Analyzing behavioral economics in enhancing voluntary tax compliance in the us among self-employed informal sector workers

 Corresponding author: Charles Dickson Usen Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Analyzing behavioral economics in enhancing voluntary tax compliance in the us among self-employed informal sector workers Charles Dickson Usen * Legal and Tax Analyst, Getcorp Payroll Accounting and Tax, USA. World Journal of Advanced Research and Reviews, 2025, 26(03), 366–384 Publication history: Received on 28 April 2025; revised on 01 June 2025; accepted on 04 June 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.26.3.2226 Abstract Voluntary tax compliance remains a significant challenge in the United States, particularly among self-employed workers operating within the informal sector. Traditional enforcement mechanisms—such as audits and penalties— often prove inefficient and costly in curbing tax evasion in this decentralized and under-regulated population. This paper explores how insights from behavioral economics can offer a more nuanced and cost-effective approach to fostering tax compliance. It begins by examining foundational behavioral economic principles, including loss aversion, social norm activation, mental accounting, and the power of framing, which collectively influence taxpayer decisionmaking beyond purely rational calculations. Drawing on empirical studies and experimental interventions from both domestic and international contexts, the paper highlights how behavioral nudges—such as personalized communication, moral reminders, simplified filing processes, and timely prompts—can improve compliance rates without increasing enforcement intensity. Special focus is given to the unique cognitive biases and economic realities of self-employed informal sector workers, including gig workers, freelancers, and sole proprietors, who often lack employer-based withholding systems and face complex tax filing requirements. The paper reviews IRS pilot programs and third-party research that reveal promising behavioral strategies tailored for this demographic. In narrowing its focus, the study proposes a framework for integrating behavioral tools into federal and state-level tax outreach programs targeting informal earners. Policy implications include the need for cross-agency collaboration, digital platform engagement, and privacy-conscious nudging mechanisms that respect autonomy while promoting civic duty. The paper concludes that behavioral economics offers a valuable, scalable supplement to traditional tax policy tools, capable of enhancing compliance and public revenue while preserving taxpayer trust and dignity. Keywords: Behavioral Economics; Tax Compliance; Informal Sector; Self-Employment; IRS Nudges; Public Finance 1. Introduction 1.1. Contextualizing Tax Compliance Challenges in the U.S. Tax compliance is essential for maintaining the fiscal health of governments, yet in the United States, voluntary compliance remains uneven, particularly among certain economic groups. The Internal Revenue Service (IRS) has estimated the annual tax gap—the difference between taxes owed and collected—at over $500 billion, a figure that undermines both government revenue and perceived fairness in the tax system [1]. While wage earners operating under third-party reporting systems (such as W-2 employees) generally demonstrate high compliance, this is not the case for taxpayers whose incomes are self-reported and unverified. The U.S. tax system largely relies on voluntary compliance, which is inherently influenced by perceptions of fairness, fear of audits, cognitive biases, and socioeconomic realities [2]. World Journal of Advanced Research and Reviews, 2025, 26(03), 366–384 367 1.2. Importance of Informal Sector and Self-Employed Populations The informal sector, while traditionally associated with developing economies, has seen a growing footprint within the United States, particularly through the rise of gig economy platforms, freelance markets, and off-the-books labor arrangements. Approximately 60 million Americans engage in freelance or contract-based work, accounting for nearly 36% of the workforce [3]. These individuals, who include rideshare drivers, home-based entrepreneurs, and digital freelancers, often fall outside conventional payroll systems. Consequently, their income reporting, tax filing, and payment compliance are often inconsistent and prone to underreporting [4]. Self-employed individuals face distinct challenges: lack of withholding mechanisms, variable incomes, complex filing requirements, and limited access to advisory services. The informal nature of their earnings often translates to fragmented recordkeeping and reduced institutional engagement, further exacerbating non-compliance risks. Tax agencies, in response, have struggled to effectively reach and regulate this group using traditional tools [5]. 1.3. Limitations of Traditional Tax Enforcement Mechanisms Historically, U.S. tax compliance policy has emphasized audit-based deterrence, civil penalties, and information return systems. While these mechanisms work relatively well for salaried employees, they are less effective among selfemployed or informal earners due to the absence of real-time third-party verification. Audit rates for individual returns have steadily declined over the past decade, especially for filers reporting less than $200,000, making enforcement increasingly reactive and selective [6]. Moreover, the administrative cost of auditing self-employed taxpayers is disproportionately high compared to the revenue it generates. For this demographic, fear of penalties often competes with short-term cash flow constraints and cognitive biases, such as discounting future obligations. Consequently, deterrence-based approaches tend to overlook the behavioral and psychological dynamics underlying tax decisions, particularly in decentralized, informal settings [7]. A one-size-fits-all strategy, focused narrowly on enforcement, fails to address the nuanced barriers facing these workers. 1.4. Rationale for Applying Behavioral Economics Given the limitations of conventional compliance frameworks, behavioral economics offers a compelling alternative. Behavioral economics recognizes that individuals are not purely rational actors; rather, their financial decisions are shaped by heuristics, biases, social norms, and cognitive constraints [8]. In the context of tax compliance, this means that even when individuals understand their legal obligations, they may not act on them unless prompted, reminded, or socially motivated. Insights from behavioral economics have been increasingly applied to public policy through mechanisms known as "nudges"—subtle interventions that influence decision-making without restricting choice [9]. For instance, simplification of tax forms, personalized reminders, and norm-based messages can significantly enhance compliance at a fraction of the cost of audits. Several IRS pilot programs have already demonstrated the potential of such behavioral interventions in improving payment rates among delinquent taxpayers [10]. Moreover, behavioral tools align well with the decentralized nature of the informal sector, offering scalable, low-friction solutions. Importantly, applying behavioral economics to tax compliance also respects taxpayer autonomy and dignity. Unlike punitive approaches, nudges are generally non-intrusive and can be designed to promote a sense of civic responsibility. For self-employed and informal workers—who may already feel disconnected from formal institutions—such humancentered strategies are not only more effective but also more equitable [11]. 1.5. Research Objectives and Scope This paper aims to explore how behavioral economics can be systematically leveraged to improve voluntary tax compliance among self-employed informal sector workers in the United States. Specifically, it addresses the following research questions: • What are the primary behavioral barriers to tax compliance for informal and self-employed individuals? • Which behavioral interventions have shown the greatest efficacy in promoting compliance in similar populations? • How can tax authorities incorporate these insights into policy design and implementation frameworks? World Journal of Advanced Research and Reviews, 2025, 26(03), 366–384 368 The scope of this research includes empirical analysis of U.S.-based behavioral interventions, comparative studies from international tax authorities, and synthesis of behavioral theory. The target population consists of self-employed and informal earners not covered by employer withholding, including but not limited to gig workers, sole proprietors, and freelance professionals [12]. This investigation draws on interdisciplinary perspectives, integrating public finance, psychology, behavioral economics, and administrative law. The aim is not to propose a singular intervention, but rather to outline a framework for behaviorally informed tax compliance strategies that can be adapted across contexts. To understand how behavioral insights can improve voluntary tax compliance, it is essential first to explore the theoretical underpinnings of behavioral economics. Unlike neoclassical economics—which assumes individuals are rational utility maximizers—behavioral economics posits that decision-making is bounded by cognitive limitations, emotional influences, and context-dependent judgments [13]. These tendencies are particularly pronounced in environments of uncertainty, complexity, or temporal delay—all of which characterize the informal tax environment. In the following section, the paper delves into the major behavioral concepts relevant to taxpayer decision-making, including loss aversion, social norm activation, mental accounting, and present bias. This theoretical foundation provides the lens through which real-world compliance behavior can be better understood, predicted, and influenced— particularly among self-employed individuals navigating the ambiguous and fluid terrain of informal economic activity. 2. Theoretical framework and literature review 2.1. Behavioral Economics and Tax Compliance Traditional economic theory assumes that individuals behave rationally, acting in their own self-interest to maximize utility. In tax contexts, this means individuals are presumed to comply when the expected cost of non-compliance— fines, audits, penalties—exceeds the expected benefit of evasion. This deterrence model, based on Allingham and Sandmo’s economic theory of income tax evasion, has long guided tax policy frameworks [6]. Yet, empirical evidence consistently shows that compliance rates often exceed what would be expected from purely rational cost-benefit calculations. Taxpayers frequently choose to comply despite minimal risk of detection or penalty. Such anomalies have led to a broader reconsideration of the psychological and social dimensions of tax behavior. Behavioral economics responds to this gap by recognizing that individuals are not fully rational agents. Instead, they are subject to bounded rationality, relying on mental shortcuts (heuristics) to make decisions under uncertainty [7]. In tax compliance, individuals often misjudge the probability of audits or overweigh the cognitive burden of tax preparation. These bounded decision processes are compounded by behavioral biases such as present bias (overvaluing immediate rewards) and optimism bias (underestimating future obligations or penalties) [8]. Furthermore, behavioral insights demonstrate that decisions are not made in a vacuum; they are shaped by context, peer behavior, framing of options, and perceptions of fairness. For example, a taxpayer’s willingness to comply may hinge less on economic incentives and more on whether they believe others are paying their fair share or whether the tax authority treats them respectfully [9]. Thus, while classical economics assumes tax behavior is an outcome of enforcement pressure, behavioral economics reframes it as a product of psychological, social, and contextual forces. This shift allows for more human-centric, costefficient interventions that go beyond punitive measures. 2.2. Key Behavioral Concepts Relevant to Tax Behavior Several core behavioral concepts offer specific insight into how taxpayers make decisions—and how those decisions can be influenced through non-coercive means. One of the most widely observed is loss aversion, which posits that people experience the pain of losses more intensely than the pleasure of equivalent gains. In tax compliance, this means that taxpayers may be more responsive to messages that highlight what they stand to lose (e.g., penalties, future benefits, reputation) than what they might gain by being compliant [10]. Closely linked to this is the framing effect, where the way choices are presented—positively or negatively—influences decisions. A tax reminder that states “most people in your area pay their taxes on time” can increase compliance by leveraging descriptive social norms. Conversely, emphasizing penalties without context can induce anxiety or avoidance [11]. World Journal of Advanced Research and Reviews, 2025, 26(03), 366–384 369 Mental accounting is another critical concept. Taxpayers often allocate income and expenses into different mental “buckets,” which may lead them to perceive tax obligations as less urgent than other liabilities. This can be especially relevant for self-employed individuals who receive gross income and must later remit taxes, unlike salaried workers whose taxes are withheld automatically [12]. Another powerful behavioral lever is social norm activation. Humans are inherently social, and compliance increases when individuals believe their peers are also compliant. Research shows that norm-based messaging—especially personalized to neighborhoods or income brackets—can significantly influence behavior without coercion [13]. Finally, intrinsic motivation—the internal drive to act ethically or civically—can be harnessed to encourage voluntary compliance. When tax systems are perceived as fair, and authorities are seen as legitimate, individuals are more likely to comply voluntarily, even in the absence of enforcement [14]. These behavioral concepts form the basis for designing “nudges” that subtly guide taxpayer behavior. Importantly, they also support more empathetic public policy approaches, particularly for underserved or marginalized groups. Figure 1 Conceptual framework linking behavioral economics to tax compliance behavior 2.3. Voluntary Tax Compliance in the Informal Sector The informal sector in the United States is diverse, encompassing a wide range of workers including gig economy participants, sole proprietors, home-based service providers, and undocumented laborers. While their economic contributions are substantial, their relationship with the tax system is tenuous. Unlike salaried employees, informal workers often lack automatic payroll deductions, standardized income reporting, and institutional support for tax preparation [15]. These structural gaps are compounded by psychological and contextual factors. Informal workers often face income volatility, lack clarity on tax obligations, and exhibit high levels of distrust toward government institutions. In behavioral terms, they may be more prone to present bias, prioritizing immediate financial needs over future tax obligations [16]. Additionally, complex filing requirements and perceived procedural unfairness act as demotivators, further reducing voluntary compliance. In international contexts, several governments have experimented with behavioral interventions targeting informal earners. In Guatemala, simple SMS reminders increased compliance among market vendors by framing tax as a shared civic duty. In Kenya, commitment devices—like pre-filing pledges—were used to reinforce intrinsic motivations [17]. These findings suggest that even among marginalized groups, behavioral nudges can shift compliance outcomes when tailored to local realities. Within the U.S., the IRS has begun to recognize these challenges. Pilot programs involving behaviorally designed notices, personalized risk indicators, and simplified digital communication channels have shown promising results. One such World Journal of Advanced Research and Reviews, 2025, 26(03), 366–384 370 initiative targeted freelance workers through an online tax prep tool that broke tax obligations into weekly “bites,” reducing mental accounting errors and improving on-time payment [18]. However, gaps remain. Many informal workers still operate outside formal banking or digital ecosystems. Others fear exposing themselves to scrutiny due to prior non-compliance or immigration concerns. These concerns highlight the need for behaviorally sensitive policy tools that do not rely on enforcement alone but instead engage informal earners respectfully, predictably, and transparently [19]. As such, voluntary compliance in the informal sector must be seen not merely as a legal issue, but as a behavioral one. Tax design for this population must integrate empathy, simplicity, and psychological realism, emphasizing fairness, trust-building, and gradual onboarding into the formal economy. These principles will be critical in shaping effective interventions. 3. Methodological considerations 3.1. Research Design This study employs a qualitative, multi-source synthesis design anchored in case study methodology. The decision to focus on qualitative evidence stems from the need to understand the behavioral mechanisms, decision-making contexts, and institutional nuances that underlie voluntary tax compliance among self-employed and informal sector workers. Quantitative studies often underrepresent informal earners due to sampling limitations, while qualitative inquiry allows for richer, contextual interpretation of behavioral cues and motivations [11]. The design is informed by an exploratory multiple-case approach, allowing for triangulation across diverse evidence types. It incorporates case data from internal IRS behaviorally-informed outreach pilots, academic behavioral experiments, and cross-national tax compliance studies. The integration of these varied sources enhances generalizability without sacrificing contextual specificity [12]. The research also adopts principles from realist evaluation, which emphasize "what works, for whom, and under what circumstances"—a critical lens when dealing with informal economic actors who vary widely in socio-economic profile, trust in institutions, and tax knowledge [13]. By applying thematic synthesis to existing cases, the study moves beyond surface-level outcomes to uncover underlying cognitive, emotional, and situational determinants of behavior. This methodological triangulation ensures that insights are not solely derived from one organizational or national lens. Instead, they reflect a convergence of insights from U.S.-based tax administrations, behavioral economic trials, and secondary literature on compliance psychology. The qualitative case study framework is therefore both flexible and robust, suitable for capturing the nuanced interactions between policy design, behavioral nudges, and informal tax behavior [14]. 3.2. Data Sources Three categories of data inform this study: (1) Internal and publicly available IRS pilot programs, (2) published behavioral intervention studies, and (3) curated secondary datasets from policy and research institutions. The IRS data includes reports from randomized control trials (RCTs) targeting underreporting and late payment among gig workers, independent contractors, and Schedule C filers. Several pilot programs tested personalized letter formats, reminder framing, and simplified digital notices, yielding measurable differences in compliance behavior [15]. These data provide insight into message framing, demographic responsiveness, and administrative feasibility. Academic behavioral intervention studies contribute further granularity. Field trials from institutions like Harvard, MIT, and the University of Chicago evaluated nudges ranging from loss-framed communications to moral suasion prompts. The studies offer robust empirical evidence on cognitive triggers, temporal discounting, and perceived legitimacy, particularly among marginalized populations [16]. Lastly, secondary datasets—such as Pew Research Center’s gig economy findings, the Tax Policy Center’s compliance metrics, and the National Bureau of Economic Research's working papers—offer demographic and attitudinal insights into the broader landscape of self-employed informal workers [17]. These are particularly valuable for profiling nonfilers and low-visibility earners not captured in IRS administrative datasets. World Journal of Advanced Research and Reviews, 2025, 26(03), 366–384 371 Table 1 Summary of Key Data Sources and Behavioral Insights Extracted Data Source Type Coverage Key Behavioral Insights Extracted World Bank Informal Sector Enterprise Surveys Quantitative Multiple LMICs High perceived compliance burden and preference for cash transactions among informal workers OECD Tax Morale Surveys Quantitative + Qualitative Global (including OECD and nonOECD) Trust in government strongly correlates with willingness to comply voluntarily IRS Gig Economy Compliance Reports Administrative Data United States Many gig workers unaware of filing requirements; temporal discounting impacts quarterly payments Kenya Revenue Authority (KRA) iTax Pilot Evaluation Pilot Evaluation Data Kenya SMS nudges increased mobile filing rates by 22% during tax season Behavioral Insights Team (BIT) Field Experiments RCT/Experimental UK, Nigeria, Colombia Norm-based messages and simplification boosts tax compliance; framing effects matter significantly South Africa National Informal Economy Dashboard Mixed Methods South Africa Informal earners cite complexity and fear of penalties as barriers; mental accounting observed Nigeria Fintech Association Platform Usage Report Digital Transaction Logs Nigeria Strong potential for in-app behavioral prompts; gig workers more responsive to peer-behavior cues McKinsey Global Institute Digital Finance Reports Policy + Case Studies Global (inc. India, China, Africa) Digital financial services reduce friction but must be paired with trust-building interventions 3.3. Analytical Approach The analytical strategy employed a hybrid of policy mapping and behavioral thematic synthesis. First, tax compliance interventions were categorized using a logic model framework adapted from the OECD’s Tax Administration Behavioral Insights Toolkit [18]. This model categorizes interventions by intent (e.g., cognitive simplification, social norm activation, commitment devices), delivery medium (e.g., digital, mail, in-person), and behavioral outcome targeted (e.g., timely filing, full payment, accurate reporting). Second, a behavioral impact evaluation matrix was constructed to align interventions with their documented outcomes across three axes: behavior change, taxpayer feedback, and administrative scalability. For example, an SMS-based prepayment prompt that increased filing compliance by 18% was rated as high-impact but moderate-scalability due to digital access limitations [19]. Thematic synthesis was then applied to distill patterns across the interventions. Emergent themes included trust in government, procedural fairness, message timing, and personalization. NVivo software aided the coding process, although this study emphasizes interpretive over statistical significance. Codes were grouped into higher-order constructs that informed the conceptual framework presented in Section 2 (see Figure 1). Finally, findings were iteratively mapped against compliance behavior models (e.g., the Slippery Slope Framework and the Responsive Regulation Model), validating the behavioral mechanisms observed in empirical data [20]. This multilayered approach enables the generation of a context-rich, behaviorally informed framework for understanding and improving tax compliance among informal sector actors. World Journal of Advanced Research and Reviews, 2025, 26(03), 366–384 372 4. Behavioral interventions and evidence from practice 4.1. IRS Behavioral Pilot Programs Over the past decade, the U.S. Internal Revenue Service (IRS) has piloted a range of behaviorally informed interventions aimed at boosting voluntary tax compliance. These initiatives emerged from a growing recognition that traditional enforcement and penalty-based approaches were insufficient in influencing compliance behavior among self-employed and informal sector taxpayers. Behavioral pilot programs tested various psychological nudges, particularly framing techniques in tax notifications, appeals to civic duty, and the strategic use of reminders to encourage timely filing and payment. A seminal experiment modified standard IRS correspondence by incorporating moral suasion cues—reminding taxpayers that most Americans pay their taxes on time. This social norm framing was based on the premise that individuals are more likely to comply when they believe their peers are doing the same. In randomized trials, such letters increased on-time payment rates by up to 5 percentage points. Another variant of the pilot included framing late payment penalties in terms of loss aversion—emphasizing what the taxpayer stood to lose rather than gain— demonstrating measurable effects on compliance behavior. Critically, these interventions revealed substantial variation across demographics and income brackets. Low-income filers showed greater responsiveness to reminder-based nudges, whereas higher-income self-employed workers reacted more favorably to messages highlighting detection probability or moral imperatives. In one pilot, timely response increased by over 30% among gig workers receiving repeated, SMS-based filing reminders with simplified language and pre-calculated estimates. However, challenges emerged. Some taxpayers perceived behavioral nudges as manipulative or overly paternalistic, potentially eroding trust in the IRS. Moreover, while the short-term improvements were clear, long-term effects— especially across repeat filing cycles—remained underexplored. Nevertheless, these findings laid the groundwork for broader application of behavioral economics in tax compliance, especially when paired with digital delivery systems for cost-effective scaling. Figure 2 Sample design of IRS behaviorally framed tax letters World Journal of Advanced Research and Reviews, 2025, 26(03), 366–384 373 These pilots collectively demonstrated that nudging, when thoughtfully designed and appropriately segmented, can improve voluntary compliance while reducing administrative burdens. The IRS now continues to refine its behavioral toolkit as part of broader modernization efforts. 4.2. Digital Nudges and Platform Interventions With the rise of gig work and digital platforms, behavioral interventions have extended beyond traditional IRS channels to fintech ecosystems. Companies like PayPal, Etsy, Uber, and Venmo have partnered with third-party advisors and tax authorities to incorporate nudges directly into their platforms. These digital nudges—delivered via push notifications, dashboard alerts, or automated emails—aim to address timing gaps and information asymmetries in the tax compliance process. One such collaboration saw digital payment platforms prompt users with pre-tax season alerts, reminding them of the need to report earnings and offering automated expense logs. The interventions were especially targeted toward informal vendors and freelancers—groups traditionally outside formal compliance nets . A/B tests showed that users receiving early alerts were 25% more likely to visit IRS-related tax help portals or download tax documents before filing deadlines. Additionally, app-integrated nudges embedded behavioral design principles like salience and present bias mitigation. For instance, gig economy workers on task platforms received mobile prompts tied to earnings thresholds. When surpassing $400—a self-employment tax trigger—a customized prompt would notify them of their filing obligations and offer links to tax prep tools or accountant directories. Crucially, digital nudges also enabled real-time feedback loops. Platforms could track user interaction data, allowing dynamic optimization of message content and timing. This level of agility offered promise in scaling interventions in a cost-effective, privacy-preserving manner. Nonetheless, data-sharing agreements and regulatory oversight were essential to maintain user trust and comply with legal boundaries surrounding taxpayer information. Despite these benefits, several limitations persisted. Message fatigue, opt-out rates, and reduced engagement over time affected the efficacy of digital nudges. Moreover, there were concerns about equitable access to digitally delivered messages—particularly among non-English speakers or those without reliable internet access. As a whole, digital behavioral interventions offered scalable, adaptive tools to augment compliance in underregulated segments. Their integration into everyday platforms provided new touchpoints for behavioral influence that complemented traditional IRS efforts. 4.3. Comparative International Case Studies International tax administrations have likewise adopted behavioral economics to tackle compliance in informal and self-employed segments. These comparative experiences yield valuable insights into the translatability of behavioral nudges and the contextual constraints shaping their outcomes. In the UK, HM Revenue & Customs (HMRC) deployed a range of randomized control trials testing behavioral framings in tax letters and emails. One prominent example emphasized peer compliance norms: taxpayers were told that “nine out of ten people in your town pay their taxes on time.” This approach boosted compliance by 6 percentage points in some cohorts. Another message variant reminded taxpayers of public service linkages—connecting taxes to funding for hospitals and schools—which also proved effective, especially among middle-income self-employed workers. India’s implementation of Goods and Services Tax (GST) offered another relevant case. Under its GST framework, the Indian government employed voluntary disclosure prompts for small traders and service providers operating below the formal registration threshold. These prompts leveraged simplified language, pre-filled forms, and text-based moral suasion to encourage compliance. Behavioral pilots in Maharashtra and Tamil Nadu revealed that simplified compliance options combined with behavioral nudges increased first-time tax registration among micro-entrepreneurs by up to 18%. Other jurisdictions, such as Colombia and South Africa, have embedded behavioral interventions into digital tax portals. These include automated reminders, interactive filing guidance, and gamified penalty calculators. While results varied, key lessons emerged around message clarity, channel optimization, and cultural relevance. For example, appeals to national duty resonated more in countries with high civic trust, whereas fear-based messaging often backfired in regions with historical distrust of tax authorities. World Journal of Advanced Research and Reviews, 2025, 26(03), 366–384 374 These international examples suggest a few transferable practices. First, segmentation and tailoring of messages—by sector, geography, or behavior—consistently enhance efficacy. Second, digital delivery mechanisms must accommodate users’ literacy, language, and connectivity constraints. Third, transparency in messaging (e.g., who is sending it and why) builds trust and sustains engagement. Table 2 Comparison of International Behavioral Compliance Interventions by Sector Country Sector Intervention Type Behavioral Mechanism Reported Impact United Kingdom Small Businesses Personalized letters + gain-framing Social norms, loss aversion 5.1% increase in timely tax filings Kenya Informal Retail SMS nudges with local language framing Salience, community norms 10% increase in mobile tax declarations Chile Freelancers Pre-filled returns with income estimate Ease, mental accounting Reduced filing errors by 15% India Microenterprises In-app reminders in GST portal Timeliness, present bias 6% increase in on-time payments Australia Sole Traders Email prompts with peer benchmarks Descriptive norms, identity 8.2% compliance rise among target group Mexico Transport Sector Digital wallet tax tools + visuals Simplification, habit formation Increased savings and tax prep engagement South Africa Informal Services Radio ads + mobile help desks Familiarity, emotional resonance Increased tax inquiries by 12% U.S. tax policymakers can benefit by selectively adapting global behavioral insights while accounting for federalism, diversity, and platform governance constraints. The international landscape affirms the broader potential of behavioral economics in voluntary compliance—if designed with context sensitivity, ethical safeguards, and continuous evaluation mechanisms. 5. Understanding the informal sector in the U.S. 5.1. Who Are Informal Self-Employed Workers? Informal self-employed workers encompass a broad spectrum of economic agents operating outside the purview of formal labor market structures. This group includes gig economy participants such as ride-share drivers and food delivery personnel, sole proprietors of small-scale businesses, home-based service providers, and unregistered professionals like tutors, mechanics, and hairdressers. These workers typically engage in activities characterized by minimal or no regulatory oversight, unstable income flows, and limited access to formal financial and legal systems [19]. The socioeconomic profiles of informal self-employed individuals often reflect vulnerabilities and structural exclusion. Many possess limited educational backgrounds, lack employer-provided benefits, and operate without access to legal protections or retirement plans. Gender disparities are also notable, with women frequently overrepresented in lowerincome informal roles such as domestic work and food vending [20]. Moreover, ethnic minorities and immigrants are disproportionately involved in informal employment due to barriers to formal sector integration [21]. Compliance with tax regulations is typically low among informal self-employed workers, not necessarily due to intentional evasion but rather as a consequence of structural and psychological barriers. Limited financial literacy, perceived complexity of tax systems, and the absence of tailored outreach from tax authorities create an environment where compliance is either deprioritized or viewed as unattainable [22]. Many workers in this category operate in cashbased economies, reducing traceability and further entrenching informality [23]. Access to digital platforms has increased participation in the gig economy; however, this shift has not been accompanied by formalization. Digital intermediaries rarely classify workers as employees, absolving themselves of payroll World Journal of Advanced Research and Reviews, 2025, 26(03), 366–384 381 such systems lay the foundation for sustainable public finance ecosystems. Ultimately, when citizens perceive taxation not as a burden but as a gateway to opportunity and fairness, it enhances both the efficiency and legitimacy of government interventions. 8.4. Recommendations for Researchers and Policymakers For researchers, there is a pressing need to deepen the empirical evidence base around behavioral interventions in tax contexts, particularly in lowand middle-income countries. Future studies should adopt experimental methods, including randomized control trials (RCTs) and longitudinal surveys, to assess the long-term impacts of nudges on tax compliance across diverse informal sectors. Research should also explore cultural, gender, and regional differences in behavioral responsiveness to develop more tailored intervention strategies. Policymakers, on the other hand, must institutionalize behavioral insights within tax agencies. This involves establishing behavioral units, investing in upskilling staff, and building collaborative networks with fintech platforms and civil society organizations. All interventions must be governed by ethical standards that ensure transparency, consent, and data protection. Furthermore, digital tax platforms should be co-designed with users, particularly marginalized groups, to ensure accessibility. Governments should commit to iterative learning—evaluating what works, what doesn’t, and why—and use those insights to refine policies. Finally, integrating behavioral nudges with broader social protection mechanisms can enhance legitimacy, offering tangible incentives for compliance and signaling that taxation is part of a reciprocal social compact rather than a one-sided obligation. 8.5. Closing Thoughts on the Balance Between Enforcement and Engagement The challenge facing modern tax administrations is not simply to enforce compliance but to cultivate it. While legal frameworks and audit systems remain essential tools, an overreliance on coercive enforcement can alienate citizens, especially in informal sectors where mistrust of state authority is already high. Conversely, engagement strategies rooted in behavioral science offer a more sustainable and dignified pathway to revenue mobilization. The future of tax policy lies in balancing enforcement with empathetic engagement. This involves recognizing that many informal earners operate under constraints, not criminal intent, and that compliance is often a function of system design, not individual morality. By leveraging behavioral insights, tax systems can lower psychological barriers, foster trust, and build a culture of shared fiscal responsibility. Ultimately, the most effective tax systems are those that taxpayers feel they have a stake in—systems where compliance is not feared, but understood, facilitated, and valued. As governments face increasing fiscal pressures and rising public expectations, embedding behavioral economics into tax policy is not just innovative—it is imperative for building equitable, resilient, and citizen-centered public finance systems. References [1] Kleven Henrik Jacobsen. Bunching. Annual Review of Economics. 2016;8:435–464. https://doi.org/10.1146/annurev-economics-080315-015234 [2] Chetty Raj, Looney Adam, Kroft Kory. Salience and taxation: Theory and evidence. American Economic Review. 2009;99(4):1145–1177. https://doi.org/10.1257/aer.99.4.1145 [3] Slemrod Joel, Weber Caroline. Evidence of the invisible: Toward a credibility revolution in the empirical analysis of tax evasion and the informal economy. International Tax and Public Finance. 2012;19(1):25–53. https://doi.org/10.1007/s10797-011-9171-0 [4] Alm James, McClelland Gary H, Schulze William D. Why do people pay taxes? Journal of Public Economics. 1992;48(1):21–38. https://doi.org/10.1016/0047-2727(92)90040-M [5] World Bank. Doing Business 2020: Comparing Business Regulation in 190 Economies. Washington, DC: World Bank Group; 2020. https://www.doingbusiness.org/ [6] OECD. Tax Morale: What Drives People and Businesses to Pay Tax? OECD Tax Policy Studies. 2019;No. 29. https://doi.org/10.1787/f3d8ea10-en [7] International Labour Organization. Women and Men in the Informal Economy: A Statistical Picture. 3rd ed. Geneva: ILO; 2018. https://www.ilo.org/global/publications/books/WCMS_626831/lang--en/index.htm World Journal of Advanced Research and Reviews, 2025, 26(03), 366–384 382 [8] World Bank. The Economics of the Informal Sector: A Simple Model and Some Empirical Evidence from Latin America. World Bank Policy Research Working Paper 3850. 2006. https://openknowledge.worldbank.org/handle/10986/8827 [9] Lopez Rafael, Marchese Marco, Stojanovic Aleksandar. The future of work in the informal economy. ILO Employment Working Paper No. 249. 2019. https://www.ilo.org/wcmsp5/groups/public/--- ed_emp/documents/publication/wcms_734503.pdf [10] Adekoya Yetunde Francisca. Optimizing debt capital markets through quantitative risk models: enhancing financial stability and SME growth in the U.S. International Journal of Research Publication and Reviews. 2025 Apr;6(4):4858-74. Available from: https://ijrpr.com/uploads/V6ISSUE4/IJRPR42074.pdf [11] Sunstein Cass R, Reisch Lucia A. Automatically green: Behavioral economics and environmental protection. Harvard Environmental Law Review. 2014;38:127–158. https://harvardelr.com/wpcontent/uploads/sites/12/2014/04/Sunstein-Reisch.pdf [12] Thaler Richard H, Sunstein Cass R. Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven: Yale University Press; 2008. [13] KRA & World Bank Group. Behavioral Insights for Tax Compliance in Kenya: Pilot Evaluation Report. Nairobi: Kenya Revenue Authority; 2019. https://documents.worldbank.org/en/publication/documentsreports/documentdetail/369721585823355481/ [14] Enemosah A, Chukwunweike J. Next-Generation SCADA Architectures for Enhanced Field Automation and RealTime Remote Control in Oil and Gas Fields. Int J Comput Appl Technol Res. 2022;11(12):514–29. doi:10.7753/IJCATR1112.1018. [15] OECD. Behavioural Insights for Public Integrity: Harnessing the Human Factor to Counter Corruption. Paris: OECD Publishing; 2019. https://doi.org/10.1787/c22004b4-en [16] de Neve Jan-Emmanuel, Ward George, De Keulenaer Frederic, Van Praag Bernard, Ferrer-i-Carbonell Ada. Income and happiness: Evidence, explanations and economic implications. Journal of Economic Literature. 2018;56(2):478–539. https://doi.org/10.1257/jel.20160965 [17] Fowosere Sodiq, Esechie Courage Obofoni, Namboozo Sarah, Anwansedo Friday. The role of artificial intelligence in green supply chain management. International Journal of Latest Technology in Engineering Management & Applied Science. 2025;14(2):33. doi: 10.51583/ijltemas.2025.14020033 [18] Ekundayo F, Ikumapayi OJ. Leadership practices in overseeing data engineers developing compliant, highperformance REST APIs in regulated financial technology environments. Int J Comput Appl Technol Res. 2022;11(12):566–577. doi:10.7753/IJCATR1112.1021. [19] Dal Bó Ernesto, Finan Frederico, Rossi Martín A. Strengthening state capabilities: The role of financial incentives in the call to public service. Quarterly Journal of Economics. 2013;128(3):1169–1218. https://doi.org/10.1093/qje/qjt008 [20] Adekoya Yetunde Francisca. Optimizing debt capital markets through quantitative risk models: enhancing financial stability and SME growth in the U.S. International Journal of Research Publication and Reviews. 2025 Apr;6(4):4858-74. Available from: https://ijrpr.com/uploads/V6ISSUE4/IJRPR42074.pdf [21] Kahneman Daniel, Tversky Amos. Prospect theory: An analysis of decision under risk. Econometrica. 1979;47(2):263–292. https://doi.org/10.2307/1914185 [22] BIT (Behavioural Insights Team). Behavioural Insights and Tax: Evidence from Around the World. London: UK Cabinet Office; 2012. https://www.bi.team/publications/behavioural-insights-and-tax/ [23] Emmanuel Oluwagbade and Oluwole Raphael Odumbo. Building resilient healthcare distribution networks: Adapting to crises, securing supplies and improving scalability. Int J Sci Res Arch. 2025;14(01):1579–98. Available from: https://doi.org/10.30574/ijsra.2025.14.1.0265 [24] Narayan Ambar, Van der Weide Roy, Cojocaru Alexandru, Lakner Christoph, Redaelli Silvia, Mahler Daniel Gerszon, Ramasubbaiah Rakesh, Thewissen Stefan. Fair Progress? Economic Mobility across Generations around the World. Washington, DC: World Bank Group; 2018. https://openknowledge.worldbank.org/handle/10986/28428 [25] Brockmeyer Anne, Hernandez Manuel. Taxation, Information, and Withholding: Evidence from Costa Rica. World Bank Policy Research Working Paper No. 7600. 2016. https://doi.org/10.1596/1813-9450-7600 World Journal of Advanced Research and Reviews, 2025, 26(03), 366–384 383 [26] Ekundayo F. Strategies for managing data engineering teams to build scalable, secure REST APIs for real-time FinTech applications. Int J Eng Technol Res Manag. 2023 Aug;7(8):130. Available from: https://doi.org/10.5281/zenodo.15486520 [27] Muralidharan Karthik, Niehaus Paul, Sukhtankar Sandip. Building state capacity: Evidence from biometric smartcards in India. American Economic Review. 2016;106(10):2895–2929. https://doi.org/10.1257/aer.20141346 [28] Lolade Hamzat, Yetunde Adekoya, Andrew Ajao. INNOVATIONS IN EMERGING MARKET DEBT RISK MANAGEMENT: COMPLEMENTARY INSIGHTS FOR U.S. FINANCIAL RISK MODELING. International Journal of Engineering Technology Research & Management (IJETRM). 2025May19;08(01):167–86. [29] Ogundu PG. Decentralized housing finance models: Blockchain-based mortgage systems and crowdfunded real estate investment for affordability. International Research Journal of Modernization in Engineering, Technology and Science. 2025 Feb;7(2):1916. Available from: https://www.doi.org/10.56726/IRJMETS67513 [30] Adekoya Y, Oladimeji JA. The impact of capital structure on the profitability of financial institutions listed on the Nigerian Exchange Group. World Journal of Advanced Research and Reviews. 2023 Dec;20(3):2248–65. doi: 10.30574/wjarr.2023.20.3.2520. [31] Emmanuel Ochuko Ejedegba (2024) ‘INTEGRATED STRATEGIES FOR ENHANCING GLOBAL FOOD SECURITY AMID SHIFTING ENERGY TRANSITION CHALLENGES’, International Journal of Engineering Technology Research & Management (ijetrm), 08(12). doi: 10.5281/zenodo.14502251, [32] Adeniyi A, Adekoya Y, Namboozo S. Bridging the infrastructure gap: assessing the impact of critical infrastructure investments on economic growth in the United States and emerging markets. Int J Fundam Multidiscip Res. 2024 Sep–Oct;6(5). DOI: https://doi.org/10.36948/ijfmr.2024.v06i05.28772 [33] Ejedegba Emmanuel. Innovative solutions for food security and energy transition through sustainable fertilizer production techniques. World Journal of Advanced Research and Reviews. 2024 Dec;24(3):1679–1695. Available from: https://doi.org/10.30574/wjarr.2024.24.3.3877 [34] Ogunkoya TA. Smart hospital infrastructure: what nurse leaders must know about emerging tech trends. Int J Comput Appl Technol Res. 2024;13(12):54–71. doi:10.7753/IJCATR1312.1007. [35] Ekundayo Foluke, Adegoke Oladimeji, Fatoki Iyinoluwa Elizabeth. Machine learning for cross-functional product roadmapping in fintech using Agile and Six Sigma principles. International Journal of Engineering Technology Research & Management. 2022 Dec;6(12):63. Available from: https://doi.org/10.5281/zenodo.15589200 [36] Karlan Dean, McConnell Margaret, Mullainathan Sendhil, Zinman Jonathan. Getting to the top of mind: How reminders increase saving. Management Science. 2016;62(12):3393–3411. https://doi.org/10.1287/mnsc.2015.2296 [37] Adekoya YF. Optimizing debt capital markets through quantitative risk models: enhancing financial stability and SME growth in the U.S. Int J Res Publ Rev. 2025 Apr;6(4):4858–74. Available from: https://ijrpr.com/uploads/V6ISSUE4/IJRPR42074.pdf. [38] Osinaike T, Adekoya Y, Onyenagubo CV. A survey of AI-powered proactive threat-hunting techniques: challenges and future directions. Int J Fundam Multidiscip Res. 2024 Nov–Dec;6(6). DOI: https://doi.org/10.36948/ijfmr.2024.v06i06.29183. [39] Ikumapayi Olumide Johnson. The convergence of FinTech innovations, AI, and risk management: Transforming traditional banking, accounting, and financial services. International Research Journal of Modernization in Engineering Technology and Science. 2025 Apr;7(2). doi:10.56726/IRJMETS67253. [40] Duflo Esther, Hanna Rema, Ryan Stephen. Incentives work: Getting teachers to come to school. American Economic Review. 2012;102(4):1241–1278. https://doi.org/10.1257/aer.102.4.1241 [41] Emmanuel Agbeni, K., Akanni, O., Yetunde Francisca , A., Judith Gbadebo, A., Chioma Ejikeme , P., Alexander Nwuko, O., & Ezeokolie, C. (2025). The Government Expenditures, Economic Growth and Poverty Levels in Nigeria: A Disaggregated Approach . INTERNATIONAL JOURNAL OF ECONOMICS AND MANAGEMENT REVIEW, 3(1), 18–33. https://doi.org/10.58765/ijemr.v3i1.249 [42] Scholz John T, Lubell Mark. Adaptive political attitudes: Duty, trust, and fear as monitors of tax policy. American Journal of Political Science. 1998;42(3):903–920. https://doi.org/10.2307/2991734 World Journal of Advanced Research and Reviews, 2025, 26(03), 366–384 384 [43] Akobundu Uchenna Uzoma, Igboanugo Juliet C. Enhancing equitable access to essential medicines through integrated supply chain digitization and health outcomes-based resource allocation models: a systems-level public health approach. Int J Eng Technol Res Manag. 2021 Aug;5(08):159. Available from: https://doi.org/10.5281/zenodo.15593726 [44] Raymond Antwi Boakye, George Gyamfi, Cindy Osei Agyemang. DEVELOPING REAL-TIME SECURITY ANALYTICS FOR EHR LOGS USING INTELLIGENT BEHAVIORAL AND ACCESS PATTERN ANALYSIS. International Journal of Engineering Technology Research & Management (IJETRM). 2023Jan21;07(01):144–62. [45] Hallsworth Michael, List John A, Metcalfe Robert D, Vlaev Ivo. The behavioralist as tax collector: Using natural field experiments to enhance tax compliance. Journal of Public Economics. 2017;148:14–31. https://doi.org/10.1016/j.jpubeco.2017.02.003 [46] Ikumapayi Olumide Johnson, Ayankoya Bisola Beauty. AI-powered forensic accounting: Leveraging machine learning for real-time fraud detection and prevention. International Journal of Research Publication and Reviews. 2025 Feb;6(2):236–250. doi: https://doi.org/10.55248/gengpi.6.0225.0712. [47] Adekoya YF, Oladimeji JA. The impact of capital structure on the profitability of financial institutions listed on the Nigerian Exchange Group. World J Adv Res Rev. 2023;20(3):2248–65. DOI: https://doi.org/10.30574/wjarr.2023.20.3.2520. [48] Oladokun P, Adekoya Y, Osinaike T, Obika I. Leveraging AI algorithms to combat financial fraud in the United States healthcare sector. Int J Innov Sci Res Technol. 2024 Oct; DOI: 10.38124/ijisrt/IJISRT24SEP1089.