Social network

Purpose The purpose of this paper is to test for the predictive power of each of the dimensions of social network in explaining financial inclusion of the poor in rural Uganda. Design/methodology/approach The study employed a cross-sectional research design and data were collected from a total of 400 poor households located in Northern, Eastern, Central and Western Uganda. The authors adopted ordinary least square hierarchical regression analysis to test for the predictive power of each of the dimensions of social network in explaining financial inclusion of the poor in rural Uganda. The effects were determined by calculating the significant change in coefficient of determination (R2) between the dimensions of social network in explaining financial inclusion. In addition, analysis of variance was also used to test for variation in perceptions of the poor about being financially included. Findings The findings revealed that the dimensions of ties and interaction significantly explain financial inclusion of the poor in rural Uganda. Contrary to previous studies, the results indicated that interdependence as a dimension of social network is not a significant predictor of financial inclusion of the poor in rural Uganda. Combined together, the dimensions of social network explains about 16.6 percent of the variation in financial inclusion of the poor in rural Uganda. Research limitations/implications The study was purely cross-sectional, thus, ignoring longitudinal survey design, which could have investigated certain characteristics of the variable over time. Additionally, although a total sample amounting to 400 poor households was used in the study, the results cannot be generalized since other equally marginalized groups such as the disabled persons, refugees, and immigrants were not included in this study. Furthermore, the study used only the questionnaire to elicit responses from the respondents. The use of interview was ignored during data collection. Practical implications Policy makers, managers of financial institutions, and financial inclusion advocates should consider social network dimensions of ties and interaction as conduits for information flow and sharing among the poor including the women and youth about scarce financial resources like loans. Advocacy towards creation of societal network that brings the poor together in strong and weak ties is very important in scaling up access to and use of scarce financial services for improving economic and social well-being. Originality/value Contrary to previous studies, this particular study test the predictive power of each of the dimensions of social network in explaining financial inclusion of the poor in rural Uganda. Thus, it methodologically isolates the individual contribution of each of the dimensions of social network in explaining financial inclusion of the poor. The authors found that only ties and interaction are significant predictors of financial inclusion of the poor in rural Uganda. Therefore, the findings suggest that not all dimensions of social network are significant predictors of financial inclusion as opposed to previous empirical findings.

[1]  Sydney Chikalipah.  What determines financial inclusion in Sub-Saharan Africa? , 2017 .

[2]  Alex Bavelas,et al.  Communication Patterns in Task‐Oriented Groups , 1950 .

[3]  Abu Kasim Ali,et al.  Social Capital and Farmer Welfare in Malaysia , 2009 .

[4]  Tyler Biggs,et al.  Ethnic networks and access to credit : evidence from the manufacturing sector in Kenya , 2002 .

[5]  W. Powell,et al.  Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology. , 1996 .

[6]  David Lazer,et al.  Network Theory and Small Groups , 2004 .

[7]  Mark S. Granovetter.  T H E S T R E N G T H O F WEAK TIES: A NETWORK THEORY REVISITED , 1983 .

[8]  C. Grootaert.  Does social capital help the poor? - a synthesis of findings from the local level institutions studies in Bolivia, Burkina Faso, and Indonesia , 2001 .

[9]  Ronald S. Burt,et al.  A note on sociometric order in the general social survey network data , 1986 .

[10]  Mark S. Granovetter,et al.  Polanyi Symposium: a conversation on embeddedness , 2004 .

[11]  B. Wellman.  Structural analysis: From method and metaphor to theory and substance. , 1988 .

[12]  P. Kalmi,et al.  Social Capital and Access to Credit: Evidence from Uganda , 2016 .

[13]  R. Burt.  The Network Structure Of Social Capital , 2000 .

[14]  Susan Johnson,et al.  Financial Access and Exclusion in Kenya and Uganda , 2011 .

[15]  B. H. Mayhew,.  Structuralism Versus Individualism: Part 1, Shadowboxing in the Dark , 1980 .

[16]  Thierry van Bastelaer.  Imperfect Information, Social Capital and the Poor's Access to Credit , 1999 .

[17]  L. Pritchett,et al.  Sociability : Household Income and Social Capital in Rural Tanzania , 1999 .

[18]  Daniel J. Brass,et al.  POTENTIAL POWER AND POWER USE: AN INVESTIGATION OF STRUCTURE AND BEHAVIOR , 1993 .

[19]  R. Burt.  Structural Holes versus Network Closure as Social Capital , 2001 .

[20]  Mark S. Granovetter.  The Impact of Social Structure on Economic Outcomes Social Networks and Economic Outcomes: Core Principles , 2022 .

[21]  B. Omonona,et al.  EFFECTS OF SOCIAL CAPITAL ON CREDIT ACCESS AMONG COCOA FARMING HOUSEHOLDS IN OSUN STATE, NIGERIA , 2009 .

[22]  Mark S. Granovetter.  The Strength of Weak Ties , 1973, American Journal of Sociology.

[23]  Cagla Okten,et al.  Social Networks and Credit Access in Indonesia , 2004 .

[24]  Eric B. Santor,et al.  Membership has its privileges: the effect of social capital and neighbourhood characteristics on the earnings of microfinance borrowers , 2001 .

[25]  Dean S. Karlan,et al.  Social Connections and Group Banking , 2005 .

[26]  Leora F. Klapper,et al.  Financial Inclusion in Africa , 2015 .

[27]  R. Townsend,et al.  Using Repayment Data to Test Across Models of Joint Liability Lending , 2007 .