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\hypersetup{pdftitle={Credit Constraints and Agricultural Productivity of Rural Households in Nigeria},pdfauthor={Abimbola Adepoju, Olugbenga Omolade},pdfsubject={Tropentag 2016: Abstract},pdfkeywords={Agricultural productivity, credit constraint, rural households},pdfpagemode=None,colorlinks=true}
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\parbox[b]{13.4cm}{\centering \large{\textbf{Tropentag, September 18-21, 2016, Vienna, Austria}}\\[1ex] \Large{``Solidarity in a competing world ---\\fair use of resources''\\[2ex]}}
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\Large{\textbf{Credit Constraints and Agricultural Productivity of Rural Households in Nigeria\footnote{\textbf{Contact Address:} Olugbenga Omolade, University of Ibadan, Department of Agricultural Economics, No 1 Omolade Close Ajayi Amodu Community Wakajaye, 234~Ibadan, Nigeria, \mbox{e-mail}: \email{omoladekayode@gmail.com}}\\[0.8ex]}}
\normalsize{\textsc{Abimbola Adepoju$^{1}$, Olugbenga Omolade$^{2}$}}
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\begin{itemize*}
\item[]{\small{\textit{$^{1}$University of Ibadan, Ibadan., Department of Agricultural Economics, }}}
\item[]{\small{\textit{$^{2}$University of Ibadan, Department of Agricultural Economics, Nigeria}}}
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\index[author]{Adepoju, Abimbola}
\index[author]{Omolade, Olugbenga}
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\textbf{Abstract}
\begin{abstract}
\normalsize{
Agriculture a significant sector in Nigeria's economy and the economic activities of the majority of rural households in Nigeria. Painfully, the sector is plagued with multifarious problems such as low level of farmer's income, constant rise in importation of agricultural commodities, lack of agricultural credit, inadequate essential industrial agro raw materials etc. The aforementioned inherent problems necessitated the investigation of factors influencing credit constraint and its effect on agricultural productivity of rural households in Nigeria. Secondary data used in this study is the second wave of General Household Survey-Panel Data. The total number of rural households interviewed for the survey was 2431, out of which 655 households constituted the sample size for this study. The study was analysed using descriptive statistics, probit regression model, and endogenous switching regression model. The results show that 69 percent of the households had not obtained credit from any source while 27.9 percent obtained credit from friends and relatives. Household size, farm size, household savings and value of household assets were among other variables influencing the probability of households being credit constrained or not. Also, findings indicate that under credit constraints, education, labour, technology and other production inputs were not optimally utilised. Households that were credit constrained had lower productivity than a random household from the sample would have had, and those who were not credit constrained had no better nor worse productivity than a random household. The study however recommended that formal credit institution should diversify their loan scheme to incorporate different financial needs of rural households. }

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\noindent \textbf{Keywords:} Agricultural productivity, credit constraint, rural households
\index[key]{Agricultural!productivity}
\index[key]{Credit constraint}
\index[key]{Rural!households}
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