Ex Post Impacts of Improved Maize Varieties on the Poor in Rural Ethiopia Di Zeng, Jeffrey Alwang, George Norton, Bekele Shiferaw, Moti Jaleta, Chilot Yirga

Poverty Impact Assessment: Ex Ante vs. Ex Post

Ex ante

Observed income distribution Predicted poverty impact Poor Poverty Line

Ex post

Counterfactual income distribution Estimated poverty impact Poor Poverty Line Predicted income distribution Rich Observed income distribution Rich

Maize Production in Ethiopia

• A major maize producer in Sub-Saharan Africa • 19% daily energy contribution (Smale, Byerlee and Jayne, 2011) • Mainly cropped in central highlands (>93% total yield, Schneider and Anderson, 2010) • Over 40 improved varieties released since 1970s (hybrid and OPV)

Data Description

• Four regions surveyed in 2010 • 1,359 households with 2,443 maize plots • • 564 adopters, 535 non-adopters, and 260 partial adopters 43.3% of maize area under improved varieties • Woreda-level monthly precipitation datal for the past 5-10 years from National Meteorology Agency of Ethiopia Tigray Amhara Oromia SNNPR

Kernel Density of Yields

Empirical Specification

• Normalize the utility from local varieties to zero, and denote the utility from improved varieties as • The decision rule of adoption • The potential outcomes (Rubin, 1974) in logarithm form are or • The generalized Roy model (Heckman et al., 2006)

Treatment Effect Estimation

• Endogenous adoption decision: IV methods • Homogeneity • • Probit-2SLS (Wooldridge, 2002) Selection model (Heckman, 1979) • Heterogeneity • Marginal treatment effect via semiparametric local IV estimation (Björklund and Moffitt,1987; Heckman et al., 2006) • Obtain estimates of percentage yield increase (treatment effect)

Welfare Changes: the Economic Surplus Model

Welfare Changes: Small Open Economy

• Directly estimated at household level • • Plot level income change: 

I ik

 

PY ik obs



C ik obs

 

PY ik

* 

C ik

*  

P



Y ik

 

C ik

Aggregated to household: 

I i

 

k



P



Y ik

 

C ik

 • Δ

C ik

— IV cost function estimation • Counterfactual income distribution computed

Welfare Changes: Closed Economy

STEP 1: Estimate market-level economic surplus changes • The

k

-shift (Alston et al., 1995) • The counterfactual price level (elasticities synthesized from literature) • The aggregate surplus changes

Welfare Changes: Closed Economy

STEP 2: Allocate market level surplus changes to households • Decomposition of Δ

PS



PS

 

PS yield

 

PS price

where 

PS price

 

P

*

Q

*

Z

( 1  0 .

5

Z

 ) • • Δ

PS price

Δ

PS yield

— allocated to all maize sellers by market shares — allocated to all adopters by the yield increases' shares • Δ

CS

— allocated to all maize buyers by purchase shares among total supply • Counterfactual income distribution computed

Poverty Impacts

• Foster-Greer-Thorbecke (FGT, 1984) poverty indices calculated for both observed and counterfactual income distributions • The differences are poverty impacts

Instrumental Variables

• Production • • • • Rainfall intensity of the sowing month Local population density Distance to the nearest agricultural extension office Temporary seed supply shortage (yes / no) • Cost • • Rainfall intensity of the sowing month Distance to the nearest agricultural extension office

Yield Impact: Mean Estimates

C-D Translog PSM-NN PSM-Radius PSM-Kernel Partial Adopter FD: C-D Partial Adopter FD: Translog Probit 2SLS .474** .552*** ATT Esimates Selection .551*** .584*** LIV .662*** .514*** Robustness check .419*** .442*** .454*** .386*** .409***

Yield Impact: MTE Estimates

C-D technology Translog technology

Other Parameter Estimates

• Cost increase due to adoption — 32.5% • The

k

-shift — 39.1% cost reduction per kilogram • Elasticities •

ε

— 0.5

•

η

— -1 • Aggregate impacts • Δ

PS

in small open economy — 135.9 thousand USD • • Δ

PS

Δ

CS

in closed economy — 101.3 thousand USD in closed economy — 50.7 thousand million USD • Only 6.37% sold maize is consumed by surveyed households

Poverty Impacts: Small Open Economy

Poverty Line $1 $1.25

$1.45

FGT Index

Headcount Depth Severity Headcount Depth Severity Headcount Depth Severity Poverty impact under Homogeneity .0095

.0029

.0015

.0103

.0042

.0023

.0103

.0049

.0029

Poverty impact

under Heterogeneity

.0088

.0032

.0017

.0089

.0045

.0025

.0118

.00453

.0031

Poverty Impacts: Closed Economy

Poverty Line $1 $1.25

$1.45

FGT Index

Headcount Depth Severity Headcount Depth Severity Headcount Depth Severity Poverty impact under Homogeneity .0110

.0048

.0027

.0162

.0064

.0038

.0147

.0073

.0046

Poverty impact

under Heterogeneity

.0066

.0031

.0019

.0089

.0040

.0025

.0081

.0047

.0030

Further Interpretation

• Individual level • A typical adopter with average maize area (0.39 ha) observe 440.5 kg yield increase • Such an adopter observe an income increase of

45.6 - 72.4 USD

(evaluated using average per-capita maize consumption) • Population level • • Sensitivity analyses lend credence to previous estimates 0.7 - 1.2 percentage headcount poverty reduction means

0.48 - 0.83 million

rural people have escaped poverty • A major achievement

Further Interpretation: Producer Benefits

Concluding Remarks

• Maize research and variety diffusion has had a substantial effect on poverty in rural Ethiopia • The poor benefit the least from maize technologies due to resource constraints: still much room for micro-level policies to work • Methodological remarks

References

• • • • • • • • • Smale, M., D. Byerlee, and T. Jayne. 2011. Maize revolutions in Sub-Saharan Africa. World Bank Policy Research working paper. No. WPS 5659.

Schneider, K., and L. Anderson. 2010. Yield Gap and Productivity Potential in Ethiopian Agriculture: Staple Grains & Pulses. Evans School Policy Analysis and Research (EPAR) Brief No. 98.

Rubin, D. 1974. Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies. Journal of Educational Psychology 66: 688-701 Wooldridge, J. 2002. Econometric Analysis of Cross Section and Panel Data, MIT Press.

Heckman, J.J., S. Urzua, and E. Vytlacil. 2006. Understanding Instrumental Variables in Models with Essential Heterogeneity. The Review of Economics and Statistics 88: 389-432.

Heckman, J.J. 1979. Sample Selection Bias as a Specification Error. Econometrica 47: 153-61.

Björklund, A., and R. Moffitt. 1987. The Estimation of Wage and Welfare Gains in SelfSelection Models. Review of Economics and Statistics 69: 42-49.

Alston, J.M., G.W. Norton, and P.G. Pardey. 1995. Science under Scarcity: Principles and Practice for Agricultural Research Evaluation and Priority Setting. Ithaca, NY: Cornell University Press.

Foster, J., J. Greer, and E. Thorbecke. 1984. A Class of Decomposable Poverty Measures. Econometrica 52: 761-766.

Thank you.