PurposeThis paper aims to make a modest attempt to classify the Asia-Pacific countries in terms of the access to information and communication technology (ICT) to unearth the prevalence of digital divide (if any) in the Asia-Pacific region. In addition to that, this paper also examines the role played by the digital skill in bridging the digital divide in the context of Asia–Pacific countries.Design/methodology/approachSecondary data on 43 Asia-Pacific countries for the period from 2012 to 2017 was collected from International Telecommunication Union (ITU) database and World Development Indicators, World Bank. K-means clustering technique was applied to explore the natural grouping of the Asia-Pacific countries based on ICT access. The role of digital/ICT skill in narrowing the access-based digital divide was investigated using panel data regression technique.FindingsClustering of countries suggested a significant difference amongst the Asia-Pacific countries in terms of ICT access, signifying the prevalence of access based digital divide. Digital skill played pivotal role in promoting ICT access and thereby reducing the digital divide during the period of the study. Per capita income level, level of education, openness of the economy and urbanisation were observed to be the determining factors in reducing the digital divide during the period of study in the Asia-Pacific region.Originality/valueThe study makes an unique attempt to explore the role of digital/ICT skill in tapering the access-based digital divide in the context of Asia-Pacific region.
PurposeThe article makes a modest attempt to explore the level of financial literacy (FL) amongst the farmers in India. An effort was also made to unearth the factors affecting such FL.Design/methodology/approachThe study used secondary data on 11,030 farmers across various regions of India from the Financial Inclusion Insight Survey, 2017. Standard and Poor Global FL questions were used to measure the level of FL amongst the respondents. In addition to the appropriate statistical tools and techniques, the censored tobit regression model and generalized structural equation model were applied to explore the determinants of FL of the Indian farmers.FindingsThe outcome of the study indicated that the majority of Indian farmers are financially illiterate. The average FL score obtained by the sample farmers was found to be only 33%. The results of the study signaled significant regional variation in FL amongst the farmers across India. Apart from the regional variation in FL, farmer type, state-specific agricultural productivity, gender, marital status, age, educational attainment and financial inclusion were found to be the major determinants of the FL amongst the farmers.Originality/valueEvaluation of FL amongst farmers is scanty in the literature in developed nations and especially in the context of emerging economies, like India. The authors tried to fill this gap by exploring FL and its determinants amongst Indian farmers. In addition to this, the study for the first time used a comprehensive and rich dataset of 11,030 Indian farmers while exploring the level of FL and its determinants.
The paper seeks to examine the cross-country variation in the state of digital economy in Asia-Pacific region. In addition, this paper also identifies the role played by the digital skill in fostering the development of the digital economy in the Asia-Pacific region using secondary data of 43 Asia-Pacific countries over 2012 to 2017. K-means clustering approach and panel regression method were used for the purpose of analysis of data. The outcome of the study revealed stark variations in use, access, and overall digital economy amongst the Asia-Pacific countries signifying the prevalence of digital inequality. The findings of the study highlighted that secondary and tertiary education as the proxies of digital skill exerted significant positive effect on the development of digital economy. However, the effect of tertiary education was found to be stronger than that of the secondary education. The outcome of the study also identified income level to be affecting the development digital economy.
In this paper an effort was made to evaluate the level of efficiency of the firms that belong to the selected manufacturing sub-sectors in India for the period 1999-2000 to 2013-2014 using Stochastic Frontier Analysis. Subsequently, the microeconomic and macroeconomic determinants of efficiency were analysed applying Panel Censored Tobit Regression Model. The study revealed that Electrical Equipment sub-sector was found to be the most efficient sector followed by the sub-sectors Auto Parts and Equipment, Pharmaceutical and Biotechnology, Chemicals, Textile, Food products and Steel respectively. The study also showed that leverage, size of the firm, age of the firm, openness of the firm (microeconomic) and inflation (macroeconomic) made notable contribution towards changing the level of efficiency of manufacturing firms during the study period. However, their contributions were not the same in all sub-sectors under study.
Determination of significant sector specific macroeconomic factors under the board manufacturing industry is an important task. In Indian context, using the monthly data on five major manufacturing sector specific indices (such as BSE-Basic Materials, BSE-Consumer Discretionary Goods and Services, BSE-Fast Moving Consumer Goods, BSE-Health Care and BSE-Industrials) and the macroeconomic variables (gold price, index of industrial production, wholesale price index, money supply, foreign portfolio investment ratio (FPIR), rate of interest, real effective exchange rate and crude oil price and economic policy uncertainty) for the period September, 2005 to November, 2016, the present study attempted to explore the significant sector specific macroeconomic variables in long run as well as short run. The empirical results obtained by applying the ARDL-UECM model suggested that economic policy uncertainty, FPIR and price factor were observed to be the most important determinants of all the five sectoral stock indices for the study period.
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