Economic Notes最新文献

The COVID-19 crisis has revived an old heated debate on whether significant increases in the money supply ultimately lead to higher inflation. Some observers have alluded to the quantity theory of money for that purpose, though in our view, this has sometimes been in a misleading way. Against this background, this paper seeks to clarify several aspects of the quantity theory of money, which are useful to apply it fairly in the current world. First, we review the meaning of the velocity term in the quantity equation. We argue that it has no relevance as a behavioural concept: there is no such thing as a 'desired velocity'. Rather, income velocity should be seen as a variable deriving from a system of parameters and variables related to money demand, as the monetarist approach clearly puts it, with no intrinsic relevance. Second, we clarify the practical relevance that the quantity theory approach can bear in the 21st century. Third, we review the channels and assumptions underlying the asserted quantity theory link between money growth and inflation. In light of our analysis, we conclude that the high money growth rates seen since the pandemic outbreak are unlikely to translate into higher inflation rates.

Financial inclusion programs are used as a major economic development tool in emerging economies. Human development is the major determinant of financial inclusion, creating opportunities for people to better access financial services. The study's major aim is to examine the technical efficiency of financial inclusion of Indian states by Data Envelopment Analysis using human development as input. To achieve this objective, a 3-dimensional Financial Inclusion Index (FII) is constructed for 28 major Indian states from 2010 to 2017. Empirical findings suggest most Indian states are under low and medium financial inclusion, and states with better human development have better FII status. Crucially, technical efficiency results reveal states with better human development perform better in terms of FII. The inconsistency in FII and human development ranks is majorly found in North-eastern and high-income Indian states. Therefore, policymakers in India should focus on promoting human development in low Human Development Index states.

This paper surveys the theoretical and empirical literature on the contributions of financial structure to economic growth. Whereas the theoretical literature clarifies the channels through which banks and financial markets may affect resource allocation and hence economic performance, the empirical evidence on the connection between financial structure and economic growth is mixed. There are several methodological problems inherent in the literature, including disparate studies that have been frequently mixed and confused, unsolved econometric issues (endogeneity, heterogeneity and omitted variables) and failure to consider the interaction between banks and capital markets. More rigorous single-country studies are called for.

Community banks (CBs), despite holding a fairly small share of US banking assets, provide vital financial services to key segments of the economy and fill a void untapped by larger non-community banks (Non-CBs). They face challenges brought on by a fast-changing banking landscape, evolving technology, and ever-increasing regulatory burden. To remain competitive and to gain scale-related efficiencies, CBs have been seeking mergers even as greater institutional size causes a departure from the classical relationship-based business model. This study examines performance of US CBs and Non-CBs post the Great Recession to reveal how size of these institutions may affect their business operations. Empirical findings show that CBs, compared with their larger counterparts, tend to maintain higher levels of liquidity and lower levels of capital, and demonstrate a greater dependence on core deposits, confirming that CBs focus on deposit taking and soft information-based lending strategies. Furthermore, this study suggests that CBs should not be considered a homogenous group operating under a singular business model and cautions that regulatory dialectics aimed at the banking industry should not employ a one-size-fits-all approach.

A hidden Markov model is proposed for the analysis of time-series of daily log-returns of the last 4 years of Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash. These log-returns are assumed to have a multivariate Gaussian distribution conditionally on a latent Markov process having a finite number of regimes or states. The hidden regimes represent different market phases identified through distinct vectors of expected values and variance–covariance matrices of the log-returns, so that they also differ in terms of volatility. Maximum-likelihood estimation of the model parameters is carried out by the expectation–maximisation algorithm, and regimes are singularly predicted for every time occasion according to the maximum-a-posteriori rule. Results show three positive and three negative phases of the market. In the most recent period, an increasing tendency towards positive regimes is also predicted. A rather heterogeneous correlation structure is estimated, and evidence of structural medium term trend in the correlation of Bitcoin with the other cryptocurrencies is detected.