Linard_2006-System Dynamics Modeling & Probabilistic BCA_Sexual Harrassment in Workplace

8/9/2019 Linard_2006-System Dynamics Modeling & Probabilistic BCA_Sexual Harrassment in Workplace

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SYSTEM DYNAMICS & EVALUATION OF

SEXUAL HARRASSMENT INTERVENTION POLICIESKeith T Linard

Director, Ankie Consulting Pty Ltd

Email: keithlinard#@#yahoo.co.uk

Summary

This paper reports on a consulting project which formed part of the overall Future Directions of EEO in the New Zealand Public Service. The Future Directions project aim ed gaincommitment from chief executives to a Public Service direction for EEO to 2010, and tostrategies for implementation.

The specific aim of the consultancy was to iden tify the net economic and financial benefits of implementing EEO policy and programs for the P ublic Service over the long term, and in the

process, to design a rigorous economic and financial evaluation methodology for future use inevaluating HR policies in general.

This involved the following key steps:

1. the developm ent of an analytical fram ework within which to specify and describecosts and benefits of EEO

2. calculation of net costs, or benefits, of EEO and

3. extrapolation of costs and benefits over time.

It became apparent early in the project that earlier costing methodologies involved significantassumptions which had not been justified and double counting. To underpin the credibility of the financial modelling, it was decided to do a detaile d analysis of the systemic forces at play inworkplace sexual harassm ent and of the pr esumed system ic interventions through EEO policy.

Systems Thinking

Systems thinking (which is em erging as one of the m ost powerful business paradigm sfollowing the publication of ‘The Fifth Discipline’ 1) lies at the basis of BCA. It is also critical to theevaluation of the full economic and financial benefits (and costs ) of EEO.

Systems thinking views an organization and its environment as a comp lex whole of inter-relating, interdependent parts. It stresses the relationships and the processes that m ake up theorganizational context, rather than the separate entities or the sum of the parts.

1 Senge. P., The fifth discipline – the art and practice of the learning organisation . Random House, NewYork, 1990.



Systems thinking focuses on theinterrelated chain of events, not justthe event itself, and seeks to addressthe full spectrum of consequences.(Figure 1)

Consider, for example, the traditionalaccounting perspective, pre-EEO, of asexual harassm ent or racialvilification incident by a m anager.Even had it generated a form alcomplaint, at best it m ight haveresulted in a ‘quiet talking to’ f or themanager; at worst, further victim is-ation of the victim . No direct costswould enter the accounting balancesheet.

A systems thinking, perspective, however, would recognise the sense of anger, frustration and powerlessness of the victim s and their work co lleagues; it would identify the im pact on productivity and staff turnover, in the short term, and the ability to recruit and retaincompetent staff in the longer term . The sy stems thinking view would identify the verysignificant long run opportunity costs – reduction in total profitability.

Benefit cost analysis (BCA) is a system s based management evaluation fram ework whichseeks to identify, and put a dollar value on, all direct and indirect costs and all benefits whichcan reasonably be assum ed to result from a policy or decision. It is particularly designed toidentify and account for the cost or benefit im plications of longer term or indirect im pacts of

policies and decisions.

Systems thinking, and particularly one of the key tools of system s thinking, causal loop

diagrams, are central to the logic of the costing assumptions that underlie this evaluation.

Causal Loops and Logic Analysis Framework

The logic analysis fram ework was developed in the early 1950’s for the evaluation of international aid program s. Since the 1980’s it has becom e a basic tool in public sector

program evaluation. It seeks to m ake exp licit the assum ed interrelationships between

program inputs (e.g., EEO resourcing) and the program outcomes, so that these assumptionsmay be challenged and validated . The rigour of such analyses is enhanced through form al

logic modelling using system dynamics tools such as Powersim™.

The essential logic which underlies the econom ic and financial evaluation m odels isillustrated in Modules 1 to 4 below.

The language of the Logic Model

The system dynam ics m odelling fram ework include s four (4) variable types, stock, flow,auxiliary and link. The link may be a delayed link as noted.

Figure 1: Systems thinking focuses on theinterconnected chain of events . . .



STOCK: An accumulation of physical objects ($, people etc)

or of qualitative features (morale, knowledge etc)

FLOW: The instantaneous process by which a stock is filled

or emptied. The 'tap' is controlled by the decision rules

AUXILIARIES: These contain the decision rules and

assumptions which drive the mathematics.

LINK: Indicates a causal relationship between parameters.

DELAYED LINK (parallel bars across link):

Indicates a time delay in a relationship.

Module 1 – Managing of harassment incidents

Module 1 explores the presumed relationship between the level of EEO_Resourcing and legaland court expenses. In particular it illustrate s that EEO m ay be presum ed to af fect anorganisations’ costs structure through a variety of direct and indirect paths.

Thus, EEO_Resourcing is presumed to act directly on five key variables:

• Willingness_to Pursue action : As EEO supports and em powers wom en it can beassumed that the proportion of those affected by harassm ent who form ally notify incidentswill increase. W ere the proporti on of notified incidents that proceed to court to rem ainconstant, costs to the agency would increase.

• Management_EEO_Effectiveness: As a result of EEO, m anagers are presum ed to bemore sensitive to the issues, and hence m ore lik ely to be able to resolve the issue to theclaimants’ satisfaction without m atters proceed ing to court. This factor would tend tocounterbalance the increased reporting of incidents.

• Number_of_PHUGS (power hungry ugly guys) recruited : Through m ore appropriate

recruitment screening, potential harassees are likely to be reduced, tending to decrease thenumber of incidents.



• Number_of_WIMPS ( weak insensitive male persons) recruited : Through m oreappropriate recruitment screening, potential harassees are likely to be reduced, and those whoare recruited are m ore likely to be positivel y influenced by EEO training, again tending toreduce the number of incidents.

• Propensity_to_Harass: Given potential harassees exist in the workf orce, training andawareness raising (of men and women) is presumed to lead to a lowering of the propensity toharass.

In addition there are two indirect forces at play as a consequence of EEO_Resourcing. Theawareness of Cases_Fixed_by_Management_Action and Cases_Proceeding_to_Court amongthe workforce will feedback to further reduce the propensity to harass.

Module 2 – Women reacting to sexual harassment

This module explores the presum ed relationship between the level of EEO_Resourcing andtwo of the key benefit factors in the analysis, unscheduled leave (EEO related) and staff resignations (EEO related).

This Module shows (simplified) the possible consequences of an harassment incident:

1. incident is speedily resolved satisfactorily (from the viewpoint of the harassee);

2. unsatisfactory resolution – harassee continues working but with lower morale;

3. unsatisfactory resolution – harassee resigns

4. unsatisfactory resolution – harassee takes unscheduled leave and m ay subsequentlyresign or return to work but with lowered morale.





Module 4 – Managing Potential Harassers

Module 4 depicts the logical m echanisms by which EEO_Resourcing im pacts on the general

climate of EEO awareness in an organisation.

How EEO_Resourcing impacts on awareness, however, is not simple.

In modelling the logic of this aspect, we need to take into account the following:

• EEO ‘success stories’ have a ‘use-by’ date , they age – vigorous m anagement action 6years ago, or a hefty court penalty 4 year s ago do not impact significantly on people’sawareness;

• the mere existence of a ‘success story’ is but the start – the intended audiences m ustknow about it and take it into their awareness.

EEO_Resourcing generates the success stories in the first place via the mechanisms discussedin Module 1. Then, EEO_Resourcing, acting indi rectly via EEO networks, EEO training and

EEO public relations etc, assists in increasing the awareness of the success stories.

Application of this EEO Logic Model

The logic m odel has been used first to ensure that underlying assum ptions are not buried inobscure equations and secondly to assist judge ment on what costs and benefits should andshould not be included, and what effects m ight be lumped together in a single im pact. Inrelation to the latter point, for exam ple, all the positive and negative forces at play in Module1 are represented in the spreadsheet simply as “reduced sexual harassment claims liability”.



SUMMARY OF RESULTS OF BENEFIT COST ANALYSIS

Steps in applying the BCA methodology

Clearly, the systems thinking analysis was only one step in the overall analysis, albeit a very

important step from the perspective of giving credibility to the overall results. The f ollowing phases needed to be completed in order to achieve the overall project aim.

1. the developm ent of an analytical fram ework within which to specify and describecosts and benefits of EEO, this has been outlined above;

2. specification of the base case and comparison case;

3. development of the logic model to clarify the presumed interrelationships between

EEO resourcing and organisational impacts and outcomes;

4. identification of significant costs and be nefits (as noted, this som etimes involvedaggregating the end result of a combination of positive and negative forces);

5. specification of the m athematical relationships which determ ine the dollar value of

these costs and benefits;6. for all significant param eters in these equations, specification of their most likely

value, and the likely range within which the parameter m ight vary (i.e., the lowestlikely value and the highest likely value);

7. extrapolation of costs and benefits over tim e and discounting these back to presentvalues

8. development of the Lotus 1-2-3 Spr eadsheet, using the @RISK (Monte Carlosimulation ‘add-in’);

9. analysis of results.

Results

For completeness, the following summary is given of the BCA results.

Notwithstanding the various assum ptions, the study provides a strong indication that the NetPresent Value of the benefits of the EEO Consolidation significantly outweigh the costs from

both the economic and the financial perspectives.

The economic analysis, which takes into account all economic benefits and costs, suggests anexpected Net Present Value of $34 million.

Taking into account uncertainty in various assum ptions, the range of the NPV is from $14million to $55 million.

The financial analysis, which sim ply accounts for those dollar costs and benefits whichultimately impact on the agency’s balance sheet , suggests an expected Net Present Value of $32.5 million.

Taking into account uncertainty in various assum ptions, the range is from $11.5 m illion to$53 million.

Figure 5 depicts the probability distribution of Net Present Values for the financial analysis of the EEO Consolidation Option. This distribu tion reflects the uncertainty in the various

assumptions on which the com putations ar e based. Notwithstanding the significant



uncertainty in some of these assumptions, the analysis shows that benefits outweigh the costs by a minimum of $12.5 million, but could be as high as $47.5 million.

When one compares the EEO Consolidation Op tion with the ‘Free Market’ Option, the casefor EEO appear even stronger, as indicated in Figure 6.

The study has identified those param eters to which the results are m ost sensitive and whichdeserve more attention in agency data collections.

0

0.05

0.1

0.15

0.2

Values in $million

P R O B A B I L I T Y

0 7.5 15 22.5 30 37.5 45

Distribution of NPV (financial)EEO CONSOLIDATION SCENARIO

Figure 5: Probability distribution of NPV (financial) for the EEO

Consolidation Option

Figure 6: Financial Analysis -- Net Presen t Value of EEO Consolidation c.f. ‘Free

Market’ Option

‘Free Market’

Comparison

Scenario

($m)

EEO

Consolidation

Scenario

($m)

EEO Consolidation

compared with

‘Free Market’ Case

$m

Net Present Value - 2.2 30.4 32.6

Standard

Deviation

2.5 6.2 5.8



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Linard_2006-System Dynamics Modeling & Probabilistic BCA_Sexual Harrassment in Workplace