Practice non-financial reporting in accordance with the recommendations of the global reporting initiative

Volume of disclosure = 100 %



For the regression purposes, values normalized to 1 were used. Let us consider the volume of disclosure calculation on an example for one company from the sample.

Table 3. Volume of disclosure calculation for “NVIDIA” company (abridged version for convenience)

Indicator	Indices: “1” if reported, “0,5” if partially disclosed, “0” otherwise
G 1.1	1
G 1.2	0
G 2.1	1
G 2.2	1
G 2.3	0,5
G 2.4	1
…	…
EC1	1
EC2	0
EC3	0,5
…	…
EN13	1
EN14	0
…
LA6	0
LA7	0,5
…	…
PR8	0
PR9	0
Volume of total disclosure as :

Source: Author's accounts.

Table 3 shows (in abridged form) how the total percentage of sustainability information disclosure was calculated for “NVIDIA” company (USA). First reporting year - 2010. Similar tables were constructed for each company in the sample and their first year reports were analyzed. The procedure was the following. For a given company, the first year report was examined for compliance with GRI suggestions about indicators to report on. For each indicator stated by GRI, if required information was disclosed in company's report, “1” was assigned; if disclosure was not full (meaning that not all required aspects were reflected), “0,5” was assigned; if no necessary information was found in company's first year report, “0” was assigned. For instance, in its report, “NVIDIA” fully disclosed data on restored or protected habitats (EN13), partially reported on amount of labor fatalities, injury and disease rates, and absenteeism (LA7), and gave no information on the number of customer complaints about privacy encroachment and private data loss (PR9) [GRI Guidelines, version 3.0 (GRI website)]. After assignment of indices to all indicators, the former were summed up and divided by the total number of indicators specified by relevant version of GRI guidelines (each report, depending on the year of issuance, was analyzed according to corresponding version of guidelines). Thus, more formally, percent of transparency was calculated as:

Volume of disclosure = .

The obtained number represented the volume of non-financial information disclosed in company's first report (from 0 to 1). For “NVIDIA”, the disclosure volume was 0,4221 (or 42,21%).

Now it is time to introduce dependent and explanatory variables in the model developed. As discussed above, the main explanatory variable is the volume of disclosed non-financial information in the first year report taking values from 0 to 1. We will regard this variable as “SR_i”. The dependent variable in the model is financial performance. For assessment of firms' financial results, both A-B and M-B measures were taking, as suggested by Schreck (2011): ROA, Earnings per share ratio (EPS), ROE (A-B) and Tobins' Q (M-B).

ROA = , where denominator is the assets' book value average of beginning and end of the period (Lie, 2001).

ROE = ,

EPS = , (Murphy, 1967).

Tobin's Q = , (Chung and Pruitt, 1994).

The dependent variables in the model (universally regarded as FP) were calculated as a percentage change in next year FP in comparison to FP of first report year:

Delta FP = , where “t” is the year of first report and “t+1” is the following year.

The logic behind the use of such form of financial performance assessment is the following. Since first sustainability reports are released in the period t, SR_t and FP_t correspond to the same period and are obtained simultaneously. Thus, the only financial results that can be influenced by non-financial information disclosure in period t correspond to the next period t+1. In order to enable comparison of FP data between companies, relative, not absolute changes in financial results should be considered. Thus, percentage change in FP ratios (normalized to 1) is employed. Hence, the model suggests that the beginning of sustainability reporting practice contributes to positive change in financial results. Moreover, the greater the volume of first year non-financial information disclosure is, the larger is the positive shock to FP ratios.

Several control variables were used in model testing. In their study, Lopez, Garcia and Rodriguez (2007) employed size (as total assets), risk (as debt to assets) and industry measures. Following their logic, this model introduced size [SIZE] and risk [RISK] control variables, measured as book value of tangible assets and financial debt to common equity ratio (leverage) at time t respectively. The industry variable was not explicitly included in this model as company's industry characteristics are partly reflected by assets value, so potential problem of industry - size control variables correlation is avoided. In addition, to control for possible influence of R&D expenses on change in FP, R&D expenses to sales ratio [RD] (to normalize to firm's size) measured at time t was introduced as another variable. All dependent variables, as well as controls, were measured for each company depending on its first report year. Bloomberg database was used for financial data collection.

Let us now turn to hypotheses formulation. For the first part of the research, four hypotheses were proposed:

H1: Next year percentage change in companies' EPS ratios increases with the volume of sustainability information disclosed in the year of first report.

Percentage change could be positive if FP_t+1 ratios increased relative to FP_t , and could be negative if FP_t+1 ratios appeared to be lower. Thus, “increase in percentage change” means movement from its negative values to positive ones, while “decrease in percentage change” corresponds to the reverse movement.

H2: Next year percentage change in companies' ROA ratios increases with the volume of sustainability information disclosed in the year of first report.

H3: Next year percentage change in companies' ROE ratios increases with the volume of sustainability information disclosed in the year of first report.

H4: Next year percentage change in companies' Tobin's Q ratios increases with the volume of sustainability information disclosed in the year of first report.

Before the introduction of formal regression equations, last point on approach to potential endogeneity problem should be made. As it was discussed in the previous sections, it may be thought that good past financial results may influence the amount of non-financial information disclosed which in turn would be transferred into better future financial performance. To account for that, FP _t will be included in the model as another explanatory variable and its significance will be tested, as it was suggested by Brammer, Brooks and Pavelin (2006). FP _t values are good representatives of past years financial results as they are correlated with other lagged FP. Moreover, since SR _t and FP _t appear in the same period and FP _t clearly cannot influence the volume of disclosed information in the period t (the two variables move simultaneously in time), the correlation between these explanatory variables should not be high (however, formal tests for multicollinearity were performed as well [shown below]). Thus, FP _t can be considered as a valid explanatory variable for the model.

The regression equations constructed are the following:

EPSDEL_i = б + в₁ SR_i + в₂ SIZE_i + в₃ RISK_i + в₄ RD_i + в₅ EPS_i + u_i (H1),

where [EPSDEL] = and [EPS] = EPS at year t. SR, RD, SIZE and RISK correspond to time t as well. Following the same logic, other three regression equations were constructed:

ROADEL_i = б + в₁ SR_i + в₂ SIZE_i + в₃ RISK_i + в₄ RD_i + в₅ ROA_i + u_i (H2),

ROEDEL_i = б + в₁ SR_i + в₂ SIZE_i + в₃ RISK_i + в₄ RD_i + в₅ ROE_i + u_i (H3),

Q_TOBINDEL_i = б + в₁ SR_i + в₂ SIZE_i + в₃ RISK_i + в₄ RD_i + в₅ Q_TOBIN_i + u_i (H4).



4. Results

For data analysis obtained for specified sample of 202 companies, OLS regression method was used and EViews software was employed.

Table 4. Statistics summary and correlation matrix.

	Mean	SD	SR	SIZE	RISK	RD
EPSDEL	3,7476	11,5774
ROADEL	1,7158	5,0894
ROEDEL	1,5636	5,0490
Q_TOBINDEL	-0,0216	0,2577
SR	0,5976	0,2145	1
SIZE	6471659492	8676903989	0,1168	1
RISK	0,9840	1,4439	-0,0468	-0,0009	1
RD	0,0242	0,0748	0,1168	0,0373	-0,1224	1
EPS	2,0962	4,2750	-0,1032	0,2394	0,0568	-0,0147
ROA	0,05647	0,11615	-0,1815	-0,0409	-0,1286	0,1
ROE	0,12287	0,22187	-0,1206	0,0644	-0,2928	0,0749
Q_TOBIN	1,2717	0,7945	-0,0239	-0,2127	-0,1580	0,2428

Source: Author's accounts.

Table 4 represents mean and standard deviation (SD) values for all variables involved in the analysis. Moreover, it shows correlation coefficients for explanatory variables. As can be seen, all coefficients are relatively small (less than 0,3 in absolute values). Thus, it seems unlikely that multicollinearity problem could take place, however to obtain more reliable proof, variance inflation factor (VIF) analysis was undertaken (results are below).

Table 5. VIF analysis results.

Variable	VIF
SR	1,0815
RISK	1,0228
SIZE	1,0185
RD	1,0582
EPS	1,1542
ROA	1,0573
ROE	1,1174
Q_TOBIN	1,1365

Source: Author's accounts.

Table 5 indicates that all explanatory variables' VIFs are near 1, so the absence of multicollinearity can be concluded. The next table represents regression results.

Table 6. Regression results.

	(H1)	(H2)	(H3)	(H4)
	EPSDEL	ROADEL	ROEDEL	Q_TOBINDEL
C	-1,3244 (0,1869)	-1,7730 (0,0777)	-0,8962 (0,3712)	0,5100 (0,6106)
SR	2,0409 (0,0426)	4,4149 (0,0000)	2,7346 (0,0068)	0,4142 (0,6792)
RISK	0,6731 (0,5017)	-0,9691 (0,3336)	-0,9670 (0,3347)	-1,2341 (0,2186)
SIZE	-0,8342 (0,4052)	-1,7180 (0,0873)	-0,9524 (0,3420)	-2,5072 (0,0130)
RD	0,0302 (0,9759)	0,0329 (0,9738)	0,0629 (0,9499)	4,6046 (0,0000)
EPS	-0,1861 (0,8526)	-	-	-
ROA	-	-1,2815 (0,2015)	-	-
ROE	-	-	-1,2375 (0,2173)	-
Q_TOBIN	-	-	-	-1,0716 (0,2852)
D-W stat	2,1505	1,9898	1,9616	2,0416

Source: Author's accounts.

Values in the cells represent t-statistics for corresponding explanatory variables and associated p-values (in parentheses). The last line of the table indicates statistics for Durbin-Watson (D-W) autocorrelation test obtained for each regression. As can be seen, D-W statistics are relatively big. The upper critical values d_u for 202 observations and 5 explanatory variables are equal to 1,8094 at 5% significance level and 1,7143 at 1% significance level; all obtained D-W statistics exceed these critical values, so the null hypothesis about autocorrelation presence is rejected. No autocorrelation was found in the models.

As for t-coefficients, the results are the following. For each H1, H2 and H3 models, t-statistics of SR variable appeared to be significant at 5% significance level (s.l.), as corresponding p-values 0,0426, 0,0000 and 0,0068 do not exceed 0,05 (here and later on, if p-value < 0,05, coefficient's significance is observed). Moreover, for these models, none of the control variables' coefficients is significant at 5% s.l. (however, the direction of their influence on dependent variables is as anticipated). It should also be noted, that coefficient insignificance of lagged FP variables (EPS, ROA, ROE) indicates the absence of endogeneity problem in the models. Thus, hypotheses H1, H2, and H3 are not rejected at 95% confidence level, so it may be concluded that the volume of sustainability information disclosure positively influences the percentage change in EPS, ROA and ROE ratios, and that past financial results do not interrupt the relation.

However, result obtained for H4 model appeared to be fairly interesting. While SR coefficient is not significant at any reasonable s.l. (p-value is equal to 0,6792), RD and SIZE control variables showed their significance. According to regression results, RD positively influences Tobin's Q ratio (almost zero p-value). It can be proposed, that company's involvement in R&D practices is favorably appraised by the market, and as a result firm's market valuation rises (so Tobin's Q rises as well). As for SIZE control, the corresponding coefficient is significantly negative (p-value = 0,0130). Such result is fully anticipated, as increase in company's size, i.e. rise in its tangible assets book value, boosts denominator's share in Tobin's Q ratio, which exactly corresponds to assets book value. Thus, Tobin's Q decreases. Significance of lagged FP value coefficient (0,2852 p-value for Q_TOBIN variable) is not confirmed, thus no endogenous relation is present. For H4 model, the hypothesis about significant positive influence of disclosure volume on percentage change in Tobin's Q ratio is rejected at any reasonable s.l. No relation is found.



Table 7. White test results.

	(H1)	(H2)	(H3)	(H4)
Ч2 (20) probability	0,4438	0,2058	0,8921	0,5924

Source: author accounts.

Before we proceed to interpretation of observed results, it should be mentioned that White test for heteroskedasticity identification was performed for each regression model. Table 7 below depicts key statistics.

As can be seen, probabilities for chi-square values with 20 degrees of freedom (number of estimated auxiliary regression parameters minus 1) are fairly high (greater than 0,05 for 5% s.l., indicating that auxiliary regression coefficients are insignificant). Thus, at any reasonable s.l. we do not reject null hypothesis about homoskedasticity. Heteroskedasticity is not found.

Now let us provide the intuition for obtained results on models testing. As mentioned before, H1, H2, and H3 hypotheses were not rejected, while H4 was. That means that, first, there exists a positive influence of the volume of first year disclosure on relative change in ROA, ROE and EPS ratios (A-B measures), and, second, there is no significant influence of the percentage of disclosed information in the first year sustainability report on the relative change in Tobin's Q ratio (M-B measure). Let us suggest an intuition for such results. As significant influence was found only for A-B measures of FP, the following logic applies. The fact that a company starts to publish non-financial information may be regarded as signal to clients, employees, and suppliers about firm's increased transparency. Identified stakeholders are unlikely to penetrate into the essence of reported information right away; they rather increase their valuation of the company, which employed a new socially responsible practice of accountability. Furthermore, the higher volume of reported information in the first year may be viewed as the as a more serious attempt to become transparent and sustainable. Next, discussed in the previous sections causal mechanisms of SR-FP link take place: increased reputation and higher external valuation lead to increased sales due to positively inclined customers and reduces costs due to employees relation improvement, higher skilled labor attracted, and newly built suppliers relations gained. All these factors lead to improvement in company's net income and, thus, ROA, ROE, EPS ratios. Hence, the higher the disclosure volume is, the greater is the increase in company's valuation by external parties, which leads to greater positive change in FP.

However, the situation is different for firm's valuation by the market. It may happen, that markets are nearly perfect, and possess all external (and possibly private) information. Thus, for the moment of first report publication, markets either already know information about disclosures, or instantly react. Hence, neither the fact of company's beginning of sustainability reporting nor the volume of information being disclosed can significantly influence company's valuation by the market. As Tobin's Q ratio reflects company's market value, there is no significant relation between it and disclosure volume.



5. Further analysis

The next part of the research was dedicated to comparative analysis of long term financial performance of firms once started and then continues reporting practice with financial results of firms never reported of their sustainability development. For that purposes, graphical analysis was used.

In their recommendations, GRI suggests, that reporting firms appear to be more stable in their financial results over time as compared to non-reporting peers. The reason is that since reporting companies reflect most of the sustainability information in their reports from year to year, investors and stakeholders are “familiar” with its SR practices, so any new information released by the company will not sharply affect interested parties and, thus, firm's financial results. As for non-reporting businesses, their SR practices (if exist) are not known by external agents, so a realize of any piece of sustainability information will be fully unexpected for stakeholders and thus reflected via some FP shocks [GRI website].

This research tried to assess such GRI statement. For the purpose of conducting the analysis, a sample of not reporting peers was chosen for existing sample of companies. Specifically, each company was analyzed via Bloomberg database and a suggested peer (the most suitable in respect to the size measured by the value of total assets) was picked. Thus, a matching sample of companies-analogies was obtained.

For the analysis of time trends in FP, EPS and Tobin's Q ratios were chosen. The first one is an A-B measure (above results revealed similar behavior of all three A-B measures analyzed, so no need to additionally test ROA and ROE variables), while the second is M-B one. For each company (of both types) relative changes in FP measures (applying methodology from the firs part of the research) were calculated for four years before and four years after the first report year. For peers, first report year FP measures were calculated as well. It should be noted that the choice of “four years” step was made in order to obtain representative visual results and embrace as much reporting firms as possible (some firms were excluded from the initial sample as the publication of first report in 2014, 2013, or 2012 made it impossible to have “+4” step; however, the percent of excluded firms was relatively small). The first report year was indicated as “0 period”, so analysis included “-4” - “+4” relative changes in financial results.

In order to obtain an aggregated results for reporting companies and their peers separately, for each group averaged values of relative changes were obtained. For each reporting company “0” period was determined, which was also used for its peer. Next, for all firms “-4” - “+4” FP changes were calculated. Finally, for each of two samples, all firms' measures were summed up and divided by the number of firms in the sample revealing the aggregated tend in relative FP measures changes for reporting and not reporting companies.

The obtained trend sare represented on the graphs below.

Figure 5. Average trend in EPS relative changes.

Source: Author's accounts.



Figure 6. Average trend in Tobin's Q relative changes.

Source: Author's accounts.

As can be seen from the graphs, reporting companies (as compared to their peers) behave more stably over time (after the first positive jump in the year following the first report year - another confirmation of results of models above) only for EPS measure. For Tobin's Q, their financial performance resembled that of their peers. The obtained results perfectly suit the intuition discussed earlier: for A-B measures, the disclosure of sustainability information represents a positive shock in the year following the first report year and no significant contributions thereafter (as majority of non-financial information is already revealed); for M-B measure, SR disclosure does not affect the market that possess all relevant information. The last point is confirmed by the fact that all reporting and non-reporting companies move together (as seen on the Figure 6). Thus, GRI statement can be considered as valid only for the assessment of FP that uses A-B approach.

The overall conclusion for the research performed is that the fact of non-financial information disclosure and its volume is relevant only for “non-professionals”, i.e. users that does not possess the full set of information at any point of time: consumers, employees, suppliers, social and environmental organizations, but not for the professional market players. However, such non-professionals' valuation of the company still constitutes to the latter a very important intangible asset that may greatly contribute to company's financial health.



Conclusion

The work performed under this research was aimed at the analysis of existing studies on SR-FP link, assessment of their approaches to models construction and enrichment of existing findings by development of a very new methodology on the subject. This study suggested looking at the possible link not between the nature of firms' sustainability practices (assessed by rating agencies or some other external parties) and their financial performance, but rather between firm's involvement in evaluation of its own SR behavior and financial results. The research turned to the companies' practice of sustainability reporting, popular and valuable nowadays, and was first which implemented analysis of non-financial disclosures contained in voluntary reports (by means of disclosure volume index development) for assessment of companies' financial performance. Moreover, the paper was also the first to investigate the very beginning of firms' sustainability reporting practices. Non-financial reports studied during the research were prepared according to one of the most popular and widely used sustainability reporting framework - GRI - which represented another innovation for SR-FP link assessment. The hypotheses proposed in the study suggested the possibility of positive influence of the volume of disclosed information in the first sustainability report on relative change in company's financial results. It was found that while such influence is indeed significant for accounting-based measures of FP, there is no evidence for significant dependence of market-based measures. As an explanation of such results, differences in the possession and use of new non-financial information released by external agents were proposed. Also, graphical analysis of FP trends confirmed the obtained results and added that long-term financial stability is associated with the use of sustainability information by “non-professional” users.

After having obtained new findings and having identified new dependencies in SR-FP link, the study is ready to suggest fields for further investigation. Firstly, time analysis of the link between the disclosure volume and financial performance using panel data may be performed. Also, investigation of the samples from developing markets may be undertaking.

This paper analyzed whether there is any short term, as well as long term benefit for firms beginning their sustainability reporting practices. Such results, if augmented by further findings, could represent a high value for business.



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