integrated agricultural system, migration, and social protection strategies to reduce vulnerability...

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)

1

2

0

2

0 2

1ppR

X

B

22

)(

EEVarPQVar

2

2

1)(

PVar

PQd

PQs 1

2

0

2

0 2

1YYR

Y

B

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

DroughtMild 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

DroughtNo 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 TotalLosses

(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. NMaize Share of Cropped Land 0.60 0.27 6098Number of Adult Equivalent 5.37 2.68 6098Female Headed Household 0.21 0.41 6098Net Income (less Maize) (US Dollars) 553.80 979.62 6098Total Assets 71.40 188.60 6098Owns Livestock (dummy) 0.83 0.38 6098Total Landholdings 2.53 3.05 6098Hectares Cultivated 1.99 2.03 6098Had Fallow Land (dummy) 0.33 0.47 6098Use Fertilizer (dummy) 0.40 0.49 60982008 Dummy 0.52 0.50 6098Lag Groundnut Price 0.28 0.03 6098Lag Sweet Potato Price 0.05 0.01 6098Lag Maize Price 0.09 0.01 6098Grew Cash Crops previous Survey Year 0.21 0.41 6098Grew High Value Crops previous Survey Year 0.45 0.50 6098Grew Other Staple Crops previous Survey Year 0.52 0.50 6098Lagged Planting Season Coef. of Var. (5 yr) 0.69 0.12 6098Lagged Growing Season Coef. of Var. (5 yr) 0.68 0.14 6098Lagged Planting Season 10 day Rainfall (5 yr avg.) 53.14 12.55 6098Lagged Growing Season 10 day Rainfall (5 yr avg.) 59.59 10.91 6098

Summary Statistics: Maize Share

Variable Mean Std. Dev. NMaize yield per hectare 1619.39 1308.20 9929Number of Adult Equivalent 5.30 2.74 9929Female Headed Household 0.21 0.40 9929Net Income (less Maize) (US Dollars) 465.08 859.41 9929Total Assets 273.26 1702.91 9929Owns Livestock (dummy) 0.83 0.38 9929Total Landholdings 2.63 3.07 9929Hectares Cultivated 2.03 2.06 9929Had Fallow Land (dummy) 0.37 0.48 9929Use Fertilizer (dummy) 0.36 0.48 9929Fertilizer per Hectare 101.10 203.68 9929Grew Cash Crops (dummy) 0.21 0.40 9929Grew High Value Crops (dummy) 0.49 0.50 9929Grew Other Staple Crops (dummy) 0.51 0.50 9929Share of Maize planted/Total Cropped land 0.60 0.29 9929Total Rainfall (mm) over growing season 621.80 155.40 9929Planting Season Coef. of Var. (5 yr) 0.70 0.12 9929Growing Season Coef. of Var. (5 yr) 0.62 0.12 9929Harvest 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 SeasonClimate Variables Mean Std. Dev Mean Std. DevLagged Planting Season Coef. of Var. (5 yr) 0.69 0.11 0.68 0.12Lagged Growing Season Coef. of Var. (5 yr) 0.64 0.12 0.71 0.14Lagged Planting Season Total Rainfall (5 yr avg.) 56.73 13.39 49.79 10.68Lagged 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* 8.974Female Headed Household -22.608 74.699

Net Income (less Maize) (US Dollars) 0.087*** 0.022Total Assets 0.014 0.016Owns Livestock (dummy) 28.712 42.012Total Landholdings 4.618 6.831

Hectares Cultivated -103.778*** 13.262Had Fallow Land (dummy) -9.961 34.326Use 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** 58.140Grew 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*** 64.982Constant 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

02

02

02

02

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

avra

inbp

(m

m)

year=1999, 2004, 2009Graphs by village code

Mean rainfall in Belg planting season over past 5 years (mm)

0.5

11.5

0.5

11.5

0.5

11.5

0.5

11.5

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

sdra

inbp

(m

m)

year=1999, 2004, 2009Graphs by village code

Std. dev. of rainfall in Belg planting season over past 5 years

Preliminary results: variablesVariable 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

nsdrainbpRevised 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)

nsdrainbgRevised standard deviation of rainfall during Belg growing season over last 5 years (=std. dev. for


nsdrainkpRevised standard deviation of rainfall during Kirmet planting season over last 5 years (=std. dev. for


nsdrainkgRevised standard deviation of rainfall during Kirmet growing season over last 5 years (=std. dev. for


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.


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.


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.


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.


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.


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