The Community Trust Index measures the level of trust in the Nepal Red Cross Society by assessing its competencies and core values. It examines key subdimensions that shape trust perceptions, alongside community views on the Early Warning System. The findings highlight both strengths and areas for improvement, with the aim of strengthening community engagement and guiding policy development. Ultimately, the index seeks to foster a more trusted and cohesive environment, contributing to Nepal’s sustainable development and the well-being of its communities.

Sampling

The sampling employed a random stratified approach in selected districts where the Nepal Red Cross Society (NRCS) operates. Stratification was based on age group, gender, education status, with additional distinctions made between rural and urban municipalities to ensure representation across diverse communities.

The survey was conducted by the Nepal Red Cross Society (NRCS) in April and May 2025 as part of the Community Trust Index project. NRCS volunteers administered the questionnaire, focusing on issues of trust within the framework of the Building Trust initiative. In total, the survey reached 3,538 respondents across selected districts in the western provinces and the Kathmandu Valley.

See metrics: Metrics

Geographic

The sample distribution broadly reflects the population structure across the selected districts. Kathmandu (32.1% vs. 37.1%) is slightly under-represented, while Kailali (18.5% vs. 16.4%) and Banke (12.6% vs. 11%) are somewhat higher in the sample. Other districts show only minor differences compared to census data. At the province level, Sudur Paschim is over-represented, which will require post-stratification adjustment in the analysis.

Coverage

Gender and Age

The sample broadly reflects the population structure by age and gender. Women 18–39 are well aligned with census data, though women 40+ are slightly under-represented. Among men, the 18–39 group is close to population levels, while older men (60+) are over-represented. These small imbalances may require post-stratification adjustment.

Education

The education profile of the sample is closely aligned with population data. University and secondary levels match well (23.3% vs. 22.7% and 40.7% vs. 43.4%). Primary education is slightly under-represented (24.7% vs. 27.6%), while those with no formal education are somewhat over-represented (11.2% vs. 6.4%).

Employment

he available data on employment status is not directly comparable with official labour force statistics. According to the 2021 census, the employment rate was 49%, whereas the survey indicates a higher rate of over 70%. Within the survey, business (28.8%) and agriculture (24%) are the main sources of work, while 22% of respondents reported being unemployed (6% looking for work and 16% not looking). Smaller groups are engaged in full-time jobs (9.3%), irregular or informal work (7.1%), or are full-time students (8.9%). As no comparable population breakdown is available, these results should be interpreted with caution.

Source: https://censusnepal.cbs.gov.np/results/files/result-folder/Labour%20Force%20and%20Economic%20Activities%20in%20Nepal.pdf

Limitations

The data presented in this study should be interpreted with caution due to methodological limitations related to sampling and representativeness. While the survey provides valuable insights, certain groups and regions are over- or under-represented compared with census distributions. For example, Sudur Paschim province is over-represented, women aged 40+ are slightly under-represented, and older men are over-represented. Similarly, differences are observed in education levels, employment categories, and district representation.

To reduce these imbalances, post-stratification adjustments were applied using key variables such as age, gender, education, and employment status. These corrections improve comparability but cannot fully eliminate bias introduced by the sampling approach. As a result, the findings should be viewed as broadly indicative rather than fully representative of the wider population, and caution is advised when generalizing beyond the surveyed groups.

Survey Results

The charts below present the survey answers as percentages, offering visualization of the Community Trust levels by subdimensions. They illustrate the distribution of community’s perceptions of the competencies and values.

Perception of trust

Competencies

Values

Additional Questions

Contextual questions

This section presents findings on community members’ experiences with and behaviors toward the Red Cross. These questions explore interactions, perceptions, and engagement patterns, offering insights into how the Red Cross is viewed and utilized within the community.

Experiences

The survey shows that a large majority of respondents reported not having donated (76.1%) or volunteered (84.9%) with the Nepal Red Cross Society. By contrast, 42.8% reported having received aid or support, highlighting that engagement is more common through assistance than through contributions or volunteering.

Behaviours

The data shows strong community trust in NRCS, with high likelihoods of sharing information, seeking support, volunteering, recommending, providing feedback, donating, and following crisis advice.

Intention

The data shows strong community trust in NRCS, with high likelihoods of sharing information, seeking support, volunteering, recommending, providing feedback, donating, and following crisis advice.

Score

This score is derived from responses to questions that assess perceptions of competencies and values, providing a comprehensive measure of trust. A higher score indicates stronger trust, suggesting that community members believe their needs are being addressed and their values are respected. Learn more about scoring method: Methods

Overall Score

The following chart presents an analysis of competencies and values, each rated on a scale from 0 to 10. For competencies, the strongest perceptions are in Capability (7.64), Accessibility (7.37), and Responsiveness (7.33), while Openness (6.5) scores the lowest, highlighting an area where improvement may be needed.

For values, the highest ratings are for Inclusiveness (7.74), Respectfulness (7.55), and Fairness (7.62), reflecting positive perceptions of these qualities. In contrast, Transparency (5.02) and Integrity (5.69) score significantly lower, suggesting challenges in these dimensions.

Overall, the data indicates that while competencies such as capability and responsiveness are viewed positively, and values like inclusiveness and fairness are well-regarded, there is a clear need to strengthen openness, integrity, and especially transparency.

Learn more about weighting process: Weighting

Score by factors

The chart illustrates perceived competencies and values across various demographics, including age, gender, area of residence, main language, education, province, district, and employment status, as well as experiences with the Nepal Red Cross Society (NRCS) such as being a beneficiary or volunteering. It evaluates how different groups rate attributes like capability and inclusiveness, highlighting both strengths and areas needing improvement. This helps stakeholders tailor their strategies to better address the needs and perceptions of diverse communities.

Distribution of mean scores for values and competencies per demographic questions

Score by respondent profile

The score analysis by respondent profile reveals that volunteers generally rate competencies and values higher than beneficiaries and others. Among competencies, the strongest perceptions are in Capability (8.34 for volunteers, 8.29 for others) and Effectiveness (8.07 for volunteers), while Openness (as low as 6.03 among others) emerges as a weaker area across groups. In terms of values, Inclusiveness (8.34) and Fairness (8.24) stand out positively for volunteers, whereas Transparency scores lowest across all groups (4.31 for beneficiaries, 6.03 for volunteers, 5.85 for others).

This indicates that while trust is particularly strong among volunteers, weaknesses in openness and transparency are consistent across groups, highlighting the need to strengthen these dimensions to enhance overall trust and engagement.

Methods and Metrics

Metrics

Gender

Respondents by Gender
Gender Total Respondents Percentage (%)
Female 1643 50.2
Male 1632 49.8
Other or did not answer 0 0.0
Total 3275 100.0

Age

Respondents by Age Group
Age Group Total Respondents Percentage (%)
18-39 1780 54.4
40-59 938 28.6
60+ 557 17.0

Geographic

Respondents by District and Region
Region District Total Respondents Percentage (%)
Bagmati Kathmandu 1051 65.9
Bagmati Lalitpur 307 19.2
Bagmati Bhaktapur 238 14.9
Bagmati TOTAL 1596 100.0
Lumbini Banke 412 56.4
Lumbini Bardiya 319 43.6
Lumbini TOTAL 731 100.0
Sudur Paschim Kailali 607 64.0
Sudur Paschim Kanchanpur 341 36.0
Sudur Paschim TOTAL 948 100.0

Relationship with RC

Respondents by relationship with RC
Profile Total Respondents
Aid recipient 1401
Volunteer 495
Other 1699

Methods

Scoring methodology

To determine the score, we employ the following method:

  1. Survey Structure The CTI survey includes multiple questions grouped under sub-dimensions of two main categories:
    • Competencies (e.g., reliability, effectiveness, technical proficiency)
    • Values (e.g., ethics, integrity, fairness, transparency)

  1. Sub-Dimension Scoring

    Each sub-dimension comprises several survey items (questions).Respondents answer on a Likert-type scale (1 to 4 - Don’t not is excluded). For each sub-dimension:

    Sub-dimension Score = ∑ (Weighted Response Scores) / Number of Items

If weights are not empirically derived, equal weighting is typically applied to each item.

  1. Dimension Scoring

Once all sub-dimension scores are calculated, the Competency Score and Values Score are each derived as the arithmetic mean of their respective sub-dimension scores:

  • Competency Score = ∑(Sub-dimension Scores for Competency) /𝑛

  • Values Score = ∑(Sub-dimension Scores for Values)/𝑚

where 𝑛 and 𝑚 are the number of sub-dimensions in each category.

  1. Overall Scoring

    The final Community Trust Index score is the arithmetic mean of the Competency and Values scores:

  • CTI Score = (Competency Score + Values Score)/2

Weighting

Weighting vs. unweighting

To correct demographic deviations from the overall population, we applied a technique called raking. This method adjusts results based on variables such as age, gender, province, education level, and geographic (urban/rural) to align the sample with the population distribution. Data sources included the 2021 Nepal Population and Housing Census (Central Bureau of Statistics, https://censusnepal.cbs.gov.np/Home/Index/EN).

The weighted results are broadly consistent with the unweighted data, showing only small differences across most dimensions. The largest variation appears for openness and transparency, where weighted scores drop more noticeably compared with unweighted results. This suggests that weighting slightly reduces overall averages but does not substantially alter the general trends in competencies and values across respondent groups.

Drivers Correlation

Correlation matrix

Significance testing

Significance testing

When checking for significant differences between the groups we use a t-test to compare means of the competency and value questions, for all the questions, the results are indeed not significant due to the small sample size. The table shows whether a results for beneficiaries, volunteers and others are significantly different form each other. We used a 95% confidence level and corrected the p-values using a multiple comparisons correction.

Dimension Drivers Volunteer-Other Volunteer-Beneficiary Benficiary-Other
Competency Capabillity Yes No Yes
Competency Responsiveness Yes No Yes
Competency Awareness Yes No Yes
Competency Accessible Yes Yes Yes
Competency Relevance Yes No Yes
Competency Effectiveness Yes Yes Yes
Competency Openness Yes Yes Yes
Value Kindness Yes Yes Yes
Value Fairness Yes No Yes
Value Inclusiveness Yes No Yes
Value Respectfulness Yes No Yes
Value Engagement Yes No Yes
Value Integrity Yes No Yes
Value Transparency Yes No Yes
Value Neutrality Yes No Yes