Purpose: The study was conducted to formulate the relationship between the three dimensions of institutes, namely cognitive, regulative and, normative dimensions of institutes. The model was formulated using SmartPLS 3. Research Methodology: In this study, the Five-point Likert scale was used and the data were collected from postgraduate students in two states of Uttar Pradesh and Jharkhand. SmartPLS 3 was used to formulate the model and establish the relationship between the three dimensions of institutes. Results: There exists a positive relationship between "Cognitive dimension and normative dimension"; "Regulative dimensions and Cognitive dimensions; and between Regulative dimensions and Normative dimensions" respectively of institutes. Limitations: Study is conducted on a small sample of 100 postgraduate students from two states of India namely Jharkhand and Uttar Pradesh which may decrease the reliability of the study. Contribution: In this study, a relationship is established by using smart PLS 3 between the three dimensions of institutes required for entrepreneurship development with the help of Likert scale developed based on previous studies which can help in measuring the country institutional profile and provide the base for studying the role of these dimension of the institute in entrepreneurial intention growth among the postgraduate students in states of India.

Purpose: This paper predicted the direct relationship between the four indicators of “Financial Inclusion” and “GDP per-capita” of the country. Previous studies presented in this scenario are qualitative in nature. Research methodology: In this paper, “step-wise multiple linear regression” is used to establish the cause-and-effect relationship between the four indicators of “financial inclusion”; “Deposit accounts per 1000 population”; “Number of credit accounts per 1,000 people”; “Bank branches per 100,000 of adult population”, and “ATMs per 100,000 of adult population” and “GDP per capita”. Results: Regression model showed only “Credit accounts per 1,000 people” have a significant relationship with the “GDP per capita”. In this article, secondary data were obtained from the RBI website and the reports of international financial institutes. Limitations: Data on “ATMs” and “Bank branches per 100,000 of the adult population” is not present before 2004, decreasing the depth of analysis. Contribution: There is a cause-and-effect relationship between the country’s “GDP per capita” and the “F.I.” “Credit accounts per 1,000 people” only have a significant relationship with GDP per capita, so the change in the number of credit account will show a change in GDP per capita for Indian economy. Keywords: Financial inclusion (F.I), GDP (Gross Domestic Product) per capita, Deposit accounts, Credit accounts, ATMs (Automated Teller Machines), Bank branches