The impact of low quality fertilizer on wealth effect Ling Yee Khor, Manfred Zeller University of Hohenheim, Stuttgart, Germany 17 th ICABR Conference Ravello, Italy, June, 2013

Ling Yee Khor, Manfred Zeller Page 2 Motivation IntroductionTheoryEmpirical MethodologyResultsSummary How does wealth level of farmers affect fertilizer use? Positive ( Lamb, 2003; Abdoulaye and Sanders, 2005 ) Negative ( Ariga et al., 2008 ) Insignificant ( Kaliba et al., 2000 )

Ling Yee Khor, Manfred Zeller Page 3 Motivation IntroductionTheoryEmpirical MethodologyResultsSummary Value of substandard and fake inputs in China in first half of 2011 US$ 24 million (Source: Xinhua News Agency) Substandard and fake inputs in Bangladesh, Cambodia, Nigeria, Tanzania, Vietnam

Ling Yee Khor, Manfred Zeller Page 4 IntroductionTheoryEmpirical MethodologyResultsSummary Data Research Center for Rural Economy (RCRE) of China Stratified random sampling in Hebei province Year 2004 to households 4218 observations

Ling Yee Khor, Manfred Zeller Page 5 IntroductionTheoryEmpirical MethodologyResultsSummary Fertilizer Use in Hebei Mean: 600 kg / hectare Maximum: 4000 kg / hectare Sources: China Rural Statistical Yearbook (2003) and RCRE Data (2004 – 2008)

Ling Yee Khor, Manfred Zeller Page 6 Uncertainty Model Robison and Barry (1987) Expected Value – Variance (EVV) method IntroductionTheoryEmpirical MethodologyResultsSummary Certainty equivalent of profit Risk aversion of an individual Expected value of profit Variance of profit

Ling Yee Khor, Manfred Zeller Page 7 Wealth Effect on Fertilizer Use High wealth: Wealth effect is positive Low wealth: Wealth effect is not clear F = fertilizer use; W = wealth, λ = risk aversion IntroductionTheoryEmpirical MethodologyResultsSummary

Ling Yee Khor, Manfred Zeller Page 8 Wealth Index vs Income Income Livestock Wealth index Henry, Sharma, Lapenu, and Zeller (2003) Multi-dimensional 4 categories: human capital, food security, living condition, and other assets IntroductionTheoryEmpirical MethodologyResultsSummary

Ling Yee Khor, Manfred Zeller Page 9 Constructing a Wealth Index (I) 13 variables left: Human capital (5) Food security (0) Living condition (3) Other assets (5) 13 variables left: Human capital (5) Food security (0) Living condition (3) Other assets (5) 45 potential variables from Research Center of Rural Economy (RCRE) Correlation with benchmark Expected sign Statistically significant Few missing data Principal Component Analysis Loading factor Source: Henry et al. (2003) Correlation with benchmark Expected sign Statistically significant Few missing data Principal Component Analysis Loading factor Source: Henry et al. (2003) IntroductionTheoryEmpirical MethodologyResultsSummary

Ling Yee Khor, Manfred Zeller Page 10 Constructing a Wealth Index (II) 13 variables left: Human capital (5) Food security (0) Living condition (3) Other assets (5) 13 variables left: Human capital (5) Food security (0) Living condition (3) Other assets (5) Loading factor as weight Household data of 13 wealth variables  1 wealth index for each household Source: Henry et al. (2003) Loading factor as weight Household data of 13 wealth variables  1 wealth index for each household Source: Henry et al. (2003) IntroductionTheoryEmpirical MethodologyResultsSummary

Ling Yee Khor, Manfred Zeller Page 11 Preliminary Results IntroductionTheoryEmpirical MethodologyResultsSummary Theoretical Wealth ) 2) 3)

Ling Yee Khor, Manfred Zeller Page 12 Preliminary Results IntroductionTheoryEmpirical MethodologyResultsSummary Empirical VariableProvince Wealth category LowHigh Wealth index Hebei ** Yunnan91.818** Notes: ** p < 0.05 Annual net income per cap in Hebei villages ~ US$ 360 (one of the highest in China) Annual net income per cap in Yunnan villages ~ US$ 210 (one of the lowest in China) Source: China Rural Statistical Yearbook (2003)

Ling Yee Khor, Manfred Zeller Page 13 Summary and Outlook Theoretical section shows that wealth effect changes due to uncertainty and risk averseness cannot test this empirically, due to a lack of dataset in our research region with risk preference data Both theoretical and empirical analyses show that wealth effect can change when we move from low wealth farmers to high wealth farmers IntroductionTheoryEmpirical MethodologyResultsSummary

Abdoulaye, T. and Sanders, J. H. (2005). Stages and determinants of fertilizer use in semiarid African agriculture: The Niger experience. Agricultural Economics, 32(2): Ariga, J., Jayne, T. S., Kibaara, B., and Nyoro, J. K. (2008). Trends and patterns in fertilizer use by smallholder farmers in Kenya, Food security collaborative working paper no , Department of Agricultural, Food, and Resource Economics, Michigan State University. Henry, C., Sharma, M., Lapenu, C., and Zeller, M. (2003). Microfinance Poverty Assessment Tool. Technical Tools Series No. 5. Consultative Group to Assist the Poor and The World Bank, Washington, DC. Kaliba, A. R., Verkuijl, H., and Mwangi, W. (2000). Factors affecting adoption of improved maize seeds and use of inorganic fertilizer for maize production in the intermediate and lowland zones of Tanzania. Journal of Agricultural and Applied Economics, 32(1): Lamb, R. L. (2003). Fertilizer use, risk, and off-farm labor markets in the semi-arid tropics of India. American Journal of Agricultural Economics, 85(2): Robison, L. J. and Barry, P. J. (1987). The Competitive Firm's Response to Risk. Macmillan Publishing Company, New York. Wang, G China cracks down on counterfeit seeds, pesticides. Internet. Xinhua News Agency. Available Zeller, M., Sharma, M., Henry, C., and Lapenu, C. (2006). An operational method for assessing the poverty outreach performance of development policies and projects: Results of case studies in Africa, Asia, and Latin America. World Development, 34(3): Ling Yee Khor, Manfred Zeller Page 14 References IntroductionTheoryEmpirical MethodologyResultsSummary