ETHIOPIAN DEVELOPMENT RESEARCH INSTITUTE

Land constraints and agricultural intensification in Ethiopia:

A village level analysis of high potential areas

Derek Headey, Mekdim Dereje, Jacob Ricker-Gilbert, and Alemayehu Seyoum Taffesse

Ethiopian Economic Association ConferenceJuly 18, 2013Addis Ababa

1

Overview of the presentation

• Introduction• Data and methods• Results and Discussion• Conclusion and policy implication

Introduction• Two seminal but opposing views on the impact of

shrinking farm size on the face of fixed resources.– Malthusian theory (1795) – Boserup’s (1965) theory of agricultural intensification.

• Ethiopia seems to have the experience of both in highly populated and land constrained areas.

― Severe famines of early 1970s and 1980s― Both endogenous & policy induced agricultural

intensification.

Introduction, cont’d• Objectives of the study:1. Examine the evolution of farm sizes overtime2. Identify drivers of agricultural intensification, particularly

land constraints (i.e. Boserup)• Basic results of the study: Strong support for Boserupian intensification:

―More inputs use per hectare,―More purchased input costs per hectare, ―More farm labor per hectares, ―Higher cereal yields, and ―Higher gross farm income from crops.

Introduction, cont’d However, net farm income from crops per

hectare (i.e. net of input costs) is not responsive to rising land constraints

Total farm income and net of input costs per capita shrinks with farm size

Moreover, we find no evidence that land constrained households are more likely to engage in off-farm work, or more likely to send their children to school

Data and Methods• Data:• Description and coverage

―AGP baseline to promote agricultural intensification on small farms.

―93 of Ethiopia’s 450 highland woredas – with 304 enumeration areas (EAs)

―Detailed information on farming practices, along with community questionnaire and Focus group discussion

―We merged this data with GIS data from IFPRI/CSA on access to markets & agroecological potential

F1. AGP enumeration areas (red), major markets (yellow) and woreda level population density (green & blue shading)

Notes: Population density categories (in persons per square kilometer) from lightest to darkest are” 0-31, 31-101, 101-139, 139-195, 195-537, 537 and above.

Data and Methods, cont’d

• Pros and cons of the data―Spatial richness―Wide array of intensification indicators―Plenty of controls for market access and agro-

ecological potential―The data is not yet a panel survey, so limited potential

to address endogeneity concerns

Data and Methods, cont’d• Farm intensification variables (dependent variables):

– Net & gross farm income per capita– Net & gross farm income per hectare– Total input cost per ha– Fertilizer & Improved seed per ha (kg)– Plough & handheld equipment indices (PCA)– Daily wage rate, men– Maize and Teff yields

• Non-farm intensification variables (dependent variables)– Non-farm work (yes=1); No. months off-farm work– Secondary schooling

Data and Methods, cont’d• Explanatory variables:

– Average farm sizes (EA level average), proportion of small farms in EA; farm inequality (coef. of variation)

– Agro-ecological factors (LGP, slope, elevation, soil fertility (%), and Land slopes (%))

– Market access (Nearest market town (km), and nearest 50K city (minutes))

– Farm policies/institutions at the community (access to cooperatives and other farmer groups).

– potentially relevant household characteristics, particularly demographics, education and wealth.

Data and Methods, cont’d

• The model to be estimated has a form:

– Where Y/L is some intensification indicator (nearly

always measured in per hectare terms)– L/N is land per capita (or prevalence of small farms)– M is access to markets, – A is agro-ecological potential, – P is institutional factors (e.g. farm policies) and– X is other local characteristics (e.g. wealth, education)

Data and Methods, cont’d• Estimation challenges & solutions:1. Control for unobservable HH characteristics Measure all variables at village level to purge regressions

of household unobservables (we assume)2. Simultaneity biases if population pressures are driven by agro-ecological and market access variables Measure these as best we can3. Measurement error in farm sizes affects both RHS and LHS variables Cleaning, clustering, SUR (common shocks), rreg, qreg

ResultsPanel A – Nationally representative statistics from CSA (2012)

Oromia SNNP Amhara Tigray Ethiopia

Average farm size (ha)1.15 0.49 1.09 0.91 0.96

Farm size inequality (Gini, 0-1) 0.43 0.44 0.41 0.43 0.46

% with less than 0.5 ha30.0% 61.7% 33.4% 41.4% 39.7%

Total no. of holders (millions) 5.46 3.39 4.00 0.96 14.29

Panel B – AGPS Statistics

Oromia SNNP Amhara Tigray All AGPAverage cultivated area (ha) 1.32 0.93 1.37 1.56 1.46

% with less than 0.5 ha18% 35% 22% 17% 23%

Number of holders 4.15 2.38 2.54 0.28 9.36

Results, cont’d• Distribution of farm size by household head age

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1.4

1.5

1.6

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20 30 40 50 60Age of the househld head in completed years

Residual of average farm size per holder Average farm size per holder

Dep. Var. Fertilizer (kg /ha)

Imp. seed (kg /ha)

variable inputs(birr /ha)

Plow index (std. dev.)

Handheld index (std. dev.)

EA average farm size (ha)-11.17*** -2.016*** -481.42*** 0.166* 0.015

EA farm coef. variation -8.478*** -1.900* 41.489 -0.510*** -0.248**

Nearest market (km) -0.188** 0.01 -6.21 -0.007 0.010

Nearest city (minutes) -0.572 -0.24 -9.729 0.064*** -0.011

Savings loan=1 -2.344 0.34 -79.757 -0.274** -0.174**

Heads w/ 2nd educ. (0-1)76.5*** 32.4*** 3205.6** 0.687 2.4**

EA ave. wealth (std. dev.) 1.798 1.403 137.345** 0.003 0.111**

R-squared 0.44 0.24 0.42 0.65 0.36

Farm size elasticity -0.82 -1.85 -0.88 0.61 Not significant

Results, cont’d

Results, cont’dDep. Var. EA Wages

(Birr/day)Hired labor (man-days/ha)

Family labor (man-days/ha)

Teff yields (kg/ha)

Maize yields (kg/ha)

EA average farm size (ha) 1.51 0.03* -40.01*** -334.84*** -562.76***

EA farm coef. variation -1.41 -0.051 31.83** 118.91 91.09

Extension office=1 0.92 -0.019 -18.23* 187.09** 31.21

Savings credit coop.=1 -1.24 0.083*** 12.011 88.65 -162.29

Savings loan=1 1.72 -0.04 -24.07*** -5.056 -4.13

Bank/MFI=1 -0.32 -0.02 67.53*** 43.11 -167.34

Heads w/ sec. educ. (0-1) 1.76 0.66 58.4 550.6 4724.9***

Heads w/ ter. educ. (0-1) 123.3*** -0.96 -355.5 -10040.1*** -6288.7**

EA average wealth 1.481* 0.06** 6.97 34.50 90.92

R-squared 0.32 0.29 0.44 0.35 0.39

Farm size elasticity N.A. 0.00 -0.41 -0.53 -0.54

Results, cont’d

Dep. Var.

Net crop income (birr) exc. labor

Net crop income (birr) inc. labor

Net farm income (Birr/capita)

Wealth index (std dev.)

EA average farm size (ha) -4215.9*** -681.7 1028.9*** 0.12*

EA farm coef. variation 243.3 360.2 681.8* 0.18

Nearest market (km) 3.06 -55.7 -11.5 -0.01**

Nearest city (minutes) -293.7* -119.1 3.4 0.01

Savings credit coop.=1 865.2 489.3 599.1** -0.12

Heads with sec. educ. (0-1) 11406.7 -761.2 -1510.7 3.55***

EA average wealth (sd) 857.424* 271.4 120.8

R-squared 0.26 0.20 0.36 0.24

Farm size elasticity -0.54 Not significant 0.84 0.06

Sensitivity tests• We ran different estimators (OLS, rreg, lad, SUR), but

these made little difference• Also ran some sensitivity tests with the proportion of

farms in village cultivating less than 1 hectare• 1 hectare cut-off is somewhat arbitrary, but we argue

that almost any household can exploit 1 ha• Another benefit is that this indicator is less affected by

the presence of a few large farms in the village, which can push up the village average

Results, cont’dDep. Var.

Fertilizer (kg /ha)

Improved seed (kg /ha)

variable inputs (birr)

Plow index (sd)

Handheld index (sd)

Farms less than 1 ha (0-1) 28.3*** 5.2*** 1380.6*** -0.85*** 0.01R-squared 0.43 0.24 0.43 0.65 0.34Small farms elasticity 0.87 2.01 1.05 -1.31 N.A.Dep. Var. Daily wages

(birr/day)Hired labor (man-days/ha)

Family labor(man-days)

Teff yields (kg/ha)

Maize yields (kg/ha)

Farms less than 1 ha (0-1) -4.95*** -0.08 125.4*** 1,049.1*** 1,720.0***R-squared 0.31 0.28 0.45 0.38 0.42Farm size elasticity -0.09 n.a. 0.54 0.69 0.69Dep. Var. Net farm

income after exc. labor (birr)

Net farm income (birr/ha)

Net farm income (birr/capita)

Wealth index (sd)

Farms less than 1 ha (0-1) 13,859.5*** 2,666.4 -1,790.4*** -0.38**R-squared 0.33 0.21 0.30 0.24Farm size elasticity 0.75 n.a. -0.61 -0.09

Conclusion and policy implication• Summary of results1. Young farmers cultivate substantially less land compared to

previous generations >> will they catch up over their lifecycle or will land continue to be a binding constraint?

2. Strong support for Boserup’s hypothesis. As land constraints increasingly bind, small farmers:

– apply more fertilizer and other purchased inputs – use more and more family labor per hectare– increase cereal yields (Teff and maize).– Increase the overall value of output per hectare (HVCs)• Other technologies (plow) have ambiguous effects

Conclusion and policy …, cont’d

• Nevertheless, the picture is not rosy . . .• Land constraints are strongly linked to lower incomes• Rural population growing rapidly• Potential for land expansion and rural resettlement widely

thought to be limited (for smallholders, at least)• Water another major constraint• Currently very little nonfarm diversification – Ethiopia has the

lowest share of rural nonfarm employment in the world

Conclusion and policy …, cont’d

• What are the implications of this research for policies & future research? (the “So what?” question)

1. Still focus on increasing cereal yields, but also give more attention to high value crops in the most land constrained areas (and focus on income growth rather than just yields)

3. Prioritize rural nonfarm activities (progress on infrastructure, but very little growth in rural nonfarm sector – why?)

4. Assess potential for large commercial farms to create opportunities for seasonal off-farm employment

5. Facilitate rather than hinder migration, including rural-urban6. Maintain progress in reducing fertility (some success here!)

Conclusion and policy …, cont’d

• Finally, one challenge for highly land-constrained countries is to coordinate policy efforts across different sectors

• e.g. Ministries of agriculture are not responsible for migration, rural nonfarm sector, family planning or even land policies

• Yet it seems likely that developments in all of these sectors are required to avert the potentially disastrous consequences of shrinking farm sizes

• Ethiopia’s development strategy is probably more cognizant of these coordination challenges than most African countries

• But in some areas much more can be done, • Moreover, land needs to be more widely recognized as one of

the most fundamental constraints to poverty reduction

Thank You!