Demographic Disparities in Bhutan’s Integrity Landscape: Unpacking Age-Driven Corruption Experiences

Demographic Disparities in Bhutan’s Integrity Landscape: Unpacking Age-Driven Corruption Experiences

Kinzang Gyeltshen^1*, Pema Tenzin², Kinley Yangzom³

¹Division of Analysis & Research, Anti-Corruption Commission, Thimphu 11001, Bhutan

^2,3Evaluation & Assessment Division, Department of Prevention & Education, Anti-Corruption Commission, Thimphu 11001, Bhutan

^*Corresponding author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90600047

Received: 06 June 2025; Accepted: 10 June 2025; Published: 28 June 2025

ABSTRACT

This paper investigates the relationship between age and experiences of corruption in Bhutan, revealing a significant negative correlation. Data from the 2022 National Integrity Assessment indicates that younger individuals are more likely to encounter or report corrupt practices, supported by a regression coefficient of = -0.447 (=−0.372, p < .001) and a Pearson’s correlation of r = -0.372 (p < .001). The findings suggest that older individuals typically exhibit stronger ethical standards and moral reasoning, contributing to a decreased likelihood of experiencing corruption. Theoretical frameworks, including Life Course Theory, Generational Theory, and Social Learning Theory, enhance our understanding of how demographic characteristics shape perceptions and experiences of corruption. Additionally, contextual factors such as socioeconomic status and cultural influences are identified as critical to the dynamics of corruption in Bhutan. The implications underscore the necessity for targeted anti-corruption strategies that address the specific vulnerabilities of different age groups. This study contributes to a nuanced understanding of Bhutan’s integrity landscape, advocating for inclusive approaches to foster ethical behavior across generations.

Keywords: Corruption Experiences, Age and corruption, National Integrity Assessment, Corruption Perception, Anti-Corruption Strategies

INTRODUCTION

Corruption involves the misuse of public authority for personal gain, which continues to pose a serious challenge to governance worldwide. Even in Bhutan, a nation known for its Gross National Happiness (GNH) philosophy, corruption erodes public trust, distorts developmental outcomes, and compromises institutional integrity. While Bhutan ranks relatively well on global indices compared to other South Asian nations, it continues to encounter subtle forms of corruption that adversely impact the provision of public services and the efficiency of bureaucratic operations (Transparency International [TI], 2024).

Bhutan has achieved considerable advancements in the institutionalization of anti-corruption mechanisms, particularly with the establishment of the Anti-Corruption Commission (ACC) in 2006.  TI (2024) ranks Bhutan among the least corrupt countries in South Asia. The ACC has played a pivotal role in fostering a culture of integrity and enhancing public awareness through educational initiatives, advocacy efforts, and enforcement strategies. Its 2023 Annual Report emphasizes that, while overt forms of corruption may be relatively limited, more insidious practices such as favoritism, nepotism, and the misuse of public resources remain prevalent and concerning (ACC, 2023).

While much of the discourse concerning corruption in Bhutan has concentrated on institutional mechanisms and policy frameworks, there exists a paucity of research examining how demographic characteristics, particularly age, influence individuals’ experiences of corruption. The literature indicates that younger populations are frequently more susceptible to corruption due to their regular interactions with employment, education, and service systems (TI, 2019; Mocan, 2008). Conversely, older individuals may perceive or report corruption distinctively based on their social positions, diminished engagement, or cultural norms.

This study aims to bridge this gap by examining demographic disparities in experiences of corruption, with a particular emphasis on age-related trends. Drawing data from Bhutan’s 2022 National Integrity Assessment (NIA), the research explores how age influences citizens’ reported experiences with corruption. By unpacking this demographic dimension, especially the increased exposure among young adults, this study contributes to a deeper understanding of Bhutan’s integrity landscape. The findings seek to inform more inclusive and targeted anti-corruption strategies that align with the experiences of diverse population groups.

Research Objective

Investigate the variations in the corruption experience concerning the participants’ demographic profiles.

Research Question

How do corruption experiences differ within Bhutan’s various demographic profiles?

Research Hypothesis

• H₀:  There is no significant difference in the experience of corruption within different age groups.
• H₁: There is a significant difference in the experience of corruption within different age groups.

LITERATURE REVIEW

Demographic Disparities in Corruption

Demographic disparities in corruption refer to the varying methods by which different population groups, characterized by age, gender, education, income, ethnicity, geographic location, and occupation, are affected by corruption (Dollar & Gatti, 1999; Gupta et al., 2002; Persson et al., 2013; Hassan, 2022). Research indicates that women are generally less involved in corrupt activities than men, which is attributed to differences in socialization, moral judgment, and risk aversion (Wei, 2023; Bullock & Jenkins, 2020; TI, 2023; UNODC, 2023). Further, higher levels of education are typically associated with lower corruption due to increased ethical awareness and legal understanding, although in some cases, educated individuals may engage in more complex forms of corruption (TI, 2023; Bullock & Jenkins, 2020). Income also plays a key role, with lower-income individuals often being both victims and perpetrators of petty corruption, while wealthier individuals may engage in grand corruption to protect or enhance their economic status (UNODC, 2023)

These distinctions underscore that corruption is not uniformly distributed throughout society; instead, specific demographic groups are more susceptible to, or disproportionately impacted by, corrupt practices attributable to structural inequalities, social norms, institutional biases, and power imbalances (UNODC, 2020; TI, 2019).

For instance, age serves a critical role in these disparities. Younger individuals, particularly those entering the workforce or navigating public systems, often display heightened vulnerability to minor corruption due to their limited experience and social capital (Bokor, 2018). Conversely, older individuals, especially those in positions of authority, may possess increased opportunities to engage in or resist corruption, although their decisions may be influenced by their career stage, ethical maturity, or considerations regarding their reputation (Bokor, 2018; Elder, 1998). Further, Sim (2024) reveals that age has a negative effect on corruption perception, indicating that older individuals tend to perceive less corruption. Studies report a correlation between the willingness to commit corruption and age; younger generations are more likely to commit fraud (Mangafić & Veselinović, 2020; Kasa et al., 2023).

The age-related variations in perception and experience of corruption illustrate that one’s life stage and generational context can significantly affect both the likelihood of encountering corruption and the inclination to respond (Sim, 2024).  Moreover, marginalized ethnic or rural communities frequently lack access to formal grievance mechanisms, elevating their susceptibility to exploitation and diminishing their capacity to report or resist corruption (OECD, 2021). These disparities underscore the imperative for targeted, equity-focused anti-corruption initiatives that acknowledge various population groups’ diverse experiences and vulnerabilities (Heywood, 2017).

Generational Dynamics in Corruption Susceptibility

Young people make up a substantial proportion of the general population and in some countries a majority are liable to be exposed to the same everyday forms of corruption faced by others (Murimi, 2018 as cited in Bergin, 2024). Age significantly influences how individuals experience corruption.  In a survey commissioned by the African Union Commission in 2018, 63 per cent of young people said they had been directly affected by corruption (Bullock & Jenkins, 2020). Furthermore, studies by Arce et al. (2024) and Owan et al (2024) on university students revealed a significant effect of age on corruption tolerance, with older students reporting lower tolerance to corrupt practices. Younger people, particularly those new to the workforce, may be more vulnerable to corrupt practices due to their limited work experience and lower organizational positions (Owan, 2024). Similarly, the Youth Integrity Assessment [YIA] 2022 reveals that the majority of parents and teachers feel that values in youth have degenerated compared to those of past generations (ACC, 2022). However, Phuntsho and Gyeltshen (2024) noted an improved level of values in youth, especially those enrolled in the Integrity Club initiative during school days.

As younger individuals navigate job markets and public services, they might encounter corruption more frequently (Mangafić & Veselinović, 2020). On the other hand, older individuals, especially those in positions of power, have greater opportunities to engage in corruption. However, they often have more established ethical standards and a greater fear of reputational damage, which can deter them from corrupt practices (TI, 2023). Research indicates a complex relationship, showing that younger and older populations experience and engage in corruption differently, influenced by their societal roles and career stages (Arce et al., 2024).

Age-related differences in digital literacy and access to information can also shape individuals’ exposure to and engagement with corruption. Guriev and Papaioannou (2022) noted that older generations may have fewer opportunities to observe or report corruption due to lack of technology or repressive regimes. Younger generations, being more digitally connected, often have a greater awareness of their rights and are more likely to encounter anti-corruption campaigns through social media and online platforms (Guriev & Papaioannou, 2022; OECD, 2021). This empowers them to recognize and resist corrupt practices, especially in contexts where e-governance and transparency tools are strong. However, digital exposure alone does not guarantee anti-corruption action. Without institutional support, civic engagement, and trust in the justice system, both younger and older citizens may struggle to report or confront corruption (Guriev & Papaioannou, 2022).

Individuals in the younger demographic frequently experience transitional life stages pursuing education, seeking employment, and interacting with public services, resulting in more frequent exposure to bureaucratic systems (Kasa et al, 2023; Mangafić & Veselinović, 2020). Such points of interaction may elevate their likelihood of encountering petty corruption. For instance, a study conducted by McGee and Benk (2023) using data from the World Values Survey, encompassing 48 countries, found younger adults in transitional economies were more inclined to report having paid bribes than older citizens, attributable to their dependence on public systems and diminished social capital. Similarly, the Global Corruption Barometer Survey 2019 published by TI (2019) underscored that Asian youth are more susceptible to bribery during employment, licensing, or education-related procedures, particularly in developing democracies. In Bhutan, an increasing number of young people are exposed to corruption related to health, education, and employment services (ACC, 2022).

Theoretical Perspectives on Age and Corruption Experiences

Life-Course Theory

Life-course theory offers a developmental perspective that facilitates understanding how individuals’ behaviors and attitudes evolve over time in response to changing roles and institutional interactions (Elder, 1998). This significantly enhances the understanding of age-related differences. With respect to corruption, this theory posits that individuals’ experiences and perceptions of corrupt practices vary among different age groups due to their respective stages in the life cycle.

Younger individuals, motivated by ambition, peer influence, or a desire to navigate complex bureaucratic systems, may justify corrupt acts as necessary for career advancement or survival (Hauk & Saez-Marti, 2002). In contrast, older individuals may have encountered corruption more frequently due to their prolonged interactions with bureaucratic systems, which can result in either desensitization or strategic navigation of such circumstances. As asserted by Elder (1998), behavioral patterns are shaped by age-related life transitions and the cumulative impact of experiences over time, thereby underscoring the importance of age in comprehending experiences of corruption. However, it is imperative to note that prolonged exposure to corrupt systems without accountability can normalize unethical practices even among older individuals (Persson et al.,  2013).

Social Learning Theory

This theory posits that individuals develop attitudes and behaviors, including those regarding corruption, through observing others within their social and institutional environments. This modeling is influenced by reinforcement processes and the perceived legitimacy of the observational behaviors (Bandura, 1977). Age plays a significant role in this learning process. An analysis by Lee et al. (2023) on role of social changes in shaping civil servants motivation to engage in unethical behaviour found out that civil servants who are more aware of the policy are not only willing to behave more ethically than those less aware but also increase their motivation to do so over time. Younger individuals tend to be more impressionable, responding acutely to behavioral cues provided by teachers, family members, and mentors, particularly in contexts that foster ethical conduct. In contrast, older individuals are likely to have encountered corrupt practices through their diverse organizational experiences, which may have become normalized within their professional networks. Such extended exposure can foster a deeper understanding of corrupt systems, potentially leading to either complicity or a motivation to resist, contingent upon their experiences and values.

Generational (Cohort) Theory

Generational or cohort theory asserts that individuals who grow up during the same historical period develop shared attitudes and values influenced by their formative years’ socio-political and institutional context  (Inglehart, 1997; Twenge et al., 2012).  According to Sim (2024), age effects reflect developmental changes within individuals over time, while cohort effects reflect differences across groups defined by birth period. These shared experiences give rise to persistent generational differences in the perceptions of governance and corruption. For instance, younger cohorts, raised during a period characterized by democratic reforms, digital transparency, and anti-corruption advocacy, tend to exhibit a lower tolerance for unethical practices and possess heightened expectations regarding institutional accountability (Sim, 2024; Welzel & Inglehart, 2006). In contrast, older generations, who matured in contexts defined by limited transparency or entrenched patronage systems, may regard certain forms of corruption as normative or even essential for effectively navigating public services. Furthermore, these generational shifts may elucidate discrepancies in the perception of corruption and reporting behaviors among different age groups.

METHODOLOGY

Research Design and Participants

This study employed a quantitative correlational design, utilizing linear regression analysis to investigate the relationship between age and the frequency of corruption experiences among individuals who made payments in cash or kind to avail services from public agencies. The study draws data from the National Integrity Assessment (NIA) 2022 dataset, which provides insights into the corruption experiences reported by service users from various public agencies. The dataset comprises 6,761 service users who have availed services from various public agencies. This study focuses on 91 respondents who reported making payments in the form of cash or in-kind to public officials while availing public services.

Variables

Independent Variable: Age Group (treated as an ordinal variable with evenly spaced intervals (such as 20 & Below, 21-30, 31-40, 41-50, 51-60, 61-70 and 71 & Above), coded numerically)

Dependent Variable: Frequency of corruption experiences (measured by frequency of payment in cash or kind)

Statistical Model and Analysis

Descriptive statistics are used to assess the frequency of various types of corruption across different demographic parameters, where no significant differences are observed except for age groups. Responses were coded and entered into STATA and SPSS for analysis. To test the hypothesis, a Simple Linear Regression (SLR) model was employed to analyze the relationship between the predictor (age group) and the outcome (frequency of corruption experiences). The following formula defines the regression model:

Where:

Y = Frequency of corruption experiences

X = Age group

 = Intercept (constant)

 = Slope coefficient (effect of age group)

 = Error term

RESULTS

Experiences of corruption in relation to Demographic Characteristics

Table 1 Educational group and corruption experiences

Table 1 highlights the experiences of corruption across different educational backgrounds. Those with ‘No Education/Others’ encounter corruption at 30.77%, indicating that limited education raises susceptibility, possibly due to a lack of awareness about ethics and legal implications. Conversely, higher education is associated with a lower likelihood of corruption.

Figure 1 shows experiences of corruption by income groups, highlighting notable disparities. Respondents in the Nu. 100,001 to Nu. 400,000 brackets report the highest corruption rate at 39.56%, followed closely by those earning Nu. 50000 or less. This indicates a greater susceptibility to corruption among lower income levels.

Figure 1 Income group and corruption experiences

Figure 2 shows 54.95% of those experiencing corruption were male, and 45.05% were female, indicating a moderate gender disparity. This suggests males are more likely to experience corruption, possibly due to social roles or greater interaction with bureaucratic systems.

Figure 2 Sex group and corruption experience

As presented in Figure 3, Individuals aged 20 and below report the least corruption. The 21-30 age group sees significant rises as they enter the workforce. Those aged 31-40 experience the highest incidence of corruption, suggesting challenges arising from increased responsibilities or economic pressures. Conversely, the 41-50 age group reports fewer experiences of corruption, indicating better stability and stronger social networks. The 51-60 cohort and older groups report fewer incidents of corruption.

Figure 3 Age group and corruption experiences

Regression Analysis Predicting Frequency Based on Age Group

Assumptions of Linear Regression

• Linearity: A scatterplot confirmed a linear relationship between age and the frequency of corruption (see Figure 4).
• Independence of Errors: The Durbin-Watson statistic is 1.112, which falls within the acceptable range (1.0–3.0), indicating no significant autocorrelation in residuals, supporting the model’s validity (see Table 4).
• Homoscedasticity: Residual plots showed randomly scattered around y=0 with no clear pattern, indicating roughly constant variance. This randomness suggests the linear regression model is appropriate.

Figure 4 Scatterplot of Standardized Residuals vs. Standardized Predicted Values

Normality of Residuals: Both the histogram (Figure 5) and Q-Q plots (Figure 6) of residuals showed approximate normality, especially between the 0.2 and 0.8 quantiles.

Figure 5 Histogram

The histogram indicates that the residuals are approximately normally distributed, as the bars closely follow the shape of the superimposed normal curve.

Figure 6 Q-Q Plot of Standardized Residuals

Points closely align with the diagonal line, particularly between 0.2 and 0.8 probabilities, suggesting normal distribution. The alignment indicates that the normality assumption for residuals is mostly satisfied.

• No Significant Outliers or Influential Points: All standardized residuals were within the range of -2 to +2, indicating no extreme outliers (see Figure 4).
• Interval or Ratio Level of the Dependent Variable: The frequency of corruption experiences was measured on an interval scale, appropriate for linear regression.

Table 2 Descriptive Statistics

The mean frequency of corruption incidents is 2.09 (SD = 1.78), which indicates a moderate level of corruption frequency on average. The mean age group is 2.68 (SD = 1.48), suggesting that participants are distributed across different age categories but cluster slightly higher than the midpoint of the age scale.

Table 3 Correlations

There is a moderately significant negative correlation between the age group and corruption frequency (r = -0.372, p < .001). This suggests that as age increases, the incidence of corruption tends to decrease.

 Table 4 Regression Model Summary, ANOVA, and Coefficient predicting corruption frequency from Age Group

The regression model explains 13.9% of the variance in corruption frequency (R² = 0.139), indicating a modest but meaningful relationship. The regression model is statistically significant (F(1,89)=14.328, p<.001), confirming that age group reliably predicts corruption frequency. The constant intercept () is 3.287 (p<.001), indicating that when the predictor (20 & below) is coded as 1, the predicted frequency of corruption is approximately 3.29. The coefficient for age group is =−0.447 (=−0.372, p<.00), meaning that for every one-unit increase in age group, the frequency of corruption experiences decreases by 0.447 units. The confidence interval for the coefficient (-0.682 to -0.212) does not include 0, further confirming the effect is statistically significant.

DISCUSSIONS 

The relationship between age and experiences of corruption has garnered attention, revealing significant insights into how ethical standards evolve across the lifespan. This study suggests a notable negative correlation between age and the reported experiences of corruption in Bhutan. This correlation, evidenced by a regression coefficient of =−0.447 (=−0.372, p<.001), and a Pearson’s correlation of r = -0.372 (p < .001), invites an exploration of the underlying factors that contribute to such disparities.

Supporting this observation, Eisenberg (2000) and McAdams (2006) articulate that moral development is a progressive journey, often enhanced by the cumulative life experiences that older individuals accrue. As people age, they develop a more nuanced understanding of the implications of corrupt behavior, which can be attributed to the development of stronger ethical standards and more refined moral reasoning. This perspective is further reinforced by Life-Course Theory, as posited by Elder (1998), which asserts that behavioral changes are closely aligned with the evolution of social roles and familiarity with institutional frameworks. Consequently, older individuals, equipped with internalized norms and competencies, navigate systems with increased ethical awareness, thereby exhibiting a decline in reported experiences of corruption.

However, it is imperative to recognize that age is not the sole predictor of corruption experiences. The model accounts for only 13.9% of the variance in corruption frequency (R² = 0.139), suggesting that other contextual variables, such as socioeconomic status, education, and cultural influences, may play a pivotal role. Treviño et al. (2000) emphasize the significance of these contextual factors in ethical decision-making, advocating for a multi-faceted approach to understanding corruption dynamics. This perspective underscores the complexity of the issue, as various social determinants converge to shape individual experiences.

Additionally, social learning theory, as articulated by Bandura (1977), explains how observational learning and reinforcement influence the development of behavior. Young individuals may observe peers or authority figures engaging in corrupt practices, which, if unpunished, can lead to the normalization of such behaviors. This observation aligns with the findings of Persson et al. (2013), who argue that tolerant institutional environments can socialize youth into complicity regarding unethical actions. Therefore, the strong inverse relationship between age and corruption frequency suggests that older individuals, having had more time to reflect on the consequences of unethical behavior, may exhibit greater resistance to engaging in corrupt activities.

Generational theory, as proposed by Inglehart (1997) and furthered by Twenge et al. (2012), introduces another layer of complexity to this discussion. These scholars suggest that generational shifts heighten sensitivity to corruption and willingness to report unethical behavior. However, younger generations, shaped by distinct societal contexts, may perceive actions as corrupt that older individuals consider routine, complicating the relationship between age and corruption experiences. This divergence in values and coping strategies, rooted in differing historical backgrounds, underscores the importance of understanding corruption through a generational lens.

Policy Implications and Recommendations

The observed age-related disparities in experiences of corruption indicate the necessity for targeted anti-corruption strategies that specifically address the distinct vulnerabilities and perspectives of various age cohorts. For younger populations, initiatives may include educational programs designed to raise awareness about corruption and empower individuals to report unethical practices. Furthermore, anti-corruption educational programs should be targeted at the middle-aged (21-40 years) group to achieve a greater impact.

Additionally, mentorship programs and active youth participation in governance processes could contribute to fostering a culture of integrity from an early age. Reiterating ethical standards through continuous training and accountability mechanisms is imperative for senior individuals, particularly those in leadership positions. Promoting transparency and fostering open communication within organizations can significantly mitigate opportunities for corrupt behavior.

Research Gap and Future Research

The Anti-Corruption Commission (ACC) of Bhutan has emphasized the necessity for gender-sensitive strategies to combat corruption, acknowledging that women’s perspectives are critical for formulating effective anti-corruption methodologies (Yangzom et al., 2024). Similarly, age, income, and educational attainment also contribute to an individual’s susceptibility to corruption within Bhutanese society. These multifaceted factors operate in various contexts concerning corruption scenarios. While global literature reiterates the importance of demographic and intersectional factors in shaping experiences of corruption, research conducted within Bhutan has not systematically explored these disparities. The present study aims to address the gap by investigating the experiences of corruption related to age, using quantitative data obtained from the NIA 2022. However, other demographic characteristics, such as educational background, gender, income, and socioeconomic status, are least considered in this study. Future research can explore these additional variables to provide a more comprehensive understanding of corruption. Longitudinal studies could be particularly valuable in assessing how changes in age and associated life stages impact corrupt behavior over time.

Limitations

While the findings establish a robust correlation between age and experiences of corruption, the analysis relies on self-reported data, which generational differences may influence in perceptions, memory, and the willingness to disclose sensitive experiences of corruption. Future studies could enhance survey data with qualitative narratives to gain a deeper understanding of the motivations underlying reported behaviors. Additionally, further analysis considering digital access, rural-urban location, and occupation may reveal intersectional vulnerabilities.

CONCLUSION

This study reveals a significant negative correlation between age and experiences of corruption in Bhutan, indicating that younger individuals are more susceptible to corrupt practices than their older counterparts. The findings suggest that as individuals age, their ethical standards and moral reasoning tend to strengthen, leading to a decrease in experiences of corruption. These insights underscore the necessity for targeted anti-corruption strategies that specifically address the vulnerabilities of younger populations, promoting educational initiatives and fostering a culture of integrity to mitigate corruption within Bhutan.

Ethical Consideration

The research design underwent a comprehensive ethical review to ensure the safeguarding of identity and privacy. Data was anonymized and securely stored to prevent unauthorized access. Participants provided their consent and were assured of the stringent confidentiality and voluntary nature of their participation in the study. This work has received ethical approval from the agency’s research committee and fully adheres to the ethical standards for research involving human subjects.

ACKNOWLEDGEMENT

 The authors express gratitude to the Anti-Corruption Commission of Bhutan (ACC) for funding this paper and sharing primary data for the study. The authors also thank the reviewers for their valuable comments and suggestions. The views and opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the Anti-Corruption Commission (ACC) of Bhutan.

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