Relevant and even prescient commentary on news, politics and the economy.

Job Creation Follow Up

For those interested in more information on job creation in the Employment Dynamics data
base these three article provide very good information.

Cordelia Okolie, “Why Size Class Methodology Matters in Analyses of Net and Gross Job Flows.” July 2004 Monthly Labor Review

Jessica Helfand, Akbar Sadeghi and David Talan, “Employment Dynamics: Small and Large Firms Over the Business Cycle.” March 2007 Monthly Labor Review

Tim Kane, The Importance of Startups in Job Creation and Job Destruction (PDF from the Kauffman Foundation Research Series, July, 2010)

All three are pdf files and for the second article the link takes you to the Monthly Labor Review where you can directly access the article.

The subject is more complex than generally thought as different methodologies can create significantly different results.

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Are you better off than you were a year ago? 28 States Say No.

The WSJ Economics Blog, discussing June 2010 unemployment rates by state, uses the headline “Most Regions Show Improvement“*

I suppose we should be encouraged by the headline and not look at the text:

Washington, DC and 16 states recorded jobless rates in excess of 10%. North and South Dakota continued to have the lowest rates in the country, at 3.6% and 4.5%, respectively.

Despite the improvements in the jobless rates, 27 states posted a decline in payroll employment, while 21 notched increases. Montana and Alaska had the highest percentage increase from the previous month, while New Mexico and Nevada reported the largest percentage drops. [emphasis mine]

Less money is being paid in a majority of states. The clearest explanation, then, remains that the “decline” in U-3 reflects people dropping out of the work force, not being employed.

It gets more interesting if you look at the Year-on-Year Change. There, 28 of the 50 states show a U-3 unemployment rate that is higher than or equal to last year’s. (The District of Columbia’s U-3 rate declined by 0.1% over that time, so it is only 10.0% now.)

And the improvements are, lest we forget, from a high plateau. The 14 states with the greatest drop in their unemployment rate year-on-year have an average current rate of 8.4%—and a median rate of 8.95%, the average being skewed by the above-mentioned North Dakota, with it’s 9.3 people per square mile and total population under 650,000, 37% of which are not of working age.

Dropping North Dakota from the “biggest YoY winners” moves the median current unemployment rate to 10.0%, while the average is slightly above 8.75%.

If this is a recovery, then my December 2009 prediction that this will turn out to be a “cursive-zed” recession may turn out to be optimism.

*Also, “Jobless Rates Drop in Most States.”

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Relative employment is shifting

Today Statistics Canada released impressive June employment figures from its Labour Force Survey (LFS). In case you missed it, the April gains, +109,000 new jobs, set a record. And the June gains, +93,000, were nearly as spectacular. (Note: the unemployment rate for Canada in the chart to the left is through May, not June)

Canada’s labor market bounced back fully and then some. Spanning May 2008, when job loss became the norm as the global credit crunch started to take hold, to December 2009, 259k jobs were lost. However, this year through June 2010, the labour market added back 308k jobs, which is +50k new jobs during the expansion or roughly +500k in “US”.

I’m afraid that the US labour market is a far different story. To regain employment lost since June 2008, 6.9 MILLION jobs need to be added back to the employment figures of the current population survey.

I digress. Every time I hear the Canadian statistics, I immediately multiply the statistic by 10 to control for the population differential; thus, +109,000 new jobs in Canada would be equivalent to roughly +1,090,000 in the US, all else equal. In translating the job gains into “U.S”, I understand the magnitude with more clarity – not very different form learning a new language by translating the words in your head.

Is +50k Canadian still equivalent (roughly) to +500k US? The short answer is pretty much – the 2009 US/CAN relative population was just over 9; but in thinking about relative population figures, I stumbled upon a rather remarkable relative employment figure between the US and Canada. The Canadian employment picture has become much much brighter than that in the US over the last decade.

The chart illustrates US employment relative to that in Canada, Germany, and Japan (Germany and Japan are there for comparison). As you can see, employment in the US relative to our neighbor to the North has dropped markedly. There is a secular downward trend in US employment relative to that in Canada.


And it’s not just a population issue. On a population-adjusted basis, the employment figures in Germany, Canada, and Japan are trending upward relative to that in the US – and for Canada, this is a secular trend rather than a cyclical phenomenon.

The US employment picture is fading compared to other developed nations. And remember, Japan and Germany saw near-zero annual population growth spanning the years 2000-2009.

Rebecca Wilder

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EMPLOYMENT REPORT

The employment report was bad, as private payroll employment only rose 83,000. Moreover, the household survey showed a drop in employment of -350,000.


The unemployment rate fell. But that is because the – 350,000 fall in household survey was offset by a -652,000 drop in the labor force. A labor force contraction is really bad news as it implies public confidence in the availability of jobs deteriorated.


Moreover, the average workweek on private nonfarm payrolls fell 0.1 to 34.1 hours.
As a consequence of modest employment gains and a drop in the workweek the index of aggregate hours worked only rose 0.1%. However, the three month growth rate of hours worked did expand t0 3.3% (SAAR), almost exactly the same as last month.

In addition, average hourly earnings also fell and the year over year growth in average hourly earnings fell to 1.7% . The drop in workweek also caused the gain in average weekly earnings to tick down as well. Average hourly earnings growth is now where it bottomed in the 1986 and 2004 wage cycles– the lowest gains in this series since it was first recorded in 1967.

Regardless of how it is calculated, the current gap between employment and the long term trend of employment is at post WW II record levels.

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Employment Report


The employment report was basically more of the same and was very discouraging.
Even though the headline number reported a large 431,00 increase in employment, most of this was temporary Census jobs. Private employment only increased 41,000, significantly less than in the previous few months.

The unemployment rate fell, but that was just as much a result of a rise in discouraged workers as a rise in employment as measured by the household survey.



Even the diffusion index ticked down.

As in previous months the rise in aggregate hours worked rose nicely even though it was only up 0.3% as compared to 0.4% in the last few months.


Wage gains were also moderate as average hourly earnings rose 0.3% and were up only 1.9% over the last 12 months. But because of the gains in hours worked average weekly earnings continued to improve.

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Working the Refs

So there was this big snowstorm that hit the East Coast a couple of weeks ago. (Not the one this weekend, that dumped about 2′ of snow on Upstate New York and a little more than a foot here in suburban New Jersey; the one that wiped out D.C. and gave the Party of No an excuse to do nothing.)

Snow in February. What a surprise! Clearly, not something that happens every year.

My high school classmates and others in the Midwest see the notice and say, “Yeah, gosh, sounds like January and February here.”

But This One is Different. Maybe because it gave the U.S. press an excuse to pay no attention to Haiti. Maybe because closing down D.C. meant that all the pundits got to whine and reveal their suffering.

And, just maybe, because it has become the all-purpose excuse for the February Employment Report. Or any other hint that the world is not perfect, and those “green shoots” haven’t been eaten by starving deer who were then shot by Big Bank Hunters.

The Usual Suspects are already out in force.* And the hedging (not in the risk management sense) has begun:

“We will have to wait until March to see if February is an aberration or a fundamental sign that the recovery in sales will be more subdued than hoped,” [Jessica Caldwell, Edmunds’ director of industry analysis said].

So anything that can be marginally interpreted as positive will be The Crest of a Wave, while anything that makes those legendary shoots look as if they were artificial flowers will get the rousing “Wait Until March!” cry.

All we really know is that—thanks to Senator Bunning and a pliant Democratic “leadership”—March, not April, is the Cruelest Month for about 1.2 million normally-working Americans.

But, gosh, the job gains for February might be understated by 5-8% of that total. So let’s not do anything hasty.

*Yes, it’s “pick on Brad DeLong day.” Didn’t you get the memo? (Also, I can’t find discussion of the topic at any of the Other Usual Suspects, though I haven’t checked The Big Picture.)

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How to Build an Economic Model

Let us see if we can translate my previous post on job selection into an economic model.  Start with a basic formula:

(1) AcceptOffer = a(1) + a(2)*w + a(3)*b + a(4)*oa + a(5)*t

where a is a constant, w is wages, b is benefits, oa is “opportunity for advancement” and t is treatment received in the workplace.

The first observation we make is that several of these variables are difficult to quantify—and even more difficult to objectify. So let’s start with the easy ones.

w is very identifiable: reported (on a per capita aggregate basis), subject to enforcement penalties (e.g., minimum wage laws), and used in “downstream applications” (e.g., tax filings) and therefore relatively verifiable.

b is (1) known to be non-negative and (2) often variable within, let alone between, organizations. (Vacation time, sick days, insurances offered and costs to the employee all may vary depending on level, time of service, location of office, etc.)

This could present a problem, but here we can use standard economic theory to our advantage. We do not know the amount of b, but we can assume that the employer is rational, and is offering a total compensation to the worker that s/he expects will be less than or equal to the marginal product of that person’s labor. We therefore can reasonably assume that b is related to w. If we then review the available aggregate data we can approximate that benefits offered will be approximately a certain percentage of w—and that workers will assume that assumption (and, in most cases, verify that assumption within a margin of error) before accepting the job.

We then restate the equation as

(2a) AcceptOffer = a(1) + a(2’)*w’ + a(4)*oa + a(5)*t

where w’ is the weighted combination of w and b above, and a(2’) is the restated coefficient.

If we then assume that all parties have full information of the ratio of wages to benefits, then a(2’) = a(2)=a(3), so we simplify to:

(2b) AcceptOffer = a(1) + a(2)*w’ + a(4)*oa + a(5)*t

We now have to consider opportunities for advancement and treatment. Here, we have two problems that are difficult, possibly insurmountable, for modeling.

The first is a lack of measurability. There are no public records for “didn’t get promoted.” Nor, except in extreme cases, is there a way to measure treatment by supervisors. The data that might be available&mdassh;lawsuits, official complaints, even Human Resources files (for which there are significant privacy considerations)—is all negative and, accordingly, skewed (biased). This is because (a) ninety percent or so of all workers and/or bosses will never have a complaint filed against them and (b) the ability to file a complaint may be present because the general work atmosphere is more amenable to filing one than not, so the presence of a complaint is not in itself a good or bad thing for the overall measure.

The second is that tolerances vary by person. To use an absurd example, people who use “Every Breath You Take” for their wedding may be more likely to tolerate attentions that others view as harassment. Similarly, forcing people to clock out for a “smoke break” will be viewed differently depending upon whether one is a smoker or not. General policies are just that—general.

So, if we are building an economic model, we must come up with a reasonable approximation of these last two variables. The most direct way to do this is the standard method: assume each individual has their own Utility Curve, and “prices” accordingly.

Based on their preferences and options, then, we map the compensation required to offset negative consequences from oa and t. While the variables still are not directly observable, we can make a simplifying assumption:

Assume that the compensation required to do the work is a factor of w’.

Have to work in the sewer system? Change w’ to compensate. Need to work the night shift and/or weekends? Same type of adjustment. Boss clearly favors buxom blondes and you’re a petite redhead? Adjust current salary requirements to compensate for lowered opportunity for advancement/promotion. You’re a b.b. who will have trouble getting work done because the boss will harass you? Adjust accordingly.

We assume—due to the constraint: a lack of available data—that we can reduce “a(4)*oa + a(5)*t” to some proportion of w that will compensate the worker for the environment into which they are being placed. If we further assume that the worker has complete information as to hisser preferences, the worker will not accept a job that does not offer that level of compensation.

So we can restate equation 2(b) using the Utility Curve assumption. Assume

(3a) a(6)*w” =a(4)*oa + a(5)*t

such that w” also proportionate to w(and therefore w’ as well) and a(6) is the coefficient selected by the individual that makes the offered wage compensatory to the opportunities for advancement and expected treatment on a Present Value basis.

We can then reduce equation (2b) to

(3b) AcceptOffer = a(1) + a(2)*w’ + a(6)*w”

or, given that (a) w” is proportionate to w and w’ and (b) that the multiplier in most cases is 1, and (c) the constant (e.g., signing bonus) can be assumed without loss of generality to be 0,

(3b) AcceptOffer = clip_image002[10]

to indicate that the value varies with individuals.

To concretize the example, assume that a redhead and a blonde, as above, are both offered a job. Assume further that the redhead’s compensation requirement—lower-but-still-positive opportunity for advancement—is lower than the blonde’s for will-be-harassed-and-work-will-be-impeded. That is

clip_image002(r) < clip_image002[4](b)

There are four possibilities:

  1. The offered wage will be belowclip_image002[12](r), in which case neither will accept the job
  2. The offered wage will be below clip_image002[14](b) but above clip_image002[16](r), in which case one of the two positions will be filled
  3. The offered wage will be above clip_image002[18](b), in which case both will accept the offer and the company will have offered a higher wage than was required to fill both positions. (That the offer is what the company believes will be the employees’ s marginal product of labor [MPL] is a collateral issue.), or
  4. The company will negotiate with each, offering the redhead clip_image002[20](r) and the blonde clip_image002[22](b), and everyone will be happy—so long as initial expectations were accurate (or, if you prefer, the new employees both had full information).

Note also that there is a learning process for both the applicant and the employer. Offers and demands will be adjusted based on historic data (if both decline the offer, the next candidates of similar background will be offered more, and perceptions of growth (improvements in experience and/or education by the worker).

If we generalize this, we note that there is a distribution of clip_image002[24] (due to Individual Preferences). If we further make simplifying assumptions—e.g., a normal distribution of clip_image002[26] among the population—we come to the conceit of the “reservation wage,” and all the economic literature that is attendant upon it.

So that is how you build an economic model.  The question then becomes: how do you use it? A relatively short (though it does incorporate a micro model) discussion of that continues below the fold.


The problem—if it is one (I’m inclined to argue it is; YMMV)—is that, having built a model in which all the proxying assumptions are “simplified” into a single variable, we lose some granularity, having made a trade-off for the sake of measurement.

Accordingly, a change in the “reservation wage” may not in itself tell us whether the real wage has gone up or the work environment has become, on balance, more or less acceptable.

Again, an example, one that will be familiar to students of microeconomics. You are given two choices: (1) you can receive $100 right here and now or (2) you can travel a known distance (say, five miles)—with a finite chance of death or injury—and receive $1,000,000 on completion.

Surroundings, at this point, matter. If the five miles traveled is as an American soldier in full uniform walking outside of the Green Zone, you might well choose $100. Even when you are native to the area, the choice may vary: walking five miles through one gang’s territory is a different option than traveling that distance through different territories. Or even a pure environmental matter may have an effect: Walking five miles through a desert with no canteen, or having to swim five miles from shore in a dangerous waters, is not the same risk as walking five miles down an unpaved dirt road in the middle of the day. Even if you would be required “only” to walk down a heavily-used interstate highway with no shoulder or sidewalks, discretion may be the better part of valor.

Over time, through the “learning process” (op cit. Arrow, 1962, as all good op cits must), the dollars offered will be adjusted so that the payouts balance on a risk-adjusted basis. (Collaterally, there may be other reasons for the greater payouts; signaling by any other name.)

Now suppose the landscape changes. There is a canteen every half-mile in the desert. A gang is run out of its territory, or takes over another’s territory. The Green Zone becomes larger.

The balance has changed; the risk is different. The job is demonstrably different, and therefore requires lower (higher) compensation. But the difference has nothing directly to do with the base salary/benefits requirement and everything to do with the overall attractiveness and/or treatment received.

If we were to forget that, we would conclude that there is more demand for the job itself, and therefore people are willing to take a lower salary. If, on the other hand, we keep in mind that there are more factors to the reservation wage than just the salary itself, we realize that producing a more pleasant work atmosphere is beneficial to our firm, as it enables us both to present a good face to clients and to reduce our cost of labor.

The first type of economist probably should be avoided, as he adds very little value to the discussion of how to use the model.

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Trade-Offs and Revealed Preferences, Republican Leadership edition

Even more than Digby on CalPERS, the one piece everyone should read today is Charlie Stross on International Travel. Since this is an economics blog, let’s pull a key section:

Here’s the rub: security is a state of mind, not a procedure. Procedures can’t cope with attackers, because they’re inflexible. If you search passengers for guns, someone will carry a knife. If you search for knives, someone will sew themselves a set of underwear full of PETN. And so on. To deal with a threat — say, someone who wants to attack your air travel infrastructure — you must look for the attacker, not their tools, because they can change their tools at will to exploit weaknesses in your procedure for identifying tools.

JFK is wide open to terrorists intent on causing mass casualties….

Schiphol — Amsterdam airport — gets the security screening right, or at least less wrong than JFK and most other airports. Rather than having a hideous bottleneck between check-in and the departure area, security screening is carried out at each depature gate, with a separate metal detector and X-ray belt; no huge crowds form in unsecured areas. On US-bound flights, someone who clearly isn’t a minimum-wage drone checks ID documents and asks a couple of questions that seem to me to the aimed at flushing out anyone who is disturbed or tense — a crude form of profiling.[italics his; boldfacing mine]

South Carolina Senator Jim DeMint preferred to let the TSA remain leaderless for the past year in fear of unionization of the workers. As he explained to CNN:

Or, as quoted by Mark “neither Ernest nor earnest” Hemingway the Washington Examiner, in a piece oh-so-sensibly entitled Napolitano wants to unionize TSA employees despite safety concerns:

The administration is intent in on unionizing and submitting our airport security to union bosses [and] collective bargaining, and this is at a time, as Senator Lieberman says, we’ve got to use our imagination we’ve got to be constantly flexible. We have to out think the terrorists. When we formed the airport security system we realize we could not use collective bargaining and unionization because of that need to be flexible. Yet that appears to be the top priority of the administration.

But DeMint was much clearer on the Senate floor, and speaking to Fox:

It makes absolutely no sense to submit the security of our airports and the passengers here in this country to collective bargaining with unions.

Which, of course, is why police and fire departments are all non-union as well.

The people you attract to any job—by your deliberate practices, not “unintended consequence”—are those who cannot get a job that they know to be more stable, pays better, has better benefits, or provide a more friendly work atmosphere. By your policies and procedures, you reveal the type of worker you prefer. This is as true of the TSA as it is of Goldman Sachs.

In the case of the TSA, though, the combination produces the natural hire as the people who couldn’t get a job at Applebee’s, The Olive Garden, or Ruby Tuesday’s.

As Paul Kedrosky recently noted, it’s more “security theater” than security. So when DeMint compares the TSA to the FBI, he’s neglecting that the average staring salary at the FBI eight years ago was over $43,000—with an increase of at least $10,000 upon completion of training. This is $20,000-$30,000 a year more than the $12/hour my neighbor made when he started with the TSA. (He quit quickly, finding restaurant work more profitable.)

If you want security, you pay for people who know how to do security. If you want theater, you depend on Jim DeMint to ensure that the TSA remains leaderless, and then have no right to be surprised when a British novelist points out that your security isn’t secure. Even when he says:

Suppose I wanted to attack the US air travel infrastructure….I can kill lots of passengers! All I need to do is to buy a maximum-size carry on bag (US dimensions: 7″ x 13″ x 20″) and build the biggest, heaviest bomb into it that I can wheel behind me….

All I would have to do then is buy a ticket…and go queue. Then, when I get to the middle of the crowd, detonate the device. (For added horrors: have an accomplice with a similar device hang back, to detonate their bomb amidst the fleeing survivors.)

[S]ecurity checkpoints are a target, too, because they slow down travellers and cause crowds to form, and another term for “crowd” is “convenient target”. And because the attacker has not been separated from their weapon at the point when they reach such a target, it’s the logical weak point for causing maximum damage.

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Another View of the Data

While I applaud the cautious optimism of Spencer and Tom, I’m more inclined to quote Joseph Brusuelas:

[T]he January payrolls added a dollop of Zen like logic to a recovery that is shaping up like no other. An additional 111,000 workers entered the labor force, yet the unemployment rate fell to 9.7% while private sector employment continued to contract. Hours worked, demand for temporary workers and the hiring in the service sector all improved. However, without the benchmark revisions, the unemployment rate would have increased to 10.6% which better captures the condition of an economy that has seen 8.4 million workers displaced during the recession.

The bump in manufacturing was more than balanced by the drop in the Service Sector, as more and more flower shops cut staff in the face of slack demand and unavailable credit.

If the bank bailout was to bailout the banks—defibrillating them to kick-start the economy’s heart, as it were—then it appears to be time to admit that that program was too small. Or to stop the other programs that are making it more advantageous for banks to hold funds than lend them. Any way you look at it, the optimistic view that declining unemployment has started doesn’t appear to be the way to bet.

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