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Crop diversity and welfare dynamics: Empirical Evidence from Nigeria

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- Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 1/24
- Crop diversity and welfare dynamics: Empirical Evidence from Nigeria Eleni Yitbarek, Hiywot Girma, Wondimagegn Tesfaye 2023 AGRODEP Annual Conference, Kigali, Rwanda 21-23 March 2023 Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 2/24
- Outline 1 Motivation 2 Objective 3 Data 4 Estimation strategy 5 Results 6 Conclusion Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 3/24
- Motivation After a hiatus, promoting sustainable agricultural growth is back on the development agenda. Climate change is emerging as a major threats to the development of the sector and might worsen food insecurity Climate change might aect smallholder farmers disproportionately: a moderate increase in temperatures will have a negative impact on the production of stable crops There is an public policy demand for identifying sustainable agricultural practices that can improve welfare and help farm households withstand the deleterious eect of climate change. Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 4/24
- Motivation Crop diversication is identied as the most ecologically feasible and cost eective CSA to improve agricultural production and improve food and nutrition security Two possible channels: Diversication towards nutrient dense crops has the potential to improve nutrition for farm households Enhancing farm household's income. Though the pathway is not always direct and linear (food market, knowledge and preferences) However, the direct causal link between crop diversication and nutrition is not simple and the existing empirical evidence is mixed. Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 5/24
- LiteratureSnapshot The existing scant empirical evidence suggest that adopting crop diversication improves the consumption of the poorest and reduces poverty (Tesfaye and Tirivayi, 2020; Sibhatu and Qaim, 2018). Nevertheless, studies has not moved down to an empirical analysis of how crop diversication aect poverty dynamics. Static poverty measures fail to distinguish between an individual who has been in poverty all her life, and another who happens to have had a small misfortune for the year the measurement was carried out. Signicant numbers of people move into poverty throughout their lives, one third African population move into and out of poverty (Dang and Dabalen, 2019). Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 6/24
- Objective Assess the eect of crop diversication on farm household's poverty dynamics Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 7/24
- Data Nigeria General Household Survey (NGHS)- 2010/11, 2012/13, and 2015/16 Living Standards Measurement Study Integrated Surveys on Agriculture (LSMS-ISA) program of the World Bank in collaboration with the Nigerian National Bureau of Statistics (NBS) Panel of 5000 hh and 14,000 individuals Geo-reference households Temperature and precipitation data The Climatic Research Unit (CRU-TS-4.03), University of East Anglia Outcome variable: Poverty status of households = HH consumption per adult equivalent + national poverty line (137,430 Naira or 382 USD per year) Determinants of poverty status of households - household characteristics + household head characteristics + crop diversity indices + climate variability Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 8/24
- Data Table: Denition of crop diversity indices Index Mathematical Construction Explanation Adaptation in this paper Number of crops D=S Richness A Household produced S number of crops Shannon-Weaver D = −Σpi ln pI , D 0 Proportional abundance and Richness pi is proportion, or relative abun- dance of a species D 0 Composite Entropy D = − Pp i pi lns (pi ) (1 − 1/S), Proportional abundance and Richness p_i is proportion, or relative abundance of a species 0 ⩽ D ⩽ 1 Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 9/24
- Data Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 10/24
- Data Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 11/24
- Data Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 12/24
- Endogenous switching model Cappellari and Jenkins (2002, 2004). It models poverty transitions between two consecutive waves using a trivariate probit model. Four parts: Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 13/24
- Endogenous switching model Cappellari and Jenkins (2002, 2004). It models poverty transitions between two consecutive waves using a trivariate probit model. Four parts: The determination of poverty status at t, Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 13/24
- Endogenous switching model Cappellari and Jenkins (2002, 2004). It models poverty transitions between two consecutive waves using a trivariate probit model. Four parts: The determination of poverty status at t, The determination of household retention between t − 1 and t, Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 13/24
- Endogenous switching model Cappellari and Jenkins (2002, 2004). It models poverty transitions between two consecutive waves using a trivariate probit model. Four parts: The determination of poverty status at t, The determination of household retention between t − 1 and t, The determination of poverty status at t − 1 in order to account for the initial conditions problem, Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 13/24
- Endogenous switching model Cappellari and Jenkins (2002, 2004). It models poverty transitions between two consecutive waves using a trivariate probit model. Four parts: The determination of poverty status at t, The determination of household retention between t − 1 and t, The determination of poverty status at t − 1 in order to account for the initial conditions problem, The correlations between the unobservables aecting all the three processes Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 13/24
- Endogenous switching model Cappellari and Jenkins (2002, 2004). It models poverty transitions between two consecutive waves using a trivariate probit model. Four parts: The determination of poverty status at t, The determination of household retention between t − 1 and t, The determination of poverty status at t − 1 in order to account for the initial conditions problem, The correlations between the unobservables aecting all the three processes The combination of all the four parts characterizes the determinants of poverty persistence and poverty entry rates Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 13/24
- Initial poverty status equation p∗ it−1 = β′ xit−1 + uit−1 (1) where: i = 1, · · · , N indexes households and t = 1, · · · , T time span, p∗ it−1 is the latent dependent variable, xit−1 is a vector of controls describing i's household characteristics, β is a vector of parameters to be estimated and the error term, uit−1 = δi + µit−1 (the sum of an household-specic eect and an orthogonal white noise error), pit−1 = 1[p∗ it−10] (2) Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 14/24
- Retention equation r∗ it = γ′ wit−1 + εit (3) where i = 1, · · · , N indexes households and t = 1, · · · , T time span, r∗ it 's is the latent retention propensity, the error term εit = ηi + ϑit (the sum of an household-specic eect ηi plus an orthogonal white noise error ϑit) wit−1 is a vector of controls describing i's household characteristics and γ is a vector of parameters to be estimated rit = 1[r∗ it 0] (4) Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 15/24
- Poverty transition equation p∗ it = [(pit−1)λ′ 1 + (1 − pit−1)λ′ 2] zit−1 + ϵit (5) where: i = 1, · · · , N indexes households and t = 1, · · · , T time span p∗ it is the latent dependent variable λ′ 1, λ′ 2 are parameter vectors to be estimated zit−1 denotes vector of controls the error term ϵit = τi + ξit (the sum of an household specic eect τi plus an orthogonal white noise error ξit) (pit|pit−1, rit = 1) = 1[{(pit−1)λ′ 1+(1−pit−1)λ′ 2}zit−1+ϵit κt ] (6) Note that pit is only when rit = 1 Estimation method: Maximum likelihood (multivariate approach of Cappellari and Jenkins, 2004). Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 16/24
- The joint distribution of the error terms uit−1, εit and ϵit is trivariate standard normal - unrestricted and estimable. The distribution of unobserved households' heterogeneity is parameterized via the cross-equation correlations. Other things being equal, ρ1 (corr b/n unobservable aecting pit−1, and rit) and ρ3 (corr b/n unobservable aecting rit and pit) are equal to zero, panel attrition is random and joint estimation of retention equation can be ignored, the model reduces to a bivariate model. Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 17/24
- The joint distribution of the error terms uit−1, εit and ϵit is trivariate standard normal - unrestricted and estimable. The distribution of unobserved households' heterogeneity is parameterized via the cross-equation correlations. Other things being equal, ρ1 (corr b/n unobservable aecting pit−1, and rit) and ρ3 (corr b/n unobservable aecting rit and pit) are equal to zero, panel attrition is random and joint estimation of retention equation can be ignored, the model reduces to a bivariate model. ρ2 (corr b/n unobservable aecting (pit−1 and pit) and ρ1 (corr b/n unobservable aecting pit, and rit) are equal to zero, the initial condition can be ignored and past poverty experience can be treated as exogenous. Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 17/24
- The joint distribution of the error terms uit−1, εit and ϵit is trivariate standard normal - unrestricted and estimable. The distribution of unobserved households' heterogeneity is parameterized via the cross-equation correlations. Other things being equal, ρ1 (corr b/n unobservable aecting pit−1, and rit) and ρ3 (corr b/n unobservable aecting rit and pit) are equal to zero, panel attrition is random and joint estimation of retention equation can be ignored, the model reduces to a bivariate model. ρ2 (corr b/n unobservable aecting (pit−1 and pit) and ρ1 (corr b/n unobservable aecting pit, and rit) are equal to zero, the initial condition can be ignored and past poverty experience can be treated as exogenous. ρ1 = ρ2 = ρ3 = 0 both initial poverty and sample attrition are exogenous and the model reduces to a univariate probit model. Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 17/24
- Testing validity of estimation strategy Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 18/24
- Results Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 19/24
- Results Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 20/24
- Results Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 21/24
- Results Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 22/24
- Conclusion We bring a new applied evidence from one of the poorest country in SSA to bear on the on-going debate whether crop diversication.... We found initial conditions and sample retention are endogenous to poverty transitions in our panel. Adopting crop diversity is negatively associated with poverty entry but does not aect poverty persistence. Mitigating the eect of climate change on welfare might require adoption of more than one CSA than one-size ts all interventions. Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 23/24
- Thank you! This work was supported through the Climate Research for Development (CR4D) Postdoctoral Fellowship [CR4D-19-17]. Eleni Yitbarek #2023 AGRODEP Conference March 22, 2023 24/24

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