DEPARTMENT OF POLITICAL SCIENCE LEGAL CLARITY AND IMPARTIALITY An Experimental Study of Consistency in Decision- Making Among Government Officials Worldwide Joakim Nilsson Master’s Thesis: 30 credits Programme: Master’s Programme in Political Science Date: 2025-05-25 Supervisor: Marina Nistotskaya Words: 11836 Abstract The language used in legal texts is often ambiguous, hindering bureaucrats' ability to understand, interpret, and apply the law consistently, thereby threatening impartiality. The Quality of Government (QoG) literature foregrounds the importance of impartiality. However, it largely overlooks how legal language clarity affects this principle in practice. This thesis bridges two unconnected literatures: QoG and legal scholarship. Although legal origin theory links legal traditions to government performance, it overlooks the role of legal clarity. Legal research highlights the importance of clarity for legal comprehension, yet it does not address its implications for bureaucratic decision-making. This thesis addresses this gap by asking: How does the clarity of legal language affect bureaucratic impartiality? It advances a theoretical argument linking legal clarity to impartiality via the mechanism of consistent application. To test the argument empirically, the study conducted an online survey experiment with over 900 former and current government officials worldwide. Participants were randomly assigned to read a case based on a real-life scenario where the law was framed in either ambiguous or clearer legal language. The findings provide empirical support to the proposed theory that legal clarity promotes impartiality: individuals exposed to ambiguous wording applied the law less consistently. The thesis broadens the prevailing perception of QoG by advancing a nuanced understanding of impartiality, which considers the role of legal language clarity. Key words: Impartiality, Legal clarity, Consistency, Bureaucracy, Experiment. I Table of Contents 1. Introduction ........................................................................................................................................................ 4 2. Literature Review ............................................................................................................................................... 5 2.1. Quality of Government as Impartiality ......................................................................................................... 5 2.2. Legal Research ............................................................................................................................................. 6 2.2.1. Legal Origin Theory ............................................................................................................................. 6 2.2.2. Regulatory Burden ................................................................................................................................ 7 2.2.3. Legal Comprehension ........................................................................................................................... 8 2.2.4. Research Gap ........................................................................................................................................ 9 3. Theoretical Argument ...................................................................................................................................... 10 3.1. Legal Clarity & Legal Linguistics .............................................................................................................. 10 4. Assessing Consistency: A Survey-Based Experiment .................................................................................... 13 4.1. Recruitment Strategy ................................................................................................................................... 14 4.1.1. Population under study: Recruiting Government Officials ................................................................ 14 4.2. Sample target, Power Analysis, Exclusion Criteria ................................................................................... 15 4.3. Survey Design & Operationalization .......................................................................................................... 16 4.3.1. Procedure & Vignette ......................................................................................................................... 16 4.3.2. Experimental Condition: Legal Text .................................................................................................. 18 4.3.3. Decision-Making: Answer Alternatives ............................................................................................. 20 4.3.4. Sample Demographics ........................................................................................................................ 20 4.3.5. Reading Comprehension Check .......................................................................................................... 21 4.3.6. Manipulation Checks .......................................................................................................................... 22 5. Analysis .............................................................................................................................................................. 22 5.1. Descriptive Statistics .................................................................................................................................. 23 5.2. Analysis of Variance Across Groups .......................................................................................................... 24 5.3. Regression Analysis .................................................................................................................................... 26 6. Discussion .......................................................................................................................................................... 28 7. Conclusion ......................................................................................................................................................... 31 References .............................................................................................................................................................. 35 Appendix A: The Survey ...................................................................................................................................... 40 Appendix B: Compensation & Ethical Consideration ...................................................................................... 47 Appendix C: Case Description and Design Rationale ....................................................................................... 48 Appendix D: Treatment Explanation ................................................................................................................. 49 Appendix E: Distributions ................................................................................................................................... 52 Appendix F: Country of Residence ..................................................................................................................... 54 Appendix G: Variables ......................................................................................................................................... 55 II Acknowledgement I would like to thank Marina Nistotskaya for her excellent supervision. I am grateful to Aksel Sundström, Maria Solevid, and Đorđe Milosav (Humboldt-Universität zu Berlin) for their valuable comments on the research design, and to Natalia Alvarado Pachon for her technical guidance. I also appreciate Erik Ottosson and Jan Ericson—members of the Swedish Parliament—for their insights on the importance of language in legislative work, along with Mikael Pauli, legal director at the Ministry of Health and Social Affairs, for sharing his perspectives. Additionally, I thank Maria Bylin, at the Secretariat for Legal and Linguistic Draft Revision of the Prime Minister's Office, for our discussions regarding the role of language in laws for legal interpretation, and her literature recommendations. I would also like to express my gratitude to the Quality of Government Institute at the Department of Political Science at the University of Gothenburg for providing the necessary funding for this study. III 1. Introduction Legal language is often unclear, hindering bureaucrats from correctly understanding, interpreting, and consistently applying laws. This complexity threatens impartial application of laws—a foundational pillar of high-quality government, according to the influential Quality of Government (QoG) literature (Rothstein & Teorell, 2008) High QoG is associated with various positive societal outcomes (Ahlerup et al., 2016; Dahlberg & Holmberg, 2014; Kolvani & Nistotskaya, 2025; Nistotskaya et al., 2015; Nistotskaya & Cingolani, 2016; Povitkina & Bolkvadze, 2019). Although the concept of bureaucratic impartiality is central for QoG, existing research largely neglects the role of law in shaping it. While legal origin theory addresses the role of legal systems in government performance (La Porta et al., 1999; La Porta et al., 2008), it falls short in accounting for legal clarity. Similarly, legal research demonstrates that clarity is essential for understanding and interpreting laws, but it limits its focus to the judicial sphere (Charrow & Charrow, 1979; Diamond & Levi, 1995; Randall, 2014) and offers no insight into how legal clarity affects bureaucratic decision-making. These two strands of literature—QoG and law scholarship—remain largely disconnected: there is currently no research linking legal clarity and bureaucratic impartiality. By asking: how does the clarity of legal language affect bureaucratic impartiality?, I aim to fill this research gap. By answering this question, the thesis makes three important contributions. First, building on the legal linguistics literature, it develops a theoretical argument that greater linguistic clarity in legal texts leads to more consistent application of law across similar cases, thereby enhancing impartiality. Second, the thesis tests this proposition through an online survey experiment involving more than 900 current and former government officials worldwide. The findings provide empirical support for the hypothesis: subjects that were exposed to unclear legal language applied the law less consistently than those exposed to clearer language. Finally, by demonstrating the effect of legal clarity on impartiality, it advances a more nuanced understanding of the concept of impartiality. 4 The thesis progresses as follows: Section 2 reviews the literature on the quality of government and legal studies.1 Section 3 discusses the theoretical framework that connects legal clarity to impartiality. Section 4 outlines the study design, methodology, and process, while Section 5 presents the results of the analysis. Lastly, the thesis presents a discussion and conclusion, including avenues for further research. 2. Literature Review 2.1. Quality of Government as Impartiality Over the past several decades, the field of comparative politics has evolved from the focus on institutions regulating access to political power (or the input side of the political system) to incorporate the output side of the political system—concerning how political power is exercised (La Porta et al., 2008; Rothstein & Teorell, 2008). Within this, research has shifted from broad and often ambiguous concepts such as good governance to more precise definitions (Rothstein & Teorell, 2008), thereby enhancing the conceptual clarity and measurability (Nistotskaya, 2020). For example, the influential concept of the quality of government (QoG) boils down to impartiality in the implementation of law: “When implementing laws and policies, government officials shall not take into consideration anything about the citizen/case that is not beforehand stipulated in the policy or the law” (Rothstein & Teorell, 2008, p. 170). Extensive empirical research shows that high QoG, or impartiality, is associated with a number of positive government outputs and societal outcomes (for review, see Nistotskaya 2020). For example, Holmberg et al. (2009) report a positive association of impartiality with economic growth, democracy, reduced corruption, the rule of law, public health, environmental sustainability, and social well-being. Furthermore, research links QoG to enhanced political legitimacy and public trust, increased political participation, improved public goods provision, economic growth, and diminished public support for populist parties (Ahlerup et al., 2016; Dahlberg & Holmberg, 2014; Kolvani & Nistotskaya, 2025; Nistotskaya et al., 2015; Nistotskaya & Cingolani, 2016; Povitkina & Bolkvadze, 2019). 1 Some parts of sections 2 and 3 includes adapted materials from my earlier work: “Final paper”, submitted for the course: SK2212, Quality of Government in Comparative Perspective 5 While Rothstein and Teorell's (2008) definition of impartiality is analytically rigorous, the concept has been subject to critique. Some scholars argue that this perspective overlooks the role of the content of the laws, advocating for a broader understanding of impartiality—one that takes into account the normative content of laws, which is presumed to influence their outcomes (Agnafors, 2013; Olander, 2021; Sparling, 2018). Although moral and ethical considerations may be important, they belong to a different debate. In Rothstein and Teorell’s (2008) framework, impartiality is deliberately restricted to the application of laws, emphasizing the equal and unbiased implementation of rules by public officials. I argue, however, that their definition contains a critical shortcoming: it treats the law language as a “black box”, which presumes a degree of legal clarity and precision that precludes misinterpretation or ambiguity. In other words, Rothstein and Teorell’s (2008) framework implies that no misunderstandings or interpretive challenges can arise that could jeopardize the impartial application of the law, and bureaucrats apply the law exactly as it is written, without distortion resulting from legal ambiguity. Since the clarity of the law cannot be taken for granted, it is essential to consider how it affects the practice of impartiality in real-life settings. While Rothstein and Teorell's (2008) definition of impartiality emphasizes the absence of influence from factors not “stipulated in the policy or the law” (p. 170), this thesis argues that the clarity of what is stipulated in the law also plays a critical role in enabling impartial implementation. In light of the foundational principle of impartiality that “we must treat like cases alike” (Dworkin, 1986, p. 165), it becomes evident that the ambiguity of wording of laws (hereafter legal ambiguity) can jeopardize the consistent application of the law, thereby undermining impartiality itself. 2.2. Legal Research 2.2.1. Legal Origin Theory A prominent strand of research, known as Legal Origin Theory (LOT), argues that different legal traditions have a lasting effect on government performance and societal outcomes. LOT distinguishes between common law and civil law traditions, which influence institutional mechanisms and outcomes. Common law systems are generally associated with stronger property rights, lighter regulation, and better performance than civil law systems (La Porta et al., 6 1999; La Porta et al., 2008). Common law originates from England and has primarily been exported to Anglo-Saxon countries; it relies on judicial precedents alongside statutes, and the judicial branch collaborates with the legislative branch to form legislation (Gibbons, 1999; La Porta et al., 1999; Pejovic, 2001). In civil law, the legislative branch seeks to create comprehensive laws limiting room for interpretation (Gibbons, 1999; Pejovic, 2001). Critics highlight LOT’s inconsistencies, for example, Scandinavian civil law countries outperform LOT predictions (Dahlström & Lapuente, 2017). Others argue that institutional performance is better explained by historical political developments and bureaucratic structures rather than by legal origin alone (Charron et al., 2012). Despite its critics, LOT raises an important point by drawing attention to legal traditions and laws as predictors of bureaucratic performance. It emphasizes the differences between different legal systems but attributes superior performance primarily to other institutional mechanisms, such as stronger property rights, rather than to the content or clarity of the laws themselves. Thus, although LOT elevates the issue of legal variation, it leaves the role of legal clarity unexamined. Existing literature points to a difference between different legal traditions regarding the clarity of legal language. First, legislation originating from common law systems is more challenging to interpret, as they tend to have a more intricate structure and greater technical details (Vanterpool, 2007, p. 186). Second, plain language reforms—key to enhancing legal clarity—have been primarily implemented in Anglo-Saxon countries with common law traditions (Vanterpool, 2007, p. 168). Consequently, it is reasonable to expect variation in legal clarity depending on the legal tradition from which a given law derives. 2.2.2. Regulatory Burden Further insights come from studies that examine regulatory burden as a mechanism for QoG. An increasing volume of legislation can overwhelm and hinder bureaucrats’ ability to implement policies effectively (Fernández-i-Marín et al., 2024b). Legislators may introduce new regulations to appear effective but frequently fail to allocate sufficient resources for their enforcement, which leaves bureaucracies ill-equipped to manage the additional demands (Fernández-i-Marín et al., 2024a). Challenges occur when regulations exceed available capacity, highlighting the 7 importance of better aligning laws with bureaucratic resources to improve implementation (Fernández-i-Marín et al., 2024c). Duvanova (2017) contributes to this discussion by examining how details and the structure of legislation influence bureaucratic discretion by comparing building codes in two Kazakhstani provinces. One province is governed by a highly detailed 11,000-word regulation, and the other by a concise version, including significantly fewer details. The author demonstrates how overly complex laws can create implementation challenges. Although the regulatory burden literature highlights how both the volume and level of detail in legislation can affect QoG, it largely overlooks the role of legal clarity. Yet within this context, it is reasonable to assume that legal clarity—or the lack thereof—can influence how laws are applied and, in turn, shape the quality of policy execution, including the degree of impartiality. 2.2.3. Legal Comprehension Empirical studies directly testing the connection between legal clarity and impartiality are lacking, but some insights can be drawn from legal scholarship that has examined legal comprehension. There is a small number of studies that has explored how the clarity of legal texts influence individuals’ ability to understand legal texts. In one of the first studies of this kind, Charrow and Charrow (1979) provided insight into legal language by focusing on instructions for trial juries and identifying specific language traits that hinder understanding. Their study demonstrates that unclear legal language makes legal texts less comprehensible to laypeople working in courts. Diamond and Levi (1995) argue that legal misunderstandings can have serious, detrimental outcomes. In U.S. courts, jurors without formal legal training play a significant role in decisions involving the death penalty. In their study, jurors recruited from a Chicago court were presented with hypothetical cases and legal instructions written in complex language. To assess laypeople’s understanding of the instructions, participants were asked to reproduce parts of the legal instructions. The results indicate that many participants had difficulty understanding essential aspects, which, in worst case, could lead to arbitrary death sentences being issued by jurors. 8 Similar findings were obtained by Randall (2014) from experimental studies. In one experiment, participants listened to six sample jury instructions and responded to true/false questions assessing their comprehension. The results revealed that understanding declined when the instructions included linguistically complex features—such as passive constructions and presupposed information—both linked to the increased cognitive processing load. In a follow-up experiment, the instructions were rewritten in plain English to eliminate these features, resulting in significantly improved comprehension. The legal comprehension literature provides a point of departure for understanding the relationship between unclear legal language and comprehension. However, it addresses neither how legal clarity affects individuals’ ability to treat “like cases alike” (Dworkin, 1986, p. 165)— a core aspect of impartiality—nor how it affects this ability in the context of public administration. 2.2.4. Research Gap The predominant definition of the quality of government is understood as the impartial implementation of laws and policies by the bureaucracy (Rothstein & Teorell, 2008). Impartiality implies that the bureaucracy should act as a neutral entity, ensuring that the application of laws considers only what is outlined in the law. Although QoG research has demonstrated the benefits of bureaucratic impartiality, Rothstein & Teorell’s (2008) definition overlooks the laws themselves. Existing critique emphasizes the content of laws as a crucial factor for QoG, yet it pays no attention to the wording of laws—legal clarity. On the other hand, the existing legal research engages with the law in several distinct ways. The literature on legal origins links institutional performance with laws, highlighting the historical roots of legal systems as having lasting effects on government performance. The literature on regulatory burden reveals how the volume and complexity of legislation can hinder effective policy implementation. Finally, empirical research on legal comprehension shows that unclear wording reduces understanding and increases the risk of misinterpretation. However, none of these strands directly examine how legal clarity affects impartiality, particularly in the context of public administration. 9 In sum, existing literature overlooks the question of whether legal clarity affects the bureaucracy's ability to apply laws impartially. This thesis seeks to close this gap by examining the impact of legal clarity on the consistency of decision-making within the bureaucracy of bureaucrats. 3. Theoretical Argument 3.1. Legal Clarity & Legal Linguistics Legal studies discuss the quality of laws to distinguish between effective and ineffective legislation. However, “quality is a vague and elusive term” (Mousmouti, 2012, p. 191). It is divided into two broad dimensions. One concerns the substance of laws in relation to their performance (Mousmouti, 2012). Here I focus on the other dimension, its clarity. Clarity can be defined as ‘the state or quality of being easily perceived and understood’ (Tullock, 1978:80, cited in Majambere, 2011, p. 417). It does not refer to content, but to how effectively language communicates that content (Vanterpool, 2007). Clear legal language reduces ambiguity, enhances precision, and improves implementation (Majambere, 2011; Mousmouti, 2012, p. 194; Vanterpool, 2007; Wydick, 1978, p. 728). Legal texts are often criticized for their complexity and dense language (Vanterpool, 2007, p. 167; Voermans, 2011, p. 40). Voermans (2011) notes that: “Complex verbose legislation, full of jargon and legal constructs, not only complicates comprehension and interpretation but also irritates its audience and may otherwise tend to undermine its authority” (Voermans, 2011, p. 40). Wydick (1978, p. 727) argues that its verbosity and redundancy may be intentionally exclusionary, a view shared by Majambere (2011), who calls much of legal writing “Meaningless jargon” (p. 424). Wydick (1978) emphasizes the need for clearer legal language to reduce confusion and ambiguity, and Voermans (2011) adds that complex language makes interpretation and understanding difficult, thereby weakening its authority (p. 40). The use of plain language in legal texts has gained increasing attention; it seeks to improve understandability and applicability through enhancing clarity (Voermans, 2011, pp. 46–47; Wydick, 1978, p. 727). Plain language eliminates unnecessary complexity by avoiding “obscurity, inflated vocabulary and convoluted sentence structure” (Eaglesson, 1990, as cited in 10 Majambere, 2011, p. 422). Plain language reforms have been introduced in several countries, with the European Union as a notable example. Since multilingualism increases complexity, it is particularly vital to provide clarity in its legal framework (Voermans, 2011). Wydick (1978) emphasizes that clarity can be achieved by simplifying legal language. Legal writing becomes clearer and more effective by focusing on essential “working” words and eliminating unnecessary “glue” words. Wydick explains that clarity can be achieved by using active voice, shortening sentences to under 25 words, and ensuring that ideas follow a logical sequence. For instance, “a trial by jury was requested by the defendant” becomes clearer as “the defendant requested a jury” (Wydick, 1978, p. 729). Additionally, terms like “for the purpose of” can be replaced with “to” (p. 731), and “the fact that she died” can be simplified to “her death” (p. 732). In this thesis, I define unclarity in legal texts based on specific linguistic features identified in legal linguistic research that are known to reduce comprehension and complicate interpretation. Legal clarity is defined by the absence of these features. Linguistic problems are often presented in legal language; these features provide a framework for assessing clarity. First, ambiguous pronoun placement can lead to multiple interpretations, as in “John thought he should be more polite to Bill,” “he” is unclear (Solan, 1995, p. 1070). Second, nominalization— turning verbs into nouns—adds complexity, such as in “the determination was made” instead of “they determined” (Charrow & Charrow, 1979, p. 1321). Third, embedded clauses stack multiple ideas into one sentence, extracting its meaning (Charrow & Charrow, 1979, p. 1327). Fourth, a passive voice obscures agency, for instance, writing: “the defendant was struck by the car” rather than “the car struck the defendant” (Charrow & Charrow, 1979, p. 1321). It becomes unclear who is being referred to. Fifth, negative sentence structures, particularly multiple negatives, hinder comprehension, for example: “It is not uncommon not to know” is less clear than its positive form (Charrow & Charrow, 1979, p. 1321). Technical vocabulary, such as “credibility”, “stipulate”, and “imputed”, often confuses laypeople and can lead to misinterpretations (Charrow & Charrow, 1979, p. 1324). The same applies to Latin phrases, which are common in legal texts; they add complexity (Gibbons, 1999, p. 158). Wydick (1978) states that ambiguous formulations hinder understanding. For example, “In our current circumstances, the budgetary aspect is a 11 factor which must be taken into consideration to a greater degree” (Wydick, 1978, p. 738) could be rewritten more concretely to “more attention needs to be paid to the budget”. The presence of these linguistic features makes legal texts difficult to read and reduces their clarity. Consistent application of the law is central to impartiality, yet the QoG literature largely overlooked the role of clarity in legal language. This thesis argues that legal clarity is essential for impartiality by drawing on the legal linguistics literature—which shows how ambiguous language hinders comprehension and allows for multiple interpretations—legal clarity leads to less consistent application of the law. Suppose the law is ambiguous, allowing for multiple interpretations and resulting in similar—or even identical—cases being decided differently. Since impartiality entails that that “we must treat like cases alike” (Dworkin, 1986, p. 165), variation in outcomes indicates a breach of the impartiality principle. In other words, impartiality depends not only on refraining from considering factors not stipulated in the law (as emphasized by Rothstein & Teorell, 2008), but also on the consistent application of the law—something that, in turn, hinges on the clarity of the law itself. Figure 1 summarizes my argument: while legal research shows that legal ambiguity reduces understanding and increases the scope for divergent interpretations, I expect interpretation and comprehension to affect the consistency with which the law is applied—a critical aspect of impartiality. Figure 1: Theoretical Argument Note. The link between legal clarity and consistency is empirically examined. Interpretation, comprehension, and impartiality are not empirically examined, but are part of the theoretical framework. Based on the discussion above, I put forward the following hypothesis: 12 H1: Reduced legal clarity (i.e., legal ambiguity) diminishes the consistency with which the law is applied across similar cases, thereby undermining impartiality. 4. Assessing Consistency: A Survey-Based Experiment To test the hypothesis, I employed a population-based survey experiment using a two-group, between-subjects design. The participants—current and former government officials from around the world—were recruited via an online crowdsourcing platform (described in detail below). The experiment examined whether variations in the linguistic features of a legal text affect the consistency of decision-making. Specifically, I investigated whether the clarity of legal language influences how consistently officials interpret and apply the law by randomly assigning them to either a clear or an unclear version of the same legal scenario concerning zoning regulations. The participants were presented with a vignette—a structured hypothetical scenario (Mutz, 2011, p. 54)—and asked to assume the role of case officers responsible for issuing building permits on behalf of a local government agency. They were provided with background information, an excerpt from the law, and a description of a specific case for which they had to determine the appropriate outcome. Vignettes are well-suited for this type of study, as they help to redirect participants’ focus away from the experimental manipulation and toward the task at hand (Mutz, 2011, p. 64). Through convenience sampling, I collected responses anonymously from 920 individuals who voluntarily opted into a survey via an internet-based crowdsourcing platform, Prolific. The subjects were randomly assigned to either a treatment or a control group. The legal text presented to both groups was substantively identical, containing the same conditional elements. The experimental manipulation consisted of varying the linguistic style of the text. The treatment group received a version written in a more complex legal style incorporating legal linguistic elements that may negatively influence comprehension and potentially affect the consistency of participants’ judgments. In contrast, the control group received a plain language version designed to increase the clarity of the text and reduce the scope for divergent interpretations. The questionnaire was designed in Qualtrics, and participant recruitment was facilitated via Prolific. This crowdsourcing platform provides access to a high-quality global participant pool 13 (Douglas et al., 2023; Peer et al., 2021) and ensures respondents receive fair monetary compensation. The questionnaire can be found in Appendix A. 4.1. Recruitment Strategy Conducting survey-experiments and recruiting participants through crowdsourcing platforms is a relatively new phenomenon that, in recent years, has gained increased popularity among researchers (Milosav & Nistotskaya, 2024; Kennedy et al., 2020; Palan & Schitter, 2018). Several prominent social science studies have conducted survey-experiments using such platforms (e.g., Gerber et al., 2016; Huber et al., 2012). Using crowdsourcing platforms to conduct survey-experiments holds many benefits, not least in terms of convenience and cost- effectiveness (Peer et al., 2021, p. 1643). The use of crowdsourcing platforms has proven to be a reliable method, as demonstrated by numerous successful replications of prior experimental studies (Palan & Schitter, 2018, p. 22). Among crowdsourcing platforms, Amazon Mechanical Turk (MTurk) is the most well-known (Kennedy et al., 2020; Palan & Schitter, 2018). However, as indicated by recent reports, MTurk has faced increasing criticism for poor data quality, including frequent use of bots to answer surveys (Kennedy et al., 2020); inattentive responding by participants; and issues related to underpayment, raising both ethical concerns and problems with participants’ motivation (Palan & Schitter, 2018, p. 23). I chose Prolific as the study’s platform, as it emerges a viable alternative; it is a bit more expensive than MTurk, but in contrast, studies show that Prolific offers high-quality data compared to competitors (Douglas et al., 2023; Peer et al., 2021). Prolific offers access to over 200,000 active participants worldwide, with a particular emphasis on individuals from OECD countries (Prolific, 2025a). 4.1.1. Population under study: Recruiting Government Officials All recruited participants have prior or current experience in government and public administration. Prolific offers a pre-screening service for potential participants, featuring three options: “Currently in a Governmental Organization”, “Currently Working in Government & Public Administration”, and “Past Experience in Government & Public Administration”. I chose the last option because it provides the largest number of potential participants, and the group includes both currently working government officials and those with previous experience. No 14 country restrictions were set, meaning anyone working in this sector globally could participate in the survey. Focusing on government officials enhances the study's external validity. The project intends to explore decision-making behavior in government institutions and generate insights about this population. Therefore, a general participant pool from Prolific would limit the generalizability of the findings as they would not adequately represent the professional experience of the target group. By targeting individuals with prior government and public administration experience, I can assume a baseline familiarity with decision-making contexts, which is relevant to the study. The experiment focuses on legal language, which is likely more familiar to bureaucrats than the general public. Although the case focuses on the construction sector, an area where many respondents may have limited or no prior experience or knowledge, the scenario does not require technical expertise and is devoid of specialized terminology. The construction-related terms used in the survey were selected to be accessible to non-experts (which was pre-tested in a pilot study), and all complex concepts (e.g., zoning laws) were clearly explained to participants. 4.2. Sample target, Power Analysis, Exclusion Criteria To ensure the ability to detect even small effects, the target sample size is based on the assumption of a small effect size (Cohen’s d ≤ 0.2). The expected effect size is uncertain, as prior studies provide no clear indications. In light of this, it is rational to assume a small effect to reduce the risk of an insufficient sample size that might fail to detect meaningful differences. The effect size analysis suggested a minimum of 800 respondents (400 per group). Given this, and the available financial resources (see Appendix B), I anticipate that some of the data may not meet the desired quality standards, so I set the target sample size at 1,000. To enhance data quality, the following exclusion criteria are applied. First, responses that are flagged as incomplete, invalid, or returned are excluded. Second, participants are required to complete a reCAPTCHA test, which helps detect and filter out automated responses (i.e., bots) (Kennedy et al., 2020, p. 621). Responses with a reCAPTCHA score below 0.5, indicating a high likelihood of bot activity (as per Qualtrics methodological advice), are excluded from the dataset. 15 Third, an attention check2 is administered to screen out participants who fail to answer the question correctly, as such responses suggest a high likelihood of inattentiveness or random answering. In line with Prolific’s policy, these participants are excluded and not compensated (Prolific, 2025b). 4.3. Survey Design & Operationalization 4.3.1. Procedure & Vignette This study was pre-registered on March 28 before data collection on the Open Science Framework (registration DOI: https://doi.org/10.17605/OSF.IO/SQ8XJ), stating the design plan, sampling plan, experiment design, treatment condition, and hypothesis. The study commenced at 10 AM on April 8, 2025; the final response was recorded at 11 AM on April 9th. All responses in the dataset are complete (100%), and participants provided active consent to participate. 920 participants were approved and compensated at an hourly rate of £6.55 (i.e., on average £0.5 per response). It was conducted as follows: Prolific's website presents a list of potential studies to participants who meet the pre-specified eligibility criteria. A title and a brief description are displayed for each study, along with the estimated completion time and the remuneration amount. Participants who choose to take part in the study are automatically directed to Qualtrics, where the survey is administered. Participants can only complete the survey once, and upon completion, they are automatically returned to Prolific, where they receive the payment. Through Qualtrics, the questionnaire’s layout, style, and format are adapted for mobile devices and computers, making the survey accessible to a wider audience. After reading the law, both groups receive the same brief case summary concerning a building permit application to construct an attic apartment. Participants are then asked to resolve the case by selecting one of four response options, only one of which is correct. This experimental design allows for assessing whether language ambiguity results in greater variation in decision-making. 2 The attention check question: ”Please select 'Strongly agree' to show you are paying attention to this question, answer alternatives: “Strongly agree”, “Agree”, “Disagree”, “Strongly disagree”. 16 The questionnaire is organized as follows. The introductory page provides a brief overview of the survey and its subject matter, and participants must actively consent to participate. Following this, a demographics section and covariates, such as age, education level, legal background, and a comprehension check, are included. The third block contains an attention check, while the fourth consists of a test for reading and comprehension skills. After this, participants are randomly assigned3 to either the treatment or the control group4. Both groups receive the same background scenario: they are asked to assume the role of a case officer working for the planning office in “Town A”, responsible for reviewing building permit applications and ensuring regulatory compliance. Subsequently, both groups are presented with an excerpt of a realistic invented zoning law—“Act (2025:1742) – The Zoning Regulation”. The experimental manipulation occurs at this point: only the language of the legal text differs between the two groups, and all other information remains identical. After reading the law, both groups receive the same brief case description concerning a building permit application to construct an attic apartment. Participants are then asked to resolve the case by selecting one of four response options, only one of which is correct. This experimental design enables an assessment of whether the complexity of legal language leads to greater variation in decision-making. The rationale behind the case design is elaborated in Appendix C. Briefly, it is inspired by a real case based on documents obtained from Gothenburg’s municipality. A tenant-owned association submitted a building permit application to the City of Gothenburg, followed by the City Planning Office's review statement. The association sought permission to build an apartment in an unused attic space within a densely populated area governed by a zoning plan managed by the City of Gothenburg. The excerpt outlines an application from a housing association and the municipality's responses over multiple exchanges, where they request various documents, 3 To avoid the risk of skewed group distributions the randomized assignment, to either control or treatment group, occurs after participants completed the initial section, and just before they reach the experiment block. This approach decreases the likelihood that early dropouts get assigned to a group without finishing the study, thereby helping to prevent imbalances between the groups due to attrition. 4 I use the terms “treatment” and “control”, even though both groups are exposed to a legal text. The treatment group receives a version modified with legal-linguistic elements. The control group receives a plain-language version, which serves as a basis for comparison. 17 including those related to heritage conservation approval, accessibility adaptations, and other technical requirements. Placing the case description immediately after the legal text ensures that participants have the necessary information available, enabling them to make informed decisions without needing to revisit earlier sections. 4.3.2. Experimental Condition: Legal Text The two versions of the text convey the same substantive content (requirements for a building permit) but differ in linguistic complexity due to variations in legal linguistics. The manipulation is designed to reduce the clarity of the legal text by introducing legal-linguistic complexity, which may influence how participants interpret the law and, consequently, make decisions. The text was designed to strike a balance between reflecting the real-world complexity of legal documents, to ensure validity, and maintaining a level of accessibility that would not impede respondents’ comprehension. All legal requirements reflect provisions from the Swedish Planning and Building Act (SFS 2010:900) and the Swedish administrative authority “Boverket” Building Regulations (BBR, 2011, 3:1 & 3:2), which strengthens its credibility. The laws include four requirements that are easy to understand even if participants are unfamiliar with the construction sector. These are: documentation, heritage approval, accessibility provision, and a final paragraph describing conditions for the building permit and when the construction can begin. A comprehensive explanation of the treatment, including all requirements and linguistic elements, can be found in Appendix D, Table 1, and Table 2. The treatment is binary: participants are either assigned to the treatment (1) or the control group (0). The treatment group receives a version of the legal text incorporating seven linguistic features—discussed in the theory section (Charrow & Charrow, 1979; Gibbons, 1999; Solan, 1995; Wydick, 1978)—that have been shown to hinder legal comprehension. These include passive voice, negative constructions, nominalizations, syntactic embedding, a Latin expression, and ambiguous formulations and pronoun placement. Care was taken not to overload the text with too many comprehension-inhibiting features to prevent participants from disengaging and responding at random. In contrast, the control group is presented with a clear version of the legal text, which explains the regulatory requirements in language free from these problematic features. 18 The first paragraph in the law concerns the documentation requirement (SFS 2010:900, 9 kap, 21 §)5, which comprises information regarding the submission of documents—a floor plan, section drawing, and elevation drawing need to be submitted. The treatment consists of a passive voice and a negative sentence (e.g., “shall not be issued”), nominalization (e.g., “nonconforming”), syntactic embedding (“including but not limited to a floorplan”), and a Latin phrase (“ipso facto”). The second paragraph states that if the building is located in a densely constructed area covered by a zoning plan, a heritage conservation6 approval is required. It features passive voice, a negative sentence (e.g., “is not admissible”), and a nominalization (e.g., “determination”). Third, the accessibility provision requirement states that apartments larger than 35 square meters must be accessible for people with disabilities7. Here, a passive voice (e.g. “shall be precluded”), nominalization (e.g. “demonstration”), ambiguous pronoun placement (the pronoun “their” may be perceived as unclear; it could refer to “residential units” or “persons with disabilities”), and an ambiguous formulation (“as deemed satisfactory by the authorities” opens up for interpretation) can be found. The fourth and final paragraph and element (conditions)8 explains that construction cannot start until the planning office has issued a construction permit. It includes a passive voice and a negative sentence (“shall not commence”) and a nominalization (“issuance”). These legal provisions establish a foundation for decision-making regarding the case presented. However, the linguistic interventions make it more difficult to discern the necessary requirements in the text presented to the treatment group. 5 21 § “An application for permission or prior notice shall be in writing and contain […] the documents required for a decision on a starting notice in accordance with Chapter 10, if the application concerns an extension or other (translation of SFS 2010:900, 9 kap, 21 §) 6 “In a zoning plan, the municipality may determine [...] how public places that are particularly valuable from a historical, cultural-historical, environmental or artistic point of view shall be protected” (translation of: SFS 2010:900, 4 kap, 8 §). And “a building that is particularly valuable from a historical, cultural, environmental or artistic point of view must not be defaced.” (translation of: SFS 2010:900, 9 kap, 13 §). 7 “A building shall […] be accessible and usable for people with reduced mobility or orientation” (translation of: SFS 2010:900, 9 kap, 1 §). Boverket (BBR, 2011, 3:223) stipulate that homes over 35 square meters need to be adapted for accessibility, in order to follow the requirements in SFS 2010:900. 8 “An action may not be started before the building committee has given a start notice, if the action requires” (translation of: SFS 2010:900, 10 kap, 3 §). 19 4.3.3. Decision-Making: Answer Alternatives While real-life decision-making may involve an infinite range of possibilities, it was necessary to limit the number of answer options to ensure the experiment remained manageable while still allowing for effective measurement of variance between the groups. Furthermore, the number of possibilities must not make the correct choice too obvious. At the same time, too many options could introduce excessive complexity, especially given the limited time available for the survey and the potential challenges participants may face in interpreting the case. After reviewing plausible misinterpretations of the law (see decision-alternative breakdown and legal reasoning in Appendix D, Table 2), I determined that four answer alternatives strike the right balance: sufficiently complex without being overly so. The answer options include four decision alternatives: “Pending—request revision”, “Approve”, “Reject”, and “Approve with an exemption”. These options were designed to offer a relatively equal chance to be selected. Considering both linguistic complexity, potential misinterpretation, and findings from a pilot study (N = 40), which supported this approach. To mitigate potential response bias, the order of the answer options is randomized in the survey. Concludingly, the variable is nominal; the values do not follow a consistent order. 4.3.4. Sample Demographics Socio-demographic and background variables were collected to characterize participants and enable control for individual differences between the groups. Some variables were derived directly from Prolific’s prescreening data (e.g., gender, age, first language, and country of origin), while others were obtained through the survey (e.g., legal background, education level, and government employment). These questions are placed at the beginning of the study, prior to the experimental block. All participants are required to have proficient English language skills, so a dummy variable is created to distinguish between participants for whom English is the first language and those for whom it is not. Gender is categorized into three groups: “Male,” “Female,” and “Prefer not to say”. Age is a continuous variable that is ranging from 18 to 99 (the minimum age of participants is 18 on 20 Prolific), then recoded into a 4-point categorical variable9. Education is measured on a 7-point scale and recoded into three categories: “Low”, “Intermediate”, and “High”10. Additionally, a variable indicating whether participants have completed any formal legal education is included; it is reported as a dummy, either as “Yes, I have taken at least one law course” or “No, I have never taken a law course”. Furthermore, the government position variable is also binary, indicated as either “Currently working in government” or “Previously working in government”. Lastly, participants’ country of origin is included, and is based on Prolific’s data. Prolific collects approval ratings, reflecting the participants' experiences using Prolific and their approval levels in previous studies. The more studies a participant has been approved for, the higher the score (Prolific, 2025c). I classify this variable as follows: New to moderate experience, ranging from 0 to 100; Experienced participants, from 100 to 1000; and Highly Experienced Participants, 1000 and above. 4.3.5. Reading Comprehension Check To assess individual reading comprehension, I include a multiple-choice question (see Appendix A) adapted from Shohamy (1984, p. 169). Research in educational psychology supports the reliability of multiple-choice formats for measuring reading comprehension (Çetinkaya Özdemir & Akyol, 2019, p. 567; Pearson & Hamm, 2005). As discussed in the theory section, reading comprehension influences participants’ ability to understand complex legal texts (Charrow & Charrow, 1979; Diamond & Levi, 1995; Randall, 2014). While the question provides only a rough baseline and lacks nuances, it serves two purposes: as an approximate measure of comprehension and as a validity check. Failure to answer the question correctly may indicate difficulty in understanding the legal scenario. The variable is coded as a binary (0 = fail, 1 = pass). 9 Age categories: 16–29, 30–49, 50–64, 65–99. 10 “No formal education,” “Elementary school or equivalent,” and “College/vocational training” are considered low education. “High school or equivalent” and “College/vocational training” are considered intermediate. “Master or equivalent and “Doctoral or equivalent” are coded as high education category. 21 4.3.6. Manipulation Checks Manipulation checks are commonly used in experimental studies to ascertain if the manipulation of the independent variable had the intended effect. In this study, the block after the treatment includes an objective manipulation check that asks participants to recall essential information from the legal text. This question evaluates their understanding of key elements of the legal text. The intention is to assess whether participants in the unclear condition will be less likely to extract the same basic information. The question is accompanied by four answer options with one correct answer. A subjective manipulation check is included in the subsequent block to complement this objective measure. Participants were asked to rate their perception of the legal text’s clarity on a Likert scale from 0 (Not at all clear) to 10 (Extremely clear). I follow Mutz’s (2011, p. 65) suggestion and place the manipulation checks after the treatment. By doing so, I arguably avoid influencing their decision-making. Placing it after treatment may weaken the effects observed in the manipulation check. However, I avoid the risk of potential influence of the dependent variable, which could occur if it is positioned before (Ejelöv & Luke, 2020, p. 3). 5. Analysis As I am interested in the causal effect of legal clarity on the consistency of decision-making, I formally express the assumption of the counter-factual outcomes using the potential outcome framework: 𝐻!: 𝐸[𝑌"(1)] = 𝐸[𝑌"(0)] 𝐻#: 𝐸[𝑌"(1)] < 𝐸[𝑌"(0)] Here, Yi (1) denotes the consistency of decision-making for individual i when exposed to legal clarity treatment (absence of legal clarity), and Yi (0) denotes the consistency under the control condition (presence of legal clarity). The null hypothesis posits that legal clarity does not affect consistency—that is, the average consistency is the same across the groups. The alternative hypothesis suggests that individuals exposed to the treatment make less consistent decisions on average. 22 5.1. Descriptive Statistics After applying the exclusion criteria, the final dataset includes 885 respondents, with 445 in the treatment group and 440 in the control group. The randomization process was successful, strengthened by a randomization check on key variables; the groups are evenly distributed (see Table 1 and 2 in Appendix E). The average time to complete the survey was 5 minutes and 35 seconds. Table 1 reports descriptive statistics, and Appendix G reports all categories for each variable. Participants in the treatment have an average age of 41 years, ranging from 19 to 87, and 46% are male.11 The average in the control is 42, ranging from 18 to 81, and 44% are male. Participants come from 33 countries, but most reside in the UK, South Africa, or the US (see country list in Appendix F). Most had completed a high level of education, and around 60% had completed at least one formal legal course. Roughly 80% were native English speakers. Most participants passed the reading comprehension check, 82% in the treatment group and 85% in the control group. For experience in government, 58% of the treatment group and 61% of the control group had prior government experience; respectively, 42% and 59% were currently employed in the sector. Regarding decision outcomes, 49% of the treatment group and 59% of the control group selected the legally correct decision (Pending—request revision). 26% of the treatment group and 24% of the control group chose “Approve the application”. 9% of the treatment group and 7% of the control group chose “Reject”. 16% of the treatment group and 10% of the control group selected “Approve with an exemption.” Manipulation checks confirmed the treatment effect: 88% in the treatment group and 90% in the control group answered the objective question correctly. On the subjective clarity rating, the treatment group rated the text lower (M = 6.49, SD = 2.37) than the control group (M = 7.98, SD = 1.69). 11 Percentages rounded to nearest whole number 23 Table 1: Descriptive Statistics Variable obs mean SD min max Treatment Group Decision-Making 440 1.92 1.10 1 4 Gender 440 1.46 0.50 1 3 Age 440 40.56 13.67 19 87 Education 440 2.81 0.39 2 3 Legal Education 440 1.58 0.49 1 2 Government experience 440 1.58 0.49 1 2 English first language 440 0.79 0.41 0 1 Approval rating 440 1.99 0.69 1 3 Reading comprehension 440 0.82 0.39 0 1 Objective manipulation 440 0.88 0.32 0 1 check Subjective manipulation 440 6.49 2.37 1 10 check Control Group Decision-Making 445 1.67 0.97 1 4 Gender 445 1.44 0.51 1 3 Age 445 41.52 13.88 18 81 Education 445 2.82 0.39 2 3 Legal Education 445 1.62 0.49 1 2 Government experience 445 1.61 0.49 1 2 English first language 445 0.85 0.35 0 1 Approval rating 445 1.98 0.71 1 3 Reading comprehension 445 0.85 0.36 0 1 Objective manipulation 445 0.90 0.30 0 1 check Subjective manipulation 445 7.98 1.69 2 10 check 5.2. Analysis of Variance Across Groups I examine the effect of law clarity on consistency in decision-making by testing for differences in variance across the groups. More specifically, I evaluate whether participants in the treatment group, presented with the less clear law, demonstrate greater variability in their decision-making compared to the control group, who received the clearer version of the law. Graph 1 visualizes the distribution of answers between clear (Control) and non-clear (Treatment) language groups. The difference in variance can be discerned visually; the answers are more spread out in the non-clear language group (Treatment), suggesting greater inconsistency. 24 Graph 1: Distribution of Answer Alternatives To assess whether the distribution of responses differs significantly between groups, I employ a Chi-square test for homogeneity, designed to compare the distribution of categorical variables across two or more groups (Agresti, 2008, pp. 225–239; StataCorp, 2023). Since a Chi-square test for homogeneity computes differences in frequencies between categories, normality in distribution is not a prerequisite, unlike when working with continuous data. The Chi-square test requires each category to hold at least five observations (Agresti, 2008, p. 228), a condition fulfilled by this sample. Following Agresti (2008, p. 225), the test is computed as: (𝑂 − 𝐸)$ χ$ =/ 𝐸 O denotes the observed frequencies, and E is the expected frequencies calculated under the assumption that there is no difference in the distribution of outcomes across groups. To reject the null hypothesis and to conclude that the distributions differ, the test statistic must be associated with a p-value below the conventional significance threshold of 5% (p < 0.05). 25 Table 2: Chi-squared test for homogeneity: Comparison of Decision Consistency Across Groups Decision Alternatives Group Pending— Approve Reject Approve with Total request exemption revision Control Group 265 (54%) 107 (48%) 29 (41%) 44 (39%) 445 (100%) Treatment Group 215 (46%) 114 (52%) 42 (59%) 69 (61%) 440 (100%) Total 480 (54%) 221 (25%) 71 (8%) 113 (13%) 885 Chi-Squared. 13.31 Degrees of Freedom. 3 p-value. 0.004 Note. Frequency distributions are presented for each category, and the corresponding percentage within parentheses. As shown in Table 2, the Chi-squared test indicates a statistically significant difference in variance across the groups (p < 0.004), I can therefore reject the null hypothesis. This result suggests that increased linguistic complexity in legal texts is associated with less consistent decision-making. 5.3. Regression Analysis In addition to analyzing variance in decision-making, I also examine the determinants of participants’ perceived clarity of the legal texts. The experiment participants were asked to rate the clarity of the legal text on a Likert scale 0-10. Graph 2 depicts the distribution of responses by group, suggesting that participants in control group (blue) perceived their legal text as clearer, compared to the treatment group (red). 26 Graph 2: Perceived Clarity of Legal Text I perform an OLS regression (Table 3) testing whether the treatment influences the participants' perception of clarity, controlling for a set of individual-level characteristics. A binary variable, where unclear law = 1 and clear law = 0, is used as the key explanatory variable (Treatment). Table 3: Result of OLS Regression M1 M2 M3 M4 Treatment -1.49*** -1.49*** -1.53*** -1.5*** (0.14) (0.14) (0.14) (0.14) Age -0.07 -0.01 0.07 (0.08) (0.09) (0.09) Gender 0.18 0.17 0.23 (0.14) (0.14) (0.14) Education 0.27 0.09 0.09 (0.18) (0.18) (0.18) Legal education 0.61*** 0.49*** (0.14) (0.15) Government position -0.11 -0.11 (0.14) (0.14) English first language -0.18 -0.15 (0.18) (0.18) Approval rating -0.45*** (0.11) Reading comprehension check -0.14 (0.19) Objective manipulation check 0.55* (0.23) Constant 7.98*** 7.12*** 8.81*** 8.91*** (0.01) (0.62) (0.69) (0.70) Observations 885 885 885 885 Adjusted R2 0.12 0.12 0.13 0.15 Note. Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001 27 In a bivariate regression (M1), which only includes the binary treatment and the clarity rating, the coefficient for Treatment is statistically significant and negatively signed, suggesting that the less clear text affected the perception of clarity. On average, participants in the treatment group rated the clarity of the legal text approximately 1.5 points lower than those in the control group. This finding is robust to the introduction of basic demographic variables (M2), specific skills that may affect the perception of legal clarity (M3), as well as the approval rating, the reading comprehension check, and the objective manipulation check (M4). Treatment remains statistically significant at the 99% confidence level and negatively signed across all models. Furthermore, the size of the coefficient remains about the same: membership in the treatment group corresponds to a 15% reduction in the perceived clarity of the text. It is worth noting that legal education is associated with a higher subjective perception of the clarity of legal text. Additionally, a higher approval rating is linked to a reduced clarity rating, while a correctly answered objective manipulation check is associated with a slightly higher clarity rating. The results of the standard OLS diagnostic tests suggest that the key assumptions (linearity, homoscedasticity, normality of residuals, no multicollinearity, influential observations) of the OLS analysis are met. The results of the OLS analysis show that participants in the treatment group, who were presented with the less clear legal text, rated its clarity significantly lower than those in the control group. This finding, combined with the finding from the analysis of variance of decision- making outcomes, suggests that less clear legal texts are more difficult to comprehend and also contributes to greater inconsistency in decision-making. 6. Discussion In this section, I reflect on potential limitations of the study, including concerns related to cognitive load, generalizability, internal validity, measurement reliability, and representativeness of the sample. First, the treatment group may have been exposed to a higher cognitive load than the control group. This could have led to confusion, prompting some participants to disengage and select an answer without fully processing the content. The experiment was carefully 28 designed to mitigate such risks. On one hand, it needed to incorporate appropriate legal linguistic features to evaluate their impact on decision-making. On the other hand, it required a concise and focused design to maintain participant attention and ensure decisions were based solely on the legal text and considerations of case descriptions. In real decision-making, legal texts are often complex. If the experimental material were overly simplified, it would fail to reflect real challenges. Therefore, a certain degree of complexity was necessary to create a credible scenario and to enable participants to make as informed decisions as possible given the legal language provided. To further address concerns about cognitive load, I have simplified the study by reducing both the number of questions and response options, in line with recommendations by Stockemer and Bordeleau (2019, pp. 38–43). Additionally, I have minimized the condition elements to help participants to focus on the core aspects of the decision-making task. These measures aim to ensure that responses are influenced primarily by the clarity of the legal text, rather than by extraneous complexity. Both versions of the legal texts contain approximately the same number of words; the key difference lies in the clarity of their presentation. Furthermore, vignette experiments face criticism for lacking realism and for being improbable (Mutz, 2011, p. 59). While still hypothetical in its nature, the scenario used in this experiment is based on actual laws and real-life cases. This helps to mitigate concerns associated with entirely fictional scenarios. Moreover, for concerns about social desirability in survey experiments, that is, when respondents base their decisions on what is considered wrong according to social norms rather than on their own beliefs (Stockemer & Bordeleau, 2019, p. 41). Experiments containing hypothetical scenarios can suffer from this phenomenon (Mutz, 2011, p. 61). However, the questionnaire avoids value-laden or sensitive topics, so I do not expect participants to respond in a socially desirable way. The responses are neutral and cannot be considered controversial; participants simply reflect on the decisions based on the law. In survey research, including one or several attention checks is standard practice. Concerns have been raised that its inclusion may threaten validity. However, evidence suggests that such problems do not significantly affect the research results (Kung et al., 2018, p. 264). The attention check was positioned before the experimental manipulation to take extra precautions. 29 Another query concerns how reading comprehension can be assessed through just one multiple- choice question. It can present a challenge, partly because a single question may be insufficient to reflect participants’ abilities; however, more extensive approaches are not feasible due to the constraints of a five-minute survey. This variable is not part of the main analysis, and if you cannot handle this simple task, you may struggle to understand complicated legal language. Therefore, I believe that it gives an appropriate indication. For the variable approval rating, new members on Prolific consequently have low ratings, as the variable reflects their experience in answering surveys. It provides a measure on experience on Prolific, but it does not indicate how proficient participants are in decision-making scenarios. Government officials may be experienced in interpreting legal texts but less experienced in answering surveys. Consequently, these individuals will naturally have a low approval rating. Participants with lower ratings may take longer to answer surveys than participants with high ratings. However, this is not a major problem since most participants have high approval ratings. Regarding the main analysis—testing the differences in variance across the groups—the analysis concludes that unclear texts lead to inconsistent decision-making, which is a reasonable conclusion. However, extended individual-level predictors (e.g., reading comprehension or legal knowledge) could be used to discern further nuances. As a final point, I consider the generalizability of the findings. The project’s several features make its findings generalizable to broader bureaucratic decision-making. First, the study employs a carefully designed experiment that captures the dimensions it aims to investigate. Second, the experimental materials are based on real laws and incorporate linguistic elements commonly found in formal legislation. Third, the sample consists of current and former government officials, ensuring that the decision-making context closely mirrors real-world bureaucratic processes. Finally, the case scenario itself is inspired by a real case from the urban planning administration in Gothenburg, and the decisions made in the experiment are grounded in the same legal framework used in that setting. Although the case pertains to zoning regulation, the linguistic features under examination are not unique to this legal domain. These elements are broadly characteristic of legal language across different sectors. Therefore, the findings are not confined to urban planning but may plausibly 30 extend to other areas of law and administration. Moreover, the participants, all of whom are or have been public officials, were not limited to roles within a specific sector, further reinforcing the broader applicability of the results. 7. Conclusion This project began with the observation that the language used in legal texts is often characterized by a lack of clarity. This ambiguity can lead to misunderstandings and leave greater room for subjective interpretation, making it more difficult for bureaucrats to apply the law consistently. Such inconsistency in decision-making poses a threat to the principle of impartiality—one of the foundational pillars of high-quality government (QoG). A review of the existing QoG literature revealed a striking absence of research examining the relationship between legal clarity and impartiality. This thesis set out to address that gap. The literature review uses legal studies to map the relationship between legal clarity and impartiality. The legal origin theory explains differences between legal traditions related to bureaucratic performance, but highlights property protection as an explanatory mechanism. Furthermore, legal clarity also seems to vary between different legal traditions; plain language reforms have mainly been adopted in countries with a common law tradition. Some empirical evidence suggests that legal clarity improves understanding and interpretation. Despite these valuable contributions, theoretical frameworks and empirical evidence explaining the link between legal clarity and impartiality remain underdeveloped. The first contribution of this thesis is the development of a novel theoretical framework. I argue that legal clarity affects impartiality in bureaucratic decision-making through consistent application of the law. The legal linguistics literature identifies specific linguistic features commonly found in legal texts that help distinguish between unclear and clear legal language. These features include ambiguous pronoun placement, passive voice, negative constructions, nominalizations, syntactic embedding, Latin expressions, and ambiguous phrasing. The prevailing definition of impartiality states that laws should be applied solely based on what is prescribed in the law. This is a view that is consistent with the legal normative principle that laws should be applied equally in similar cases—that is, like cases should be treated alike. Ambiguity of wording of laws (legal ambiguity) can therefore reduce understanding, allow for divergent 31 interpretations, and lead to greater inconsistency in the application of the law—ultimately threatening impartiality. The second contribution is an empirical test of the hypothesis that clarity in legal language affects consistency in decision-making. To explore this, I conducted an online survey experiment involving over 900 former and current government officials worldwide. The participants were divided into two groups, each of which was given a case inspired by a real building permit application for an attic apartment handled by the Gothenburg City Planning Office. They were exposed to a legal scenario that included legal requirements for building permits based on Swedish zoning laws. The experimental treatment consisted of seven linguistic elements identified in the theory section, used to create an ambiguous variant of the law. To capture the variations in decision-making, participants were asked to determine the outcome of the legal scenario using four decision options, one of which was correct. The experimental findings support the theoretical claim that unclear legal language leads to less consistent decision-making, whereas clearer language tends to produce more uniform application of the law. By demonstrating that ambiguity in legal texts can lead to unintended variations in decision- making or even incorrect application of the law—risking similar cases being treated differently—this thesis highlights the need to broaden the definition of Quality of Government. While Rothstein and Teorell's (2008) definition emphasizes the absence of influence from factors not “stipulated in the policy or the law” (p. 170), this thesis argues that the clarity of what is stipulated in the law is equally critical for ensuring impartial implementation. Integrating legal clarity into the Quality of Government (QoG) framework requires rethinking both the concept’s scope and its operationalization. Impartiality has often been operationalized by the absence of discretionary bias or favoritism. Adding legal clarity reframes impartiality as also requiring that different implementers arrive at the same interpretation of a norm. Measuring interpretive variance (e.g., through vignettes or linguistic analysis) becomes a legitimate indicator of QoG. Readability scores (e.g., Flesch-Kincaid), counts of complex constructions (nominalizations, embedded clauses), or expert surveys on textual clarity can supplement existing QoG indices. This enriches cross-national comparisons by capturing variation in statutory legibility, including comparing across legal families. Empirical QoG assessments can 32 incorporate experimental or survey-based modules (as in this thesis) to test whether legal clarity predicts outcomes such as case-processing times, appeal rates, or administrative complaints— bridging micro-level linguistic features to macro-level governance quality. While unclear laws undeniably affect QoG, in countries where public administration is grappling with high levels of corruption, politicization, or nepotism, the factors not “stipulated in the policy or the law” (Rothstein & Teorell, 2008, p. 170) may carry greater weight than the clarity of legal provisions. In other words, the effect of legal clarity may be more pronounced in countries where the quality of government is already relatively high. This conjecture warrants empirical investigation in future research. A certain degree of vagueness may sometimes be necessary in complex or unique cases when there is uncertainty about the outcome in the legislative process. However, one must remain aware that such ambiguity increases discretion at the implementation stage, reducing predictability and potentially undermining public trust. To support impartial implementation, policymakers should prioritize the simplification of legal language. Unclear laws increase the need for legal support to understand and apply laws impartially. As Fernández-i-Marín et al. (2024b) stress, it is essential to align the burdens of regulation placed on agencies with the resources available to them. Simplifying legal language is one way to reduce this burden and promote more consistent and impartial decision-making. Politicians could adopt plain language drafting manuals, statutory templates, and regular legislative “health checks” as part of their QoG enhancement toolkit. Recognizing that clarity and comprehension are two sides of the same coin, governments might invest not only in textual reform but in ongoing training for drafters, judges, and administrators on linguistic best practices. As the study demonstrates, individuals with formal legal education are better equipped to understand unclear legal texts than those without such training. 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Introduction End of Block Start of Block (ReCAPTCHA, bots capture) 40 End of Block Start of Block (Socio demographic questions) 41 End of Block Start of Block (Attention check) End of Block Start of Block (reading comprehension check) 42 End of Block Randomized assignments to groups (Treatment/Control) Start of Block 43 Treatment group 44 Control group Answer alternatives End of Block 45 Start of Block (Objective manipulation check) End of Block Start of Block (Subjective manipulation check End of Block Start of Block (Prolific ID capture) End of Block 46 Appendix B: Compensation & Ethical Consideration Providing fair compensation is crucial to encouraging participant engagement and addressing ethical considerations; it also motivates participants and improves data quality (Prolific, 2025b). Payment size can influence if participants choose to opt into the study, previous studies imply that data quality is generally not affected by the level of compensation (Buhrmester et al., 2011, cited in Palan & Schitter, 2018, p. 23). Nonetheless, appropriate compensation remains important, given that the survey arguably entails a higher cognitive load than typical questionnaires. Participants received an average reward of £6.55 per hour, which exceeds Prolific's minimum wage policy of £6 per hour, mirroring the US minimum wage of $7.25 (i.e., about £5.4) (Prolific, 2025b; U.S Department of labor, n.d.). The actual pay is proportional to the time spent on answering the questions and should not be excessively high to attract participants who engage in the study solely for monetary reasons (Etikprövningsmyndigheten, 2023, p. 33). This study was funded (100%) by the Quality of Government Institute at the University of Gothenburg, and the approximate total cost of carrying out the experiment was £700. Participants’ responses were collected anonymously, and personal data cannot be linked to any individual. The participants are fairly compensated for taking the survey. They were introduced to the topic before they chose to move on from Prolific; they were briefed on the study’s purpose, estimated completion time, compensation level, and hourly wage, and they were also encouraged to reach out with any queries. The introduction states that the survey was conducted by the Quality of Government Institute at the University of Gothenburg, and participants were informed that the project follows the EU General Data Protection Regulation (GDPR) (Etikprövningsmyndigheten, 2023, pp. 86–89), all collected data will only be used for social science research and Individual responses will not be shared with a third party. Participants must actively consent to proceed; if they do not, the survey closes. Throughout the survey, neutral language is employed, and I do not consider the subject to be sensitive; neither political affiliation, corruption, political behavior, nor any other related subject is investigated. 47 Appendix C: Case Description and Design Rationale I have immersed myself in a building permit process in Sweden by requesting all documents from the City of Gothenburg regarding a building permit application to construct an attic apartment in central Gothenburg. I followed the ongoing process until the final building permit was completed and reviewed the application, together with the city planning office’s response over several rounds. This has allowed me to create a comprehensive understanding of the legislation in the area, outlining how a case progresses from application to a finished building permit in a municipality in Sweden. I have drawn inspiration from this case in designing what different response alternatives are possible and reasonable. Application and the first response: The response to the first application submission from the case officer states that the construction “Cause a distortion” (my translation of Gothenburg City Planning Office, [internal document], April 27, 2023a, p. 1) to the zoning regulated area, with a legal reference to (SFS 2010:900, 4 kap, 8 §), requires a heritage approval and additional specified documents from the applicant. After this, the applicant handed in a heritage approval, to which the officer replied: “We cannot grant your application, and we therefore suggest that you change your application” (my translation of Gothenburg City Planning Office, [internal document], June 20, 2023b, p. 1). Even though a heritage assessment was received, the officer proposed yet another suggestion that the applicant “should take a holistic approach to the roof landscape” (my translation of Gothenburg City Planning Office, [internal document], June 20, 2023b, p. 1), suggesting another revision. In the third revision, the case officer determined that the suggested plan does not fulfill the accessibility requirements specified in BBR (2011, 3:223), and demanded another revision. The applicant finally received a partial start notice to begin construction of certain parts in November 2023 (Gothenburg City Planning Office, [internal document], November 8, 2023c). However, only for part of the construction, more documents were still required. I am not insisting that this case has been handled subjectively or faultily; rather, it illustrates a typical and similar application process, including various review comments that relate to the legal requirements created in my case. 48 Appendix D: Treatment Explanation Table 1: Treatment Breakdown 49 Table 2: Legal Interpretation and Rational for Decision Alternatives Answer Option Interpretation Reasoning 1. Pending— Correct The application does not include an elevation drawing, only a floor plan and section drawing. The law requires an request revision elevation drawing. The case officer should therefore ask the applicant for a revision. The law states it is “Subject to revision” (T) and “Shall be revised” (C). The linguistic complexity in “Subject to revision” (T) might cause participants to overlook the revision requirement. Additionally, the building is in a “Densely built area covered by a zoning plan,” (Case) and there is no heritage approval submitted, requiring a revision. 2. Approve Incorrect The application lacks required documentation, specifically an elevation drawing. The treatment version reads: “Including but not limited to a floor plan, section drawing, and elevation drawing” (T), embedded in complex language. Passive voice and nominalization like “Subject to revision” (T) might cause confusion. Some participants might incorrectly approve due to included drawings and the case stating that the building is accessible. They might not realize the elevation drawing is missing. 3. Reject Plausible, but The proposal meets the accessibility requirements, as stated in the case description. However, the treatment incorrect version includes a clause: “As deemed satisfactory by the competent authorities” (T), which introduce ambiguity, this alternative may appear reasonable to participants in the treatment group due to linguistic ambiguity. The control version makes the requirement clearer. Participants might also misinterpret the missing elevation drawing as grounds for rejection, or reject the application based on the lack of heritage approval, even though it can be submitted later. 4. Approve with an Incorrect The elevation drawing is missing and cannot be exempted. The law requires all three documents: floor plan, exemption section drawing, and elevation drawing and the officer must request a revision if any of these are missing. This alternative might lead participants to overlook or even misunderstand the revision requirement as it introduces a false assumption that an exemption is possible Note. (C) indicate the control group, and (T) indicate the treatment group, and (Case) denote the case description text. There are four different decisions to choose from, each with its own unique characteristics. Based on the reasoning in Table 2, each decision has a relatively equal chance of being selected; however, there is only one accurate answer. I present each decision along 50 with the underlying reasoning for the alternatives. The framing of the text presented for the treatment group is more complex, which could lead to a lack of comprehension and create more room for interpretation. Moreover, during the design phase of the questionnaire, I considered including a fifth option in which I would add a clause to the legal text. This clause would introduce a time period in which the applicant could submit a heritage approval. However, this option was excluded because it could create unwanted variance and risks, which could excessively complicate the decision-making situation and likely lead participants to choose an answer randomly. Limiting respondents to the four presented options ensures the mutually exclusive criteria. I also considered including another option, explaining that an approval can be given against the required payment to the case officer. This alternative was discarded, partly due to problematic ethical reasons (a socially undesirable choice). It is merely an act of corruption and is not tied to the law; there is no allowance for extra fees described. 51 Appendix E: Distributions Table 1: Randomization Check – Chi-square Test for Homogeneity Variable Control group Treatment group Chi² (df) p-value Gender χ²(2) = 0.60 0.740 Female 56% 54% Male 44% 46% Prefer not to say 0.4% 0.2% Age χ²(3) = 0.88 0.830 16–29 years 23% 24% 30–49 years 48% 49% 50–64 years 22% 22% 65–99 years 7% 6% Education Level χ²(1) = 0.03 0.868 Intermediate 18% 19% High 82% 81% Legal Education χ²(1) = 0.92 0.337 Taken Law 38% 42% Course No Law Course 62% 58% Government pos. χ²(1) = 0.68 0.410 Currently in 39% 42% Government Previously in 61% 58% Government Note. The distributions are reported in percentages. I employ a Chi-square test for homogeneity— Following Agresti (2018, p. 225–239)—to check for differences in distribution. None of the variables present statistically significant results, which indicates no difference between the groups, suggesting that the randomization process has worked sufficiently. 52 Table 2: Visualizations of Distributions Note. The visualizations present distributions for each group; they account for key demographic variables over each group; they do not cover all variables. For a thorough explanation and exact coding of the variables, see Section 4.3. 53 Appendix F: Country of Residence Table 1 Country of residence Control Treatment Total n = 33 United Kingdom 175 177 352 South Africa 119 129 248 United States 66 44 110 Poland 17 11 28 Canada 10 10 20 Australia 9 6 15 Germany 3 12 15 Kenya 7 6 13 Italy 7 4 11 Portugal 4 9 13 Hungary 3 4 7 Netherlands 3 4 7 Greece 2 3 5 Spain 3 2 5 France 0 4 4 Ireland 3 1 4 Sweden 3 1 4 Belgium 1 1 2 Brazil 0 2 2 Denmark 2 0 2 Japan 1 1 2 Austria 1 1 2 New Zealand 0 2 2 Slovenia 2 0 2 Czech Republic 0 1 1 Estonia 0 1 1 India 1 0 1 Indonesia 1 0 1 Israel 1 0 1 Latvia 0 1 1 Malaysia 1 0 1 Mexico 0 1 1 Norway 0 1 1 Other 0 1 1 Note. This table reports the frequencies for each country by each group 54 Appendix G: Variables Table 1 Variable and Categories Control Treatment Total Total 445 440 885 Dependent variable Decision-making (DV) Pending—request revision (1) 265 215 480 Approve (2) 107 114 221 Reject (3) 29 42 71 Approve with an exemption (4) 44 69 113 Demographical variables Gender Female (1) 249 239 488 Male (2) 194 200 394 Prefer not to say (3) 2 1 3 Age 16–29 years 103 105 208 30–49 years 213 215 428 50–64 years 97 95 192 65–99 years 32 25 57 Education Low (1) - - - Intermediate (2) 82 83 165 High (3) 3 63 357 720 Legal Education Yes, I have taken at least one law course (1) 171 183 354 No, I have never taken a law course (0) 2 74 257 531 Government Position Currently working in government (1) 174 184 358 Previously worked in government (2) 271 256 527 English as First Language Yes (1) 380 346 726 No (0) 65 94 159 Other Measures Approval rating New/moderate (0 and 100 approvals) 117 107 224 Experienced (100-1000 approvals) 219 232 451 H ighly Experienced (>1000 approvals) 109 1 01 210 Reading Comprehension Fail (0) 66 81 147 Pass (1) 379 359 738 Objective Manipulation Check Incorrect answer (0) 44 51 95 Correct answer (1) 401 389 790 Subjective Manipulation Check 1-2 1 27 28 3-4 23 76 99 5-6 46 82 128 7-8 196 167 363 9-10 179 88 267 55