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The Contribution of Trees to Livelihoods: A Panel Analysis of Living Standards Surveys in Tanzania

  1. Socio-Economic Data Research Question Empirical Challenge Results The Contribution of Trees to Livelihoods: A Panel Analysis of Living Standards Surveys in Tanzania Anil K. Bhargava Postdoctoral Research Fellow University of Michigan CGIAR Side Event September 10th, 2015 World Forestry Congress, Durban, South Africa Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
  2. Socio-Economic Data Research Question Empirical Challenge Results Living Standards Measurement Study Sources of Income Agriculture Living Standards Measurement Study Integrated Surveys on Agriculture (LSMS-ISA) Nationally representative multi-period panel datasets Same households and plots over time Household, agricultural, and community surveys Environmental and geophysical data (at low resolution). Geo-referenced household and plot locations available Eight SSA partner countries Tanzania, Ethiopia, Malawi, Nigeria, Uganda, Mali, Niger, and Burkina Faso Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
  3. Socio-Economic Data Research Question Empirical Challenge Results Living Standards Measurement Study Sources of Income Agriculture Household Income by Source: Tanzania Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
  4. Socio-Economic Data Research Question Empirical Challenge Results Living Standards Measurement Study Sources of Income Agriculture Agriculture Trends: Tanzania Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
  5. Socio-Economic Data Research Question Empirical Challenge Results How Can Trees Contribute to Livelihoods How much is land quality, associated with existence of trees on plots and proximity to forests, contributing to these increasing agricultural values? Africa is thought to be below productive potential in agriculture Inputs and technologies consistently estimated as underutilized Need to improve understanding of land quality's role, extending recent ndings of forest and tree impacts on soil quality to agricultural productivity and poverty alleviation Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
  6. Socio-Economic Data Research Question Empirical Challenge Results How Can Trees Contribute to Livelihoods How much is land quality, associated with existence of trees on plots and proximity to forests, contributing to these increasing agricultural values? Africa is thought to be below productive potential in agriculture Inputs and technologies consistently estimated as underutilized Need to improve understanding of land quality's role, extending recent ndings of forest and tree impacts on soil quality to agricultural productivity and poverty alleviation Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
  7. Socio-Economic Data Research Question Empirical Challenge Results Identication Strategy Fixed Eects Matching Identication of Causal Impact We want to know impact of trees on agricultural outcomes Concern of endogeneity: intercropping of trees may lead to higher agricultural returns via improved land quality higher agricultural returns occur on better land, which is more conducive to intercropping of trees (e.g. Kilamanjaro) Unobserved sources of potential bias (omitted variable bias): better farmers may tend to grow permanent crops is productivity higher because of trees or farmer ability? = Must account for confounding plot and farmer characteristics Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
  8. Socio-Economic Data Research Question Empirical Challenge Results Identication Strategy Fixed Eects Matching Identication of Causal Impact We want to know impact of trees on agricultural outcomes Concern of endogeneity: intercropping of trees may lead to higher agricultural returns via improved land quality higher agricultural returns occur on better land, which is more conducive to intercropping of trees (e.g. Kilamanjaro) Unobserved sources of potential bias (omitted variable bias): better farmers may tend to grow permanent crops is productivity higher because of trees or farmer ability? = Must account for confounding plot and farmer characteristics Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
  9. Socio-Economic Data Research Question Empirical Challenge Results Identication Strategy Fixed Eects Matching Identication of Causal Impact We want to know impact of trees on agricultural outcomes Concern of endogeneity: intercropping of trees may lead to higher agricultural returns via improved land quality higher agricultural returns occur on better land, which is more conducive to intercropping of trees (e.g. Kilamanjaro) Unobserved sources of potential bias (omitted variable bias): better farmers may tend to grow permanent crops is productivity higher because of trees or farmer ability? = Must account for confounding plot and farmer characteristics Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
  10. Socio-Economic Data Research Question Empirical Challenge Results Identication Strategy Fixed Eects Matching Potential Solutions 1) Fixed Eects Regression Model Panel dataset allows use of changes in number or existence of trees on same plot over time to create counterfactual Controls for unobserved, time-invariant land characteristics that contribute to land quality Controls for unobserved, time-invariant farmer characteristics that contribute to farm productivity (e.g. ability, risk) Observable time-varying farmer and farm factors can be explicitly controlled for: changes in non-ag income, wealth, fertilizer, pesticides, irrigation Still leaves out time-varying unobservables and thus must assume common time trends in outcomes between two groups If this holds, assignment of trees to plots is as good as random Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
  11. Socio-Economic Data Research Question Empirical Challenge Results Identication Strategy Fixed Eects Matching Potential Solutions 1) Fixed Eects Regression Model Panel dataset allows use of changes in number or existence of trees on same plot over time to create counterfactual Controls for unobserved, time-invariant land characteristics that contribute to land quality Controls for unobserved, time-invariant farmer characteristics that contribute to farm productivity (e.g. ability, risk) Observable time-varying farmer and farm factors can be explicitly controlled for: changes in non-ag income, wealth, fertilizer, pesticides, irrigation Still leaves out time-varying unobservables and thus must assume common time trends in outcomes between two groups If this holds, assignment of trees to plots is as good as random Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
  12. Socio-Economic Data Research Question Empirical Challenge Results Identication Strategy Fixed Eects Matching Fixed Eects Estimation Equation yit = βi +γt +δtreatit +θ ·Xit +εit, where yit is the outcome of interest for plot i in year t, treatit is the treatment (e.g., existence or number of trees), and Xit is a vector of control variables that may also aect y. The coecient β is the farmer-plot xed eect, γ is the time xed eect, and the main coecient of interest is δ, or the within estimator, which gives the treatment eect of trees on agricultural outcomes. Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
  13. Socio-Economic Data Research Question Empirical Challenge Results Identication Strategy Fixed Eects Matching Propensity Score Matching 2) Matching Create treatment and control groups based on a set of observable characteristics, such as household income, landholdings, input use, plot slope. Obtain average treatment eect (ATE) in population: Start with control plots, then nd treated plots with similar initial probabalities of treatment Average treatment eect on treated (ATET): Start with treated plots, then nd non-treated plots with similar initial probabalities of treatment Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
  14. Socio-Economic Data Research Question Empirical Challenge Results Fixed Eects Results Matching Results Conclusion FE Results: Agricultural Production Value per Acre Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
  15. Socio-Economic Data Research Question Empirical Challenge Results Fixed Eects Results Matching Results Conclusion Matching: Value per Acre Figure: Matching with Costs, ATET Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
  16. Socio-Economic Data Research Question Empirical Challenge Results Fixed Eects Results Matching Results Conclusion Matching: Value per Acre (no costs) Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
  17. Socio-Economic Data Research Question Empirical Challenge Results Fixed Eects Results Matching Results Conclusion Conclusion Results are preliminary but suggest large gains in agricultural production are possible with investment in trees on plots. But requires big push in number of trees. Important to control for confounding physical land properties and socio-economic household and farmer characteristics over time (e.g. with panel datasets). Could this be leading to enhanced adoption rates of complementary agricultural technologies? How does additional income from agriculture due to trees enter into household expenditures (nutrition, investment)? Policymakers may consider these indirect linkages to and from the socio-economic side of households when considering future environmental and social policies Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
  18. Socio-Economic Data Research Question Empirical Challenge Results Fixed Eects Results Matching Results Conclusion Next Steps Improve soil, erosion, and other environmental data using plot geo-locations to strengthen understanding of this impact channel Split impacts by crop, tree type, and region Methodologically, combine matching plus xed eects to potentially improve empirical design Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
  19. Socio-Economic Data Research Question Empirical Challenge Results Fixed Eects Results Matching Results Conclusion Thank you Anil K. Bhargava, University of Michigan The Contribution of Trees to Livelihoods
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