18  Causal mechanisms

Gow et al. (2016) argue that, while causal inference is the goal of most accounting research, it is extremely difficult to find settings where straightforward application of statistical methods can produce credible estimates of causal effects (and the remaining chapters of this part arguably support this claim). Does this mean accounting researchers must give up making causal statements? Gow et al. (2016) argue that the answer is “no” and suggest an increased focus on causal mechanisms as one path forward.

In this chapter, we aim to help the reader understand the idea of causal mechanisms, which can suggest empirical analyses that do not rely on, say, random assignment to treatment, which is very rarely found in accounting research. Unlike the other chapters in this part, this chapter is largely qualitative and contains no empirical analysis.

Accounting research is not alone in its reliance on observational data with the goal of drawing causal inferences. For this reason, it is natural to look to other fields using observational data to identify causal mechanisms and ultimately to draw causal inferences. Epidemiology and medicine are two fields that are often singled out in this regard. In the next two sections, we briefly provide examples and highlight the features of the examples that enhanced the credibility of the inferences drawn.1 A key implication of this discussion is that accounting researchers need to identify clearly and rigorously the causal mechanism that is producing their results.

Tip

While there is no R code in this chapter, a Quarto template for the discussion questions below is available on GitHub.

18.1 John Snow and cholera

A widely cited case of successful causal inference is John Snow’s work on cholera. As there are many excellent accounts of Snow’s work, we will focus on the barest details. As discussed in Freedman (2009, p. 339), “John Snow was a physician in Victorian London. In 1854, he demonstrated that cholera was an infectious disease, which could be prevented by cleaning up the water supply. The demonstration took advantage of a natural experiment. A large area of London was served by two water companies. The Southwark and Vauxhall company distributed contaminated water, and households served by it had a death rate ‘between eight and nine times as great as in the houses supplied by the Lambeth company,’ which supplied relatively pure water.” But there was much more to Snow’s work than the use of a convenient natural experiment. First, Snow’s reasoning (much of which was surely done before “the arduous task of data collection” began) was about the mechanism through which cholera spread. Existing theory suggested “odors generated by decaying organic material.” Snow reasoned qualitatively that such a mechanism was implausible. Instead, drawing on his medical knowledge and the facts at hand, Snow conjectured that “a living organism enters the body, as a contaminant of water or food, multiplies in the body, and creates the symptoms of the disease. Many copies of the organism are expelled with the dejecta, contaminate water or food, then infect other victims” (Freedman, 2009, p. 342).

With a hypothesis at hand, Snow then needed to collect data to prove it. His data collection involved a house-to-house survey in the area surrounding the Broad Street pump operated by Southwark and Vauxhall. As part of his data collection, Snow needed to account for anomalous cases (such as the brewery workers who drank beer, not water). It is important to note that such qualitative reasoning and diligent data collection were critical elements in establishing (to a modern reader) the “as if” random nature of the treatment assignment mechanism provided by the Broad Street pump. Snow’s deliberate methods contrast with a shortcut approach, which would have been to argue that in his data he had a natural experiment.

Another important feature of this example is that widespread acceptance of Snow’s hypothesis did not occur until compelling evidence of the precise causal mechanism was provided. “However, widespread acceptance was achieved only when Robert Koch isolated the causal agent (Vibrio cholerae, a comma-shaped bacillus) during the Indian epidemic of 1883” (Freedman, 2009, p. 342). Only once persuasive evidence of a plausible mechanism was provided (i.e., direct observation of micro-organisms now known to cause the disease) did Snow’s ideas become widely accepted.

We expect the same might be true in the accounting discipline if researchers carefully articulate the assumed causal mechanism for their observations. It is, of course, necessary for researchers to show that a proposed mechanism is consistent with observed behaviour in the institutional setting being examined. As discussed below, detailed descriptive studies of institutional phenomena provide an important part of the information used to evaluate a proposed mechanism.

18.2 Smoking and heart disease

A more recent illustration of plausible causal inference is discussed by Gillies (2011). Gillies (2011) focuses on the paper by Doll and Peto (1976), which studied the mortality rates of male doctors between 1951 and 1971. The data of Doll and Peto (1976) showed “a striking correlation between smoking and lung cancer” (Gillies, 2011, p. 111). Gillies (2011) argues that “this correlation was accepted at the time by most researchers (if not quite all!) as establishing a causal link between smoking and lung cancer.” Indeed Doll and Peto themselves say explicitly (p. 1535) that “the excess mortality from cancer of the lung in cigarette smokers is caused by cigarette smoking.” In contrast, while Doll and Peto (1976) had highly statistically significant evidence of an association between smoking and heart disease, they were cautious about drawing inferences of a direct causal explanation for the association. Doll and Peto (1976, p. 1528) point out that “to say that these conditions were related to smoking does not necessarily imply that smoking caused … them. The relation may have been secondary in that smoking was associated with some other factor, such as alcohol consumption or a feature of the personality, that caused the disease.”

Gillies (2011) then discusses extensive research into atherosclerosis between 1979 and 1989 and concludes that “by the end of the 1980s, it was established that the oxidation of LDL was an important step in the process which led to atherosclerotic plaques.” Later research (Morrow et al., 1995, p. 1201) provided “compelling evidence that smoking causes oxidative modification of biologic components in humans.”2 Gillies (2011, p. 120) points out that this evidence alone did not establish a confirmed mechanism linking smoking with heart disease because the required oxidation needs to occur in the artery wall, not in the bloodstream, and it fell to later research to establish this missing piece.3 Thus, through a process involving multiple studies over two decades, a plausible set of causal mechanisms between smoking and atherosclerosis was established.

Gillies (2011) avers that the process by which a causal link between smoking and atherosclerosis was established illustrates the Russo-Williamson thesis. Russo and Williamson (2007, p. 159) suggest that “mechanisms allow us to generalize a causal relation: while an appropriate dependence in the sample data can warrant a causal claim ‘\(C\) causes \(E\) in the sample population,’ a plausible mechanism or theoretical connection is required to warrant the more general claim ‘\(C\) causes \(E\).’ Conversely, mechanisms also impose negative constraints: if there is no plausible mechanism from \(C\) to \(E\), then any correlation is likely to be spurious. Thus mechanisms can be used to differentiate between causal models that are underdetermined by probabilistic evidence alone.” The Russo-Williamson thesis was arguably also at work in the case of Snow and cholera, where the establishment of a mechanism (i.e., Vibrio cholerae) was essential before the causal explanation offered by Snow was widely accepted. It also appears in the case of smoking and lung cancer, which was initially conjectured based on correlations, prior to a direct biological explanation being offered.4

18.3 Causal mechanisms in accounting research

Gow et al. (2016) suggest that accounting researchers can learn from fields such as epidemiology, medicine, and political science, which grapple with observational data yet eventually draw inferences that are causal. While randomized controlled trials are a gold standard of sorts in epidemiology, it is often unfeasible or unethical to use such trials. For example, in political science, it is impossible to randomly assign countries to treatment conditions such as democracy or socialism. Nevertheless, these fields have often been able to draw plausible causal inferences by establishing clear mechanisms, or causal pathways, from putative causes to putative effects.

One paper that has a fairly compelling identification strategy is Brown et al. (2015), which examines “the influence of mobile communication on local information flow and local investor activity using the enforcement of state-wide distracted driving restrictions.” The authors find that “these restrictions … inhibit local information flow and … the market activity of stocks headquartered in enforcement states.” Miller and Skinner (2015) (p. 229) suggest that “given the authors’ setting and research design, it is difficult to imagine a story under which the types of reverse causality or correlated omitted variables explanations that we normally worry about in disclosure research are at play.” However, notwithstanding the apparent robustness of the research design, the results would be much more compelling if there was more detailed evidence regarding the precise causal mechanism through which the estimated effect occurs, and the authors appear to go to lengths to provide such an account.5 For example, evidence of trading activity by local investors while driving prior to, but not after, the implementation of distracted driving restrictions would add considerable support to conclusions in Brown et al. (2015).6

As another example, many published papers have suggested that managers adopt conditional conservatism as a reporting strategy to obtain benefits such as reduced debt costs. However, as Beyer et al. (2010, p. 317) point out, an ex-ante commitment to such a reporting strategy “requires a mechanism that allows managers to credibly commit to withholding good news or to commit to an accounting information system that implements a higher degree of verification for gains than for losses,” yet research has only recently begun to focus on the mechanisms through which such commitments are made (e.g., Erkens et al., 2014).7 It is very clear that we need a much better understanding of the precise causal mechanisms for important accounting research questions. A clear discussion of these mechanisms will enable reviewers and readers to see what is being assumed and assess the reasonableness of the posited causal mechanisms.

18.4 Discussion questions

18.4.1 Li et al. (2018)

  1. Let’s think about the causal mechanism in Li et al. (2018).

    Suppose you are a manager involved in the process of preparing information about significant customers for your firm’s 10-K. What do you think your main area of expertise would be? If a court upheld or rejected the IDD while you are in this role, would you expect to learn about that in your role? If so, how would you learn this information? If not, how might the court ruling nonetheless have an effect on your decisions regarding what to include in the filing?

    Assume that the relevant managers are aware of relevant court rulings on IDD, how would you expect this to affect disclosure decisions? When would this effect occur? How long would it last?

  2. Note that PepsiCo is headquartered in the state of New York. Reading the discussion of the PepsiCo Inc. v. Redmond case (Li et al., 2018, pp. 272–3), does this fact have implications for their research design. How might Li et al. (2018) need to do things differently?

  3. Look at the plots in Li et al. (2018, p. 284). What do we expect to see here? Try to imagine the plots without the fitted curves and red vertical dashed line. How compelling is this evidence? Why do you think the authors included this? How might one depict the results reported in Table 2 on the figures in Li et al. (2018, p. 284) (pay attention to magnitudes)?

  4. In Section 3.2, we ran regressions with two-way fixed effects (i.e., fixed effects for grade and for id, the student identifier). What variables are analogous to grade and for id in regressions (3) and (4) in Table 2 of Li et al. (2018)? Why is there no \(\mathit{POST} \times \mathit{TREAT}\) coefficient reported in Table 2? How would you code \(\mathit{POST}\) for a firm in a state that never has IDD?

  5. Compare Tables 2 and 3 of Li et al. (2018). Which regressions are most comparable across the two tables? What insight does Table 3 provide over Table 2?

18.4.2 Burks et al. (2018)

  1. Suppose you are a senior executive at a bank. What opportunities might implementation of IBBEA by a state other than your own provide to you? How would you evaluate such opportunities? How long would it take to evaluate them?

  2. Suppose you are a senior executive at a bank in state that is about to implement IBBEA. What actions might you take if you anticipate increased interest in your state by interstate banks? Would the actions you take depend on how out-of-state banks enter?

  3. Burks et al. (2018) do not explain what a “deposit cap” is. Rice and Strahan (2010) describe it thus: “IBBEA specifies a statewide deposit concentration limitation of 30% with respect to interstate mergers that constitute an initial entry of a bank into a state.” What might drive differences in the deposit cap for different states? (See Table 1.) Would this restriction affect behaviour of all banks in an IBBEA-implementing state in the same way?

  4. “The results are consistent with disclosure significantly increasing upon introduction of the IBBEA (the main effect) … we find that the restriction imposing a deposit cap of less than 30% has a significant mitigating effect on disclosure.” How many states implemented a cap of less than 30%? (See Table 1.) We checked one of these states (Colorado) and it had a cap of 25%. In 1997, the bank with the largest market share had 17.87%. With these facts, what is the practical difference between 25% and 30% in this case?

18.4.3 Christensen et al. (2017); Glaeser and Guay (2017)

  1. Christensen et al. (2017) claim to find a causal effect of requiring disclosure in SEC filings on mine safety. Why is it significant that the information was already available on MSHA website? How persuasive do you find the evidence of a causal effect to be? What elements do you find particularly persuasive (or problematic)?

  2. What is the causal mechanism through which Christensen et al. (2017) argue (or conjecture) this effect occurs?

  3. Glaeser and Guay (2017) suggest that use of securities laws to enforce safety regulations is another mechanism through which the causal effect occurs? Does this undermine the validity of the estimate of the causal effect?

  1. This material is largely based on material in Gow et al. (2016).↩︎

  2. This evidence is much higher levels of a new measure (levels of \(F_2\)-isoprostanes in blood samples) of the relevant oxidation in the body due to smoking. This conclusion was greatly strengthened by the finding that levels of \(F_2\)-isoprostanes in the smokers fell significantly after two weeks of abstinence from smoking” (Morrow et al., 1995, pp. 1201–1202).↩︎

  3. “Smoking produced oxidative stress. This increased the adhesion of leukocytes to the … artery, which in turn accelerated the formation of atherosclerotic plaques” (Gillies, 2011, p. 123).↩︎

  4. The persuasive force of Snow’s natural experiment, coming decades before the work of Neyman (1923) and Fisher (1935), might be considered greater today.↩︎

  5. Brown et al. (2015, pp. 277–278) “argue that constraints on mobile communication while driving could impede or delay the collection and diffusion of local stock information across local individuals. Anecdotal evidence suggests that some individuals use car commutes as opportune times to gather and disseminate stock information via mobile devices. For instance, some commuters use mobile devices to collect and pass on stock information either electronically or by word-of-mouth to other individuals within their social network. Drivers also use mobile devices to wirelessly check stock positions and prices in real-time, stream the latest financial news, or listen to earnings calls.”↩︎

  6. Note that the authors disclaim reliance on trading while driving: “our conjectures do not depend on the presumption that local investors are driving when they execute stock trades … [as] we expect such behavior to be uncommon.” However, even if not necessary, given the small effect size documented in the paper (approximately 1% decrease in volume), a small amount of such activity could be sufficient to provide a convincing account in support of their results.↩︎

  7. We discuss conditional conservatism in Section 4.1.2.↩︎