Straight Talk about Corporate Social Responsibility

Critical thinking about “corporate social responsibility” (CSR) is needed, because there are few topics where discussions feature greater ratios of heat to light.  With this in mind, two of my Harvard colleagues – law professor Bruce Hay and business school professor Richard Vietor – and I co-edited a book, Environmental Protection and the Social Responsibility of Firms: Perspectives from Law, Economics, and Business.

At issue is the appropriate role of business with regard to environmental protection.  Everyone agrees that firms should obey the law. But beyond the law – beyond compliance with regulations – do firms have additional responsibilities to commit resources to environmental protection?  How should we think about the notion of firms sacrificing profits in the social interest?

Much of what has been written on this question has been both confused and confusing.  Advocates, as well as academics, have entangled what ought to be four distinct questions about corporate social responsibility:  may they, can they, should they, and do they.

First, may firms sacrifice profits in the social interest – given their fiduciary responsibilities to shareholders?  Does management have a fiduciary duty to maximize corporate profits in the interest of shareholders, or can it sacrifice profits by voluntarily exceeding the requirements of environmental law?  Einer Elhauge, a professor at Harvard Law School, challenges the conventional wisdom that managers have a simple legal duty to maximize corporate profits.  He argues that managers have freedom to diverge from the goal of profit maximization, partly because their legal duties to shareholders are governed by the “business judgment rule,” which gives them broad discretion to use corporate resources as they see fit.

If a company’s managers decide, for example, to use “green” inputs, devise cleaner production technologies, or dispose of their waste more safely, courts will not stop them from doing so, no matter how disgruntled shareholders may be at such acts of public charity.  The reason is that for all a judge knows, such measures – particularly when they are well publicized – will add to the firm’s bottom line in the long run by increasing public goodwill.  But this line of argument contradicts the very premise, since it is based upon the notion that the actions are not sacrificing profits, but contributing to them.

This leads directly to the second question.  Can firms sacrifice profits in the social interest on a sustainable basis, or will the forces of a competitive market render such efforts transient at best?  Paul Portney, Dean of the Eller College of Management at the University of Arizona, notes that for firms that enjoy monopoly positions or produce products for well-defined niche markets, such extra costs can well be passed on to customers.  But for the majority of firms in competitive industries – particularly firms that produce commodities – it is difficult or impossible to pass on such voluntarily incurred costs to customers.  Such firms have to absorb those extra costs in the form of reduced profits, reduced shareholder dividends, and/or reduced compensation, suggesting that, in the face of competition, such behavior is not sustainable.

This leads to the third question of CSR:  even if firms may carry out such profit-sacrificing activities, and can do so, should they – from society’s perspective?  Is this likely to lead to an efficient use of social resources?  To be more specific, under what conditions are firms’ CSR activities likely to be welfare-enhancing?  Portney finds that this is most likely to be the case if firms pursuing CSR strategies are doing so because it is good business – that is, profitable.  Once again, a positive response violates the premise of the question.  But for more costly CSR investments, concern exists about the opportunity costs that will be involved for firms. Further, in the case of companies that behave strategically with CSR to anticipate and shape future regulations, welfare may be reduced if the result is less stringent standards (that would have been justified).

Finally, do firms behave this way?  Do some firms reduce their earnings by voluntarily engaging in environmental stewardship?  Forest Reinhardt of the Harvard Business School addresses this question by surveying the performance of a broad cross-section of firms, and finds that only rarely does it pay to be green.  That said, situations do exist in which it does pay. Where one can increase customers’ willingness to pay, reduce one’s costs, manage future risk, or anticipate and defer costly governmental regulation, then it may pay to be green.  Overall, Reinhardt acknowledges the existence of these opportunities for some firms – examples such as Patagonia and DuPont stand out – but the empirical evidence does not support broad claims of pervasive opportunities.

So, where does this leave us?  May firms engage in CSR, beyond the law? An affirmative though conditional answer seems appropriate.  Can firms do so on a sustainable basis?  Outside of monopolies and limited niche markets, the answer is probably negative.  Should they carry out such beyond-compliance efforts, even when doing so is not profitable?  Here – if the alternative is sound and effective government policy – the answer may not be encouraging.  And the last question – do firms generally carry out such activities – seems to lead to a negative assessment, at least if we restrict our attention to real cases of “sacrificing profits in the social interest.”

But definitive answers to these questions await the results of rigorous, empirical research.  In the meantime, we ought to prevent muddled thinking by keeping separate these four questions of corporation social responsibility..

Does economic analysis shortchange the future?

Decisions made today usually have impacts both now and in the future. In the environmental realm, many of the future impacts are benefits, and such future benefits — as well as costs — are typically discounted by economists in their analyses.  Why do economists do this, and does it give insufficient weight to future benefits and thus to the well-being of future generations?

This is a question my colleague, Lawrence Goulder, a professor of economics at Stanford University, and I addressed in an article in Nature.  We noted that as economists, we often encounter skepticism about discounting, especially from non-economists. Some of the skepticism seems quite valid, yet some reflects misconceptions about the nature and purposes of discounting.  In this post, I hope to clarify the concept and the practice.

It helps to begin with the use of discounting in private investments, where the rationale stems from the fact that capital is productive ­– money earns interest.  Consider a company trying to decide whether to invest $1 million in the purchase of a copper mine, and suppose that the most profitable strategy involves extracting the available copper 3 years from now, yielding revenues (net of extraction costs) of $1,150,000. Would investing in this mine make sense?  Assume the company has the alternative of putting the $1 million in the bank at 5 per cent annual interest. Then, on a purely financial basis, the company would do better by putting the money in the bank, as it will have $1,000,000 x (1.05)3, or $1,157,625, that is, $7,625 more than it would earn from the copper mine investment.

I compared the alternatives by compounding to the future the up-front cost of the project. It is mathematically equivalent to compare the options by discounting to the present the future revenues or benefits from the copper mine. The discounted revenue is $1,150,000 divided by (1.05)3, or $993,413, which is less than the cost of the investment ($1 million).  So the project would not earn as much as the alternative of putting the money in the bank.

Discounting translates future dollars into equivalent current dollars; it undoes the effects of compound interest. It is not aimed at accounting for inflation, as even if there were no inflation, it would still be necessary to discount future revenues to account for the fact that a dollar today translates (via compound interest) into more dollars in the future.

Can this same kind of thinking be applied to investments made by the public sector?  Since my purpose is to clarify a few key issues in the starkest terms, I will use a highly stylized example that abstracts from many of the subtleties.  Suppose that a policy, if introduced today and maintained, would avoid significant damage to the environment and human welfare 100 years from now. The ‘return on investment’ is avoided future damages to the environment and people’s well-being. Suppose that this policy costs $4 billion to implement, and that this cost is completely borne today.  It is anticipated that the benefits – avoided damages to the environment – will be worth $800 billion to people alive 100 years from now.  Should the policy be implemented?

If we adopt the economic efficiency criterion I have described in previous posts, the question becomes whether the future benefits are large enough so that the winners could potentially compensate the losers and still be no worse off?  Here discounting is helpful. If, over the next 100 years, the average rate of interest on ordinary investments is 5 per cent, the gains of $800 billion to people 100 years from now are equivalent to $6.08 billion today.  Equivalently, $6.08 billion today, compounded at an annual interest rate of 5 per cent, will become $800 billion in 100 years. The project satisfies the principle of efficiency if it costs current generations less than $6.08 billion, otherwise not.

Since the $4 billion of up-front costs are less than $6.08 billion, the benefits to future generations are more than enough to offset the costs to current generations. Discounting serves the purpose of converting costs and benefits from various periods into equivalent dollars of some given period.  Applying a discount rate is not giving less weight to future generations’ welfare.  Rather, it is simply converting the (full) impacts that occur at different points of time into common units.

Much skepticism about discounting and, more broadly, the use of benefit-cost analysis, is connected to uncertainties in estimating future impacts. Consider the difficulties of ascertaining, for example, the benefits that future generations would enjoy from a regulation that protects certain endangered species. Some of the gain to future generations might come in the form of pharmaceutical products derived from the protected species. Such benefits are impossible to predict. Benefits also depend on the values future generations would attach to the protected species – the enjoyment of observing them in the wild or just knowing of their existence. But how can we predict future generations’ values?  Economists and other social scientists try to infer them through surveys and by inferring preferences from individuals’ behavior.  But these approaches are far from perfect, and at best they indicate only the values or tastes of people alive today.

The uncertainties are substantial and unavoidable, but they do not invalidate the use of discounting (or benefit-cost analysis).  They do oblige analysts, however, to assess and acknowledge those uncertainties in their policy assessments, a topic I discussed in my last post (“What Baseball Can Teach Policymakers”), and a topic to which I will return in the future..

What Baseball Can Teach Policymakers

With the Major League Baseball season having just begun, I’m reminded of the truism that the best teams win their divisions in the regular season, but the hot teams win in the post-season playoffs.  Why the difference?  The regular season is 162 games long, but the post-season consists of just a few brief 5-game and 7-game series.  And because of the huge random element that pervades the sport, in a single game (or a short series), the best teams often lose, and the worst teams often win.

The numbers are striking, and bear repeating.  In a typical year, the best teams lose 40 percent of their games, and the worst teams win 40 percent of theirs.  In the extreme, one of the best Major League Baseball teams ever ­- the 1927 New York Yankees – lost 29 percent of their games; and one of the worst teams in history – the 1962 New York Mets – won 25 percent of theirs.  On any given day, anything can happen.  Uncertainty is a fundamental part of the game, and any analysis that fails to recognize this is not only incomplete, but fundamentally flawed.

The same is true of analyses of environmental policies.  Uncertainty is an absolutely fundamental aspect of environmental problems and the policies that are employed to address those problems.  Any analysis that fails to recognize this runs the risk not only of being incomplete, but misleading as well.  Judson Jaffe, formerly at Analysis Group, and I documented this in a study published in Regulation and Governance.

To estimate proposed regulations’ benefits and costs, analysts frequently rely on inputs that are uncertain —  sometimes substantially so.  Such uncertainties in underlying inputs are propagated through analyses, leading to uncertainty in ultimate benefit and cost estimates, which constitute the core of a Regulatory Impact Analysis (RIA), required by Presidential Executive Order for all “economically significant” proposed Federal regulations.

Despite this uncertainty, the most prominently displayed results in RIAs are typically single, apparently precise point estimates of benefits, costs, and net benefits (benefits minus costs), masking uncertainties inherent in their calculation and possibly obscuring tradeoffs among competing policy options.  Historically, efforts to address uncertainty in RIAs have been very limited, but guidance set forth in the U.S. Office of Management and Budget’s (OMB) Circular A‑4 on Regulatory Analysis has the potential to enhance the information provided in RIAs regarding uncertainty in benefit and cost estimates.  Circular A‑4 requires the development of a formal quantitative assessment of uncertainty regarding a regulation’s economic impact if either annual benefits or costs are expected to reach $1 billion.

Over the years, formal quantitative uncertainty assessments — known as Monte Carlo analyses — have become common in a variety of fields, including engineering, finance, and a number of scientific disciplines, as well as in “sabermetrics” (quantitative, especially statistical analysis of professional baseball), but rarely have such methods been employed in RIAs.

The first step in a Monte Carlo analysis involves the development of probability distributions of uncertain inputs to an analysis.  These probability distributions reflect the implications of uncertainty regarding an input for the range of its possible values and the likelihood that each value is the true value.  Once probability distributions of inputs to a benefit‑cost analysis are established, a Monte Carlo analysis is used to simulate the probability distribution of the regulation’s net benefits by carrying out the calculation of benefits and costs thousands, or even millions, of times.  With each iteration of the calculations, new values are randomly drawn from each input’s probability distribution and used in the benefit and/or cost calculations.  Over the course of these iterations, the frequency with which any given value is drawn for a particular input is governed by that input’s probability distribution.  Importantly, any correlations among individual items in the benefit and cost calculations are taken into account.  The resulting set of net benefit estimates characterizes the complete probability distribution of net benefits.

Uncertainty is inevitable in estimates of environmental regulations’ economic impacts, and assessments of the extent and nature of such uncertainty provides important information for policymakers evaluating proposed regulations.  Such information offers a context for interpreting benefit and cost estimates, and can lead to point estimates of regulations= benefits and costs that differ from what would be produced by purely deterministic analyses (that ignore uncertainty).  In addition, these assessments can help establish priorities for research.

Due to the complexity of interactions among uncertainties in inputs to RIAs, an accurate assessment of uncertainty can be gained only through the use of formal quantitative methods, such as Monte Carlo analysis.  Although these methods can offer significant insights, they require only limited additional effort relative to that already expended on RIAs.  Much of the data required for these analyses are already obtained by EPA in their preparation of RIAs; and widely available software allows the execution of Monte Carlo analysis in common spreadsheet programs on a desktop computer.  In a specific application in the Regulation and Governance study, Jaffe and I demonstrate the use and advantages of employing formal quantitative analysis of uncertainty in a review of EPA’s 2004 RIA for its Nonroad Diesel Rule.

Formal quantitative assessments of uncertainty can mark a truly significant step forward in enhancing regulatory analysis under Presidential Executive Orders.  They have the potential to improve substantially our understanding of the impact of environmental regulations, and thereby to lead to more informed policymaking..