The Dangers of Sustainability Metrics

Yves here. I’m waiting for VoxEU to run the generalized version of the argument it makes here, “The Dangers of Metrics.” A bizarre vogue for metrics started in the late 1990s, and it became even more acute in the dot bomb era. You’d see resumes suddenly claiming that the individual increased sales by x% or lowered complaints by y%, when nearly all the time, if you looked at their position, it would be impossible for them to have achieved that on their own. This was shades of resume fabulist Charles Asubonten, who did a brief turn as CalPERS CFO, claiming he was responsible for the increase in stock price as one of his earlier employers. Not only did we describe why that was bunk, Los Angeles Times columnist Mike Hiltzik supplied more evidence.

Another practice we’ve observed is consultants taking credit for improvements in clients’ businesses, quantifying those claims, when it’s impossible for them to take credit unless they sent in a large team and actually ran the operation.

From our 2006 Conference Board Review article, Management’s Great Addiction:

Corporate America is obsessed with numbers. Analyst meetings focus on earnings expectations, revenue growth, and margins rather than business fundamentals. PowerPoint presentations look naked if they lack charts and graphs to buttress their three-point message. Lofty corporate mission statements are often trumped by pressure to “hit the targets.”….

This love affair with figures increasingly looks like an addiction. Numbers serve to analyze, justify, and communicate. But they are, fundamentally, abstractions. When numbers begin to assume a reality of their own, independent of the reality they are meant to represent, it’s time for a reality check. Some are already frustrated with the trend:In a recent McKinsey survey of more than a thousand public-company directors, most said they wanted to hear less about financial results and more about things not so readily quantified, such as strategy, risks, leadership development, organizational issues, and markets.

Metrics presuppose that situations are orderly, predictable, and rational. When that tenet collides with situations that are chaotic, non-linear, and subject to the force of personalities, that faith—the belief in the sanctity of numbers—often trumps seemingly irrefutable facts. At that point, the addiction begins to have real-world consequences.

Here, Professor Edmans describes a specific danger of heavy reliance on metrics: promoting short-termism.

By Alex Edmans, Professor of Finance, London Business School and CEPR Research Fellow. Originally published at VoxEU

See original post for references

Policymakers, investors, and stakeholders are demanding that companies report sustainability metrics so that they can be held accountable for delivering social performance. Doing so increases the total amount of information in the market and reduces the cost of capital. However, real decisions depend on not the total amount of information, but the balance between ‘hard’ (quantitative) and ‘soft’ (qualitative) information. Since sustainability metrics only contain the former, they distort this balance – skewing managers’ sustainability investments to ones with short-term payoffs.

Sustainability is the corporate issue of the day. It was the theme of the 2020 World Economic Forum in Davos, and the call for companies to serve the wider society – not just shareholders – has only intensified in the COVID-19 pandemic.

A key challenge, however, is to measure the sustainability of a company. Accordingly, global consortiums are devising an ever-increasing set of sustainability metrics for companies to report. One example is the World Economic Forum’s framework, released in September 2020 in collaboration with the Big Four accounting firms.

The value of these initiatives is backed up by decades of academic research showing that financial efficiency – the amount of information in financial markets – boosts real efficiency. Focusing on primary financial markets, Bernanke and Gertler (1989) and Kiyotaki and Moore (1997) show that transparency reduces the cost of capital and thus increases investment.

Turning to secondary financial markets, the survey of Bond et al. (2012) discusses how financial efficiency allows managers to glean more information from prices and also increases their incentives to improve fundamental value since their compensation is tied to prices.

However, in Edmans et al. (2016), we highlight that financial efficiency – and thus information disclosure – can sometimes harm real efficiency. Central to our argument is the observation that not all information can be credibly disclosed. ‘Hard’ (quantitative and verifiable) information can be, such as the number of jobs created, but ‘soft’ information, such as the quality of those jobs, cannot.

It may seem that this distinction doesn’t matter. Even if companies can’t disclose soft information, frameworks can at least force them to disclose hard information. Our model shows that such disclosure indeed increases the total amount of information into prices and reduces the cost of capital, consistent with common wisdom. However, we also show that real efficiency (the quality of a firm’s decisions) depends not on financial efficiency (the total amount of information in prices), but the balance between hard and soft information.

The idea is as follows. If no information is disclosed, the manager will take the investment decision that improves long-term value. However, if companies are forced to disclose hard information, then it will be fully reflected in stock prices, on which the manager’s compensation and reputation are based. While investors can still gather soft information through analysing a company’s fundamentals, doing so is costly, so it will only be partially reflected in prices. Knowing that prices will depend more on hard information than soft, managers will take investment decisions that boost hard information, even if they don’t maximise long-term value.

When we originally wrote the paper, our main interpretation of hard information was ‘quarterly earnings’, and soft information referred to ‘intangible assets’. We used our model to highlight the danger of quarterly reporting, a key topic at the time: the EU Transparency Directive Amending Directive was passed in 2013, allowing companies to stop quarterly reporting, but many companies continued due to it, potentially due to the cost of capital impact.

However, hard information can equally be interpreted as sustainability metrics. Going back to the previous example, consider a company that can invest in either creating more jobs or improving the quality of existing jobs, and that the latter creates more long-term value. Unconstrained, the company will take the latter investment. But if it’s forced to disclose new job creation, it may be skewed towards the former.

Other examples are as follows:

  • Disclosure of worker wages may lead to companies providing eye-catching salary raises, rather than meaningful work and skills development.
  • Diversity metrics reward adding a minority to the board to tick the box, rather than developing a culture that actively encourages dissent.
  • Highlighting emissions may punish products like semiconductors, which release perfluorocarbons when manufactured, yet may power the solutions to climate change.

We already recognise the incompleteness of numbers when it comes to financial information – hence the arguments against quarterly reporting. We also recognise the incompleteness of non-financial numbers in non-business settings: school league tables based on exam results may turn them into exam factories. But the same problem is overlooked in business settings.

Stakeholder value is often assumed to be ‘long-term’, in contrast to shareholder value which is labelled ‘short-term’. But stakeholder metrics can be equally as short-term as quarterly earnings, and an excessive focus on them can lead to myopic decisions.

What’s the solution? Not to throw the baby out with the bathwater and allow companies to report nothing; else, they can’t be held accountable. Instead, it’s to recognise the dangers of these metrics and interpret them with caution.

Reporting frameworks should not be prescriptive, but allow companies to opt out of reporting certain metrics if doing so may distort behaviour. Investors shouldn’t take sustainability metrics at face value, but conduct their own analysis to understand the context behind them – for example, what a company has done to promote diversity of thinking, beyond improving diversity statistics. This requires investors to take large stakes to begin with so that they have the incentive to do their own research, as studied by an extensive academic literature on blockholders (see Edmans 2014 and Edmans and Holderness 2017 for surveys).

A common question is ‘how do you measure sustainability?’ As explained in Edmans (2020), this is the wrong question. Sustainability isn’t something that you can measure; it’s something you assess. This involves starting with quantitative metrics but then supplementing them with qualitative information, just as the impact of a researcher is more than her Google scholar count and number of publications in top journals.

Critics argue that such an assessment is subjective and unreliable. They bemoan the inconsistency of ESG ratings, documented by Berg et al. (2020), and argue that greater disclosure will allow sustainability to be objectively assessed. But sustainability, just like a researcher’s impact, is inherently subjective – there’s no getting around the fact that it depends on soft information.

Even the financial value of a company is subjective because it depends not only on current financials but also management quality, competitive position, and strategic outlook. As a result, equity analysts disagree on whether a stock is a ‘buy’ or a ‘sell’ and what its price target should be, but no one bemoans this inconsistency. Moreover, the importance of soft information is an attraction, not a drawback.

If sustainability could be measured with a single, unambiguous set of quantitative metrics, there would be no need for human investors, as it could be assessed by machines and priced into the market. For investors to add value, they need to look beyond the metrics and get into the weeds of a company, rather than trying to assess it using an Excel spreadsheet.

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