Category Archives: Jobs creation

5/5/20: A V-Shaped Recovery? Ireland post-Covid


My article for The Currency on the post-Covid19 recovery and labour markets lessons from the pst recessions: https://www.thecurrency.news/articles/16215/the-fiction-of-a-v-shaped-recovery-hides-the-weaknesses-in-irelands-labour-market.


Key takeaways:
"Trends in employment recovery post-major recessions are worrying and point to long-term damage to the life-cycle income of those currently entering the workforce, those experiencing cyclical (as opposed to pandemic-related) unemployment risks, as well as those who are entering the peak of their earnings growth. This means a range of three generations of younger workers are being adversely and permanently impacted.

"All of the millennials, the older sub-cohorts of the GenZ, and the lower-to-middle classes of the GenX are all in trouble. Older millennials and the entire GenX are also likely to face permanently lower pensions savings, especially since both cohorts have now been hit with two systemic crises, the 2008-2014 Great Recession and the 2020 Covid-19 pandemic.

"These generations are the core of modern Ireland’s population pyramid, and their fates represent the likely direction of our society’s and economy’s evolution in decades to come."


26/7/19: Stop Equating Low Unemployment Rate to High Employment Rate


There is always a lot of excitement around the unemployment stats these days. Why, with near-historical lows, and the talk about 'full employment', there is much to be celebrated and traded on in the non-farm payrolls stats and Labor Department press releases. But the problem with all the hoopla around these numbers is that it too often mixes together things that should not be mixed together. Like, say, mangos and frogs, or apples and moths.

Take a look at the following data:

Yes, unemployment is low. Civilian unemployment rate is currently at seasonally-adjusted 3.7% (June 2019), and Unemployment rate for: 20 years and over, at 3.3%, seasonally adjusted. On 3mo average basis, last time we have seen comparable levels of Civilian unemployment was in 1969, and 20+ Unemployment rate was in 2000. Kinda cool, but also revealing: historical lows in unemployment require  Civilian unemployment metric to confirm. Which means that factoring in Government employment, things are bit less impressive today. But let us not split hairs.

Here is the problem, however: record lows in unemployment are not the same as record levels in employment. Low unemployment, in fact, does not mean high employment.

To see this, look at the solid red line, plotting Employment rate for 20 years and older population. The measure currently sits at 71.2 percent and the last three months average is at 71.1 percent.  Neither is historically impressive. In fact, both are below all months (ex-recessions) for 1990-2008. Actually, not shown in the graph, you would have to go back to 1987 to see the same levels of employment rate as today. Oops...

But why is unemployment being low does not equate to employment being high? Well, because of a range of factors, the dominant one being labor force participation. It turns out (as the chart above also shows), we are near historical (for the modern economy's period) lows in terms of people willing to work or search for jobs. Or put differently, we are at historical highs in terms of people being disillusioned with the prospect of searching for a job. Darn! The 'best unemployment stats, ever' and the worst 'willingness to look for a job, ever'.

U.S. Labor Force Participation rate is at 62.9 percent (62.8 percent for the last three months average). And it has been steadily falling from the peak in 1Q 2000 (at 67.3 percent).

When we estimate the relationship between the Employment rate and the two potential factors: the Unemployment rate and the Participation rate, historically (since 1970s) and within the modern economy period (since 1990) as well as in more current times (since 2000), and since the end of the Great Recession (since 2010) several things stand out:

  1. Unemployment rate is weakly negatively correlated with Employment rate, or put differently, decreases in unemployment rate are associated with small increases in employment; across all periods;
  2. Labor force participation rate is strongly positively correlated with Employment rate. In other words, small increases in labor force participation rate are associated with larger increases in employment; across all periods;
  3. Labor force participation rate, in magnitude of its effect on Employment rate, is roughly 14-15 times larger, than the effect of Unemployment rate on Employment rate; across all periods; and
  4. The relatively more important impact of Labor force participation rate on Employment, compared to the impact of Unemployment rate on Employment has actually increased (albeit not statistically significantly) in the last 9 years.
These points combined mean that one should really start paying more attention to actual jobs additions and employment rate, as well as participation rate, than to the unemployment rate; and this suggestion is more salient for today's economy than it ever was in any other period on record.

But above all, please, stop arguing that low unemployment rate means high employment. Bats are not cactuses, mangos are not moths and CNN & Fox kommentariate are not really analysts.

13/7/19: Mapping the declines in jobs creation


Increasing market power concentration, falling entrepreneurship, rising concentration amongst the start ups, unicorns and billions in investment, the markets have been rewarding larger companies at the expense of the smaller and medium enterprises for years. And this has had a problematic impact on human capital and jobs creation.

Here is the data on the levels of employment in medium-large companies over the years, based on the U.S. markets data:


In simple terms, per each dollar of investors' money, today's companies are creating fewer jobs - a trend that was present since at least 2000, and consistent with the onset of the Goldilocks Economy. But the most pronounced collapse in jobs creation from investment has been since 2017. Excluding recessionary periods, in 2002-2006 average annual decline in the number of employees per $1 billion in market valuation was 3.45%. Over 2009-2013 this number rose to 4.73% and in 2014-2019 the rate of decrease averaged 8.05% per annum.

18/6/19: Obama v Trump: Jobs Creation


Who had the more impressive numbers in terms of jobs creation: President Obama or President Trump? This question is non-trivial. For a number of reason.

Take first the superficially-simple comparative:

  • On a y/y basis, average monthly change in total non-farm payrolls under the last 28 months of President Obama Administration was 2,704,000 using non-seasonally-adjusted data. For the first 28 months of the Trump Administration, the same figure was 2,394,000. So by this metric, things were better under Obama Administration last 28 months in office.
  • The caveat to the above is that as jobs numbers grow, each consecutive period, new additions of jobs should be harder and harder to come up with, especially during the mature period of the expansion cycle. In other words, after some number of quarters of economic recovery, creating more new jobs gets harder, primarily because the pool of potential employees to be hired into jobs shrinks. So, adjusting Obama figures and Trump figures for this, we can use rate of change in 28 months averages. This is not easy to do, because we do not have consecutive 28 months periods of first rising, then falling jobs additions averages for any period, except for the 1990s. Back then, jobs creation first run at 483,000 monthly average in 1991-1993, 3,124,000 in 1993-1995, 2,889,000 in 1996-1998 and 3,080,000 in 1998-2000. So within upside cycle, the net decline in jobs creation was between 1.74% and 7.2%. Applying these to Obama Administration’s peak jobs creation rate over any 28 months period gives us the rate of Obama Administration cycle-adjusted jobs creation of between 2,509,150 and 2,656,775 - both of these figures are higher than the raw numbers for the Trump Administration’s first 28 months in office. 
  • In monthly average jobs creation measured on m/m basis, Obama Administration’s last 28 months in offer yielded 128,000 monthly jobs additions on average. The Trump Administration’s comparable figure is 294,000, vastly outpacing Obama Administration’s record. This means that, in total,  during the Obama Administration last 28 months in office, the U.S. economy has created net 2,527,000. In Trump’s Administration 28 months in office, the economy generated 7,206,000 jobs. 
  • The above figures, however, is heavily weighted against the last 28 Obama Administration period due to the final two months of the period coinciding with heavily seasonality-related effects (December and January effects). Controlling for seasonality effects, Obama Administration comparable net jobs creation over that period was 7,139,000 against Trump’s 7,206,000.
  • Finally, looking at the entire jobs cycle, as illustrated in the chart below:


Note, I consider the period of Obama Administration with sustained jobs creation - a sort of
‘jobs creation upside cycle’ that started in March 2011. Based on this comparative, Obama Administration did outperform Trump Administration so far into the latter tenure in office (see steeper slope in the trend line for Obama Administration, and flatter slope for Trump Administration.


Draw your own conclusions out of all of this, but there are my top level ones:

  1. Whilst it is other daft to argue whether one Administration was able to ‘create’ more jobs than the other - the comparatives are a bit too sensitive to differences in economic environments and yearly cycles, overall, Obama Administration’s last 28 months in office seem to have been creating comparable number of jobs to the Trump Administration’s first 28 months in office.
  2. Trump Administration has seen more substantial monthly increases than Obama Administration did, but annually, Obama Administration outperformed Trump Administration in this comparative.
  3. In overall terms, jobs creation remained similar across both Administrations to-date, once we adjust for skewed seasonality effects, but Obama Administration appears to have outperformed the Trump Administration over the cycle of jobs expansion.

16/5/17: Technology: Jobs Displacement v Enhancement


Technological innovation is driving revolutionary changes across the labour markets and more broadly, markets for human capital. These changes are structural, deep and accelerating, and, owing to their nature, are not yet sufficiently understood or researched.

One theoretically plausible aspect of the technological innovation in terms of human capital effects is the expected impact of technology on demand for (and therefore supply of) different occupations. For example, we know that technology can act as a complement to or a substitute for labour.

In the former case, we can expect advancement of technology to create more jobs that are closely linked to enhancing technological innovation, deployment and productivity. In other words, we can expect more geeks. And we can expect - given lags in education and training - that as demand for geeks rises, their wages will rise in the short run before falling rather rapidly in the longer term.

In the latter case, there is a bit less certain, however. Yes, technology’s primary objective is to lower costs of production and increase value added. As a result, it is going to displace vast numbers of workers who can be substituted for via technological innovation. However, not all substitutable workers are made of the same cloth and not all technological innovation is capable of achieving unambiguous returns on investment necessary to sustain it. Take, for example, an expensive robot that costs, say, USD 600.000 a pop, but can only replace 3 lower skilled workers in a laundromat, earning USD16,000 per annum. So with benefits etc factored in, the cost of these 3 workers will be around USD70,000 per annum. It makes absolutely zero sense to replace these workers with new tech at least any time before the tech systems become fully self-replicating and extremely cheap. So, for really lower skills distributions, we can expect that jobs displacement by technology is unlikely to materialise soon. But for mid-range wages, consistent with mid-range skills, there is a stronger case for jobs displacement.

All of which suggests that we are likely to see a U-shaped polarisation process arising when it comes to jobs distribution across the skills segments: higher wage segment rising in total share of employment, as complementarity effects drive jobs creation here; and the lower wage segment also rising in total employment, as robots-induced increase in value added across the economy translates into greater demand for low-skills jobs that cannot be efficiently displaced by technology, yet. In the middle, however, we are likely to witness a cratering of employment. Here, the workers are neither complementary to robots, nor are they earning low enough wages to make expensive robots non-viable as a replacement alternative for labour.

Interestingly, we are already witnessing this trend. In fact, we have been witnessing it since the early 1990s. For example, Harrigan, James and Reshef, Ariell and Toubal, Farid paper titled “The March of the Techies: Technology, Trade, and Job Polarization in France, 1994-2007”, published March 2016, by NBER (NBER Working Paper No. w22110: http://ssrn.com/abstract=2755382) looked into “employee-firm-level data on the entire private sector from 1994 to 2007” in France.

The authors “show that the labor market in France has polarised: employment shares of high and low wage occupations have grown, while middle wage occupations have shrunk.” So the story is consistent with an emerging U-shaped labour market response to technological innovation on the extensive margin (in headcount terms). And more, the authors also find that inside margin also polarised, as “…the share of hours worked in technology-related occupations ("techies") grew substantially, as did imports and exports.”

However, the authors also look at a deeper relationship between technology and jobs polarisation. In fact, they find that, causally, “polarisation occurred within firms”, but that effect was “…mostly due to changes in the composition of firms (between firms). [And] …firms with more techies in 2002 saw greater polarization, and grew faster, from 2002 to 2007. Offshoring reduced employment growth. Among blue-collar workers in manufacturing, importing caused skill upgrading while exporting caused skill downgrading.”