Institutional transparency concerns the amount of information government bodies provide about their decision-making and the implementation of their policies (e.g., Stasavage, Reference Stasavage2003; Hollyer et al., Reference Hollyer, Rosendorff and Vreeland2014). In recent years, transparency has gained increased attention in the good governance literature, as it is claimed to result in better decisions, policies, and processes without causing a fundamental shift in the political regime (Öge, Reference Öge2016). Importantly, transparency is not a coherent concept, with several different types having been identified: goal transparency, operational transparency, and procedural transparency (Libich, Reference Libich2006). The focus of what is made transparent varies between the three types. Goal transparency means communicating institutional goals openly, operational transparency implies performance measurement and admission of errors in planning or meeting goals, and procedural transparency relates to the provision of information regarding how decisions are arrived at by the institution.

In general, it is argued that good transparent policies contain methods of accountability. Transparent policies provide information to citizens that improve their ability to make choices about the services they receive and the rules and regulations they are required to follow. Finkelstein (Reference Finkelstein2000) asserts that a transparent policy is in fact deemed effective when the public acts on the information the policy provides. Therefore, a reasonable gauge of the success of institutional transparency, in which ever guise it takes, is the extent to which actions are motivated by the policy information communicated.

The present study focuses specifically on procedural transparency and examines the impact it has on actions taken by businesses, specifically compliance with authorities’ regulations. The role of procedural transparency for rule compliance is highly relevant, since enforcement mechanisms and the so-called management approach based on trust-building mechanisms such as making procedures transparent are not exclusively perceived as competing strategies but even claimed to be most effective when combined (Tallberg, Reference Tallberg2002).

If as is outlined by Finkelstein (Reference Finkelstein2000), the utilization of policy information communicated is effective because a causal association can be drawn between it and actions in accord with it, then by extension we test the underlying assumption that procedural transparency, if successful, will encourage compliance. Some support for this claim can be found in the domain of behavioral change. Defaults used to steer choice behavior often work in the background without the individual's knowledge. Paunov et al. (Reference Paunov, Wänke and Vogel2019a, Reference Paunov, Wänke and Vogel2019b, Reference Paunov, Wänke and Vogel2020) successfully showed that under conditions where their presence and a rationale for them were made explicit, more people maintained the default than opting out. Support for the impact of transparency on behavior has also been reported in the domain of tax behavior. Here transparency is considered as a prerequisite of citizens’ tax compliance (Torgler, Reference Torgler2002) and a causal connection is often drawn between transparency of procedures and higher acceptance of authorities, ultimately enhancing trust and leading to voluntary compliance (Kirchler, Reference Kirchler2007).

Contrary to these findings, there is also work suggesting that there are limits to the constructive effects of transparent policies. For instance, recent work examining behavioral change interventions also shows that transparency does not guard against backfires and may in fact contribute to them because explicit communication of policies (e.g., public campaigns) is misaligned with public motivations to change (Osman et al., Reference Osman, McLachlan, Fenton, Neil, Löfstedt and Meder2020). In line with this, there is work showing that increasing transparency does not necessarily increase trust in the judgment of public officials and public institutions (De Vries & Sobis, Reference De Vries and Sobis2016). Also, it is suggested that enforcement agencies should be cautious in expecting reliable improvements resulting from increased transparency. In this vein, Meijer (Reference Meijer2007) emphasizes that differences in enforcement organizations and their policies and how these relate to transparency have to be taken into account. He argues transparency might be applicable for a certain policy whereas it may cause frictions with other policies and claims that enforcement agencies should try to enhance their understanding of the relation between transparency and compliance.

As the current scientific evidence concerning the effect of procedural transparency is rather inconclusive, the present study explores the effect of procedural transparency on compliance with authorities. Importantly, we focus on a situation where transparency might have a potentially negative effect on compliance. Given that there is some evidence of backfiring effects of procedural transparency, we focus on whether transparency can lead to reduced compliance with regulations. Accordingly, we investigate whether the usually positive effect of higher penalties on compliance can be diminished due to transparently communicating information on penalty rates for other stakeholders.

The specific setting of the study refers to businesses’ compliance with safety rules. Research in the domain of safety rules has often focused on increasing compliance in the workplace by encouraging employees to operate more in line with the companies’ guidelines. For instance, making more transparent safety guidelines was aimed at targeting job demands in reducing emotional exhaustion and workplace injuries (Li et al., Reference Li, Jiang, Yao and Li2013). Other work has examined efforts to improve communication of safety rules to generally improve the relationship between perceptions of fairness and organizational safety climate (Ayim Gyekye & Haybatollahi, Reference Ayim Gyekye and Haybatollahi2014), as well as actual safety behavior in the fire service (Smith et al., Reference Smith, DeJoy, Dyal, Pu and Dickinson2019). In contrast to these studies, the novelty of the present work is that it deals with businesses’ adherence to safety regulations administered by responsible governmental authorities.

To consider the impact of increasing transparency of safety guidelines on business compliance, we consider work on corporate illegality. In general, theories of corporate illegality (e.g., Finney & Lesieur, Reference Finney and Lesieur1982; Coleman, Reference Coleman1987; Baucus, Reference Baucus1994) assume three preconditions for illegal business behavior: (1) the motivation to break the law in order to achieve goals that either proactively maximize profits or proactively prevent the business from failing; (2) the opportunity to engage in illegal behavior; and (3) no effective controls to deter from illegal behavior. Where the first precondition can be seen as attributing responsibility to the business and the last two might reflect limitations in how regulations are devised and how they are enforced. In line with the economics-of-crime paradigm (Becker, Reference Becker, Fielding, Clarke and Witt1968), the traditional view is that illegal behavior, as noncompliance with safety standards and regulations, should be negatively related to the level of deterrence. Thus, high compared to low penalties are expected to result in a lower level of noncompliance.

It is worth also highlighting that research on procedural fairness revealed that in certain situations, severe penalties might backfire and have a negative effect on compliance with authorities’ decisions. For instance, Verboon and Van Dijke (Reference Verboon and Van Dijke2011) reported that procedural fairness moderates the effect of sanction severity on compliance with authorities’ regulations. Severe sanctions increased compliance with the authority more than mild sanctions, but only when authorities’ actions were perceived as fair. Similarly, Van Prooijen et al. (Reference Van Prooijen, Gallucci and Toeset2008) observed that inconsistent punishment procedures can have detrimental effects on cooperation, as participants in their study cooperated less if punishment procedures were inconsistent between persons.

Importantly, the impact of transparency and how it is assumed to be related to perceived fairness and trust in governmental institutions has to be considered, especially in the context of punitive actions taken resulting from noncompliance. For instance, in the domain of tax compliance research, it is argued that transparency provides a great opportunity to enhance taxpayers’ compliance, and conversely, when taxpayers perceive that the government is not transparent, they will react negatively, attempting to evade taxes (Siahaan, Reference Siahaan2014). However, disclosure of information that outlines strategies and elements certain policies are based on can be influenced by subtle differences in the presentation of such information, and this can ultimately affect compliance (Porumbescu et al., Reference Porumbescu, Lindeman, Ceka and Cucciniello2017).

Relating work on transparency to work on the deterrent effect of penalties in the present study, we investigate experimentally how transparent communication of penalties influences compliance with safety regulations. More specifically, we examine how the relative size of fines for businesses’ noncompliance with safety regulations impacts the decision to comply or not. The structure of penalty rates for different-sized businesses offers a relevant context to study transparency, as previous research on safety compliance has identified a positive association between the size of a business and the level of safety compliance (Arpanutud et al., Reference Arpanutud, Keeratipibul, Charoensupaya and Taylor2009).

In line with this finding, there are several studies that report differences in compliance considering the size of businesses. Parker and Nielsen (Reference Parker and Nielsen2011) argue that larger, better-resourced, and better-managed businesses might have a stronger sense of the calculative reasons for compliance and do more to comply because they are institutionally programed to act in conformance with social norms of compliance. In the same vein, Bickerdyke and Lattimore (Reference Bickerdyke and Lattimore1997) present evidence for small firms exhibiting lower compliance rates than large businesses, as about 40% of 360 small businesses in their study did not fully comply with the regulations. These authors cite ignorance with respect to the rules as the most common cause of this noncompliance. They also argue that typically large firms face a higher probability of inspection, so that small companies may face rather low expected penalties from noncompliance.

Importantly, small businesses often argue that regulations and the tax system involve unfair burdens on small businesses, as they lack the resources of larger businesses, but are still expected to undertake similar types, and volume, of paperwork in order to comply with uniform regulations (Bickerdyke & Lattimore, Reference Bickerdyke and Lattimore1997). Accordingly, several studies suggest that size is a dominant corporate characteristic in explaining business compliance, indicating a positive relationship between firm size and the firm's level of compliance (e.g., Cerf, Reference Cerf1961; Al-Shammari, Reference Al-Shammari2011; Galani et al., Reference Galani, Alexandridis and Stavropoulos2011; Juhmani, Reference Juhmani2012; Sucuahi, Reference Sucuahi2013).

To sum up, we connect three different strands of the literature on compliance with authorities’ regulations, which allow the following inferences: First, in general, a positive effect of transparency on compliance is assumed. However, some insist that a more fine-grained approach is necessary, especially with regard to procedural transparency. Second, higher penalties for noncompliance are expected to increase compliance, but some studies indicate that the disciplining effect of higher penalties is sometimes attenuated, for instance, in case of perceived unequal or unfair treatment. Third, small- and big-size businesses often differ in their compliance motivations, for instance, due to differences in fairness perceptions with regard to the bureaucratic burden they face or diverging subjective perceptions of the likelihood to be audited.

Based on our interpretation of this relevant literature as offering rather inconclusive evidence, the aim of the present study was (i) to investigate whether an unequal penalty rate for small-size in contrast to big-size businesses has a different effect on compliance when this difference is transparent compared to when it is not transparent. Accordingly, we tested whether the effectiveness of different proportional penalty rates for small- and big-size businesses is attenuated or even undermined when such policies are transparent. We also tested (ii) how different income levels, different relative compliance costs, and different audit probabilities influence compliance with safety regulations over repeated compliance decisions. The respective assumptions were that lower compliance costs and higher probability of an audit result in higher compliance, while there was no clear expectation regarding the effect of income level. Additionally, we explored (iii) whether business size (small-size vs big-size companies) affects relative compliance when equal penalty rates were applied. These research questions were preregistered on the Open Science Framework (OSF) website using the As-Predicted templateFootnote ¹. Data and analyses code are also available on the OSFFootnote ².

Method

Participants

A total of 666 participants (243 males, 418 females, 5 other; M _age = 34.89 years, Mdn = 31; SD = 12.86) living in the UK were recruited via the research platform Prolific Academic between March 24 and 26, 2020. Their mean payoff was £ 6.18 (SD = 0.89), consisting of a basic payment (£ 3.50) and additional incentive compatible payment dependent on the decisions made in the study. The sample size decision was based on the available monetary resources. A sensitivity power analysis in G*Power (Faul et al., Reference Faul, Erdfelder, Lang and Buchner2007) for an ANOVA with three factors (two levels each) indicates that the smallest effect detected given N = 666 for power = 80% is d = 0.26Footnote ³. The Ethics Review Board (ERB) of Tilburg University granted ethics approval for this study to the first author.

Design

The experiment was programed on the SoSci Survey online research platform (https://www.soscisurvey.de) and had the following 2 (Business Size: small vs big) × 2 (Penalty Rate: equal vs unequal) × 2 (Penalty Scheme: transparent vs nontransparent) between-subjects design. Thus, participants were either assigned a small or a big business, and the penalty rates were equal (both business sizes paid 50% of evaded compliance costs) or unequal (small-size businesses paid 50% of evaded compliance costs and big-size businesses paid 150%). Importantly, the penalty scheme was either nontransparent (participants were only informed about own penalty rate) or transparent (participants were also informed about penalty rate for the other business size). Figure 1 presents an overview of these different experimental conditions.

Figure 1. Overview of the eight conditions in the experiment. Note: These condition result from three factors with two levels each: transparency of penalty scheme (nontransparent/only informed about own penalty rate vs transparent/also informed about penalty rate for other business size); penalty rates (equal: 50% of evaded compliance costs for both business sizes vs unequal: 50% of evaded compliance costs for small-size businesses and 150% of evaded compliance costs for big-size businesses); business size (small-size vs big-size business).

Participants in all conditions went through 18 rounds of compliance decisions. Each round represented a unique combination of the different levels of the within-subject factors business income, compliance costs due, and audit probability (3 × 2 × 3 levels). Income was either 1,000, 1,500, or 2,000 ECU (Experimental Currency Units; small-size companies) or 1,500, 2,000, or 2,500 ECU (big-size companies). Compliance costs were either 20% or 40% of the round income. The audit probability was set at 10%, 15%, or 20%, and audits were determined based on these probabilities in each round. The key dependent variable was relative compliance with safety regulations, measured as the amount of paid safety costs divided by the due safety costs.

Procedure

Participants were informed that during the study they would take on the role of owner of a business that manufactures toys. Before the actual experiment started, participants were presented with three exemplary rounds of safety compliance decisions, for which they were asked to indicate the resulting net income in ECU to check whether they understood the instructions.

Next, in each round of the actual experiment, they received business income which varied for both small- and big-size businesses over the rounds but was always higher for the big-size businesses in comparison to the small-size businesses. They were instructed to spend a certain amount of their income as compliance costs to adhere to safety rules. Accordingly, on the screen, they were informed about their income in the respective round and the due safety compliance costs, as well as the chance to be audited by the responsible authorities and the potential penalties in case of detected noncompliance.

In each round, participants indicated on a form their decision as to whether or not to comply with the safety regulations. The final payoff was determined by one randomly drawn round, depending on the compliance decision in this round and whether an audit took place and—subsequently—a penalty was collected.

Post-experimental questionnaire

After the experiment, participants filled in a questionnaire consisting of six blocks of questions (all items are listed in the Appendix). First, participants were presented with four memory/attention check items (e.g., “What was the penalty rate that applied to your company?”), with three to five multiple-choice options per item. Second, a manipulation check item referred to the penalty size information (“Which statement/s is/are true about the penalties?”), with four multiple-choice options addressing whether there was only information about the penalty for the own business or also for other business sizes, and whether the penalty was the same, relatively smaller, or relatively larger for big-size businesses. Third, the severity of fines and perceived fairness was assessed with one or two items depending on whether participants were in one of the transparent or nontransparent conditions (“The penalty rate that applied to my company was too high.”; “Comparing the penalty rates for small size and big size companies, the penalty scheme was fair.”; with the second item only present when penalty rates for other businesses were transparent) (all rated on: 1 = do not agree at all; 7 = fully agree). Forth, risk propensity was assessed using six items by indicating the likelihood of engaging in different described activities (e.g., “Betting a day's income at the horse races.”) (1 = extremely unlikely; 7 = extremely likely). Fifth, norm following was measured with seven items, where participants had to indicate how characteristic presented descriptions were of them or their beliefs (e.g., “I always do my best to follow society's rules.”) (1 = extremely uncharacteristic; 5 = extremely characteristic). Sixth, three questions relating to the COVID-19 pandemic (e.g., “How worried are you about the current COVID-19 pandemic?”) (1 = not at all; 7 = extremely), in order to control for potential undesired effects in the experiment. Finally, demographic data (age, gender, country of residence) and language skills, attention to and understanding of the presented information were recorded.

Results

Below, we first report on the manipulation check and items that assessed attention to and understanding of the presented information. Next, we present the analyses of the main research questions investigating the effects of the experimental manipulations on relative compliance in the study. Finally, we analyze the extent to which the concepts measured in the post-experimental questionnaire are related to the actual decisions in the study.

Attention, understanding, and manipulation check

The answers to the questions before the start of the experiment indicate that the participants paid attention. The questions as to how their decisions and potential audits influence their payoff were answered correctly by the vast majority of participants (correct answers: 83.3%, 87.5%, and 91.6%, respectively). Regarding the memory and attention checks, 42.9% recalled the presented compliance cost rates correctlyFootnote ⁴, 75.1% the audit probabilities, 92.8% the penalty size that applied to their business, and 88.4% the size of their business. Furthermore, the participants indicated that they did carefully read all instructions (M = 4.82; SD = 0.42), that they completely understood all instructions (M = 4.59; SD = 0.67), and they rated their English skills as very high (M = 4.92; SD = 0.30; all on a five-point scale).

The manipulation check was a multiple-choice item referring to several details. It checked for accuracy of knowledge of information on the penalty size, whether or not information was presented about the penalty size for other business sizes, and if so, whether that penalty was the same, relatively smaller, or relatively larger to the penalty imposed on the participant's own business. Of the sample, 65.2% responded completely correct on all four presented statements and another 17.3% were incorrect on only one of the presented options.

Compliance with safety regulations

Effect of transparent different penalty schemes on compliance (between-subject effects)

Figure 2 indicates the overall mean compliance for all eight experimental conditions. To analyze whether an unequal penalty rate for small compared to big-size businesses affected compliance differently based on whether it was transparent or not, a linear mixed-effects model with the dependent variable relative complianceFootnote ⁵ over all 18 rounds of the experiment and the independent variables business size, penalty rate, and transparency of penalty scheme, as well as the respective two-way and the three-way interactions were run (see Table 1). As the results reveal, a higher penalty rate led to more compliance for big businesses in comparison to small businesses when this unequal penalty scheme is nontransparent, with compliance of big-size businesses over 10% higher than smaller ones. This is indicated by the significant interaction effect of business size × penalty rate. We do not observe this pattern when the different penalty scheme was transparent, since the three-way interaction business size × penalty rate × transparency was not significant. In this case, compliance of big-size businesses was not different from small-size companies, although the big businesses faced a higher penalty rate. This suggests that the deterring effect of a higher penalty rate was attenuated when participants were aware of the different relative penalties.

Figure 2. The effect of transparent and different penalty rates on compliance of small-size and big-size companies. Note: The y-axis indicates relative compliance, the x-axis the experimental condition, as indicated by business size (small vs big), penalty rate (equal vs unequal), and transparence (nontransparent vs transparent). Points represent means and the lines represent the standard errors of the means.

Table 1. The effect of business size, penalty scheme, and transparency on compliance

Note: N = 11,988 observations. Random intercept for N = 666 participants. Independent variables were dummy coded with the following reference categories (Business size = small; Penalty rate = equal; Transparency = nontransparent).

Effect of income, compliance costs, and audit probability (within-subject effects)

Relative compliance was clearly influenced by the level of compliance costs as well as the probability of an audit, and—to a lesser extent—by the size of income (see Figure 3). As can be seen in Table 2, higher compliance costs (i.e., 40% of the round income) resulted in lower relative compliance compared to lower compliance costs (i.e., 20% of round income). Higher levels of audit probability increased relative compliance, with a probability of 20% leading to higher compliance than a probability of 15%, and both elevating relative compliance compared to an audit probability of 10%. We also observe a—rather small—effect of income size. In the rounds with the highest level of income, compliance was lower than in the rounds with the lowest level of income, while the medium income level did not differ from the lowest level.

Figure 3. The effect of audit probability, compliance costs and income size on compliance with safety regulations. Note: The y-axis indicates relative compliance, the x-axis audit probability (10%, 15%, or 20% probability), safety compliance costs (20% or 40% of round income) and income size (low, medium, or high amount). Note that in each round big-size businesses have a higher income than small-size businesses, but for both business sizes, three different income levels were provided. Points represent means and the lines represent the standard errors of the means.

Table 2. The effect of audit probability, compliance costs and income size on compliance.

Note: N = 11,988 observations. Random intercept for N = 666 participants. Independent variables were dummy coded with the lowest level as reference category (Income = low; Compliance costs = 20%; Audit probability = 10%).

Effect of business size on compliance (between-subject effects)

When focusing only on the experimental conditions where big- and small-size companies faced the same penalty rates, we find a difference in compliance. Big businesses exhibited significantly lower compliance compared to small businesses (N = 332; B = −0.075, SE = 0.031, p < 0.001)Footnote ⁶.

Post-Experimental Questionnaire

The post-experimental questionnaire reveals that the evaluation of the size of the penalty rate for the own business did not differ between different experimental conditions, F(7, 658) = 0.74, p = 0.641. In the four transparent conditions, participants were also asked to evaluate the penalty scheme considering the penalty size for both types of businesses; a one-factorial ANOVA did not reveal a significant difference, F(3, 327) = 1.76, p = 0.155. However, for the transparent conditions, perceived fairness of the penalty scheme in general (i.e., independent of the respective experimental condition) was associated with higher compliance (N = 331; B = 0.02, SE = 0.01, p = 0.034). Overall, risk preference was negatively associated with compliance (N = 666; r = −0.22, p < 0.001) and norm following was positively associated with compliance (N = 666; r = 0.09, p = 0.028). Also, when looking at the questions specifically probing the impact of COVID-19, it is evident that participants were personally affected (M = 5.45; SD = 1.40) and worried (M = 5.59; SD = 1.40), but they indicated that these experiences did not influence their behavior in the experiment (M = 1.80; SD = 1.41).