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This article examines the empirical relationship between electricity consumption, economic growth, energy prices and technology development for India by taking annual time series data from 1981 to 2017. By using the ARDL bounds testing approach to co-integration, the study found long-run equilibrium relationship does exist among the variables. The article reports the existence of positive and significant impact of economic growth on electricity consumption, whereas technological development negatively affects electricity consumption in both the long run and short run. The Granger causality results reveal the presence of unidirectional causality from economic growth and technological development to electricity consumption in India. Therefore, the present study suggests policy makers in India to increase investment in electricity infrastructure to support high economic growth in the country. Further, the policy makers and the government should encourage more technological innovation to minimise usage of fossil fuels and support the use of green energy. This action could help the economy achieve a sustainable economic growth with better environmental quality. JEL Codes: C22, O4, O13, Q43, Q48

Abstract
This present study aims to examine the dynamic relationship between globalization, institutional reforms, and financial development in the context of South Asian economies over the period 1985–2015 by employing panel data methods. The Westerlund (2007) panel co-integration test result indicates that there exists a long-run equilibrium relationship between globalization, institutional reforms, and financial development. The findings of panel dynamic ordinary least squares (PDOLS) indicate that the effect of globalization is positive and statistically significant to financial development. Furthermore, the empirical evidence of a dynamic panel error-correction model reveals a unidirectional causal relationship running from globalization and institutional reform to financial development. In terms of policy recom-mendations, the study suggests that it is vital to focus on globalization and institutional reforms to promote effectiveness of the financial system and economic performance in the region.
JEL Classifications: F19, G29
globalization, institutional reforms, South Asia, Panel Dynamic OLS (PDOLS), panel causality

Purpose– The purpose of this paper is to examine the contribution of financial development to poverty reduction in 11 South Asian developing countries using panel data set over the time period 1990-2012.Design/methodology/approach– The stationarity properties are checked by using Levin-Lin-Chu and Im-Pesaran-Shin panel unit root tests. The paper applied the Pedroni's panel co-integration test to examine the existence of long-run relationship. The coefficients of co-integration are examined by fully modified OLS (FMOLS) and the causal link is checked by panel causality test.Findings– The empirical results of Pedroni co-integration test confirm a long-run relationship between financial development and poverty reduction in South Asian developing economies. The findings of FMOLS method confirm a strong and positive relationship between financial development, trade openness, inflation and poverty reduction. Results of panel causality test indicate that there is a unidirectional causality running from financial development to poverty reduction variable.Research limitations/implications– The present study recommends appropriate economic and financial reforms focussing on financial inclusion to reduce poverty in selected South Asian economies.Originality/value– This paper is the first of its kind to empirically examine the causal relationship between financial sector development and poverty reduction in South Asian economies using modern econometric techniques.

Purpose– The purpose of this paper is to examine the relationship between financial sector development and poverty reduction in India using annual data from 1970 to 2012. The paper attempts to answer the critical question: does financial sector development lead to poverty reduction?Design/methodology/approach– Stationarity properties of the series are checked by using Ng-Perron unit root test. The paper uses the Auto Regressive Distributed Lag (ARDL) bound testing approach to co-integration to examine the existence of long-run relationship; error-correction mechanism for the short-run dynamics and Granger non-causality test to test the direction of causality.Findings– The co-integration test confirms a long-run relationship between financial development and poverty reduction for India. The ARDL test results suggest that financial development and economic growth reduces poverty in both long run and short run. The causality test confirms that there is a positive and unidirectional causality running from financial development to poverty reduction.Research limitations/implications– This study implies that poverty in India can be reduced by financial inclusion and financial accessibility to the poor. For a fast growing economy with respect to financial sector development this may have far-reaching implication toward inclusive growth.Originality/value– This paper is the first of its kind to empirically examine the causal relationship between financial sector development and poverty reduction in India using modern econometric techniques.

Purpose– The purpose of this paper is to examine the relationship between financial development and income inequality in India using annual data from 1982-2012.Design/methodology/approach– Stationarity properties of the series are checked by using ADF, DF-GLS, KPSS and Ng- Perron unit root tests. The paper applied the auto regressive distributed lag (ARDL) bound testing approach to co-integration to examine the existence of long run relationship; and error correction mechanism for the short run dynamics.Findings– The co-integration test confirms a long run relationship between financial development and income inequality for India. The ARDL test results suggest that financial development, economic growth, inflation aggravates the income inequality in both long run and short run. However, trade openness reduces the gap between rich and poor in India.Research limitations/implications– The present recommend for appropriate economic and financial reforms focussing on financial inclusion to reduce income inequality in India.Originality/value– Till date, there is hardly any study that makes a clear comparison between market-based indicator and bank based indicator of financial development in India and those examining the relationship between finance and income inequality nexus. Further there is hardly any study to include gini coefficient as a proxy for inequality for India and apply ARDL techniques of co-integration, using the basic principles of GJ hypothesis and provide short run and long run dynamics for India. So the contribution of the paper is to fill these research gaps.

AbstractThe present study aims to explore the relationship between ICT diffusion and poverty reduction in SAARC countries using panel data from 2005 to 2020. This study uses econometric techniques robust to cross‐sectional dependence (CSD) including Pesaran's CSD tests; second‐generation unit root test; Pedroni, Kao, Westerlund cointegration tests; CS‐ARDL, Driscoll‐Kraay (DK) standard error approach; and D&H causality test. The investigation is based on the ICT diffusion index constructed using principal component analysis (PCA). The study's major finding shows that ICT diffusion reduces poverty both in the long and short run, indicating the favorable impact of ICT on the development process in SAARC countries. Further, economic growth, financial development, and remittances all serve to minimize the poverty level. The causality test reveals bidirectional causation between ICT diffusion and poverty reduction. The study highlights the crucial role of ICT diffusion and selected economic variables in reducing poverty. The findings of the present research shall benefit policymakers to formulate appropriate policies and programs to improve the well‐being of people and enhance macroeconomic performance, which impacts both the societal and environmental development of a country.

AbstractThis paper examines the payments made to minority producers, focused on African American producers, from the COVID‐19 program, Coronavirus Food Assistance Program (CFAP), of the United States Department of Agriculture (USDA) and compares it with one of the other more recent ad hoc program payments, the Market Facilitation Program (MFP). There were two rounds of the CFAP, and combinedly (as of March 2022), the program made direct payments of $31.0 billion ($11.8 billion from CFAP 1 and $19.2 billion from CFAP 2) starting in 2020. The MFP made a total payment of $23.5 billion (in two rounds, MFP 2018 and MFP 2019) to producers affected by the retaliatory tariffs placed on US producers by trade partners across multiple years. CFAP made almost $600 million in direct payments to minority producers, including Black or African American producers. Black or African American only producers received more than $52 million in CFAP payments. CFAP payments were proportional to the value of agricultural commodity sold for most minority producers. The 2017 Census of Agriculture showed that the majority of minority producers, including African American producers but excluding Asian producers, raised livestock. CFAP made the highest payments to livestock minority producers. The CFAP payment distribution pattern shows that payments reached minority producers who often did not receive Government payments. CFAP made more payments and as a share of total program outlays to minority producers compared to MFP. However, for Black or African American only producers, even though the magnitude increased (because CFAP disbursed more funds compared to MFP), the share of payment received did not increase.

Funding Information: The authors thank Dr. Pirkko M Mattila, Dr. Jenni Lahtela and Bhiswa Ghimire for their valuable suggestions on how to improve the web-tool, Olle Hansson for the FIMM cluster server machine to host the web-tool and the database, and all the beta-testers for confirming the smooth operation and reproducibility of the analyses. This work was supported by the Academy of Finland (grants 295504, 310507, 326238, 340141 and 344698 to TA), European Union's Horizon 2020 Research and Innovation Programme (ERA PerMed JAKSTAT-TARGET), the Cancer Society of Finland (TA), the Sigrid Jusélius Foundation (TA), and the Norwegian Cancer Society (grant 216104 to TA). Publisher Copyright: © 2022, The Author(s). ; Identification of cell populations often relies on manual annotation of cell clusters using established marker genes. However, the selection of marker genes is a time-consuming process that may lead to sub-optimal annotations as the markers must be informative of both the individual cell clusters and various cell types present in the sample. Here, we developed a computational platform, ScType, which enables a fully-automated and ultra-fast cell-type identification based solely on a given scRNA-seq data, along with a comprehensive cell marker database as background information. Using six scRNA-seq datasets from various human and mouse tissues, we show how ScType provides unbiased and accurate cell type annotations by guaranteeing the specificity of positive and negative marker genes across cell clusters and cell types. We also demonstrate how ScType distinguishes between healthy and malignant cell populations, based on single-cell calling of single-nucleotide variants, making it a versatile tool for anticancerapplications. The widely applicable method is deployed both as an interactive web-tool (https://sctype.app), and as an open-source R-package. ; Peer reviewed

Funding Information: Academy of Finland [292611, 310507, 313267, 326238 to T.A.]; Cancer Society of Finland [to T.A.]; Sigrid Jus?lius Foundation [to TA]. Funding for open access charge: European Union's Horizon 2020 Research and Innovation Programme [ERA PerMed JAKSTAT-TARGET project]. Funding Information: Academy of Finland [292611, 310507, 313267, 326238 to T.A.]; Cancer Society of Finland [to T.A.]; Sigrid Jusélius Foundation [to TA]. Funding for open access charge: European Union's Horizon 2020 Research and Innovation Programme [ERA PerMed JAKSTAT-TARGET project]. Conflict of interest statement. None declared. Publisher Copyright: © The Author(s) 2020. ; SynergyFinder (https://synergyfinder.fimm.fi) is a stand-alone web-application for interactive analysis and visualization of drug combination screening data. Since its first release in 2017, SynergyFinder has become a widely used web-tool both for the discovery of novel synergistic drug combinations in pre-clinical model systems (e.g. cell lines or primary patient-derived cells), and for better understanding of mechanisms of combination treatment efficacy or resistance. Here, we describe the latest version of SynergyFinder (release 2.0), which has extensively been upgraded through the addition of novel features supporting especially higher-order combination data analytics and exploratory visualization of multi-drug synergy patterns, along with automated outlier detection procedure, extended curve-fitting functionality and statistical analysis of replicate measurements. A number of additional improvements were also implemented based on the user requests, including new visualization and export options, updated user interface, as well as enhanced stability and performance of the web-tool. With these improvements, SynergyFinder 2.0 is expected to greatly extend its potential applications in various areas of multi-drug combinatorial screening and precision medicine. ; Peer reviewed