Growth Plus: Policies for Agricultural Productivity Growth

and Poverty Reduction inRural Ethiopia

Zewdu Ayalew Abro

Bamlaku Alamirew Alemu (PhD)

Munir A. Hanjra (PhD)

CSAE conference

March 24 2014

Oxford, UK

Outline of the presentation

1. Brief introduction

2. Data

3. Research Method

4. Findings

5. Concluding remarks

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).

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)

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.

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.

3. Research Methods

We used fixed effects regressionTwo 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.

3. Research Methods

Our model is:

is measured by TE, land and Labor productivity.Corresponding to every predicted level of

consumption per capita, we calculated a class of decomposable poverty measures ( using:

where =0 if , 1 otherwise; Z is the poverty line; is weight for the ith household. is poverty aversion parameter.

3. Research Methods

The problem in equation (1) is that the productivity indicator is endogenous.

To mitigate the problem, we used 2SLS by finding “appropriate” instruments.

we rewrite the endogenous variable as:

Two identification conditions for the IV to work (Wooldridge, 2009). The , : cannot be tested. , : can be tested against the two side alternative that

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).

4. FindingsA) 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 13.9191999 37.84 16.308 8.1492004 35.85 15.012 7.872009 49.45 21.069 11.013

1995/96 45.5 12.9 5.11999/00 44.2 11.9 4.52004/05 38.7 8.3 2.72010/11 29.6 7.8 3.1

National Poverty Figures

ERHS Village Poverty Figures

4. Findings

B) Movements in and out of Poverty 21 %

9%

70%

4. FindingsC) 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. intervalAlways-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

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>zAlways-poor 0.13445 0.031854 4.22 0.000 0.072 0.197Sometimes-poor 0.113184 0.020282 5.58 0.000 0.073 0.153dummy1999 -0.03774 0.020424 -1.85 0.065 -0.078 0.002dummy2004 -0.16683 0.020424 -8.17 0.000 -0.207 -0.127dummy2009 -0.0427 0.020424 -2.09 0.037 -0.083 -0.003Constant 0.327368 0.02176 15.04 0.000 0.285 0.370

95% Conf. interval

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

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

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>z95% 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

4. Findings

Summary of variables used for the FEVariables 1994 1999 2004 2009Real consumption per capita 71.7 86.1 91 61.5 Sex of the head of the household 0.86 0.83 0.85 0.68Age of the head of the household 45 49 51 53Head’s years of schooling 1.76 1.81 1.91 2.44Household size 6.57 6.32 6.02 5.95Dependency ratio 1.49 0.47 0.55 0.5Percentage 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 352Participation 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

4. FindingsExplanatory Variables:

Coefficients: dependent variable log

RCPPMModel 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

F test that all ai =0 1.84* 1.74* 1.85* 1.86*

Wald test statistic for random effects 1567.7* 88.37* 951.41* 522.16*

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.

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