Leading by Example: Reflecting on Gender at Supervisory Agencies

1. Underlying institutional and cultural barriers that might prevent an organization from collecting, analyzing and disseminating gender diversity and pay equality data include lack of leadership commitment, limited resources or technical capacity, insufficient or fragmented HR and payroll systems, organizational norms that deprioritize gender equality, fear of reputational risk and resistance to transparency or change among staff
2. Challenges in analyzing licensing and supervisory outputs by the gender of staff and Financial Services Providers (FSPs) include limited or inconsistent data, small sample sizes, confidentiality concerns, resource constraints and organizational resistance while the results could reveal potential biases, differences in outsourcing and opportunities to promote gender equity and inclusion

1. So far within our organization gender diversity is recognized and implemented. Payment is as per scale of the company and working conditions and hierarchy, not gender based. If you are doing same level of work and if you are on similar position pay grade is same let alone in ECMA all over Ethiopia.
2. So far, we have only three women led licensee out of 17 licensed institutions. And the gender diversity in the staff composition is minimal as the corporate governance guide that requests ESG consideration is not yet mandatory. However, the Authority points out the gender inclusion and diversity concepts with stakeholder engagements.

1. Currently the institution collects, analyses, and disseminates gender diversity data, challenges remain due to limited depth of analysis, particularly regarding pay equality and promotions. This arises from privacy concerns.
2. Analysing licensing and supervisory outputs by gender faces challenges, including limited data and privacy concerns. Although such analysis can highlight workforce gender distribution and identify areas for targeted interventions to support female participation in the financial sector, its contribution to FSPs’ gender diversity is likely to be low, as company formation is largely a voluntary choice influenced by individual exposure, education, and societal or organisational norms.

A key barrier could be cultural discomfort around discussing pay gaps or internal imbalances, along with concerns about reputational risk if disparities are made public.
Analysing licensing and supervisory outcomes by gender could be sensitive and technically challenging due to data gaps and small samples. However, it could uncover unconscious biases or confirm that processes are fair.

1. Institutional barriers, limited systems for granular tracking, no dedicated budget staff for gender analytics, competing priorities, absence of mandatory internal gender reporting.
Cultural barriers, low internal awareness of gender equity's link to better supervision outcomes, entrenched patriarchal norms undervaluing women's barriers, and reluctance to expose imbalances due to potential reputational sensitivity or resistance to change in a male-dominated leadership environment.
2. Challenges; small sample sizes in supervisory teams limit statistical reliability, lack of gender data in licensing files, lack of analytical tools &expertise for intersectional breakdowns, privacy concerns with staff/FSP leader data, time/resource strain on already stretched supervisors, and potential bias in interpretation without clear methodology.
Likely results; Analyses might reveal underrepresentation of female supervisors in high-impact roles, potential correlations between female-led teams and more gender-sensitive outcomes, or biases where female-led FSPs face stricter/harsher supervisory actions due to stereotypes, highlighting needs for diversity training and proportionate approaches to avoid unintended exclusionary effects.

The only barrier could be lack of awareness of the need for disseminating such information. Once the maangement is convinced that it is important for progress and equity, I strongly feel it is possible.
The possible challenge could be around proper handling of confidentiality data and risk of mis-use of the information by some outsiders for selfish reasons. Duty of care must be applied properly to ensure safe disclosures which could help reduce unconsious bias.

1. In my organization, there are no barriers to disseminating gender diversity data. Currently, the organization upholds gender equity in its appointments to key positions and at the Job Manning level. As a supervisory organization that leads by example, the corporate guideline issued to regulated entities (Banks and Other Financial Institutions) sets a minimum requirement for the number of females on the board. As for pay. The salary/ emolument is based on job grade, it is irrespective of gender, age, or any other conditions

2. The availability of correct data could be a significant challenge. In some instances, the ultimate beneficial owner of the FSP may be masked by entities seeking a license. Some might choose to use a proxy for reasons best known to them. Although we unmask, but after the third layer of unmasking without reaching the UBO, we reject the applications.

1. Institutionally, gender is often viewed as a financial inclusion issue, rather than a supervisory concern. As a result, gender disaggregated data is usually excluded from regulatory reporting frameworks and risk assessment tools. Culturally, some of Uganda's regulatory frameworks portray a belief that financial risks affect all customers equally, leading to limited awareness of how gender differences influence risk faced by consumers or provider behavior.

2. Supervisory organizations may be uncertain about how to present gender indicators in a way that links findings to their supervisory frameworks or policies. This presents a challenge where regulators fail to identify gender differentiated risks and biases in their supervisory decisions, hence weakening both supervisory effectiveness and financial inclusion outcomes.

1.Institutional or cultural barriers may include low leadership prioritization, concerns about reputational risk if gaps are identified, and the perception that such data is not central to regulatory objectives. Confidentiality concerns around salaries, and organizational norms that discourage discussion of pay can also limit transparency. In addition, fear of internal resistance or the resource implications of addressing identified inequalities may reduce willingness to analyze and disseminate the data.

Analyzing, licensing and supervisory outputs by the gender of supervisory staff and financial service providers (FSPs) could be challenging due to limited or incomplete data, small sample sizes, and difficulty separating gender effects from factors like firm size or risk profile. There may also be sensitivity around perceived bias. However, the analysis could reveal useful insights, such as whether women-led firms face different regulatory experiences, whether supervisory approaches vary, or whether diverse teams influence outcomes, helping identify potential gaps and improve fairness in supervision.

1. At Bank of Uganda, gender diversity and pay equality data may be deprioritized because such concerns are seen as secondary to the Bank's core monetary and supervisory mandate. Limited HR information systems, staff reluctance to disclose personal information, and hierarchical workplace norms further constrain data collection and open discussion of pay disparities.

2. Breaking down licensing and supervisory outputs by the gender of supervisory staff and financial service providers could prove difficult due to gaps or inconsistencies in available data, insufficient numbers of cases for meaningful comparison, and the complexity of isolating gender-specific patterns from other influencing variables such as institutional size or risk exposure. Additionally, there may be discomfort around the perception that linking gender to supervisory performance implies bias or unfair judgment of individuals or institutions.

1. One of the barriers is this not clearly being a core mandate of the Central Bank's monetary and financial stability priorities. However, recently Gender diversity especially Executive and Board level reporting is now included in the Bank's annual report as part of the ESG framework. Dissemination of Pay equality data is limited partly requiring that at a strategic stakeholder level where as a country, we want to address equal opportunities challenges and its now mandated for institutions to report on this.
2. Limted data, comparability across different payment services based on founders and funding of the FSP, discomfort partly due to cultural perceptions of gender roles, trend analysis would be difficult as data collection may not be certain. The results may be biased which may not present the intended outcome.

I think there may be reputational concerns as it may appear to the public that our institution also acknowledges that such gender diversity gaps exist as it would be expected that we would be neutral and merit based

For the second question I think that there may be a risk in the interpretation of the results, as correlation may not necessarily imply bias. Some outcomes could be influence by other factors such as capital strength of an institution e.g one financial institution which may be performing poorly primary because of low capital compared to its pears may be being run by one gender while the rest are being run by another gender, and hence this may give the impression that the results in performance as a result of gender

Organisations may not disseminate such information for fear of being judged as not being gender sensitive. They may not engage in the whole process if they believe that they are already compliant.

1. No internal policies requiring gender pay-gap reporting, discussions about pay equality or diversity may be perceived as controversial and legacy databases may not integrate payroll, promotions, and workforce demographics.
2. Incomplete records of supervisory staff involvement in decisions, findings may be politically or institutionally sensitive and staff may feel evaluated based on personal attributes.

Some underlying institutional and cultural barriers that may prevent the Bank from collecting, analyzing, and disseminating its own gender diversity and pay equality include:
A. Weak enforcement of gender polices in institutions. Laws and policies to reduce gender inequality often suffer poor implementation and minimal or no monitoring mechanism. This becomes a challenge to the CBN in measuring and disclosing appropriate gender and pay data.

B. Women in financial institutions often face institutionalized barriers that relates from culture, hierarchy, and gender norms. These in turn affect hiring, promotion, and pay decisions. the continuous existence of these inequalities may influence internal Human Resources
processes in the CBN.


Analysing licensing and supervisory outputs by the gender of supervisors and FSP leadership could be challenging due to data gaps, cultural sensitivities, capacity constraints, and risk of misinterpretation. However, if conducted properly would produce valuable insights on whether women who establish new financial service providers, fintech face unique regulatory hurdles.

In the context of the Ethiopian financial landscape, the Ethiopian Capital Market Authority (ECMA) and other regulatory bodies face a distinct set of internal and external barriers when it comes to managing gender and pay data.
1. Internal Barriers to Collecting Own Gender & Pay Data
Even if an organization is committed to external gender advocacy, several "invisible" institutional and cultural barriers can hinder internal data transparency:
The "Pipeline" Defense: A strong cultural and institutional narrative exists that the lack of women in senior regulatory roles is purely a "pipeline issue" (e.g., fewer female graduates in specialized finance). This belief can lead to a lack of urgency in collecting pay equality data, as leadership may assume disparities are purely based on seniority rather than systemic bias.
Confidentiality and "Saving Face": In Ethiopian organizational culture, salaries and internal hierarchies are often treated with high levels of discretion. There is a fear that publishing internal pay gap data might lead to industrial unrest or "name and shame" the institution, especially if it reveals that high-profile leadership roles remain disproportionately male-dominated.
Standardized HR Silos: Historically, HR systems in many Ethiopian public institutions were designed for administrative compliance rather than strategic data analysis. Moving toward gender-disaggregated pay reporting requires a technical overhaul of payroll systems that many institutions view as a secondary priority to their primary market-oversight mandate.
2. Challenges in Disaggregating Licensing and Supervisory Outputs
Analyzing regulatory outputs (like license approvals) by the gender of both the supervisor and the Financial Service Provider (FSP) presents unique hurdles:
Data Integrity and "The Default Male": Many licensing applications are submitted under corporate names or by "lead consultants." Attributing an FSP’s gender (e.g., distinguishing a woman-led fintech from a male-led one) is difficult without a mandatory eKYC field.
Sample Size Constraints: Because the number of female-led FSPs in Ethiopia’s capital market and DFS sectors is currently small, any statistical analysis might lack significance or inadvertently "identify" specific individuals, leading to privacy concerns.
The "Mentorship vs. Bias" Paradox: In supervisory work, if a female supervisor is assigned to a female-led FSP, is a positive outcome the result of better understanding (mentorship) or favoritism (bias)? Disentangling these motives requires qualitative data that standard supervisory logs do not capture.
3. Predicted Results of Such an Analysis
If the ECMA were to successfully conduct this disaggregated analysis, the results would likely mirror global trends found by organizations like the IMF and the World Bank:
Higher Compliance Resilience: Research suggests that female supervisors often focus more on conduct and risk culture. An analysis might show that FSPs supervised by women have fewer repeated administrative breaches, as female supervisors may prioritize long-term stability over aggressive market expansion.
The "Affinity Effect": We might see that female-led FSPs (fintechs) have a higher success rate in the NBE Regulatory Sandbox when matched with diverse supervisory teams who understand the unique barriers (like collateral constraints) that female entrepreneurs face.
The "Double Burden" in Licensing: The data might reveal that female applicants for capital market licenses undergo longer "vetting" periods. This is often not due to overt discrimination but to "unconscious skepticism" where female leaders are asked for more documentation to prove their financial "standing" compared to their male counterparts.

Collecting and disseminating gender diversity and pay equality data may be constrained by outdated HR systems that do not easily generate gender-disaggregated reports. Challenges like limited in-house analytical capacity, and concerns about employee privacy also need to be mentioned. At times, central banks prefer to exercise caution about releasing sensitive data that could be misinterpreted or create reputational and industrial relations risks. All these remain key challenges.

In addition, culturally, gender metrics are normally viewed as secondary to the mandates of central banks including Bank of Uganda who core mandate is ensuring price stability and a sound financial system.

For question 2, lack of standard metrics for assessing compliance is a key challenge. As such, the results of such analysis may be less meaningful.

1) In my view, my organization has no real barriers to sharing gender diversity data because gender equity is already part of how we hire and appoint people. Roles and pay are strictly based on job grade, not gender similar to the wider Nigerian financial sector where corporate culture bias has been identified as the main barrier, not pay structures.
One challenge in analysing licensing or supervisory outcomes by the gender of staff and the gender of the FSP is getting reliable and complete data, especially since some applicants hide their real owners behind several layers of companies, making it hard to link decisions to actual people. It could also be easy for people to misunderstand the results if they don’t consider other factors that affect decisions, like risk or transparency. If the analysis is done carefully, it could show whether decisions are truly fair and gender neutral or whether there are any small differences that need attention, helping the system become more open and inclusive.

One barrier could be that gender data is not treated as a priority within the organisation , especially if the focus is mainly on financial stability rather than inclusion. There may also be cultural resistance, , weak data systems or confidentiality issues which make collecting and analysing geder data more difficult.