American Journal of Theoretical and Applied Statistics

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Food Production Modelling Using Fixed Effect Panel Data for Nigeria and Other 14 West African Countries (1990-2013)

Received: 22 May 2016    Accepted: 31 May 2016    Published: 08 July 2016
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Abstract

In this research, the fixed effect panel data predictive model was employed to formulate panel regression models of food production of 15 selected Economic Community of West African States (ECOWAS) using four (4) World Development Indicators (WDI) as explanatory variables. Data were collected from 1990 to 2013. The four WDI are Food imports (% of merchandise imports), Agricultural land (% of land area), Fertilizer consumption (kilograms per hectare of arable land) and Inflation (consumer prices annual %). The fixed effect with cross-sectional seemingly unrelated regression (SUR) static panel data method was employed. The result of the analysis shows that agricultural land and fertilizer consumption have significant positive effect on the food production index of ECOWAS countries, while food imports and rate of inflation have significant negative effect on food production index of the ECOWAS countries. It is seen that 98.8% of the variation in food production among ECOWAS countries can be explained by the variations in food imports, agricultural land, fertilizer consumption and inflation. We therefore recommend that ECOWAS countries should increase agricultural land and fertilizer consumption and reduce food imports and rate of inflation in order to boost their food production level and have excess to export.

DOI 10.11648/j.ajtas.20160504.17
Published in American Journal of Theoretical and Applied Statistics (Volume 5, Issue 4, July 2016)
Page(s) 208-218
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

Food Production Index, Cross Section, Time Series, Panel Data, Fixed Effect, World Development Indicators

References
[1] Ekum, M. I., Farinde, D. A. and Ayoola, F. J. (2013), “Panel Data: The Effects of Some World Development Indicator (WDI) on GDP Per Capita of Selected African Union (AU) Countries (1981-2011)”, International Journal of Science and Technology (IJST Publications UK), vol. 2 No. 12, December, 2013. Pp 900-907.
[2] Wood, A. (2002), “Could Africa Be Like America?” A paper presented at the Annual Bank Conference on Development Economics, Washington, D. C. Available at: http://siteresources.worldbank.org/INTABCDEWASHINGTON2002/Resources/Wood.pdf. Accessed on 25/05/2015.
[3] Lopriore, C. and Muehlhoff, E. (2010), “Food Security and Nutrition Trends in West Africa - Challenges and the Way Forward”, Nutrition Programmes Service, Food and Agriculture Organization Rome, Italy.
[4] Samuelson, P. A., Koopmans, T. C. and Stone, J. R. N. (1954), “Report of the evaluative committee for Econometrica”. Econometrica 22, 141–6.
[5] Zakari, S., Ying, L. and Song, B. (2014), “Factors Influencing Household Food Security in West Africa: The Case of Southern Niger”. Sustainability, 6, 1191-1202.
[6] Marcantonio, F. D, Opazo, C. M, Hurle J. B and Demeke, M (2014), “Determinants of Food Production in Sub Sahara Africa: The Impact of Policy, Market Access and Governance”, Paper Presented at EAAE 2014 Congress (Agri-Food and Rural Innovations for Healthier Societies), Ljubljana, Slovenia.
[7] Hedeh, L. (2013), “The role of Nigerian Agriculture in West African Food Security”, International Food Policy Research Institute (IFPRI). Jan 14, 2013. Available at http://nssp.ifpri.info/page/5/.Accessed on 25/05/2015.
[8] Obasi, P. C, Ukoha, H. A, Ukewuihe, I. S. and Mark, N. M. (2013). “Factors Affecting Agricultural Productivity among Arable Crop Farmers in Imo State, Nigeria”, American Journal of Experimental Agriculture 3 (2): 443-454, 2013.
[9] Chauvin, N. D., Mulangu, F. and Porto, G. (2012), “Food Production and Consumption Trends in Sub-Saharan Africa: Prospects for the Transformation of the Agricultural Sector”, African Center for Economic Transformation. Guido Porto, Universidad Nacional de La Plata, Working Paper, United Nations Development Programme, Regional Bureau for Africa, WP 2012-011: February 2012. Available at http://ideas.repec.org/p/rac/wpaper/2012-011.html (accessed March 10, 2015).
[10] Tesfaye, E. (2014), “Determinants of Agricultural Export in Sub-Saharan Africa: Evidence from Panel Study”, American Journal of Trade and Policy, Vol. 1, No. 3, 2014 (Issue 3).
[11] Drukker, D. M. (2003), “Testing for serial correlation in linear panel-data models”. Stata Journal 3: Pp 168|177.
[12] Baltagi, B. H. (1980), “On seemingly unrelated regressions with error components”, Econometrica 48, 1547-1551.
[13] Bacchetta, M. (2007), “Releasing Export Constraints: The Role of Governments”, ERSD, WTO, available online at http://www.aercafrica.org/documents/export-supply-working-paper/Bachetta 18DB37.pdf accessed on 15/03/,2015.
[14] World Bank (2007), “World Development Indicators”, World Bank, Washington, D. C. Available at http://data.worldbank.org/indicator.Accessed on 25/05/2015.
[15] Biggs, T. (2007), “Assessing Export Supply Constraints: Methodology, Data, and Measurement”, available online at http://www.aercafrica.org/documents/export_supply workingpapers/BiggsT- Assessing.pdf.Accessed on 15/03/2015.
[16] Kandiero, T. and Randa, J. (2004), “Agricultural Exports: Important Issues for Sub-Saharan Africa”, African Development Review, Vol. 16, No. 1, Pp 1-35.
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Author Information
  • Department of Mathematics and Statistics, Lagos State Polytechnic, Ikorodu, Lagos, Nigeria

  • Department of Mathematics and Statistics, Lagos State Polytechnic, Ikorodu, Lagos, Nigeria

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  • APA Style

    Olatunji Taofik Arowolo, Matthew Iwada Ekum. (2016). Food Production Modelling Using Fixed Effect Panel Data for Nigeria and Other 14 West African Countries (1990-2013). American Journal of Theoretical and Applied Statistics, 5(4), 208-218. https://doi.org/10.11648/j.ajtas.20160504.17

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    ACS Style

    Olatunji Taofik Arowolo; Matthew Iwada Ekum. Food Production Modelling Using Fixed Effect Panel Data for Nigeria and Other 14 West African Countries (1990-2013). Am. J. Theor. Appl. Stat. 2016, 5(4), 208-218. doi: 10.11648/j.ajtas.20160504.17

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    AMA Style

    Olatunji Taofik Arowolo, Matthew Iwada Ekum. Food Production Modelling Using Fixed Effect Panel Data for Nigeria and Other 14 West African Countries (1990-2013). Am J Theor Appl Stat. 2016;5(4):208-218. doi: 10.11648/j.ajtas.20160504.17

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  • @article{10.11648/j.ajtas.20160504.17,
      author = {Olatunji Taofik Arowolo and Matthew Iwada Ekum},
      title = {Food Production Modelling Using Fixed Effect Panel Data for Nigeria and Other 14 West African Countries (1990-2013)},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {5},
      number = {4},
      pages = {208-218},
      doi = {10.11648/j.ajtas.20160504.17},
      url = {https://doi.org/10.11648/j.ajtas.20160504.17},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtas.20160504.17},
      abstract = {In this research, the fixed effect panel data predictive model was employed to formulate panel regression models of food production of 15 selected Economic Community of West African States (ECOWAS) using four (4) World Development Indicators (WDI) as explanatory variables. Data were collected from 1990 to 2013. The four WDI are Food imports (% of merchandise imports), Agricultural land (% of land area), Fertilizer consumption (kilograms per hectare of arable land) and Inflation (consumer prices annual %). The fixed effect with cross-sectional seemingly unrelated regression (SUR) static panel data method was employed. The result of the analysis shows that agricultural land and fertilizer consumption have significant positive effect on the food production index of ECOWAS countries, while food imports and rate of inflation have significant negative effect on food production index of the ECOWAS countries. It is seen that 98.8% of the variation in food production among ECOWAS countries can be explained by the variations in food imports, agricultural land, fertilizer consumption and inflation. We therefore recommend that ECOWAS countries should increase agricultural land and fertilizer consumption and reduce food imports and rate of inflation in order to boost their food production level and have excess to export.},
     year = {2016}
    }
    

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  • TY  - JOUR
    T1  - Food Production Modelling Using Fixed Effect Panel Data for Nigeria and Other 14 West African Countries (1990-2013)
    AU  - Olatunji Taofik Arowolo
    AU  - Matthew Iwada Ekum
    Y1  - 2016/07/08
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    N1  - https://doi.org/10.11648/j.ajtas.20160504.17
    DO  - 10.11648/j.ajtas.20160504.17
    T2  - American Journal of Theoretical and Applied Statistics
    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
    SP  - 208
    EP  - 218
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20160504.17
    AB  - In this research, the fixed effect panel data predictive model was employed to formulate panel regression models of food production of 15 selected Economic Community of West African States (ECOWAS) using four (4) World Development Indicators (WDI) as explanatory variables. Data were collected from 1990 to 2013. The four WDI are Food imports (% of merchandise imports), Agricultural land (% of land area), Fertilizer consumption (kilograms per hectare of arable land) and Inflation (consumer prices annual %). The fixed effect with cross-sectional seemingly unrelated regression (SUR) static panel data method was employed. The result of the analysis shows that agricultural land and fertilizer consumption have significant positive effect on the food production index of ECOWAS countries, while food imports and rate of inflation have significant negative effect on food production index of the ECOWAS countries. It is seen that 98.8% of the variation in food production among ECOWAS countries can be explained by the variations in food imports, agricultural land, fertilizer consumption and inflation. We therefore recommend that ECOWAS countries should increase agricultural land and fertilizer consumption and reduce food imports and rate of inflation in order to boost their food production level and have excess to export.
    VL  - 5
    IS  - 4
    ER  - 

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