The Labor Credit Scoring Model for the Gig Economy in Developing Countries

The Labor Credit Scoring Model for the Gig Economy in Developing Countries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Labor Credit Scoring Model for the Gig Economy in Developing Countries Nattapong Anekadhana This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7198891/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Gig workers in developing countries are part of an informal labor system, which means unstable jobs and being far from accessible capital from the banking system. This study presents Labor Credit Scoring (LCS), a novel framework that turns worker reputation into a transferable asset across platforms. A combination of quantitative, qualitative, and alternative data that was never used in platforms before, like skilled certifications or behavior data, LCS opens the black box of rating in traditional platforms and enables credible assessments without formal records. The model contributes to lifelong learning and incentivizes upskilling, laying the groundwork for a Labor Credit Bureau (LCB) that connects labor histories with financial institutions and public services. A 12-month field experiment in Thailand involving 300 participants across three cities demonstrated LCS’s effectiveness in improving worker performance, income, and trustworthiness. The results suggest that LCS is scalable, adaptable, and fits diverse labor platforms in the Global South that are professionally licensed, weak, or volunteer. It offers a policy-relevant solution for building a skills-based labor market with a central standard across platforms that will turn informal labor back to formality at the end. reputation system gig economy Labor credit scoring Labor credit bureau Figures Figure 1 Figure 2 1. Introduction The gig economy, broadly defined as short-term or task-based employment mediated by digital platforms, has emerged as a vital source of income in many developing countries. Unlike traditional employment systems, gig work provides alternative access to income generation for marginalized groups, including women, youth, and individuals with limited technological or language proficiency. As stated in the World Bank’s report (World Bank, 2024), local online platforms in developing countries play a significant role in helping labor groups access more dynamic jobs. These local platforms reduce obstacles present on global platforms, such as the necessity of English communication and the unacceptability of local currencies for payments. Furthermore, these local platforms contribute to digital inclusion and may help achieve national public policy goals related to training or access to social security in the future. (World Bank, 2024) Despite offering flexible work opportunities, the gig economy’s informal and fragmented structure poses persistent challenges for workers. These include job insecurity, lack of access to benefits, and the absence of standardized mechanisms to fairly evaluate worker competence or credibility. Current reputation systems used by global platforms such as Uber, Grab, or Upwork remain platform-specific, non-transferable, and opaque in terms of how ratings are calculated. Moreover, these systems tend to prioritize short-term customer satisfaction over long-term workforce development. As a result, gig workers are left with limited incentives to invest in upskilling or to build verifiable records of their performance. Importantly, there remains a notable gap in research on how workers—particularly those in the informal sectors of developing countries—can develop portable and trusted reputational capital, or so-called “skill passports,” that facilitate mobility and inclusion across platforms and sectors. Although in the past, performance assessments have often chosen to use either quantitative or qualitative data, several studies have indicated that using both types of data together will provide more reliable, comprehensive, and flexible results, especially when using weighting methods or converting them into composite indexes to systematically reflect the potential of the workforce. (Silva & Ribeiro, 2021). This article therefore proposes the concept of the Hybrid Competency Index (HCI), an assessment framework that combines quantitative data such as work volume, income, and skill certifications with qualitative data such as consistency, work behavior, and customer feedback to reflect both the past performance and future growth potential of gig workers, which will be applied to both types of workers: technicians, who require higher work standards, and servicers, who focus more on customer satisfaction and consistency of service standards. The concept of a Labor Credit Bureau (LCB) is also proposed, which will apply the LCS approach, acting as a centralized data repository that allows workers to hold their creditworthiness data (Dynamic Skill Passport) and freely share it across platforms, employers, and financial institutions. To make the proposed approach relevant to real contexts in developing countries in different regions of the world, this study presents a comparative analysis of case studies in Southeast Asia (Thailand, Sri Lanka, Pakistan), Sub-Saharan Africa (Kenya, Nigeria), and Latin America (Brazil, Mexico), which differ in terms of technology adoption levels, labor laws, and digital platform structures. These comparative results lead to the design of an LCS that is flexible and adaptable to various contexts. This article is an important contribution to the advancement of knowledge on innovation design for labor systems by providing a practical and ethical framework to promote trust, mobility, and financial inclusion for gig workers in developing countries, thus laying the foundation for a more just and sustainable digital labor structure. 2. Literature Review 2.1 The Gig Economy in Developing Countries: Trust, Reputation, and Financial Inclusion The gig economy is rising up and gaining prominence across the globe. This model is characterized by short-term hiring and task-based work that is operated through digital platforms. Particularly in developing countries, this model became a significant alternative employment trend. It would be beneficial for groups that were never included in formal labor markets before, neither youth, women, nor individuals living in remote or underserved areas (Datta et al., 2023). Local and regional platforms have become essential intermediaries in this transformation, offering flexible job access without the barriers typical of traditional employment. This expansion, however, is not without its challenges. While digital gig platforms create opportunities, they also perpetuate issues of job precarity, lack of social protections, and the absence of formal work documentation (Banik & Padalkar, 2021). These problems are further exacerbated by structural factors such as high unemployment, the need for supplementary income, and the growing availability of gig-based platforms across emerging markets. One of the most critical factors in platform-based labor systems is the trust and reputation model. That star ratings, customer reviews, or performance metrics are typically worked. In theory, even systems are intended to reduce uncertainty and support trust between clients and workers.However, as Wood et al. (2019) emphasize, reputation systems on gig platforms in developing countries often suffer from serious limitations. Rating delays make new workers suffer from prior inexperience, and biases in client feedback distort true performance assessments. Moreover, many platforms exhibit uniformly high average scores, which undermines the utility of ratings as discriminating indicators of worker quality.Rather than promoting equity, these flawed systems may reinforce exclusion by making it harder for capable workers—especially newcomers—to gain visibility and compete fairly. This issue is even more highlighted in low-infrastructure regions, such as parts of Sub-Saharan Africa, where scarcity means they have limited internet access, low English literacy, and expensive devices. All are barriers to either platform entry or reputation-building. These challenges extend beyond hiring to the financial ecosystem that surrounds informal labor. Although skilled and productive, many gig workers remain financially struggling. As Sutherland et al. (2019) inform, the inconsistency of gig income and no evidence of formal employment records mean that even capable workers are often perceived as below-rated borrowers. Traditional credit rating systems, which rely on documents such as tax returns or bank statement records, The system did not include freelancers or small entrepreneurs. The system did not include freelancers or small entrepreneurs. Therefore, these individuals face high barriers to accessing loans, insurance, or any financial products at the end. Even workers are low-risk in practice (Hlongwane et al., 2024). To address these structural blind spots, researchers have proposed the integration of alternative data—drawn directly from platform behaviors—to assess creditworthiness. Indicators such as work acceptance rates, frequency of transactions, and earnings consistency offer a more accurate and dynamic view of a worker's reliability and financial stability. Hlongwane et al. (2024) argue that this shift toward behavior-based scoring not only improves fairness, but also greatly enhances access to financial inclusion for the informal workforce. These data-driven approaches lay the foundation for new labor credit scoring mechanisms that are more responsive to real-world working patterns. Especially within the gig economy of developing countries. Meanwhile, concepts of the Labor Credit Bureau (LCB) were inspired by traditional financial credit bureaus that succeeded in financial market trustability by reducing asymmetric data and supporting responsible financial behavior through incentives that let borrowers access better loan conditions (Hlongwane et al. 2024). As developing countries' official credit statement records are limited, LCB can take an important role in declaring transparency in the system for labor history data (Chen et al., 2023). In the same way, LCB establishments in developing countries can collect LCS of labor data to make transparent the working efficiency for employers and financial institutions. Such an approach will foster greater trust and provide more convenience for making better decisions. However, Volpone et al. (2015) criticize traditional credit bureaus for their selective treatment of specific population groups. Therefore, LCB systems have to be designed for labor participants based on their data and use multidimensional consistency and real experience rather than financial conditions alone. 2.2 Key Factors in Designing Credit Scoring Models for Gig Workers As credit rating methodologies developed, there has been an increasing focus on combining both quantitative indicators and qualitative criteria. Either managerial quality, account conduct, or sector-specific characteristics, etc., serve to better reflect borrower risk (Roy & Shaw, 2023). Researchers have specifically recognized the value of this dual approach for assessing worker performance among gig workers in developing countries. Quantitative dimensions may include indicators such as total jobs completed, overall platform income, or evidence of training and professional certifications. Meanwhile, qualitative factors include punctuality, customer feedback, trustworthiness, and service consistency. Those offer a more faceted perspective on worker behavior and potential. However, these factors do not operate in isolation. Many gig workers engage in multi-homing strategies, working across several platforms simultaneously to optimize job access and income stability. These activities are often mediated by platform algorithms that monitor and evaluate worker behavior in real time, frequently through opaque and unexplainable logic (Chimhutu, Munoriyarwa, & Rudman, 2023). The diversity of work styles, job types, and algorithmic interactions calls for credit scoring systems that can adapt to this complexity—rather than imposing rigid, one-size-fits-all models. Thus, the assessment criteria between a capable technician might be significantly different with a general service provider. Different scoring models that reflect these occupational and contextual differences are needed. A compelling example of this diversity is offered by Koene and Pichault (2020), who conducted a study of platform workers in China and proposed a typology of three primary worker groups, each with distinct motivations and relationships with platforms. The first group, Embedded Fixers, are workers who work closely and tie with specific clients or communities. This group often possesses specialized skills such as plumbing, tutoring, or childcare and relies on repeat engagements. Therefore, long-term trust, responsibility, and continuity are crucial. In contrast, pragmatic experimenters adopt a flexible and adaptive approach, working across multiple platforms and various jobs. Their success depends on short-term behavioral patterns, either frequency, variety of gigs, or responsiveness to dynamic conditions. Finally, Dedicated Activists represent a small but critical cohort of workers committed to changing the structural inequalities of platform work itself. They may not always score highly on conventional performance assessments, but their advocacy is supportive of the long-term sustainability and fairness of the gig economy. Together, these profiles underscore the need for labor assessment systems that are both context-aware and worker-specific. Credit scoring models must be designed with sufficient flexibility to accommodate diverse forms of labor, motivations, and strategies—rather than relying solely on transactional metrics or static behavioral assumptions. A fair and meaningful system must account for who the worker is, how they operate, and why they work the way they do. 2.3 Research Gaps and Novel Contributions Despite much research in platform reputation systems, the current operations remain mistaken. This is true in the context of developing countries. Möhlmann et al. (2023) have shown that the entire platform rating works as "black boxes," which show little transparency in how scores are manipulated and no channel for appealing their ratings. The lack of transparency creates an environment where workers struggle to understand the reasons behind their ratings, resulting in frustration, inequality, and systemic inefficiency. This issue has shown a significant problem in the gig economies of developing countries, where millions of workers rely on platforms for their primary income. In these facts, the lack of a comprehensive, fair, and understandable credit or rating system becomes more than just a technical oversight. That becomes a structural barrier to equity. While many studies acknowledge the role of reputation systems in regulating digital labor, there remains a striking absence of a unified framework that supports trust-building, feedback loops, and policy-level enforceability. These limitations create significant challenges. Although they are capable and have a desire to work, low scores keep them away from jobs. At the same time, financial inclusion, an increasingly important role of development policy, remains elusive for many informal or gig workers due to the absence of credible, portable reputation data (Lukac & Grow, 2021). Current financial systems depend on any formal employment records, tax returns, or loan history, leaving the workforce overlooked and underserved. To address these gaps, this paper proposes a Labor Credit Scoring (LCS) framework specifically designed for the gig economy in developing countries. Unlike conventional models, LCS combines both quantitative and behavioral indicators to form a multidimensional assessment of worker credibility. It is built to be worker-centric, transparent, and portable, allowing for cross-platform compatibility and long-term value generation. In doing so, LCS represents not merely a new technical tool but a foundational redesign of how informal labor is recognized, validated, and empowered in a digital economy. 2.4 Comparative Landscape of Reputation Systems In the current digital labour landscape, multiple types of technologies and methods are applied to rating and reputation systems. The main purpose is to build trust between clients and workers and provide quality service control as well. However, these systems differ significantly in terms of transparency, portability, data sources, and fairness. To systematically evaluate in empirical terms the strengths and weaknesses of these models—particularly in the context of gig economies in developing countries—this section presents a comparative review of four major types of reputation systems currently in use or in research development. These include: (1) Platform-Bound Reputation Systems (PBRS) – used in platforms like Uber and Upwork, etc. (2) AI-Based Rating Systems – such as those used by Amazon Flex, (3) Blockchain-Based Reputation Systems – are conceptual papers by BFCRI and WorkerRep, and (4) Alternative Credit Scoring Systems (ACSS) – such as Tala and Branch. Each system is evaluated based on its key strengths, limitations, and how the proposed Labour Credibility System (LCS) addresses specific gaps that will show in the comparison table as last. 1. PBRS , used by platforms like Uber and Upwork, rely on reviews and Linkert ratings to build a worker’s credibility at their postwork. These systems are user-friendly and help customers make quick decisions, but their credit was built to be non-portable, and their rating scores were also untransferable between platforms. This rating system is often subject to inflation and bias (Fu et al., 2023). It is widely used in developing countries where gig platforms are dominant. There is no appeal system in place to ensure the fairness of workers. 2. AI-based rating systems , such as those used in Amazon Flex, use AI-generated reputation scores from quantitative behavioural data only. Either delivery time or acceptance rate, etc. Though efficient, these models are opaque and provide no explanation or appeal mechanism when automated decisions affect livelihoods. Such systems are increasingly adopted in developing countries such as India but pose risks of “algorithmic injustice” when workers face unpredictable problems in working, such as traffic jams, rain, accidents, etc. Meanwhile, improvements are difficult because the logic behind their scores is in a black box (Fu et al., 2023).3. Blockchain-Based Reputation Systems 3. Blockchain-based reputation systems , exemplified by BFCRI and B-Ride prototypes, use smart contracts to manage reputation securely and transparently. These complex systems are tamper-resistant and can potentially be portable across platforms due to decentralised technology. Meanwhile, a complicated system will make it limit use. Furthermore, their works are only at the conceptual papers and simulation only. So these works have never been commercialised for real-world deployment on a large scale yet (Fu et al., 2023; Baza et al., 2021). 4. ACSS platforms like Tala and Branch use mobile data—SMS logs, GPS, and social media app usage—to assess creditworthiness for microfinance for informal workers mainly. Even systems support data for financial consideration to informal labor but lack transparency and often operate without regulatory oversight. Despite security and privacy being concerns, they are widely used in Africa, South Asia, and Latin America, where traditional credit bureaus are weak or absent (Baza et al., 2021). The following four tables present a comparative analysis between the proposed Labor Credibility System (LCS) and these four distinct types of rating and reputation systems commonly found in digital labor platforms. Regards outlines of their key strengths and limitations. The tables illustrate how LCS addresses the gaps in each system. 3. Conceptual Framework The conceptual framework proposed in this research treats Labor Credit Scoring (LCS) as a multi-dimensional index that combines indicators of a worker’s competence, reliability, and trustworthiness, the same as how financial credit scoring combines various data to assess creditworthiness. In this model, the LCS is calculated by using a weighted sum of three sub-scores, extending prior two-factor models to include an alternative data dimension. The formula is defined as LCS=wQ×ScoreQ+wR×ScoreR+wAD×ScoreAD In this formula, ScoreQ represents the quantitative component, ScoreR denotes the qualitative (reputation) component, ScoreAD signifies the alternative data component, and the sum of wQ, wR, and wAD equals 1. The weighting parameters (wQ, wR, wAD) are adjustable to fit different professional contexts or stakeholder concerns. Some such jobs might put more weight on reputation over competence or might oppose it. This adaptable scoring method is similar to current trends in credit analytics, which use alternative data to assess people who don't have formal records as they should (Alliance for Financial Inclusion [AFI], 2025).Importantly, research from developing countries shows that unusual factors like a gig worker's reputation on a platform and their earnings record can be good indicators of their credit risk or trustworthiness. Likewise, favorable online ratings have been shown to increase real-world financial credibility (e.g., higher customer ratings correlating with greater business debt capacity) (Derrien, Garel, Romec, & Weisskopf, 2024). 3.1 Components of the LCS Model The LCS model is composed of three components: a quantitative score (Score Q), a qualitative reputation score (Score R), and an alternative data score (Score AD). Each component consists of multiple indicators that reflect different angles of a worker’s competence or credibility. The final LCS uses these components in the different weights described above. As below, we detail each component and its combined factors: Quantitative Component (Score Q): This component reflects measurable outcomes and tangible qualifications, captured through variables such as Job quantity and volume: e.g., the number of completed jobs, total working hours, or service transactions over a given period. High volumes of successfully finished tasks indicate experience and reliability in sustaining work (Porter et al., 2020). Income level and consistency: the total earnings accrued in the period and their consistency (for instance, steady income without prolonged gaps). A stable income stream suggests sustained employability and commitment. Experience: the length of employment or service (years of work or number of service cycles), repeat hire rates, and diversity of projects or clients. Greater experience and repeat engagements imply trust gained from employers and versatility in skills. Certifications/Skills: such as relevant licenses, professional certifications, or accredited training course certificates. Meanwhile, formal credentials may be difficult in some informal labors; any verifiable skill endorsement (e.g., a trade certificate or training completion) can support the quantitative score by evidencing proven competencies. Job completion rate: result of assigned jobs that are successfully completed. A high completion rate signals dependability and efficiency in honoring commitments. These quantitative metrics are objective indicators of productivity and capability. Many online labor platforms track similar metrics (e.g., number of gigs done, earnings, and completion percentages) as key performance indicators for workers (Ovezmyradov, 2022). Qualitative Component (Score R): This reputation-based component collects subjective and behavioral data relevant to the worker’s working reliability. that consist of: Customer's ratings and reviews: Each score and review received showed customer satisfaction with the worker’s efficiency, quality of service, politeness, and professionalism. Therefore, high ratings and positive reviews received represent the quality of their capability. Timeliness and accountability: punctuality, commitment to deadlines, and job cancellation or no-show rates. These indicate a strong sense of responsibility. Conversely, frequent delays or cancellations would negatively affect this score. Complaint and dispute recorded: Any customer complaints, reported incidents, or disputes related to the worker services. A history free of these serious complaints will be a higher score, while negative incidents will lower the reputation score to reflect their behavior and concerns. Together, these qualitative components reflect a picture of the worker’s reputation among customers and within the community. In that gig platform and informal work system, a solid reputation is crucial (Benavides et al., 2022). A worker with excellent reviews and demonstrated reliability is likely to attract new job opportunities and repeat orders, whereas a poor reputation leads them to less business. Alternative Data Component (Score AD): This component describes non-traditional data about a worker beyond direct job performance or customer satisfaction. These alternative data have the purpose of capturing dimensions of trust and stability that traditional metrics might miss, especially for workers with a lack of formal working documents. Examples of such data, followed by: Financial and payment history will indicate a worker’s personal financial behaviors, including the timely payment of utility bills, rent, mobile phone subscriptions, and more. These demonstrate worker financial responsibility and reliability outside the workplace and point to how the worker manages commitments reliably. (World Bank, 2024, pp. 7-9). Digital footprint and platform data: Data is generated from the worker’s activities on any digital platforms beyond the workplace. Data sources cover the worker's mobile wallet or digital payment usage patterns, transaction records from other gig platforms or online marketplaces, and also ride-sharing and delivery logs. These data will reflect a broader view of the worker’s economic activity and reliability. For instance, a driver’s or courier’s earnings and completion rating on another platform, or a seller’s history on an e-commerce platform, can be affected by their LCS profile as evidence of consistent engagement and income generation across platforms. (World Bank, 2024, p. 28) Social media and network recognition: these represent a worker’s body and behavior on social or professional networks, such as recognition on LinkedIn or influence on Facebook's community. Positive matters such as customer admiration can be used as a trustworthy reference, while unprofessional digital footprints might be a red flag. However, using social media data must be done carefully, although online can show the worker’s dimension in a broader context, such as personal attitude, etc. (Alliance for Financial Inclusion [AFI], 2025). Psychometric and behavioral assessments include the results from any psychology tests or personality assessments that the worker may have taken, which evaluate traits such as honesty, diligence, risk tolerance, and interpersonal skills. Studies in the financial sector found that psychology test results, such as integrity assessments, help improve credit scoring and consideration in cases of borrowers with weak evidence, particularly those engaged in informal remote labor (Alliance for Financial Inclusion [AFI], 2025). Collecting alternative data in LCS recognizes those workers, especially in informal sectors, leave many digital footprints, which help assess their credibility. By using these additional data sources, the LCS model approach provides a more integrated and fair assessment of a worker. Particularly valuable is information such as integrity for those who may lack lengthy work histories or formal credentials, such as poor women or youth laboring in remote areas of sub-Saharan Africa, etc. Alternative data use for scoring has been shown to broaden financial inclusion—for example, incorporating telco, utility, or e-commerce records can significantly improve the risk prediction for individuals with no traditional credit files (World Bank, 2024, pp. 8–9). In the same spirit, an LCS enriched with alternative data offers a more robust picture of a worker’s trustworthiness beyond what standard on-platform metrics alone can capture. Each of these parts (quantitative, qualitative, and alternative) works together to create a complete system for evaluating workers, much like how different factors are used in credit scoring systems. Each of these components—quantitative, qualitative, and alternative—supports each other. These form a framework for scoring labor systems in a manner similar to multi-dimensional credit scoring systems such as credit bureaus. Importantly, the LCS model is designed with customizable weighting, allowing the relative importance of each component to be tailored according to occupational context, policy priorities, or empirical insights from field testing. For instance, an initial implementation might assign equal weight (w) to quantitative (Q) and qualitative (R or Reputation) scores (e.g., wQ=0.5, wR=0.5, wAD=0, w_Q = 0.5, w_R = 0.5, w_AD = 0, wQ=0.5, wR=0.5, wAD=0). However, in technician roles that skill verification is critical for, such as electrician or plumber, etc., greater emphasis may be placed on the quantitative component, especially certifications and job completion indicators. In practice, for general service workers such as cleaners or delivery personnel, behavioral consistency and platform reliability (reflected in the reputation or alternative data scores) are needed. So these data may have higher weights. The ability to fine-tune the model enhances its flexibility and contextual sensitivity so that the LCS can adapt more effectively to the needs of diverse labor markets while maintaining core standards of fairness and rigor. 3.2 Governance Principles and System Architecture of the LCS Framework In addition to its scoring methodology, the LCS framework also works on principles that promote transparency, fairness, and accountability in how scores are calculated and maintained. These principles include real-time updates, in which a worker’s score reflects their most recent activity by continuously retrieving live data from the platform. Transparency is another core critical; workers must have full access to their scoring records and a clear understanding of which behaviors influence their score. Furthermore, the system ensures protection of personal information: sensitive data such as chat histories with clients are either anonymized or aggregated to protect worker privacy and uphold data rights. The system architecture of the LCS framework is composed of three working layers. The data layer captures and aggregates quantitative and behavioral inputs from platform activities and verified records. Next, the Scoring Algorithm Layer processes this data using the formula outlined earlier to compute the LCS score, dynamically weighted according to policy or job-specific configurations. Finally, the Policy Layer manages how the logic works on the system, such as defining rules around weighting schemes, screening data sources, transparency criteria, and procedures for dispute resolution or score appeals. Together, these design elements ensure that the LCS functions not only as a technical scoring model but also as a worker-centric governance system —balancing algorithmic precision with ethical and contextual accountability. 4. Proposed Model of LCS This section explains the concept design of the labor credit scoring (LCS) model, which combines many attributes into a real-time score that reflects a gig worker’s performance, credibility across platforms, and behavioral engagement. The model is built to be flexible and scalable, particularly in developing country contexts where formal credentials may be scarce and worker performance is often invisible to third parties. The LCS model consists of three components: quantitative (or Score Q), reputational (or Score R), and behavioral data (or Score AD). These are combined together into a total score. This was transformed into a weighted formula: LCS = wQ × ScoreQ + wR × ScoreR + wAD × ScoreAD Each component captures a different dimension of the worker’s profile, with the overall score designed to provide more portability and fairness than traditional star-rating systems. The Quantitative Score (ScoreQ) reflects tangible, measurable work outcomes derived directly from transactional data. It includes the number of jobs completed over a specified period (or job volume), the consistency of income over time (e.g., in weekly or monthly earnings), and the job completion rate. Each indicator is normalized to a 0–100 scale and combined using a weighted sum. Therefore, the formula will be updated to ScoreQ = 0.5 × (Job volume) + 0.3 × (Income stability) + 0.2 × (Completion rate) This component representsthe worker’s performance and reliability.By focusing on outputs that are both measurable and trackable, ScoreQ is the basis of the LCS through objective indicators. The Reputation Score (ScoreR) evaluates how the worker is perceived by clients and the platform community. It includes variables such as the average customer satisfaction rating (e.g., converted from a 1–5 star system to a 0–100 score), punctuality or on-time arrival rates, and the worker’s complaint history. A high number of timely and well-rated completions boosts the score, while repeated complaints or dispute cases result in deductions. Positive recognitions—such as platform badges for high performance or consistency—are also factored in. A representative computation might be ScoreR = 0.5 × (Customer satisfaction) + 0.3 × (Punctuality) + 0.2 × (Complaint and award index) This score captures professionalism and interpersonal qualities that are often invisible in transaction data but crucial to client trust and satisfaction. The third and increasingly critical component is the Alternative Data Score (ScoreAD) . This score incorporates behavioral and contextual data that serve as proxy indicators of worker engagement and reliability. either frequency of app usage, how quick they are in job acceptance, cancellation behavior, etc. Importantly, certifications and training completions —especially those issued by the platform itself—are included in this category, as they signal self-directed learning and motivation even in the absence of formal licensing. For instance, a worker who consistently logs in daily, promptly responds to job offers, completes optional training modules, and passes in-app skill assessments would earn a high ScoreAD. In contrast, a worker who has less cancellation discipline, a low engagement streak, or long periods of no work would receive a lower score. An example formula might be ScoreAD = 0.35 × (Responsiveness) + 0.25 × (Training and certification completion) + 0.2 × (Engagement streak) + 0.2 × (Cancellation discipline) All behavioral signals are collected automatically through the system backend and translated into structured variables using rule-based algorithms. These indicators enrich the LCS by accounting for factors not visible in ratings or job logs, which is especially useful in early-stage user profiling or for workers who operate in informal settings. All three components—ScoreQ, ScoreR, and ScoreAD—are processed through a centralized credit engine governed by the Labour Credit Bureau (LCB) , as depicted in Figure 1 . The LCB collects data via APIs from various sources, either platforms, clients, or external institutions such as banks or mobile operators, and applies scoring policies that are set by job category and context. The system works with real-time updates and feedback and also allows dispute resolution. In sum, the LCS model provides a reliable evaluation tool across platforms for gig workers. By mixing work performance, client-based reputational data, and behavioral data together, it allows platforms and partner institutions such as banks to recognize and reach them for capital. even when formal qualifications are lacking. This structure is not only trustworthy for both worker and client but also encourages continuous skill development through incentive alignment. 4.2 Integrated LCS Formula, Worker Segmentation, and Calibration Logic The Labor Credit Scoring (LCS) system consolidates multiple dimensions of worker performance into a unified formula that is adaptable by worker type and job context. At its conceptual level, the system evaluates a worker’s credibility using the following weighted formula: LCS = α × Score Q + β × Score R + γ × Score AD , where α + β + γ = 1 Score Q represents the quantitative dimension. Objective indicators such as job volume, income stability, and certifications are used. Score R represents qualitative feedback. Simply speaking, reputation from clients is either based on behavioral ratings or dispute history. Score AD , also called alternative data , will capture behavioral, contextual, and self-reported indicators that extend beyond traditional platform ratings. These three components ensure real-time integration and fair assessment, especially for gig workers in developing countries where formal records may be difficult to obtain. Therefore, while a technician’s job volume boosts Score Q, their consistent client ratings impact Score R, and their responsiveness or app engagement is recorded to Score AD in automated real time. By explaining the weight of each component, this concept creates new transparency and a fair basis. Workers can understand how each behavior or factor impacts their score; this solves the "black box" that is a big problem in platform ratings currently (Möhlmann et al., 2023). However, by the nature of the work and the country's context, α, β, and γ can be adjusted. For example: For technicians (e.g., plumbers, electricians), who require specialized competencies, α = 0.5 , β = 0.3 , and γ = 0.2 For servicers (e.g., cleaners, delivery workers), who rely heavily on client interaction, α = 0.3 , β = 0.4 , and γ = 0.3 In practice, if a worker completes an online training module, Score AD will increase instantly. Likewise, if a negative customer review is filed, Score R will be adjusted downward in real time as well. By integrating diverse data sources—including self-declared skills, app usage behavior, and verified digital certifications—LCS reduces bias from the client by capturing data from diverse sources of gig work and weights them differently. This framework makes the system more trustworthy, aligning with fairer algorithmic management (Hlongwane et al., 2024; Alliance for Financial Inclusion, 2025). While the scoring formula provides a flexible foundation, its effectiveness depends on how its customization fits the real-world dynamics of each worker type and country's context. Therefore, to make more meaningful applications We found that recognizing the diverse nature of gig work, the LCS system defines platform workers into two main groups. Technicians and servicers will manipulate its scoring differently. As we found, each group emphasizes different combinations of quantitative metrics (Score Q), reputation (Score R), and alternative data (Score AD) based on the job’s nature and customer expectations mainly. Technicians Technicians are skilled or semi-skilled workers—such as electricians, plumbers, and mechanics—whose work tends to technical execution and is often related to safety and compliance standards. Given the structured nature of their work, clients typically expect predictable, verifiable outcomes. Score Q : will focus on objective work outcomes, such as number of jobs completed, job completion rate, and consistency of income. These reflect a technician's ability to deliver results at scale and with reliability mainly. Score R : Reflects behavioral qualities—such as punctuality, communication, and professionalism—drawn from client reviews and platform interactions. While important, reputation tends to be more consistent for technical jobs because of clear expectations and job descriptions. Score AD : This metric is a higher priority for technicians. caused verified credentials to be involved, such as vocational training certificates, professional licenses like electrician, etc. Furthermore, the platform's microlearning records or engagement in upskilling modules also contribute here. So, these indicators use both long-term competence data and formal validation to make more reliability. Example weight: α = 0.4 (Q), β = 0.2 (R), γ = 0.4 (AD) Servicers Servicers include general gig workers such as cleaners, delivery drivers, or movers. These jobs are more customer-facing and relationship-based, requiring less formal training but a higher basis of professionalism and relying more on client satisfaction. Score Q : Focuses on metrics like job frequency, job consistency (e.g., working days per week), and income stability. These reflect availability and discipline more than technical complexity. Score R : Plays a central role in evaluating servicers. Customer ratings, repeat requests, and qualitative reviews (e.g., courtesy, friendliness, cleanliness) are often the main basis for hiring decisions in this group. Score AD : Includes participation in non-certified training (e.g., online hospitality or customer service modules), behavioral consistency indicators (e.g., low cancellation rate), and platform engagement rate (e.g., daily logins or prompt job acceptance). Even if formal licensing may not apply, these alternative data still validate trust and commitment. Example weight: α = 0.3 (Q), β = 0.5 (R), γ = 0.2 (AD) However, no one size fits all. So defining score components only for each worker type is insufficient to reflect actual capabilities. To ensure that the assigned weights (α, β, γ) reflect actual labor conditions, systematic calibration is necessary. Therefore, the initial weightings for α, β, and γ may follow the logic described above; ensuring validity and fairness requires systematic calibration. Meanwhile, literature suggests three viable approaches (Gompf et al., 2021; Li et al., 2021; Zhang et al., 2023): Stakeholder Surveys : Collecting input from workers, clients, and platforms via tools such as Likert scales or the Analytic Hierarchy Process (AHP) to assign importance scores to each factor. Empirical Analytics : Retrospective analysis of platform data—examining correlations between performance factors and tangible outcomes like income, retention, or customer rehire rates. Contextual Adaptation : Customizing weights by job type, platform culture, or region (e.g., giving more weight to alternative data in areas with poor certification infrastructure). A/B testing or prototype validation can then fine-tune this logic. This weight-based adjustment helps reduce unintentional bias in algorithms and promotes a fairer labor platform, aligning with the principles of involving both sides, either customer or worker, in AI development (Zhang et al., 2023). 4.3 Operational Models and Score Computation There are two options for operationalizing the LCS framework across a platform: 1. Segment-Specific Models Each worker category (technician vs. servicer) has its own formula, customized with distinct weights and sub-indicators. This model aligns with Möhlmann et al. (2023), who argue that each labor group requires its own sense-making process for how platform logic works. 2. Unified Model with Type Adjustment A single LCS formula is maintained across the platform, but worker type is maintained as a variable that influences the weight vector (α, β, γ). This streamlines computation but still respects role-specific nuances. In either case, the system must avoid cross-domain bias: Technicians shouldn’t be rated based on delivery speed. Servicers shouldn’t be penalized for lack of technical licenses. To illustrate, Table 5 presents two LCS calculation examples: Worker Type Work Volume Certifications Client Rating LCS Formula Final Score Technician 50 jobs Electrician License 4.5 / 5 (0.4 × 70) + (0.3 × 90) + (0.3 × 90) 82 / 100 Servicer 200 jobs Online Training 4.8 / 5 (0.3 × 90) + (0.2 × 80) + (0.5 × 96) 88 / 100 This approach ensures contextual accuracy while maintaining a shared LCS framework across the ecosystem.Whether using a unified formula or separate ones by category, the effectiveness of the LCS model depends on how and what inputs are picked and weighted. The following section presents our scoring logic in detail, including how key indicators are normalized and how weight distributions vary by worker type. That will make the Labour Credit Score (LCS) both interpretable and actionable; the system defines how raw indicators such as work volume, certifications, and customer reviews are converted into normalized scores and combined with predefined weights to generate a total score. Component Weighting by Worker Type The system assigns specific weights to each component—Score Q (quantitative), Score R (reputation), and Score AD (alternative data)—tailored to each worker profile: Worker Type Score Q (α) Score R (β) Score AD (γ) Technician 0.4 0.2 0.4 Servicer 0.3 0.5 0.2 This configuration reflects the realities of each job category: Technicians rely more on formal competencies and verified qualifications (AD), whereas Servicers depend heavily on ongoing client feedback and behavior (R). Score Conversion Guidelines Each raw input is translated to a normalized score on a 0–100 scale based on set criteria: Work Volume (Q) Technician: 50 jobs = 70 points Servicer: 200 jobs = 90 points Client Rating (R) 5 stars = 100 points 4.5 stars = 90 points 4.8 stars = 96 points Alternative Data (AD) Electrician License = 90 points Customer Service Training = 80 points Verified completion of microlearning module = 60–75 points High responsiveness rate = up to 90 points Engagement streak (e.g., working >20 days/month) = 85+ These conversions are periodically reviewed based on platform-wide distributions and A/B tested for impact on hiring behavior. Example Calculation Worker Type Work Volume Certification (AD) Rating LCS Calculation LCS Score Technician 70 (Q) 90 (AD) 90 (R) (0.4×70) + (0.2×90) + (0.4×90) = 82 82/100 Servicer 90 (Q) 80 (AD) 96 (R) (0.3×90) + (0.5×96) + (0.2×80) = 88.2 88/100 Practical Implications The framework is designed to support multi-skill workers . For example, one worker does perform both plumbing and food delivery. In such cases, the system assigns distinct LCS profiles per skill category . This separation makes a worker’s credibility in one domain (e.g., certified plumbing) not influence their other unrelated domains (e.g., parcel delivery). This approach also mitigates algorithmic control risks. As studied by Möhlmann et al. (2023), many digital platforms deploy “one-size-fits-all” systems that avoid worker diversity and create unfair conditions. Meanwhile, the LCS model enables a multi-central scoring system grounded in the actual structure of each work, credentials, and behavior patterns specific to each job type. 5. Pilot Implementation and Preliminary Findings: The KRIB Use Case 5.1 Pilot Objectives and Context The Skill-Based Hiring Enforcement System (SHES) is a platform-level mechanism designed to enforce skill development, align incentives, and enhance labor quality in gig economies. This system consists of three main engines: First is the Skill Engine, for basic upskilling by micro-learning; the Rate Engine is second, which assesses worker performance; the last one is the Visibility Engine, which ranks workers on the hiring system based on verified score level. At the heart of SHES’s Rate Engine lies the Labor Credibility Scoring (LCS) model, which assigns a composite credibility score to each worker based on work history (Q-score), customer ratings (R-score), and alternative data (AD-score) such as certifications. This scoring mechanism adjusts weighting by job type—ensuring performance is assessed in a context-sensitive way. This pilot aimed to evaluate how well the SHES–LCS system performs when implemented within an actual job-matching platform (KRIB). Unlike prior LCS framework discussions that support reputation metrics across platforms. Meanwhile, this pilot focused on practical performance within a single-platform context. In particular, it aimed to validate whether an occupation-weighted scoring logic could operate effectively within a real-world platform—improving trust, reducing complaints, and enhancing worker outcomes. That we provide a visual explanation of how the SHES system and LCS interoperate with the KRIB hiring platform as below. To test this, we designed a 12-month comparative field experiment involving 300 participants across three cities in Thailand: Bangkok, Chiang Mai, and Nakhon Ratchasima. Participants were divided evenly between two platforms: KRIB, which implemented the SHES–LCS system (experimental group), and TPQI-net, a conventional public-sector job platform from the Thailand Professional Qualification Institute (Public Organization) that runs without any structured scoring or incentive mechanisms (control group). Each city hosted 50 participants per platform, yielding 150 experimental and 150 control participants. These workers were drawn from two occupational groups: technicians (e.g., electricians, plumbers) and servicers (e.g., cleaners, helpers). All participants started with zero ratings and underwent onboarding procedures appropriate to their assigned platform. 5.2 Evaluation Design and Methodology Participants in the KRIB group were onboarded through a structured SHES workflow, including identity verification, skill declaration, and optional submission of credentials. They received status as "new" at an initial LCS scoring and then continuously gained scores throughout the pilot. Workers on TPQI-net registered through a basic process with no scoring, skill verification, or incentives. The LCS scores on KRIB consisted of three weighted components: Q-score (quantitative work metrics), R-score (client reviews), and AD-score (alternative data). Weightings were adjusted by role: Technicians had a weighting of 60% Q, 25% R, and 15% AD, while Servicers used a 35% Q, 55% R, and 10% AD formula. Performance was measured through skill assessments, income, job volume, customer ratings, and complaint frequency, complemented by qualitative interviews. 5.3. Key Findings Workers in the SHES–LCS system showed strong improvement across all metrics. Skill assessments rose by +10.1 points on average (from 63.9 to 74.0), compared with only a +0.9 increase in the control group. Job ratings averaged 4.8 out of 5 (vs. 4.6 in the control group), and complaints dropped to 2.5%—less than half of the 5.8% observed in TPQI-net. Our tracking reported trust increasing, with confidence scores rising from 5.0 to 9.0 in the experimental group. Furthermore, KRIB workers earned ฿6,987 per month on average—a 34% increase from baseline—and significantly more than TPQI-net participants, who reached only ฿6,050. These workers also completed more jobs per week, driven by more visibility in the hiring system (KRIB) linked to their LCS scores gained. Meanwhile, retention rates exceeded 90% in KRIB, pointing to labor's higher satisfaction and good engagement. Qualitative interviews revealed that participants valued the fairness of the system and the opportunity to develop their reputation through transparent performance tracking. Some expressed a desire for clearer feedback on score calculation, suggesting that explainability will be key in future refinements. 5.4 Lessons, Limitations, and Next Steps The pilot generated several important lessons. First, the modular architecture of SHES–LCS shows high adaptability along the pilot period and in three major cities of Thailand with a 90% retention rate. It was embedded into KRIB without disrupting core functionality, confirming that gig platforms can scale up without overhauls. Workers responded positively to the SHES + LCS system. Either performance-based visibility or wage tiering incentivized engagement, and maintaining the LCS score reinforced upskilling as a closed loop as planned. Occupation-specific weighting within the LCS algorithm also worked as intended, keeping fairness and relevance across any job types on the platform. Transparency has become a main concern. Even though workers look for their scores, several voices show worry about how those scores were derived.This points to a need for more visible scoring logic or educational content in future iterations. Furthermore, the study confirmed that trustworthy raised income also can be achieved even within a single-platform ecosystem. That said, the pilot had its limitations. The LCS score was not tested in a multi-platform context; therefore, its portability and working across platforms are unproven. Nor was the system linked to any financial services, such as credit scoring or insurance underwriting—an area of clear potential that remains unexplored. The sample was limited to the home service sector only; this raises questions about whether similar gains would occur in logistics, transport, or digital freelance work. Additionally, the AD-score component had limited impact due to the scarcity of formal credentials among gig workers. Finally, while 12 months is efficient for midterm results, it remains insufficient for career-long outcomes. Future work will thus focus on scaling the model horizontally and vertically. A multi-platform rollout will be necessary to evaluate whether Skill Passports can be carried and recognized across ecosystems. Longitudinal studies should follow, tracking gig workers over multiple years to determine how credibility scores influence skill development, career mobility, and economic resilience. Especially partnerships with financial institutions could unlock further value, allowing verified scores as famous metrics to be used for access to capital. On the technical side, algorithm refinements must ensure fairness, interpretability, and resilience to gaming. Finally, pilot implementations in adjacent sectors—such as transport, warehousing, and digital micro-tasks—will test the universality of the SHES–LCS approach. Ultimately, this pilot demonstrated that SHES–LCS can elevate worker performance, enhance trust, and reduce transactional friction in gig economies. With proper expansion and policy alignment, the system offers a blueprint for establishing a national labor reputation infrastructure that is equitable, skill-based, and responsive to real-world performance. 6. Discussion and Implications The pilot implementation of the Labor Credit Scoring (LCS) system, embedded within the SHES architecture, approaches automated digital feedback loop mechanisms. which, combined with motivational theories, can fill structural gaps of upskilling in the gig economy. As the findings from the KRIB platform indicate, the LCS model demonstrates not only technical feasibility but also strong practical and behavioral impacts. that we found worker performance elevated, customer trust increased significantly, and income more consistent than previously fragmented labor platforms. At the core of this success lies LCS’s ability to transform abstract concepts like “trust” and “reliability” into quantifiable, portable metrics. By integrating quantitative (Q-score), reputational (R-score), and alternative data (AD-score) components, the system offered an integrated worker score that was updated in real time depending on job category. The LCS model goes beyond traditional rating systems such as star ratings on Uber or Grab (in Southeast Asia). by filling the gaps that traditional platforms have with new qualities that have been overlooked, such as portability, upskill certification, payment consistency, or even psychometric data. Simply speaking, LCS established a central credible foundation for worker validation, which is valuable in regions where formal documentation or certifications remain rare. Particularly the role of alternative data brought to the involved both the technical and ethical sides. In developing countries where many gig workers are in informal labor systems, their economic identity is invisible from formal labor systems. Therefore, behavioral data used by LCS, such as app engagement, upskill certificates, and digital payment patterns, etc., enabled LCS to retrieve previously unrecognized qualities of workers. This more insightful view aligns with the growing global interest in using non-traditional data for financial inclusion (World Bank, 2024; AFI, 2025) and has become a key factor in bringing informal labor back to formal systems. However, this approach also demands strict attention to data privacy, informed consent, and algorithmic fairness to prevent unintended exclusion or exploitation. The system’s transparency and real-time feedback design partially addressed these concerns, but future iterations must further empower users with score explanations, opt-out controls, and mechanisms to contest unfair evaluations. From a policy standpoint, the study’s findings point toward the emergence of a Labor Credit Bureau (LCB)—an institutional body analogous to financial credit bureaus but operating within the labor market. The LCB could serve as a central repository of worker reputation data, aggregating performance metrics across platforms and enabling portability of skills and credibility. For governments, especially in developing nations, this represents a leap forward in labor market formalization, where workers previously overlooked by financial institutions or public policy can now be evaluated, supported, and included based on verifiable, systematized data. A well-structured LCB would reduce information asymmetries, allow for better job-matching services, and act as a gateway for gig workers to access government subsidies, reskilling programs, or microloans based on performance—rather than pedigree. The implications of LCS also extend to platform design and business operations. By incentivizing workers to complete verified training and maintain high behavioral standards, platforms can reduce the cost of quality control and increase user retention. More importantly, platforms that integrate such scoring logic gain reputational legitimacy and can differentiate themselves in competitive labor markets. In regions such as ASEAN, Africa, and Latin America, LCS systems could be adapted to local infrastructures—for example, by integrating with M-Pesa in Kenya or national training registries in Brazil—demonstrating LCS's scalability across diverse economic ecosystems. Nonetheless, critical design challenges remain. First, the algorithm must avoid penalizing newcomers who lack job history. Initial scores should be adaptive and allow for rapid growth through effort-based achievements. Second, the risk of score manipulation or fake reviews must be mitigated through verified data inputs and continuous audit mechanisms. Third, the ethical use of alternative data—especially personal, financial, and behavioral traces—must be guided by human rights principles and contextual regulation. Looking forward, the systemic potential of LCS is transformative. If embedded within public-private ecosystems like SHES and linked to financial institutions, social welfare agencies, or employer networks, LCS could act as both a labor market signal and a developmental tool. Workers who maintain high scores could gain faster access to jobs, higher pay, or even loan eligibility—shifting the gig economy from an exploitative model to a system of upward mobility. Finally, future research should expand the LCS implementation across multiple platforms, explore longitudinal effects on career mobility and skill accumulation, and examine the interplay between scoring systems and worker motivation under varied cultural conditions. This is particularly relevant for designing skill-based welfare programs , where government benefits, tax credits, or social protections are triggered by verified labor performance rather than static employment status. In sum, the LCS model offers more than a technical fix for fragmented reputation systems—it presents a new institutional layer in the governance of digital labor. If coupled with ethical safeguards and policy-level alignment, it can lay the foundation for a national labor infrastructure that is inclusive, trustworthy, and oriented toward continuous development. 7. Conclusion This study delivers a novel and empirically tested framework for Labor Credit Scoring (LCS) tailored to the gig economy in developing countries. A system combining quantitative, qualitative, and alternative data together, LCS transforms fragmented platform rating systems and Blackbox into portable and transparent scoring that empowers informal workers to build, maintain, and carry their earned reputation across platforms. That also brings them back to the formal labor system. Through a year-long field experiment across three major cities in Thailand—Bangkok, Chiang Mai, and Nakhon Ratchasima—the LCS model was embedded within the SHES enforcement system on a real platform (KRIB) and proved effective. Workers using LCS showed significant improvements in verified skill levels (+10.1 points), monthly income (+34.4%), customer trust (from 5.0 to 9.0/10), and retention rates (>90%). All show both behavioral qualities and labor revenue uplift. Critically, LCS introduced a "reputation-as-asset" paradigm, enabling workers to retain their reputation data beyond a single platform, thus fostering labor mobility without resetting credibility. This shift aligns incentives: maintaining a high score becomes essential not only for current income but also for future employability across platforms—a vital step in promoting quality labor in fragmented digital labor markets. The system’s modular and data-driven architecture ensures adaptability to diverse occupational groups (e.g., technicians vs. servicers), while the pilot confirms its scalability and cultural fit across urban zones in developing economies. In addition, by establishing a foundational infrastructure for a Labor Credit Bureau (LCB), this model paves the way for cross-sectoral applications—linking verified labor performance with financial access, public reskilling programs, and broader social inclusion. In sum, the LCS framework contributes not just a technical tool but a policy-relevant institutional innovation. If extended and governed ethically, it offers a practical route toward building a trustworthy, skills-based, and inclusive labor market infrastructure—fit for the future of work in the Global South. Declarations Ethics Approval Statement: This study was approved by the Human Ethics Committee of Thammasat University (Faculty of Medicine), Thailand. 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Washington, D.C.: World Bank. https://openknowledge.worldbank.org/handle/10986/40066 Zhang, A., Boltz, A., Lynn, J., Wang, C., & Lee, M. K. (2023). Stakeholder-centered AI design: Co-designing worker tools with gig workers through data probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1–19). ACM. https://doi.org/10.1145/3544548.3581354 Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7198891","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489920577,"identity":"0330090a-2d2e-48a3-adb3-e164ec93df49","order_by":0,"name":"Nattapong Anekadhana","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYHACZgYGtgMQ5geEEJFaGGdARBibidbCzEOMFv7ZzYcNfpTdkTdnP8D22ObPYQb+9gPsjwvwaJG4cyw5sefcM8OdPQnsxrlthxkkziQwNs/AZ82NHOMDvG2HGTccSGCTzm04DBQBOowHjw75G/mfD/5tO2y/4fwDNmkLoMPkCWkxuJHDnAy0JXHDDaAtDGyHgSIEtBjeSDM2ljl3OHnDjQdskr1t6TyGZxIbZ+PTIncj+bHkm7LDthvOJ7BJ/PhjLSd3/PCBz/i0IAF+cNwDFTM2EKdhFIyCUTAKRgFOAAAJOE+ZEaazUAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0004-1858-5331","institution":"silpakorn university","correspondingAuthor":true,"prefix":"","firstName":"Nattapong","middleName":"","lastName":"Anekadhana","suffix":""}],"badges":[],"createdAt":"2025-07-23 17:45:40","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7198891/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7198891/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87895703,"identity":"9eba08db-c4d0-4a3b-8b24-a6f4d18bf765","added_by":"auto","created_at":"2025-07-30 07:30:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":30169,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eshows the conceptual model of the three-component Labor Credit Scoring (LCS) framework.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7198891/v1/fcb1fb1aa33a39848f95b5b4.jpg"},{"id":87895702,"identity":"5ddd60c9-905f-465e-901e-2093ba9710f3","added_by":"auto","created_at":"2025-07-30 07:30:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72367,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLCB Ecosystem — illustrating how gig workers' data are collected, processed, and governed by the Labor Credit Bureau (LCB) to generate context-specific credit scores (LCS), which are then shared with multiple labor and financial institutions\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7198891/v1/0495c5c91248d10a57da2169.jpg"},{"id":87898644,"identity":"9daf4749-bd52-498c-baf3-24e17ea8a746","added_by":"auto","created_at":"2025-07-30 07:54:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1678554,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7198891/v1/a27a471a-1713-4ddc-95a1-2b20a60057a7.pdf"},{"id":87895707,"identity":"7791548e-cf9d-4659-bbce-cc989109ea0e","added_by":"auto","created_at":"2025-07-30 07:30:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":657202,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7198891/v1/08c611d3796b55b7a88f639a.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eThe Labor Credit Scoring Model for the Gig Economy in Developing Countries\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe gig economy, broadly defined as short-term or task-based employment mediated by digital platforms, has emerged as a vital source of income in many developing countries. Unlike traditional employment systems, gig work provides alternative access to income generation for marginalized groups, including women, youth, and individuals with limited technological or language proficiency. As stated in the World Bank\u0026rsquo;s report (World Bank, 2024), local online platforms in developing countries play a significant role in helping labor groups access more dynamic jobs. These local platforms reduce obstacles present on global platforms, such as the necessity of English communication and the unacceptability of local currencies for payments. Furthermore, these local platforms contribute to digital inclusion and may help achieve national public policy goals related to training or access to social security in the future. (World Bank, 2024)\u003c/p\u003e\u003cp\u003eDespite offering flexible work opportunities, the gig economy\u0026rsquo;s informal and fragmented structure poses persistent challenges for workers. These include job insecurity, lack of access to benefits, and the absence of standardized mechanisms to fairly evaluate worker competence or credibility. Current reputation systems used by global platforms such as Uber, Grab, or Upwork remain platform-specific, non-transferable, and opaque in terms of how ratings are calculated. Moreover, these systems tend to prioritize short-term customer satisfaction over long-term workforce development. As a result, gig workers are left with limited incentives to invest in upskilling or to build verifiable records of their performance. Importantly, there remains a notable gap in research on how workers\u0026mdash;particularly those in the informal sectors of developing countries\u0026mdash;can develop portable and trusted reputational capital, or so-called \u0026ldquo;skill passports,\u0026rdquo; that facilitate mobility and inclusion across platforms and sectors.\u003c/p\u003e\u003cp\u003eAlthough in the past, performance assessments have often chosen to use either quantitative or qualitative data, several studies have indicated that using both types of data together will provide more reliable, comprehensive, and flexible results, especially when using weighting methods or converting them into composite indexes to systematically reflect the potential of the workforce. (Silva \u0026amp; Ribeiro, 2021). This article therefore proposes the concept of the Hybrid Competency Index (HCI), an assessment framework that combines quantitative data such as work volume, income, and skill certifications with qualitative data such as consistency, work behavior, and customer feedback to reflect both the past performance and future growth potential of gig workers, which will be applied to both types of workers: technicians, who require higher work standards, and servicers, who focus more on customer satisfaction and consistency of service standards.\u003c/p\u003e\u003cp\u003eThe concept of a Labor Credit Bureau (LCB) is also proposed, which will apply the LCS approach, acting as a centralized data repository that allows workers to hold their creditworthiness data (Dynamic Skill Passport) and freely share it across platforms, employers, and financial institutions.\u003c/p\u003e\u003cp\u003eTo make the proposed approach relevant to real contexts in developing countries in different regions of the world, this study presents a comparative analysis of case studies in Southeast Asia (Thailand, Sri Lanka, Pakistan), Sub-Saharan Africa (Kenya, Nigeria), and Latin America (Brazil, Mexico), which differ in terms of technology adoption levels, labor laws, and digital platform structures. These comparative results lead to the design of an LCS that is flexible and adaptable to various contexts.\u003c/p\u003e\u003cp\u003eThis article is an important contribution to the advancement of knowledge on innovation design for labor systems by providing a practical and ethical framework to promote trust, mobility, and financial inclusion for gig workers in developing countries, thus laying the foundation for a more just and sustainable digital labor structure.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 The Gig Economy in Developing Countries: Trust, Reputation, and Financial Inclusion\u003c/h2\u003e\n \u003cp\u003eThe gig economy is rising up and gaining prominence across the globe. This model is characterized by short-term hiring and task-based work that is operated through digital platforms. Particularly in developing countries, this model became a significant alternative employment trend. It would be beneficial for groups that were never included in formal labor markets before, neither youth, women, nor individuals living in remote or underserved areas (Datta et al., 2023). Local and regional platforms have become essential intermediaries in this transformation, offering flexible job access without the barriers typical of traditional employment. This expansion, however, is not without its challenges. While digital gig platforms create opportunities, they also perpetuate issues of job precarity, lack of social protections, and the absence of formal work documentation (Banik \u0026amp; Padalkar, 2021). These problems are further exacerbated by structural factors such as high unemployment, the need for supplementary income, and the growing availability of gig-based platforms across emerging markets.\u003c/p\u003e\n \u003cp\u003eOne of the most critical factors in platform-based labor systems is the trust and reputation model. That star ratings, customer reviews, or performance metrics are typically worked. In theory, even systems are intended to reduce uncertainty and support trust between clients and workers.However, as Wood et al. (2019) emphasize, reputation systems on gig platforms in developing countries often suffer from serious limitations. Rating delays make new workers suffer from prior inexperience, and biases in client feedback distort true performance assessments. Moreover, many platforms exhibit uniformly high average scores, which undermines the utility of ratings as discriminating indicators of worker quality.Rather than promoting equity, these flawed systems may reinforce exclusion by making it harder for capable workers\u0026mdash;especially newcomers\u0026mdash;to gain visibility and compete fairly. This issue is even more highlighted in low-infrastructure regions, such as parts of Sub-Saharan Africa, where scarcity means they have limited internet access, low English literacy, and expensive devices. All are barriers to either platform entry or reputation-building.\u003c/p\u003e\n \u003cp\u003eThese challenges extend beyond hiring to the financial ecosystem that surrounds informal labor. Although skilled and productive, many gig workers remain financially struggling. As Sutherland et al. (2019) inform, the inconsistency of gig income and no evidence of formal employment records mean that even capable workers are often perceived as below-rated borrowers. Traditional credit rating systems, which rely on documents such as tax returns or bank statement records, The system did not include freelancers or small entrepreneurs. The system did not include freelancers or small entrepreneurs. Therefore, these individuals face high barriers to accessing loans, insurance, or any financial products at the end. Even workers are low-risk in practice (Hlongwane et al., 2024).\u003c/p\u003e\n \u003cp\u003eTo address these structural blind spots, researchers have proposed the integration of alternative data\u0026mdash;drawn directly from platform behaviors\u0026mdash;to assess creditworthiness. Indicators such as work acceptance rates, frequency of transactions, and earnings consistency offer a more accurate and dynamic view of a worker\u0026apos;s reliability and financial stability. Hlongwane et al. (2024) argue that this shift toward behavior-based scoring not only improves fairness, but also greatly enhances access to financial inclusion for the informal workforce. These data-driven approaches lay the foundation for new labor credit scoring mechanisms that are more responsive to real-world working patterns. Especially within the gig economy of developing countries.\u003c/p\u003e\n \u003cp\u003eMeanwhile, concepts of the Labor Credit Bureau (LCB) were inspired by traditional financial credit bureaus that succeeded in financial market trustability by reducing asymmetric data and supporting responsible financial behavior through incentives that let borrowers access better loan conditions (Hlongwane et al. 2024). As developing countries\u0026apos; official credit statement records are limited, LCB can take an important role in declaring transparency in the system for labor history data (Chen et al., 2023). In the same way, LCB establishments in developing countries can collect LCS of labor data to make transparent the working efficiency for employers and financial institutions. Such an approach will foster greater trust and provide more convenience for making better decisions.\u003c/p\u003e\n \u003cp\u003eHowever, Volpone et al. (2015) criticize traditional credit bureaus for their selective treatment of specific population groups. Therefore, LCB systems have to be designed for labor participants based on their data and use multidimensional consistency and real experience rather than financial conditions alone.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Key Factors in Designing Credit Scoring Models for Gig Workers\u003c/h2\u003e\n \u003cp\u003eAs credit rating methodologies developed, there has been an increasing focus on combining both quantitative indicators and qualitative criteria. Either managerial quality, account conduct, or sector-specific characteristics, etc., serve to better reflect borrower risk (Roy \u0026amp; Shaw, 2023). Researchers have specifically recognized the value of this dual approach for assessing worker performance among gig workers in developing countries. Quantitative dimensions may include indicators such as total jobs completed, overall platform income, or evidence of training and professional certifications. Meanwhile, qualitative factors include punctuality, customer feedback, trustworthiness, and service consistency. Those offer a more faceted perspective on worker behavior and potential.\u003c/p\u003e\n \u003cp\u003eHowever, these factors do not operate in isolation. Many gig workers engage in multi-homing strategies, working across several platforms simultaneously to optimize job access and income stability. These activities are often mediated by platform algorithms that monitor and evaluate worker behavior in real time, frequently through opaque and unexplainable logic (Chimhutu, Munoriyarwa, \u0026amp; Rudman, 2023). The diversity of work styles, job types, and algorithmic interactions calls for credit scoring systems that can adapt to this complexity\u0026mdash;rather than imposing rigid, one-size-fits-all models. Thus, the assessment criteria between a capable technician might be significantly different with a general service provider. Different scoring models that reflect these occupational and contextual differences are needed.\u003c/p\u003e\n \u003cp\u003eA compelling example of this diversity is offered by Koene and Pichault (2020), who conducted a study of platform workers in China and proposed a typology of three primary worker groups, each with distinct motivations and relationships with platforms. The first group, Embedded Fixers, are workers who work closely and tie with specific clients or communities. This group often possesses specialized skills such as plumbing, tutoring, or childcare and relies on repeat engagements. Therefore, long-term trust, responsibility, and continuity are crucial. In contrast, pragmatic experimenters adopt a flexible and adaptive approach, working across multiple platforms and various jobs. Their success depends on short-term behavioral patterns, either frequency, variety of gigs, or responsiveness to dynamic conditions. Finally, Dedicated Activists represent a small but critical cohort of workers committed to changing the structural inequalities of platform work itself. They may not always score highly on conventional performance assessments, but their advocacy is supportive of the long-term sustainability and fairness of the gig economy.\u003c/p\u003e\n \u003cp\u003eTogether, these profiles underscore the need for labor assessment systems that are both context-aware and worker-specific. Credit scoring models must be designed with sufficient flexibility to accommodate diverse forms of labor, motivations, and strategies\u0026mdash;rather than relying solely on transactional metrics or static behavioral assumptions. A fair and meaningful system must account for who the worker is, how they operate, and why they work the way they do.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Research Gaps and Novel Contributions\u003c/h2\u003e\n \u003cp\u003eDespite much research in platform reputation systems, the current operations remain mistaken. This is true in the context of developing countries. M\u0026ouml;hlmann et al. (2023) have shown that the entire platform rating works as \u0026quot;black boxes,\u0026quot; which show little transparency in how scores are manipulated and no channel for appealing their ratings. The lack of transparency creates an environment where workers struggle to understand the reasons behind their ratings, resulting in frustration, inequality, and systemic inefficiency.\u003c/p\u003e\n \u003cp\u003eThis issue has shown a significant problem in the gig economies of developing countries, where millions of workers rely on platforms for their primary income. In these facts, the lack of a comprehensive, fair, and understandable credit or rating system becomes more than just a technical oversight. That becomes a structural barrier to equity. While many studies acknowledge the role of reputation systems in regulating digital labor, there remains a striking absence of a unified framework that supports trust-building, feedback loops, and policy-level enforceability.\u003c/p\u003e\n \u003cp\u003eThese limitations create significant challenges. Although they are capable and have a desire to work, low scores keep them away from jobs. At the same time, financial inclusion, an increasingly important role of development policy, remains elusive for many informal or gig workers due to the absence of credible, portable reputation data (Lukac \u0026amp; Grow, 2021). Current financial systems depend on any formal employment records, tax returns, or loan history, leaving the workforce overlooked and underserved.\u003c/p\u003e\n \u003cp\u003eTo address these gaps, this paper proposes a Labor Credit Scoring (LCS) framework specifically designed for the gig economy in developing countries. Unlike conventional models, LCS combines both quantitative and behavioral indicators to form a multidimensional assessment of worker credibility. It is built to be worker-centric, transparent, and portable, allowing for cross-platform compatibility and long-term value generation. In doing so, LCS represents not merely a new technical tool but a foundational redesign of how informal labor is recognized, validated, and empowered in a digital economy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Comparative Landscape of Reputation Systems\u003c/h2\u003e\n \u003cp\u003eIn the current digital labour landscape, multiple types of technologies and methods are applied to rating and reputation systems. The main purpose is to build trust between clients and workers and provide quality service control as well. However, these systems differ significantly in terms of transparency, portability, data sources, and fairness. To systematically evaluate in empirical terms the strengths and weaknesses of these models\u0026mdash;particularly in the context of gig economies in developing countries\u0026mdash;this section presents a comparative review of four major types of reputation systems currently in use or in research development.\u003c/p\u003e\n \u003cp\u003eThese include:\u003c/p\u003e\n \u003cp\u003e(1) Platform-Bound Reputation Systems (PBRS) \u0026ndash; used in platforms like Uber and Upwork, etc.\u003c/p\u003e\n \u003cp\u003e(2) AI-Based Rating Systems \u0026ndash; such as those used by Amazon Flex,\u003c/p\u003e\n \u003cp\u003e(3) Blockchain-Based Reputation Systems \u0026ndash; are conceptual papers by BFCRI and WorkerRep, and\u003c/p\u003e\n \u003cp\u003e(4) Alternative Credit Scoring Systems (ACSS) \u0026ndash; such as Tala and Branch.\u003c/p\u003e\n \u003cp\u003eEach system is evaluated based on its key strengths, limitations, and how the proposed Labour Credibility System (LCS) addresses specific gaps that will show in the comparison table as last.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1. PBRS\u003c/strong\u003e, used by platforms like Uber and Upwork, rely on reviews and Linkert ratings to build a worker\u0026rsquo;s credibility at their postwork. These systems are user-friendly and help customers make quick decisions, but their credit was built to be non-portable, and their rating scores were also untransferable between platforms.\u003c/p\u003e\n \u003cp\u003eThis rating system is often subject to inflation and bias (Fu et al., 2023). It is widely used in developing countries where gig platforms are dominant. There is no appeal system in place to ensure the fairness of workers.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2. AI-based rating systems\u003c/strong\u003e, such as those used in Amazon Flex, use AI-generated reputation scores from quantitative behavioural data only. Either delivery time or acceptance rate, etc. Though efficient, these models are opaque and provide no explanation or appeal mechanism when automated decisions affect livelihoods.\u003c/p\u003e\n \u003cp\u003eSuch systems are increasingly adopted in developing countries such as India but pose risks of \u0026ldquo;algorithmic injustice\u0026rdquo; when workers face unpredictable problems in working, such as traffic jams, rain, accidents, etc. Meanwhile, improvements are difficult because the logic behind their scores is in a black box (Fu et al., 2023).3. Blockchain-Based Reputation Systems\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3. Blockchain-based reputation systems\u003c/strong\u003e, exemplified by BFCRI and B-Ride prototypes, use smart contracts to manage reputation securely and transparently. These complex systems are tamper-resistant and can potentially be portable across platforms due to decentralised technology.\u003c/p\u003e\n \u003cp\u003eMeanwhile, a complicated system will make it limit use. Furthermore, their works are only at the conceptual papers and simulation only. So these works have never been commercialised for real-world deployment on a large scale yet (Fu et al., 2023; Baza et al., 2021).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4. ACSS platforms\u003c/strong\u003e like Tala and Branch use mobile data\u0026mdash;SMS logs, GPS, and social media app usage\u0026mdash;to assess creditworthiness for microfinance for informal workers mainly.\u003c/p\u003e\n \u003cp\u003eEven systems support data for financial consideration to informal labor but lack transparency and often operate without regulatory oversight. Despite security and privacy being concerns, they are widely used in Africa, South Asia, and Latin America, where traditional credit bureaus are weak or absent (Baza et al., 2021).\u003c/p\u003e\n \u003cp\u003eThe following four tables present a comparative analysis between the proposed Labor Credibility System (LCS) and these four distinct types of rating and reputation systems commonly found in digital labor platforms. Regards outlines of their key strengths and limitations. The tables illustrate how LCS addresses the gaps in each system.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Conceptual Framework","content":"\u003cp\u003eThe conceptual framework proposed in this research treats Labor Credit Scoring (LCS) as a multi-dimensional index that combines indicators of a worker\u0026rsquo;s competence, reliability, and trustworthiness, the same as how financial credit scoring combines various data to assess creditworthiness. In this model, the LCS is calculated by using a weighted sum of three sub-scores, extending prior two-factor models to include an alternative data dimension. The formula is defined as\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003cem\u003eLCS=wQ\u0026times;ScoreQ+wR\u0026times;ScoreR+wAD\u0026times;ScoreAD\u003cbr\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u0026nbsp;In this formula, ScoreQ represents the quantitative component, ScoreR denotes the qualitative (reputation) component, ScoreAD signifies the alternative data component, and the sum of wQ, wR, and wAD equals 1.\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003cbr\u003eThe weighting parameters (wQ, wR, wAD) are adjustable to fit different professional contexts or stakeholder concerns. Some such jobs might put more weight on reputation over competence or might oppose it. This adaptable scoring method is similar to current trends in credit analytics, which use alternative data to assess people who don\u0026apos;t have formal records as they should (Alliance for Financial Inclusion [AFI], 2025).Importantly, research from developing countries shows that unusual factors like a gig worker\u0026apos;s reputation on a platform and their earnings record can be good indicators of their credit risk or trustworthiness. Likewise, favorable online ratings have been shown to increase real-world financial credibility (e.g., higher customer ratings correlating with greater business debt capacity) (Derrien, Garel, Romec, \u0026amp; Weisskopf, 2024).\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Components of the LCS Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe LCS model is composed of three components: a quantitative score (Score Q), a qualitative reputation score (Score R), and an alternative data score (Score AD).\u003c/p\u003e\n\u003cp\u003eEach component consists of multiple indicators that reflect different angles of a worker\u0026rsquo;s competence or credibility. The final LCS uses these components in the different weights described above. As below, we detail each component and its combined factors:\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003cstrong\u003eQuantitative Component (Score Q):\u003c/strong\u003e This component reflects measurable outcomes and tangible qualifications, captured through variables such as\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eJob quantity and volume: e.g., the number of completed jobs, total working hours, or service transactions over a given period. High volumes of successfully finished tasks indicate experience and reliability in sustaining work (Porter et al., 2020).\u003c/li\u003e\n \u003cli\u003eIncome level and consistency: the total earnings accrued in the period and their consistency (for instance, steady income without prolonged gaps). A stable income stream suggests sustained employability and commitment.\u003c/li\u003e\n \u003cli\u003eExperience: the length of employment or service (years of work or number of service cycles), repeat hire rates, and diversity of projects or clients. Greater experience and repeat engagements imply trust gained from employers and versatility in skills.\u003c/li\u003e\n \u003cli\u003eCertifications/Skills: such as relevant licenses, professional certifications, or accredited training course certificates. Meanwhile, formal credentials may be difficult in some informal labors; any verifiable skill endorsement (e.g., a trade certificate or training completion) can support the quantitative score by evidencing proven competencies.\u003c/li\u003e\n \u003cli\u003eJob completion rate: result of assigned jobs that are successfully completed. A high completion rate signals dependability and efficiency in honoring commitments.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese quantitative metrics are objective indicators of productivity and capability. Many online labor platforms track similar metrics (e.g., number of gigs done, earnings, and completion percentages) as key performance indicators for workers (Ovezmyradov, 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQualitative Component (Score R):\u003c/strong\u003e This reputation-based component collects subjective and behavioral data relevant to the worker\u0026rsquo;s working reliability. that consist of:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eCustomer\u0026apos;s ratings and reviews: Each score and review received showed customer satisfaction with the worker\u0026rsquo;s efficiency, quality of service, politeness, and professionalism. Therefore, high ratings and positive reviews received represent the quality of their capability.\u003c/li\u003e\n \u003cli\u003eTimeliness and accountability: punctuality, commitment to deadlines, and job cancellation or no-show rates. These indicate a strong sense of responsibility. Conversely, frequent delays or cancellations would negatively affect this score.\u003c/li\u003e\n \u003cli\u003eComplaint and dispute recorded: Any customer complaints, reported incidents, or disputes related to the worker services. A history free of these serious complaints will be a higher score, while negative incidents will lower the reputation score to reflect their behavior and concerns.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTogether, these qualitative components reflect a picture of the worker\u0026rsquo;s reputation among customers and within the community. In that gig platform and informal work system, a solid reputation is crucial (Benavides et al., 2022). A worker with excellent reviews and demonstrated reliability is likely to attract new job opportunities and repeat orders, whereas a poor reputation leads them to less business.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlternative Data Component (Score AD):\u003c/strong\u003e This component describes non-traditional data about a worker beyond direct job performance or customer satisfaction. These alternative data have the purpose of capturing dimensions of trust and stability that traditional metrics might miss, especially for workers with a lack of formal working documents. Examples of such data, followed by:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eFinancial and payment history will indicate a worker\u0026rsquo;s personal financial behaviors, including the timely payment of utility bills, rent, mobile phone subscriptions, and more. These demonstrate worker financial responsibility and reliability outside the workplace and point to how the worker manages commitments reliably. (World Bank, 2024, pp. 7-9).\u003c/li\u003e\n \u003cli\u003eDigital footprint and platform data: Data is generated from the worker\u0026rsquo;s activities on any digital platforms beyond the workplace. Data sources cover the worker\u0026apos;s mobile wallet or digital payment usage patterns, transaction records from other gig platforms or online marketplaces, and also ride-sharing and delivery logs. These data will reflect a broader view of the worker\u0026rsquo;s economic activity and reliability. For instance, a driver\u0026rsquo;s or courier\u0026rsquo;s earnings and completion rating on another platform, or a seller\u0026rsquo;s history on an e-commerce platform, can be affected by their LCS profile as evidence of consistent engagement and income generation across platforms. (World Bank, 2024, p. 28)\u003c/li\u003e\n \u003cli\u003eSocial media and network recognition: these represent a worker\u0026rsquo;s body and behavior on social or professional networks, such as recognition on LinkedIn or influence on Facebook\u0026apos;s community. Positive matters such as customer admiration can be used as a trustworthy reference, while unprofessional digital footprints might be a red flag. However, using social media data must be done carefully, although online can show the worker\u0026rsquo;s dimension in a broader context, such as personal attitude, etc. (Alliance for Financial Inclusion [AFI], 2025).\u003c/li\u003e\n \u003cli\u003ePsychometric and behavioral assessments include the results from any psychology tests or personality assessments that the worker may have taken, which evaluate traits such as honesty, diligence, risk tolerance, and interpersonal skills. Studies in the financial sector found that psychology test results, such as integrity assessments, help improve credit scoring and consideration in cases of borrowers with weak evidence, particularly those engaged in informal remote labor (Alliance for Financial Inclusion [AFI], 2025).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCollecting alternative data in LCS recognizes those workers, especially in informal sectors, leave many digital footprints, which help assess their credibility. By using these additional data sources, the LCS model approach provides a more integrated and fair assessment of a worker. Particularly valuable is information such as integrity for those who may lack lengthy work histories or formal credentials, such as poor women or youth laboring in remote areas of sub-Saharan Africa, etc. Alternative data use for scoring has been shown to broaden financial inclusion\u0026mdash;for example, incorporating telco, utility, or e-commerce records can significantly improve the risk prediction for individuals with no traditional credit files (World Bank, 2024, pp. 8\u0026ndash;9). In the same spirit, an LCS enriched with alternative data offers a more robust picture of a worker\u0026rsquo;s trustworthiness beyond what standard on-platform metrics alone can capture. Each of these parts (quantitative, qualitative, and alternative) works together to create a complete system for evaluating workers, much like how different factors are used in credit scoring systems.\u003c/p\u003e\n\u003cp\u003eEach of these components\u0026mdash;quantitative, qualitative, and alternative\u0026mdash;supports each other. These form a framework for scoring labor systems in a manner similar to multi-dimensional credit scoring systems such as credit bureaus.\u003c/p\u003e\n\u003cp\u003eImportantly, the LCS model is designed with customizable weighting, allowing the relative importance of each component to be tailored according to occupational context, policy priorities, or empirical insights from field testing. For instance, an initial implementation might assign equal weight (w) to quantitative (Q) and qualitative (R or Reputation) scores (e.g., wQ=0.5, wR=0.5, wAD=0, w_Q = 0.5, w_R = 0.5, w_AD = 0, wQ=0.5, wR=0.5, wAD=0). However, in technician roles that skill verification is critical for, such as electrician or plumber, etc., greater emphasis may be placed on the quantitative component, especially certifications and job completion indicators.\u003c/p\u003e\n\u003cp\u003eIn practice, for general service workers such as cleaners or delivery personnel, behavioral consistency and platform reliability (reflected in the reputation or alternative data scores) are needed. So these data may have higher weights.\u003c/p\u003e\n\u003cp\u003eThe ability to fine-tune the model enhances its flexibility and contextual sensitivity so that the LCS can adapt more effectively to the needs of diverse labor markets while maintaining core standards of fairness and rigor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Governance Principles and System Architecture of the LCS Framework\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eIn addition to its scoring methodology, the LCS framework also works on principles that promote transparency, fairness, and accountability in how scores are calculated and maintained. These principles include real-time updates, in which a worker\u0026rsquo;s score reflects their most recent activity by continuously retrieving live data from the platform. Transparency is another core critical; workers must have full access to their scoring records and a clear understanding of which behaviors influence their score. Furthermore, the system ensures protection of personal information: sensitive data such as chat histories with clients are either anonymized or aggregated to protect worker privacy and uphold data rights.\u003c/p\u003e\n\u003cp\u003eThe system architecture of the LCS framework is composed of three working layers. \u003cstrong\u003eThe data layer\u003c/strong\u003e captures and aggregates quantitative and behavioral inputs from platform activities and verified records. Next, the \u003cstrong\u003eScoring Algorithm Layer\u003c/strong\u003e processes this data using the formula outlined earlier to compute the LCS score, dynamically weighted according to policy or job-specific configurations. Finally, the \u003cstrong\u003ePolicy Layer\u003c/strong\u003e manages how the logic works on the system, such as defining rules around weighting schemes, screening data sources, transparency criteria, and procedures for dispute resolution or score appeals.\u003c/p\u003e\n\u003cp\u003eTogether, these design elements ensure that the LCS functions not only as a technical scoring model but also as a \u003cstrong\u003eworker-centric governance system\u003c/strong\u003e\u0026mdash;balancing algorithmic precision with ethical and contextual accountability.\u003c/p\u003e"},{"header":"4. Proposed Model of LCS","content":"\u003cp\u003eThis section explains the concept design of \u003cstrong\u003ethe labor credit scoring (LCS) model, which combines many attributes into a real-time score that reflects a gig worker\u0026rsquo;s performance, credibility across platforms, and behavioral engagement.\u003c/strong\u003e The model is built to be flexible and scalable, particularly in developing country contexts where formal credentials may be scarce and worker performance is often invisible to third parties.\u003c/p\u003e\n\u003cp\u003eThe LCS model consists of three components: quantitative (or Score Q), reputational (or Score R), and behavioral data (or Score AD). These are combined together into a total score. This was transformed into a weighted formula:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLCS = wQ \u0026times; ScoreQ + wR \u0026times; ScoreR + wAD \u0026times; ScoreAD\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach component captures a different dimension of the worker\u0026rsquo;s profile, with the overall score designed to provide more portability and fairness than traditional star-rating systems.\u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003eQuantitative Score (ScoreQ)\u003c/strong\u003e reflects tangible, measurable work outcomes derived directly from transactional data. It includes the number of jobs completed over a specified period (or job volume), the consistency of income over time (e.g., in weekly or monthly earnings), and the job completion rate. Each indicator is normalized to a 0\u0026ndash;100 scale and combined using a weighted sum. Therefore, the formula will be updated to\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eScoreQ = 0.5 \u0026times; (Job volume) + 0.3 \u0026times; (Income stability) + 0.2 \u0026times; (Completion rate)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis component representsthe worker\u0026rsquo;s performance and reliability.By focusing on outputs that are both measurable and trackable, ScoreQ is the basis of the LCS through objective indicators.\u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003eReputation Score (ScoreR)\u003c/strong\u003e evaluates how the worker is perceived by clients and the platform community. It includes variables such as the average customer satisfaction rating (e.g., converted from a 1\u0026ndash;5 star system to a 0\u0026ndash;100 score), punctuality or on-time arrival rates, and the worker\u0026rsquo;s complaint history. A high number of timely and well-rated completions boosts the score, while repeated complaints or dispute cases result in deductions. Positive recognitions\u0026mdash;such as platform badges for high performance or consistency\u0026mdash;are also factored in. A representative computation might be\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eScoreR = 0.5 \u0026times; (Customer satisfaction) + 0.3 \u0026times; (Punctuality) + 0.2 \u0026times; (Complaint and award index)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis score captures professionalism and interpersonal qualities that are often invisible in transaction data but crucial to client trust and satisfaction.\u003c/p\u003e\n\u003cp\u003eThe third and increasingly critical component is the \u003cstrong\u003eAlternative Data Score (ScoreAD)\u003c/strong\u003e. This score incorporates behavioral and contextual data that serve as proxy indicators of worker engagement and reliability. either frequency of app usage, how quick they are in job acceptance, cancellation behavior, etc. Importantly, \u003cstrong\u003ecertifications and training completions\u003c/strong\u003e\u0026mdash;especially those issued by the platform itself\u0026mdash;are included in this category, as they signal self-directed learning and motivation even in the absence of formal licensing.\u003c/p\u003e\n\u003cp\u003eFor instance, a worker who consistently logs in daily, promptly responds to job offers, completes optional training modules, and passes in-app skill assessments would earn a high ScoreAD. In contrast, a worker who has less cancellation discipline, a low engagement streak, or long periods of no work would receive a lower score. An example formula might be\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eScoreAD = 0.35 \u0026times; (Responsiveness) + 0.25 \u0026times; (Training and certification completion) + 0.2 \u0026times; (Engagement streak) + 0.2 \u0026times; (Cancellation discipline)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll behavioral signals are collected automatically through the system backend and translated into structured variables using rule-based algorithms. These indicators enrich the LCS by accounting for factors not visible in ratings or job logs, which is especially useful in early-stage user profiling or for workers who operate in informal settings.\u003c/p\u003e\n\u003cp\u003eAll three components\u0026mdash;ScoreQ, ScoreR, and ScoreAD\u0026mdash;are processed through a centralized credit engine governed by the \u003cstrong\u003eLabour Credit Bureau (LCB)\u003c/strong\u003e, as depicted in \u003cstrong\u003eFigure 1\u003c/strong\u003e. The LCB collects data via APIs from various sources, either platforms, clients, or external institutions such as banks or mobile operators, and applies scoring policies that are set by job category and context. The system works with real-time updates and feedback and also allows dispute resolution.\u003c/p\u003e\n\u003cp\u003eIn sum, the LCS model provides a reliable evaluation tool across platforms for gig workers. By mixing work performance, client-based reputational data, and behavioral data together, it allows platforms and partner institutions such as banks to recognize and reach them for capital. even when formal qualifications are lacking. This structure is not only trustworthy for both worker and client but also encourages continuous skill development through incentive alignment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Integrated LCS Formula, Worker Segmentation, and Calibration Logic\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Labor Credit Scoring (LCS) system consolidates multiple dimensions of worker performance into a unified formula that is adaptable by worker type and job context. At its conceptual level, the system evaluates a worker\u0026rsquo;s credibility using the following weighted formula:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLCS = \u0026alpha; \u0026times; Score Q + \u0026beta; \u0026times; Score R + \u0026gamma; \u0026times; Score AD\u003c/strong\u003e, where \u003cstrong\u003e\u0026alpha; + \u0026beta; + \u0026gamma; = 1\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eScore Q\u003c/strong\u003e represents the quantitative dimension. Objective indicators such as job volume, income stability, and certifications are used.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eScore R\u003c/strong\u003e represents qualitative feedback. Simply speaking, reputation from clients is either based on behavioral ratings or dispute history.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eScore AD\u003c/strong\u003e, also called \u003cstrong\u003ealternative data\u003c/strong\u003e, will capture behavioral, contextual, and self-reported indicators that extend beyond traditional platform ratings.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese three components ensure real-time integration and fair assessment, especially for gig workers in developing countries where formal records may be difficult to obtain. Therefore, while a technician\u0026rsquo;s job volume boosts Score Q, their consistent client ratings impact Score R, and their responsiveness or app engagement is recorded to Score AD in automated real time.\u003c/p\u003e\n\u003cp\u003eBy explaining the weight of each component, this concept creates new transparency and a fair basis. Workers can understand how each behavior or factor impacts their score; this solves the \u0026quot;black box\u0026quot; that is a big problem in platform ratings currently (M\u0026ouml;hlmann et al., 2023).\u003c/p\u003e\n\u003cp\u003eHowever, by the nature of the work and the country\u0026apos;s context, \u0026alpha;, \u0026beta;, and \u0026gamma; can be adjusted. For example:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eFor \u003cstrong\u003etechnicians\u003c/strong\u003e (e.g., plumbers, electricians), who require specialized competencies, \u003cstrong\u003e\u0026alpha; = 0.5\u003c/strong\u003e, \u003cstrong\u003e\u0026beta; = 0.3\u003c/strong\u003e, and \u003cstrong\u003e\u0026gamma; = 0.2\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003eFor \u003cstrong\u003eservicers\u003c/strong\u003e (e.g., cleaners, delivery workers), who rely heavily on client interaction, \u003cstrong\u003e\u0026alpha; = 0.3\u003c/strong\u003e, \u003cstrong\u003e\u0026beta; = 0.4\u003c/strong\u003e, and \u003cstrong\u003e\u0026gamma; = 0.3\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn practice, if a worker completes an online training module, Score AD will increase instantly. Likewise, if a negative customer review is filed, Score R will be adjusted downward in real time as well.\u003c/p\u003e\n\u003cp\u003eBy integrating diverse data sources\u0026mdash;including self-declared skills, app usage behavior, and verified digital certifications\u0026mdash;LCS reduces bias from the client by capturing data from diverse sources of gig work and weights them differently. This framework makes the system more trustworthy, aligning with fairer algorithmic management (Hlongwane et al., 2024; Alliance for Financial Inclusion, 2025).\u003c/p\u003e\n\u003cp\u003eWhile the scoring formula provides a flexible foundation, its effectiveness depends on how its customization fits the real-world dynamics of each worker type and country\u0026apos;s context. Therefore, to make more meaningful applications\u003c/p\u003e\n\u003cp\u003eWe found that recognizing the diverse nature of gig work, the LCS system defines platform workers into two main groups. \u003cstrong\u003eTechnicians\u003c/strong\u003e and \u003cstrong\u003eservicers\u003c/strong\u003e will manipulate its scoring differently. As we found, each group emphasizes different combinations of quantitative metrics (Score Q), reputation (Score R), and alternative data (Score AD) based on the job\u0026rsquo;s nature and customer expectations mainly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTechnicians\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTechnicians are skilled or semi-skilled workers\u0026mdash;such as electricians, plumbers, and mechanics\u0026mdash;whose work tends to technical execution and is often related to safety and compliance standards. Given the structured nature of their work, clients typically expect predictable, verifiable outcomes.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eScore Q\u003c/strong\u003e: will focus on objective work outcomes, such as number of jobs completed, job completion rate, and consistency of income. These reflect a technician\u0026apos;s ability to deliver results at scale and with reliability mainly.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eScore R\u003c/strong\u003e: Reflects behavioral qualities\u0026mdash;such as punctuality, communication, and professionalism\u0026mdash;drawn from client reviews and platform interactions. While important, reputation tends to be more consistent for technical jobs because of clear expectations and job descriptions.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eScore AD\u003c/strong\u003e: This metric is a higher priority for technicians. caused \u003cstrong\u003everified credentials to be\u0026nbsp;\u003c/strong\u003einvolved, such as vocational training certificates, professional licenses like electrician, etc. Furthermore, the platform\u0026apos;s microlearning records or engagement in upskilling modules also contribute here. So, these indicators use both long-term competence data and formal validation to make more reliability.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003eExample weight: \u0026alpha; = 0.4 (Q), \u0026beta; = 0.2 (R), \u0026gamma; = 0.4 (AD)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eServicers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eServicers include general gig workers such as cleaners, delivery drivers, or movers. These jobs are more customer-facing and relationship-based, requiring less formal training but a higher basis of professionalism and relying more on client satisfaction.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eScore Q\u003c/strong\u003e: Focuses on metrics like job frequency, job consistency (e.g., working days per week), and income stability. These reflect availability and discipline more than technical complexity.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eScore R\u003c/strong\u003e: Plays a central role in evaluating servicers. Customer ratings, repeat requests, and qualitative reviews (e.g., courtesy, friendliness, cleanliness) are often the main basis for hiring decisions in this group.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eScore AD\u003c/strong\u003e: Includes participation in non-certified training (e.g., online hospitality or customer service modules), behavioral consistency indicators (e.g., low cancellation rate), and platform engagement rate (e.g., daily logins or prompt job acceptance). Even if formal licensing may not apply, these alternative data still validate trust and commitment.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003eExample weight: \u0026alpha; = 0.3 (Q), \u0026beta; = 0.5 (R), \u0026gamma; = 0.2 (AD)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHowever, no one size fits all. So defining score components only for each worker type is insufficient to reflect actual capabilities. To ensure that the assigned weights (\u0026alpha;, \u0026beta;, \u0026gamma;) reflect actual labor conditions, systematic calibration is necessary. Therefore, the initial weightings for \u0026alpha;, \u0026beta;, and \u0026gamma; may follow the logic described above; ensuring validity and fairness requires systematic calibration.\u003c/p\u003e\n\u003cp\u003eMeanwhile, literature suggests three viable approaches (Gompf et al., 2021; Li et al., 2021; Zhang et al., 2023):\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eStakeholder Surveys\u003c/strong\u003e: Collecting input from workers, clients, and platforms via tools such as \u003cstrong\u003eLikert scales\u003c/strong\u003e or the \u003cstrong\u003eAnalytic Hierarchy Process (AHP)\u003c/strong\u003e to assign importance scores to each factor.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEmpirical Analytics\u003c/strong\u003e: Retrospective analysis of platform data\u0026mdash;examining correlations between performance factors and tangible outcomes like income, retention, or customer rehire rates.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eContextual Adaptation\u003c/strong\u003e: Customizing weights by job type, platform culture, or region (e.g., giving more weight to alternative data in areas with poor certification infrastructure). A/B testing or prototype validation can then fine-tune this logic.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis weight-based adjustment helps reduce unintentional bias in algorithms and promotes a fairer labor platform, aligning with the principles of involving both sides, either customer or worker, in AI development (Zhang et al., 2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Operational Models and Score Computation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are two options for operationalizing the LCS framework across a platform:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Segment-Specific Models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach worker category (technician vs. servicer) has its own formula, customized with distinct weights and sub-indicators. This model aligns with M\u0026ouml;hlmann et al. (2023), who argue that each labor group requires its own sense-making process for how platform logic works.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Unified Model with Type Adjustment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA single LCS formula is maintained across the platform, but worker type is maintained as a variable that influences the weight vector (\u0026alpha;, \u0026beta;, \u0026gamma;). This streamlines computation but still respects role-specific nuances.\u003c/p\u003e\n\u003cp\u003eIn either case, the system must avoid cross-domain bias:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eTechnicians shouldn\u0026rsquo;t be rated based on delivery speed.\u003c/li\u003e\n \u003cli\u003eServicers shouldn\u0026rsquo;t be penalized for lack of technical licenses.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo illustrate, Table 5 presents two LCS calculation examples:\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWorker Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWork Volume\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCertifications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eClient Rating\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLCS Formula\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFinal Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTechnician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50 jobs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eElectrician License\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.5 / 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.4 \u0026times; 70) + (0.3 \u0026times; 90) + (0.3 \u0026times; 90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82 / 100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eServicer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e200 jobs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOnline Training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.8 / 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.3 \u0026times; 90) + (0.2 \u0026times; 80) + (0.5 \u0026times; 96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e88 / 100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThis approach ensures contextual accuracy while maintaining a shared LCS framework across the ecosystem.Whether using a unified formula or separate ones by category, the effectiveness of the LCS model depends on how and what inputs are picked and weighted. The following section presents our scoring logic in detail, including how key indicators are normalized and how weight distributions vary by worker type.\u003c/p\u003e\n\u003cp\u003eThat will make the Labour Credit Score (LCS) both interpretable and actionable; the system defines how raw indicators such as work volume, certifications, and customer reviews are converted into normalized\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003escores and combined with predefined weights to generate a total score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComponent Weighting by Worker Type\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe system assigns specific weights to each component\u0026mdash;Score Q (quantitative), Score R (reputation), and Score AD (alternative data)\u0026mdash;tailored to each worker profile:\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWorker Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eScore Q (\u0026alpha;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eScore R (\u0026beta;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eScore AD (\u0026gamma;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTechnician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eServicer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThis configuration reflects the realities of each job category:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eTechnicians\u003c/strong\u003e rely more on formal competencies and verified qualifications (AD), whereas\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eServicers\u003c/strong\u003e depend heavily on ongoing client feedback and behavior (R).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eScore Conversion Guidelines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach raw input is translated to a normalized score on a \u003cstrong\u003e0\u0026ndash;100 scale\u003c/strong\u003e based on set criteria:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eWork Volume (Q)\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Technician: 50 jobs = 70 points\u003cbr\u003e\u0026nbsp;Servicer: 200 jobs = 90 points\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eClient Rating (R)\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;5 stars = 100 points\u003cbr\u003e\u0026nbsp;4.5 stars = 90 points\u003cbr\u003e\u0026nbsp;4.8 stars = 96 points\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAlternative Data (AD)\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Electrician License = 90 points\u003cbr\u003e\u0026nbsp;Customer Service Training = 80 points\u003cbr\u003e\u0026nbsp;Verified completion of microlearning module = 60\u0026ndash;75 points\u003cbr\u003e\u0026nbsp;High responsiveness rate = up to 90 points\u003cbr\u003e\u0026nbsp;Engagement streak (e.g., working \u0026gt;20 days/month) = 85+\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese conversions are periodically reviewed based on platform-wide distributions and A/B tested for impact on hiring behavior.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExample Calculation\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWorker Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWork Volume\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCertification (AD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRating\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLCS Calculation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLCS Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTechnician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70 (Q)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90 (AD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90 (R)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.4\u0026times;70) + (0.2\u0026times;90) + (0.4\u0026times;90) = 82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82/100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eServicer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90 (Q)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80 (AD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96 (R)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.3\u0026times;90) + (0.5\u0026times;96) + (0.2\u0026times;80) = 88.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e88/100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePractical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe framework is designed to support \u003cstrong\u003emulti-skill workers\u003c/strong\u003e. For example, one worker does perform both plumbing and food delivery. In such cases, the system assigns \u003cstrong\u003edistinct LCS profiles per skill category\u003c/strong\u003e. This separation makes a worker\u0026rsquo;s credibility in one domain (e.g., certified plumbing) not influence their other unrelated domains (e.g., parcel delivery).\u003c/p\u003e\n\u003cp\u003eThis approach also mitigates algorithmic control risks. As studied by M\u0026ouml;hlmann et al. (2023), many digital platforms deploy \u0026ldquo;one-size-fits-all\u0026rdquo; systems that avoid worker diversity and create unfair conditions. Meanwhile, the LCS model enables a \u003cstrong\u003emulti-central\u003c/strong\u003e \u003cstrong\u003escoring system\u003c/strong\u003e grounded in the actual structure of each work, credentials, and behavior patterns specific to each job type.\u003c/p\u003e"},{"header":"5. Pilot Implementation and Preliminary Findings: The KRIB Use Case","content":"\u003cp\u003e\u003cstrong\u003e5.1 Pilot Objectives and Context\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Skill-Based Hiring Enforcement System (SHES) is a platform-level mechanism designed to enforce skill development, align incentives, and enhance labor quality in gig economies. This system consists of three main engines: First is the Skill Engine, for basic upskilling by micro-learning; the Rate Engine is second, which assesses worker performance; the last one is the Visibility Engine, which ranks workers on the hiring system based on verified score level. At the heart of SHES\u0026rsquo;s Rate Engine lies the Labor Credibility Scoring (LCS) model, which assigns a composite credibility score to each worker based on work history (Q-score), customer ratings (R-score), and alternative data (AD-score) such as certifications. This scoring mechanism adjusts weighting by job type\u0026mdash;ensuring performance is assessed in a context-sensitive way.\u003c/p\u003e\n\u003cp\u003eThis pilot aimed to evaluate how well the SHES\u0026ndash;LCS system performs when implemented within an actual job-matching platform (KRIB). Unlike prior LCS framework discussions that support reputation metrics across platforms. Meanwhile, this pilot focused on practical performance within a single-platform context. In particular, it aimed to validate whether an occupation-weighted scoring logic could operate effectively within a real-world platform\u0026mdash;improving trust, reducing complaints, and enhancing worker outcomes. That we provide a visual explanation of how the SHES system and LCS interoperate with the KRIB hiring platform as below.\u003c/p\u003e\n\u003cp\u003eTo test this, we designed a 12-month comparative field experiment involving 300 participants across three cities in Thailand: Bangkok, Chiang Mai, and Nakhon Ratchasima. Participants were divided evenly between two platforms: KRIB, which implemented the SHES\u0026ndash;LCS system (experimental group), and TPQI-net, a conventional public-sector job platform from \u003cstrong\u003ethe Thailand Professional Qualification Institute\u003c/strong\u003e (Public Organization) that runs without any structured scoring or incentive mechanisms (control group). Each city hosted 50 participants per platform, yielding 150 experimental and 150 control participants. These workers were drawn from two occupational groups: technicians (e.g., electricians, plumbers) and servicers (e.g., cleaners, helpers). All participants started with zero ratings and underwent onboarding procedures appropriate to their assigned platform.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 Evaluation Design and Methodology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants in the KRIB group were onboarded through a structured SHES workflow, including identity verification, skill declaration, and optional submission of credentials. They received status as \u0026quot;new\u0026quot; at an initial LCS scoring and then continuously gained scores throughout the pilot. Workers on TPQI-net registered through a basic process with no scoring, skill verification, or incentives.\u003c/p\u003e\n\u003cp\u003eThe LCS scores on KRIB consisted of three weighted components: Q-score (quantitative work metrics), R-score (client reviews), and AD-score (alternative data). Weightings were adjusted by role: \u003cstrong\u003eTechnicians\u003c/strong\u003e had a weighting of 60% Q, 25% R, and 15% AD, while \u003cstrong\u003eServicers\u003c/strong\u003e used a 35% Q, 55% R, and 10% AD formula. Performance was measured through skill assessments, income, job volume, customer ratings, and complaint frequency, complemented by qualitative interviews.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3. Key Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWorkers in the SHES\u0026ndash;LCS system showed strong improvement across all metrics. Skill assessments rose by +10.1 points on average (from 63.9 to 74.0), compared with only a +0.9 increase in the control group. Job ratings averaged 4.8 out of 5 (vs. 4.6 in the control group), and complaints dropped to 2.5%\u0026mdash;less than half of the 5.8% observed in TPQI-net. Our tracking reported trust increasing, with confidence scores rising from 5.0 to 9.0 in the experimental group.\u003c/p\u003e\n\u003cp\u003eFurthermore, KRIB workers earned ฿6,987 per month on average\u0026mdash;a 34% increase from baseline\u0026mdash;and significantly more than TPQI-net participants, who reached only ฿6,050. These workers also completed more jobs per week, driven by more visibility in the hiring system (KRIB) linked to their LCS scores gained. Meanwhile, retention rates exceeded 90% in KRIB, pointing to labor\u0026apos;s higher satisfaction and good engagement. Qualitative interviews revealed that participants valued the fairness of the system and the opportunity to develop their reputation through transparent performance tracking. Some expressed a desire for clearer feedback on score calculation, suggesting that explainability will be key in future refinements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.4 Lessons, Limitations, and Next Steps\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pilot generated several important lessons. First, the modular architecture of SHES\u0026ndash;LCS shows high adaptability along the pilot period and in three major cities of Thailand with a 90% retention rate. It was embedded into KRIB without disrupting core functionality, confirming that gig platforms can scale up without overhauls. Workers responded positively to the SHES + LCS system. Either performance-based visibility or wage tiering incentivized engagement, and maintaining the LCS score reinforced upskilling as a closed loop as planned. Occupation-specific weighting within the LCS algorithm also worked as intended, keeping fairness and relevance across any job types on the platform.\u003c/p\u003e\n\u003cp\u003eTransparency has become a main concern. Even though workers look for their scores, several voices show worry about how those scores were derived.This points to a need for more visible scoring logic or educational content in future iterations. Furthermore, the study confirmed that trustworthy raised income also can be achieved even within a single-platform ecosystem.\u003c/p\u003e\n\u003cp\u003eThat said, the pilot had its limitations. The LCS score was not tested in a multi-platform context; therefore, its portability and working across platforms are unproven. Nor was the system linked to any financial services, such as credit scoring or insurance underwriting\u0026mdash;an area of clear potential that remains unexplored. The sample was limited to the home service sector only; this raises questions about whether similar gains would occur in logistics, transport, or digital freelance work. Additionally, the AD-score component had limited impact due to the scarcity of formal credentials among gig workers. Finally, while 12 months is efficient for midterm results, it remains insufficient for career-long outcomes.\u003c/p\u003e\n\u003cp\u003eFuture work will thus focus on scaling the model horizontally and vertically. A multi-platform rollout will be necessary to evaluate whether Skill Passports can be carried and recognized across ecosystems. Longitudinal studies should follow, tracking gig workers over multiple years to determine how credibility scores influence skill development, career mobility, and economic resilience. Especially partnerships with financial institutions could unlock further value, allowing verified scores as famous metrics to be used for access to capital. On the technical side, algorithm refinements must ensure fairness, interpretability, and resilience to gaming. Finally, pilot implementations in adjacent sectors\u0026mdash;such as transport, warehousing, and digital micro-tasks\u0026mdash;will test the universality of the SHES\u0026ndash;LCS approach.\u003c/p\u003e\n\u003cp\u003eUltimately, this pilot demonstrated that SHES\u0026ndash;LCS can elevate worker performance, enhance trust, and reduce transactional friction in gig economies. With proper expansion and policy alignment, the system offers a blueprint for establishing a national labor reputation infrastructure that is equitable, skill-based, and responsive to real-world performance.\u003c/p\u003e"},{"header":"6. Discussion and Implications","content":"\u003cp\u003eThe pilot implementation of the Labor Credit Scoring (LCS) system, embedded within the SHES architecture, approaches automated digital feedback loop mechanisms. which, combined with motivational theories, can fill structural gaps of upskilling in the gig economy. As the findings from the KRIB platform indicate, the LCS model demonstrates not only technical feasibility but also strong practical and behavioral impacts. that we found worker performance elevated, customer trust increased significantly, and income more consistent than previously fragmented labor platforms.\u003c/p\u003e\u003cp\u003eAt the core of this success lies LCS\u0026rsquo;s ability to transform abstract concepts like \u0026ldquo;trust\u0026rdquo; and \u0026ldquo;reliability\u0026rdquo; into quantifiable, portable metrics. By integrating quantitative (Q-score), reputational (R-score), and alternative data (AD-score) components, the system offered an integrated worker score that was updated in real time depending on job category. The LCS model goes beyond traditional rating systems such as star ratings on Uber or Grab (in Southeast Asia). by filling the gaps that traditional platforms have with new qualities that have been overlooked, such as portability, upskill certification, payment consistency, or even psychometric data. Simply speaking, LCS established a central credible foundation for worker validation, which is valuable in regions where formal documentation or certifications remain rare.\u003c/p\u003e\u003cp\u003eParticularly the role of alternative data brought to the involved both the technical and ethical sides. In developing countries where many gig workers are in informal labor systems, their economic identity is invisible from formal labor systems. Therefore, behavioral data used by LCS, such as app engagement, upskill certificates, and digital payment patterns, etc., enabled LCS to retrieve previously unrecognized qualities of workers. This more insightful view aligns with the growing global interest in using non-traditional data for financial inclusion (World Bank, 2024; AFI, 2025) and has become a key factor in bringing informal labor back to formal systems. However, this approach also demands strict attention to data privacy, informed consent, and algorithmic fairness to prevent unintended exclusion or exploitation. The system\u0026rsquo;s transparency and real-time feedback design partially addressed these concerns, but future iterations must further empower users with score explanations, opt-out controls, and mechanisms to contest unfair evaluations.\u003c/p\u003e\u003cp\u003eFrom a policy standpoint, the study\u0026rsquo;s findings point toward the emergence of a Labor Credit Bureau (LCB)\u0026mdash;an institutional body analogous to financial credit bureaus but operating within the labor market. The LCB could serve as a central repository of worker reputation data, aggregating performance metrics across platforms and enabling portability of skills and credibility. For governments, especially in developing nations, this represents a leap forward in labor market formalization, where workers previously overlooked by financial institutions or public policy can now be evaluated, supported, and included based on verifiable, systematized data. A well-structured LCB would reduce information asymmetries, allow for better job-matching services, and act as a gateway for gig workers to access government subsidies, reskilling programs, or microloans based on performance\u0026mdash;rather than pedigree.\u003c/p\u003e\u003cp\u003eThe implications of LCS also extend to platform design and business operations. By incentivizing workers to complete verified training and maintain high behavioral standards, platforms can reduce the cost of quality control and increase user retention. More importantly, platforms that integrate such scoring logic gain reputational legitimacy and can differentiate themselves in competitive labor markets. In regions such as ASEAN, Africa, and Latin America, LCS systems could be adapted to local infrastructures\u0026mdash;for example, by integrating with M-Pesa in Kenya or national training registries in Brazil\u0026mdash;demonstrating LCS's scalability across diverse economic ecosystems.\u003c/p\u003e\u003cp\u003eNonetheless, critical design challenges remain. First, the algorithm must avoid penalizing newcomers who lack job history. Initial scores should be adaptive and allow for rapid growth through effort-based achievements. Second, the risk of score manipulation or fake reviews must be mitigated through verified data inputs and continuous audit mechanisms. Third, the ethical use of alternative data\u0026mdash;especially personal, financial, and behavioral traces\u0026mdash;must be guided by human rights principles and contextual regulation.\u003c/p\u003e\u003cp\u003eLooking forward, the systemic potential of LCS is transformative. If embedded within public-private ecosystems like SHES and linked to financial institutions, social welfare agencies, or employer networks, LCS could act as both a labor market signal and a developmental tool. Workers who maintain high scores could gain faster access to jobs, higher pay, or even loan eligibility\u0026mdash;shifting the gig economy from an exploitative model to a system of upward mobility.\u003c/p\u003e\u003cp\u003eFinally, future research should expand the LCS implementation across multiple platforms, explore longitudinal effects on career mobility and skill accumulation, and examine the interplay between scoring systems and worker motivation under varied cultural conditions. This is particularly relevant for designing \u003cem\u003eskill-based welfare programs\u003c/em\u003e, where government benefits, tax credits, or social protections are triggered by verified labor performance rather than static employment status.\u003c/p\u003e\u003cp\u003eIn sum, the LCS model offers more than a technical fix for fragmented reputation systems\u0026mdash;it presents a new institutional layer in the governance of digital labor. If coupled with ethical safeguards and policy-level alignment, it can lay the foundation for a national labor infrastructure that is inclusive, trustworthy, and oriented toward continuous development.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis study delivers a novel and empirically tested framework for Labor Credit Scoring (LCS) tailored to the gig economy in developing countries. A system combining quantitative, qualitative, and alternative data together, LCS transforms fragmented platform rating systems and Blackbox into portable and transparent scoring that empowers informal workers to build, maintain, and carry their earned reputation across platforms. That also brings them back to the formal labor system.\u003c/p\u003e\n\u003cp\u003eThrough a year-long field experiment across three major cities in Thailand—Bangkok, Chiang Mai, and Nakhon Ratchasima—the LCS model was embedded within the SHES enforcement system on a real platform (KRIB) and proved effective. Workers using LCS showed significant improvements in verified skill levels (+10.1 points), monthly income (+34.4%), customer trust (from 5.0 to 9.0/10), and retention rates (\u0026gt;90%). All show both behavioral qualities and labor revenue uplift.\u003c/p\u003e\n\u003cp\u003eCritically, LCS introduced a \"reputation-as-asset\" paradigm, enabling workers to retain their reputation data beyond a single platform, thus fostering labor mobility without resetting credibility. This shift aligns incentives: maintaining a high score becomes essential not only for current income but also for future employability across platforms—a vital step in promoting quality labor in fragmented digital labor markets.\u003c/p\u003e\n\u003cp\u003eThe system’s modular and data-driven architecture ensures adaptability to diverse occupational groups (e.g., technicians vs. servicers), while the pilot confirms its scalability and cultural fit across urban zones in developing economies. In addition, by establishing a foundational infrastructure for a Labor Credit Bureau (LCB), this model paves the way for cross-sectoral applications—linking verified labor performance with financial access, public reskilling programs, and broader social inclusion.\u003c/p\u003e\n\u003cp\u003eIn sum, the LCS framework contributes not just a technical tool but a policy-relevant institutional innovation. If extended and governed ethically, it offers a practical route toward building a trustworthy, skills-based, and inclusive labor market infrastructure—fit for the future of work in the Global South.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics Approval Statement: This study was approved by the Human Ethics Committee of Thammasat University (Faculty of Medicine), Thailand. The research was conducted in accordance with international standards for research involving human participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipant Consent Statement: All participants in the study provided informed consent prior to their participation. The consent process was approved by the Human Ethics Committee of Thammasat University (Faculty of Medicine), ensuring compliance with ethical standards.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlliance for Financial Inclusion. (2025). \u003cem\u003eAlternative data for credit scoring: Special report\u003c/em\u003e. https://www.afi-global.org/wp-content/uploads/2025/02/Alternative-Data-for-Credit-Scoring.pdf\u003c/li\u003e\n\u003cli\u003eBanik, N., \u0026amp; Padalkar, M. (2021). 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(2024). \u003cem\u003eThe use of alternative data in credit risk assessment: Opportunities, risks, and challenges\u003c/em\u003e. https://documents1.worldbank.org/curated/en/099031325132018527/pdf/P179614-3e01b947-cbae-41e4-85dd-2905b6187932.pdf\u003c/li\u003e\n\u003cli\u003eWorld Bank. (2024). \u003cem\u003eWorking Without Borders\u003c/em\u003e\u003cem\u003e: The Promise and Peril of Online Gig Work\u003c/em\u003e\u003cem\u003e\u0026mdash;Short Note Series #5\u003c/em\u003e\u003cem\u003e: The Role of Local Online Gig Platforms\u003c/em\u003e. Washington, D.C.: World Bank. https://openknowledge.worldbank.org/handle/10986/40066\u003c/li\u003e\n\u003cli\u003eZhang, A., Boltz, A., Lynn, J., Wang, C., \u0026amp; Lee, M. K. (2023). Stakeholder-centered AI design: Co-designing worker tools with gig workers through data probes. In \u003cem\u003eProceedings of the 2023 CHI Conference on Human Factors in Computing Systems\u003c/em\u003e (pp. 1\u0026ndash;19). ACM. https://doi.org/10.1145/3544548.3581354\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Silpakorn University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"reputation system, gig economy, Labor credit scoring, Labor credit bureau","lastPublishedDoi":"10.21203/rs.3.rs-7198891/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7198891/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGig workers in developing countries are part of an informal labor system, which means unstable jobs and being far from accessible capital from the banking system. This study presents Labor Credit Scoring (LCS), a novel framework that turns worker reputation into a transferable asset across platforms. A combination of quantitative, qualitative, and alternative data that was never used in platforms before, like skilled certifications or behavior data, LCS opens the black box of rating in traditional platforms and enables credible assessments without formal records. The model contributes to lifelong learning and incentivizes upskilling, laying the groundwork for a Labor Credit Bureau (LCB) that connects labor histories with financial institutions and public services. A 12-month field experiment in Thailand involving 300 participants across three cities demonstrated LCS\u0026rsquo;s effectiveness in improving worker performance, income, and trustworthiness. The results suggest that LCS is scalable, adaptable, and fits diverse labor platforms in the Global South that are professionally licensed, weak, or volunteer. It offers a policy-relevant solution for building a skills-based labor market with a central standard across platforms that will turn informal labor back to formality at the end.\u003c/p\u003e","manuscriptTitle":"The Labor Credit Scoring Model for the Gig Economy in Developing Countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 07:30:43","doi":"10.21203/rs.3.rs-7198891/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d12cf755-8ab6-481d-97ac-03df047b2fd1","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-30T07:30:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-30 07:30:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7198891","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7198891","identity":"rs-7198891","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}