This document summarizes a presentation on agricultural productivity growth and poverty reduction in rural Ethiopia. It finds that the rural poor have lower productivity due to fewer assets, shocks, and inefficiencies. Regression analysis shows that higher productivity is associated with increased consumption and lower poverty. However, the poor benefit less from growth due to constraints. Poverty reduction requires combined efforts of productivity growth and other programs to address these constraints.

1. Growth Plus: Policies for
Agricultural Productivity Growth
and Poverty Reduction in
Rural Ethiopia
Zewdu Ayalew Abro
Bamlaku Alamirew Alemu (PhD)
Munir A. Hanjra (PhD)
CSAE conference
March 24 2014
Oxford, UK

2. Outline of the presentation
1. Brief introduction
2. Data
3. Research Method
4. Findings
5. Concluding remarks

3. 1. Introduction
Poverty is pervasive in rural Ethiopia (Bogale et
al., 2005; Dercon and Christiaensen, 2011;
Dercon et al., 2012; Kumar and
Quisumbing, 2012).
29.6% of the population are below the poverty
line. Of which
30.4% are rural and 25.7% are urban (MOFED,2012)
Other studies also indicated that poverty
disproportionately affects the rural poor
(IFAD, 2001; Bogale et al., 2005; World
Bank, 2008; IFAD, 2010).

4. 1. Introduction
Since agriculture is the main source of income
and livelihood, growth in agricultural productivity
directly affects the welfare of the poor (Irz et
al., 2001).
The empirical evidence at country level and cross
country studies indicated that productivity growth
has a positive effect (eg. Christiaensen and
Demery, 2007; Christiaensen et al., 2010; Minten
and Barrett, 2008; Thirtle et al., 2003)

5. 1. Introduction
The linkages between agricultural productivity
and poverty in Ethiopia has been studied by
(Alemu, 2010; Geda et al., 2009; Tafesse, 2003)
 But this studies used a static framework lacking
the analytical ability to see the dynamics of
productivity and poverty.
Using the advantages of having panel data, this
study attempted to fill this gap by analyzing the
impact of agricultural productivity on household
poverty.

6. 2. Data
Ethiopian Rural Household Survey (ERHS).
The survey spans 16 years with 7 rounds.
We took four rounds for this study.
We created the panel based on two criteria.
households must have cultivated some plot of land and
They have to have positive value of production.
We omitted households with zero or missing values for
these two variables.
Finally, a balanced panel of 1007 households
consisting of 4028 observations over four rounds was
created.

7. 3. Research Methods
We used fixed effects regression
Two approaches are widely used in modeling
poverty.
Direct approach: modeling poverty as binary outcome
as a function of regressors.
Indirect approach: two step procedure.
We followed a two step procedure.
First, we undertake a regression of consumption per
capita per month,
Second, predict poverty using the Foster et al. (1984)
class of poverty measures.

8. 3. Research Methods

9. 3. Research Methods

10. 3. Research Methods
Instruments for technical Efficiency:
a) number of plots,
b) If crop failure due to drought,
c) If output affected because someone in the family
labor was too ill
d) participation in the extension program,
e) If farmers used irrigation and
f) If farmers owned more than one ploughing oxen.
Variables (a) and (e) are used for land
productivity and (c)-(f) for labor productivity.
Credit: area cultivated and value of farming
assets (Geda et al, 2006).

11. 4. Findings
A) Poverty Profiles
 What explains the rise in poverty in the ERHS villages?
Localized droughts in several villages(Dercon et al., 2012).
the global food price rise in 2008 and the subsequent high
village inflation.
Year Head count poverty Poverty gap Squared poverty gap
1994 46.18 23.858 13.919
1999 37.84 16.308 8.149
2004 35.85 15.012 7.87
2009 49.45 21.069 11.013
1995/96 45.5 12.9 5.1
1999/00 44.2 11.9 4.5
2004/05 38.7 8.3 2.7
2010/11 29.6 7.8 3.1
National Poverty Figures
ERHS Village Poverty Figures

12. 4. Findings
B) Movements in and out of Poverty 21 %
9%
70%

13. 4. Findings
C) From cross tabulation of variables with
persistence of poverty four important points
emerged.
First, the poorest households have significantly
lower amount of farming assets. eg.
Area(hectare) Coef. Std. Err. z P>z 95% Conf. interval
Always-poor -1.20505 0.12917 -9.33 0.000 -1.458 -0.952
Sometimes-poor -0.54744 0.082245 -6.66 0.000 -0.709 -0.386
dummy1999 -0.23005 0.044716 -5.14 0.000 -0.318 -0.142
dummy2004 0.175699 0.044716 3.93 0.000 0.088 0.263
dummy2009 0.164376 0.044716 3.68 0.000 0.077 0.252
Constant 2.018175 0.077226 26.13 0.000 1.867 2.170

14. 4. Findings
Second, the poorest households are more prone
to shocks such as illness of a cultivator and
drought which affect their production. eg.
drought Coef. Std. Err. z P>z
Always-poor 0.13445 0.031854 4.22 0.000 0.072 0.197
Sometimes-poor 0.113184 0.020282 5.58 0.000 0.073 0.153
dummy1999 -0.03774 0.020424 -1.85 0.065 -0.078 0.002
dummy2004 -0.16683 0.020424 -8.17 0.000 -0.207 -0.127
dummy2009 -0.0427 0.020424 -2.09 0.037 -0.083 -0.003
Constant 0.327368 0.02176 15.04 0.000 0.285 0.370
95% Conf. interval

15. 4. Findings
Third, wealth represented by ownership of
livestock was also the lowest among the poorest
households. eg.
Livestock ownership
(TLUs Coef. Std. Err. z P>z 95% Conf. interval
Always-poor -3.27792 0.401232 -8.17 0.000 -4.06 -2.49
Sometimes-poor -2.12055 0.255471 -8.3 0.000 -2.62 -1.62
dummy1999 0.260528 0.115581 2.25 0.024 0.03 0.49
dummy2004 0.385151 0.115581 3.33 0.001 0.16 0.61
dummy2009 2.583694 0.115581 22.35 0.000 2.36 2.81
Constant 4.692473 0.235198 19.95 0.000 4.23 5.15

16. 4. Findings
Fourth, Productivity is invariably lower among
the poorest households. eg.
Labor productivity
(Real VP in Birr/no. of
labor) Coef. Std. Err. z P>z 95% Conf. interval
Always-poor -870.893 91.12924 -9.56 0.000 -1049.50 -692.28
Sometimes-poor -466.368 58.02331 -8.04 0.000 -580.09 -352.64
dummy1999 223.7107 49.05364 4.56 0.000 127.57 319.85
dummy2004 478.3434 49.05364 9.75 0.000 382.20 574.49
dummy2009 510.9694 49.05364 10.42 0.000 414.83 607.11
Constant 1010.314 59.1398 17.08 0.000 894.40 1126.23

17. 4. Findings
The poor are not only poor but also inefficient!
 the next question will be, controlling for other
factors, to what extent productivity affects household
welfare measured by RCPC and hence poverty ?
Technical Efficiency Coef. Std. Err. z P>z
95% Conf.
Interval]
Always-poor -0.08999 0.018232 -4.94 0.000 -0.126 -0.054
Sometimes-poor -0.04865 0.011609 -4.19 0.000 -0.071 -0.026
dummy1999 0.056529 0.000726 77.88 0.000 0.055 0.058
dummy2004 0.110226 0.000726 151.87 0.000 0.109 0.112
dummy2009 0.160061 0.000726 220.53 0.000 0.159 0.161
Constant 0.571706 0.010202 56.04 0.000 0.552 0.592

18. 4. Findings
Summary of variables used for the FE
Variables 1994 1999 2004 2009
Real consumption per capita 71.7 86.1 91 61.5
Sex of the head of the household 0.86 0.83 0.85 0.68
Age of the head of the household 45 49 51 53
Head’s years of schooling 1.76 1.81 1.91 2.44
Household size 6.57 6.32 6.02 5.95
Dependency ratio 1.49 0.47 0.55 0.5
Percentage of households who have access to credit
(1=Yes,0 otherwise)
0.13 0.54 0.56 0.64
Livestock ownership in tropical livestock units
(TLUs)
2.9 3.16 3.29 5.49
Village food price index 100 114 115 352
Participation in off-farm activities (1=Yes,0
otherwise)
0.35 0.22 0.36 0.41
Technical efficiency 0.53 0.59 0.64 0.69
Distance to the nearest towns in kilometers 11.3 10.2 9.6 12

19. 4. Findings
Explanatory Variables:
Coefficients: dependent variable log
RCPPM
Model 1 Model 2 Model 3 Model 4
Technical Efficiency 2.49**
Logarithm of labor productivity (Birr/labor) 0.16**
Logarithm of land productivity (Birr/hectare) 0.19* 0.18*
Land to family labor ratio 0.05*
Sex of the head of the household 0.16* 0.13* 0.08*** 0.09**
Household head’s years of schooling 0.01*** 0.01*** 0.01*** 0.01***
Age of the head of the household 0.00 0.00 0.00 0.00
Household size -0.11* -0.10* -0.12* -0.11*
Dependency ratio -0.07*** -0.14* -0.12* -0.12*
Logarithm of Village food price index -0.48* -0.31* -0.29* -0.29*
Probability of obtaining credit 0.38*** 0.10 1.25* 1.07*
Participation in off-farm activities (1=Yes,0 No) 0.08* 0.09* 0.07** 0.07*
Livestock ownership (Tropical Livestock Units) 0.02* 0.01*** 0.00 0.00
Distance to the nearest towns in km -0.01* -0.01* -0.01* -0.01*
Constant 5.36* 5.11* 4.11* 4.25*
Overall Wald test statistic 174361* 172434* 170134* 171481*
R-squared 0.17 0.24 0.18 0.19
corr(ai, Xb) -0.33 0.06 -0.03 -0.01

20. 4. Conclusion
 Agricultural productivity growth could benefit the poor.
 But the poor
 possess fewer farm assets
 Less productive farm implements and
 face other socioeconomic constraints in factor-product markets and
 thus benefitted less from growth.
 The econometric results show productivity indeed helps to reduce
poverty.
 Other factors are also at work:
 inflationary pressures,
 Demographic variables (eg. dependency ratio),
 Liquidity constraints and
 Availability of off-farm work opportunities.
 Poverty reduction needs combined efforts : GROWTH PLUS.

21. 4. Conclusion
Caveats and further research:
 further analysis is necessary to make more tangible claims
about the poverty impact of agricultural productivity.
Because the research focused on the direct effects of farm
productivity.
It is also good to see the general equilibrium effect to the
rural sector and its linkage with other sectors.
The indicator of welfare used is also weak:
Good to expand the welfare measures using MPI, asset
based poverty measures and food security indicators among
others.
Small sample, use of nationally representative data