Corruption Vs Human Development: An Empirical Analysis

Abstract

Background: Corruption is a significant issue facing the modern world. Corruption hinders social, political, economic, and environmental growth, causing inequality and a decrease in public spending. Research demonstrates that corruption adversely affects human capital improvement, ecological sustainability, and political frameworks, hindering progress at both high and low levels. Method: This paper examines the relationship between corruption and human development and how each influences the other, using 2018 data from 100 countries. This study uses a linear regression model and statistical methods like goodness of fit, hypothesis testing, and correlation coefficients to look into how the Corruption Perception Index (CPI) and the Human Development Index (HDI) affect each other. Result: The corruption perception index (CPI) and the human development index (HDI) have an important positive correlation (0.7708). Models explain 62.08% of HDI variation and 57.28% of CPI variation, depending on regression analysis. This implies that higher CPI (less corruption) notably enhances HDI (human development), and vice versa. Discussion: Findings show a strong correlation between higher human development (higher HDI) and lesser corruption (higher CPI), which has a major impact on social and economic circumstances. Though several countries have progressed with decreasing corruption, perceptions across the world remain unchanged, and numerous fields are still experiencing difficulties. Conclusion: This study's findings demonstrate a significant inverse relationship between corruption and human development. With the goal of fighting corruption and enhancing human development, it emphasizes the importance of a wide range of indicators, including technical developments, institutional reforms, and international cooperation.

Introduction

Improvement in human well-being has been facing various hindrances over the time. While some are natural, artificial, or man-made. Corruption is one of those such man-made hindrances. Many studies have been carried out till date to measure and explain how this corruption by a few is disintegrating the prospects for the rest of society. The Oxford English Dictionary describes corruption as “dishonest or fraudulent conduct by those in power, typically involving bribery.” According to Transparency International, corruption is “the abuse of entrusted power for private gain” (Montagna & Harris, 2019). Corrupt practices by the government have wide-reaching social, political, economic, and environmental impacts on society, leading to reduced investments in public goods, damaging market competition, and increasing inequality. The Human Development Index (HDI) is a statistic developed and compiled by the United Nations to measure and various countries' levels of social and economic development. It consists of three main topics of interest: education, life expectancy, and gross national income per capita. By using this index, one may compare the development levels of various nations and trace changes in development over time. The ecological balance is harmed by the widespread use of priceless natural resources for personal benefit and for indiscriminate mining, logging, petroleum drilling, etc. many groups provide bribes.

The influence of corruption on the rise of human capital in developing nations shows that corruption in both direct and indirect ways hampers the growth of human capital, mainly through its impact on standard of living and knowledge (Linhartova & Pucek, 2024). Green growth and sustainable environments are seriously hampered by corruption, which has negative effects all over the countries and hinders the improvement of ecological outcomes in parallel with economic growth (Tawiah, Zakari & Alvarado, 2023). High as well as low levels of corruption restrict growth, whereas moderate levels may result in a positive effect. Optimal level of corruption at which its effects change from advantageous to disadvantageous (Trabelsi, 2024). To combat corruption, import and export operations also need to simplify, increase in income, standard of living, and enhance political systems, particularly democracy (Audi & Ali, 2017). In terms of government expenditures and per capita income, the perfect extent of monitoring is for preventing corruption. Due to budgetary constraints, supervisory input should be minimal at low-income levels but increases significantly with income. Since honest leaders are unable to totally eradicate others' dishonest acts, partial corruption persists in middle-class societies (Zhang et al., 2023). Corruption in the healthcare industry during COVID-19, stressing major issues such as medical supply fraud, worse service quality, and the demand for public participation in detecting corruption (Ruzanov & Zharlygassinov, 2021).

Objective of Study

This particular study investigates statistically the interaction of corruption and human development across 100 countries, attempting to explain both how corruption varies and how variations in human development vary.

I. Find the correlation coefficient between HDI and CPI.

II. Regress HDI on CPI using a linear regression model.

i. Test the hypothesis that the slope coefficient is equal to zero.

ii. Find the Goodness of Fit (r²) of the model.

III. Regress CPI on HDI using a linear regression model.

i. Test the hypothesis that the slope coefficient is equal to zero.

ii. Find the Goodness of Fit (r²) of the model.

Methodology

Theoretical Background of Correlation Coefficient:

This study uses the tools of simple correlation and regression. Correlation means the association or interdependence between two random variables. If two variables are so related that a change in the magnitude of one of them is accompanied by a change in the magnitude of the other, they are said to be correlated.

Figure 1.

Where σ_x and σ_y are the standard deviations of x and y respectively, and cov (x, y) denotes the covariance of x and y.

Theoretical Background of Hypothesis Testing

In order to reach decisions about population on the basis of sample observations drawn from the population, it is required to make certain assumptions or guesses about the population parameter. Such an assumption is called a statistical hypothesis, which may or may not be true, and the procedure is called testing of hypothesis or test of significance.

Y_{i =} α+ βx_i+u_i where i=1, 2, 3…………., n (For regression HDI on CPI, we denote α=β₁ and β=β₂. Similarly, regression CPI on HDI, we denote α=β₃ and β=β₄)

Where u_i represents the stochastic disturbance terms or random errors. The estimated regression equation be: Yi​​^=α^+β^​X_i, . Where α^ and β^ are the estimates of the parameters α and β, respectively, Yt​​^and is the estimated value of Y for any given value of X=X_i. It is not expected that all observations will fall on the estimated regression line. This implies the true value yi- α^-β^​X_i ​and the estimated value will differ, and this difference is denoted by: [e_i can be positive, negative or zero]. After estimating the values of α and β, the level of significance is calculated. For the purpose of the paper, we will concentrate on the significance test of β. Then constructed H₀: β=0 (null) and H₁: β<0 (alternative) hypothesis.

Since σ2/u is not known, the above test static cannot be calculated numerically. s2/u may use in place of σ2/u. After that t-value is used where,

Figure 2.

Next TSS, ESS, and RSS values are determined. H₀ will be rejected at 5% level of significance if the calculated value of t < -t_{0.05, n-2}

Goodness of fit of the regression equation

The coefficient of determination (r_xy²) is commonly used to measure the goodness of fit of a regression line. It measures the proportion or percentage of the total variation in the regress and explained by the regression model.

Figure 3.

Results

This is a cross-section study, which is based on secondary data, includes data from the HDI and CPI for 100 countries in 2018. Corruption Perceptions Index (CPI), a well-known measure of perceived level of corruption published by Transparency International. It is measured on a scale of 0 to 100, where 0 is highly corrupt and 100 is incorrupt. The well-known measure of human development was first published by UNDP (United Nations Development Programme) in 1990. The value of HDI lies between 0 and 1, where 0 is low development and 1 is highest development.

The CPI data for the year 2018 has been collected from the Transparency International website. (https://www.transparency.org/en/cpi/2018) and the HDI data for the year 2018 has been collected from (Human Development Index - HDI 2018 | countryeconomy.com)

Figure 4. Table 1: HDI & CPI Data and Calculations for Correlation Coefficient and Regression

Figure 5. Figure 1 : Dual-Axis Scatter Plot with Trend Lines of HDI and CPI

Figure 1 shows the graphical representation of values of CPI and HDI of 100 countries, where the left-side vertical axisdenotes CPI and the right-side vertical axis illustrate HDI. The CPI line goes up and down, indicating that the level of perceived corruption varies greatly across the countries. The red dashed line represents the linear trend for the CPI, which is relatively flat, showing a slight decrease over the data points, indicating a small downward trend in perceived corruption. The purple dashed line represents the linear trend for the HDI. This line is also relatively flat, showing a slight increase over the data points indicating a small upward trend in human development. The HDI values are more stable, generally staying in the middle to upper range. The trend lines indicate that while both indices fluctuate, their overall averages are relatively steady.

Statistically Analysis:

The correlation coefficient between two variables, is r_{xy= 0.7708 (approx)} which is positive and moderately high, implies that less corruption (higher value of CPI) is positively associated with higher human development. r²= 0.59, it implies the explanatory variables explain about 59% of the explained variable.

Regressing HDI on CPI

In our model: Y_i​=β₁​+β₂​X_i​+u_i​, (For regression HDI on CPI, we denote α=β₁ and β=β₂.). The regression equation is Y_i​​^=0.46054+0.006X_i​

Test of Hypothesis: Null Hypothesis H_{o : β2=0}

Alternative Hypothesis H_{o : β2≠0}

Under the normality assumption, the test statistic- ‘t’ follows the t-distribution with (n – 2) degrees of freedom.

Figure 6.

The critical Region at 5% level of significance is | t | ≥ 1.9739. Since the calculated t is much greater than the tabulated t, the null hypothesis is rejected, and the alternative hypothesis is accepted. A 10% rise in CPI causes a 6% rise in the HDI.

Goodness of fit of the regression equation

Figure 7.

The coefficient of determination (r_xy²) is 0.6208, means fluctuations in the rate of corruption in a country have been able to explain the fluctuations in human development by 62.08%.

Regressing CPI on HDI:

In our model: X_i​=β₃​+β_4​Y_i​+v_i​ (regression CPI on HDI, we denote α=β₃ and β=β₄

Regression equation of CPI on HDI: x _i ^ ​=−26.538+99.4Y_i

Test of significance:

Figure 8.

The critical Region at 5% level of significance is | t | ≥ 1.9739. Calculated t is much greater than tabulated t; hence the alternative hypothesis is accepted. A 10% rise in HDI causes a 9.9% rise in the CPI.

Goodness of fit of the regression equation

Figure 9.

The coefficient of determination (r_xy²) is 0.5728, which implies fluctuation in human development in a country has been able to explain the fluctuations in the rate of corruption by 57.28%.

Here r² ≠ r_xy2​ ≠ r_yx2 due to the different parameters, TSS and ESS use for calculating the regression of HDI on CPI (β_1,β₂₎ and the regression of CPI on HDI (β_3, β₄). And mostly r_xy² ≠ r_xy² due to different disturbance terms mentioned as u and v.

Discussion

The socio-economic conditions of countries are often analysed through indices like the Corruption Perception Index (CPI) and the Human Development Index (HDI). The nonlinear effects of corruption on economic development are examined via regression-tree analysis utilising statistics from 103 countries (1996–2017), and the Solow model's heterogeneity is shown by identifying two different types of nations with comparable development models and illustrating how the impact of traditional growth factors is influenced by levels of corruption (Beyaert, García-Solanes & Lopez-Gomez, 2023). The relationship between unemployment and democracy in evaluating corruption levels in 80 underdeveloped countries between 1990 and 2018 shows that, although democracy has a positive effect on lowering corruption, this impact is hampered by higher rates of unemployment. The result suggests that reducing unemployment is crucial for enhancing corruption prevention (Oueghlissi & Derbali, 2023). The influence of corruption on the rise of human resources in Sub-Saharan Africa using data from 35 countries (1996–2018), showing that corruption decreases life expectancy, average study duration, and the quality of educational and health amenities (Bazie, Thiombiano, & Maiga, 2023). Empirical studies using disaggregated data demonstrate the adverse relationship between sustainable development and corruption in sub-Saharan Africa (Hope, 2024). The robustness analysis found a nonlinear relation between GDP and poverty, highlighting the various manners in which corruption, particularly in developing nations, contributes to poverty (Castro, 2019). Although corruption's adverse effects often hamper development, especially in 83 developing countries, it may be beneficial in regions like North Africa, the Middle East, and Latin America. Building public institutions and arguing that composite indicators offer an expanded view of the complexity of corruption are crucial (Spyromitros & Panagiotidis, 2022). A statistical model demonstrates a positive correlation between the Human Development Index (HDI) and the Corruption Perception Index (CPI), indicating the rise of populism in European democracies. Also, a significant link exists between these indicators and the rise of populism, particularly after the financial crisis-induced social dissatisfaction (Sarabiaet al., 2019). Based on data from 48 sub-Saharan African countries between 2012 and 2020, perceptions of corruption are positively associated with a greater Human Capital Index (HCI) and inversely associated with a greater Online Service Index (OSI), while the Telecommunication Infrastructure Index (TII) had no significance (Paul & Adams, 2023).

Following several prior studies, the current study tries to show a significant association between the Corruption Perception Index (CPI) and the Human Development Index (HDI) by using specific statistical factors. The correlation value of 0.7708 indicates that HDI, which represents greater human development, tends to increase together with CPI, which suggests less corruption. Based on the regression analyses, there is a substantial relationship between the two variables. About 62.08% of the deviation in HDI and 57.28% of the variation in CPI are explained by the models for the regression of HDI on CPI and CPI on HDI, respectively. The statistical relevance of these connections is verified by the hypothesis tests, suggesting that higher CPI (less corruption) leads to higher HDI, and higher HDI promotes less corruption. Overall, the results show a strong correlation between degrees of corruption and human development, with improvements in one leading to considerable progress in the other. This emphasises the importance of anti-corruption initiatives as an approach to boost human development and vice versa.

Despite increased transparency over the past 30 years, perceptions of corruption have remained stagnant. Transparency International (2021) notes that the global average score for corruption perception has remained at 43 out of 100 for the last decade, with two-thirds of countries scoring below 50. However, nations like Mexico, India, Brazil, and Ukraine have made significant strides in increasing public access to demographic, administrative, and judicial data (Mungiu-Pippidi, 2023). Around the world, countries have improved their scores, proving that development is possible in any circumstance. Western Europe and the European Union (EU) experienced a decline in their average score to 65 out of 100 as political integrity and checks and balances were undermined (United Nations Development Programme, 2023/2024). With an average score of 35, Eastern Europe and Central Asia struggle with widespread corruption, growing authoritarianism, and an unstable legal system. While some Sub-Saharan African nations have improved, the majority still have low scores. The Middle East and North Africa region's average score of 38 indicates minimal progress, indicating ongoing difficulties with governmental corruption and war. Asia Pacific's average score is a persistently stable 45, while formerly top-scoring nations are regressing. The Americas have an average of 43 percent amnesty rate, which is being made possible by a lack of judicial independence and a weaker rule of law (Transparency International the global coalition against corruption, 2023).

Limitations

This study analyses statistics from only 100 different countries to explore the relationship between HDI and CPI using a linear regression model and statistical methods like goodness of fit, hypothesis testing, and correlation coefficients. Further studies may expand to a larger global dataset with the goal of discovering patterns, recording long-term or regional changes using different models, and studying specific regional or cultural factors affecting corruption and human development.

Conclusion

This study highlights a critical link between human development and corruption, demonstrating that higher levels of human development are associated with lower levels of corruption and vice versa across 100 countries. Two regression analyses show that both CPI and HDI influence each other confidently. The results highlight a significant positive link between the Corruption Perception Index (CPI) and the Human Development Index (HDI) and how important it is to eradicate corruption as a complicated issue that affects public infrastructure, political stability, economic equality, and transparency. In order to effectively combat corruption, broad strategies involving institutional, social, and economic improvements are required. Preventing corruption perception may involve better internet services and improved human capacity through training. There should be improvements in the political system and the establishment of the power of democracy to get rid of corruption. To combat corruption in recent years, it is crucial to strengthen institutional frameworks by enhancing transparency and accountability. Additionally, leveraging technology for greater transparency in government and business practices, along with fostering international cooperation, can help address and mitigate corruption on a broader scale. Future studies should look at the long-term, regional patterns between the HDI and CPI, with an emphasis on the effects of corruption on income equality, healthcare, and education. Research on the efficaciousness of anti-corruption measures, like legal reforms and transparency initiatives, may shed light on human development. Along with examining the influence of cultural norms, comparative studies on nations that have successfully or unsuccessfully combated corruption may also be able to provide best practices for promoting integrity and accountability on a global scale.

Conflict Of Interest

The author declares that they have no conflict of interests.

Acknowledgement

I have heartfelt gratitude to my college, my professors, and the institution for their guidance and direction. Additionally, I am grateful to my family and workplace for their support throughout this journey.