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This study attempts to understand the determinants of saving behaviour using the Global Findex micro-database of India and China. Further, this study has also tried to identify the gender gap in saving behaviour for both the countries. Empirical (pooled logistic regression) results suggest that being rich, educated, employed and old favour saving than others. Women are more prone to save informally than men. The main contribution of this article is the analytical comparison between India and China, which demonstrates that in terms of saving Chinese adults are ahead of Indian adults. However, informal saving is more prevalent in India. The gender gap in saving behaviour is higher in China than in India. Our research also discovered that China’s age saving pattern is U-shaped, that is, younger and older are more likely to save than the middle-aged, which contradicts the standard life cycle model whereas this model holds for India.

Different financial literacy models adopted by formal and semi‐formal institutes have dissimilar impacts on individuals' decision‐making and financial behavior. Individuals who participated in semi‐formal financial literacy training are more likely to borrow than those who participated in the formal financial literacy training program. Decomposition suggests that people who participated in the formal financial literacy program are more likely to subscribe to insurance services than people trained from semi‐formal financial literacy program. Individuals who participated in the semi‐formal financial literacy training program are more satisfied with their current financial situation than those who participated in the formal literacy training program.

This article has proposed a new five‐dimensional financial inclusion index (FII) incorporating important indicators of digital financial services and insurance services to evaluate the scenario of financial inclusion across 136 countries for the year 2017. Here, dimensions have been created depending upon the characteristics of variables. To carry out the analysis, we have used the two‐stage principal component method. First, all five dimension indices (geographic outreach, demographic outreach, access, usage population, and usage volume) have been constructed, and then finally, the composite FII has been computed. One important addition in building this index is the inclusion of insurance‐related and digital finance‐related variables, which have not been included in previous cross‐country studies. Another contribution of this study is the identification of the dimension(s) that ultimately have the greatest influence on the financial inclusion score. We discovered that dimensions (access and usage population) constructed based on demand‐side factors dominate the level of financial inclusion. Therefore, top‐ranking countries in the access and usage population index are more likely to score higher than others in the FII. This underlines the relevance of demand‐side factors as indicators of financial inclusion over supply‐side factors.