Agricultural Growth and Poverty Alleviation: Empirical Evidence from 34 African Countries Using System Dynamic Panel Data Estimations

Author(s)

Enock Darkwah , Huang Qifa , Yvonne Rommel Anselim ,

Download Full PDF Pages: 195-203 | Views: 836 | Downloads: 247 | DOI: 10.5281/zenodo.3614751

Volume 8 - October 2019 (10)

Abstract

Agriculture is the backbone of every economy as every country feeds on its natural produces. However, if a country will progress then much effort should be channeled into agriculture in order to feed the industrial sector to add value to them. On this tenet, the study aims to assess the impact that agricultural growth has on poverty alleviation in Africa. The data used for the study were sourced from 1996 to 2017, a panel of 34 African countries. In a panel study, system dynamic panel data estimation method was used in its analysis and the results confirm that agricultural growth positively and consistently impacts poverty alleviation. Moreover, governments’ effectiveness in making good policies and providing sound intervention to propagate economic growth will positively reduce poverty

Keywords

Agricultural growth; Poverty alleviation; Africa; System dynamic panel data estimations; Homogeneous causality 

References

i.        Aboyade, B. Olabimpe. (1987) Communication in rural development; Rural development; Literacy; Libraries; Africa; Nigeria; Developing countries. Fountain Publications, Ibadan.

ii.      Arellano, M., Bond, S. (1991). “Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations”. Review of Economic Studies 58 (2): 277 – 297.

iii.    Dumitriscu, E.I., Hurlin, C. (2012). “Testing for Granger non-causality in heterogeneous panels”. Econometric modelling 2: 1450 -1460.

iv.     Fan (2005) Shenggen Fan (2005). Agricultural R&D and poverty reduction in China. International Food Policy Research Institute, Beijing China.

v.       Gollin, D. (2010). Agricultural productivity and economic growth. In: Handbook of Agricultural Economics. P. Pingali and R. Evenson, eds. New York, Elsevier.

vi.     Im, K.S., Pesaran, Y.S. (2003). “Testing for unit roots in heterogeneous panels”. Journal of Econometrics 115: 53 -73.

vii.   Kao, C., Chiang, M.H. (2000). On the estimation and inference of a cointegrated regression in panel data. Adv. Econ. 15, 179 – 222.

viii. Levin, A., Lin, C.F., Chu, J. (2002). “Unit root tests in panel data: asymptotic and finite sample properties”. Journal of Econometrics 108: 1 -24.

ix.     Lingyun H., Xiaolu Z. (2018). “Impact of financial development on trade-embodied carbon dioxide emissions: Evidence from 30 provinces in China”. Journal of Cleaner Production 198:

x.       Maddala, G.S., Wu, S. (1999). “A comparative study of unit root tests with panel data and a new simple test”. Oxford Bulletin of Economics and Statistics 61: 631 – 652.

xi.     McNamara, E. (1998) Motivational Interviewing – A Form Teacher‘s manual. Ainsdale, Merseyside: Positive behaviour management.

xii.   Mellor, J. W. (1995). Introduction in Agriculture on the road to Industrialization. J. W. Mellor, ed. Baltimore, John Hopkins University Press for the International Food Policy Research Institute (IFPRI).

xiii. Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics 61, 53 -70.

xiv. Pedroni P. (2004). Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis: New results. Economic Theory 20, 597 – 627.

xv.   Schlutz, T. W. (1964). Transforming Traditional Agriculture. New Haven, Yale University Press.

xvi. Seligman, T. O., Johnson, R. A (1933): Encyclopedia of Social Studies: The Macmillian Company U.S.A. pp 47,530.

xvii.           World Bank (1996): Nigeria Poverty in the midst of plenty: The challenge of growth with inclusion. A World Bank Poverty Assessment vol.15 p.13.

xviii.         World Bank (2004). Development indicators. Oxford University Press for the World Bank, Oxford.

xix. World Development Indicators

xx.   Worldwide Governance Indicators

xxi. Zipora, A. O. (2007). Agricultural growth, rural poverty and hunger in Africa. AAAE Conference Proceedings, 465-468.

Cite this Article: