This study explores the rationality behind firms’ decision to admit or deny their involvement in bribery when responding to confidential surveys conducted by international agencies (such as the World Bank). Specifically, we posit that firms’ reluctance to provide accurate information about their engagement in bribery is at least to some extent contingent on certain situational factors. In other words, we claim that this behavior is context dependent. The paper uses the notions provided by the theory of planned behavior to understand the way in which the corruption of the legal environment, the intensity of market competition, and identification risk influence firms’ decision to lie about their involvement in bribery. To test these notions, we use databases from the fifth wave of the EBRD-World Bank Business Environment and Enterprise Performance Survey, country-level data from the Kauffman Foundation and macroeconomic (i.e., country-level) information from the World Bank database. We run ordinary least squares with geographic region-clustered standard errors on data from 30 countries and 6122 individual firms during the period 2012–2013. Consistent with our expectations, the results indicate that firms operating within more corrupt legal environments, facing more competition, and bearing a higher risk of being identified are less likely to deny their involvement in bribery. We conclude that not all firms have the same incentives to lie about their participation in bribery, and therefore, identifying the drivers of this heterogeneity may help policymakers better assess the reliability of bribery information collected through confidential surveys.
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Considering its magnitude as well as its negative consequences for economic development and international trade (e.g., Organization for Economic Cooperation and Development (2016), it is not surprising that the battle against bribery is a priority for policymakers throughout the world. The International Monetary Fund (2016) states that the cost of bribery might well be between $1.5 and $2 trillion per year. To put this figure into context, this estimate is roughly equivalent to 2% of the global gross domestic product (GDP) and is greater than the current GDP of Canada (i.e., the 10th largest economy in the word). In recent decades, the U.S. and other developed countries have assessed fines against firms that participate in bribery in their national and international businesses (Criminal Division of the U.S. Department of Justice and Enforcement Division of the U.S. Securities and Exchange Commission 2012). This anti-bribery effort seemed to result in investors from these countries reducing their investments in corrupt countries (Cuervo-Cazurra 2008). Some examples of legislation that includes a penalty are the 1977 U.S. Foreign Corrupt Practices Act, the 1997 OECD Convention on Combating Bribery, and the 2010 British Bribery Act.
TPB is defined at the individual (i.e., person) level. We extrapolate the notions of TPB to firms’ decisions, given that firms’ decisions are likely to depend on the capabilities, motivations, and preferences of executives, which is consistent with the upper echelons theory (e.g., Hambrick and Mason 1984).
We do not foresee a firm’s perceived behavioral control to exert a direct influence on its intention to deny its corrupt behavior. According to TPB, perceived behavioral control is unlikely to exert an important influence on firms’ intentions in situations in which firms have volitional control over performing (or not) the behavior of interest (Ajzen 1991). From a strict decision-making perspective, the firms in our setting have complete control over their disclosure practices. Participation in the BEEPS is voluntary. Likewise, even if a firm participates in the BEEPS, disclosing (or not) information about its bribery practices is not mandatory. Moreover, firms have no limitations or obstacles to provide false (or biased) information. The information provided by firms is not questioned, and for the most part, it is taken as true. Hence, we do not consider behavioral control to be a relevant factor in the context of our study. This direct application of the definition of perceived behavioral control is consistent with the approach taken in prior research using TPB to explain firms’ disclosure decisions (e.g., Carpenter and Reimers 2005). Nevertheless, some other studies use a broader concept of behavioral control in which it is understood based on not only the limitations or resources but also other factors related to the consequences of other risks associated with the behavior of interest (e.g., Powpaka 2002; Park and Blenkinsopp 2009).
Bribery is more profitable in corrupt legal environments because corrupt agents face lower potential prosecution and litigation risks (Bond 2008; Brunetti and Weder 2003). Therefore, when a corrupt legal environment is in place, firms have more economic incentives to engage in bribery (e.g., Becker 1974; Cooter and Ulen 2011; Harcourt et al. 2013).
To exemplify the costs of being publicly associated with bribery, one may consider the case of Walmart. In March 2012, The New York Times released a report about the bribery practices potentially employed by Walmart in its foreign operations. Consistent with the notion of reputational costs, the company’s stock price decreased by approximately 5 percent after the release of this information (Forbes). In terms of legal costs, Fortune magazine notes that since 2012, Walmart spent more than $800 million in legal fees and costs for its internal investigation into alleged payments to government agents in markets such as Mexico, India and China as well as to reinforce its compliance systems globally.
This expectation is consistent with the notions discussed in the voluntary disclosure literature, which predict that a firm’s willingness to provide accurate information about its involvement in bribery is likely to be higher when the associated proprietary costs of disclosure are lower (e.g., Verrecchia 1983).
As mentioned in note 5, we consider the effect of perceived behavioral control to be marginal for the purposes of our study. Likewise, we do not foresee the subjective norm to be a function of firms’ perceived identification risk. As explained in “Bribery Information and Competition” section, the subjective norm refers to the social disapproval that a firm may face if it denies its participation in bribery. We do not expect a firm’s perception of this subjective norm to vary according to their identification risk. Thus, we focus our analysis on the effect of identification risk on firms’ attitude toward denying its bribery practices.
Bribery has a bad economic reputation. It is usually a “grabbing” hand for markets. It generates harmful effects on market efficiency (Shleifer and Vishny 1993; Rose-Ackerman 1997) and distortions in the prices of goods, services, resources, and factors of production (Jain 2001, 2011). It also reduces the intensity of human capital and the firm assessments of private investors (Mo 2001).
This occurs not only because of lower operating costs but also because if firms do not have to pay bribes, they can allocate their economic resources to other productive activities.
As mentioned in note 5, we consider the effect of perceived behavioral control to be marginal for the purposes of our study. Likewise, we do not foresee the subjective norm to be a function of firms’ perceived identification risk. As explained in “Bribery Information and Competition” section, the subjective norm refers to the social disapproval that a firm may face if it denies its involvement in bribery. We do not expect a firm’s perception of this subjective norm to vary according to their identification risk. Thus, we focus our analysis on the effect of identification risk on firms’ attitude toward denying its bribery practices.
To encourage individual firms’ participation in the BEEPS, the World Bank conducts the survey confidentially. In addition, the World Bank publishes the results without revealing the identity of the individual respondents.
Identifying survey respondents is even easier for those economic agents, such as competitors or authorities, with advanced knowledge about a certain industry or sector.
Size is not necessarily related to being listed. Larger companies are not necessarily listed, and yet they may still be highly known and recognizable. For instance, one can consider the case of Mars or Cargill, which are large non-listed firms (usually on the Fortune list).
The BEEPS had four previous waves. However, the items on the survey change from one wave to another. Given this lack of homogeneity, we conducted a cross-sectional analysis. This approach is consistent with previous studies that used the BEEPS or similar databases to explore research questions related to our main topic (e.g., Jensen et al. 2010; Diaby and Sylwester 2015). In “Additional Analysis” section, we present the results of models using data from the fourth wave of the BEEPS. The results of this additional analysis are qualitatively similar to the results of our main tests.
See Appendix 1 for the list of countries included in the sample.
Although this variable does not measure the precise degree of false responses, it should be significantly correlated with them (Jensen et al. 2010).
In our additional analysis section, we show the results of the analysis using the alternative dependent variable of individual firms’ perception of the level of corruption in the environment (instead of the Kauffman Corruption Index) as a benchmark to estimate the likelihood of false responses.
The full ranking is (1) strongly disagree, (2) tend to disagree, (3) tend to agree, and (4) strongly agree.
The results are qualitatively the same when using it as a categorical variable.
We added one to the number of competitors prior to calculating the natural logarithm to keep observations of firms reporting zero competitors in the sample.
Item e2b_2013 on the BEEPS asks respondents to estimate the number of competitors of their main product/service in their main market. We eliminate observations corresponding to firms that responded either that they did not know or that they had too many competitors to count.
The full ranking is (1) No obstacle, (2) Minor obstacle, (3) Moderate Obstacle, (4) Major obstacle, and (5) Very severe obstacle.
We use Stata 13 to conduct our empirical tests.
We conducted an endogeneity test based on the results of a two-stage least squares (2SLS) model. Overall, the results indicate that endogeneity is unlikely to generate significant bias in our estimations. The results of this test are available from the authors upon request.
The results hold if we use the variables as categorical.
The correlation between the Kauffman Corruption Index and the variable Legal Environment is low (r = 0.04). Therefore, it is unlikely that this correlation is driving the results of our main analysis. In the same vein, given that our dependent variable in the main analysis is calculated as the residuals of a model using the Kauffman Index as an explanatory variable, our dependent variable is not directly correlated with the Kauffman Index. For these reasons, the correlation between Legal Environment and the Kauffman Index is unlikely to represent a major issue in the results of our main analysis.
Although this variable does not measure the precise degree of false response, it should be significantly correlated with it (Jensen et al. 2010).
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This study was funded by Spanish Ministry of Economy and Competitiveness (Grant Numbers ECO-2010-22105-C03-03 and ECO2013-45864-P); Community of Madrid (Grant Number S2015/HUM-3417); and FEDER (Grant Number UNC315-EE-3636).
Conflict of interest
Susana Gago-Rodríguez declares that she has no conflict of interest. Gilberto Márquez-Illescas declares that he has no conflict of interest, and Manuel Núñez-Nickel declares that he has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
See Table 8.
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Gago-Rodríguez, S., Márquez-Illescas, G. & Núñez-Nickel, M. Denial of Corruption: Voluntary Disclosure of Bribery Information. J Bus Ethics 162, 609–626 (2020). https://doi.org/10.1007/s10551-018-3989-9
- Voluntary disclosure