Common-sense metrics about the learning curve for high-turnover, technical positions, particularly in IT, can yield shorter ramp-up time, more efficient staffing policies and decisions - and increased profitability. This paper presents the practical issues, the theory behind a low-cost solution, a straight-forward implementation plan, talking points to justify long-overdue change in staffing practices and true Human Capital accounting

1. SHORT-TERM STAFFING FOR THE LONG HAUL
July, 2014 1
SHORT-TERM STAFFING FOR THE LONG HAUL
TOWARD PRACTICAL HUMAN CAPITAL
DAN SHUMAKER
MBIS, BUSINESS SYSTEMS ANALYST AT CIBER, INC.
DShumaker@ciber.com
ABSTRACT
GAAP say salary is an expense. But some of it may be neither overhead nor revenue-productive. This is particularly true of
technical positions with significant ramp-up times, even for the highly qualified. An estimate of the cost of expensive
learning curves can provide major, positive impacts to business continuity, knowledge retention, fact-based staffing
decisions, and even the bottom line. This paper explains job learning cost estimation, its significant implications and an
implementation.
Keywords: staffing, temporary, staffing theory, contingency, turnover, business continuity, contract, contractor, consultant,
worker training cost, human capital
INTRODUCTION - THE CONTEXT
First, one might say that most staffing is short-term, temporary, that the term permanent job is an oxymoron, especially in the
field of IT. Sure, company cultures span a broad spectrum of turnover rates, so some may have positions held by the same
person for decades, even in IT. But my focus here is on positions that have high turnover and expensive ramp-up investment.
Such positions can carry an ignored but significant financial waste that can be addressed – profitably.
Half of my 30-year IT career has been consumed in (or by) consulting. I’d prefer to invest myself in a solid company as a
long-term employee but corporate staffing policies can actually discourage long-term relationships. I’ve observed that short-
term – contingency – technical staffing carries a high cost to companies that escapes scrutiny, either because businesses
aren’t aware of it, don’t know how to measure it or don’t bother. I label this stealth cost, “The Discarded Job Learning
Curve.” I’ll explain how it can be significant.
THE COST
My clients have paid me – a lot – to learn what I call the job context. That’s the sum of all the “how we do it here” stuff, plus
all about the client’s specific business functions and work flows, as well as how the software and data are structured to
support those functions. This is knowledge I would only know if I’ve held the same position at that company before. They
hire me because I meet the job requirements; but I become a revenue contributor to the project for which I am hired only
after I have learned the job’s technical and business context. In most cases, my managers and coworkers in these technical
positions have agreed that it takes 6 months or more to become fully productive. One of my managers, of Project Managers,
estimated a 9-month learning curve, and we PMs agreed.
My fellow consultants and I know all too well that workforce reductions are a fact of today’s job market. But we have
frequently lamented the seeming disregard for the waste of a company’s investment to train us in the job context. This is an
investment that’s discarded when our contracts have matured or we’re laid off. Many such positions will cycle from one
consultant to another every year or two. Yes, we’d like a justification to keep working after our contracts expire; but it just
doesn’t make sense for the business to ignore that investment when deciding to lay off workers, policy or not, temporary or
not. And if/when those positions are re-filled, the invest-discard cycle repeats.

2. SHORT-TERM STAFFING FOR THE LONG HAUL
July, 2014 2
There are at least three reasons why the job learning investment in workers is not factored into staffing decisions: Generally
Accepted Accounting Principals (GAAP), arbitrary contract renewal limitations and the lack of a practical metric for the
financial loss.
THE CONSTRAINTS
The GAAP
The GAAP, as I’ve said, treat salaries as expenses, a cost of generating revenue. They do not measure the salary paid for on-
the-job or formal training differently from salary that actually contributes to revenue. The cost of the job learning curve is
assumed to be insignificant or negligible, and unmeasurable.
The Administration of Contracts and Legal Risk
One common policy prevents the protection of job learning investment: arbitrary limitations on contract renewals. Contracts
vary in term, from 1 to 12 months, and are often renewed, depending on the need. But contracts will generally not be
renewed beyond the company’s limit on the number of consecutive months a consultant can work at the company. These
limits vary by company but the range is typically 12 to 24 months.
A pervasive rationale for contract term limits goes back to a 1998 lawsuit brought by Microsoft contractors. They were
treated like Microsoft employees in every respect except eligibility for stock options, for which they sued – and won. That
caught the attention of the legal department of every other company that employs consultants. Businesses now maintain all
sorts of arbitrary distinctions between employees and consultants, ranging from different ID badges, restrictions from
company social functions or fitness facilities, to scaled-down workplace accommodations, employment only through a 3rd
party staffing agency – and contract renewal limitations. The objective is to make sure consultants remember they aren’t
employees.
But any limitation on contract terms and renewals mandates and inflates turnover that is already high in IT. That, together
with the high cost of training technical employees in the job context, has resulted in inadvertent but significant waste of
business financial resources. But how significant is it?
THE CAPITAL VALUE OF JOB LEARNING
Estimating the Learning Curve Duration
So I contend that accounting treatment and arbitrary contract term limits disregard the cost of job learning and you might
agree in principle. But, to my knowledge, nobody attempts to measure that cost, so it’s easy to ignore. Is there a way to
measure it? Yes.
I’ve already mentioned a variable that is a good starting point: the length of time for a worker to reach full productivity or,
the length of the learning curve. This is easier to measure for some blue collar positions because productivity can be
measured in terms of discrete units of acceptable work per unit of time. Many white collar technical positions however have
learning curves that span months and productivity measurement is very subjective.
Truly objective measurements for the length of a white collar learning curve are not cost effective. The cost to develop and
administer a good, written test of job learning for each position would be prohibitive. And such tests – even if they provide
good measurement – would always be chasing a moving skill set target in a fast-paced corporate setting, especially a
technical one.
But we need not shy away from subjective estimation. Managers routinely award performance-based raises on a similar,
subjective measurement so this isn’t uncharted territory. In accounting, the depreciation of capital assets is, of course, an
estimate. And my experience on all of my contracts has been that managers and workers generally agree on the length of a
position’s learning curve.

3. SHORT-TERM STAFFING FOR THE LONG HAUL
July, 2014 3
A Valid Rationale
Subjective expectations of productivity will obviously vary by position, individual aptitude and prior experience, but work
teams can reach a consensus with their managers about the average duration of the learning curve. But aside from consensus,
what makes it a valid estimate?
There are balancing pressures against over- or underestimating. A low estimate will make it difficult to explain to
management when productivity is lower or of less quality than expected. A high estimate will also raise the eyebrows of
upper management, especially when compared with the industry or internal work groups having similar positions. When
asked to come up with an estimate, managers and workers who understand these implications of their estimates will be
conscientious and realistic. And in the absence of more a precise metric, this estimate is a reasonable one.
A Conceptual Model
Once we have the learning curve duration estimate, we can use a “standard” learning curve formula to plot job learning.
Here’s a conceptual view.
Figure 1: Conceptual Learning Curve Cost Model
We’ll assume that the Salary Expense is constant over the length of the learning curve. And since no worker’s time is 100%
productive toward the primary assignment(s) due to overhead, we should allow that overhead can range from 5 to 20% of
work time (and therefore of salary).
The lower boundary of the overhead margin forms the upper boundary of the learning curve itself. But since job learning
increases (at a decreasing rate) as long as a worker holds a job – we’ll consider, say, 95% job learning as “Fully Productive,”
the target for measuring the cost of the learning curve.
I’ve labeled the shaded area above the curve and below the Full Productivity line, “Contribution to Human Capital,” to
indicate the portion of Salary Expense that pays for job learning. (More on Human Capital later.) The area under the curve
represents the portion of Salary Expense that is actually contributes to revenue. I call this true Salary Expense. The unit of
measure for the Learning Curve Duration could be days, weeks, months, or more conveniently, pay periods.
A Mathematical Model
If we’re going to change the accounting treatment of some salary expenses – and yes, that’s what I’m proposing – we need a
mathematical model with a solid rationale and hard numbers. An exponential curve will do but I’ll let mathematicians
determine if another one would fit better. An exponential function produced the curve below and it follows my intuitive

4. SHORT-TERM STAFFING FOR THE LONG HAUL
July, 2014 4
assessment of job learning. It assumes 5% overhead and that full productivity of 5% below that, or 90% of job learning
potential.
Figure 2: Mathematical Learning Curve Cost Model
Let’s look at the components of this function and discuss how it might be used to model the kind of technical, white collar,
job learning we’ve been discussing.
Here’s the equation:
y = L – e – k t
… where …
 L is the upper limit of % productivity that job learning enables a worker to approach,
 e is the base of the natural logarithm, approximately 2.718,
 t is the time estimate for the length of the learning curve, measured in the number of pay periods (to reach “Full
Productivity,” which we’ll say requires 90% of job context learning),
 k is a constant that gauges the initial steepness of the curve (we can derive this), and
 Y is the percentage of productivity that we will consider Full Productivity; here, it’s 90%
For our purposes, L will always be 0.95, or 95%, since we’re assuming this as the upper limit of productivity. We’ll measure
t in terms of pay periods, because we want to estimate in that increment how much of a worker’s pay is for job learning and
how much is for productive work. We’ll assume a 6-month learning curve here and that payroll runs every week, so we’ll
allocate a portion of salary for Human Capital for only the first 26 pay periods. (But we’ll also remember that job learning
continues to increase, at a decreasing rate, as long as the worker remains with the company.)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 6 11 16 21 26 31 36 41 46 51
Pay Periods
JobLearning%,akaProductivity

5. SHORT-TERM STAFFING FOR THE LONG HAUL
July, 2014 5
We’ll first need to use the values we know (or have set) and solve for k, the steepness constant.
Ln (Y – L x Y)
-1 x t x ln (e)
k =
(The math might be a little unusual for ordinary accounting but once the model is built, it need be revisited only periodically
for possible revision.)
Once we have the value of the curve steepness constant, k, we can determine the height of the learning curve at each pay
period, from the first one ( t = 1) through the one when the employee is deemed fully productive: here, that’s 26. And let’s
assume an annual salary of $80,000. Here are the resulting values.
Table 1: Mathematical Learning Curve Cost Model - Example
Salary Allocation
Pay
Per.
%
Productivity Expense
Human
Capital
Periods to Learning Target 26 (weeks) 1 5.88% 90.51 1,447.95
annual salary 80,000.00 2 15.58% 239.72 1,298.74
period gross 1,538.46 3 24.22% 372.69 1,165.77
maximum productivity 95% 4 31.93% 491.19 1,047.27
Target Learning % 90% 5 38.79% 596.79 941.67
k = 0.115220 6 44.91% 690.90 847.56
e = 2.7182818 7 50.36% 774.77 763.69
e = 2.7182818 8 55.22% 849.51 688.95
e = 2.7182818 9 59.55% 916.12 622.34
10 63.41% 975.48 562.98
11 66.84% 1,028.38 510.09
12 69.91% 1,075.52 462.94
13 72.64% 1,117.53 420.93
14 75.07% 1,154.97 383.49
15 77.24% 1,188.33 350.13
16 79.17% 1,218.06 320.40
17 80.90% 1,244.56 293.90
18 82.43% 1,268.18 270.29
19 83.80% 1,289.22 249.24
20 85.02% 1,307.97 230.49
21 86.10% 1,324.69 213.78
22 87.07% 1,339.58 198.88
23 87.94% 1,352.85 185.61
24 88.70% 1,364.68 173.78
25 89.39% 1,375.22 163.24
26 90.00% 1,384.62 153.85
Totals for Learning Curve 26,032.03 13,967.97
% of Learning Curve Salary ($40,000, over 6 months) 35%
% of 1st Year's Salary 17%
% of 2 Years' Salary 9%

6. SHORT-TERM STAFFING FOR THE LONG HAUL
July, 2014 6
Using this formula, the estimate is that 35% of salary paid a worker to reach Full Productivity is the cost of job learning
(regardless of the learning curve duration). In this case, that’s about $14,000, out of $40,000 paid over the course of the 6-
month learning curve. That money does not generate revenue; it is an investment in future revenue and therefore, I contend,
capital in nature.
Or graphically, the portion of the salary enclosed in the shaded square below is what we’re focusing on. The area in the box
above the curve is 35% of the total area. The curve continues to grow in height because job learning continues to grow,
though at an ever-decreasing rate.
Figure 3: Graphical View of the Learning Curve Cost Model
Every time the contract matures or attrition occurs and the position is re-filled by another worker, the company discards that
investment and pays it again. If the point of an arbitrary contract tenure limitation is to insure the company against a
consultant suit for employee benefits, this is a very high premium for a self-insurance policy.
A Philosophy of Human Capital
My concern about accounting for the investment in people harkens back to a concept that was very popular in the ‘80s, with
ebbs and flows since: Human Capital. The term has regained popularity recently but, from outside HR looking in, the current
motivation seems to be to re-label & rejuvenate the traditional Human Resources functions.
We all adapt terms to refocus each others’ attention, to jar stereotypes. But this use of “Human Capital” (Human Capital
Acquisition / Development / Management, etc.) dilutes the traditional meaning of the word capital, from being an accounting
container of measurable, monetary value to a vague reference to what amounts to a very generic label of the unquantifiable
value of a company’s workforce. We talk these days about relational capital, which we accumulate by helping others so
they’ll want to reciprocate. But there are understandably no metrics for such terms.
Truth be told, I’ve never liked being labeled a resource either, as in Human Resources. It has the connotation of some
humans being used by others to achieve an objective, like chess pieces on a project management Pert Chart board. A resource
is something expended, used up, in the process of building or achieving something. So being called a resource isn’t exactly
motivating.
Learning Curve Salary Allocation
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 6 11 16 21 26 31 36 41 46 51
Pay Periods
JobLearningas%ofSalary

7. SHORT-TERM STAFFING FOR THE LONG HAUL
July, 2014 7
If this sounds like I’m whining over what others would deem semantics, I suppose I am. But I’m speaking to the basic need
of rank and file workers to feel significant, not manipulated or mollified. This need can be served legitimately by focusing on
Human Capital investment in the language of the bean counters, and the implications its estimation will have on how we treat
people. And I contend that this change will make a subtle but perceptible, psychological change in how a company views and
treats its workers. No, I don’t have a metric for that yet but I believe it will happen. Thus this philosophical aside has a
purpose here, to argue for policies that value people.
One reason few of us have ever believed the empty mantra, “Our employees are our most valuable asset,” is that the financial
statements don’t say so and profit-motivated staffing decisions for instant bottom-line improvement say otherwise.
Stockholders want their consciences free from the guilt of mistreated, abused workers but beyond that, an over-worked, over-
stressed workforce with high turnover is ignored as an acceptable cost of profit. And that’s because there are no metrics (of
which I’m aware) to compare that quality of workforce and productivity with another. Well, I’m introducing a metric here
that I hope will help. But a little more about Human Capital.
There have been erstwhile attempts to measure the monetary value of workers to the company. Some promoters of Human
Capital statistics ruminated in the ‘80s compulsively over metrics for knowledge, skill and experience, but that problem has
exponential proportions. Highly fluctuating skill set requirements, high turnover as well as frequent, knee-jerk staffing and
layoff decisions make that kind of valuation impossible. If number-crunchers really need to know the value of their
workforce, let them state it simply in terms of the size of the payroll; that’s the job market’s metric. But, as I’ve shown
above, we can estimate the investment in Human Capital: the cost of the learning curve, bundled with hiring and training
costs, and this will make a difference.
HUMAN CAPITAL ACCOUNTING - IMPLEMENTATION
Implementation – Data Collection
We now have a model to calculate a reasonable estimate of the cost of new-hire, on-the-job training. So what? How do we
record human capital investment and use this information?
First, executives, managers and Human Resources will want to approach their auditors and board with this change in the
accounting treatment of salaries. Assuming they get buy-off – OK, maybe a big if, but go with me here – they will need to
time the transition, say, between financial reporting periods or the end and beginning of the financial year. They will also
need to reach a consensus on which positions and levels qualify for this change in accounting treatment. Professional,
technical white-collar positions with the highest and/or mandated turnover, employee and consultant, would be the best
candidates, particularly those with significant learning curves.
Initially, and once a year thereafter, HR will ask supervisors of the targeted positions, and their direct reports, to discuss the
learning curve and estimate its average length. That’s a half-hour meeting, at most. Supervisor estimates could be weighted in
the averaging, if desired, but workers’ opinions are valid, too. Prior year estimates may differ but probably not significantly.
HR would collect estimates for each job class, category and position title, average them across similar departments if desired,
and enter them into the payroll accounting system.
Implementation – Payroll Accounting & Financial Reporting; Staffing Decisions, Procedures and Policies
New and revised HR software will be needed for indicating which positions are targeted for capital investment treatment and
for capturing the learning curve estimates. There will need to be a new Human Capital account, of course. Each payroll cycle,
salary will be posted according to the computation that segregates salary expense from capital investment for those positions.
That dual posting transaction of salary will occur with each payroll cycle until the employee leaves the company, even
though the contribution to capital will increase at a decreasing rate over time. There will also be software revisions to
financial & General Ledger reports, revisions to accounting period transactions, etc. Upon termination, the worker’s
accumulated capital investment will be subtracted from Human Capital and added to Salary Expense, partially offsetting the
reduction in Salary Expense.
Upper management will then need to revisit the policies and procedures for staffing, layoffs and other decisions, based on
this new financial data and it’s implications to true cost effectiveness.

8. SHORT-TERM STAFFING FOR THE LONG HAUL
July, 2014 8
Implications: Costs and Benefits
There will be up-front costs involved to identify positions targeted for Human Capital accounting, to collect and revise the
learning curve duration estimates and to modify payroll accounting software. But the benefits are many times greater, all
other factors being equal.
Staffing Policies The future value of a Human Capital investment should moderate the setting of contract renewal
limitations. Management can determine the optimal, minimum tenure of a new worker, whether
consultant or employee, based on the costs of recruitment, hiring and job learning. Yes, turnover
is high in today’s job market but eliminating or reducing mandated turnover means lower staffing
costs. That’s good news for HR and every department that utilizes consultants. That’s also good
news to staffing firms, which would see more stable income. And that’s good news to workers
who want to stay in a position as long as possible.
Staffing Decisions The net effect of a layoff and attrition will be both a reduction in salary and the associated
reduction of Human Capital investment that becomes an offsetting increase in Salary Expense.
The financial reports will reflect this reality and the tactic of layoffs as an artificial, instant
improvement to the bottom line will be moderated – by the reality of lost capital. This will provide
more continuity of trained workers, improving efficiency and profitability. And management will
have to seek cost reductions elsewhere.
Productivity Metrics Management expectations of the contribution from newly hired workers to revenue production
will be more substantiated, formally established, and be a benchmark for performance reviews.
Training Costs Rather than handling training costs as expenses, often used up at the end of a budget year to
preserve the current allocation, they will be treated as investments in human capital. As such,
management and training developers should feel pressure to increase their diligence in the timing,
quality and placement of training investments so that they generate real returns in work
performance – and reductions in the length of the learning curve.
In addition, management will more readily accept the costs involved in developing new-employee
orientation, structured on-the-job training – and documentation, especially in IT – as investments
in a shorter learning curve. In software development, this perspective may justify a dedicated
technical writer to maintain documentation on business/work flow, data flow and system design.
Technical writers are often the first to go when IT management considers reductions. But solid,
up-to-date system and business process flow documentation is essential in any work group
plagued with high turnover – if we want to shorten the learning curve. And a shorter learning
curve estimate justifies the cost of that documentation.
It is worth noting that this model does not take into account the time spent by trained workers to
transfer job learning to new workers. Accounting policies should require that experienced workers
keep track of this time separately so it can be added to the human capital value of the new
workers. The effect would be an even higher Human Capital investment value - and a lower, true
overall cost of productivity.
Financial Statements Reallocating a portion of salary expense as Human Capital investment reflects the reality that
revenue-productive expenses are actually lower than previously reported and the capital value of
the company is higher. The accounting period in which Human Capital accounting is introduced
will see improved financial reports (all other factors being equal) – because they will reflect a
reality not previously measured – and those reports will contain explanations of this change in
accounting treatment.

9. SHORT-TERM STAFFING FOR THE LONG HAUL
July, 2014 9
Legal Protection Back where we started. The Legal Department may be nervous about modifying or eliminating
contract tenure limitations: “What about protecting the company from lawsuits by consultants who
think they’re employees?” First, I think the legal departments over-reacted big-time to that ’98
lawsuit against Microsoft and to the subsequent court ruling. Go ahead and maintain all those
other arbitrary distinctions between employees and consultants if you have to; but drastically relax
or eliminate the contract renewal limitations and simply require consultants to sign company-
protecting statements upon each contract term renewal. We consultants wouldn’t mind certifying,
“I recognize I am not an employee of and am not treated like an employee of <company-name>. I
hereby revoke any future claims to benefits of employee-hood (unless extended an offer of direct
employment by said company) and promise never to sue said company for employee benefits
while I am a consultant (who is actually an employee of the staffing vendor). This statement is
hereby sealed by my signature and a drop of my blood below.” Something like that should do it.
CONCLUSION
Whether corporate accountants, auditors, board members and executives will recoil from this change in the GAAP status quo
in the accounting treatment of salaries is up to them. But the cost of the learning curve is significant, no matter how you
measure it. Even if this record-keeping doesn’t make it into the accounting process, companies owe it to their owners /
stockholders to consider it seriously, for employees as well as consultants, when considering staffing decisions. And we
consultants wouldn’t mind a little more job security either. Oh, right – another oxymoron.
References and Acknowledgements
Due to his arrogance or forgetfulness of inadvertently appropriating others’ ideas, the author may be in unknown debt to
others’ genius – though he really doesn’t recall this topic ever receiving attention or treatment any place else. He can and
does attribute the general concepts and foundational business content to what he can remember of the instruction his
professors gave him in pursuit of his master’s degree. One topic, however, that required some research was the mathematics
of the learning curve. Learning theory is not his area and neither is what he would term esoteric mathematics.
Several web sites provided a good summary of the issues, problems and solutions of this topic:
Federal Aviation Administration, Guidance, Pricing Handbook, Chapter 18: “The Learning Curve,”
http://fast.faa.gov/pricing/98-30c18.htm
“The Learning Curve or the Experience Curve,” Summary by James R. Martin, Management and Accounting Web,
http://maaw.info/LearningCurveSummary.htm
http://www.business-analysis-made-easy.com/Learning-Curve-Spreadsheet.html
“The Learning Curve for Industry,” by Erik van der Merwe, (The University of Cambridge, Institute for Manufacturing)
http://www.ifm.eng.cam.ac.uk/csp/summaries/learningcurve.html
However, these publications are all predicated on the study of reducing the length of time needed to complete discrete units
of work. Those models are not applicable to the kind of job learning required for the positions that see the most turnover in
technical positions: programmers, analysts, testers, project managers and the like.
Realizing the need for a curve formula that matches the general, intuitive sense of job learning for technical jobs, the author
received direction from mathematician friends to a basic exponential function and tailored it to meet the context.
__________________
The author holds a Master of Business Information Systems degree from the G. Robinson Mack School of Business at
Georgia State University in Atlanta, GA. His 30-year career spans every aspect and role of software development: computer
operations; application and systems programming; systems analysis and design; quality assurance; business, data and process
analysis; training development and delivery; college-level instruction; and project management. For the past 16 years he has
worked as a consultant while living in Colorado with his wife and daughter. But to his shame, he has only been skiing once in
the Rockies – on the bunny slope – on the last day of a season.