Scenario Analysis

Best-Practices Management: Scenario Analysis

A common form of risk-reduction management in industries, like finance, where results matter, Scenario Analysis is increasingly being adopted to assess the impact of policy proposals on climate change, another field where growing consensus holds that real-world, in-situ results need to be optimized. There is also enough at stake in seemingly small middle-management decisions made within institutions that collect and house valuable public and private assets and income to warrant the adoption of Scenario Analysis to mitigate risks to the enterprise.

At stake: Reducing Perverse-incentive Spillover

One of the standard problems over the past half century of institutional reforms has been the adoption of Isolated Optimization analysis and decision making throughout institutions. In the Isolated Optimization management approach, variables that the policy designer can control are optimized in isolation from the context that they will operate in. Factors that the manager cannot immediately control are excluded from the analysis and the decision-making process.

It is one thing to distinguish controllable, “Close to Home” factors from environmental factors. It is a serious overstep to ignore those environmental factors, or dismiss them prematurely as unknowable, in a strategic analysis and in making organizational decisions. Analytical reduction generally can be a useful tool for making decisions; however, it would be a mistake to equate the Isolated Optimization analytical reduction with analytical reduction generally. Isolated Optimization is just one, excessively-parsimonious variant of analytical reduction, and the risks and costs associated with this reductive variant  over the past half-century have proven too high.

Isolated Optimization policies are generally understood to be the product of the best of intentions, including positive intentions expressed toward growth, efficiency, seniority, and diversity. The problem that Isolated Optimization policies create is not bad intentions. The problems they create when they hit the road include insufficient decision-making method, and resulting bad decisions and perverse incentives.

The perverse incentive epidemic we have lived through ranges from the tax reduction movement’s perverse incentivization of labour oversupply and wage and economic stagnation, to excess carceral build up on behalf of rationalization and its perverse incentives to war, concentration camps for immigrant children, and the far-reaching reduction of public, working class life-supports in expensive economies, to financial deregulation and socio-economically perverse incentives resulting from automatic state convertibility responses to financial failures. The empirical record of perverse-incentive spillover resulting from management optimizing “controllable” immediate variables in isolation from their context goes on and on and on. It is unfortunately the management aesthetic that we have known in our era. But we have enough data on the results to respectfully require  managers to adopt the more fully-specified cost-benefit risk-assessment and decision-making methodology that is used when resources are at stake and outcomes matter.

Scenario Analysis: The Benefits

Scenario Analysis by contrast is designed to mitigate the avalanche of unintended consequences of Isolated Optimization decisions. It can be particularly appropriate and beneficial for those institutions and organizations that have insufficiently accounted for their own access to assets, income streams, and benefits relative to that same accounting made by better-organized, for-profit institutions like finance and investment firms, tech and management firms, and management-directed accounting consultants.

By extrapolating the introduction of proposed policy changes to a fully-specified environment of institutional connections and their operational mandates, including and analyzing Best and Worse-case Scenarios within policy proposals, Scenario Analysis allows a decision-making collective to make better decisions–still optimizing controllable variables, but with respect to the context they operate within.

Key benefits include:

  • Future planning – gives public-sector stakeholders a peek into the expected returns and risks involved when planning to shift budgets.
    • The goal of any business venture is to grow–to increase revenue over time, and it is best to use informed calculations when deciding to begin a transfer of public budgets to private businesses.
  • Proactive – University communities can avoid or decrease potential losses that result from uncontrollable factors by being aggressively preventive during worst-case scenarios, by analyzing events and situations that may lead to unfavorable outcomes. As the saying goes, it is better to be proactive than reactive when a problem arises.
    • Worst case scenario example:In 2012 Bain Financial (owners of Dollar Stores, inter alia) famously identified regional (hinterlands) universities as under-tapped resources for investors wanting low-risk publicly-provided assets in their tranches and portfolio mixes. In 2019 the Republican government of Alaska reinterpreted the financial industry’s asset vein interpretation of universities to mean that the public was excessively larding universities with income and assets. In lieu of inter-regional income transfer, the conservative government of Alaska stripped 41% of university funding, targeting 1,300 university workers for layoffs.

      What will happen to 40% of each regional university’s budget over the upcoming years? Will it be diverted to investment portfolios and for-profit management and software sales/data-accumulation firms? Or will it be stripped by governments and converted into regional tax reductions? Can university stakeholders and managers identify strategies alternative to facilitating or  private v. public income and asset mining?

  • Avoiding risk and failure – to avoid poor budgeting, priorities, planning, and policy decisions, scenario analysis allows the university organizations and communities to assess prospects detracting from or fortifying institutional integrity and degrees of freedom. It takes the best and worst probabilities into account so that stakeholders can make an informed decision.
  • Projecting investment returns or losses – the analysis makes use of tools to calculate the values or figures of potential gains or losses of an investment. This gives concrete, measurable data that stakeholders can base the approaches they take for a better outcome.

Case Study: Replacing University Courses with Software Rental

Let’s take the scenario of a project by a regional university’s management to replace first-year university courses with renting delegated-education technology (DET) from an international for-profit Health and Education Management and Software corporation with national and regional offices. The Isolated Optimization management approach would both design and market the policy as optimizing controllable immediate, organization variables, while black boxing how those variables will perform within the context of the same business environment it institutionalizes. In the Isolated Optimization approach, management would be simply required to present outcomes of this policy that we would expect if the organization and institution were operating in a black box, rather than in a specifiable context.

Rose colored glasses: For example, sans context, the replacement of first-year courses with capital-intensive, reduced-labour costs training software could be expected to a) allow scholars employed by the university to better use their skills, concentrating on university-level education and research (assuming the university has previously established loose income-driven or immigration-driven admissions criteria, flooding first-year courses with seriously-underprepared education-credential consumers). Sans context, and less realistically, the replacement of first-year courses with training software might be hoped to be designed to b) professionally, neutrally, incrementally introduce students of all identities to the university experience and basic work expectations, or perhaps instead to c) efficiently, objectively weed out underprepared education credential consumers after they have contributed income to the university. Sans context, the replacement of first-year courses with capital-intensive, reduced-labour costs training software could be expected to d) allow management to institute a “cost-saving” (substituting capital for labour) managerial strategy recognized by the business community and its legislatures for its contribution to profits or economic control, or e) build strong, portable relationships within international Health and Education management and software market networks. In an Isolated Optimization analysis, the policy change is presented–marketed–as a win-win all-benefits proposition. It lacks a realistic accounting of probable costs in situ. Yet in a specifiable context that such policy change will help institutionalize and operate within, these win-win scenarios can very well fail to pan out, or even produce the perverse incentives that have been a hallmark of Isolated Optimization.

Financial management corporations such as the ubiquitous Bain Capital (owner of Dollar Stores, inter alia) are part of that identifiable context that should be incorporated in analysis and accounted for in decision-making. They have long advised that privatizing multiple functions and assets of regional universities and redirecting regional universities’ public funding into financial instruments channeling organization income into private investors’ income streams will permit investors around the world to expand their opportunities to earn income on their otherwise-underemployed wealth, while the financial advisors themselves enjoy income and profits from managing these privatized assets and innovating the financial instruments that are built upon investor credence that these assets will provide lucrative income to financial firm shareholders.

The perverse incentive characteristic of an era in which a small number of people around the globe own more assets than they can productively employ in wealth-generating production is that commonly–as in the famous case of Lehman Brothers inter alia–the real, amassed assets that global shareholders rely upon to deliver market-busting returns, such as those contained in the regional public university–are simply depleted, the debts owned by financial firms are transferred into the depleted organization, and that underlying productive organization feeding the financial-instrument strategy is allowed to die, stripped of assets and burdened with transferred debt.

Financial advisors identify regional universities in particular as promising sources of private income and debt sinks because global investors are thought to be distant from the effects of regional asset liquidation. The current financial system is awash in moral hazard. As well, dedicated to private wealth, legislatures can be relied upon to let the public resources die rather than restore the public institution by taxing the very investors (possibly beyond the jurisdictional state’s reach, and due to wealth transfer, increasingly wealthier and more powerful than regional actors) whose financial investment earnings were taken out of the public resources, and debts transferred to the public resources, to boost private wealth in the first place. Socially-rational taxation currently violates wealth entitlement. Under current conditions, the perverse incentive cascade can only be nipped in the policy proposal stage, for example with effective barriers to the privatization/expropriation of public budgets.

Fortunately, the management corrective to analysis overlooking these salient contextual factors is available. Organizations can require their management to pursue a more fully-specified analysis, the Scenario Analysis. The key is requiring management modeling to incorporate context research and specification, which management will deploy in crafting Best and Worst-case Scenarios concerning the policy innovation.

If organization members have adequate freedom to hold management to competent research into contextual factors and analysis of Best and Worst-case Scenarios, these scenarios will allow management to make more valid analyses, and will allow the organization to make better decisions preserving member goals and organization integrity, including as that integrity supports regional socio-economic integrity.

The Scenario Analysis Method

Identifying Optimization Parameters

The first step is asking the members of the organization what it is that they value about their work, in this case as scholars. Compiling these answers will inform the organization’s policy optimization parameters, which will be reintroduced after the contextual identification and analysis and the Best and Worse-case Outcomes analysis stages.

Contextual Identification and Analysis – TBD

An organization needs to understand the nature of the market-related risks and opportunities it may face.

  • Each organization faces a different blend of market-related risks and opportunities.
  • The enterprise impacts related to market change may vary significantly depending on the economic sector(s)/sub-sector(s) in which an organization operates.
  • Enterprise impacts may also vary significantly depending on the following:
    • the geographic location of the organization’s value chain (both upstream and downstream).
    • the organization’s assets and nature of operations.
    • the structure and dynamics of the organization’s supply and demand markets.
    • the organization’s customers.
    • the organization’s other key stakeholders.

Best and Worse-case Outcomes Analysis, Prep

There are 3 major categories of considerations organizations face in constructing scenarios and conducting scenario analysis: parameters/assumptions, analytical choices, and impacts.

Parameters/Assumptions

Discount rate – what discount rate does the organization apply to discount future value?, see Best-case/Worst-case Scenarios, below.

Labour & technology commodity prices – what assumptions are made about how labour v. technology prices would develop over time, including economic incentives and disincentives to the in-house or outsourced development and maintenance of the skilled v. deskilled or unskilled university labour force, and multiplier effects and their social and geographic distribution? How does the distribution of in-house v. outsourced skilled v. unskilled labour impact socio-economic inequality and attendant political shifts recursively impacting university funding? How does the distribution of multiplier effects impact socio-economic inequality and attendant political shifts recursively impacting university funding?

As technology inputs allow private for-profit companies to monopolize data on stratified consumer-products (students and their households) over time, how will private data pricing impact the cost structure, to the university, to the public financers, and to its consumer-products and their end-users, of the education commodity?

In the university enterprise, as with online media, the consumers are also the product. As management and technology allow higher-education and credentialing inputs to be standardized, optimized for efficiency, and cheapened, what conclusions does the organization draw about the development over time of quality and market prices for the student consumer-product outputs of public financing, private financing, labour, and technology? How might optimizing efficiency of production in regional universities, v. flagship national universities, impact the consumer-product’s capacity to penetrate the higher-value labour markets increasingly concentrated in metropoles in a period of declining economic mobility, or contribute to stagnation or exceptional economic dynamism in regional networks?

Work demand and mix – what would be the resulting total work demand and work mix across different sources of primary work (labour, technology)? How does this develop over time assuming supply/end-use efficiency improvements? What factors are used for work conversion efficiencies of each source category and for end-use efficiency in each category over time?

Macro-economic Variables – what public financing rate, consumer-financing rate, employment rate, and other economic variables are used?

Demographic variables – what assumptions are made about population growth, migration, labour mobility and its distribution, economic mobility, socio-economic network development, and socio-economic inequality?

Institutional, social and economic distribution of benefits and costs – to what extent are distributions of income and assets, efficiency (type specified) gains and losses, sovereignty and strategic degrees of freedom gains and losses, clean energy transition, and ecologically-driven regional physical changes incorporated into scenarios, priorities and planning?

Geographical tailoring of transition impacts – what assumptions does the organization make about potential differences in input and output parameters across regions, countries, asset locations, and markets?

Technology – does the organization make assumptions about the development of performance/cost and resulting levels of deployment over time of various key supply and demand-side technologies (e.g. education and research labour, and technologies in interested sectors including financial, managerial, data accumulation, and patent rentiers)?

Policy – what are assumptions about the strength of different private-public policy coalitions and signals, and their development over time and across jurisdictions (e.g. provincial university funding, university managerial independence, collective bargaining and organized labour, Academic Freedom, Collegial Governance; subsidies for technology; subsidies for construction; marketing budgets; accounting budgets; Black Budgets; managerial and other administrative budgets; the university as conduit for increasing the public funding of international private marketing, management, education services, social services, immigration services, and financial services providers). What can we assume about the upcoming likelihood of private-public growth political parties v. anti-public political parties and governments?

Expropriation sensitivity assumptions – assumptions of privatization increase v. taxation stagnation or decrease; increased demand by financial, technology, and managerial firms for income streams, public access; assumptions about university consolidation in Canada v. spinning off regional universities or their parts into online course credential sales?

Analytical Choices

Scenarios – what scenarios does the organization use for transition impact analysis and which sources are used to assess physical impact both for central/base case and for sensitivity analyses?

Quantitative vs. qualitative or “directional” – is the scenario exercise fully quantitative or a mix of quantitative and qualitative?

Timing – how does the organization consider timing of implications under scenarios e.g. is this considered at a decadal level 2020; 2030; 2040; 2050

Scope of application – is the analysis applied to the whole value chain (inputs, operations and markets), or just direct effects on specific organization units / operations?

Financial, tech, and managerial models/data sets – which models and data sets support the assessment of privatization-related risks?

Risks to scholarship, schools, and the university – when assessing market risks, which specific risks have been included, including tje severity of their probable impact? To what extent has the organization assessed the impact to its portfolio (e.g. largest assets, most vulnerable assets) and to what extent have risks been incorporated in future organization strategy?

To what extent has the impact on prices and availability in the whole value chain been considered, including knock-on effects from suppliers, infrastructure, and access to consumers?

Enterprise Impacts/Effects

Earnings – what conclusions does the organization draw about impact on earnings and how does it express that impact (e.g. as EBITDA, EBITDA margins, EBITDA contribution, dividends)?

Costs – what conclusions does the organization draw about the implications for its operating/production costs and their development over time?

Revenues – what conclusions does the organization draw about the implications for the revenues from its key commodities/ products/ services and their development over time?

Assets – what are the implications for asset values of various scenarios?

Capital Allocation/ investments – what are the implications for capex and other investments?

Timing – what conclusions does the organization draw about development of costs, revenues and earnings across time (e.g. 5/10/20 year)?

Responses – what information does the organization provide in relation to potential impacts (e.g. intended changes to capital expenditure plans, changes to portfolio through acquisitions and divestments, retirement of assets, entry into new markets, development of new capabilities etc.)?

Enterprise Interruption due to physical impacts – what is the organization’s conclusion about its potential enterprise interruption/productivity loss due to market impacts– both direct effects on the organization’s own assets and indirect effects of supply chain/product delivery.

 

Best case-Worst case Scenarios

When performing the analysis, managers and executives at a university, school or department will generate different future states of the university, higher education, and the economy. These future states will form discrete scenarios that include assumptions about supplier business plans, product prices, student-consumer data, operating costs, politics and public funding, and other drivers of the  enterprise.

Managers typically start with 3 basic scenarios:

  • Base case scenario – this is the average scenario based on management assumptions.
    • Note: When calculating the net present value, the rates most likely to be used are the discount rate, or the cash flow growth rate.
  • Worst case scenario – considers the most serious or severe outcome that may happen in a given situation.
    • Note: When calculating the net present value, one would take the highest possible discount rate and subtract the possible cash flow growth rate.
  • Best case scenario – this is the ideal projected scenario, and is almost always assumed by management to market and institute their pre-existing preferences.
    • Note: When calculating the net present value, use the least possible discount rate, highest possible growth rate, or lowest possible tax rate.

 

Preventing Garbage In, Garbage Out: Distributed Power in Organizations

While a significant improvement on Isolated Optimization, Scenario Analysis is not immune to GIGO–Garbage In, Garbage Out hazard. Though we would like to believe that all managers are competent at identifying and analyzing environmental factors and their likely interactions with policy, it also remains that managers can often have seriously-compromised incentive to constructing effective Scenario Analyses.

This is not just because Scenario Analysis requires more work of managers, but because the same firms with a business model tapping into public-organization budgets standardly also provide incentivizing career-building, income-enhancing, and network opportunities to helpful agents within the target organization. This business model has been particularly common within sales to the public sector, which labor market is subject to legislatively-imposed income compression, stagnation, and in the course of anti-public campaigns, status degradation. In fact, recognition of this standard business model–selling both products to organizations and managerial job opportunities to helpful agents within those organizations– should be built into the Scenario Analysis, as the business model itself imposes environmental costs upon the organization.


It is easy to identify whether this business model is an environmental factor: Does the firm selling the product also support helpers’ career advancement within customer organizations, or, more ostentatiously, hire helpful agents from within client organizations? Is the for-profit goods or service provider also a management or consulting firm? Huron Consulting Group is an example of such a firm (see appendix), advertising to cooperative health care and university managers and purchasers career-mobility opportunities in its own international management and sales network expansion.


An important cost of this business model to be analyzed in decisions to adopt their products, to transfer public funding to the private for-profit corporation, is that organization members serving as agents of private firm product adoption are opening exclusively for themselves a wider field of credible employment opportunities, an advantage in employment negotiations that can allow them to command a larger share of organizational resources at coworkers’ expense. These employment opportunities may also engage moral hazard by incentivizing predatory, organization-depleting decisions from which the facilitating agent is uniquely shielded by virtue of their employment mobility through their relationship with the management and product sales firm.

The integrity of the cost-benefit analysis requires that managers not be allowed to exclude this prevalent contemporary context from analysis, and regardless of whether organization intermediaries admit an intention to take advantage of perks on offer, no policy change proposal should be allowed to proceed until such cost-benefit distribution factors are incorporated in the Scenario Analysis and Best- and Worst-case Scenarios. After all, even respected and well-remunerated professionals like medical doctors have been known to overprescribe medications under pharmaceutical rep influence.

The safeguard against disincentivized, half-hearted, ineffective Scenario Analysis is an organizational structure of distributed power, wherein organization members have the capacity to push managers for analytical improvements and policy options based on fully-specified analysis.

This is also to point out another common contextual factor today: Because hierarchical decision making undermines the conditions required for effective, fully-specified Scenario Analysis, and so permits greater opportunities for predatory decision-making capture, interested corporations, such as financial, technology, and management-consultant firms, have an especial, compelling interest in supporting hierarchical decision making in the organizations whose income and assets they target, as Bain Capital has also indicated. Therefore, in universities, active Collegial Governance is an institution essential to fully-capacitated policy analysis and sound decision making.

 

References

Bain Capital. 2012. “The Financially Sustainable University.” https://www.bain.com/insights/financially-sustainable-university/

Corporate Finance Institute. “Scenario Analysis.” https://corporatefinanceinstitute.com/resources/knowledge/modeling/scenario-analysis/

Kishita et al. 2016. “Scenario Analysis.” https://www.sciencedirect.com/topics/earth-and-planetary-sciences/scenario-analysis

Pistor, Katharina. 2019. The Code of Capital: How the Law Creates Wealth and Inequality. Princeton.

TCFD. “The Use of Scenario Analysis in Disclosure of Climate-related Risks and Opportunities.” https://www.tcfdhub.org/home/scenario-analysis

 

Appendix: Tech Sales-Driven Management Goals, Stage One

The following images summarize institutional optimizations that for-profit management and tech sales corporations are geared to sell to universities via their managers. Recall also that most tech sales firms have a less-public, longer-term business plan to eventually monopolize and monetize the data (eg. on consumer-products) that they will gain through organizations adopting their technology; this typical, staged business strategy in the present era of private property law-making can create and lock in future increased–constraining and possibly prohibitive–costs for the technology-adopting (university) organization and its (student) consumer-product base.

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