Discriminant analysis of livestock farmers credit worthiness potential under rural banking scheme in Abia State, Nigeria

Mbanasor, JA; Nto, Philips O.

doi:10.4314/naj.v39i1.3259

Cited by 4 publications

(5 citation statements)

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“…Its negative coeffi cient means that if distance from tea farm to tea-processing factory increased by one kilometre, ineffi ciency would decrease by 0.003%. This fi nding is in line with that of Nto and Mbanasor (2011), who found that nearness to market negatively aff ected productivity of agribusiness fi rms in Nigeria.…”

Section: Sources Of Technical Ineffi Ciencysupporting

confidence: 90%

Technical Efficiency of Smallholder Tea Production in South-Eastern Malawi: A Stochastic Frontier Approach

2017

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Section: Sources Of Technical Ineffi Ciencysupporting

confidence: 90%

Technical Efficiency of Smallholder Tea Production in South-Eastern Malawi: A Stochastic Frontier Approach

2017

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“…These values are indication that the discriminant function used in this study provided the high significant amount of information required for determining household unemployment rate. The result provided a similar result with that of Mbanasor and Nto (2008), and is a better test when compared with previous studies (Arene, 1993;Onyemucheya, 2005;Eze, 2003). Also the chi-square test was found to be significant at 1%, implying that all the discriminant coefficients were not equal to zero, thereby confirming that the estimated function can be used to discriminate between unemployed and not unemployed households as originally defined.…”

Section: Discriminant Scoresupporting

confidence: 77%

“…The technique attempted to derive a linear combination of these factors which "best" discriminated between the two groups. The importance of the derived discriminant function for the study was assessed using the squared canonical correlation, wilk's Lamda and an associated chi-square statistics and the percentage of unemployed households classified into group (Madukwe and Ayichi, 1997;Klecka, 1975;Mbanasor and Nto, 2008).…”

Section: Thismentioning

confidence: 99%

Discriminant Analysis of Rural Households Unemployment status in Imo State, Nigeria

Ohajianya¹

2012

GJSS

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This study analyzed the household unemployment rate in Imo State. Data were collected with validated questionnaire from 540 proportionately and randomly sampled rural households in Imo state. Data were analyzed using descriptive statistics and Discriminant function model. The household unemployment rate in Imo state was found to be 61.4% , therefore the households in Imo state are categorized as unemployed households. About 71% of the household members were frictionally unemployed. Determinants of household unemployment rate were found to be wage rate in non-paid employment, dependents, socioeconomic status, level of education, type of Occupation, health status, and frequency of use of information sources on jobs. The most significant discriminator of unemployed households is education.

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“…The discriminant analysis technique was applied by Madukwe (1995) to identify discriminating variables between two groups of contact farmers (effective and ineffective). Mbanasor and Nto (2008) categorized potential beneficiaries of livestock credit as credit-worthy or non-credit-worthy. In this study, a stepwise technique was used to determine factors or variables that differentiate or discriminate between farmer users and nonusers of mass media.…”

Section: Statistical Tools For Data Analysismentioning

confidence: 99%

Characterizing Farmer Users and Nonusers of Mass Media as Channels of Agricultural Information in Benue State, Nigeria

Okwu

2011

Journal of Agricultural & Food Information

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This study characterized farmer users and nonusers of mass media as channels of agricultural information. A structured interview was used to obtain data from 316 randomly selected farmers in Benue State, Nigeria. The data were subjected to discriminant analysis and the following socioeconomic characteristics significantly differed at a 5% level of probability between users and nonusers of mass media (and their F-ratio values): education (6.30), income (5.12), gender (4.84), and socioeconomic status (4.83). Farmer users of mass media are therefore those who have good level of education, belong to a relatively high income bracket, and are typically male and of a relatively high socioeconomic status. Farmers' socioeconomic characteristics should be considered in planning mass media usage in agricultural information dissemination.

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Discriminant analysis of livestock farmers credit worthiness potential under rural banking scheme in Abia State, Nigeria

Cited by 4 publications

References 0 publications

Technical Efficiency of Smallholder Tea Production in South-Eastern Malawi: A Stochastic Frontier Approach

Technical Efficiency of Smallholder Tea Production in South-Eastern Malawi: A Stochastic Frontier Approach

Discriminant Analysis of Rural Households Unemployment status in Imo State, Nigeria

Characterizing Farmer Users and Nonusers of Mass Media as Channels of Agricultural Information in Benue State, Nigeria

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