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This paper aims to test for spatial convergence in financial inclusion across major Indian states and union territories.

After initially building an Index of Financial Inclusion (IFI) for major Indian states between 2003 and 2016, exploratory spatial data analysis (ESDA) is employed to draw inferences about mean and variance of IFI. The paper then seeks to confirm the ESDA results through spatial panel regression techniques. Finally, spatial results are correlated with results from aspatial convergence measures.

The study finds that there is no evidence of spatial convergence in financial inclusion over the study period, suggesting that those states that were relatively less financially included remained so through the study period. The study also asserts the relevance of certain important determinants, namely, per capita income, infrastructure, industrialization and gender.

This study has two limitations. First, only banking institutions are considered in measuring financial inclusion. Second, due to lack of a consistent indicator of gender participation across states, we had to employ sex ratio as a proxy.

The study suggests that policies to expand financial inclusion in Indian states, especially those with low inclusion levels are likely to benefit neighbouring states also, thereby accelerating the financial inclusion drive across states.

The study is a first in the Indian context to estimate the spatial dependence of financial inclusion and provides relevant implications for policymakers and bankers to target financial inclusion schemes in backward states.

The purpose of this paper is to assess the dynamic impact of financial inclusion on economic growth for a large number of developed and developing countries.

This study uses some panel data models such as country-fixed effect, random effect and time fixed effect regressions, panel cointegration, and panel causality tests to examine the linkage between financial inclusion and economic growth. Panel cointegration is being used to test the long run association between financial inclusion and economic growth, whereas panel causality test is used to find the direction of causality between financial inclusion and economic growth. The data on financial inclusion are taken from Sarma (2012) for the period 2004-2010.

The empirical findings reveal that there is a positive and long run relationship between financial inclusion and economic growth across 31 countries in the world. Further, panel causality test shows a bi-directional causality between financial inclusion and economic growth Thus, the study confirms that financial inclusion is one of the main drivers of economic growth.

This study has two limitations. First, this study considers only banking institutions in the analysis. Second, the period tested for the long run relationship is not long enough.

This study empirically measures the quantitative impact of financial inclusion policies pursued across the world. The study also suggests that policies emphasizing financial sector reforms in general and promoting financial inclusion in particular shall result in higher economic growth in the long run.

This study attempts to assess the long run relationship between financial inclusion and economic growth with the help of a multidimensional index of financial inclusion. Therefore, this can be a valuable contribution to the banks and policymakers.

The purpose of this paper is to examine the price–volume relationship in the bitcoin market to validate near-stock properties of bitcoin.

Daily data of bitcoin returns, returns volatility and trading volume (TV) are utilized for the period August 17, 2010–April 16, 2017. Linear and non-linear causality tests are employed to examine price–volume relationship in the bitcoin market.

The linear causality analysis indicates that the bitcoin TV cannot be used to predict return; however, the reverse causality is significant. In contrast, the non-linear causality analysis shows that there are non-linear feedbacks between the bitcoin TV and returns. The bitcoin TV, which represents new information, leads to price changes, and large positive price changes lead to increased trading activity. Similarly, in recent periods (post-break period), the results of the non-linear causality test show a unidirectional causality from TV to the volatility of returns.

This study uses the average index value of major bitcoin exchanges. But further research on this relationship using data from different bitcoin exchanges may provide further insights into the price–volume relationship of bitcoin and its near-stock properties.

These findings from the non-linear causality analysis, therefore, suggest that investors cannot simply base their decisions on the linear dynamics of the bitcoin market. This is because new information in terms of the TV is neither linearly related to the price nor it is a one-to-one kind of relationship as most investors commonly understand it to be. Rather, investors’ decisions should be based on non-linear models, in general, and the best-fitting non-linear model, in particular.

The study examines bitcoin’s near-stock properties in a price–volume relationship framework with the help of both linear and non-linear causality tests, which to the best of the authors’ knowledge remains unexplored.

Though an accumulating body of study has analysed monetary policy transmission in India, there are few studies examining the differential impact of monetary policy action. Against this backdrop, this study aims to analyse the differential impact of monetary policy on aggregate demand, aggregate supply and their components along with the general price level in India.

The study develops a structural macroeconometric model, which is primarily aggregate and eclectic in nature. The generalized method of movements is used for estimation of behavioural equations, while a Gauss–Seidel algorithm is used for model simulation purposes.

The paper presents the results of two policy simulations from the estimated model that highlight the differential impact of monetary policy. The first one, hike in the policy rate by 5% and second is a reduction in bank credit to the commercial sector by 10%. The results from the first policy simulation experiment reveal that interest hike has a significant negative impact on aggregate demand, aggregate supply and general price level. However, the maximum impact is borne by investment demand and imports followed by private consumption. While as among the components of aggregate supply maximum impact is born by infrastructure output followed by the manufacturing and services sector with the agriculture sector found to be insensitive in nature. The results from the second policy simulation experiment revealed that pure monetary shocks have a significant negative impact on aggregate demand, aggregate supply and general price level. However, the maximum impact is born by private consumption and imports followed by investment demand. While as among components of aggregate supply maximum impact is borne by infrastructure followed by the manufacturing and services sector with the agriculture sector found to be insensitive in nature. From both policy simulation experiments, the study highlighted the relative importance of the income absorption approach as opposed to the expenditure switching effect.

The results obtained in this study provides a strong framework for design the monetary policy framework. The results are in a view of the differential impact of monetary policy action among the components of both aggregate demand and aggregate supply. This reflection of differential impact has immense significance for the macroeconomic stabilization as the central bank will have to weigh the varying repercussion of its actions on different sectors. For instance, the decline in output after monetary tightening might be conceived as mild from an overall perspective, but it can be appreciable for some sectors. This differential influence will have an implication for policy design to care for distributional aspects, which otherwise could be neglected/disregarded. Similarly, the output decline may be as a result of either consumption postponement or a temporary slowdown in investment. However, the one emanating due to investment decline will have lasting growth implications compared to a decline in consumer demand. In addition, the relative strength of expenditure changing or expenditure switching policies of trade balance stabilization may have varying consequences in the aftermath of monetary policy shock. Accordingly information on the relative sensitiveness/insensitiveness of different sectors/ components of aggregate demand towards monetary policy actions furnish valuable insights to monetary authorities in framing appropriate policy.

The work carried out in the present paper is motivated by the fact that although a number of studies have examined the monetary transmission mechanism in India, a very few studies examining the differential impact of monetary policy action. However, to the best of the knowledge, there is no such studies, which have examined the differential impact of monetary policy in the structural macro-econometric framework. The paper will enrich the existing literature by providing a detailed account of the differential impact of monetary policy among the components of both aggregate demand and aggregate supply in response to an interest rate hike, as well as a decrease in the money supply.

The purpose of this paper is to investigate empirically the price discovery and volatility spillovers in Indian spot-futures commodity markets.

The study has used four futures and spot indices of Multi-Commodity Exchange, Mumbai. The study also employs vector error correction model (VECM) and bivariate exponential Garch model (EGARCH) to analyze the price discovery and volatility spillovers in Indian spot-futures commodity market.

The VECM shows that agriculture future price index (LAGRIFP), energy future price index (LENERGYFP) and aggregate commodity index (LCOMDEXFP) effectively serve the price discovery function in the spot market implying that there is a flow of information from future to spot commodity markets but the reverse causality does not exist. There is no cointegrating relationship between metal future price index (LMETALFP) and metal spot price index (LMETALSP). Besides the bivariate EGARCH model indicates that although the innovations in one market can predict the volatility in another market, the volatility spillovers from future to the spot market are dominant in the case of LENERGY and LCOMDEX index while LAGRISP acts as a source of volatility toward the agri-futures market.

The results are aggregate in nature. Further study at disaggregated level will provide further insights on behavior of specific commodity prices and the price discovery process.

The paper provides useful information about the evolution and structures of futures commodity trading in India, related literature and relevant methodology concerning the hypotheses.

The purpose of this paper is to test a discrete time asset pricing model where a non‐marketable asset (human capital), along with other factors predicting stock returns, explain risk return relationship. The paper will add to the literature on risk return relationship with human capital by investigating the hypothesis that human capital is a significant factor affecting stock prices.

The dynamic inter‐linkages of factors representing financial and human components of wealth in predicting stock returns is tested in the Indian market for the period of 1996:04 to 2005:06. The procedures employed include Granger causality tests, impulse response functions and seemingly unrelated regression estimates.

Empirical findings validate the model that including human capital as a proxy for aggregate wealth in the economy can better predict stock prices than the standard empirical capital asset pricing model. There is a Granger cause relationship between security prices and labor income and it is further concluded that labor and dividend are significant factors affecting security prices.

This is one of the first papers to study the human capital aspect in predicting stock returns in the Indian market. In addition, the paper provides important insights into the causal relationship of human capital and market return in explaining the risk return relationship.

The purpose of this paper is to construct a robust macroeconomic performance (MEP) index of the State economies of an emerging market economy, i.e. India.

Two variants of data envelopment analysis (DEA) models – radial and non‐radial – are proposed to construct the macroeconomic policy performance of 22 Indian State economies in the post‐economic reforms era covering the period: 1994‐1995 to 2001‐2002, using three macroeconomic indicators: growth in gross state domestic product, price stability, and fiscal deficit.

The authors' three broad empirical findings are: first, the radial and non‐radial DEA models yield significantly different rankings of State economies in terms of their MEP index scores; second, as against the use of only growth in gross state domestic product and price stability for MEP measure, the inclusion of fiscal deficit as an additional indicator yields a noticeable improvement not only in the State MEP index scores, but also in their rankings, thus providing the evidence of relatively successful attempt by the Indian States in reducing fiscal deficit, in general, and legislating FRBM bill, in particular; and third, a positive significant correlation between foreign direct investment (FDI) and MEP indicates that a State's overall macroeconomic policy performance does matter to attract FDI.

Since the DEA models employed in this study ignore the possibility of asymmetric shocks, the MEP results might be questioned in this deterministic setting. However, the study period has been smooth and has not been subject to any major changes in the State economic policies. Therefore, the MEP results might not be susceptible such changes. However, further research is desired on examining the macroeconomic policy performance behavior of Indian States using bootstrapping DEA.

None of the past Indian studies were able to give a comprehensive picture concerning the MEP behavior of Indian State economies, since the methodologies adopted in those studies were not suitable to take into consideration all the macro indicators at a time. Therefore, this present study is considered the first of its kind in assessing the MEP index of the Indian State economies by simultaneously considering all the macro indicators.