The document summarizes research analyzing the contribution of trees to livelihoods in Tanzania using panel data from Living Standards Measurement Surveys. The research aims to quantify the impact of trees on agricultural productivity and incomes. Fixed effects regressions and propensity score matching techniques are used to control for endogeneity and identify causal effects. Preliminary results suggest large gains in agricultural production and value are possible from increasing the number of trees on plots, though more research is needed to better understand impacts by crop, tree type, and region.

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