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      The role of SMEs in rural development: Access of SMEs to finance as a mediator

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      PLoS ONE

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          Abstract

          Small and Medium Enterprises (SMEs) are considered as the fundamental tool for economic growth, nevertheless, they face continuous financing challenges. SMEs are a major source for generating employment, creation of wealth and alleviating poverty from the rural regions in developing countries. Their access to finance is key to the expansion of this sector. The paper aims to discover the intervening role of “access of SMEs to finance” in the link between SME’s evolution and rural development, in the context of Pakistan. In total 338 entrepreneurs operating SMEs in rural areas completed a survey for the study. Through a multi-stage stratified random sampling technique, entrepreneurs were selected from three districts. Confirmatory factor analysis and structural equation modeling were used to test hypotheses. This study shows that SME’s evolution has a positive and optimistic influence on rural development. Further, the study also reveals that on SME’s progress a positive influence happens by the “access of SMEs to finance”. Particularly, the study finds that “access of SMEs to finance” significantly mediated the effect of SME’s evolution on rural development. The findings of this paper hold significant implications for both the research society and loan-issuing institutions and departments.

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          Most cited references61

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          Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

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            Missing data: our view of the state of the art.

            Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Writing – original draft
                Role: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                8 March 2021
                2021
                : 16
                : 3
                Affiliations
                [1 ]Department of Agricultural Economics and Management, School of Public Affairs, Zhejiang University, Hangzhou, China
                [2 ]Department of City & Regional Planning, Mehran University of Engineering & Technology, Jamshoro, Pakistan
                International Centre for Integrated Mountain Development (ICIMOD), Kathmandu, Nepal, NEPAL
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Article
                PONE-D-20-34673
                10.1371/journal.pone.0247598
                7939373
                33684146
                d729e125-ff89-4c95-9e79-313b62788b34
                © 2021 Manzoor, Wei

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 4, Tables: 7, Pages: 18
                Product
                Funding
                The authors received no specific funding for this work.
                Categories
                Research Article
                Social Sciences
                Economics
                Finance
                Earth Sciences
                Geography
                Geographic Areas
                Rural Areas
                People and Places
                Geographical Locations
                Asia
                Pakistan
                Social Sciences
                Economics
                Development Economics
                Economic Growth
                Social Sciences
                Economics
                Labor Economics
                Employment
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Factor Analysis
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Factor Analysis
                Social Sciences
                Economics
                Finance
                Public Finance
                Money Supply and Banking
                Social Sciences
                Political Science
                Public Policy
                Poverty Reduction
                Custom metadata
                All relevant data are within the paper and its Supporting information files.

                Uncategorized

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