countries catch up on innovation, to the point where it makes sense to invest more on human capital. They were already having few children, so investing more in education, etc., per kid, comes naturally. Average human capital is higher, they become more adept at using human capital-intensive technology, and they become wealthier than the southern countries (vice versa in the southern hemisphere). The loop between development, fertility, and human capital investment reinforces itself, and the high latitude countries start the demographic transition earlier than their tropical counterparts.

I don't know enough about biology--or development economics--to add much here. My prior is that cultural reasons exert great influence on fertility, creating demographic inertia, and working against the type of "structural change" that inverts the relationship between latitude and development. And how about government policy? France has a significantly higher fertility rate than Spain or Italy.

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