4.2.3. Overview of Corporate Metric Results and Meaning

The discussion above has investigated the application of metrics used in the more expansive environmental disclosure field to the narrower, in reporting terms at least, of anti-corruption disclosure. The metrics differ from each other in a number of ways and hence, by design or default, seek to inform on slightly different issues or questions. SQNI, using the main calculation above, seeks relative disclosure by word count for each year without any clear concern for change over time. Other measures seek to inform on the number and/or depth of questions/categories answered and therefore can inform on not

just relative performance between companies over one year but over time too. TQLI seeks to combine SQNI with a depth of questions answered measure to give a broader assessment of quality across companies and over time.

In this section, the relative performance or ranking of the companies across the measures is considered, and this follows Helfaya and Whittington (2019, Table 7, page 537). Table 10 seeks to rank the best to worst disclosers according to each measure and, finally, a rough overall average score over the measures together. The three top performers for each column are highlighted in green, whilst the bottom three are shown in red. This shows a fairly consistent ranking for the companies at the bottom of the rankings whilst slightly more diversity at the top.


**Table 10.** Rank (Top 1, bottom 10) of companies by measure across the total sample period.

As mentioned throughout, some sample companies have not been part of the FTSE100 for the entire sample period. From 2011 all companies were part of the FTSE100, so Table 11 shows the same measures, but just for the final nine years of the sample period. If there is a rising trend or a discontinuity in disclosure following the 2010 Corruption Act, then it would be more reasonable to compare all companies across the same timeframes. The four companies at the base of the table are those with the restricted, later data; it is clear that Evraz and Polymetal, in particular, are now in the middle of the sample companies rather than towards the top. There would seem quite notable consistency with BP, Anglo American, and BP regularly at the top and Shell, Glencore, and Rio Tinto having the least disclosure, however, defined. Shell's ACHI score is the one major outlier to this last point; ACHI measures only the depth of questions answered with no concern for breadth, so one might conclude that Shell answers a few questions to some depth with few words (SQNI).

For each metric, the average scores across the sample companies were ranked by year from 1 (highest) to 17 (lowest). For example, 2003 has the highest rank for SQNI, 2018 is the lowest ranked, while SCI-Q ranks 2018 as the highest quality year, and 2003 is 16th out of our 17 sample years. The ranks were then compared across the metrics to see if one metric has significant power to explain or predict the level of another. The Spearman test was used for this as it requires fewer assumptions about the data and its structure. This is presented in Table 12. The two SCI variants were found to be highly correlated and also highly correlated with TQLI and SHI. The level of significance of each of these relationships is over 99%. It would be reasonable to assume that, in this context, little would be gained from working out more than one of these metrics. Intriguingly, ACHI, which assesses the depth of the questions answered, correlates negatively at 95% with all the above four metrics. This means that ACHI's interpretation of quality in anti-corruption reporting is opposite to that of the four nested metrics. Building on the previous discussion, it would seem that the increasing range of questions addressed comes at the cost of depth. SQNI has no significant positive or negative correlation with the other measures.


**Table 11.** Rank (Top 1, bottom 10) of companies by measure across 2011-2019.

**Table 12.** Spearman correlations comparing environmental reporting measure consistency over the sample period.


\*\* significant at the 99% level for the two-tailed test, \* significant at the 95% level for the two-tailed test.

#### *4.3. Research Question 2*

Our second research question considered the evaluation of the impact of the introduction of legislation, framed as: Does the use of these metrics provide consistent evidence that corporate ACD has responded to the introduction of the UK Bribery Act?

From the raw descriptive statistics tables (Tables 3 and 4), the average disclosure volume by companies for each year can be observed. The figure below shows this information graphically. Several observations can be made of this quantity graph. Firstly, the two metrics seem to closely follow each other, so more words are usually more questions answered rather than just longer answers. Secondly, there is a rising trend, but this is not consistent or uniform, as the company data in Tables 3 and 4 also demonstrates at the individual company level. Thirdly and looking more carefully, before 2010, the year of the Bribery Act, the graph seems fairly flat, but from 2010 onwards, there appears to be a jump that has continued as a somewhat inconsistent trend. This would suggest the Bribery Act may have had a positive response which companies continued to build on over the following years. Other initiatives from TI, GRI, etc., may also have impacted this positive trend.

We also considered the variation in mean values for significant differences splitting the sample into the years before the Bribery Act and the years after its introduction. Hence, the sample was split into two groups: 2003 to 2010, before the law was introduced, with 58 annual reports, 2011 and 2019, after the Bribery Act, with 90 annual reports. T-tests were conducted to compare the means of the corporate anti-corruption scores between the two groups. The results (Table 13) show a significant difference in the means of corporate anticorruption disclosure scores at a 1% level for each of the measures except SQNI and ACHI. We have already noted that the design of SQNI means it does not provide a time trend.


**Table 13.** Two-sample *t*-test before and after the UK Bribery Act 2010.

\*\*\* Significance levels—1%.

Of the six metrics applied, four, SCI-Q, SCI-C, SHI, and TQLI, showed a high level of difference between findings before and after 2010. These four measures, all with a 1% significance, show a clear change and increase in how the measure assesses the quality of anti-corruption reporting after 2010. Interestingly not only are the means significantly higher for these four measures after 2010, but the variances are also significantly higher variances following 2010. ACHI measures the depth of reporting rather than the breadth, only assessing the depth of questions actually answered. From the tables and graphs above, particularly Figure 7, we have already found that the breadth of questions addressed within anti-corruption reporting is the reason for the rise in "quality" and that it seems the depth of content for questions addressed have either stayed the same or even declined.

#### **5. Discussion and Conclusions**

Overall, it seems that there are some differences in the findings of this study compared to previous studies. For example, the differences in ACHI results across previous studies, particularly studies that focused on environmental issues, are worth noting. This study found that the mean ACHI score for its sample is 1.94, which is higher than the mean ACHI score of 0.67 for 198 US non-financial firms in the 1994 fiscal year from [34] study. Authors [34] suggested that their sample firms only disclosed qualitative information at best, while this study found that companies, on occasion, provided specific qualitative information. The mean ACHI score of 1.94 in this study is more comparable to the mean ACHI value of 2.8 from the [30] study, which is higher than that of [34]. The differences in ACHI scores across studies may be due to the timing of profile-raising of the issues concerned. For instance, since the early 2000s, calls for anti-corruption disclosure from the UN and other entities have resulted in legal and non-legal interventions that may have led to higher levels of disclosure in this study compared to the study by [34]. Additionally, Ref. [32] study on environmental issues, which analyzed a sample of 32 New Zealand companies for the fiscal year 2010–2011, reported a mean SHI value of 0.681, which is comparable to the average ACHI score in this study (Table 7). There are many differences between environmental and corruption reporting, as well as the sample being from differing countries, sectors, and timeframes, so there is no reason to expect similar results to the previous studies. We have addressed two research questions.

The first considered the applicability of quality metrics used in the environmental accounting literature to ACD, where disclosure is of significantly lower volume. Table 12 shows a high correlation between four of these measures (SCI-Q, SCI-C, SHI, and TQLI), with SQNI giving a different interpretation of relative quality and ACHI being somewhat

closer to the results from the nested four metrics. SCI-C would require the least data collection and avoid the more subjective assessment of the quality of answers needed by TQLI and SHI. Thus, if a reader were concerned about ranking the companies' reporting, then SCI-C would seem the most efficient choice. Table 4 and Figure 4 also show inconsistency in corporate reporting, with companies deciding to reduce ACD in some years and increase it in others, so the corporate-ranking-focused reader could not rely on continuing levels of reporting from a company. If a reader were concerned with reporting across the sector, then the metric recommendation is a little different. Whilst Figure 5 (both SCI metrics), Figure 9 (SHI), and Figure 11 (TQLI) show a rising trend in ACD across the sector over time, SQNI (Figure 2) reveals a trend for more companies to be closer to the lowest volume reporter than the highest. Figure 7 (ACHI versus questions answered) suggests the potential declining depth of answers across the sector whilst the number of questions answered has clearly risen. Assessment of quality and relevant metrics can depend on the precise nature of the question asked and the purpose of the person or organization investigating the reporting. Even metrics that appear poor at one task might be able to provide additional insights when used carefully. It is hard to discern any general trend of companies following each other in a deliberate way. The number of questions answered in each of the 148 annual reports correlates negatively with the average depth of answers at a significant level (see Table 12), showing that perceiving a need to answer more questions seems linked to less detailed responses. This is effectively a component analysis of SHI, with the rising number of questions addressed in a report mitigated to a degree by their decline in depth.

The second research question addressed the impact on reporting of the introduction of the UK Bribery Act of 2010. Figure 13 shows a step change from 2010 with a generally rising trend in both word count and questions answered since that point. Table 13 shows the four metrics that were mutually supportive in tracking company trends across the sample period and also support reporting after the Act being significantly greater than before. Neither ACHI nor SQNI is significant, with SQNI suggesting that reporting has actually reduced. The findings from research question 1 give us confidence in SCI-Q, SCI-C, SHI, and TQLI, providing useful information on the level of company ACD reporting year by year, enabling us to conclude that the reporting of corruption issues has increased since the Act. However, as with question 1, the finding that this is primarily about the increased breadth of answers (more questions addressed) rather than deeper answers to each question might disappoint some annual readers. The Act, then, may have triggered an awareness of more areas to cover, but this seems to be matched with opportunism to reduce depth or reluctance to add yet more pages to the annual report or to, perhaps, "unbalance" the relative content across different issues. Moreover, whilst the timing of the change in reporting fits with the introduction of the Act, we need to be cautious in assuming the Act was the only driver of this change. Non-governmental organizations have also introduced and regularly revised calls for reporting over this timeframe, and pressures to respond to events might also drive corporate behavior. Table 11 shows that our four generally preferred metrics rank the best and worst reporters similarly in the post-Act period. It would be interesting to consider why the Act might lead to more of a response from some companies than others.

Without addressing the first research question, it would not have been possible to consider the impact of the Act on ACD assessed by these metrics. Through examining the two questions together, we can conclude that some metrics (SCI, SHI, and TQLI) are more useful in assessing questions of company reporting depth, breadth, and quality and that the same metrics are consistent in assessing the impact of an event, in this case, the introduction of the Bribery Act. The applicability of these findings to alternative and broader datasets is an important question for any study. The findings here are for the ACD of the ten major extractive companies listed on the UK stock exchange from 2003 to 2019. Further studies could address other sectors, specifically those where corruption is also perceived to be a major issue. Other governance settings would also be an interesting comparison. The vagueness of the ACD reporting requirement of the 2006 Companies Act might not be matched by such a lack of clarity in other countries. Indeed, the style and history of corporate reporting might also lead to other findings [3]. ACD is not the only area of ESG reporting that might be examined in this way; modern slavery, gender pay gaps, and community engagement are just three areas where this approach could be applied, and it might then be clear whether the results here are robust across a broad range of low disclosure topics in corporate reporting that are, nevertheless, important to specific report readers and, more broadly, to those concerned with factors of reputational risk.

**Figure 13.** Volume of average company disclosure by year.

**Author Contributions:** Conceptualization, M.G., M.W. and A.H.; methodology, M.G., M.W. and A.H.; validation, M.W. and A.H.; formal analysis, M.G. and M.W.; investigation, M.G.; resources, M.G.; data curation, M.G. and M.W.; writing—original draft preparation, M.G., M.W. and A.H.; writing—review and editing, M.G., M.W. and A.H.; visualization, M.G. and M.W.; supervision, M.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
