Impact of Small-Holders’ Cattle Fattening on Household Income Generation in Fadis District of Eastern Hararghe Zone, Oromia, Ethiopia

Subject Areas : Farm Management

Jafer Mume ¹ , Fikadu Tadesse ²

1 - Agricultural economics, Oromia Agricultural Research Institute, Fadis Agricultural Research Center
2 - Agronomist,Fadis Agricultural Research Center, Oromia Agricultural Research Institute

Received: 2017-10-31 Accepted : 2018-04-21 Published : 2018-06-01

Keywords: Logit regression, cattle fattening, propensity score matching, household income,

Abstract :

At the household level, livestock plays a critical economic and social role in pastoralists and at the household level, livestock plays a critical economic and social role in pastoralists and smallholder farm households. The objectives of this study were to analyze factors affecting participation in cattle fattening and its impacts on household income in Fadis district of Eastern Hararghe. Both primary and secondary data were used. The data were collected by means of a semi-structured questionnaire from 124 samples during the period of April 20-May20/ 2017. Logit estimation revealed that participation in cattle fattening is significantly influenced by five variables. Age of household head, labor force in family member, market information, access to agricultural extension services and number of livestock are significant variables which affect the participation of the household in cattle fattening practices. Propensity score matching method was applied to analyze the impact of the cattle fattening on the household income generation. In matching processes, kernel matching with 0.25 band width was resulted in relatively low pseudo-R2with best balancing test was found to be the best matching algorithm. This method was checked for standardized bias, t-test, and joint significance level. Propensity score matching results revealed that household participated in cattle fattening practice have got 14,071 more farm income and 12,617 total household income in Ethiopian Birr (ETB) than those household that were not participated in fattening practices. This income difference shows how non-farm and off-farm income compensated for income obtained from cattle fattening activities with farm income.

References:

Aldrich, J. H., &Nelson, F. D.(1984). Liner probability, logit and probit model: Quantitative applications in the social science. Sera Miller McCun, Sage Pub. Inc, University of Minnesota and Iola, London.

 Becker, So, Ichino, A. (2002). Estimation of average treatment effects based on propensity scores: The Stata Journal, 2, (4), 1-19.

 Belete, A., Azage, T., Fekadu, B., & Berhanu, G. (2010).Cattle milk and meat production and marketing systems and opportunities for market-orientation in Fogera woreda, Amhara region, Ethiopia. IPMS K2 (Improving Productivity and Market Success) ofEthiopian Farmers Project Working Paper 19. ILRI (International Livestock Research Institute), Nairobi, Kenya, 2: 65.

 Bryson, A., Dorsett, R., & Purdon, S. (2002). The Use of propensity score matching in the evaluation of labour market policies, Working Paper No. 4, Department for Work and Pensions.

 CSA (Central Statistics Authority) (2013). Statistics report on farm management practices, livestock and farm implements. Part II, Addis Ababa, Ethiopia 1, 14-15.

 Caliendo, M., & Kopeinig, S. (2005).Some practical guidance for the implementation of propensity score matching, Discussion Paper No. 1588, University of Cologne.

 EHZARDO (East Hararghe Zone Agriculture and Rural Development Office) (2015).Annual report.Fadis District, East Hararghe Zone, Ethiopia.

 FAO (2015).Food outlook, biannual report on global food markets.Food and Agricultural organization in the United Nation.PP 6.

 Gujarati, DN. (2003).Basic econometrics (2^ndEd.).McGraw Hill, Inc., New York.

 Lee, WS. (2006).Propensity score matching and variations on the balancing test: Melbourne Institute of Applied Economic and Social Research, the University of Melbourne.

 Leuven, E., & Sianesi, B.(2003). Psmatch2, stata module to perform full propensity score matching. Retrieved from   http://ideas.repec,org/c/boc/bocode/s432001.html.

 Liao, T. F. (1994). Interpreting probability models: Logit, probit and other generalized models.Sage University paper series on Quantitative Applications in the Social Sciences, 07-101. Thousand Oaks, CA: Sage, California.

 MoARD (Ministry of Agriculture and Rural Development), (2006).Poultry and poultry product development five years plan (1998-2003). Animal and fishery development department, MoARD, Addis Ababa, Ethiopia.

 MoARD (Ministry of Agriculture and Rural Development), (2007).Livestock development master plan study: Phase I Report – Data Collection and Analysis.Volume V-Poultry Production. Addis Ababa, Ethiopia.

 Rosenbaum, PR., & Rubin, DB. (1983).Thecentral role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.

Rosenbaum, PR. (2002).Observational studies (2^nd Ed.),Springer-varlag, New York.

 Sianesi, B. (2004). An evaluation of the active labor market programs in Sweden. The Review of Economics and Statistics, 186(1), 133-155.

 SPS-LMM (2010).Trade Bulletin Issue I. Focus on Ethiopia’s meat and Live Animal Export Peters, K. J. and   Thorpe, W. 1989.Trends in On-Farm Performance Testing of Cattle and Sheep in Sub-Saharan Africa.International Livestock Centre forAfrica, Addis Ababa, Ethiopia.

 Venetoklis, T. (2004). An evaluation of wage subsidy programs to SMEs Utilising Propensity Score Matching: Government Institute of Economic Research, Helsinki.