GOVERNING DATA FOR INCLUSION: APPLYING THE UNESCO 4PS FRAMEWORK TO MICRO-PENSIONS

 INTRODUCTION

Ghana’s efforts to expand pension coverage have increasingly turned toward the informal sector, where the majority of the workforce remains outside formal retirement protection systems. Despite ongoing digital transformation across the financial sector, participation continues to lag behind expectations. The challenge is not simply one of access. It is also about designing systems that reflect the economic realities of informal sector workers, characterised by irregular incomes, limited financial buffers, and minimal engagement with structured savings mechanisms.

At the same time, the rapid growth of digital financial services has introduced new possibilities. Mobile money platforms, digital identity systems, and platform-based economic activity are generating expanding streams of data. These can be harnessed to better understand user behaviour, tailor pension products, and improve system responsiveness. This data-driven environment offers a pathway to more inclusive and adaptive pension systems, particularly for populations that have historically been difficult to reach through conventional models.

However, this shift toward data-driven inclusion also introduces significant risks. The use of alternative data, ranging from transaction histories to behavioural patterns, raises important questions around privacy, consent, fairness, and accountability. Informal sector workers, many of whom operate with limited financial and digital literacy, may be particularly vulnerable to opaque data practices or unintended exclusion arising from poorly governed systems. Without appropriate safeguards, efforts to expand inclusion could inadvertently deepen existing inequalities or erode public trust.

The central issue, therefore, is no longer whether data will play a role in micro-pension systems, but how it will be governed. Data is not neutral; it reflects the assumptions, incentives, and structures within the systems in which it is used. In the context of pension administration, where decisions have long-term implications for financial security, the governance of data becomes a matter of both technical design and public accountability.

It is within this context that the UNESCO 4Ps data governance framework, Purpose, Principles, People, and Practices, offers a useful lens for analysis. Rather than focusing solely on technological capability, the framework emphasises the need to define clear public value objectives, embed ethical and operational standards, assign institutional responsibility, and ensure that data is managed responsibly across its lifecycle. Applied to micro-pensions, it provides a structured approach to balancing innovation with inclusion, and efficiency with trust. This article applies the UNESCO 4Ps framework to examine how data governance can support the design of inclusive, secure, and trustworthy micro-pension systems for Ghana’s informal sector.

FROM DATA OPPORTUNITY TO GOVERNANCE IMPERATIVE

The expansion of digital financial services in Ghana has significantly altered the landscape within which pension inclusion is being pursued. Mobile money platforms, interoperable payment systems, and digital identity infrastructure have created new channels of engagement, particularly for individuals in the informal sector to access formal financial services. These developments have reduced traditional barriers related to distance, documentation, and transaction costs, enabling broader participation in formal systems.

Alongside this expansion is the growing availability of alternative data. Transaction histories, mobile usage patterns, and records from trade associations or cooperatives increasingly provide insights into economic behaviour that were previously difficult to capture. For populations operating outside formal payroll systems, such data offers a practical proxy for understanding financial activity, enabling more tailored and responsive service design.

In the context of micro-pensions, this data becomes particularly important. Traditional pension systems are built on stable employment relationships and predictable income streams, conditions that do not apply to most informal sector workers. As a result, the design of inclusive pension systems depends on the ability to interpret variability, including irregular earnings, intermittent contributions, and diverse livelihood patterns. Data-driven approaches make it possible to move beyond standardised models toward more flexible systems that reflect these realities.

However, the increasing reliance on data introduces a fundamental shift in how inclusion is pursued. Access is no longer determined solely by physical or institutional barriers, but by how individuals are represented within data systems. Those who are visible through digital platforms may benefit from more personalised and accessible services, while those who remain partially or entirely outside these systems risk continued exclusion. This creates a set of underlying tensions that must be carefully managed.

The first is the tension between inclusion and exploitation. While data can expand access and tailor services, it can also be used in ways that disadvantage users through opaque profiling, inappropriate targeting, or exposure to financial risks that are not fully understood. Informal sector workers, particularly those with limited digital literacy, may have limited visibility into how their data is collected, processed, or applied.

The second is the tension between innovation and rights. The drive to develop more efficient and responsive systems encourages the use of increasingly sophisticated data analytics. Without clear safeguards, such innovation may outpace the frameworks required to protect privacy, ensure fairness, and maintain accountability. In pension systems, where decisions have long-term implications, this imbalance carries significant consequences.

These tensions highlight a critical reality: the challenge is not simply to use more data, but to govern it responsibly. When left unmanaged, data does not inherently produce inclusive outcomes. Its value depends on the structures within which it is collected, interpreted, and applied.

APPLYING THE UNESCO 4PS FRAMEWORK TO MICRO-PENSIONS

As reliance on data in pension system design grow, the focus must move beyond technical capability to address questions of purpose, accountability, and trust. A more deliberate and structured approach to data management is therefore essential. In this context, the UNESCO 4Ps framework, Purpose, Principles, People, and Practices, provides a useful lens through which micro-pension systems can be designed and assessed. Applied within the context of informal sector inclusion, the framework helps ensure that data-driven approaches remain aligned with public value objectives and do not inadvertently introduce new forms of exclusion.

WHY – Purpose: Defining Public Value in Micro-Pensions

At its core, the purpose of data use in micro-pension systems must be to expand inclusion while safeguarding the rights and interests of informal sector workers. This involves addressing structural barriers that have historically limited participation, particularly the mismatch between traditional pension models and the income realities of informal employment.

Data, in this context, serves as an enabler. Alternative data sources, such as mobile money transactions or contribution patterns, can support the design of more flexible and responsive pension products. For example, a market trader with irregular daily earnings may contribute in small, variable amounts, and transaction data can help design contribution models that reflect this pattern. However, the use of such data must be clearly anchored in public value. The objective is not simply to increase system efficiency, but to ensure that individuals are better able to access, understand, and benefit from pension systems.

This requires deliberate safeguards to avoid harm. Data-driven systems, if poorly designed, may expose users to risks such as inappropriate profiling, exclusion based on incomplete data, or forms of digital surveillance that undermine trust. The purpose of data governance must therefore be to ensure that inclusion is meaningful, supporting long-term financial security without compromising individual rights.

In this sense, the guiding principle is clear: data must serve people, not just systems.

HOW – Principles: Governing Data Responsibly

Translating purpose into practice requires a clear set of guiding principles. In micro-pension systems, these principles must be adapted to the realities of informal sector participation, where users may have limited financial and digital literacy, and where traditional consent mechanisms may not be fully effective.

A human rights-based approach is fundamental. This implies that data use should respect privacy, dignity, and autonomy, ensuring that individuals are not disadvantaged by how their data is interpreted or applied. Closely linked to this is the principle of data minimisation, collecting only what is necessary to deliver the service and avoiding excessive or intrusive data practices.

Transparency is equally critical. Users should have a clear understanding of how their data is being used, particularly where it influences decisions related to contributions, benefits, or eligibility. In practice, this may require simplifying communication for users such as a market trader receiving mobile prompts, ensuring that information about data use is presented in clear, accessible terms rather than technical language.

The issue of consent must also be approached carefully. Standardised consent models may not be sufficient in informal sector contexts. Mechanisms must be designed to ensure that consent is informed, voluntary, and context-appropriate, allowing users to make meaningful decisions about their participation even where literacy levels vary.

Finally, principles of equity and non-discrimination, along with security and privacy by design, must be embedded throughout the system. This ensures that data-driven approaches do not reinforce existing inequalities and that sensitive financial information is adequately protected.

Taken together, these principles provide a foundation for responsible data use, one that supports inclusion while maintaining trust.

WHO – Institutions and Accountability

Effective data governance depends not only on principles, but on clearly defined institutional roles and responsibilities. In the context of micro-pensions, this is particularly important given the number of actors involved in data collection, processing, and use.

At the centre of the system are pension trustees and regulators, including the National Pensions Regulatory Authority (NPRA), who bear ultimate responsibility for ensuring that systems operate in the interests of contributors. Their role extends beyond financial oversight to include the governance of data practices within the system.

Supporting this are data protection authorities, such as the Data Protection Commission, which provide regulatory oversight on issues of privacy, consent, and lawful data processing. Their involvement is critical in ensuring that pension systems align with broader data protection frameworks.

Operationally, pension administrators, mobile money providers, and service partners play a central role in managing data flows. For instance, a mobile money platform facilitating contributions from a self-employed artisan must ensure that transaction data is processed securely and used only for defined purposes. These actors are often responsible for onboarding users, processing transactions, and maintaining system infrastructure. As such, their responsibilities must be clearly defined and subject to appropriate oversight.

The key challenge is to avoid fragmented accountability. Where roles are unclear or overlapping, gaps may emerge in how data is governed, increasing the risk of misuse or system failure. A layered governance structure, with clear lines of responsibility, oversight mechanisms, and audit processes, is therefore essential. In practical terms, accountability is central to preventing systemic risk.

WHAT – Implementation Across the Data Lifecycle

The effectiveness of data governance ultimately depends on how it is implemented across the data lifecycle, from collection to use.

At the point of data collection, systems must ensure that only relevant information is gathered, using approaches that are accessible to informal sector workers. For example, during onboarding, a trader using mobile money should be guided through simplified and clearly explained data capture processes, rather than complex or opaque requirements. This may include tiered KYC processes and consent mechanisms adapted to different user contexts.

During processing and storage, strong safeguards are required to protect data integrity and confidentiality. This includes encryption, role-based access controls, and clearly defined data retention policies.

Data sharing, particularly with mobile money providers, identity systems, and other partners, must be governed by formal agreements that define purpose, access, and security requirements. Uncontrolled data sharing can introduce significant risks, especially where multiple systems are involved.

At the stage of data use, transparency becomes critical. Whether data is used for benefit calculations, contribution recommendations, or analytical purposes, users should have visibility into how decisions are made and how their data influences outcomes.

Finally, cross-cutting safeguards, including audit mechanisms, grievance procedures, and continuous monitoring, must be embedded within the system. These ensure that issues can be identified and addressed in a timely manner, reinforcing accountability.

It is at this level that governance moves from concept to practice. Without effective implementation, even well-defined principles remain insufficient.

KEY RISKS IN DATA-DRIVEN MICRO-PENSION SYSTEMS

While data-driven approaches offer clear opportunities, they also introduce risks that must be actively managed.

One of the most significant is the risk of exclusion arising from incomplete or poor-quality data. Informal sector workers whose economic activity is not fully captured within digital systems may be misrepresented or overlooked entirely. For example, a small-scale trader operating largely in cash may have a limited digital footprint, reducing their visibility within these systems.

Another concern is the risk of bias in analytical or predictive models, particularly where systems rely on historical data that may not reflect the diversity of informal sector behaviour. Such biases can lead to inappropriate recommendations or unequal access to services, especially where irregular income patterns are misinterpreted.

Data misuse or overreach presents a further risk. Without clear governance, data collected for pension purposes may be used in ways that extend beyond its original intent, undermining user trust and weakening confidence in the system.

In addition, limited user understanding of how data is used can create asymmetries in power and information. This is particularly relevant in contexts where digital literacy is low, for instance where a mobile money user may not fully understand how their transaction data influences pension-related decisions.

Finally, institutional capacity constraints, including limited technical expertise and oversight capability, can weaken the effectiveness of governance frameworks.

These risks reinforce the importance of a structured approach to data governance. Without deliberate oversight, the same data systems designed to support inclusion may inadvertently produce the opposite outcome.

BUILDING TRUST: THE MISSING LAYER IN PENSION INCLUSION

At the centre of any effort to expand pension inclusion is a less tangible, but equally critical factor: trust. Access to digital platforms or financial products does not, in itself, guarantee participation. Individuals must have confidence that the system will operate fairly, that their contributions are secure, and that their data will be handled responsibly. In the absence of this trust, even well-designed systems may struggle to achieve meaningful uptake.

Building trust requires more than technical safeguards. It depends on transparency, ensuring that users understand how systems operate and how decisions are made. It also requires user control, allowing individuals to make informed choices about their participation and the use of their data.

It further depends on the availability of grievance and redress mechanisms, through which users can raise concerns and seek resolution where issues arise. These mechanisms are particularly important in informal sector contexts, where users may otherwise lack accessible avenues for recourse.

Ultimately, trust is reinforced through consistent and ethical governance. Systems that demonstrate accountability, fairness, and respect for user rights are more likely to sustain long-term engagement. In this sense, inclusion is not simply a function of access; it is fundamentally a function of trust.

CONCLUSION

Efforts to expand pension coverage to the informal sector are increasingly shaped by the intersection of digital innovation and social policy. Micro-pension systems, as they evolve, cannot be understood simply as financial products designed to mobilise savings. They represent a broader shift toward data-driven models of social protection. In these systems, access, participation, and long-term outcomes are influenced by how data is collected, interpreted, and governed.

The opportunities presented by this shift are significant. Data can enable more responsive system design, support flexible contribution models, and extend coverage to populations that have historically been excluded from formal pension arrangements. However, as this article has shown, these outcomes are not guaranteed. Without clear purpose, robust principles, defined institutional responsibilities, and effective implementation across the data lifecycle, the same systems intended to promote inclusion may inadvertently reinforce exclusion.

The UNESCO 4Ps framework provides a structured approach to navigating this complexity. By grounding data use in public value, embedding ethical safeguards, clarifying accountability, and ensuring practical implementation, it offers a pathway toward systems that are not only efficient, but also equitable and trustworthy.

Ultimately, the success of micro-pension initiatives will depend not only on their design, but on the confidence they inspire among those they are intended to serve. For informal sector workers, participation is shaped as much by trust as by access. Systems that are transparent, accountable, and responsive to user realities are more likely to achieve sustained engagement and deliver meaningful protection in old age.

Without responsible data governance, digital inclusion risks becoming digital exclusion.