The document analyzes the impact of urbanization on agricultural transformation in Ethiopia using teff production as a case study. Regression analyses find that proximity to urban centers is associated with higher agricultural input use, intensification, and profits. Specifically, transportation costs to the largest urban market, Addis Ababa, are negatively correlated with input prices, use of fertilizers, improved seeds and labor, as well as productivity, income, and profits from teff farming. Urbanization may indirectly influence agriculture through changing input-output prices and directly by improving information flows, reducing transaction costs, and strengthening institutions.

1. ETHIOPIAN DEVELOPMENT
RESEARCH INSTITUTE
Cities and agricultural transformation in
Ethiopia
PRELIMINARY RESULTS
Joachim Vandercasteelen, Seneshaw Tamru, Bart Minten and Johan Swinnen
IFPRI ESSP
EDRI
Addis Ababa, Ethiopia
1

2. 2
1. Introduction
• Agricultural transformation (higher use of improved inputs; higher
land and labor productivity) in Africa deemed important but
progress has been slow
• Several hypotheses explaining agricultural transformation:
1. “Boserup-hypothesis”: Growing population and increased land
pressure lead to incentives for technological change
2. “Induced innovation theorem”: Intensification in such a way to save
on most costly input factor (e.g. Hayami and Ruttan)
3. Market driven intensification: Access to markets will drive
intensification (e.g. Pingali and Binswanger; Reardon and Timmer)

3. 3
1. Introduction
• Urbanization important new factor for transformation in Africa:
- People living in cities in Sub-Saharan Africa increased by 160%
between 1990-2014
- Urban population in Africa expected to triple by 2050 (1.3 billion
people)
• Urbanization important economic impacts, associated with
structural transformation:
1. Shift from low productivity agriculture to more productive non-
agriculture
2. Agglomeration effects – economies of scale
3. Employment and labor markets develop
4. Spill-overs on rural areas (remittances, non-farm income)

4. 4
1. Introduction
• Important effects on agriculture and food markets:
1. Urban residents often do not grow own food; More commercial
agricultural rural-urban flows
2. Urban residents have different diets and consume more high-value
crops
3. Urban residents are often richer and are willing to pay more, leading
to higher consumption of ready-to-eat and processed foods and
demand for food safety and quality.
• Most of the literature focused of effect of urbanization on changes in
crops (von Thunen) or off-farm employment (e.g. Fafchamps and
Shilpi)
• Relatively little evidence on impacts on staple crops, that most of the
rural population makes a living from

5. 5
1. Introduction
• Look at the case of Ethiopia and at teff (most important crop area-
wise)
• Question: “How does proximity to urban centers affect farmers’
agricultural production environment and practices?”
• Important changes in Ethiopia in this area
1996/1997 2010/2011

6. 6
1. Introduction
• Urbanization: 3.7% to 14% between 1984 and 2007
• One quarter of the urban population living in Addis
• In 2012: 17% in cities
• Projections World Bank (2015):
- 5.4% annual growth
- Urban population to increase from 15.2 in 2012 to 42.3 million in 2034
- In 2028: 30% of population in cities

7. 7
2. Background on teff
• 23% of total grain area
• Most important crop in value terms in the country (2.5 billion USD in
2013/14)
• Most important cash crop in the country (750 million USD) and
major source of income for farmers
• Most expensive cereal
• Teff more readily eaten by urban consumers
• High income elasticities (1.1 in urban areas)
• Rapid growth of cities and income growth leading to increasing
demand for teff in cities

8. 8
3. Methodology
(a) Sampling and data
• Stratified random sample in 2012
• 1,200 farmers in five major teff production zones. These five zones
represent 38% of national teff area and 42% of the commercial
surplus.
• Urban proximity main independent variable: Measured by
transportation costs that farmers face when selling teff in Addis
(ETB/quintal)
• Two components:
1/ Cost of transporting teff from the farm to the market center
2/ Cost of transporting teff from the market center to the Addis
wholesale market by truck

9. 9
3. Methodology

10. 10
3. Methodology
(a) Sampling and data
Variable Unit Mean Median SD
Prices
Price of teff ETB per quintal 1,047 1,043 117
Wage rates ETB per day 37 37 12
Land rental rate ETB per ha 4,702 4,716 127
Price of DAP ETB per quintal 1,390 1,411 131
Price of Urea ETB per quintal 1,133 1,162 110
Agricultural inputs
Use of DAP kg per ha 91 82 75
Use of Urea kg per ha 64 50 67
Use of Improved Seeds kg per ha 12 0 20
Use of Agrochemicals ETB per ha 54 40 63
Use of Labor day per ha 126 108 75
Intensification outcomes
Teff Land Productivity kg per ha 1,071 978 600
Teff Labor Productivity kg per day 10 9 6
Teff Input Cost ETB per ha 3,277 2,879 1,859
Teff Non-labor Input Cost ETB per ha 2,514 2,243 1,560
Teff Profits ETB per ha 7,384 6,228 5,880

11. 11
3. Methodology
(b) Empirical strategy
• Two models: 1/ reduced form; 2/ less parsimonious
• Urban proximity (d); Prices (p); Agricultural inputs and indices (q);
Intensification outcome (y)
• Estimation using Seemingly Unrelated Regression (SUR) set-up and
bootstrapped standard errors
• Differentiate direct (due to improved information, transaction costs,
institution) and indirect effect (due to changing input and output
prices) of urban proximity; total effect is combination of both
(1) ln(b𝑖) = αw + βw ∗ d𝑖 + θw ∗ Controls + εw
2 q 𝑖 = αq + βq ∗ d𝑖 + γq ∗ ln(b 𝑞) + θq ∗ Controls + εq
3 y𝑖 = αy + βy ∗ d𝑖 + γy ∗ ln(b 𝑞) + θy ∗ Controls + εy

12. 12
3. Methodology
(b) Empirical strategy
• Controls:
1. Farm characteristics (age, gender, ethnicity, and education of
household head)
2. Household assets and household size
3. Agro-ecological conditions (altitude, share brown/black soil, share of
flat – versus sloped – land)
4. Population pressure:
- Often used GIS measures of rural population density (however, not
easily available; issue with soil quality/geography measure; strong
interpolation assumptions)
- Follow Headey et al. (2014) and use average farm size at the kebele
level (collected from the Bureau of Agriculture)

13. 13
3. Methodology
(c) Estimation issues
• Unobserved heterogeneity; cities do not develop randomly over
space; likely to emerge in areas with favourable agro-ecological
condition and potential; Settlement in hinterland also not random;
Complicated… Estimate: ln(y𝑖𝑝) = α + X 𝑖𝑝 ∗ β + ηi + εip
• Two tests: 1/ Is there heterogeneity of land over space (give all some
inputs)? 2/ Do unobserved fixed abilities of farmers vary over space?
Observed Yield
(kg/ha)
Adjusted Yield
(kg/ha)
Farming Ability
(.)
Transportation Cost
(ETB/quintal)
-2.232*** -0.975 -0.001
(0.700) (0.702) (0.001)
Constant
1,2889*** 1,181*** 0.067
(70) (65) (0.110)
Observations 2,791 2,786 2,786
R-squared 0.010 0.002 0.003

14. 14
4. Non-parametric regressions
- Advantage: No functional form specified in advance
- Local polynomial smoothing estimates
- Do for the four major outcomes:
1. Prices
2. Input
3. Input indices
4. Intensification

15. 15
4. Non-parametric regressions - prices

16. 16
4. Non-parametric regressions – use of inputs

17. 17
4. Non-parametric regressions – input ratios

18. 18
4. Non-parametric regressions – intensification

19. 19
5. Multi-variate regression results – prices
Prices
log of teff prices
(ETB/quintal)
log of wage
(ETB/day)
log of land
rent
(ETB/ha)
log of DAP
price
(ETB/qtl)
log of urea
price
(ETB/qtl)
REDUCED FORM MODEL
Transportation Cost
(ETB/quintal)
-0.86*** -3.06*** -0.18*** -0.02 -0.01
(0.22) (0.89) (0.03) (0.26) (0.30)
Constant
7,021.8*** 3,839.1*** 8,471.0*** 7,233.3*** 7,028.7***
(18.49) (86.08) (2.89) (23.77) (26.88)
R-squared 0.066 0.106 0.050 0.000 0.000
LESS PARSIMONIOUS MODEL
Transportation Cost
(ETB/quintal)
-0.55*** -2.48** -0.13*** 0.31 0.46
(0.20) (1.14) (0.04) (0.29) (0.35)
Farm Size at village level
(ha)
2.55 52.54 1.78 39.41*** 40.43***
(7.95) (47.64) (1.52) (14.36) (14.93)
Constant
6,875.39*** 3,955.94*** 8,480.4*** 7,144.6*** 6,936.6***
(65.38) (401.56) (10.94) (113.26) (99.33)
R-squared 0.126 0.161 0.282 0.202 0.180

20. 20
5. Multi-variate regression results – input use
Teff outcome
DAP
(kg/ha)
Urea
(kg/ha)
Improved
Seed
(kg/ha)
Labor
(person-
days/ha)
REDUCED FORM MODEL
Transportation Cost (ETB/quintal)
-0.47*** -0.37*** -0.19*** 0.23**
(0.13) (0.08) (0.04) (0.09)
Constant
132.27*** 96.32*** 28.19*** 106.01***
(13.70) (10.33) (4.20) (9.09)
R-squared 0.045 0.036 0.106 0.011
LESS PARSIMONIOUS MODEL
Transportation Cost (ETB/quintal)
Direct effect
-0.21 -0.35*** -0.10*** -0.20*
(0.13) (0.12) (0.04) (0.12)
Indirect effect
-0.11** -0.13** -0.02 0.01
(0.06) (0.05) (0.01) (0.03)
Total effect
-0.32** -0.47*** -0.12*** -0.18*
(0.14) (0.13) (0.03) (0.11)
Farm Size at village level (ha)
10.93 -2.15 3.02* -8.83**
(7.28) (4.47) (1.68) (4.08)
Constant
-653.83 -1,886.64* -331.21 1,195.41
(1,166.76) (1,088.00) (239.87) (917.03)
R-squared 0.167 0.291 0.210 0.169

21. 21
5. Multi-variate regression results – intensification
Teff outcome Yield (kg/ha)
Labor Prod.
(kg/day)
Input Costs
(ETB/ha)
Teff Profits
(ETB/ha)
REDUCED FORM MODEL
Transportation Cost
(ETB/quintal)
-2.30* -0.04*** -13.51*** -33.41**
(1.30) (0.01) (3.14) (14.19)
Constant
1,270.57*** 14.05*** 4,450.10*** 10,284.9***
(132.71) (1.21) (337.21) (1,476.53)
R-squared 0.017 0.060 0.061 0.037
LESS PARSIMONIOUS MODEL
Transportation Cost (ETB/quintal)
Direct effect
-2.07* -0.01 -8.12** -26.41**
(1.23) (0.01) (3.22) (12.95)
Indirect effect
-1.57*** -0.01*** -4.53** -11.85**
(0.55) (0.00) (1.78) (5.20)
Total effect
-3.65*** -0.03** -12.63*** -38.43***
(1.36) (0.01) (3.59) (13.90)
Farm Size at village level (ha)
-32.89 0.27 128.06 191.81
(53.28) (0.50) (138.36) (531.68)
Constant
-36,885*** -348*** -31,673 -330,800***
(5,902.61) (61.75) (24,580.35) (68,549.27)
R-squared 0.209 0.190 0.217 0.171

22. 22
5. Multi-variate regression results – off-farm
Wage Income Non-farm Income
REDUCED FORM MODEL
Transportation Cost (ETB/quintal)
-5.76** -14.27***
(2.86) (2.97)
Constant
1,187.76*** 7,518.66***
(297.63) (297.71)
R-squared 0.008 0.022
LESS PARSIMONIOUS MODEL
Transportation Cost (ETB/quintal)
Direct effect
-3.48 -10.26***
(2.56) (3.53)
Indirect effect
-0.20 2.31
(1.04) (1.42)
Total effect
-3.67 -7.95**
(2.58) (3.36)
Farm Size at village level (ha)
187.35* 320.49**
(104.61) (154.10)
Constant
-16,334.38 38,470.24
(22,242.48) (46,011.79)

23. 23
5. Multi-variate regression
• Strong effect of urban proximity on:
- Prices
- Use of inputs
- Measures of intensification (land and labor productivity)
- Profits
• We find no strong effects of population pressure and the smaller
farms are not associated with higher farm incomes per hectare
(similar to other findings)
• We find overall a strong direct effect (not through prices)

24. 24
6. Explaining the direct effect
• Channel 1: Transaction costs
Transaction costs to sell output or to obtain inputs are significantly
higher in areas that are more remote from cities

25. 25
6. Explaining the direct effect
• Channel 2: Monetization of production factors
Significant drop in factor monetization, the more remote; more
improved allocation of resources when better use of price signals?

26. 26
6. Explaining the direct effect
• Channel 3: Access to information and knowledge
Significant association of urban proximity with access to extension
agents, ownership of mobile phones, and awareness of improved
technologies with urban proximity

27. 27
7. Sensitivity analysis
• Run four different regression set-ups (using the less parsimonious
SUR model):
1. Add unobserved farming ability
2. Include opportunity costs of farmers’ time in transportation costs
3. Add squared transportation costs
4. Add a dummy that measures if teff was sold to Addis or not (and
therefore test if direct link has a stronger effect on outcome
variables)
• Results are robust to these four additional specifications

28. 28
8. Conclusions
• Link of urban areas with rural hinterland is not well understood,
especially so for staple crops, from which most farmers in Africa
make a living.
• Study that issue in the case of Ethiopia, where in recent decades a
significantly larger share of the rural population has become
“connected” to a city (because of infrastructure development and
city growth).
• Strong positive effect of urban proximity on:
- Output prices but also on wages and land rental rates
- Input and factor market use
- Labor and land productivity
- Profitability

29. 29
8. Conclusions
• Changing price ratios of factor and output prices because of urban
proximity important factor in explaining this effect (called “indirect
effect”)
• However, other effects matter significantly as well (transaction costs,
knowledge, information) (called “direct effect”)
• Beneficial effect of urbanization on intensification by rural producers
of staple crops
• In contrast to rural population increases (population density
increases) that do not show these positive effects on profitability
and labor and land productivity

30. 30
8. Conclusions
• Implications:
1. Access to markets and cities matter for rural populations and
ensuring appropriate infrastructure and low transportation costs to
access these markets for these rural populations is important
2. Cities an engine for agricultural transformation; ensuring that cities
can grow such that rural areas can profit from these urban growth
poles is important, e.g. stimulating rural-urban migration and
improved tenure conditions.
3. Make sure that appropriate inputs and knowledge are there for the
agricultural population so that they can profit from these new
opportunities