RatinglessPerformance Management

Lisa Harpe, PhD

Peoplefluent Research Institute (PRI)

Risks and Rewards

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Agenda

History of performance ratings

Movement to ratingless performance management

• Components

• Examples

How to reward good performance without ratings

How to include performance in studies if there are no ratings

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History of Performance

Ratings

• 1900s: Personnel Administration

Movement

• 1920s: Human Relations Movement

• 1940s: Management style

• 1950s:

• 1950 Performance Rating Act

• 1954 Incentive Awards Act

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• 1960s:

• Employer Employee X

• Salary Reform Act

• 1970s: Performance Management

• Most companies had performance

ratings

• 1980s: MBO

• To address central tendency with

ratings movement to forced

distribution systems led by GE

• 1990s: Dissatisfaction with

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• 2000s:- Lawsuits

- Technology

- Challenges

• Remote workers• Geographic dispersion• Increased dissatisfaction

• 2008: ‘Get Rid of Performance

Ratings’ by S. Culbert

• 2009: Reuters survey: 4/5 workers

dissatisfied with performance reviews

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‘Typical’ Performance Management System

Beginning of Year

• Goal Setting

End of Year

• Manager Provides Ratings

of Employee

• Employee Provides Ratings of Self

• Manager and Employee Meet to Discuss Ratings

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Example Performance Rating Form

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Example Performance Rating Form

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Example Performance Rating Form

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Example Performance Rating Form

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Types of ‘Typical’ Ratings

Categorical

• Competencies

• Goals or Objectives

Overall

• Category weighting

• Assignment or calculation

Preliminary v. final

Midyear v. annual

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Discontent with Ratings

Cumbersome

Poor implementation

Poor training

Bell curve is wrong

Static

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Discontent with Ratings

Poorly equipped to measure performance

today’s workforce

• No measurable output

• Service / knowledge workforce

• Dynamic nature of industry

• Geographically disperse workforce

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Discontent with Ratings

Backward looking

Demotivating

• SCARF hypothesis

Not reflective of performance

• Mount et all (2000)

• Joshi et al (2015)

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Performance Ratings and the Law

Siegel v. Ford

Donaldson v. Microsoft Corp

Jones v. Goodyear Tire & Rubber Co

Wal-Mart

Novartis

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Going Ratingless

Resources

• Harvard Business Review April 2015

• Culbert book 2008

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Going Ratingless

Throw out ratings, distributions,

annual review meeting

Focus on regular, on-going

feedback

Keep goal setting

Stay flexible

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Key Component: Manager-Employee Discussions

2 options

1. Highly structured conversation

2. Guided conversation

On a regular basis

Emphasize goal setting and adjustments

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Examples

Networking Company

• No ratings

• Structured conversations

• J Player v Non J Player

Clothing Retailer

• GPS

• Ongoing support

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Examples

Document and Marketing Solutions Company• Regular check-ins

• Focus on 3 elements• Expectations

• Feedback

• Growth & development

Software Company

• Until 2013, used a curve / forced ranking system

• In 2013, abandoned ratings

• Stress continual learning and growth

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Examples

Recreational Equipment Retailer• ‘anchor points’• Bi-annual calibration meetings

Financial Advisory Company• Moved away from cascading objectives, annual review,

360-degree feedback

• Performance snapshot based on 4 statements1. Given performance, I would award this person highest possible comp

and bonus.

2. Given performance, I always want this person on my team.

3. This person is at risk for low performance.

4. This person is ready for promotion.

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What about rewards for good performance?

A rating by any other name?

• J vs non J player

• Anchor points • Leading performers

• Solid performers

• Developing performers

• Performance snapshot –

agreement with 4 statements

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What about rewards for good performance?

World at Work Survey

• 80% use manager discretion

within budget

• 42% use calibration sessions

• 25% use ‘shadow’ ratings

• Many use multiple approaches

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Employment Decisions Related to Performance Ratings

Pay

• Merit increases

• Bonuses

Non-monetary rewards

Promotions

Disciplinary actions

Terminations

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Pay

Pay for performance philosophy

When pay disparities exist,

performance often cited

as justification.

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Base Pay

Identify comparison groups

Identify factors that influence pay

Analyze pay

• Regressions

• Rank sum

• T-tests

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Regression by Job

• Suppose you conduct a regression analysis by job to examine disparities in base pay along gender lines

• You identify the following factors as important determinants of base pay:

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Regression by Job: Example

The City of Oaks AAP

• 410 employees: 250 females, 160 males

Regression Model (applied, in turn, to each job)

FTE Base Pay = A base amount,

+ An increment for each year in current job,

+ An increment for each other year with company,

+ An increment for each year of prior experience,

+ An increment based on the employee’s average performance rating over the past three years,

+ An increment based on the employee’s pay grade,

+ An increment based on the employee’s job title,

+ An increment if the employee is female

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City of Oaks AAP: Regression by Job

Employees 30 R-Square 0.77

Females 15 F-Test 0.00

Parameter Estimate P-Value # Std Devs

Base Amount $35,983.96 <.0001 >3.89

Years in Current Job $2,099.19 <.0001 >3.89

Other Years With Company $536.47 0.0974 1.66

Prior Experience $21.96 0.8424 0.20

Grade B $0.00

Average Rating 1.00 – 1.99 -$3,353.99 0.5414 -0.61

Average Rating 2.00 – 2.99 -$4,709.19 0.2617 -1.12

Average Rating 3.00 – 3.99 $1,450.68 0.7202 0.36

Average Rating 4.00 – 4.99 $0.00

Female -$4,938.39 0.0032 -2.95

Male $0.00

Job IT Support

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City of Oaks AAP: Regression by Job

Employees 305 R-Square 0.91

Females 181 F-Test 0.00

Parameter Estimate P-Value # Std Devs

Base Amount $56,160.92 <.0001 >3.89

Years in Current Job $597.41 0.0112 2.54

Other Years With Company $3.13 0.9680 0.04

Prior Experience $268.17 <.0001 >3.89

Grade A $23,583.20 <.0001 >3.89

Grade B $0.00

Average Rating 1.00 – 1.99 -$4,665.44 0.2145 -1.24

Average Rating 2.00 – 2.99 -$161.61 0.9671 -0.04

Average Rating 3.00 – 3.99 -$3,041.51 0.4186 -0.81

Average Rating 4.00 – 4.99 -$714.56 0.8859 -0.14

Average Rating 5.00 $0.00

Job Title AMM -$1,513.36 0.2870 -1.06

Job Title OCW $32,446.63 <.0001 >3.89

Job Title AEC $63,926.56 <.0001 >3.89

Job Title EAG $0.00

Job Title CRC $6,736.46 <.0001 >3.89

Job Title PMK $0.00

Female -$4,458.68 <.0001 -3.99

Male $0.00

Job

Customer

Support Rep

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City of Oaks AAP: Regression by Job

Job

Project

Manager

Employees 75 R-Square 0.61

Females 54 F-Test 0.00

Parameter Estimate P-Value # Std Devs

Base Amount $31,094.59 <.0001 >3.89

Years in Current Job $716.03 <.0001 >3.89

Other Years With Company $693.97 <.0001 >3.89

Prior Experience $72.92 0.0402 2.05

Grade C $0.00

Average Rating 1.00 – 1.99 -$1,150.18 0.5969 -0.53

Average Rating 2.00 – 2.99 $1,147.69 0.4735 0.72

Average Rating 3.00 – 3.99 -$68.72 0.9656 -0.04

Average Rating 4.00 – 4.99 $0.00

Female -$1,425.69 0.0562 -1.91

Male $0.00

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Reconsidering the Pay-for-Performance Relationship

Current Base Pay = f(past starting pay rate, past merit pay increases,past promotion increases)

= f(prior experience,educational attainment,pay grade/band,geographic differentials,years of service,performance, etc.)

Merit Pay = f(performance)

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Merit Pay: Regression Example

Supervisors

• 99 employees: 57 females, 42 males

Regression Model

Merit Increase = A base amount,

+ An increment based on the employee’s most recent performance rating,

+ An increment based on the employee’s geographic pay scale,

+ An increment if the employee is female

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Regression on Merit Pay

Supervisors Employees 99 R-Square 0.83

Females 57 F-Test 0.00

Parameter Estimate P-Value # Std Devs

Base Amount $31.21 0.9083 0.12

Perf Rating: Exceeds $2,082.79 <.0001 5.59

Perf Rating: Meets All $1,515.78 <.0001 4.08

Perf Rating: Meets Most $841.20 <.0001 5.43

Perf Rating: Does Not Meet $0.00

Geo Pay Scale: A -$579.62 0.0053 -2.79

Geo Pay Scale: B -$381.22 <.0001 -4.69

Geo Pay Scale: C $0.00

Female -$1.55 0.9774 -0.03

Male $0.00

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Regression on Base Pay with Avg Perf Rating

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Merit as Proxy for Performance?

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Bonus as proxy

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Issues

Other factors influence merit (or bonus) pay

• Budget

• Department or location

• Supervisor discretion

• Unlikely a linear relationship with performance

Are there disparities in merit (or bonus) pay?

Increases for single year or across multiple years?

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Alternatives

Proxy

• % merit increase? One year? Average over multiple years?

• % bonus? One year? Average over multiple years?

• Other? Commissions? Awards?

‘Rating’ for pay purposes only

Analysis with no performance indicator supplemented with qualitative information

• Notes from performance discussions

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Possible Issues with Performance Discussions

Number of discussions by race and gender

Content of discussions by race and gender

Recorded notes by race and gender

• Number of positive vs negative comments

• Research findings

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Final Comments

Do performance ratings currently explain variance in base pay? Merit pay? Bonuses? other?

If you claim performance is related to pay, • Which kinds of pay?• What kind of relationship?• How will you quantify performance?

What factors influence pay? • Starting Pay?• Merit Increases?• Bonuses?• Other?

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Q & A

Questions?

Comments?

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