An example from goodwill research
Similar concerns can, in principle, be raised for many empirical accounting studies, including those on goodwill. For example, standard setters may currently be very interested in this (fictitious) headline: “Goodwill impairment-only approach is less useful to investors than amortization, study shows.” If accounting research were headline material (which, sadly, it is not), this is how I imagine a journalist might have summarized this paper published in the respectable European Accounting Review.
The two above concerns concerning the coronavirus research can also be brought up here. First, the paper compares data from the year before (amortization method in most countries) and after IFRS adoption in the EU (impairment method). They use a pre-post test design that, in itself, does not support causal inference. The researchers are of course aware of this, but a better experimental design was not available to them.
Second, the outcome under study is ‘usefulness to investors’, or „accounting quality“. The authors, along with a long tradition of prior papers, empirically measure this as „value relevance“, the statistical association of goodwill-related accounting amounts with stock prices. Is „value relevance“ as an outcome relevant to standard setters? We don’t really know. In academia, battles have been fought over this.
Mind you: these validity challenges do not arise because researchers do not know what they are doing! Rather, they stem from the multi-faceted challenges of setting up clean causal studies that measure phenomena of interest to standard setters. These include data availability issues, a dearth of quasi-experimental settings (where treatment and control groups are randomly formed), and ignorance (for many reasons) about exactly what outcomes standard setters are interested in.
And let’s be honest: it also reflects the fact that “applied” studies geared towards the information needs of policymakers have lower odds of getting published in the top journals that define researchers’ reputations, status, resources (including DFG funding), and salaries. All of this becomes problematic if research findings are communicated imprecisely, taken out of context, or applied to issues they don’t really speak to – as is currently happening to some corona-related research.
No silver bullet
The second condition that must be met concerns the focus on the studies generated by a field. We should not expect a single study to deliver the one causal ‘silver’ bullet. It may be more productive to think of causal statements as being made by a field, i.e., a wide range of diverse papers that bring different methods and data to bear – slowly and steadily building evidence that will end up suggesting a plausible causal effect. Let’s not forget: the research linking smoking to lung cancer built over decades, with no single causal study that settled the issue once and for all.
As such, it is important for standard setters to base their decisions on the information delivered by an entire field. This means that they need to keep up with the body of literature available, and we researchers should be clear about the implications of our studies, bridging that “expectations gap”.
From research to standard setting
So, how can we as researchers support standard setters in becoming (even) more evidence-informed?
First, we need to understand the questions that standard setters need evidence on, and the outcome measures they are interested in. For you as standard setters: You could clearly specify outcomes of interest, so what is it that you would like to have researched? Are you more interested in the value relevance, or rather in the degree of conservatism of accounting amounts? And how do you propose to measure these outcomes?
Second, we should be tuned into the timing of standard setters’ deliberations. Standard setters are not inclined to wait for years until a relevant study gets published in a top journal. Again, the corona crisis shows: research projects, publication processes and regulatory approval procedures can be sped up, if needed! Accounting standard setters, too, want the evidence when they need it.
Third, we need to communicate carefully, i.e., avoid suggesting causal effects when all we have to offer is statistical associations. Of course, a theoretically plausible and highly significant statistical association is more suggestive of an actual causal effect than some chance pattern observed in a big data mining exercise. But still, we should not mix up correlation and causation, especially when communicating with standard setters.
This brings me to my last point: our research approaches need to broaden out even more. The goodwill accounting field, for one, would benefit from the insights of more qualitative studies, including surveys and small-sample case studies. These could shed light on the motives, the actual behavior, and the interactions of the people involved, hopefully leading towards a deeper understanding of goodwill accounting decision-making in practice.
To bring these ideas together: Perhaps standard setters should take greater advantage of the possibility to commission research. Why not get together with a team of domain experts from academia and practice to “custom-build” a study to your specifications? You can then make sure to specifically ask for validity assessments, allowing you to draw clear implications from the results. You may also ask researchers to specifically consider the “real effects” of accounting treatments in an economic impact assessment.
And finally, you can help us researchers with our data collection. Where possible make relevant contacts and field data available, for example, by making yourself available for (research) interviews and by participating in and forwarding our surveys. And by attending our events that are aimed at knowledge exchange.
This blog post is loosely based on a presentation Sellhorn gave at the European Financial Reporting Advisory Group (see here).