The Unassuming Economist

From an new LSE Business Review article by Ken Fireman:

“It is an ingrained monthly ritual for U.S. economy mavens: staring intently at a computer screen or smartphone on the first Friday of the month as the clock ticks down to 8:30 a.m. Eastern time, the moment when the Labor Department releases its jobs report for the previous month.

The two numbers that draw the headlines are those that report how many jobs the U.S. economy created (or lost) and what percentage of the workforce is unemployed. The results can shape perceptions of the world’s largest economy, influence policymakers’ choices and drive investment decisions, SAGE Business Researcher freelance correspondent Victoria Finkle writes. The report is considered so market-sensitive that journalists covering it are literally locked in a room and cut off from communicating with the outside world until the instant of the release time.

“What’s codified into a couple of small numbers is a lot of knowledge about where the economy is and where the economy is going,” Michael Farren, a research fellow at George Mason University’s Mercatus Center, told Business Researcher.

But those top-line numbers are just the tip of a mountain of valuable statistical material contained in the 40-page report, Finkle writes in her account of the monthly exercise. And the data that lurk in the recesses of the report can lend texture and detail to our understanding of the economy and provide deep insights into how policymakers at the Federal Reserve and other institutions will move in the future.

Take, for example, the unemployment rate. The headline number, known as the U-3 rate from its Labor Department code, measures the percentage of the total labour force that is jobless and actively seeking work. It’s closely watched because it is tied to both halves of the Fed’s so-called dual mandate of maximising employment while maintaining price stability, Finkle writes.

But U-3 is just one way to view unemployment. It captures only those who are jobless but still looking for work; people who are so discouraged by their job prospects that they’ve simply given up searching are not counted among the unemployed in this measure. So the quants in the department’s Bureau of Labor Statistics calculate two more rates. One, known as U-5, captures those jobless and looking, plus “marginally attached” workers – those who have looked for work sometime in the past year, but not recently. U-5 is a favourite of Treasury Secretary Steven Mnuchin, who has said U-3 doesn’t provide a complete picture of the labour market. “Currently, excessive influence appears to be placed by U.S. policymakers on one metric,” he said last year, referring to the headline unemployment rate.

A second alternative measure, known as U-6, encompasses everyone unemployed and looking, plus the marginally attached – and those who are working, but in a part-time job when they would rather work full-time. This metric, sometimes known as the underemployment rate, also has its fans; it was often cited by former Fed Chair Janet Yellen during her tenure at the helm of the central bank.

Finally, the report contains data on the percentage of those unemployed who have been out of work for 27 weeks or more. Economists watch this measure, known as the long-term jobless rate, because the longer people remain out of work, the harder it becomes for them to become re-employed, Finkle writes.

No matter which unemployment rate one prefers, a constant factor in the equation is the size of the labour force, the sum of all employed and unemployed workers. And economists pay close attention to another measure in the monthly jobs report that bears directly upon the size of the labour force: the rate of participation. It has been declining in recent years – more than 3 percentage points in the past decade – as the large baby boomer generation edges into retirement, a “skills gap” widens between available workers and available jobs and opioid addiction and higher prison rates take their toll.

The monthly report also tracks a number that has been an enduring puzzle: workers’ earnings. As the economy recovered from the 2007-09 recession and the various unemployment rates fell, standard economic theory suggested that wages should rise more or less in tandem, as a stronger economy generated more demand for workers. The fact that this hasn’t happened has left many economists scratching their heads.

Each measure in the jobs report offers one piece of a puzzle that economists, money managers and other decision-makers use to better understand where the economy is headed, Finkle writes.

“If you see that employment grows by 200,000 or wages are growing by 2.5 percent year-over-year, what does that mean?” the Mercatus Center’s Farren told Business Researcher. “When you aggregate [survey data] across all businesses, that’s telling you something about the state of the economy in general. There’s a lot that’s influenced downstream by these relatively limited numbers.”

From a new IMF working paper by Luc Laeven and Fabian Valencia:

“This paper updates the database on systemic banking crises presented in Laeven and Valencia (2008, 2013). Drawing on 151 systemic banking crises episodes around the globe during 1970-2017, the database includes information on crisis dates, policy responses to resolve banking crises, and the fiscal and output costs of crises. We provide new evidence that crises in high-income countries tend to last longer and be associated with higher output losses, lower fiscal costs, and more extensive use of bank guarantees and expansionary macro policies than crises in low- and middle-income countries. We complement the banking crises dates with sovereign debt and currency crises dates to find that sovereign debt and currency crises tend to coincide or follow banking crises.”

 

From a new working paper by Susan N. Houseman:

“Two stylized facts underlie the prevailing view that automation largely caused the relative decline and, in the 2000s, the large absolute decline in U.S. manufacturing employment: first, manufacturing real output growth has largely kept pace with that of the aggregate economy for decades, and second, manufacturing labor productivity growth has been considerably higher. These statistics appear to provide a compelling case that domestic manufacturing is strong, and that, as in agriculture, productivity growth, assumed to reflect automation, is largely responsible for the relative and absolute decline in manufacturing employment. Although the size and scope of the decline in employment manufacturing industries in the 2000s was unprecedented, many
see it as part of a long-term trend and deem the role of trade small.”

“That view, I have argued, reflects a misinterpretation of the numbers. First, aggregate manufacturing output and productivity statistics are dominated by the computer industry and mask considerable weakness in most manufacturing industries, where real output growth has been much slower than average private sector growth since the 1980s and has been anemic or declining since 2000. Second, labor productivity growth is not synonymous with, and is often a poor indicator of, automation. Measures of labor productivity growth may capture many forces besides automation—including improvements in product quality, outsourcing and offshoring, and a changing industry composition owing to international competition. Indeed, the rapid productivity growth in the computer and electronics products industry, and by extension in the manufacturing sector, largely reflects improvements in product quality, not automation. In short, the stylized facts, when properly interpreted, do not provide prima facie evidence that automation drove the relative and absolute decline in manufacturing employment.”

“It is difficult to parse out the effects of various factors on manufacturing employment, and research does not provide simple decompositions of the total contribution that trade and the broader forces of globalization make to manufacturing’s recent employment decline. Nevertheless, the research evidence points to trade and globalization as the major factor behind the large and swift decline of manufacturing employment in the 2000s. Although manufacturing processes continue to be automated, there is no evidence that the pace of automation in the sector accelerated in the 2000s; if anything, research comes to the opposite conclusion.”

“Manufacturing still matters, and its decline has serious economic consequences. Reflecting the sector’s deep supply chains, manufacturing’s plight contributed to the weak employment growth and poor labor market outcomes prevailing during much of the 2000s. Research shows that such large-scale shocks have persistent adverse effects on affected communities and their residents, though these costs rarely are fully considered in policy making (Klein, Schuh, and Triest 2003). In addition, because manufacturing accounts for a disproportionate share of R&D, the health of manufacturing industries has important implications for innovation in the economy. The widespread denial of domestic manufacturing’s weakness and globalization’s role in its employment collapse has inhibited much-needed, informed debate over trade policies.”

A new IMF working paper by Davide Furceri and Aleksandra Zdzienicka provides “new evidence on the existence and magnitude of the “twin deficits” in developing economies. It finds that a one percent of GDP unanticipated increase in the government budget balance improves, on average, the current account balance by 0.8 percentage point of GDP. This effect is substantially larger than that obtained using standard measures of fiscal impulse, such as the cyclically-adjusted budget balance. The results point to heterogeneity across countries and over time. The effect tends to be larger: (i) during recessions; in countries (ii) that are more open to trade; (iii) that have less flexible exchange rate regimes; and (iv) with lower initial public debt-to-GDP ratios.”

From a new IMF working paper by Davide Furceri and Aleksandra Zdzienicka:

“We provide new evidence of the existence of the “twin deficits” in developing economies. Using unanticipated government spending shocks for an unbalanced panel of 114 developing economies from 1990 to 2015, we find that a one percent of GDP unanticipated improvement in the government budget balance improves, on average, the current account balance by 0.8 percentage point of GDP. This effect is substantially larger than usually found in the literature using standard measures of fiscal policy changes such as the CAB. This finding has important policy implications as for a given target of external adjustment less fiscal consolidation is required than normally assumed.

Beyond this average effect lies some heterogeneity, both across states of the business cycle and across countries. The effect tends to be larger: (i) during recessions; (ii) in countries that are more open to trade; (iii) that have less flexible exchange rate regimes; and (iv) with lower initial public debt-to-GDP ratios. This heterogeneity has far-reaching implications for policymakers in deciding the magnitude of the fiscal adjustment needed to address external imbalances.”