Modeling Poverty Indices Among Crop Farmers Using Beta and Dirichlet Regression Models; A Case of Uasin Gishu County, Kenya

Jackline Chepkorir Lang’at; Thomas Mageto; Irene Irungu

doi:doi:10.11648/j.ijdsa.20210701.13

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Modeling Poverty Indices Among Crop Farmers Using Beta and Dirichlet Regression Models; A Case of Uasin Gishu County, Kenya

Jackline Chepkorir Lang’at, Thomas Mageto, Irene Irungu

Published in International Journal of Data Science and Analysis (Volume 7, Issue 1)

Received: 3 February 2021 Accepted: 10 February 2021 Published: 27 February 2021

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Abstract

Poverty and its alleviation schemes remain to be of much concern to many countries in the world. In the Sub-Saharan Africa, 41% of the population live below the extreme poverty line and in Kenya, almost 80% of the population are deemed poor. The Kenyan rural sector has a contribution of 40% to this poverty levels despite agriculture being the backbone and the main source of livelihood in the rural areas. It is in this regard that the study evaluates the household characteristics effect on Poverty indices among Crop Farmer Households. The Beta and Dirichlet regression models were used in the analysis in which the Beta regression model gave a better fit to the poverty indices data. The standardized residuals, probability plots, Chi-square test of association and the Breusch Pagan test for heteroscedasticity were used as goodness of fit evaluation tests in which levels of deprivation had a significant effect on the poverty indices among the crop farmers. Data used in the study was secondary data obtained from the Kenya National Bureau of Statistics Survey Consumption Index in Uasin Gishu County for the period March 2018 to May 2018 in which a total of 489 households were employed in the survey.

Published in	International Journal of Data Science and Analysis (Volume 7, Issue 1)
DOI	10.11648/j.ijdsa.20210701.13
Page(s)	8-12
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Poverty, Beta and Dirichlet Regression Models, Households, Poverty Indices

References

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[2]	Measurement and Analysis. Oxford Poverty Human Development Initiative (OPHI) Working Paper No. 91.
[3]	Sidikat, L., Gafar, T.& Usman, A. (2009). Determinants of Poverty in Sub-Saharan Africa. African Research Review, 3 (2); 162-177.
[4]	Haradhan, K. M. (2013). Poverty and Economic Development of Kenya. International Journal of Information Technology and Business Management, 18 (1); 72-82.
[5]	AfrobarometerRound 7 SurveyResults inKenya, (2020). Institute of Development Studies, University of Nairobi-Kenya.www.afrobarometer.org.
[6]	Nekesa, M. P. (2015). Determinants of rural poverty in kenya: The cash crop growing. University of Nairobi Publications. http:erepository.uonbi.ac.ke.
[7]	Apata, T. G., Apata, O. M., Igbalajobi, O. A. & Awoniyi, S. M. O. (2010). Determinants of rural poverty in Nigeria: Evidence from small holder farmers in South-western, Nigeria. Journal of Science and Technology Education Research, 1 (4); 85-91.
[8]	Baiyegunhi, L. T. S. & Fraser, G. C. G. (2010). Determinants of Household Poverty Dynamics in Rural regions of the Eastern Cape Province, South Africa. 3rd African Association of Agricultural Economists (AAAE). University of KwaZulu-Natal.
[9]	Almas, H. & Seyoung, C. (2017). The Effects of Lifetime Work Experience on Incidence and Severity of Elderly Poverty in Korea. Institute of Labor Economics. www.iza.org
[10]	Schneider, K. & Gugerty, K. (2011). Agricultural Productivity and Poverty Reduction: Linkages and Pathways. The Evans School Review, 1 (1).
[11]	Namara, E. R, Godswill, M., Fitsum, H. & Seleshi, B. A. (2017). Rural poverty and inequality in Ethiopia: does access to small-scale irrigation make a difference? International Water Management Institute. r.namara@cgiar.org.
[12]	Chakra, P. A. & Roberto, L. G. (2012). The Impact of Remittance on Poverty and Inequality: AMicro-Simulation Study forNepal. National Graduate Institute for Policy Studies 7-22-1 Roppongi, Minato-ku, Tokyo, Japan 106 8677. http://www.grips.ac.jp/rcenter/wp-content/uploads/11-26.pdf.
[13]	Sinnathurai, V. & Brezinova, O. (2012). Poverty Incidence and its Determinants in the Estate Sector of Sri Lanka. Journal of Competitiveness, 4 (1); 44-55.
[14]	Hijazi, R. H., & Jernigan, R. W. (2009). Modelling compositional data using Dirichlet regression models. Journal of Applied Probability & Statistics, 4 (1), 77-91.
[15]	Camargo, A. P., Stern, J. M., & Lauretto, M. S. (2012, May). Estimation and model selection in Dirichlet Regression. In AIP Conference Proceedings 31st (Vol. 1443, No. 1, pp. 206-213). American Institute of Physics.

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APA Style

Jackline Chepkorir Lang’at, Thomas Mageto, Irene Irungu. (2021). Modeling Poverty Indices Among Crop Farmers Using Beta and Dirichlet Regression Models; A Case of Uasin Gishu County, Kenya. International Journal of Data Science and Analysis, 7(1), 8-12. https://doi.org/10.11648/j.ijdsa.20210701.13

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ACS Style

Jackline Chepkorir Lang’at; Thomas Mageto; Irene Irungu. Modeling Poverty Indices Among Crop Farmers Using Beta and Dirichlet Regression Models; A Case of Uasin Gishu County, Kenya. Int. J. Data Sci. Anal. 2021, 7(1), 8-12. doi: 10.11648/j.ijdsa.20210701.13

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AMA Style

Jackline Chepkorir Lang’at, Thomas Mageto, Irene Irungu. Modeling Poverty Indices Among Crop Farmers Using Beta and Dirichlet Regression Models; A Case of Uasin Gishu County, Kenya. Int J Data Sci Anal. 2021;7(1):8-12. doi: 10.11648/j.ijdsa.20210701.13

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@article{10.11648/j.ijdsa.20210701.13,
  author = {Jackline Chepkorir Lang’at and Thomas Mageto and Irene Irungu},
  title = {Modeling Poverty Indices Among Crop Farmers Using Beta and Dirichlet Regression Models; A Case of Uasin Gishu County, Kenya},
  journal = {International Journal of Data Science and Analysis},
  volume = {7},
  number = {1},
  pages = {8-12},
  doi = {10.11648/j.ijdsa.20210701.13},
  url = {https://doi.org/10.11648/j.ijdsa.20210701.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20210701.13},
  abstract = {Poverty and its alleviation schemes remain to be of much concern to many countries in the world. In the Sub-Saharan Africa, 41% of the population live below the extreme poverty line and in Kenya, almost 80% of the population are deemed poor. The Kenyan rural sector has a contribution of 40% to this poverty levels despite agriculture being the backbone and the main source of livelihood in the rural areas. It is in this regard that the study evaluates the household characteristics effect on Poverty indices among Crop Farmer Households. The Beta and Dirichlet regression models were used in the analysis in which the Beta regression model gave a better fit to the poverty indices data. The standardized residuals, probability plots, Chi-square test of association and the Breusch Pagan test for heteroscedasticity were used as goodness of fit evaluation tests in which levels of deprivation had a significant effect on the poverty indices among the crop farmers. Data used in the study was secondary data obtained from the Kenya National Bureau of Statistics Survey Consumption Index in Uasin Gishu County for the period March 2018 to May 2018 in which a total of 489 households were employed in the survey.},
 year = {2021}
}

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TY  - JOUR
T1  - Modeling Poverty Indices Among Crop Farmers Using Beta and Dirichlet Regression Models; A Case of Uasin Gishu County, Kenya
AU  - Jackline Chepkorir Lang’at
AU  - Thomas Mageto
AU  - Irene Irungu
Y1  - 2021/02/27
PY  - 2021
N1  - https://doi.org/10.11648/j.ijdsa.20210701.13
DO  - 10.11648/j.ijdsa.20210701.13
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 8
EP  - 12
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20210701.13
AB  - Poverty and its alleviation schemes remain to be of much concern to many countries in the world. In the Sub-Saharan Africa, 41% of the population live below the extreme poverty line and in Kenya, almost 80% of the population are deemed poor. The Kenyan rural sector has a contribution of 40% to this poverty levels despite agriculture being the backbone and the main source of livelihood in the rural areas. It is in this regard that the study evaluates the household characteristics effect on Poverty indices among Crop Farmer Households. The Beta and Dirichlet regression models were used in the analysis in which the Beta regression model gave a better fit to the poverty indices data. The standardized residuals, probability plots, Chi-square test of association and the Breusch Pagan test for heteroscedasticity were used as goodness of fit evaluation tests in which levels of deprivation had a significant effect on the poverty indices among the crop farmers. Data used in the study was secondary data obtained from the Kenya National Bureau of Statistics Survey Consumption Index in Uasin Gishu County for the period March 2018 to May 2018 in which a total of 489 households were employed in the survey.
VL  - 7
IS  - 1
ER  -

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Author Information

Jackline Chepkorir Lang’at

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Juja, Kenya
Thomas Mageto

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Juja, Kenya
Irene Irungu

Department of Mathematics, Statistics and Actuarial Science, Karatina University, Kenya

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APA Style

Jackline Chepkorir Lang’at, Thomas Mageto, Irene Irungu. (2021). Modeling Poverty Indices Among Crop Farmers Using Beta and Dirichlet Regression Models; A Case of Uasin Gishu County, Kenya. International Journal of Data Science and Analysis, 7(1), 8-12. https://doi.org/10.11648/j.ijdsa.20210701.13

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ACS Style

Jackline Chepkorir Lang’at; Thomas Mageto; Irene Irungu. Modeling Poverty Indices Among Crop Farmers Using Beta and Dirichlet Regression Models; A Case of Uasin Gishu County, Kenya. Int. J. Data Sci. Anal. 2021, 7(1), 8-12. doi: 10.11648/j.ijdsa.20210701.13

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AMA Style

Jackline Chepkorir Lang’at, Thomas Mageto, Irene Irungu. Modeling Poverty Indices Among Crop Farmers Using Beta and Dirichlet Regression Models; A Case of Uasin Gishu County, Kenya. Int J Data Sci Anal. 2021;7(1):8-12. doi: 10.11648/j.ijdsa.20210701.13

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@article{10.11648/j.ijdsa.20210701.13,
  author = {Jackline Chepkorir Lang’at and Thomas Mageto and Irene Irungu},
  title = {Modeling Poverty Indices Among Crop Farmers Using Beta and Dirichlet Regression Models; A Case of Uasin Gishu County, Kenya},
  journal = {International Journal of Data Science and Analysis},
  volume = {7},
  number = {1},
  pages = {8-12},
  doi = {10.11648/j.ijdsa.20210701.13},
  url = {https://doi.org/10.11648/j.ijdsa.20210701.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20210701.13},
  abstract = {Poverty and its alleviation schemes remain to be of much concern to many countries in the world. In the Sub-Saharan Africa, 41% of the population live below the extreme poverty line and in Kenya, almost 80% of the population are deemed poor. The Kenyan rural sector has a contribution of 40% to this poverty levels despite agriculture being the backbone and the main source of livelihood in the rural areas. It is in this regard that the study evaluates the household characteristics effect on Poverty indices among Crop Farmer Households. The Beta and Dirichlet regression models were used in the analysis in which the Beta regression model gave a better fit to the poverty indices data. The standardized residuals, probability plots, Chi-square test of association and the Breusch Pagan test for heteroscedasticity were used as goodness of fit evaluation tests in which levels of deprivation had a significant effect on the poverty indices among the crop farmers. Data used in the study was secondary data obtained from the Kenya National Bureau of Statistics Survey Consumption Index in Uasin Gishu County for the period March 2018 to May 2018 in which a total of 489 households were employed in the survey.},
 year = {2021}
}

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TY  - JOUR
T1  - Modeling Poverty Indices Among Crop Farmers Using Beta and Dirichlet Regression Models; A Case of Uasin Gishu County, Kenya
AU  - Jackline Chepkorir Lang’at
AU  - Thomas Mageto
AU  - Irene Irungu
Y1  - 2021/02/27
PY  - 2021
N1  - https://doi.org/10.11648/j.ijdsa.20210701.13
DO  - 10.11648/j.ijdsa.20210701.13
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 8
EP  - 12
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20210701.13
AB  - Poverty and its alleviation schemes remain to be of much concern to many countries in the world. In the Sub-Saharan Africa, 41% of the population live below the extreme poverty line and in Kenya, almost 80% of the population are deemed poor. The Kenyan rural sector has a contribution of 40% to this poverty levels despite agriculture being the backbone and the main source of livelihood in the rural areas. It is in this regard that the study evaluates the household characteristics effect on Poverty indices among Crop Farmer Households. The Beta and Dirichlet regression models were used in the analysis in which the Beta regression model gave a better fit to the poverty indices data. The standardized residuals, probability plots, Chi-square test of association and the Breusch Pagan test for heteroscedasticity were used as goodness of fit evaluation tests in which levels of deprivation had a significant effect on the poverty indices among the crop farmers. Data used in the study was secondary data obtained from the Kenya National Bureau of Statistics Survey Consumption Index in Uasin Gishu County for the period March 2018 to May 2018 in which a total of 489 households were employed in the survey.
VL  - 7
IS  - 1
ER  -

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