Integrated Agricultural System, Migration, and Social
Protection Strategies to Reduce Vulnerability to Climate
Change in East Africa
Bradford Mills - Virginia Tech
Genti Kostandini – University of Georgia
Anthony Murray – Economic Research Service, USDA
Jiangfeng Gao – Virginia Tech
Joseph Rusike - AGRA
Steven Omamo
Zhe Guo - IFPRI
Jawoo Koo - IFPRI
LSE Seminar, ILRI Nairobi, 28 January 2015

Project Objectives
• Estimate potential costs climatic changes impose on
vulnerable rural households
– yield decreases
– yield variance increases
• Identify agricultural system strategies that mitigate climate
change costs
• Rural household use of integrated agricultural system, off-
on farm employment, migration, and formal and informal
safety net strategies to reduce vulnerability to climatic
change
• Policy briefs that assist policymakers to generate country-
specific interventions to mitigate the impacts of climatic
change

Project Components
• 6 months into an 18 month project
• Component one – Climate, crop, income
linkages
– Identify the monetary costs to households and
regions that climatic change is expected to have
on agricultural systems in two East Africa
countries: Ethiopia and Zambia
– Simulation modeling (Genti)

Component one
• Identify the monetary costs of climate changes on
the agricultural systems of Ethiopia and Zambia at
the regional and household level.
• Translate rainfall change patterns into climate shocks
for major crops using DSSAT crop model for the
2000-2011 period.
• Use a methodology that takes into account the effect
on mean yields and yield variance and higher
moments of yield distribution.
• Produce ex-ante estimates based on forward looking
plausible climate change scenarios.

Methodology
• Mean yield decreases from climate change (Regional level)
Pr. Y = KPQp- ΔPQp
Cs. Yc= ΔPQc
• Producer losses due to increased risk (Regional Level)
• Consumer losses due to increased price variability (Regional Level)
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Methodology cont.
• Benefits from mean yield decreases for each
household type
• Benefits from yield variance increases
iijjjjij PQPY   )1(.Pr
(i = poor farm, average farm, rich farm: j = drought risk type)
)(5.0.Pr 22
jpijijijiij sRYRB  
(i = poor farm, average farm, rich farm: j = PFS)

Data
• Geo-referenced rainfall and temperature data for
the 2000-2011 period to characterize drought
risk using planting and harvesting dates.
• Geo-referenced farm level panel household data
(The Ethiopian Rural Household Survey seven
waves from 1994 to 2009 and the Zambian
Central Statistical Office 2000, 2004 and 2008) to
estimate the benefits for different household
types.
• Use 11 years of DSSAT crop model data to isolate
the impact of changes in rainfall pattern on yield
and yield variability.
• Use 2005 baseline SPAM production data from
IFPRI.

Table 1. Simulations of potential impacts of a 20% Maize Yield decrease and a 10% yield variance increase.
Very Severe
Drought
Moderate
Drought
Mild Drought Incipient Drought No Drought
Annual welfare changes from a 20% mean yield decrease (Thousand US $) Total Total
PR CS PR CS PR CS PR CS PR CS Total Losses (MT) Losses (%)
Ethiopia (294) (111) (23,909) (9,022) (20,686) (7,806) (11,615) (4,383) (30) (11) (77,868) (569,078) (14.71)
Zambia (3,808.2) (1,878.0) - - (12,502.4) (6,165.6) (4,527.0) (2,232.5) (1.3) (0.7) (31,115.7) (156,065.7) (7.9)
Subtotal (4,102.0) (1,988.9) (23,909.1) (9,022.3) (33,188.8) (13,971.8) (16,141.8) (6,615.4) (31.4) (12.0) (108,983.) (725,143.)
Annual welfare changes from a 10% increase in yield variance (Thousand US $)
PR CS PR CS PR CS PR CS PR CS
Ethiopia (59.6) (83.7) (4,932.9) (6,924.2) (4,438.5) (6,230.2) (2,544.7) (3,571.9) (8.9) (12.6) (28,807.1)
Zambia (87.3) (263.6) - - (286.6) (865.6) (103.8) (313.4) - - (1,920.3)
Sub-total (146.9) (347.3) (4,932.9) (6,924.2) (4,725.1) (7,095.8) (2,648.4) (3,885.3) (8.9) (12.6) (30,727.4)
Total (4,248.8) (2,336.2) (28,842.0) (15,946.5) (37,913.9) (21,067.6) (18,790.2) (10,500.8) (40.4) (24.6) (139,710.9)

Table 2. Share of losses by drought risk zone.
Total
losses
('000 US$)
Share
losses
Very
Severe
Drought
Moderate
Drought
Mild
Drought
Incipient
Drought
No Drought
Variance
share in
total losses
Producer
surplus
share in
total losses
Total
production
losses (MT)
Production
Decrease
Share
production
losses
Ethiopia (106,675) 0.76 0.01 0.42 0.37 0.21 0.00 0.27 0.64 (569,078) (0.147) 0.8
Zambia (33,036) 0.24 0.18 0.00 0.60 0.22 0.00 0.06 0.65 (156,066) (0.079) 0.2
Total (139,711) 0.05 0.32 0.42 0.21 0.00 0.22 0.64 (725,144)

Table 3. Simulations of potential impacts of different maize yield decreases and a yield variance increases.
Very Severe
Drought
(50%)
Moderate
Drought
(40%)
Mild Drought
(30%)
Incipient
Drought
(20%)
No Drought
(10%)
Welfare changes from 50%-10% mean yield decrease (Thousand US $) Total Total
PR CS PR CS PR CS PR CS PR CS Total
Losses
(MT)
Losses (%)
Ethiopia (725) (274) (47,421) (17,895) (30,907) (11,663) (11,615) (4,383) (15) (6) (124,903) (918,205) (24)
Zambia (9,168) (4,521) - - (18,522) (9,134) (4,527) (2,232) (1) (0) (48,105) (245,657) (13)
Subtotal (9,893) (4,795) (47,421) (17,895) (49,429) (20,797) (16,142) (6,615) (16) (6) (173,008) (1,163,862)
Welfare changes from 50%-10% yield variance reductions in 2016 (Thousand US $)
PR CS PR CS PR CS PR CS PR CS
Ethiopia (460) (498) (27,356) (31,653) (16,572) (20,471) (5,680) (7,484) (9) (13) (110,195)
Zambia (699) (1,569) - - (1,092) (2,844) (234) (657) - - (7,096)
Sub-total (1,159) (2,067) (27,356) (31,653) (17,665) (23,315) (5,914) (8,141) (9) (13) (117,291)
Total (11,052) (6,862) (74,776) (49,548) (67,094) (44,112) (22,056) (14,756) (25) (19) (290,299)

Annual welfare changes from a 20% yield decreases (US $/year)
Small farms Average farms Big farms
Ethiopia -28.26 -52.08 -109.67
Zambia -39.19 -73.45 -182.60
Annual welfare changes from a 10% yield variance increase (US $/year)
Ethiopia -13.83 -14.85 -19.71
Zambia -4.52 -5.56 -10.19
Table 4. Simulations of household annual welfare changes from a 20% mean yield decrease and a 10% yield variance increase.

Summary
• Potential losses are considerable given that only
maize losses were simulated.
• Estimated losses vary widely depending on the level
of drought risk.
• Welfare changes due to yield variability are an
important part of the overall welfare changes.

On-going work on component one
• Finish all major crops and use DSSAT panel
data results to estimate regional losses.
• Complete household data analysis and
estimate household losses by drought risk.
• Estimate losses/benefits based on future
climate scenarios .

Component two – integrated coping
mechanisms
Identify broader set of household
adaptations with long-term panel datasets
in Zambia and Ethiopia
(Brad for Anthony and Jiangfeng)

• Focus on agricultural adaptation: crop shares and yields
• Three-wave national representative panel:
– Created by Zambian Central Statistical Office, Ministry of Agriculture
and Cooperatives, and the Food Security Research Project
• Survey focuses on agricultural production and household
characteristics
– Survey rounds cover 1999/2000, 2002/2003, and 2006/2007
agricultural seasons
– 4,286 households successfully interviewed in all 3 panels (out of
original 7,699)
• Past research not found attrition bias (Mason and Jayne, 2013)
– This analysis only looks at households growing maize
• Climate Data:
– African Drought and Flood Monitor:
• Daily rainfall (mm)
• Aggregated for Planting (Nov-Dec), Growing (Jan-Mar), and Harvest (Apr-May)
seasons
Integrated Household Coping
Mechanisms – Zambia Data

Empirical Specification
• Assume a household fixed effects model to
exploit panel dataset:
• Two dependent variables of interest:
– Share of maize grown by farm household
– Maize yield per hectare
it it i itXy     

Variable Mean Std. Dev. N
Maize Share of Cropped Land 0.60 0.27 6098
Number of Adult Equivalent 5.37 2.68 6098
Female Headed Household 0.21 0.41 6098
Net Income (less Maize) (US Dollars) 553.80 979.62 6098
Total Assets 71.40 188.60 6098
Owns Livestock (dummy) 0.83 0.38 6098
Total Landholdings 2.53 3.05 6098
Hectares Cultivated 1.99 2.03 6098
Had Fallow Land (dummy) 0.33 0.47 6098
Use Fertilizer (dummy) 0.40 0.49 6098
2008 Dummy 0.52 0.50 6098
Lag Groundnut Price 0.28 0.03 6098
Lag Sweet Potato Price 0.05 0.01 6098
Lag Maize Price 0.09 0.01 6098
Grew Cash Crops previous Survey Year 0.21 0.41 6098
Grew High Value Crops previous Survey Year 0.45 0.50 6098
Grew Other Staple Crops previous Survey Year 0.52 0.50 6098
Lagged Planting Season Coef. of Var. (5 yr) 0.69 0.12 6098
Lagged Growing Season Coef. of Var. (5 yr) 0.68 0.14 6098
Lagged Planting Season 10 day Rainfall (5 yr avg.) 53.14 12.55 6098
Lagged Growing Season 10 day Rainfall (5 yr avg.) 59.59 10.91 6098
Summary Statistics: Maize Share

Variable Mean Std. Dev. N
Maize yield per hectare 1619.39 1308.20 9929
Number of Adult Equivalent 5.30 2.74 9929
Female Headed Household 0.21 0.40 9929
Net Income (less Maize) (US Dollars) 465.08 859.41 9929
Total Assets 273.26 1702.91 9929
Owns Livestock (dummy) 0.83 0.38 9929
Total Landholdings 2.63 3.07 9929
Hectares Cultivated 2.03 2.06 9929
Had Fallow Land (dummy) 0.37 0.48 9929
Use Fertilizer (dummy) 0.36 0.48 9929
Fertilizer per Hectare 101.10 203.68 9929
Grew Cash Crops (dummy) 0.21 0.40 9929
Grew High Value Crops (dummy) 0.49 0.50 9929
Grew Other Staple Crops (dummy) 0.51 0.50 9929
Share of Maize planted/Total Cropped land 0.60 0.29 9929
Total Rainfall (mm) over growing season 621.80 155.40 9929
Planting Season Coef. of Var. (5 yr) 0.70 0.12 9929
Growing Season Coef. of Var. (5 yr) 0.62 0.12 9929
Harvest Season Coef. of Var. (5 yr) 1.51 0.36 9929
Summary Statistics: Yield/ha

Climate: By Year & Specification
Maize yield per hectare 1999/2000 2002/2003 2006/2007
Climate Variables Mean Std. Dev Mean Std. Dev Mean Std. Dev
Total Rainfall (mm) over growing season 603.13 89.91 597.68 177.64 663.95 170.26
Planting Season Coef. of Var. (5 yr) 0.69 0.14 0.69 0.13 0.71 0.08
Growing Season Coef. of Var. (5 yr) 0.57 0.10 0.64 0.10 0.63 0.14
Harvest Season Coef. of Var. (5 yr) 1.65 0.39 1.36 0.24 1.53 0.37
Maize Share Specification 2002/2003 Season 2006/2007 Season
Climate Variables Mean Std. Dev Mean Std. Dev
Lagged Planting Season Coef. of Var. (5 yr) 0.69 0.11 0.68 0.12
Lagged Growing Season Coef. of Var. (5 yr) 0.64 0.12 0.71 0.14
Lagged Planting Season Total Rainfall (5 yr avg.) 56.73 13.39 49.79 10.68
Lagged Growing Season Total Rainfall (5 yr avg.) 59.42 10.84 59.74 10.98
N = 2943 N = 3155

Zambia: Maize Share (1 of 2)
Variable Coefficient Std. Err
Number of Adult Equivalent -0.006*** 0.002
Female Headed Household -0.002 0.017
Net Income (less Maize) (US Dollars) -1.80E-05*** 4.30E-06
Total Assets 3.73E-05 2.48E-05
Owns Livestock (dummy) -0.023** 0.011
Total Landholdings -0.001 0.001
Hectares Cultivated -0.012*** 0.003
Had Fallow Land (dummy) -0.010 0.008
Use Fertilizer (dummy) 0.026*** 0.010
2006/2007 Agricultural season (dummy) 0.101* 0.056
Changes in Household Characteristics:
• Changes in number of adults significantly decreases maize share planted
• Increases in income and ownership of livestock associated with crop diversification
• Adding more hectares cultivated reduces maize share
• Adopting fertilizer use increases maize shares
• Households in 2006/2007 ag. season grew more (p = 0.10) maize as a share of crops

Zambia: Maize Share (2 of 2)
Changes in Price & Climate Variables:
• Higher sweet potato prices in previous survey year decreases maize share
• Growing other crops (Cash/High Value/Staple) in previous survey year all show
significantly higher share of maize in current survey year
• Higher mean rainfall over past 5 years (excluding current year) for planting and
growing (p = 0.10) lead to increased share of maize
Lag Groundnut Price -7.03E-06 3.86E-05
Lag Sweet Potato Price -0.001*** 0.000
Lag Maize Price 9.89E-05 0.000
Grew Cash Crops previous Survey Year 0.099*** 0.011
Grew High Value Crops previous Survey Year 0.069*** 0.008
Grew Other Staple Crops previous Survey Year 0.080*** 0.009
Lagged Planting Season Coef. of Var. (5 yr) -0.072 0.045
Lagged Growing Season Coef. of Var. (5 yr) 0.057 0.077
Lagged Planting Season Total Rainfall (5 yr avg.) 0.002** 0.001
Lagged Growing Season Total Rainfall (5 yr avg.) 0.003* 0.001
Constant 0.368 0.123

Zambia: Maize yield/ha (1 of 2)
Changes in Household Characteristics:
• Increasing the number of adults significantly (p = 0.10) increases yield/ha
• Increases in income increases maize yield/ha
• Adding more hectares cultivated reduces yield/ha
• Fertilizer/ha increases yield/ha, but at a decreasing rate
• Households in 2002/2003 season have lower maize yield/ha relative to 1999/2000
Dependent Variable: Maize (kg) per Hectare Coefficient Std. Err
Number of Adult Equivalent 17.527* 8.974
Female Headed Household -22.608 74.699
Net Income (less Maize) (US Dollars) 0.087*** 0.022
Total Assets 0.014 0.016
Owns Livestock (dummy) 28.712 42.012
Total Landholdings 4.618 6.831
Hectares Cultivated -103.778*** 13.262
Had Fallow Land (dummy) -9.961 34.326
Use Fertilizer (dummy) -36.883 66.145
Fertilizer per Hectare 3.134*** 0.296
Fertilizer per Hectare Squared -0.001*** 0.000
2002/2003 Agricultural season (dummy) -167.697*** 38.137

Zambia: Maize yield/ha (2 of 2)
Changes in Household Characteristics & Climate Variables:
• Households in 2006/2007 season have lower maize yield/ha relative to 1999/2000
• Changes in growing cash/other staple crops led to significantly lower yield/ha
• Households with increasing shares of maize had lower yield/ha
• Increased rainfall during growing season increases yield/ha, but at a decreasing rate
• Higher Coefficients of Variation in Planting and Growing season associated with
lower yield/ha, while higher CV for Harvest associated with higher yield/ha
2006/2007 Agricultural season (dummy) -133.739*** 41.391
Grew Cash Crops (dummy) -117.635** 58.140
Grew High Value Crops (dummy) 36.012 44.143
Grew Other Staple Crops (dummy) -175.742*** 45.782
Share of Maize planted/Total Cropped land -664.952*** 93.240
Total Rainfall (mm) over growing season 3.228*** 0.739
Total Rainfall (mm) over growing season Squared -0.002*** 0.001
Planting Season Coef. of Var. (5 yr) -659.932*** 150.862
Growing Season Coef. of Var. (5 yr) -811.947*** 231.816
Harvest Season Coef. of Var. (5 yr) 202.038*** 64.982
Constant 1534.644 280.125

Ethiopia:
• Focus on broader set of adaptions
• Migration
• Off-farm labor
• Transfers
• Family
• Informal networks
• Formal networks
• Rainfall shocks influence these decisions through levels and
variance:
• Higher rainfall levels increase mean agricultural income
• Make on-farm activities more attractive
• Higher rainfall variability increases variance of agricultural
income and household vulnerabilty
• Make urban and off-farm jobs more attractive

Data
• ERHS household level data
– 3 waves (1999-2004-2009), unbalanced panel
– sample size: 1836 (1999)+1263 (2004) +1467 (2009)
– demographics, assets, expenditures, migration,
remittance, social safety networks, and off-farm
activities
• Climatic data
– precipitation (mm) on a daily basis
– mean and variance are calculated for each
Belg/Kiremt planting and growing season

Ethiopian cropping calendar
Belg planting: 1/16-3/31; Belg growing: 4/1-5/31; Kiremt planting: 6/1-8/10;
Kiremt growing: 8/11-9/30

Ethiopian rainfalls in Belg planting
season
02020202
2000 2005 2010 2000 2005 2010
2000 2005 2010 2000 2005 2010 2000 2005 2010
1 2 3 5 6
7 8 9 10 12
13 14 15 16 17
21 22 23
year=1999,2004,2009
Graphs byvillagecode
Meanrainfall in Belgplanting seasonoverpast5 years(mm)
0
.5
1
1.5
0
.5
1
1.5
0
.5
1
1.5
0
.5
1
1.5
2000 2005 2010 2000 2005 2010
2000 2005 2010 2000 2005 2010 2000 2005 2010
1 2 3 5 6
7 8 9 10 12
13 14 15 16 17
21 22 23
sdrainbp(mm)
year=1999, 2004, 2009
Graphs byvillage code
Std. dev. of rainfall in Belg planting season over past 5 years

Preliminary results: variables
Variable name Label
dlabmig Dummy for HH with migrated member due to labor market reasons
sharlabmig Share of HH members who migrated due to labor market reasons
valfhhmem Monetary value of transfers from former HH members
valfgov Monetary value of public transfers
valfissn Monetary value of transfers from informal social safety nets
nddwkp4m Number of person-days worked off-farm in the past 4 months
fhhsize Household size before migration
ratiorainbp Ratio of mean rainfall over past 5 years to mean rainfall over past 30 years in Belg planting season
ratiorainbg Ratio of mean rainfall over past 5 years to mean rainfall over past 30 years in Belg growing season
ratiorainkp Ratio of mean rainfall over past 5 years to mean rainfall over past 30 years in Kiremt planting season
ratiorainkg Ratio of mean rainfall over past 5 years to mean rainfall over past 30 years in Kiremt growing season
nsdrainbp
Revised standard deviation of rainfall during Belg planting season over last 5 years (=std. dev. for
above historical average rainfall, =-std. dev. for below historical average rainfall)
nsdrainbg
Revised standard deviation of rainfall during Belg growing season over last 5 years (=std. dev. for
above historical average rainfall, =-std. dev. for below historical average rainfall)
nsdrainkp
Revised standard deviation of rainfall during Kirmet planting season over last 5 years (=std. dev. for
above historical average rainfall, =-std. dev. for below historical average rainfall)
nsdrainkg
Revised standard deviation of rainfall during Kirmet growing season over last 5 years (=std. dev. for
above historical average rainfall, =-std. dev. for below historical average rainfall)

Preliminary results: summary statistics
Variable Obs Mean Std. Dev. Min Max
dlabmig 4234 0.162 0.369 0.000 1.000
sharlabmig 4234 0.028 0.075 0.000 0.667
valfhhmem 4264 21.557 242.626 0.000 8500.000
valfgov 4264 57.513 780.711 0.000 49276.000
valfissn 4264 88.872 653.344 0.000 23778.290
nddwkp4m 4236 15.770 36.014 0.000 362.000
fhhsize 7.489 3.294 1.000 31.000
ratiorainbp 0.876 0.147 0.527 1.256
ratiorainbg 1.004 0.197 0.654 1.368
ratiorainkp 1.067 0.147 0.743 1.400
ratiorainkg 1.020 0.129 0.716 1.325
nsdrainbp -0.334 0.652 -1.335 1.261
nsdrainbg 0.024 1.312 -2.902 2.335
nsdrainkp 0.716 1.784 -2.078 5.031
nsdrainkg 0.460 1.755 -3.125 4.787

Preliminary results: migration decision
dlabmig Coef.
Robust Std.
Err. t P>|t| [95% Conf. Interval]
fhhsize 0.021*** 0.004 5.89 0 0.014 0.028
ratiorainbp -0.278*** 0.073 -3.84 0 -0.421 -0.136
ratiorainbg -0.345*** 0.069 -4.99 0 -0.481 -0.209
ratiorainkp -0.108 0.097 -1.12 0.265 -0.299 0.082
ratiorainkg -0.515*** 0.105 -4.91 0 -0.721 -0.309
nsdrainbp 0.075*** 0.016 4.65 0 0.044 0.107
nsdrainkp -0.0009 0.0095 -0.09 0.925 -0.020 0.018
nsdrainbg 0.059*** 0.011 5.56 0 0.038 0.080
nsdrainkg 0.034*** 0.009 3.96 0 0.017 0.051
_cons 1.243 0.207 6.01 0 0.837 1.649
sigma_u 0.244
sigma_e 0.342
rho 0.337

Preliminary results: migration share
sharlabmig Coef.
Robust Std.
Err. t P>|t| [95% Conf. Interval]
fhhsize 0.0015** 0.001 2.16 0.031 0.000 0.003
ratiorainbp -0.044*** 0.014 -3.05 0.002 -0.072 -0.016
ratiorainbg -0.060*** 0.014 -4.33 0 -0.087 -0.033
ratiorainkp -0.016 0.019 -0.83 0.409 -0.053 0.021
ratiorainkg -0.093*** 0.022 -4.34 0 -0.136 -0.051
nsdrainbp 0.014*** 0.003 4.57 0 0.008 0.020
nsdrainkp -0.001 0.002 -0.48 0.628 -0.005 0.003
nsdrainbg 0.0096*** 0.002 4.58 0 0.005 0.014
nsdrainkg 0.007*** 0.002 3.54 0 0.003 0.011
_cons 0.229 0.041 5.53 0 0.148 0.310
sigma_u 0.053
sigma_e 0.070
rho 0.359

Preliminary results: remittance former
household members
valfhhmem Coef.
Robust Std.
Err. t P>|t| [95% Conf. Interval]
fhhsize -7.277* 3.787 -1.92 0.055 -14.706 0.151
ratiorainbp 74.963 67.574 1.11 0.267 -57.584 207.510
ratiorainbg 171.944** 67.688 2.54 0.011 39.174 304.714
ratiorainkp 101.430* 60.018 1.69 0.091 -16.296 219.156
ratiorainkg 94.008 65.476 1.44 0.151 -34.424 222.439
nsdrainbp 22.851*** 8.826 2.59 0.01 5.539 40.163
nsdrainkp -8.015 7.572 -1.06 0.29 -22.868 6.838
nsdrainbg -27.345** 10.794 -2.53 0.011 -48.518 -6.173
nsdrainkg -0.827 5.032 -0.16 0.869 -10.698 9.044
_cons -348.987 204.214 -1.71 0.088 -749.553 51.580
sigma_u 147.019
sigma_e 250.420
rho 0.256

Preliminary results: formal remittances
valfgov Coef.
Robust Std.
Err. t P>|t| [95% Conf. Interval]
fhhsize 13.275 16.458 0.810 0.420 -19.008 45.557
ratiorainbp -528.03*** 123.797 -4.270 0.000 -770.851 -285.198
ratiorainbg -232.247** 93.743 -2.480 0.013 -416.123 -48.371
ratiorainkp -170.022 106.027 -1.600 0.109 -377.993 37.949
ratiorainkg 113.800 576.873 0.200 0.844 -1017.740 1245.337
nsdrainbp 61.405*** 20.070 3.060 0.002 22.037 100.772
nsdrainkp 7.198 12.464 0.580 0.564 -17.249 31.646
nsdrainbg 56.836*** 11.955 4.750 0.000 33.386 80.287
nsdrainkg -3.089 20.122 -0.150 0.878 -42.559 36.381
_cons 735.959 603.127 1.220 0.223 -447.074 1918.992
sigma_u 441.287
sigma_e 847.087
rho 0.2135

Preliminary results: informal
remittance equation
valfissn Coef.
Robust Std.
Err. t P>|t| [95% Conf. Interval]
fhhsize 19.971* 11.664 1.71 0.09 -2.908 42.850
ratiorainbp -211.014* 110.387 -1.91 0.06 -427.538 5.509
ratiorainbg 166.626 128.343 1.30 0.19 -85.118 418.370
ratiorainkp 350.320** 175.964 1.99 0.05 5.168 695.473
ratiorainkg -193.402** 81.336 -2.38 0.02 -352.943 -33.861
nsdrainbp 72.423*** 27.290 2.65 0.01 18.892 125.953
nsdrainkp -41.187** 16.278 -2.53 0.01 -73.116 -9.257
nsdrainbg 22.507* 13.218 1.70 0.09 -3.420 48.433
nsdrainkg 19.838** 9.282 2.14 0.03 1.631 38.045
_cons -165.305 325.026 -0.51 0.61 -802.844 472.234
sigma_u 713.442
sigma_e 541.430
rho 0.635

Preliminary results: off-farm labor
equation
nddwkp4m Coef.
Robust Std.
Err. t P>|t| [95% Conf. Interval]
fhhsize 0.450 0.335 1.34 0.18 -0.207 1.106
ratiorainbp -59.914*** 8.299 -7.22 0.00 -76.191 -43.637
ratiorainbg -6.886 7.210 -0.96 0.34 -21.028 7.256
ratiorainkp -15.273* 8.467 -1.80 0.07 -31.880 1.334
ratiorainkg -56.293*** 10.082 -5.58 0.00 -76.069 -36.517
nsdrainbp 7.354*** 1.624 4.53 0.00 4.168 10.541
nsdrainkp 3.063*** 0.810 3.78 0.00 1.474 4.651
nsdrainbg 4.502*** 1.176 3.83 0.00 2.195 6.808
nsdrainkg 3.106*** 0.717 4.33 0.00 1.700 4.511
_cons 144.093 21.548 6.69 0.00 101.829 186.358
sigma_u 23.137
sigma_e 33.697
rho 0.320

Component three – distilling policy relevant implications
• Evidence of broad adaptation
• Crop choice
• Off-farm labor
• Migration
• Safety net utilization (formal vs. informal)
• Evidence of real welfare costs of
• Mean rainfall decreases
• Variance Increases
• Further research
• Simulations linking historic and predicted rainfall to crop
changes
• Better (varying) timeframes for climate impacts
• Better specification of variance impacts
• Relative size of benefits from adaptation alternatives
• Guidelines for integrated policy support for adaptation
• Partnering with AGRA