Category: Equitablog

Should-Read: Dani Rodrik: Trump’s Trade Gimmickry: “The imbalances and inequities generated by the global economy cannot be tackled by protecting a few politically well-connected industries, using manifestly ridiculous national security considerations as an excuse…

…Trump’s trade measures to date amount to small potatoes. In particular, they pale in comparison to the scale and scope of the protectionist policies of President Ronald Reagan’s administration in the 1980s. Reagan raised tariffs and tightened restrictions on a wide range of industries, including textiles, automobiles, motorcycles, steel, lumber, sugar, and electronics. He famously pressured Japan to accept “voluntary” restraints on car exports. He imposed 100% tariffs on selected Japanese electronics products when Japan allegedly failed to keep exported microchip prices high….

Trump’s protectionism may well have very different consequences; history need not repeat itself. For one thing, even though their overall impact remains limited, Trump’s trade restrictions have more of a unilateral, in-your-face quality…. The voluntary export restraints (VERs) of the 1980s in autos and steel, for example, were administered by the exporting countries. This allowed Japanese and European companies to collude in raising their export prices for the US market…. Another contrast with the Reagan-era measures is that we are living in a more advanced stage of globalization, and the problems that have accompanied it are greater….

A serious reform agenda would instead rein in the protection of drug companies and skilled professionals such as physicians… address concerns about social dumping and policy autonomy… target areas where the gains from trade are still very large, such as international worker mobility…. But it is in the domestic arena that the bulk of the work needs to be done. Repairing the domestic social contract requires a range of social, taxation, and innovation policies to lay the groundwork for a twenty-first-century version of the New Deal…

Should-Read: Noah Smith: California Affordable Housing Is No Mystery: Just Build More: “Urban California should emulate Tokyo, which ensured the supply of dwellings stayed ahead of population growth.
By Noah Smith…

…After years of dithering and hoping the problem would go away, California is finally taking steps to address its housing crisis…. Californians have only to gaze across the Pacific, to the city of Tokyo…. As of 2015, the average residential rent in Tokyo was about $2.53 a square foot at current exchange rates. That’s about half the level for San Francisco…. Why is Tokyo housing so affordable? It’s not because Japan’s population is shrinking. More people crowd into the capital city every year…. Tokyo rent is cheaper because it builds lots of housing… building more and building up….

The national government revised regulations to allow more density. Combined with Japan’s famously simple zoning regulations, this resulted in a nation full of dense yet pleasant cities that offer decent, affordable living space. A key part of the equation, of course, is Japan’s efficient, convenient networks of public transportation….

California… is on the right track. The idea of building dense housing around transit hubs—a very Japan-like development pattern—is an especially good one. Simplified zoning codes, curbs on repeated administrative challenges to housing projects and less severe height restrictions would also be great ideas. Government-subsidized housing—which Japan also provides for its low-income citizens—is an important part of the mix…

Should-Read: So is it now time to shift to the prime-age employment rate as our principal thumbnail shorthand gauge for the state of the labor market?: Nick Bunker: Just how tight is the U.S. labor market?: “Spoiler: There’s room for the job market to improve…

…If the current unemployment rate is indicative of a very tight labor market, then why does wage growth continue to be so tepid? If the supply of potentially employable workers is tapped out, then the price of labor—wages—should grow at an increasingly faster pace. Yet as the unemployment rate declined and hit levels many associate with “full employment,” wage growth has yet to break out of the range of 2 percent to 2.5 percent per year. One simple explanation of this anomaly of a tight labor market with weak wage growth is that the labor market is not actually that tight. Indeed, the unemployment rate currently does not do a good job of predicting wage growth. What the data show is that a given unemployment rate can be associated with a wide range of wage-growth levels….

The unemployment rate is still a useful measure of the health of the labor market. But it should be taken in the context of other measures. Even if two labor markets have the same unemployment rate, one will be tighter than the other if their employment rates vary significantly. When assessing the health of the labor market, policymakers have to look at both unemployment and employment. If the U.S. labor market still has room to run, then policymakers should look favorably at monetary and fiscal policies that would increase aggregate demand. This information is particularly important for policymakers at the Federal Reserve as they consider the pace at which they raise interest rates…

Should-Read: Michael Kremer (1993): The O-Ring Theory of Economic Development: “This paper proposes a production function describing processes subject to mistakes in any of several tasks…

…It shows that high-skill workers-those who make few mistakes-will be matched together in equilibrium, and that wages and output will rise steeply in skill. The model is consistent with large income differences between countries, the predominance of small firms in poor countries, and the positive correlation between the wages of workers in different occupations within enterprises. Imperfect observability of skill leads to imperfect matching and thus to spillovers, strategic complementarity, and multiple equilibria in education….

Quantity cannot be substituted for quality… workers of similar skill will be matched together… schedule of wages as a function of worker skill. Under this production function, small differences in worker skill lead to large differences in wages and output, so wage and productivity differentials between countries with different skill levels are enormous…. Firms will offer jobs to only some workers rather than paying all workers their estimated marginal product. If tasks are performed sequentially, high-skill workers will be allocated to later stages of production…. If firms can choose among technologies with different numbers of tasks, the highest skill workers will use the highest n technology…. These predictions of the model match stylized facts about the world, and although each of these facts may be due to a variety of causes, together they suggest that O-ring production functions are empirically relevant.

Imperfect matching of workers due to imperfect information about worker skill leads to positive spillovers and strategic complementarity in investment in human capital. Thus, subsidies to investment in human capital may be Pareto optimal. Small differences between countries in such subsidies or in exogenous factors such as geography or the quality of the educational system lead to multiplier effects that create large differences in worker skill. If strategic complementarity is sufficiently strong, microeconomically identical nations or groups within nations could settle into equilibria with different levels of human capital.

Michael Kremer (1993): The O-Ring Theory of Economic Development: Quarterly Journal of Economics 100:3 (August), pp. 551-575

https://www.icloud.com/keynote/065s1qQKHgBP90nMshrWmh3sg

Should-Read: Lots to think about about how statistics and economics should be being taught these days: Drew Conway (2013): The Data Science Venn Diagram: “The primary colors of data: hacking skills, math and stats knowledge, and substantive expertise…

…On Monday we spent a lot of time talking about “where” a course on data science might exist at a university. The conversation was largely rhetorical, as everyone was well aware of the inherent interdisciplinary nature of the these skills; but then, why have I highlighted these three? First, none is discipline specific, but more importantly, each of these skills are on their own very valuable, but when combined with only one other are at best simply not data science, or at worst downright dangerous.

For better or worse, data is a commodity traded electronically; therefore, in order to be in this market you need to speak hacker…. Being able to manipulate text files at the command-line, understanding vectorized operations, thinking algorithmically; these are the hacking skills that make for a successful data hacker. Once you have acquired and cleaned the data, the next step is to actually extract insight from it. In order to do this, you need to apply appropriate math and statistics methods, which requires at least a baseline familiarity with these tools. This is not to say that a PhD in statistics in required to be a competent data scientist, but it does require knowing what an ordinary least squares regression is and how to interpret it.

In the third critical piece—substance—is where my thoughts on data science diverge from most of what has already been written on the topic. To me, data plus math and statistics only gets you machine learning…. [But] science is about discovery and building knowledge, which requires some motivating questions about the world and hypotheses that can be brought to data and tested with statistical methods….

Finally, a word on the hacking skills plus substantive expertise danger zone. This is where I place people who, “know enough to be dangerous,” and is the most problematic area of the diagram. In this area people who are perfectly capable of extracting and structuring data, likely related to a field they know quite a bit about, and probably even know enough R to run a linear regression and report the coefficients; but they lack any understanding of what those coefficients mean. It is from this part of the diagram that the phrase “lies, damned lies, and statistics” emanates, because either through ignorance or malice this overlap of skills gives people the ability to create what appears to be a legitimate analysis without any understanding of how they got there or what they have created. Fortunately, it requires near willful ignorance to acquire hacking skills and substantive expertise without also learning some math and statistics along the way. As such, the danger zone is sparsely populated, however, it does not take many to produce a lot of damage.

Should-Read: *Anatole Kaletsky *: The Market Dogs That Didn’t Bark: “The bond market’s complacency about US interest rates and inflation may be surprising… [may] turn out to be an expensive mistake… but it is a fact…

…The 30-year US bond yield is still only 3.2% – exactly where it was a year ago and in most of 2015 and 2016. It is almost a full percentage point lower than in 2013 and two full points below the level in 2007…. The bond market believes that the long-term outlook for growth and inflation is more or less the same as it was in the period from 2015 until early last year – and much weaker than it was a decade ago…. For the moment, however, the behavior of long-term US interest rates implies an almost unshakeable confidence among investors that inflation will never again become a serious threat, despite President Donald Trump’s decision to slash taxes, boost government spending, and abandon deficit limits in a US economy already nearing full employment.

This points our investigation toward the third dog that didn’t bark. Currencies were almost completely unmoved by the stock-market commotion. This quiescence makes sense: If investors are unperturbed by inflationary pressures in the US economy, they can surely be much more confident about the rest of the world. In Europe, Japan, and many emerging markets, cyclical upswings are more recent, inflation is lower, and economic management is sounder than in the US. The implication is obvious: The global expansion and bull market will continue, but leadership will move from America to the more promising economies of Europe, Japan, and the emerging world.

Should-Read: Evan A. Feigenbaum: A Chinese Puzzle: Why Economic “Reform” in Xi’s China Has More Meanings than Market Liberalization: “What is going on that produces such a gaping disconnect between Beijing’s story about reform and the views of so many in the markets?…

…Or to put that point as bluntly as possible: are Chinese politicians and bureaucrats, as some international observers would have it, uniquely dissembling? I’d like to argue that a major part of this disconnect stems from a yawning gap in the definition of what actually constitutes “reform” in the Chinese political context of today. To put it as directly as I can, “reform” simply does not have the same meaning in China today that it does to many of us, and especially to a lot of market observers. Unsurprisingly, we tend to focus on market liberalization, to the exclusion of most else. But economic “reform” in Xi Jinping’s China has at least two additional meanings—and these can actually contradict and undermine market goals.

To Beijing, “reform” means:

• market liberalization;
• administrative measures to increase bureaucratic and operational efficiencies; and
• a rebalancing of authorities and decision powers among central and local levels of government.

Viewed in those broadened terms, there is actually quite a lot of “reform” happening in China today. But so much of that reform simply does not implicate the market. And even more important, these three distinct Xi-era “reform” goals can flatly contradict each other. That, in turn, leaves Beijing often trading off one kind of reform in pursuit of another. And more often than not, it is market liberalization that slips to a (distant) third priority between administrative reform and changes to Chinese-style “federalism.”… Xi Jinping has put a very clear premium on political aims, not economic goals…. Whatever [economic] rebalancing has taken place has mostly happened organically rather than by policy intervention or design. And this means that the Chinese president’s top three priorities—a cleaner CCP, a more disciplined CCP, and a stronger and more enduring CCP—have yielded a deeper connection between political goals and economic policy outcomes than China has witnessed in a generation. Inevitably, this leads to an overemphasis on the administrative aspects of reform….

Here are the three big things I take away from the fact that debates about “reform” in China are now much broader than the one we are having outside its borders: One takeaway is that reform is, quite simply, about Beijing’s priorities, not ours…. Second, Beijing is not nearly as attuned to foreign firms as it used to be. That means the reforms American and European multinationals want are just not going to happen without a lot of backstopping from their home governments…. Third, reform in China will, for at least the next five years, be viewed almost exclusively through a domestic lens.

When Xi Jinping and his colleagues toss and turn at night, I suspect their major policy nightmares and preoccupations are entirely homegrown: (1) how to stay in power and overcome dissent; (2) how to create some 12 million new jobs each year; (3) how to maintain sufficient growth to support those employment goals; (4) how to manage the demographic challenges of an aging country through welfare and “entitlement” reforms; and (5) how to mitigate pollution and environmental challenges. These five priority agendas implicate reform in all three of the senses I outlined above. But they do not all implicate market liberalization equally, and some of them not very much at all…. Xi Jinping is going be leading the country for a very long time to come. Like it or not, this is what I suspect “reform” is going to mean in China for a long time to come as well.

Should-Read: Paul Krugman: “This might be a good time to talk about the arithmetic of trade and manufacturing… why even a full-on trade war can’t restore the manufacturing-centered economy Trump wants back…

…Once upon a time manufacturing really was a third of employment; these days it’s well under 10 percent. But how much does trade have to do with this decline? The US used to run roughly balanced trade; now it runs a deficit of about 3 percent of GDP. If that deficit were closed, most of the shift would be in manufacturing. But a dollar of trade shift in manufacturing would add much less, maybe 60 cents, to manufacturing value-added, because manufacturing uses a lot of service inputs. So were talking about maybe 1.8% of GDP added to manufacturing—or about 15% more than now. First-pass estimate would be a comparable increase in employment. So we’d be talking about raising manufacturing from 8.5% of employment to maybe 9.8%. We’d still be overwhelmingly a service economy, nothing like we once were. Not saying that trade had no role; over shorter periods, like the surge in deficits under Bush, it was a big factor in absolute changes in manufacturing employment. But over the long run manufacturing decline reflects forces much bigger than trade. Which is why the European Union, which seems to be Trump’s current focus of enmity, has also seen a steady decline in manufacturing as a share of total employment…