The State of Employee Health Care Benefits – An i4cp Research Report

The following is the executive summary of a research report I wrote for the Institute for Corporate Productivity on employee health care benefits.  The full report is available to members of i4cp. I am the director of primary research for i4cp.

The financial burden of employee health care benefits has long been a source of angst for employers and employees alike. Employers’ annual costs of providing health benefits have risen at double-digit rates the past decade and employees have increasingly taken on greater cost-sharing and diminished coverage.

With recent legislation opening up the range of employee health care solutions, employers and employees need to recalibrate their strategies and expectations. The old status quo of offering competitive yet minimal health care benefits may soon become untenable for organizations intent on attracting and retaining key talent as the economy recovers and hiring picks up.

i4cp surveyed 138 U.S. leaders and specialists in employee health care benefits in mid-2011 to find out how organizations are currently approaching health care benefits and how they are planning to do so in the coming year. The survey went beyond examining offerings and plan parameters and asked organizations about:

  • Their rationales for the current portfolio of health care plans offered;
  • how the portfolio will change and why;
  • which cost-management approaches have been most effective;
  • and which approaches are most effective in communicating to employees about health care benefits.

The survey data confirm that cost management is the prevailing strategic concern among respondents and that most organizations are delaying making major changes or overhauls to their health care benefits – at least through the next year.

And while significant change is not in the offing in terms of the types of health plans employers offer employees, high-performing organizations are differentiating themselves by communicating about benefits effectively, emphasizing wellness and health finance literacy, and incenting employees to make healthy lifestyle choices. High-performers also employ online medical expense calculation tools and online medical plan comparison tools to enable employees to model outcomes.

Despite the continuing popularity of preferred provider organizations (PPOs), there is still plenty of cost-saving potential in consumer-directed approaches that hold individual employees accountable for the lifestyle and health choices that add to employer costs. There is also room for thoughtfully designed incentives to nudge employees toward healthy and smart choices.

 

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Paradigms and People

Economics gets a bum rap as the dismal science or the undergraduate major of last resort. The truth is that the subject gains relevance only after you’ve had some real world work experience and are in a management role that requires smart decisions that have serious consequences. It’s a different story when your career is on the line rather than just a grade.

There’s a treasure trove of powerful concepts and tools that were likely over-simplified, glossed over for being too technical, or omitted from the standard undergraduate economics treatment. Together with insights from recent, revolutionary advances in the field, there’s a strong case for another look at economics and the competitive advantage it can offer you – as a worker, manager and leader.

HR leaders are accountable for human capital strategy and talent management through program design and execution. At the 2012 i4cp annual conference, I will be leading a workshop that introduces or re-acquaints participants with the key elements of economic science that will enrich their world-view of the people component of business and will help change the way they think about the fundamental aspects of their work: strategy, information, analytics, design, and planning.

The workshop will examine real-life issues and challenges faced by HR leaders through an economist’s lens. As a result of attending, you will be able to (among other things):

  • Enhance strategic thinking with key results from game theory – the science of strategy
  • Think outside the box by relying on the principles of constrained optimization
  • Appreciate the value of learning through human capital theory
  • Design water-tight contracts and optimal incentives using agency theory
  • Factor in macro- and micro-economic implications of labor economics
  • Take psychology into account with the new science of behavioral economics
  • Unleash human capital analytics with econometrics.
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“Solid State” Compensation – Part 1

There’s an established tradition in the social sciences of borrowing ready-made concepts or equations from physics and other sciences. The most famous is the use of a heat equation in the Black-Scholes option pricing model.

There’s a growing movement in talent management that favors career “lattices” over career ladders. The terminology always reminds me of the lattice structure of crystals that I encountered in a solid state physics course in college. Since talent management and reward go hand in hand, I thought it was time to introduce solid state compensation to the world.

The notion of career paths within an organization is well known. It is often offered up as part of the employee value proposition and typically implemented through the career development dimension of the performance management system. The idea is to make explicit the jobs and roles one might take through a career in an organization. Employees can have more confidence their long-term prospects if there are more career paths that lead to senior leadership positions with the organization.

However, career paths have mostly been viewed as linear, monotonic and deterministic. Hence the synonymous usage of the term “career ladder.”  The analogy captures the characteristics of career paths perfectly. A ladder is linear – one step leads to the next in equal, unchanging increments; it is monotonic – you keep going up or you keep going down (think “snakes and ladders”); and it is predictable – you see every step ahead of you.

This worked well for a while, but is wholly unsuited to how organizations now think about their human capital as well as how individuals think about their careers. Organizations who are good at talent management now think in terms of the career lattice, in which you can move up, down, and sideways – and keep doing it again and again. The fluidity is good for all.

The employee gets exposed to new challenges and situations, allowing them to diversify their individual human capital and enabling them to vie for bigger and better roles in the future. The organization gets more well-rounded employees and has more degrees of freedom in workforce planning.

The fly in the ointment – or to stick with the physics metaphor, the force that impedes this mobility – is the gravitational push on salaries. They never go down! There are some pockets opening up in this gravitational push as companies have had to take serious measures to rein in labor cost during tough economic times. Salary freezes have always been common, but now there are often salary decreases as well. In some research I did on salary outcomes for learning and development professionals, we learned that 4% received salary decreases last year.

As employees move around in the so-called lattice, especially in what are known as “lateral moves,” they sometimes end up in roles whose salary rate is lower or higher than their current role’s salary rate. Companies and individuals struggle with this. Companies are too caught up in their salary structures and rules around promotional increases. According to WorldatWork research, only 8% of companies provide salary increases for lateral moves (it’s interesting to note that the salary decrease question is not asked). Individuals have a hard time wrapping their heads around a salary decrease, even if it is temporary and even if it increases the likelihood of a much higher salary in the future.

There is a lot of confusion as a result. HR leaders want to leverage the talent management benefits of career lattices, but feel hamstrung by unclear guidelines around how the sensitive issue of pay is handled.  Compensation decisions are difficult enough in ordinary circumstances, but they take on a new level of complexity when it comes to compensation actions associated with lateral movement.

I’ve worked out a framework that will help organizations navigate the swirling waters of lateral movement compensation. It can be viewed as a series of lenses with which to examine the organization’s ability to manage lateral movement compensation. If you think through it and work out the right sequence to follow, it can serve as a roadmap to get a complete handle on lateral movement compensation.

I will introduce the framework in this post and dive deeper into it in a subsequent post.

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“Unaltyics” Award Nominee #2

Earlier this year, we launched the 2011 Human Capital Unalytics Award to recognize half-baked thinking in the field of human capital analytics.  We now have a second nominee to consider for the award.

If analytics represent the use of data, analysis and systematic reasoning to make human capital decisions, then these awards are for the opposite – the misuse of data, poor analysis and fuzzy reasoning to make human capital decisions.  Hence:  “unalytics” – short for un-analytics.

Nominee #2 is a US-based human capital institute “founded on the belief that organizations can, and must, find better ways of measuring their investment in human capital.” There is no argument there and it is only proper that Nominee #2 has joined the global struggle to link human capital investments to business outcomes.

Everyone has rightly moved beyond the obsession around demonstrating the value or efficiency of the human resources function.  All the attention is now focused on identifying and measuring the business impact of organizations’ “most valuable asset.”

One strategic front in this struggle is involves the hearts and minds of investors and financial analysts. The race is on to identify a handful of key human capital metrics that will be embraced by the likes of FASB (the Financial Accounting Standards Board) for inclusion in public companies’ financial statements. These winning metrics would offer investors information on the quality of a company’s human capital stock, allowing them to make smarter investment decisions.

Into this heroic struggle steps Nominee #2, Icarus-like in its eagerness to reduce the impact of workforce productivity to a single mathematical formula. In a white paper (what’s that?)  entitled Human Capital Financial Statements, Nominee #2 presents its total workforce productivity impact formula and components as follows.

The equation certainly looks impressive, with many variables and multi-lettered subscripts. Could this be the basis for a unified theory of human capital business impact?

It might be if sufficient explanation were provided to support the argument. However, we are left to fend for ourselves and so here we go. Let’s deal head on with some equation fundamentals and then move on to the underlying mathematics and economics to see if we can appreciate Nominee #2’s contribution.

Asterisks might be acceptable in an Excel formula, but they don’t necessarily represent the multiplication operation in mathematics. The asterisks need to be dropped altogether or replaced by “.” – that’s how multiplication is represented in equations.

Equations are meant to simplify relationships, so we suggest the use of single letters to represent variables? Perhaps replace TWPI with I (even though I is typically reserved to represent investment); TWH with H for headcount; and TCOW with C for cost? We will use these symbols from now on.

In an equation that involves different periods, the variables’ time labels are typically subscripted. TWPIn should therefore read TWPIn.  Furthermore, if you are talking about a “period n,” relative to other periods, either before or after it, you cannot throw into the mix “current year” variables. In other words, the “current year” has to be related to the “period n.”   To understand this better, let’s simplify the equation by replacing the terms in parentheses with letters for now:

Ostensibly, n can take on integer values 1, 2, 3, 4, etc. But if CY stands at 2011, the equation cannot hold – one right-hand-side value (e.g., for CY=2011) is equal to many left-hand-side values (e.g., n=1, n=2, etc.). You have to relate n to CY.

Perhaps you could replace both with t? But this doesn’t feel right; surely there was a sequence of events.

Perhaps n can be replaced by t+1 and CY can be replaced by t? Danger! This becomes a recursive equation and can be hard to compute.

The bottom line is that we have no idea how the variables are related to each other across time, even though a specific temporal relationship has to exist since we are talking about cause (human capital input) and effect (business outcome).

Although R, P and M (for revenue, profit and market capitalization) are subscripted with vfte (for variance per FTE workforce), we are told in the accompanying text that that these variables – “revenue, profit and market capitalization per full-time equivalent [are] integrated measures of financial performance represented as a ratio of the organization’s full-time equivalent (FTE) workforce.”

So there’s no variance going on at all – these are basically per-FTE figures. I think we can dispense with the cumbersome vfte notation and just define R, P and M as revenue-per-FTE, profit-per-FTE and market capitalization-per-FTE.

Uh-oh. Profit is a function of Revenue. At a high level, Profit = Revenue – Expense. If this were a fundamental equation of workforce productivity impact, then every term should be independent – i.e., not a function of another variable.  We’ll let that slide.

Now that we are no longer distracted by the notation, let’s talk units. One way to ensure an equation is correct is to check that you have the same units on both sides. Unfortunately, we are not told what units TWPI is denominated in. Let’s see if we can figure it out by working out the units on the right-hand-side of the equation.

The right-hand side has two terms. The first term has two components: (R+P+M) and H. The first component’ units are $/FTE. The second component’s units are heads. Oh wait – if only we had denominated H in terms of FTE’s, then the FTE’s in the numerator and denominator would cancel, leaving us with $. Perhaps Nominee #2 meant to define TWH as FTE? Let’s assume he did.

The second right-hand-side term also has two components: (Cr-Rr) and Ccy. Since C and R in the first term have the subscript r, denoting percentage change per period, C and R must be in %. But Ccy is in $.

Oops. You can’t add $ and %$. We are in a very Yeatsian situation: “things fall apart; the [equation] cannot hold.”

Ok, so the math doesn’t work. Perhaps the economics will? Unfortunately, economics don’t make sense if the math fails to compute.

Nominee #2, you have well and truly stumped us. Congratulations – not yet – there are many contenders yet to come!

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Career Advice on Human Capital Analytics

In my Human Capital Institute (HCI) Executive Conversations webinar on “Making the Most of Human Capital Analytics” earlier today, I did not get a chance to offer the participants advice on how to get started in analytics.

Here is the slide that I did not get a chance to cover. I think the bullet points cover the message adequately, though I’m happy to elaborate if anyone has questions.

The bottom line is that in your first foray, don’t try and boil the ocean. Find an issue, situation or problem that you can wrap your arms around and throw light on through some thoughtful analytics.

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Team Incentives and the Free Rider Problem

Team incentives, where an individual’s compensation is based on the team’s performance, are notoriously difficult to implement. Yet more and more work in companies large and small is being done through formal or informal teams. What do we need to know in order to design better team incentives?

Standard economic theory is a good place to start (though it will not provide all the answers). Economic theory assumes that people prefer leisure to work and are self-interested. Team incentives therefore fall victim to the “free rider” problem.

If all team members are rewarded for the team’s output, then there is little incentive for a self-interested, leisure-seeker to contribute toward the team goals. Since the “shirker” cannot be excluded from the reward, he becomes a free rider – enjoying the rewards without adding any effort. Game theory predicts that all team members look around and decide that their best strategy is not to work either, so the team ends up not working at all. The group incentive has led to a completely undesirable result.

Of course, this is a rather extreme portrayal, but it drives home the point and illustrates why any good compensation professional will tell you “be careful what you pay for.” There are a few ways of dealing with the free rider problem.

The simplest solution is to introduce a way of monitoring effort so that you share in the reward only if there is evidence that you have worked hard enough or well enough. This is what performance management is all about. The monitoring can be done by the firm (e.g., the manager) or by other members of the team. The latter is only practical if the team is small and each member’s effort is observable and measurable.

The situation can be saved if there is repeated interaction among the team members. If there are multiple opportunities for the team to work together for a reward in the future, then team members will be more likely to co-operate in the current project and work.  If they don’t, they may lose future earning opportunities. There may be reputational consequences for poor individual effort. This works out well if teams are formal (i.e., not ad hoc and one-time) and long-lived.

Another dimension to this is if there are social sanctions available. Groups that work together might also “play” together – i.e., have relationships beyond the confines of the team situation. Members can put pressure on team-mates to shape up or ship out.

When team members are close-knit, they internalize each others’ welfare. This builds a positive, co-operative, stable equilibrium. This approach jibes with Jon Katzenbach’s observation of high-performing teams (what he calls “real teams”): that team members become invested in each others’ development and well-being.

Given the above discussion, we can say that the factors that influence whether team incentives will work or not include team longevity, size and composition.

The longer a team stays together, the more powerful the measures of peer pressure, social sanction and internalized welfare.

The larger the team gets, the less members are able to monitor each other, reach a collusive equilibrium, exert social sanctions or internalize each others’ welfare. Other, external factors will play a more prominent role. Also, there will be fewer opportunities to measure relative performance since there will be fewer similar teams for comparison.

Team composition is an important factor. Allowing teams to form autonomously has risks: the team may play well internally but may not play well with other teams. On the other hand, teams of like-minded individuals who have self-selected into the team knowing who they will be working with are likely to perform better. Heterogeneity in team composition is a good thing and is supported by Katzenbach’s notion of complementary skills.

So what is to be done with respect to team incentives? You clearly have to be very careful to avoid the free rider problem. Ownership can play a large role, as can other ways to build and sustain high commitment to a team’s objectives. Even though everyone is not the perfectly rational economic agent that economic theory assumes, you still need to model the game-theoretic or strategic outcomes in order to thwart team members’ efforts at “gaming” the team incentive

Katzenbach’s work on teams is outstanding and serves as a firm basis for exploring team incentives. The literature on team incentives is quite thin. Is anyone interested in doing the hard work on this tough subject with me? Do you have any data on teams and team incentives that we could examine for insights?

 

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10 Grains of Salt to go with HR Research

The increasing popularity of human capital analytics has HR professionals contending with more statistics than they bargained for when they entered the field.  Is HR equipped to capitalize on the burgeoning human capital analytical research?

My sense, based on questions received from HR practitioners and fellow consultants is that the research is getting slightly ahead of the HR’s ability to absorb it, let alone act upon it. It may not be politic to say this, but we need to close this gap through a better appreciation of how the research is conducted how much credence to place in the results.

We can’t shy away from the fact that this boils down to getting a better handle on statistics.

At more and more human capital analytics conferences, I’ve noticed that the audience is reluctant to ask the presenter about the statistical model underpinning their analysis and recommendations.  On the contrary, there appears to be an eagerness to accept the results without a healthy dose of skepticism. The discussion invariably moves toward relating the results to individuals’ anecdotes.

Audiences are reluctant to ask “troublesome” questions for a number of reasons. These include respect for the presenter’s credentials; not wanting to spoil the wondrous evidence-based story being related; hesitance at side-tracking the discussion onto a boring (i.e., statistical) tangent; avoiding being seen as “geeky” for asking what are viewed as “nerdy” questions; and fear of being viewed as a mathematically challenged.

However, many of these fears are unfounded and everyone, audience and presenters alike, would be better served if people asked more such questions. Other audience members will likely be grateful for the question. The response will provide greater clarity and allow everyone to get more out of the presentation. And researchers and presenters need to be kept on their toes and accountable for their results and recommendations.

It is not necessarily a bad thing that the bulk of research presented at HR conferences does not meet academic standards of rigor. After all, practitioners want practical results, not theoretical discussions. However, the flip side is that you sometimes have to take the HR research presentations with a grain of salt.

Let’s review some aspects of HR research in terms of where it falls short of academic rigor. The purpose is not to cast HR research in poor light, but rather to appreciate the degree to which we ought to feel comfortable acting on the results.  HR research need not fulfill all the requirements in full measure, but at least we can ascertain how wide off the mark we might be and therefore how many grains of salt to add.

I’ve included some related questions that conference participants might want to ask presenters in the spirit of enriching the discussion and debate when HR research is presented.

1. The analyses have typically not been peer-reviewed. This means that there usually has not been an independent and authoritative quality check on the data, methodology and conclusions.  Even with the loftiest credentials, it is possible to omit important considerations, make errors in model estimation or misinterpret results. Review by qualified independent third parties ensures professional standards and avoids potential embarrassment for the researcher.

Questions to ask include: “Is this the first time your results have been presented? What sorts of feedback have you received on your research to date? Were you able to address all their concerns? Have you considered submitting this research to a peer-reviewed journal?”

2. Researchers are not obliged to allow their analyses to be replicated. The ability to replicate results is a scientific standard that requires complete transparency and guarantees rigor (does anyone remember the hullaballoo on cold fusion back in the ‘90s – no one could replicate the scientists’ amazing results?). HR data are proprietary, confidential and sensitive, so there may not be a way around this one. However, recent inquiries into the impact of HR policies on business outcomes often use aggregated, anonymous data and researchers should share data sets when possible.

Question to ask include: “Can you share your data set so that I can do some further analysis? If you can’t share your data, could I send you some hypotheses to be tested? Are you aware of similar results as yours by other researchers using comparable data?”

3. Results are typically presented selectively. You seldom hear about the negative results or the research that supports a contrary point of view. Even secondary research in HR, which examines primary research from various sources, neglects to tell the whole story. It would be helpful to get a summary of the current state of research on the topic.   

Questions to ask include: “Can you tell us about dead-ends your research may have led to? What are some of the contrary findings in the broader research into this topic?”

4. Correlation is sometimes packaged as causation. Or even if the usual disclaimer “correlation does not imply causation” is made, it is hard for the audience not to leave with the impression that an outcome is directly caused by an action. For example, we can’t be sure if employee engagement causes positive business results or positive business results drive employee engagement. However, investment in employee engagement is typically encouraged on the basis of impact on business results.

A question to ask is: “How comfortable are you in claiming that there is causation, not just correlation here?”

5. Multi-variate effects are not adequately treated or explained. Too much research in HR looks at one independent variable at a time. However, the world is not two-dimensional and multi-variate analysis is more appropriate. When looking at the simultaneous impact of many independent variables, you have to be careful with the interpretation of individual independent variables on the dependent variable.

Questions to ask include: “How are you controlling for the influence of other relevant factors? What are some of the other relevant factors that you were unable to find data on to include in your model?”

6. The exhibits seldom contain the information necessary to judge how much credence to place in the results. Perhaps measures of statistical significance such as p-values, t-tests, F-tests and their ilk might not resonate with many participants, but at least there is full disclosure and someone looking at the charts at a later date has the full picture.

A question to ask is: “Can you please talk about the statistical significance of your results and whether you think these results can be generalized?”

7. Crucial information about the sample is often glossed over. The size and nature of the sample as well as the sampling methodology have an important bearing on the results. Biases stemming from sample size and sample selection need to be taken into consideration when evaluating the results of the analysis. I’ve raised this issue in a previous post.

Questions to ask include: “Can you please tell us more about your sample? Why did you use this sample? How did you select the participants? Was there any randomization in the selection? Could sample selection be driving your results?”

8. Adequate context for the research is not provided. A good research product summarizes some of the historical literature and main findings – whether they support the results or not. The context and the researcher’s motivation for examining the topic help us to appreciate the specific approaches used and objectively evaluate the conclusions reached.

Questions to ask include: “Can you please talk about your motivation for exploring this topic? Is there a gap in the general research on this topic?”

9. References are not provided. Unless the research is presented in a peer-reviewed journal, it is unlikely that any sort of bibliography is provided. It is important to know what material the researcher has leveraged and how many of the ideas in the research are original. A list of references offers a number of advantages. It tells us at a glance the depth of secondary research; it alerts us to any biases the researcher might have based on what sources are mentioned; it gives us some comfort that homework has been done; and it provides a reading list for those interested in learning more about the topic.

Questions to ask include: “Can you share a list of references – i.e., articles, web-sites or books – that you used in your research? Can you suggest a few items for those of us interested in learning more about this topic? Which one article or book would you recommend we read to complement your presentation? Is your idea new?”

10. Results are over-generalized. Research results need to be qualified heavily since they are often very specific results that hold in specific situations. The results need to be qualified on the basis of sample selection, sample size, modeling assumptions, statistical significance, etc. This can become quite a mouthful when communicating the results and it is understandable why the focus is on the result not the qualification. However, we might avoid some mistakes if we check in what exact circumstances can we typically expect the results to hold.

Questions to ask include: “That’s a very strong statement; do you need to qualify that in any way? Are there situations in which you would not expect your result to hold?”

 

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