Food Security

, Volume 7, Issue 6, pp 1239–1258 | Cite as

Impact of agricultural technology adoption on asset ownership: the case of improved cassava varieties in Nigeria

  • Bola Amoke Awotide
  • Arega D. Alene
  • Tahirou Abdoulaye
  • Victor M. Manyong
Original Paper

Abstract

Using household survey data from a sample of about 850 households selected from six States in south-west Nigeria, this paper analyses the effects of the adoption of improved cassava varieties (ICVs) on asset ownership among smallholder farmers. The results of the linear regression with endogenous treatment effects showed that adoption of ICVs is positively related to asset ownership. The results further showed that ICVs had greater impact on asset ownership among female-headed households. The impact analysis using propensity score matching (PSM) showed a significant and positive effect of adoption of ICVs on asset ownership and a negative effect on asset poverty. The empirical results suggest that improved agricultural technologies can play a key role in strengthening asset ownership of smallholder farmers for increased agricultural productivity and income generation.

Keywords

Adoption Assets Poverty Impact PSM Farmer Cassava Nigeria 

JEL Classification

C14 I32 O32 Q16 

References

  1. Adewunmi, O. I., Adesimi, B., & Ezekiel, O. A. (2011). Non-farm activities and poverty among rural farm households in Yewa division of Ogun State, Nigeria. Journal of Social Science, 26(3), 217–224.Google Scholar
  2. Akinola, A. A., Alene, A. D., Adeyemi, R., Sanogo, D., & Olanrewaju, A. S. (2009). Economic impact of soil fertility management research in West Africa. African Journal of Agricultural and Resource Economics, 3(2), 159–175.Google Scholar
  3. Alene, A. D., Khataza, R., Chibwana, C., Ntawuruhunga, P., & Moyo, C. (2013). Economic impacts of cassava research and extension in Malawi and Zambia. Journal of Development and Agricultural Economics, 5(11), 547–469.CrossRefGoogle Scholar
  4. Ali, K., & Awudu, A. (2010). The adoption of genetically modified cotton and poverty reduction in Pakistan. Journal of Agricultural Economics, 61(1), 175–192.CrossRefGoogle Scholar
  5. Awotide, B. A., Karimov, A., Diagne, & Nakelse, T. (2013). The impact of seed vouchers on poverty reduction among smallholder rice farmers in Nigeria. Agricultural Economics, 44(2013), 647–658.CrossRefGoogle Scholar
  6. Barrett, C. B., & Swallow, B. M. (2006). Fractal poverty traps. World Development, 34(1), 1–15.CrossRefGoogle Scholar
  7. Bassi, L. (1984). Estimating the effects of training programs with non-random selection. The Review of Economics and Statistics, 66, 36–43.CrossRefGoogle Scholar
  8. Becerril, J., & Abdulai, A. (2010). The impact of improved maize varieties on poverty in Mexico: a propensity score matching approach. World Development, 38(7), 1024–1035.CrossRefGoogle Scholar
  9. Boahene, K., Snijders, T. A. B., & Folmer, H. (1999). An integrated socioeconomic analysis of innovation adoption: the case of hybrid cocoa in Ghana. Journal of Policy Modeling, 21(2), 167–184.CrossRefGoogle Scholar
  10. Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: Methods and applications. New York: Cambridge University Press.CrossRefGoogle Scholar
  11. Carter, M. R., & Barrett, C. B. (2006). The economics of poverty traps and persistent poverty: an asset-based approach. Journal of Development Studies, 42(2), 179–199.CrossRefGoogle Scholar
  12. Chambers, R., & Conway, G. R. (1991). Sustainable rural livelihoods: Practical Concepts for the 21st Century. Institute of Development Studies DP 296, 1991. Brighton: University of Sussex.Google Scholar
  13. Cochran, W., & Rubin, D. (1973). Controlling bias in observational studies. Sankhyā: The Indian Journal of Statistics, Series A (1961-2002), 35, 417–446.Google Scholar
  14. David, C. C., & Otsuka, K. (1994). Modern rice technology and income distribution in Asia. Boulder: Lynne Riener Publishers.Google Scholar
  15. de Janvry, A., & Sadoulet, E. (2001). World poverty and the role of agricultural technology: direct and indirect effects. Journal of Development Studies, 38(4), 1–26.CrossRefGoogle Scholar
  16. Dehejia, R. H., & Wahba, S. (1999). Casual effects in non- experimental studies: re-evaluating the evaluation of training programs. Journal of the American Statistical Association, 94, 1053–1062.CrossRefGoogle Scholar
  17. Dehejia, R. H., & Wahba, S. (2002). Propensity score matching methods for non-experimental casual studies. The Review of Economics and Statistics, 84(1), 151–161.CrossRefGoogle Scholar
  18. DFID (2000). Sustainable Livelihoods Guidance Sheets. Department for International Development. http://www.livelihoods.org/info/info_guidancesheets.html. Accessed 23 Jul 2008.
  19. Diagne, A., & Demont, M. (2007). Taking a new look at empirical models of adoption: average treatment effect estimation of adoption rates and their determinants. Agricultural Economics, 37(2–3), 201–210.CrossRefGoogle Scholar
  20. Diagne, A., Adekambi, S. A., Simtowe, F. P., Biaou, G. (2009). The impact of agricultural technology adoption on poverty: the case of Nerica rice varieties in Benin. Paper presented at the 27th Conference of the International Association of Agricultural Economists, Beijing, China, August 16–22.Google Scholar
  21. Dillon, A., & Quinones, E. (2011). Gender differentiated asset dynamics in Northern Nigeria. Food and agriculture organization of the United Nations ESA working paper 11–06. Rome: Food and Agriculture Organization of the United Nations.Google Scholar
  22. Dontsop-Nguezet, P. M., Diagne, A., Okoruwa, V. O., & Ojehomon, V. E. T. (2011). Impact of improved rice technology adoption (NERICA varieties) on income and poverty among rice farming households in Nigeria: a local average treatment effect (LATE) approach. Quarterly Journal of International Agriculture, 50(3), 267–291.Google Scholar
  23. Ellis, F. (2000). The determinants of rural livelihood diversification in developing countries. Journal of Agricultural Economics, 51(2), 289–302.CrossRefGoogle Scholar
  24. Evenson, R., & Gollin, D. (2003). Assessing the impact of the green revolution: 1960 to 2000. Science, 300(2), 758–762.PubMedCrossRefGoogle Scholar
  25. FAO & IFAD. (2005). A review of cassava in Africa with country case studies on Nigeria, Ghana, the United Republic of Tanzania, Uganda and Benin. Proceeding of the Validation Forum on the Global Cassava Development Strategy. Vol. 2.Google Scholar
  26. Fisher, M., & Weber, W. B. (2004). Does economic vulnerability depend on place of residence? Asset poverty across metropolitan and nonmetropolitan areas, USA. The Review of Regional Studies, 34(2), 137–155.Google Scholar
  27. Foster, J. E. (1984). On economic poverty: A survey of aggregate measures. In R. L. Basmann & G. F. Rhodes, Jr., (Eds.), Advances in Econometrics 3. Greenwich, CN: JAI Press.Google Scholar
  28. Foster, A. D., & Rosenzweig, M. R. (2004). Agricultural productivity growth, rural economic diversity, and economic reforms: India, 1970–2000. Economic Development and Cultural Change, 52(3), 509–542.CrossRefGoogle Scholar
  29. Foster, J. E., & Shorrocks, A. F. (1988). Poverty orderings. Econometrica, 56(1), 173–177.CrossRefGoogle Scholar
  30. Foster, J., Greer, J., & Thorbecke, E. (1984). A class of decomposable poverty measures. Econometrica, 52(3), 761–65.CrossRefGoogle Scholar
  31. Friedlander, D., Greenberg, D. H., & Robins, P. K. (1997). Evaluating government training programs for the economically disadvantaged. Journal of Economic Literature, XXXV, 1809–1855.Google Scholar
  32. Greene, W. H. (2012). Econometric analysis (7th ed.). Upper Saddle River: Prentice Hall.Google Scholar
  33. Guo, S., & Fraser, W. M. (2009). Propensity score analysis: Statistical methods and applications. Thousand Oaks: Sage Publications.Google Scholar
  34. Haveman, R., & Wolff, E. (2000). Who are the asset poor? Trends and composition, 1983–1998. Working paper 00–12. St Louis: Washington University Center for Social Development.Google Scholar
  35. Haveman, R., & Wolff, E. N. (2004). The concept and measurement of asset poverty: levels, trends and composition for the US, 1983–2001. The Journal of Economic Inequality, 2(2), 145–169.CrossRefGoogle Scholar
  36. Heckman, J., & Navarro-Lozano, S. (2004). Using matching, instrumental variables and control functions to estimate economic choice models. The Review of Economics and Statistics, 86(1), 30–57.CrossRefGoogle Scholar
  37. Heckman, J., Hidehiko, H., & Todd, P. (1997). Matching as an econometric evaluation estimator: evidence from evaluating a job training program. The Review of Economic Studies, 64, 605–654.CrossRefGoogle Scholar
  38. Heckman, J., Lalonde, R., Smith, J. (1999). The economics and econometrics of active labour market programs. In O. Ashenfelter & D. Card (Eds.) Handbook of Labor Economics, Vol. III, Amsterdam: Elsevier.Google Scholar
  39. Heckman, J., Tobias, J. L., & Vytlacil, E. (2003). Simple estimators for treatment parameters in a latent variable framework. The Review of Economics and Statistics, 85(3), 748–755.CrossRefGoogle Scholar
  40. Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81(396), 945–960.CrossRefGoogle Scholar
  41. Hossain, M., Bose, M. L., & Mustafi, B. A. A. (2006). Adoption and productivity impact of modern rice varieties in Bangladesh. The Developing Economies, 64(2), 149–166.CrossRefGoogle Scholar
  42. Igbalajobi, O., Fatuase, A. I., & Ajibefun, I. (2013). Determinants of poverty incidence among rural farmers in Ondo State, Nigeria. American Journal of Rural Development, 1(5), 131–137.Google Scholar
  43. Imbens, G., & Wooldridge, J. M. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47(1), 5–86.CrossRefGoogle Scholar
  44. Janaiah, A., Hossain, M., & Otsuka, K. (2006). Productivity impact of the modern varieties of rice in India. The Developing Economies, 64(2), 190–207.CrossRefGoogle Scholar
  45. Kijima, Y., Otsuka, K., & Sserunkuuma, D. (2008). Assessing the impact of NERICA on income and poverty in central and western Uganda. Agricultural Economics, 38(3), 327–337.CrossRefGoogle Scholar
  46. La Flamme, M., & Davies, J. (2007). Developing a shared model for sustainable aboriginal livelihoods in natural- cultural resource management, CRC–Desert knowledge. http://www.desertknowledgecrc.com.au/research/inlandpublications.html. Accessed 29 Aug 2011.
  47. Lin, J. Y. (1999). Technological change and agricultural household income distribution: theory and evidence from China. The Australian Journal of Agricultural and Resource Economics, 43(3), 179–194.Google Scholar
  48. Maddala, G. S. (1983). Limited-dependent and qualitative variables in econometrics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  49. Mendola, M. (2007). Agricultural technology adoption and poverty reduction: a propensity score matching analysis for rural Bangladesh. Food Policy, 32(3), 372–393.CrossRefGoogle Scholar
  50. Moser, C. O. N. (2006). Asset-based approaches to poverty reduction in a globalised context: an introduction to asset accumulation policy and summary of workshop findings. Brookings global economy and development working paper #01.Google Scholar
  51. Oliver, M. L., & Shapiro, T. M. (1990). Wealth of a Nation: at least one third of households are asset-poor. The American Journal of Economics and Sociology, 49(2), 129–50.CrossRefGoogle Scholar
  52. Omonona, B. T. (2001). Poverty and its correlates among rural farming households in Kogi State, Nigeria. Unpublished Ph.D. Thesis, University of Ibadan, Ibadan. Nigeria.Google Scholar
  53. Otsuka, K. (2000). Role of agricultural research in poverty reduction: lessons from the Asian experience. Food Policy, 254, 447–462.CrossRefGoogle Scholar
  54. Oyekale, A. S., Adepoju, A. O., & Balogun, A. M. (2012). Determinants of poverty among riverine rural households in Ogun State, Nigeria. Studies of Tribes and Tribals, 10(2), 99–105.Google Scholar
  55. Rahman, S. (1999). Impact of technological change on income distribution and poverty in Bangladesh agriculture: an empirical analysis. Journal of International Development, 11(7), 935–955.CrossRefGoogle Scholar
  56. Rosenbaum, P. R. (1995). Observational studies. New York: Springer.CrossRefGoogle Scholar
  57. Rosenbaum, P., & Rubin, D. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 3, 33–38.Google Scholar
  58. Roy, A. D. (1951). Some thoughts on the distribution of earnings. Oxford Economic Papers, 3, 135–146.Google Scholar
  59. Ruben, R., & van den Berg, M. (2001). Non-farm employment and poverty alleviation of rural farm households in Honduras. World Development, 29(3), 549–560.CrossRefGoogle Scholar
  60. Rubin, D. B. (1973). The use of matched sampling and regression adjustment to remove bias in observational studies. Biometrics, 29(1), 184–203.Google Scholar
  61. Rubin, D. B. (1974). Estimating causal effects of treatments in randomised and non- randomised studies. Journal of Educational Psychology, 66(5), 688–701.CrossRefGoogle Scholar
  62. Rubin, D. B. (1979). Using multivariate matched sampling and regression adjustment to control bias in observational studies. Journal of the American Statistical Association, 74(366), 318–28.CrossRefGoogle Scholar
  63. Schultz, T. W. (1982). Investing in people: the economics of population quality. Berkeley: University of California Press.Google Scholar
  64. Scoones, I. (1998). Sustainable Rural Livelihoods: A framework for analysis. In Institute of Development Studies Working Paper 72. Brighton: University of Sussex.Google Scholar
  65. Sen, A. K. (1976). Poverty: an ordinal approach to measurement. Econometrica, 44(2), 219–231.CrossRefGoogle Scholar
  66. Sherraden, M. (1991). Assets and the poor. Armonk: M.E. Sharpe, INC.Google Scholar
  67. Smith, J., & Todd, P. (2005). Does matching overcome Lalonde’s critique of non-experimnets estimators? Journal of Econometrics, 125(1–2), 305–353.CrossRefGoogle Scholar
  68. STATAcorp (2013). Endogenous treatment effects. http://www.stata.com/stata13/endogenous-treatment-effects/. Accessed 18 Feb 2015.
  69. Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data, XXIII (p. 752). Cambidge: MIT press.Google Scholar
  70. Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data (2nd ed.). Cambridge: MIT Press.Google Scholar
  71. Wu, H., Ding, S., Pandey, S., & Tao, D. (2010). Assessing the impact of agricultural technology adoption on farmers’ well-being using propensity score matching analysis in Rural China. Asian Economic Journal, 24(2), 141–160.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht and International Society for Plant Pathology 2015

Authors and Affiliations

  • Bola Amoke Awotide
    • 1
  • Arega D. Alene
    • 2
  • Tahirou Abdoulaye
    • 3
  • Victor M. Manyong
    • 4
  1. 1.Department of Agricultural EconomicsUniversity of IbadanIbadanNigeria
  2. 2.International Institute of Tropical Agriculture (IITA)IbadanNigeria
  3. 3.International Institute of Tropical AgricultureLilongweMalawi
  4. 4.International Institute of Tropical AgricultureDar Es SalaamTanzania

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