Platt Perspective on Business and Technology

Reconsidering the Phillips curve as a general indicator of how employability is being redefined

Posted in career development, job search, job search and career development, macroeconomics by Timothy Platt on January 13, 2016

The Phillips curve is a conceptual tool that was first developed by a New Zealand economist: William Phillips to describe an apparent correlation between the rate of unemployment and the rate of change of employee wages. This has, with further development and elaboration, become construed to represent a measure of correlation between the rate of unemployment and the overall rate of inflation, though for purposes of this discussion I will primarily focus on its original scope of correlational context.

Phillips himself, developed his analytical model on the basis of a longer-term data set as selected from United Kingdom records, as collected from 1861-1957. But his overall findings and the relationship that he developed from them as an analytical tool, at least as initially formulated, have been found to be overly simplistic and for a variety of reasons. A brief discussion of attempts to correct the Phillips curve, and to improve its applicability and accuracy are covered in my hyperlink reference at the start of this posting. Most importantly for purposes of this discussion, the Phillips curve and its iterations have come to be seen as applicable over short time frames only. And that detail brings me very specifically to the point of focus of this posting.

And I begin there by admittedly simplistically presenting a basic line of argument that would make a Phillips curve or iterative redevelopment thereof, seem compellingly attractive for macroeconomic systems modeling and even when it is more widely interpreted to address inflation rates.

• A reduced unemployment rate correlates with increased inflation rates because
• As the number of unemployed drops, and particularly as it drops to levels where essentially everyone seeking work either has it or can find it readily, the cost to hire and retain rises.
• Employers have to offer and pay more to secure the people they need, and particularly for higher skilled positions.
• This drives up their personnel-related expenses and their overall operating expenses, and they have to increase the price they ask for, for what they offer to their markets in order to remain profitable.
• This increases the overall inflation rate and as both end user consumers, and purchasing businesses have to pay more for what they need.
• And as this pattern continues, inflation would be expected to continue to increase too.
• And to complete this set of points I add that when the unemployment rate rises, job search and hiring become much more of a buyer’s market and employers can select and bring in higher value employees for less, reducing their personnel costs and their overall business expenses too – while maintaining or even improving productivity.
• And this makes it possible for them to price what they offer more competitively and still make a profit and both per-item sold and overall. And that, when occurring as a more general phenomenon across markets, promotes lower overall costs to consumers and it slows down any inflation taking place.

Personnel expenses are in fact among the largest and most impactful operating expenses for most businesses, and even across entire industries where production and service are highly labor-intensive and manpower requiring. So the basic principles here look to be sound. But as important as they are for this, personnel costs can at most only account for a portion of any business’ expenses, and the rate of change of wages and overall employee compensation as a whole are only two factors out of many that would be expected to correlate in some manner with employment rates. It also makes sense that competing factors and even ones that would have contradictory impact would enter into this too. And to consider one of them that arises from outside of any particular business under analytical review, here:

• Consider legislative decisions and newly enacted law, where for example minimum wages are increased for all low-skill and unskilled workers in all businesses and industries. That specific type of change in salary range is generally made completely independently of factors such as unemployment rate per se.
• Politically motivated policy changes, as realized through legislative and regulatory processes can and do serve as wild cards in shifting economies. And both arise and change business and marketplace dynamics, independently of the causally connected interactions and relationships between business-based and marketplace factors that economic models are more traditionally grounded in. Such events would be expected to cause shifts in the relative values of metrics such as unemployment rate and average wages offered, that would not arise absent such outside non-economics perturbation.
• But many of these potential perturbations from the predictions of a model such as the Phillips curve, would be expected to smooth out over time. When, for example, the minimum wage is increased in a business with a significant number of employees who would be paid that, this creates pressures for it to make compensating changes in overall business expenses, and elsewhere in the business if that were not possible in personnel costs per se, in order to keep market-facing products and services more competitively priced. And for agile and effective businesses, this rebalancing would at the very least dampen down any product cost impacts from this outside-imposed change.

So far I have been addressing what amount to self-correcting deviations from a Phillips-like curve, as they represent more single-point perturbations that businesses and marketplaces can and do find ways to accommodate. What about more structural, long-term changes that with time only increase, and certainly as a basic pattern?

Increased per-worker productivity can skew the basic relationship expressed between unemployment and wages, where fewer employees are needed in order to maintain or increase production and profitability, but with those more skilled employees expecting and receiving higher compensation for the work they perform. So at least in principle, increased overall productivity in an economy’s businesses can lead to increased per-employee wages, and even increased overall payroll, when unemployment is holding steady or even increasing (from low skills employees becoming less employable.) But I would focus here on a very different type of perturbance to this type of system in this posting: automation.

And I begin that discussion by raising what I see as a curious detail that I have noted in passing up to here, in the Phillips curve story. William Phillips initially developed his curve as an economic tool, on the basis of almost a century’s worth of data. Yet subsequent attempts to validate and use this on more recent data have found it of short-term use only. What has changed? Did Phillips in some way use a skewed or misrepresented data set in his analysis, or have new perturbing factors come into play that would lead to drift from Phillips curve predictions when that model is applied and run over anything but a relatively short time frame?

Any such perturbation would have to be long-term and structural, leading to a more single directional, incrementally increasing deviation from the predicted values that a Phillips curve analysis would offer. And as just noted, it would have to be a perturbing factor that has become increasingly significant in recent years, but that was not so important in the time frame that Phillips studied and drew his data from, at least in the United Kingdom where he gathered it from.

Manufacturing systems automation, and office systems automation have been taking place at an at least low level, niche employee context for more than a century now. When, for example, the Hollerith card, or punched card was first used in tabulating the 1890 United States census, census workers rose up in protest, burning these cards out of fear that their use would cost jobs. And automation has always led to at least some types of job reduction and even disappearance. But until recently this has always been accompanied by the creation of new types of work and new job opportunities that people who were displaced could move into. We are currently seeing what are still only the early stages of a fundamental shift there, where more and more types of work can be and are being automated, and opportunity is fundamentally beginning to disappear for whole groups of potential employees – and not just those who would seek out unskilled labor positions. Now, if a job’s primary tasks can be captured in an algorithm or through application of artificial intelligence with machine learning, that job and even its entire work category are in danger of being automated out of human hands and permanently.

This discussion fits in with a basic organizing model that I have been developing here in this blog, as to how the overall economy is changing, and for how employability is being redefined – with a fundamental shift taking place in the size and scale of the workforce needed, and with this trend increasingly impacting upon skilled and experienced members of the potential workforce as well as upon their less skilled and unskilled peers.

At the risk of oversimplifying complex issues, it is not surprising that more recent data set based analyses have found Phillips curve and related tools to only apply with any real accuracy over shorter analytical time frames. We are approaching an inflection point for the level of impact of several competing factors that would all strongly influence any empirical correlational relationships observed there. A significant overall factor that is causing, and that will increasingly cause deviation between Phillips curve predictions and empirical observations, and even for the more restricted interpretation of correlating unemployment rates and average wages, is automation. And its impact on the size and distribution of the workforce needed, has to be added in some way into those equations and into that model as a new updating iteration to it. And when this is done, the resulting new Phillips curve iteration will also map out how employability is being redefined as we proceed into the 21st century.

You can find this and related material at my Guide to Effective Job Search and Career Development – 3 and at the first directory page and second, continuation page to this Guide. I have included this posting added as a supplemental installment there. You can also find this and related material at Macroeconomics and Business and its Page 2 continuation.

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