Raising the Minimum Wage Has Not Led to Higher Unemployment: Evidence from California

Posted on July 3, 2024, 2:13 pm by aneconomicsense

A. Introduction

California has aggressively increased its minimum wage since 2014, starting on July 1 of that year and then with increases on January 1 of each year from 2016 through to 2024. Critics have argued that this would increase unemployment, saying that firms would no longer be willing to employ minimum-wage workers at the new, higher, minimum wage rates. They argued that the productivity of these workers was simply too low. If they were right, then one would have seen increases in the unemployment rate in the months following each of the steps up in the minimum wage. But there is absolutely no evidence that this happened.

The chart at the top of this post shows this lack of a response graphically. It may be a bit difficult to see as showing a lack of a response is more difficult than showing the presence of a response. The chart will be discussed in more detail below, but briefly, it shows the averages in each of the subsequent 12 months following the increases in the California minimum wage (including or excluding 2020 to 2022, as the Covid disruptions dominated in those years), of the change in the unemployment rate in California versus the change in the unemployment rate in the US as a whole. The changes are defined relative to what the unemployment rates were in the month before the increase in the minimum wage – i.e. the comparison is normally to the rate in December when the new minimum wage became effective on January 1. The unemployment rate of course goes up and down depending on macro conditions (and was normally going down for most of this period), so to control for this the changes in the unemployment rate in California are defined relative to the changes in the US as a whole.

What was the result? The chart shows that basically nothing happened. If anything, what was most common was that the unemployment rate fell slightly in California relative to the rate in the US in the months following increases in the California minimum wage. These changes were small, however, and are not really significant. But what is clear and significant is that aggressive increases in the minimum wage in California have not led to increases in unemployment in the state. The assertion that they would is simply wrong.

As noted above, this chart will be discussed in more detail below. But the post will first look at the changes in the minimum wage in California since 2014, and how the minimum wage in California compared to the federal minimum wage for the US as a whole as well as to several measures of wages in the US and to the federal poverty line. Following a look at the (non)-impact on unemployment, we will for completeness also examine what happened to labor force participation rates. Some might argue that minimum-wage workers who would have lost their jobs might then have left the labor force (in which case they would not have been counted as unemployed). But we will see that labor force participation rates in California also did not change following increases in the minimum wage. Finally, the post will discuss possible reasons for why increases in the minimum wage in California did not lead to a rise in unemployment there. Standard economics under the standard assumptions would have predicted that it would have. But those standard assumptions do not reflect well what is happening in the real world in labor markets.

B. The Minimum Wage Rate in California

The federal government sets a minimum wage that applies to the US as a whole. But due to gridlock in Congress (and opposition by Republicans), the last time the federal minimum wage was raised was in July 2009, when it was set at $7.25 per hour. As was discussed in a post on this blog from 2013, when adjusted for inflation this minimum wage was below what we had in the Truman administration in 1950, despite labor productivity now being more than three times higher than then. And from July 2009 to now, inflation has effectively reduced the value of the $7.25 wage of July 2009 to just $4.97 (based on the CPI). The federal minimum wage has simply become irrelevant.

Due to this lack of action at the federal level. many states have legislated their own minimum wage rules for their respective jurisdictions. California is one, and has been particularly aggressive. Over the past decade, the minimum wage in California has been increased to $16 per hour generally and most recently to $20 per hour for fast-food restaurant workers:

California Minimum Wage Recent History

Effective date	25 employees or less	26 employees or more

Jan 1, 2008	$8.00	$8.00
July 1, 2014	$9.00	$9.00
Jan 1, 2016	$10.00	$10.00
Jan 1, 2017	$10.00	$10.50
Jan 1, 2018	$10.50	$11.00
Jan 1, 2019	$11.00	$12.00
Jan 1, 2020	$12.00	$13.00
Jan 1, 2021	$13.00	$14.00
Jan 1, 2022	$14.00	$15.00
Jan 1, 2023	$15.50	$15.50
Jan 1, 2024	$16.00	$16.00

Fast food restaurant employees:
Apr 1, 2024	$20.00	$20.00

Sources: California Department of Industrial Relations. See here and here.

The focus of this post is on the series of increases that began on July 1, 2014, with the prior minimum wage set as of January 1, 2008, shown for reference. That 2008 rate was $8.00 per hour and was raised effective on July 1, 2014, to $9.00 per hour. California then began to increase the minimum wage annually starting January 1, 2016, with this continuing up to and including on January 1 of this year (2024). Furthermore, effective January 1, 2017, California began to set separate minimum wage rates for workers employed in businesses with 25 employees or less or with 26 employees or more. These could differ, although recently they have not.

Finally and most recently, California set a new minimum wage effective on April 1, 2024, of $20 per hour for employees of fast food restaurants (in restaurant chains with 60 or more locations nationwide). I include this here for completeness, but it is still too early to say whether this has had an impact on unemployment. So far it has not, but as I write this state-level unemployment data is available only for the months of April and May. But those figures do not provide any support for the critics: The unemployment rate in California in fact fell in those two months compared to that in the US. This will be discussed below.

The general California minimum wage has now doubled – to $16 per hour – from the $8 per hour it was prior to July 1, 2014. But for a sense of what this means, it is useful to put this in terms of various comparators:

California Minimum Wage: Selected Comparisons

California minimum wage in firms with 26 employees or more

	California Minimum Wage per hour	Ratio to US median wage of hourly workers	Ratio to US average hourly earnings of all private sector workers	Ratio to Poverty Line for family of four	Ratio to upper limit of earnings of first decile of US wage & salary workers

2008	$8.00	65%	38%	76%	93%
2014	$9.00	68%	37%	76%	94%
2016	$10.00	71%	39%	83%	102%
2017	$10.50	72%	40%	86%	103%
2018	$11.00	73%	41%	89%	104%
2019	$12.00	78%	43%	94%	109%
2020	$13.00	79%	46%	100%	111%
2021	$14.00	82%	47%	107%	115%
2022	$15.00	83%	47%	109%	113%
2023	$15.50	81%	47%	104%	108%
2024	$16.00		46%	104%	108%

Fast Food:
April 2024	$20.00		58%	129%

The comparisons here are based on the California minimum wage for employees in businesses with 26 or more employees.

The wage measures come from various reports produced by the Bureau of Labor Statistics (BLS). The first column (following the column with the California minimum wage) shows the ratio of that minimum wage to the BLS estimate of the US median hourly earnings of wage and salary workers paid an hourly wage. The ultimate source for this is the Current Population Survey (CPS) of the BLS, and this particular series is only provided annually (with 2023 the most recent year). The California minimum wage rose from 65% of this median wage of hourly workers in 2008 to 83% in 2022 and 81% in 2023). By this measure of wages – of wage and salary workers paid an hourly wage – the California minimum wage rose significantly in comparison to what a median hourly worker was being paid nationally.

A broader measure of wages is provided in the next column. The ratios here are for a worker being paid the California minimum wage to the average hourly earnings of all private sector workers – not just workers paid at an hourly rate. This is also provided by the BLS, but comes from its Current Employment Statistics monthly survey – a survey of business establishments that asks firms how many they employ and what they were paying those workers. These average wages are higher as they cover all workers and not only those paid at an hourly rate, plus the average will be higher than the median in cases such as this (as the distribution of wages paid is skewed to the right). By this measure, the California minimum wage rose from 38% of what US private sector workers were being paid on average in 2008 (and 37% in 2014) to 46-47% since 2020.

In terms of the federal poverty line, even full-time workers (40 hours per week for 52 weeks each year) paid the minimum wage in California in 2008 or even 2014 would have been able to earn only 76% of the poverty line income for a family of four. But with the increases in the minimum wage in the past decade, they would have finally been able to reach that poverty line in 2020, and then 109% of it in 2022. In 2023 and again in 2024, it would have been 104%.

The final column shows earnings at the California minimum wage compared to the earnings that would place a worker in the first decile (the bottom 10%) of the distribution of earnings of full-time wage and salary workers. These are also estimates from the BLS, are expressed in terms of usual weekly earnings, and are issued quarterly based on results from the CPS surveys.

With the increases in the California minimum wage over the past decade, full-time workers earning the minimum wage in California had incomes that exceeded the upper limit of the earnings of wage and salary workers in the US as a whole who were in the first decile of the earnings distribution – ranging from 102% of what the bottom 10% earned in 2016 to 115% in 2021 and 108% currently. Assuming the distribution of earnings in California would be similar to that in the US in the absence of the special California minimum wage laws, this can provide a rough estimate of how many workers were being affected by the California minimum wage laws.

If earnings at the California minimum wage would have matched the earnings at the upper limit of the first decile (i.e. a 100% ratio), the implication would be that the share of workers for which the California minimum wage was applicable would be 10%. With the ratio above 100% (by varying ratios up to 115%) the share affected would have been somewhat more than 10% – perhaps 11 or 12% of workers as a rough guess. But the BLS data is not for the entire labor force. Rather, it is only for wage and salary workers employed full-time. One has, in addition, part-time workers and those who are self-employed. The distribution of hourly earnings among those workers is not available, but if it is similar to the hourly earnings of full-time workers, the share affected would be the same 10% (or more).

The purpose here is just to provide a general feel for how many minimum wage workers were being affected by the changes enacted in the California minimum wage over the past decade. Various factors cannot be accounted for, but they are at least in part offsetting. For the purposes here, a reasonable estimate would be that at least 10% of the labor force had wages so low that the increases in the minimum wage in California over the last decade had an impact on what they would then be paid. That is a not insignificant share.

C. The Impact of Increases in the Minimum Wage on Unemployment

What impact did those increases in the California minimum wage then have on the employment of workers who were being paid the minimum wage? Critics of the minimum wage argue that workers are paid a wage based on their productivity, and if they are being paid at or close to the minimum wage this is only because their productivity is low. In this view, if the minimum wage that has to be paid is then raised, those workers will be let go and will become unemployed. Did we see this?

No, we did not. The evidence from the ten different increases in the minimum wage in California over the past decade (from July 2014 to January 2024) does not show any impact at all on unemployment. The chart at the top of this post summarizes the results.

The chart is based on calculations using data on the unemployment rate in California and on the unemployment rate in the US as a whole, where I calculated the unemployment rates from underlying data on the number unemployed and the number in the labor force (as published unemployment rates themselves are shown only to the nearest 0.1% point – anything less is not considered significant).

For numerous structural reasons, the unemployment rate in any particular state (including California) will differ from the rate in the nation as a whole. These structural reasons include the age structure of the population (middle-aged workers are less likely to be unemployed than young workers), the education structure (college-educated workers are less likely to be unemployed than workers with only a high school education), the industrial structure, the racial and ethnic mix of the population, and much more.

But while these structural factors affect the level of the unemployment rate in California relative to the national average, such structural factors change only slowly over time and hence do not have a significant impact on the month-to-month changes in that rate. The rate of unemployment itself can, however, change significantly from month to month at the national (as well as state) levels due to macroeconomic factors. In a recession the rate of unemployment goes up, and in a recovery or during periods of rapid growth, the rate of unemployment goes down. It is just that in the absence of some state-specific event (such as – possibly – a change in its mandated minimum wage), the month-to-month changes in the unemployment rate at the state level will generally be similar to the changes seen at the national level. They move together, as affected by macroeconomic factors. The question being examined is thus whether the increases in the minimum wage in California over the past decade led to an increase in the unemployment rate in California in the months following those changes in the minimum wage, as compared to what was observed for the unemployment rate nationally.

This is a simple form of what is called the “difference-in-difference” method. What is significant is not whether unemployment in California went up or down during the period, but whether it went up or down by more than what was seen at the national level in the same period. For example, define the changes as relative to the month prior to a change in the minimum wage law (i.e. normally relative to what the rate was in December, as all but one of the changes were effective on January 1 of each year). The employment and unemployment statistics (gathered by the BLS as part of the CPS household surveys) take place in the middle week of each month, so the mid-January unemployment rate will be treated as month one following the change in the minimum wage. The mid-February unemployment figures will then be month two, and so on until mid-December of that year will be month twelve. The minimum wage was then increased again in the next January 1, and the annual cycle was repeated for a second set of observed impacts (or non-impacts). The changes in the unemployment rate are thus defined as the difference between changes in the California rate for the given number of months following the change in its minimum wage (i.e. in month one, or in month two, and so on to month twelve), relative to what the changes were in the same period for the US as a whole.

As a concrete example using made-up numbers, suppose that in some December the unemployment rate in California was 6.0% while the unemployment rate in the US as a whole was 5.0%. Suppose then that in, say, month three (March) the observed unemployment rate in the US was 4.5% – a fall of 0.5% point over the period. If the unemployment rate in California fell to 5.5% in the same period (to March), then the change in California was the same as the change in the US as a whole, and the increase in the minimum wage on January 1 did not appear to have any differential effect. If, however, the unemployment rate in California fell only by, say, 0.3% points to 5.7%, while the US rate fell by 0.5% in the same period, one would say that it appears the increase in the minimum wage in California led to an increase in its unemployment rate by 0.2% points. And if the rate in California fell by 0.7% points to 5.3% while the US rate fell by 0.5%, then there was a 0.2% point reduction in the unemployment rate in California following the change in its minimum wage rate.

There will of course be statistical noise, as all the figures are based on household surveys. And importantly, in any given year there will also be special factors that could enter in that particular year that could affect the results. More is always happening than just a change in the minimum wage law. But to address this we have that California changed its minimum wage law on ten separate occasions over this ten-year period. We therefore have ten separate instances, and we can work out the average over those ten separate episodes. While special factors may have arisen in any given year, the only common factor in all ten was that California raised its minimum wage ten separate times.

(The exception in the averages is for the January 1, 2024, increase in the minimum wage, As I write this, we only have data for the five months through May. Thus the averages over up to the full ten instances can only be calculated for the first five months, while the averages for months six through twelve can only be for the nine cases to 2023. Also, note that for the July 1, 2014, increase in the minimum wage, the changes were defined relative to the California and US unemployment rates in June, with the subsequent twelve months then covering July 2014 to June 2015.)

Those average impacts were then remarkably small:

Average Changes in the California Unemployment Rate less Changes in the US Unemployment Rate, in the Months Following an Increase in the California Minimum Wage (in percentage points)

Months from Minimum Wage Change	July 2014 – May 2024	July 2014-2019, and 2023 – May 2024
0	0.00%	0.00%
1	-0.02%	0.01%
2	-0.06%	-0.05%
3	-0.05%	-0.04%
4	-0.07%	-0.05%
5	0.00%	-0.10%
6	-0.00%	-0.09%
7	0.03%	-0.08%
8	0.04%	-0.10%
9	-0.04%	-0.05%
10	-0.02%	-0.05%
11	-0.02%	-0.05%
12	0.02%	-0.03%

Overall average	-0.02%	-0.06%

The chart at the top of this post shows this table graphically. The two columns are for averages over the full period and with the years 2020 to 2022 excluded. The Covid disruptions dominated in those years, but the results are basically the same whether those years are included or excluded.

The changes were all essentially zero. It is not possible to see any increase in the California unemployment rate at all resulting from the increases in the minimum wage in the state over the past decade. If anything, the increases in the minimum wage were associated in most cases with a small reduction in the unemployment rates. But these are all small, and are probably simply statistical noise and not significant.

To put this in perspective, recall the discussion above that arrived at the rough estimate that the share of the labor force being paid at or close to the minimum wage might be around 10%, and possibly more. If – as the critics argue – such workers can be paid only those low wages because their productivity is so low, then they would all lose their jobs if their employers were required to pay them a higher wage. If true, the unemployment rate would then shoot up by 10% points. One obviously does not see that.

If we had over-estimated the share employed at the minimum wage by a factor of two, so that it was in fact 5% rather than 10% of the labor force, then the unemployment rate would have shot up by 5% points. One does not see that either. One does not even see an increase of 1% point, nor, for that matter, even 0.1%. The overall average change is in fact generally a small decrease in the rate of unemployment in California relative to the US rate in the months following an increase in the minimum wage, although I suspect this is just statistical noise.

Most recently, California raised the minimum wage for workers at fast food restaurants (at chains with 60 or more locations nationally) to $20 per hour effective April 1, 2024. We so far only have data for April and May as I write this, but that data provides no support for the belief that this has led to an increase in the unemployment rate. Fast-food workers are of course only a small share of the labor force: about 2.2% in California in 2023 based on BLS data for fast-food and counter workers (where fast-food workers make up about 80% of this total in national data). But in the two months since the April 1 increase to $20 per hour for fast food workers, the California unemployment rate relative to that in the US in fact fell by 0.06% points in April compared to March, and by 0.25% in May compared to March. It did not go up but rather went down.

Finally, it is possible that critics of the minimum wage may argue that low-wage workers laid off following an increase in the minimum wage will then leave the labor force entirely. If they did this, they would then not show up in the unemployment statistics and one would not see an increase in the observed unemployment rates. To be counted as unemployed in the BLS surveys, the unemployed person must have taken some positive action in the prior four weeks to try to find a job (e.g. send out applications, visit an employment center, and similarly) and yet was not employed at the time of the survey. If they did not take such an action to try to find a job, they would not be counted as “unemployed”. Rather, they would be counted as not participating in the labor force.

Therefore, for completeness, I calculated what happened to the Labor Force Participation Rate in California compared to the US rate in the months following the increases in the California minimum wage. The data comes from the BLS (but is most conveniently accessed via FRED, for the US and the California rates respectively):

Average Changes in the California Labor Force Participation Rate less Changes in the US Labor Force Participation Rate, in the Months Following an Increase in the California Minimum Wage (in percentage points)

Months from Minimum Wage Change	July 2014 – May 2024	July 2014-19, and 2023 – May 2024
0	0.00%	0.00%
1	-0.01%	-0.06%
2	-0.02%	-0.10%
3	-0.07%	-0.11%
4	0.04%	-0.10%
5	0.01%	0.01%
6	0.11%	0.00%
7	0.06%	-0.08%
8	-0.02%	-0.03%
9	-0.11%	-0.10%
10	-0.11%	-0.05%
11	-0.04%	-0.05%
12	0.02%	0.02%

Overall average	-0.01%	-0.05%

As with the unemployment rates, there was no significant impact. Had the 10% of the workers being paid at or close to the minimum wage dropped out of the labor force following the increases in the minimum wage, the figures would have shown a 10% point reduction in the California labor force participation rate. One does not see anything remotely close to that. One does not see an impact of even 1.0% point. There was simply no significant impact on labor force participation rates.

Thus, the data indicates the minimum-wage workers remained in the labor force and did not become unemployed.

D. The Economics of How Wages are Determined: In Theory and in the Real World

Economic analysis, when done well, will be clear on what conditions are necessary for certain propositions to hold. Under those conditions, one might be able to arrive at interesting conclusions. But a good analyst will examine whether there is reason to believe that those conditions reflect what we should expect in the real world. Often they do not. That is, what is of interest is not simply some proposition in isolation, but rather also under what conditions one can expect that proposition to hold.

The economics of how wages are determined is a good example of this approach. One can show that, under certain conditions, the wages paid to a worker would reflect the value of the marginal product of that worker – that is, the value of the increase in output that was made possible by hiring that worker. But one should then look at the conditions that are necessary for this to follow. And in the case of wage determination, they are not at all realistic, particularly for low-wage workers. The implication is that one should not expect the wages of these workers to reflect necessarily the value of the marginal product of such a worker.

A problem, however, is that some commentators do not follow through and examine the conditions necessary for the theoretical conclusion to hold. That is, they stop at the proposition that workers will be paid the value of their marginal product, and fail to look at whether the conditions under which that proposition would hold are realistic. They thus conclude, for example, that increases in the minimum wage will lead to the layoff of all the workers who were being paid the prior minimum wage. In their world, those workers are being paid a low wage because their productivity is low, and if firms are then required to pay a higher wage then those workers – these analysts conclude – will be laid off and indeed not be employable anywhere. They assert that their productivity is too low.

Yet as we saw above, we see nothing at all close to this in the data. California raised its minimum wage repeatedly in the last decade, and in a significant and meaningful way. We saw that it led to a significant increase in the wages of such workers compared to the overall wage structure in the US. Yet the unemployment rate in California did not increase at all in the months following those increases.

What, then, are the conditions that are necessary for this theoretical model of wage determination to hold? And how realistic are they? This section will provide a brief discussion of that theoretical model, and will then examine some of the conditions necessary for it to hold. It will not be a comprehensive discussion of all the issues that could arise. There are others as well. Rather, the purpose is to show for one set of reasons (there could be others also), the simple notion that wages will be equal to the value of the marginal product of the worker does not reflect the reality of how wages are determined.

a. The Standard Neoclassical Model of Wage Determination

In the standard model of neoclassical economics, it can be shown that the wages of a worker will equal the value of the marginal product of the worker. This can be shown to hold under the assumption of “perfectly competitive markets” for both labor (hired as an input) and for firms (hiring the labor). But for such perfectly competitive markets to exist, one needs:

1. On the side of the firms, there are many firms within a small geographic zone (small enough that commuting costs to the firms will not differ significantly) that are all competing with each other to hire labor with any given skill set. That is, the markets are “dense”, with many firms competing for that labor.

2. On the side of labor, there are many workers with each given skill set who are competing with each other and are seeking to be employed within that geographic zone.

3. There are no lumpy fixed costs incurred by the firms in hiring or firing a worker, nor are there any lumpy fixed costs for a worker in finding and being hired into a new job. Economists refer to this as no transaction costs. That is, that there are no costs incurred (neither on the part of the firm nor the worker) when a worker is fired and replaced with another.

4. There is full information freely available to all parties on what skills are required for a job, what skills each worker has, and how any worker will perform in any job. Both the firms and the workers know all this, with no cost to obtain such information.

5. Production is a smooth, upwardly rising (up to some limit), and always concave function of the hours any individual laborer provides for a job. Concave means that while the curve is rising, it is rising by less and less as the hours provided by the laborer increases. That is, there are no “bumps” in the curve. The slope of that curve at any given number of hours of labor is the marginal product of the laborer at that number of hours. That is, the slope indicates how much additional output there will be with one additional unit of labor being provided.

If all of the above holds, then one should expect that firms will pay in wages, and workers will receive, the value of the marginal product of what the workers produce. If workers were paid less than this, they would know the value of what they produce is in fact more and they would immediately move to a nearby competing firm that is willing to pay them up to the value of their marginal product. And if firms paid more than this, then competing firms could take away business from the firms paying the higher wages.

In this system, workers will thus be paid the value of their marginal product – no more and no less. And if this were true in the real world, then a mandate from the government to pay a higher minimum wage would mean that all those workers whose productivity was below the new minimum wage rate would be let go. They would become unemployed and indeed unemployable, as this set of assumptions implies that the productivity of such workers is simply too low for any firm to be willing to pay them the new minimum wage.

b. But the real world differs

Laying out the assumptions necessary for the neoclassical theory of wage determination allows us then to see whether those assumptions correspond to what we know about the world. They do not:

1. Markets are rarely dense. There are usually only a few firms – and often even no other firms hiring workers with similar skills – within a geographic zone so small that a worker is indifferent as to whom they would go to work for. There may be few or even no firms nearby that a worker could threaten to move to if they are being underpaid. And the few firms that are there may well follow what they consider to be informal “norms” on what such workers should be paid, rather than compete with each other and bid up the local wages.

2. There are transaction costs for both a firm considering to fire a worker and then to hire a new worker as a replacement, and for a worker when considering a move to a new employer. There are major costs incurred by both. Switching between employers is far from cost-free, so it is rarely done.

3. There can also be more overt constraints imposed on labor mobility and hence the ability of a worker to threaten to leave for a better-paying job. Noncompete clauses in many labor contracts – including for low-wage workers – may legally block workers from switching to a new employer in the industry where that worker has the particular skills to do well. The FTC has estimated that 18% of all US workers are covered by noncompete clauses. The FTC thus approved on April 23, 2024, new regulations banning their use. While the rule is scheduled to enter into effect on September 4, 2024, it will undoubtedly be challenged in court, with this leading to delays before it can enter into effect (if it ever does).

There is also the separate practice of antipoaching clauses. These are common in the fast-food industry as well as in other national chains of franchises. The antipoaching clauses are not in the labor contracts themselves, but rather in the franchise agreements between the franchise owner and the national firm. They require that the franchise owner not employ any individual who had worked at another franchisee’s establishment sometime before – typically at some point in the prior six months. McDonald’s claims it ended requiring those clauses in its franchisee contracts in 2017, and several states have banned the practices within their borders. But McDonald’s is still being sued in court, and it appears the practice remains common. The new FTC rule – if upheld in court – may apply to these practices as well.

4. Information is also far from complete nor is it cost-free. A firm can never know for sure how a particular worker will perform in a job until they are already on the job (with it then costly to fire and replace them in case the performance is not good). Nor will the worker easily know what all the job opportunities are out there, and what he or she would be paid at some alternative firm.

The relevant information may also be more readily available to one side of the transaction than to the other – what economists call “asymmetric information”. The worker may know well his or her skills and abilities, but the prospective hiring firm will not. Similarly, the hiring firm may know well what is needed to do well in a job, but the prospective worker will not. Also, doing well in a particular job is more than simply a skill set. It also requires an ability to work well with colleagues and a willingness to take the work seriously.

Firms will thus be cautious in hiring and may only be willing to pay a relatively low wage to new workers to start. Alternative firms will act similarly, as those firms are also unsure how well a new employee might work out (information is not complete). Thus they too will only offer a relatively low wage to start. Plus there are significant costs in the hiring and firing process itself. All this serves to lock in workers at the firms where they are now, without a credible threat to move elsewhere if their wages are not raised to reflect their full productivity.

5. Workers also gain firm-specific skills simply by the time they spend at the job. This spans the range from skills for the specific tasks that the job entails, to understanding better how the firm approaches what they want from those in these jobs, to getting to know colleagues better and their specific likes, dislikes, and how they do things. These skills are helpful, and lead to the worker becoming more productive at that particular firm.

But while a worker may see his or her productivity rise over time at some particular firm, they will not necessarily see their wage rise by the same amount. That is, the workers would be paid less than the value of their marginal product. While the firm might pay the worker somewhat more simply to help lock them in, this would not necessarily reflect the full amount of their higher productivity at that firm. The worker would not have a credible threat to leave to go to a competing firm where he or she would be paid more. Their productivity at an alternative firm – where they would once again be starting out – would not be as high and those firms would not be willing to offer a higher wage.

6. There is also a more fundamental problem in the ability (or rather inability) to ascertain what the productivity is of an individual worker. One of the assumptions of the neoclassical economic analysis noted above is that the relationship between the input of individual workers and the output of the firm is strictly concave. That is, as the input of the worker goes up (more hours) there will be a smooth decline in the extra output of the firm as a result of the increased labor input, with no “bumps” in that curve.

Economists call this diminishing marginal returns. If one increased labor input by a unit, one would see some increase in output. Increase the labor input by another unit, one would see an increase in output again, but by less than in the first step. And when the relationship is strictly convex, the increase in output would be less and less for each unit increase in labor input, up to a point where there would be no further increase in output (and after which it might even decline).

Reality is more complex. Those working in firms are not working simply as individuals but as part of teams. Adam Smith in the first few pages of The Wealth of Nations in 1776 already noted how far more productive workers can be when working in teams than when trying to do it all individually – the famous pin factory. It still applies today, and not simply in factories. Take, for example, a team working a shift at a fast food restaurant. There may normally be a team of, say, ten for a particular shift. Each worker has different responsibilities, but most of the workers have the skills to do most or perhaps all of the individual tasks.

In this made-up example, they arrived at a team of ten as normally best to handle a particular shift based on how the tasks can be divided up and given the number of customers they normally expect. It would be difficult to do with just nine, and not much gained with an extra worker and thus eleven on that shift.

What then is the marginal product of each of the workers? They need to know this to determine what wages they could pay in the standard neoclassical theory, but it is not well defined. Starting with any grouping of nine workers, the marginal product from hiring a tenth worker would be relatively high as they then could organize into the optimal team of ten. But any one of the workers could be considered to be the tenth one added to the team, and hence responsible for the jump in output in going from what is possible with just nine workers to the more productive team of ten. And if all of the workers were paid a wage corresponding to that jump in output that is possible when going to a full team of ten, they would together be paid more than the overall value of what is being produced with a team of ten.

While the workers would likely welcome such higher wages, the reality is that fast-food restaurants do not aim to operate at a loss. And they don’t. Their workers are simply not paid that much. There are fundamental conceptual problems in trying to define the marginal product of a worker when work takes place in teams (as it normally is).

E. Final Points and Conclusion

California has raised its minimum wage repeatedly in the past decade, but there is no indication in the data that this has led to an increase in unemployment. While economic theory would predict that in “perfectly competitive markets” the workers being paid below the new minimum wage would be laid off (as wages are set, under these assumptions, based on productivity, and they assert that the productivity of such workers is simply too low), this only holds under unrealistic assumptions. Wage determination is more complex. In the real-world conditions under which wages are in fact set, it is not a surprise to find that unemployment did not in fact go up.

This does not mean, however, that any increase in the minimum wage would not lead to higher unemployment. If the minimum wage was set next year at, say, $100 per hour, one should of course expect issues. What we see in the data is not that there can be any increase in the minimum wage with then no consequences for unemployment, but rather that the increases in the minimum wage that were mandated in California in the last decade did not lead to an increase in the rate of unemployment.

Increases in the minimum wage may also lead to increases in the prices of certain goods. If the production of those goods were heavily reliant on minimum wage workers, and the firms would now have to pay a higher wage for those workers, it may well be the case that such goods will now only be available at a higher price. Fast-food hamburgers may go up in price, but don’t view this as simply affecting “junk food”. The prices of blueberries and strawberries might go up as well.

Does this mean that the critics of the minimum wage are in fact right? No, it does not. First, it remains the case that unemployment did not go up following the major increases in the minimum wage in California over the past decade. The critics asserted that it would.

Second, while prices of fast-food hamburgers may have gone up following the increases in the minimum wage, those prices did not go up by as much as the minimum wage did. If wages in fact reflected the value of the marginal product of the worker, the wages of the minimum wage workers would still have gone up relative to that value – just not by as much. Under this theory of wage determination, they would still have been laid off. But there is no evidence of this in the data.

Labor markets operate far from what economists would call “perfectly”. In this reality, minimum wage laws can play a valuable and indeed important role.

The Unemployment Rate, the Growth in Employment, and Productivity

Posted on February 8, 2023, 2:53 pm by aneconomicsense

A. Introduction

The January jobs report (more properly the “Employment Situation” report) released by the Bureau of Labor Statistics (BLS) on February 3, was extraordinarily – and surprisingly – strong. The unemployment rate fell to 3.4% – the lowest it has been since May 1969 more than a half-century ago. And despite the low unemployment rate, the number of “new jobs created” (also a misnomer – it is actually the net increase in non-farm payroll employment) was a surprising 517,000. But it was not only this. The regular annual revisions undertaken each January to reflect revised population controls and weights for the employment estimates led this year to significantly higher labor force and employment estimates. With the new industry weights, the increase in the estimated number of those employed in 2022 (the number of `”new jobs”) rose to 4.8 million. The earlier estimate had been 4.5 million.

All this is an extraordinarily strong jobs report. However, one should not go too far. It is important to understand what lies behind these estimates, as well as some of the implications. For example, strong growth in the total number employed while GDP growth is more modest implies that productivity (GDP per person employed) went down. That could be a concern, except that when viewed in the context of the last several years we will see that productivity growth has in fact been rather good.

This post will first examine the new figures on unemployment and then on employment growth. We will then look at the change in productivity – both in the recent past and from a longer-term perspective.

B. The Unemployment Rate and Its (Non)-Impact on Inflation

The unemployment rate in January fell to 3.4%. This is the lowest it has been since May 1969. And if it falls a notch further to 3.3% in some upcoming month, it will have fallen to the lowest since 1953.

A 3.4% unemployment rate is certainly low. But what is more significant is that the unemployment rate has been almost as low for most of the past year. It fell to just 3.6% in March 2022, and until last month varied within the narrow range of 3.5 to 3.7% – hitting the 3.5% rate several times. It is now at 3.4%, but what is most significant is that it has been at 3.7% or less for almost a year.

One needs to recognize that the unemployment rate is derived from a survey of a sample of households (implemented by the Census Bureau) called the Current Population Survey (CPS). The CPS sample includes approximately 60,000 households each month, in a rotating panel, and from this they derive estimates on the labor force participation rate, the unemployment rate, and much more. It complements the Current Employment Statistics (CES) survey, which covers a much larger sample of 122,000 businesses and government agencies representing 666,000 individual worksites (with each employing many workers). Hence employment figures are generally taken from the CES as there will be less statistical noise. But the employers surveyed for the CES cannot know how many workers are unemployed (they will only know how many workers are employed by them), so the smaller CPS needs to be used for that. (A brief explanation of the CPS and CES is provided by the BLS as a “Technical Note” included in each of the monthly Employment Situation reports.)

Due to the size of the sample, the estimated unemployment rate is actually only known within an error limit of +/- 0.2 percentage points, using a 90% confidence interval. That is, simply due to the statistical noise a change in the unemployment rate of 0.1 percentage point from one month to the next should not be considered statistically significant, and 10% of the time even a 0.2 percentage point change may have just been a consequence of the statistical variation. However, repeated observations over several months in a row of an unemployment rate at some level will be a measurement one can have much more confidence in. That can no longer be a consequence of simply statistical noise. Thus one should not place too much weight on the January change in the unemployment rate to 3.4% from 3.5% the month before. But the fact that the unemployment rate has consistently been within the relatively narrow – and extremely low – range of 3.4 to 3.7% since March 2022 is highly significant.

An unemployment rate anywhere close to a range of 3.4 to 3.7% is also far below the rate at which economists used to believe would be possible without the rate of inflation accelerating – i.e. without inflation going higher and higher. This was given the acronym name of “NAIRU” (for Non-Accelerating Inflation Rate of Unemployment). It was held that at an unemployment rate of less than the NAIRU rate, the rate of inflation would rise from whatever pace it was at to something higher. This was viewed as unsustainable, and hence the proper goal of economic policy was, in this view, to manage macro conditions so that the unemployment rate would never fall below the NAIRU rate. That rate was also sometimes called the “full employment rate of unemployment”.

The question then is what the NAIRU rate might be. While different economists came up with different estimates, estimates generally fell within the range of 5 to 6%. An unemployment rate of less than this would then (under this theory) lead to a rise in inflation.

But that did not happen. The unemployment rate fell to below 5% in 2016, and inflation remained low. It fell to below 4% in 2018 and inflation remained low. It fell to 3.5% in 2019 and into early 2020 and inflation remained low.

With the once again very strong labor market – with unemployment hitting 3.4% – has this now changed? The rate of inflation did rise in 2021 and into 2022. But if one looks at this chart, one sees that the timing is wrong: Inflation rose earlier – in 2021 – when the unemployment rate was still well over 6% early in the year. Furthermore, nominal wages only rose later:

Inflation (measured here by the consumer price index – the CPI – for all goods and services) can be volatile, but the upward trend began already in the second half of 2020 (although in part this was initially due to a recovery in prices from depressed levels earlier in 2020 due to the Covid crisis). The chart shows the rates in terms of 3-month rolling averages (at annual equivalent rates and in arrears, so the figure for a January, say, would be for the months of November through January). The pace of change in nominal wages (also as 3-month rolling averages and at annual rates) did not start to rise until mid-2021. The increase in nominal wages appears to be more in response to the prior increase in prices – as firms found it profitable to employ more workers in an economy that grew strongly in 2021 – rather than a cause of those higher prices. This is consistent with the view that the inflation was primarily due to demand-pull, rather than cost-push, factors.

[Technical Note: The figures on changes in the nominal wage come from data assembled by the Federal Reserve Bank of Atlanta, drawing on data that can be obtained in the underlying micro-data files of the CPS. The rotating panel of households in the CPS are interviewed for four months, not interviewed for the next eight months, and then interviewed again for four months. New households are added each month and then removed after month 16 for them. This allows the researchers to match individuals with their reported wages to what they had earned 12 months before. It also allows them to examine the wage changes broken down by individual characteristics – such as age, gender, race, education level, occupation, where they are in the income distribution, and more – as these are all recorded in the CPS. It is all very interesting, and worth visiting their website where they make it easy to see the impact on the measured changes in wages of many of these different factors.

The matching of wage changes by individuals also provides a much more reliable index than the commonly cited changes in average wages provided in the monthly Employment Situation report. The latter comes from what employers report in the CES survey on the average wages they are paying. Those averages will be affected by compositional effects. For example, the reported average wages will often jump at the start of an economic downturn – such as it did in 2020 – as the less experienced and lower-wage workers are generally laid off first. This leaves a greater share of more highly paid workers, which will lead the reported average wage to rise even though the economy had entered into a downturn.]

Not only did the rise in inflation precede the more modest increase in the pace at which nominal wages rose, but since mid-2022 the rate of inflation has come down while the job market has, if anything, become tighter. The unemployment rate, as noted above, has been in the 3.4 to 3.7% range since March 2022, and is now at 3.4%. Despite this, the three-month average increase in the seasonally adjusted CPI fell from 11.0% (at an annual rate) in the three months ending in June 2022, to just 1.8% in the three months ending in December. If a tight labor market was driving inflation, one would have expected inflation to have kept going up rather than fall – and certainly not to fall by such a degree.

Furthermore, growth in nominal wages fell slightly from a peak of over 6.7% in the three months ending in June and also July 2022 (at an annual rate), to 6.1% as of December. One would have expected the pace of change in wages to have continued to go up, rather than start to ease.

It is still early to be definitive on any of this. Trends could change again. Importantly, a significant part of the sharp fall in inflation in the second half of 2022 (when measured by the full CPI) was due to a fall in the prices of oil and other energy products. However, while more recent, there are also early indications that core inflation (where food and energy prices are left out) is also falling. In terms of the core CPI (again the seasonally adjusted index), the pace of inflation fell from a peak of 7.9% (at an annual rate) in the three months ending in June 2022, to just 3.1% in the three months ending in December.

That measure of inflation – the core CPI, which is often taken to be a better measure of underlying inflationary trends than the overall CPI as food and energy prices are volatile and go down as well as up – is now falling despite unemployment at the lowest rate it has been in more than a half-century. If a tight labor market was driving inflation, then one would expect the pace of inflation to be rising, not falling.

C. Employment Growth

The January jobs report was also noteworthy for its figures on employment growth. Nonfarm payroll employment rose by 517,000 – far higher than most expected. It is not that an increase in employment of a half million in a month is unprecedented. It is rather that there was such an increase even though the unemployment rate was already at an extremely low 3.5% in the prior month. (And while nonfarm payroll employment excludes those working in agriculture, that number is now small at only 1.4% of the labor force – based on estimates from the CPS and including those in agriculture who are self-employed. It also excludes the self-employed outside of agriculture – a more substantial 5.6% of the labor force according to the CPS – but still not that large. In terms of changes in the numbers from one period to the next, the impact on the employment estimates will be small.)

In addition, the January report also reflected revisions – undertaken every January – where new weights are used to generalize from what is found in the sample in the CES of firms and other entities (such as government agencies) that employ workers to what is estimated for the economy as a whole. The re-weighting is based on a comprehensive count of payroll jobs in March of the year, with this then used to revise the estimates for all of the year (2022 in this case).

Due to the new weights, the increase in the number of jobs in the economy rose from the earlier estimate of 4.5 million in 2022 (i.e. from December 2021 to December 2022) to 4.8 million. Between January 2022 and January 2023 the increase was an estimated 5.0 million additional jobs. That is, between January 2022 and January 2023, the number employed increased by an average of 414,000 per month.

The 4.8 million growth in the number employed in 2022 was remarkable not only because it is a big number, but also because it came after the even stronger growth in employment in 2021. Employment grew by 7.3 million in 2021. In absolute terms, the 4.8 million figure in 2022 is higher than that of any year (other than 2021) in the statistics going back to when they started to be collected in the present form in 1939 (using BLS data). Such a comparison is more than a bit unfair, of course, as the US economy has been growing and there are far more people employed now than decades ago. But taking 2021 and 2022 together, the percentage growth over the two years – at 8.5% – was exceeded since 1951 only by greater increases in 1977-78 (10.2%), in 1965-66 (9.7%), and in 1964-65 (8.7% – that is, there was strong growth in the three straight years of 1964, 1965, and 1966). Joe Biden was right when he said job growth in the first two years of his presidency (of 12.1 million) was greater than that of any other president, but it is not really a fair comparison as the economy is now larger. But even in percentage terms, his record is excellent.

But such growth in the number employed cannot continue much longer. To put this in perspective, the total adult population in the US (as reflected in the CPS, and with the new population controls) rose by only 1.8 million between January 2022 and January 2023, or 150,000 per month on average. And the labor force figure, as estimated in the CPS, grew by only 1.3 million over that period, or 111,000 per month. One cannot keep adding 414,000 per month to the number employed (as we saw in the year to January 2022) when the labor force is only growing by 111,000 per month, when the unemployment rate is already at a historical low of 3.4%.

[Note that one cannot simply subtract the January 2022 figures reported from the new January 2023 figures, since in the CPS they do not go back and revise the previous year figures to reflect the new population controls. But they do show what the impact would have been on the December 2022 figures, and I assumed that they would have had the same impact on the January 2023 numbers. The impacts should be similar. One can then do the subtractions on a consistent basis.]

An increase in the number employed of an estimated 414,000 per month when the labor force was growing by only an estimated 111,000 per month was possible in 2022 in part because the unemployment rate came down (from 4.0% in January 2022 to 3.4% in January 2023), and in part because the labor force participation rate went up slightly (from 62.2% in January 2022 to 62.4% in January 2023).

But also a factor is that these are surveys from two different sources (households for the CPS and firms and other employers for the CES), and the sample estimates will not always be fully consistent with each other. As was discussed in an earlier post on this blog, the estimates can differ from each other sometimes for significant periods of time. However and importantly, over the long term the two estimates will eventually have to approach each other. The population estimates used for the CPS will yield (for a given labor force participation rate) figures on the labor force, and hence growth in the adult population will yield figures on growth in the labor force. For a given unemployment rate, the number employed – within the bounds of the statistical estimates – cannot grow faster than this.

With the unemployment rate now at 3.4%, one should not expect much if any further fall. Indeed, the general expectation (and the more or less openly stated hope of the Fed) is that it will start to rise. It is possible that the labor force participation rate will rise, but changes in this are generally pretty slow, driven mostly by demographics and social factors (the share of people aging into the normal age of retirement; the share of the young entering into the labor force given their decisions on whether and for how long to enroll in colleges and universities; decisions by households on whether one or both spouses will work; and similarly).

While there will be uncertainty in what will happen to the unemployment rate and the labor force participation rate, for given levels of each of these, employment cannot grow any faster than the labor force does. (Indeed it is slightly less: At an unemployment rate of 3.4%, employment will only grow at 96.6% of what the labor force grows by.) With the labor force growing by 111,000 per month in the year ending in January 2023 (with this already reflecting a small increase in the labor force participation rate from 62.2% to 62.4%), it will not be possible for the monthly increase in employment to grow by much more than this.

Looking forward, one should not, therefore, expect growth in the number employed to be sustained at a level that is anywhere close to the 517,000 we had in January. There will be month to month fluctuations, but one should not expect an average increase over several months that would be much in excess of the 111,000 figure for the growth in the labor force seen in the year ending in January 2023.

D. Productivity

Politicians like strong job growth. It is indeed popular. But the flip side of this is that while the number employed grew rapidly in 2021 (by 3.2% December to December), GDP growth was less (1.0% from the fourth quarter of 2021 to the fourth quarter of 2022, based on the most recent estimates). With the number employed growing faster than GDP, the mathematical consequence is that GDP per person employed went down. That is: Productivity fell in the year.

Higher productivity is ultimately what allows for higher living standards. Falling productivity would thus be a problem. However, in the context of the last several years, productivity growth has in fact been pretty good:

We are once again seeing the consequences of the highly unusual circumstances surrounding the Covid crisis. With the onset of a downturn, firms will lay off workers. But they may often lay off more workers than their output falls. This might be because of uncertainty on how much the demand for whatever they make will fall in the downturn (and they will wish to be careful and if anything to overcompensate, given the difficulty of obtaining finance in a downturn and the very real possibility of bankruptcy); or because special government programs during the downturn reduce the cost to them and their workers of these layoffs (for example through the common response of extending unemployment benefits and making them more generous); or because the first workers being laid off are the least productive ones (possibly because they are relatively new and do not yet have as much experience as others working there) so that they end up with a workforce which is on average more productive. Or, and very likely, it could be a combination of all three factors. It looks very much like Schumpeter’s “creative destruction”.

The consequence is that productivity can in fact jump up in a downturn. One sees such a clear jump in the chart in 2020, at the time of the sharp collapse due to the Covid crisis. One also sees it in 2008-09, with the financial and economic collapse in the last year of the Bush administration and then the turnaround that began in mid-2009. In terms of the numbers: Real GDP fell by 1.3% between the first quarter of 2020 and the third quarter of 2020 (in absolute terms – not annualized). But employment over this period fell by 7.4%. As a result, productivity (real GDP per person employed) jumped by 6.6% in this half year. In 2008/2009, real GDP was basically flat between the last quarter of 2008 and the last quarter of 2009 – rising by just 0.1% But employment over this period fell by 4.1%, leading to an increase in productivity of 4.4%.

Following these brief periods where businesses are scrambling to survive the downturn by producing more (or perhaps not too much less) with many fewer workers, firms then enter into a more normal period where, as the economy recovers, they are able to sell more of their product. They hire additional workers who are, by definition, less experienced in the work of that firm than their existing workforce. The new workers might also be less capable or have a less applicable skill mix. Productivity may then level off or even go down. The latter situation is in particular likely when the economy recovers quickly and firms scramble to keep up with the increased demand for their product.

The latter fits well with what we saw in 2021. GDP in 2021 rose by 5.9%, the highest of any year since 1984. And the Personal Consumption component of GDP rose by 8.3% in 2021, the highest of any year since 1946. This was spurred by the series of Covid relief packages passed in 2020 (under Trump) and in 2021 (under Biden), which totaled $5.7 trillion in the two years, or 12.8% of GDP of 2020 and 2021 together. Personal savings rose to an unprecedented level as a share of GDP (other than during World War II, with data that go back to 1929), which then supported the strong growth in personal consumption in 2021. This is consistent with a demand-led inflation that got underway in late 2020 or early 2021 (discussed above) – a risk of inflation that Larry Summers had warned of in early February 2021 when Biden’s $1.9 trillion Covid package was first proposed (and eventually passed, largely as proposed).

But what matters to long-term living standards is not so much the changes in average productivity in the periods surrounding economic downturns, but rather the trends in productivity growth over time. A ten-year moving average is a useful metric:

The chart shows rolling ten-year averages starting from 1947/57 through to 2012/22 of the growth in GDP, in employment, and in productivity (GDP per person employed). Productivity growth was relatively high at about 2% per annum in the 1950s and through most of the 1960s. But it then started to fall in the 1970s to less than 1% a year before recovering and returning to about 2% a year in the ten-year period ending in 2004. It then fell to roughly 0.8% a year since about 2017 (in terms of the ten-year averages), with some sharp fluctuations around that rate associated with the 2020 Covid crisis. As of the end of 2022, the most recent ten-year average growth rate for productivity was 0.80%.

This has important implications for GDP growth might be going forward. The labor force grew by 0.8% in 2022 (the adult population grew by 0.7%). With unemployment close to a record low, employment will not be able to grow faster than the labor force – as discussed above. And the labor force cannot grow faster than the adult population unless labor force participation rates increase. But while there major disruptions in labor force participation in 2020 and 2021 surrounding the Covid crisis – with its lockdowns, economic collapse and then recovery, as well as health concerns affecting many – labor force participation largely returned to previous patterns in 2022. Labor force participation rates have been slowly trending downwards since the late 1990s, and while it is possible this pattern might be reversed, it is difficult to see why it would. There might well be short-term fluctuations for a period of a few years, but longer-term patterns are driven mostly by demographics (the age structure of the population) and social customs (e.g. whether women decide to enter into the paid labor force).

What follows from this is that if the labor force continues to grow at 0.8% a year (as it did in 2022 – and it grew only at a lower rate of 0.6% a year in the ten-year period ending in 2022), and productivity grows at 0.8% a year (as it did in the ten-year period ending in 2022), then GDP can at most grow at 1.6% a year on average. This would be disappointing to many. While there certainly can be and will be significant year to year variation around such a trend, faster growth would require either higher productivity growth or more entering into the labor force.

E. Summary and Conclusion

The January jobs report was strong. The unemployment rate is now at the lowest it has been in more than a half-century, and the number employed grew by more than a half million – a very high figure when the unemployment rate is so low. While these are still preliminary figures and are subject to change as additional data become available, they present a picture of an extremely strong labor market.

The fall in the unemployment rate by one notch to 3.4% from the previous 3.5% should not, in itself, be taken too seriously. That is well within the normal statistical error for this figure. But what is indeed significant is that the unemployment rate has been within the narrow range of just 3.4 to 3.7% since March 2022. That is low. And it was in this low range during a period (in the second half of 2022) when inflation was coming down. While changes in the price of oil have been a major factor in driving the inflation rate in 2022, the core rate of inflation (which excludes energy prices as well as those for food) has also started to come down. The rate of change in nominal wages did start to grow in mid-2021, but this appears more to be a consequence of the rising prices rather than a cause of them. And there has been a slight reduction in the pace of change in wages in recent months.

One does not see in this any evidence that a tight labor market with extremely low unemployment (the lowest in more than a half-century), has led to higher inflation. The opposite has happened. Inflation has come down at precisely the time the labor market has been the tightest.

GDP grew rapidly in 2021, but then slowed to a more modest 1.0% rate in 2022 (from fourth quarter to fourth quarter). Coupled with rapid employment growth in the year, productivity (as measured by GDP per employed person) fell. However, this appears more to be a continued reaction to changes surrounding the disruptions resulting from the 2020 Covid crisis. During that crisis, GDP fell but employment fell by much more, leading to a jump in productivity despite the downturn. As the economy recovered and the situation normalized, workers were hired to bring workforces back to desired levels. Viewed in a longer timeframe, productivity growth has been similar to what it has now been since the mid-2010s.

That productivity growth is not especially high. It was 0.8% at an annual rate in the most recent ten-year average. Coupled with a labor force that grew at 0.8% in 2022, and going forward might grow by even less (it grew at 0.6% a year in the ten-year period ending in 2022), the ceiling on GDP growth would be 1.6% a year, or less. That is not high, but expectations need to adjust.

That is also a ceiling on what GDP growth might be. Many expect that there very well could be a recession either later in 2023 or in 2024. Much will depend on whether the government will be able to respond appropriately if the economy appears to be heading into a downturn. But with Republicans now in control of the House of Representatives, and threatening to force the US Treasury into default on the nation’s public debt if their demands for drastic spending cuts are not met, one cannot be optimistic that the government will be allowed to respond appropriately.

The Pattern of Unemployment: Fewer on Temporary Layoff, but More of the Rest

Posted on December 8, 2020, 7:10 pm by aneconomicsense

A. Introduction

The economic downturn this year has been unprecedented in many ways. Millions were laid off in March and April as the country desperately went into lockdowns to limit the spread of the virus that causes Covid-19, following the failure of the Trump administration to recognize the extent of the crisis. But it was always known that those lockdowns would be temporary (albeit with differing views on how long they would be needed), and hence those laid off in March and April were generally put on temporary layoff.

The number on temporary layoff then started to decline in May, with this continuing (although at a diminishing rate) through November. This has brought down the headline figure on total unemployment – the figure most people focus on – from 14.7% in April to 6.7% as of November. But while that focus on the overall rate of unemployment is normally appropriate (as the number on temporary layoff has usually been steady and low, while the labor force has fluctuated little), the unusual conditions of the downturn this year have masked important aspects of the story. Unemployment is a good deal worse than the traditional measures appear to suggest.

One key issue is what happened to those who were unemployed but not on temporary layoff. The Bureau of Labor Statistics (the source of the data used here) defines those on temporary layoff to be those who are unemployed but who either have been given a date for when they will be able to return to their job, or expect to return to it within six months. All other unemployed (defined by the BLS as being in the labor force but not employed, not on temporary layoff, and have taken concrete actions within the previous four weeks to look for a job), include those who were permanently laid off, who completed some temporary job, who left a job by choice (quit), or have newly entered (or re-entered) the labor force actively seeking a job but do not yet have a job.

That distinction – treating separately the unemployed on temporary layoff and the rest – will be examined in this post. Also important to the story is how many are counted in the official statistics to be in the labor force at all, as that has also changed in this unprecedented downturn. That will be examined as well.

B. The Unemployed on Temporary Layoff Spiked Up and Then Came Back Down, but Other Unemployed Rose Steadily

The chart at the top of this post shows the unemployment rates (as a percent of the labor force) for all who were unemployed (in black), for those on temporary layoff (in blue), and for all others who were unemployed (in red). Unemployment surged, at an unprecedented rate, in March and April of this year. The increase in those on temporary layoff accounted for this – indeed for all of this in those months in the estimated figures. The total increase in unemployment in March and April compared to February was 17.25 million; the increase in those on temporary layoff was almost exactly the same at 17.26 million. (But keep in mind that these figures are estimates based on household surveys, and thus that there will be statistical noise. That the numbers were almost exactly the same was certainly in part a coincidence. Still, they were definitely close.)

The total unemployment rate then came down sharply from its April peak of 14.7% to 6.7% as of November. It was led, once again. by changes in those on temporary layoff, but this time the number unemployed for reasons other than temporary layoff rose. Their rate was 3.0% in February, which then rose to 5.0% by September. It has kept at roughly this rate since (although so far with data for only two more months).

That increase – of 2.0% points – is significant but modest. With all the disruption this year, one might have expected to see more. Certainly important and effective in partially alleviating the crisis was the $3.1 trillion in several packages approved by Congress in March and April (of new government spending, tax cuts, and new loan facilities). While adding to the public debt, such spending is needed when confronted with a crisis such as this. The time to reduce the fiscal deficit would have been when the economy was at full employment. But Trump added to the fiscal deficit in those years (with both higher spending and massive tax cuts) instead of using that opportunity to prepare for when a crisis would necessitate higher spending.

C. But the Number in the Labor Force Also Fell, Which Had a Significant Impact on the Reported Unemployment Rates

There is, however, another factor important to the understanding of why the unemployment rate (for those other than on temporary layoff) rose only by this modest amount. And that is that the number in the labor force abruptly changed. This was another unusual development in this unprecedented crisis.

The labor force (formally the civilian labor force, as those on active military duty are excluded) changes only slowly. It is driven primarily by demographic factors, coupled with long-term decisions such as when to retire, whether to attend college rather than seek a job, whether both spouses in a married couple will seek to work or whether one (usually in this society the wife) will choose to remain at home with the children, and so on.

But it was different in this crisis:

The number in the labor force fell abruptly in March and April – by 8.1 million compared to February, or 4.9% of the labor force. There has never before been such an abrupt fall, at least since 1948 when such data first began to be collected. The largest previous two-month fall was just 1.0 million, in 1953 when this was 1.6% of the labor force. (And the month to month “squiggles” seen in the chart above should not be taken too seriously. They likely reflect statistical noise in the household surveys.)

Those who drop out of the labor force are not counted as unemployed, as formally defined by the BLS, as they are not actively seeking a job. And the sharp collapse in available jobs in March and April probably contributed to some dropping out of the labor force, as that scarcity of jobs would, by itself, induce some not even to try to find a job if they lost one. But probably more important in this unprecedented crisis is a parent (and usually the wife) dropping out of the labor force in order to take care of their children when the schools and/or daycare centers closed. This has never happened before.

Since April, the number in the labor force has recovered some but only partially. Compared to what the labor force likely would have been by November 2020, based on a simple extrapolation of the January 2015 to January 2020 trend (growth at an annual rate of 0.95%), the labor force in November was 5.4 million less than what it otherwise would have been.

This will have a significant impact on the unemployment figures. Since the number unemployed are, by definition, equal to the difference between the number in the labor force less the number employed, the number unemployed will be substantially higher if one counts those who abruptly dropped out of the labor force to take care of their children. These, including others who dropped out of the labor force but would prefer to be employed if labor market conditions were more hospitable, should be counted when assessing how much slack there may be in the economy. And they can be considered as part of those who are unemployed for reasons other than temporary layoff (as they are similar in nature to those who had, or in this case would have, re-entered the labor force but do not have a job).

Counting such individuals as among those who are in fact unemployed, the labor market does not look to be nearly as strong as the headline figures would suggest. Assuming that the labor force in 2020 would have continued to grow at the trend rate of the previous several years, that the number employed would have been the same as was recorded, and that the number on temporary layoff would have also been as recorded, the chart on unemployment rates then becomes:

Superficially, this chart may appear similar to that at the top of this post. But there are two important differences. First, note the scale is different. Instead of peaking in April at an overall unemployment rate of 14.7%, the unemployment rate would instead have reached over 19%. Furthermore, it would still be at 9.7% as of November, which is high. It is not far from the peak 10.0% rate reached in 2009 following the 2008 economic collapse.

Second, both the path and the levels of the unemployment rate for those other than on temporary layoff are now quite different. That rate jumps abruptly in March and April to 8.2% of the labor force, from 3.1% before, and then remains at around 7 1/2 to 8% since then. This a much more worrisome level than was seen above when no correction was made for what has happened to the labor force this year. There is also no downward trend. All the gains in the reduction of overall employment since April would have been due to the reduction in those on temporary layoff.

D. Conclusion

The economy remains weak. And president-elect Joe Biden is certainly correct that a necessary (although not sufficient) condition for the economy to recover fully will be that Covid-19 be addressed. Australia, New Zealand, and the countries of East Asia have shown that this can be done, and how it could have been done. Simply wearing masks would have been central. Dr. Robert Redfield, the head of the CDC, has noted that wearing a mask could very well be more effective in stopping the spread of the virus that causes Covid-19 than some of the vaccines now under development, if everyone wore them. But Trump has been unwilling to call on all Americans, including in particular his supporters, to wear a mask. Indeed, he has even repeatedly mocked those who choose to wear a mask.

As a longer-term solution, however, vaccinations will be key. But this also depends on most Americans (probably a minimum of 70 to 80%, but at this point still uncertain) being vaccinated. Even under the most optimistic of circumstances, constraints on vaccine availability alone means this will not be possible before the summer. But this also assumes that, once available, 70 to 80% of the population (or whatever the minimum share required will be) will choose to be vaccinated. Given how the simple wearing of face masks was politicized by Trump (and turned into a signal of whether one supports him or not), plus controversies among some on both the left and the right on vaccinations that pre-dates Trump’s presidency, it is hard to be optimistic that such a vaccination share will soon be reached.

Hopefully a sufficiently large share of the population will at some point have chosen to be vaccinated to end the spread of the virus. But until that happens, further support to the economy, and not least relief to those most affected by the crisis, needs to be passed by Congress and signed by the president. The House passed such a measure already last May, but Mitch McConnell, the Republican Majority Leader in the Senate, has so far blocked consideration of anything similar. As I write this, there appears to be a possibility of some compromise being considered in the Senate, but it remains to be seen if that will happen (and if Trump then will sign it).

It is certainly desperately needed.

An Economic Sense

It's the Economy, Stupid!

Tag Archives: unemployment

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