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The purpose of this research is to evaluate corruption in the context of India, spanning the period between 1988 and 2021. Additionally, it aims to provide an in-depth comprehension of the factors that drive its prevalence and to propose policy directives for addressing these underlying issues.

The study instead of relying on perception-based measures, takes a distinct approach by formulating a corruption index derived from reported instances, thus ensuring a more objective assessment. Furthermore, we employ stochastic frontier analysis to tackle the issue of under-reporting within the corruption index based on reported cases. Subsequently, an auto regressive distributed lag (ARDL) methodology is applied to ascertain the principal drivers of corruption, encompassing both long and short factors.

This study reveals that corruption in India is notably influenced by economic growth and income inequality. Conversely, government effectiveness and globalization display a tendency to mitigate corruption. However, our rigorous analysis demonstrates that financial development does not wield a substantial influence in our study. Moreover, our inquiry uncovers a nonlinear relationship between economic growth and corruption. Additionally, we ascertain that the long run and short run impacts of corruption remain relatively stable across both models utilized in our study.

This study differs from previous research in the subsequent manners. Primarily, we employed an objective measure to formulate the corruption index, coupled with addressing the underreporting issues via stochastic frontier analysis. Moreover, this study pioneers the identification of a non-linear relationship between corruption and economic growth within the Indian context, a facet unexplored in previous investigations.

The purpose of this paper is to measure and investigate the determinants of corruption across Indian states.

This research begins by developing a corruption index (CI) based on official data on corruption cases. Second, the authors also create an adjusted corruption index (ACI) using the stochastic frontier modelling approach to address corruption case under-reporting. Third, they use a panel feasible generalised least square (FGLS) technique to discover corruption determinants.

The results show that approximately 77% of corruption cases in India go under-reported, which is a major concern. The results also show that per capita income, government spending, law and order and urbanisation are the important factors affecting corruption at the regional level.

The findings of the study emphasise the need to address the huge under-reporting of corruption data. From a policy perspective, the governments need to emphasise factors like per capita income, government spending, law and order and urbanisation to tackle corruption in India.

Corruption is a complex global phenomenon, and several studies have conducted detailed research on the causes of corruption at all levels (regional and cross national), but this study differs from previous studies in the following ways. First, the authors used a more objective measure of corruption by developing a CI at the state level. Second, the study uses stochastic frontier analysis, which is novel in the literature on corruption analysis, to address the most critical component of under-reporting in corruption data. Finally, the study attempts to examine the factors of corruption at the regional level, which is again innovative in nature.

The idea of relating health, education and other social sector variables with growth and development, or trying to find any convergence among similar countries with respect to such macroeconomic indicators is the basic idea of this chapter, which explores the concept of enhancing welfare through a subjective route, specifically by way of expenditure in health care. By way of testing σ- and β-convergence of current healthcare expenditure per capita of the BRICS countries for the period 2006–2018, it has been found that these countries catch up with significant convergence. It has also been obtained through panel data analysis that such convergence is significantly explained by gross domestic product (GDP) per capita, life expectancy at birth, elderly ratio, CO₂ emissions and prevalence of undernourishment, all of which are in accordance with conventional hypotheses. This chapter claims that, by way of convergence, the BRICS nations may emerge as a new economic power, and expenditure on health care is one of the major areas, among other social sector variables that will play an important role.