Rising Tractor Use in sub-Saharan Africa: Evidence …...Agricultural & Applied Economics...
Transcript of Rising Tractor Use in sub-Saharan Africa: Evidence …...Agricultural & Applied Economics...
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Agricultural & Applied Economics Association (AAEA) – African Section
Washington DC05 August 2018
Rising Tractor Use in sub-Saharan Africa:
Evidence from Tanzania
D. van der WesthuizenT.S. JayneF.H. Meyer
This study was made possible by a number of
funders, including the generous support of the Bill
and Melinda Gates Foundation
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2Bureau of Food and Agricultural Policy
Introduction
• The drivers of rising use of mechanization services on smallholder farms remainpoorly understood
Objectives:
• To identify the factors driving recent rise of mechanization use by small-holderfarmers in Tanzania
• To explore the potential role of medium & large-scale farms in promoting amovement to more capital-intensive forms of farming, not only on larger farms buton smallholder farms as well
• To evaluate whether evolving trends in factor use between labor and capital onsmallholder farms in Tanzania is consistent with the Hayami-Ruttan InducedInnovation theory
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Import Data shows an Increase in Tractor Demand
Nominal value of tractor imports into region is increasing
International Trade Center &
Trademap, 2016
$-
$100 000
$200 000
$300 000
$400 000
$500 000
$600 000
2001 2003 2005 2007 2009 2011 2013 2015
No
min
al v
alu
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f im
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rts
in U
S$
th
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san
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Sub-Saharan AfricaSouthern AfricaNorth Eastern AfricaWestern Africa
$-
$10 000
$20 000
$30 000
$40 000
$50 000
$60 000
$70 000
$80 000
$90 000
$100 000
2001 2003 2005 2007 2009 2011 2013 2015
Val
ue
of
Imp
ort
s in
US
$ t
hou
san
d
Ghana Nigeria
Kenya Tanzania
Zambia Linear (Ghana)
Linear (Nigeria) Linear (Kenya)
Linear (Tanzania) Linear (Zambia)
700%
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Causes of Rising Tractor Use in SSA
Conceptual Framework
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Causes of Increased Tractor UseConceptual Framework: Hayami & Ruttan Induced Innovation
Supply:• Cost of capital has declined in Africa since 2000, real interest rates
lower & penetration of banking into rural areas has improved (Andrianaivoand Yartey, 2009; Ojah and Odongo Kodongo, 2015)
• Many medium-scale farmers own/use tractors. As these farmersexpand, there is a growing presence of tractors in rural areas
Demand:
• Rising opportunity cost of farm labor, especially in areas experiencingeconomic dynamism (Tschirley et al., 2015; Yeboah and Jayne, 2018)
• Shifts in labor force into more diversified, off-farm activities associatedwith economic transformation (Yeboah & Jayne, 2018)
• Higher global food prices Incentives to expand area under cultivation Technologies to facilitate area expansion (AGRA, 2016; Jayne et al., 2016;
Richards et al., 2016; UN Population prospects, 2017)
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Data & Methods
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Data & Methods
• Tractor importation data for 40 African countries – Trademap
• Tanzanian National Panel Survey (NPS) for 2008/09, 2010/11, 2012/13 &2014/15 (TNBS & World Bank) - 9,726 observations for pooled data &1,672 for HH-level panel)
• Demand function for tractor rental services:
• Ordinary Least Squares (OLS) multiple regression to test inducedinnovation hypothesis
1) Pooled generalized linear model (GLM) probit which provides a flexible
generalization of ordinary linear regression
2) Mundlak-Chamberlain device (Mundlak 1978; Chamberlain 1984), providing an
estimator that Woolridge (2010) refers to as the Correlated Random Effects (CRE)
model which address the issue of unobserved heterogeneity at household level
where Y = % change in the number of HH using tractors: ∆ 2008-2010; ∆ 2010-2012
∆ 2012-2014 & key variable of interest: ∆ in factor price (FP) ratio: ∆ 2008-2010; ∆
2010-2012 ∆ 2012-2014 where FP ratio = wage rate divided by tractor rental cost
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8Bureau of Food and Agricultural Policy
Data & MethodsModel specification: Demand function for tractor rental services
𝑃 𝒀𝒕𝒓𝒂𝒄𝒕𝒐𝒓𝒓𝒆𝒏𝒕 = 1 𝑋𝑘) = 𝑓 𝑋, 𝐶, 𝑅, 𝑌 + ϵi
𝑋 = ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑐ℎ𝑎𝑟𝑎𝑐𝑡𝑒𝑟𝑖𝑠𝑡𝑖𝑐𝑠
𝐶 = 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑡𝑦 𝑐ℎ𝑎𝑟𝑎𝑐𝑡𝑒𝑟𝑖𝑠𝑡𝑖𝑐𝑠
𝑅 = 𝑟𝑒𝑔𝑖𝑜𝑛 𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑠
𝑌 = 𝑦𝑒𝑎𝑟 𝑑𝑢𝑚𝑚𝑦 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠
• X: household land cultivated, gender & age of household head, assetwealth & market access conditions
• C : local wage rates, fertilizer prices, tractor rental rates, share of MSfarms as % of total number of farms in district
• R: to regional dummy variables (30 regions)
• Y: survey year dummies (3 for pooled sample; 2 for household panelanalysis)
for panel estimation ϵit =∝𝑖 + 𝜇𝑖𝑡
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• Changing tractor use in Tanzania
• Shift in rental markets, especially among small-scale producers
• Tractor rental use is concentrated in certain regions
Results:
Descriptive Statistics
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10Bureau of Food and Agricultural Policy
Changing Tractor Use in TanzaniaMore households & area using tractors; small-scale farms increasingly using rental services
World Bank online data: Tanzania National Panel
Survey, 2008/09, 2010/11, 2012/13 & 2014/15
163 029 205 390335 906
490 349
734 136625 660
856 755
1 280 714
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
-150 000
350 000
850 000
1 350 000
1 850 000
2 350 000
2008/09 2010/11 2012/13 2014/15
% o
f sm
all-
scal
e H
H u
sin
g t
ract
or
ren
tal
serv
ices
To
tal
trac
tor
use
: H
ou
seh
old
s &
Cult
ivat
ed a
rea
Number of households using tractors Hectares of cultivated land where tractors were used
% of small-scale HH using tractor rental services
0-1.99 ha group: + 135% (2009-2015)
2-4.99 ha group: + 309% (2009-2015)
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Tractor rental use is concentrated in certain regionsSome regions have experienced higher growth since 2008/09
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%A
rush
a
Man
yar
a
Kil
iman
jaro
Ku
sin
i U
ngu
ja
Moro
goro
Mji
ni
Ungu
ja
Do
dom
a
Tan
ga
Sin
gid
a
Dar
es
Sal
aam
Mtw
ara
Pw
ani
Irin
ga
Mar
a
Shin
yan
ga
Kas
kaz
ini
Pem
pa
% o
f H
H u
tili
sing
tra
cto
r re
nta
l se
rvic
es
2014/15
2008/09
World Bank online data: Tanzania National Panel
Survey, 2008/09, 2010/11, 2012/13 & 2014/15
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Estimation Results
• Pooled GLM probit
• Mundlak-Chamberlain (MC) indicator / CRE model
• Predicted Probabilities
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Pooled GLM & MC-CRE Probit Results Selective output for 4 approaches
Estimation approach Pooled GLM Probits Mundlak-Chamberlain Correlated Random
Effects
Dataset 2% rental regions 2% rental regions 2% tractor rental
regions
2% tractor rental
regions
Limited to HH located
in 0-5 ha
Limited to HH located
in 0-5 ha
Cultivated Land Size Distribution = 2 - 4.99 hectares 0.44*** 0.46*** 0.33*…. 0.33*…..
Cultivated Land Size Distribution = 5 - 9.99 hectares 0.62*** . 0.41…... .
year = 2012/13 0.31*** 0.36*** 0.50*** 0.52***
year = 2014/15 0.59*** 0.64*** . .
Household head sex: Male 0.24*** 0.26*** 0.18…… 0.26……
log_market_dist 0.01…… -0.01……. -0.20*….. -0.18……..
log_wage_rate_LP 0.19*** 0.19*** 0.21*** 0.19***
log_trac_rent_cost -0.22*** -0.31*** -0.30**… -0.31*……
hh_5_10_ha 4.37*** 4.14*** 0.63…… 0.35…….
Region = Arusha 0.76*** 0.79*** 0.95*…. 1.23**…
Region = Kilimanjaro 0.95*** 0.96*** 1.00**.. 1.27**…
Region = Morogoro 0.59*** 0.74*** 1.44*** 1.72***
Region = Pwani 0.77*** 0.79*** 1.57*…. 1.77**..
Region = Manyara 0.91*** 1.00*** 1.67**.. 1.85***
log_maize_price_mean . . 0.70*** 0.63***
log_trac_rent_cost_mean . . -0.80**… -0.83*…..
hh_5_10_ha_mean . . 8.90*…. 12.21***
. . . .
Constant -3.32*** -2.47** -0.03……. 0.02…….
Observations 3,728 3,524 1,644 1,564
pval in parentheses
*** p<0.01, ** p<0.05, * p<0.1
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14Bureau of Food and Agricultural Policy
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
0-1
.99 h
ecta
res
2-4
.99 h
ecta
res
5-9
.99 h
ecta
res
>10 h
ecta
res
Mal
e
20
14 2
5th
per
cen
tile
20
14 m
edia
n
20
14 7
5th
per
cen
tile
20
14 2
5th
per
cen
tile
20
14 m
edia
n
20
14 7
5th
per
cen
tile
20
14 2
5th
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20
14 m
edia
n
20
14 7
5th
per
centi
le
20
14 9
0th
per
cen
tile
Land size category Head
type
Wage rate Tractor rental cost Concentration of 5-20ha HH
per district
Pro
bab
ilit
y P
erce
nta
ge
Model prediction Overall model prediction
Predicted Probability ScenariosDespite overall low success rate, results change quite substantially as we control for certain variables
World Bank online data: Tanzania National Panel Survey,
2008/09, 2010/11, 2012/13 & 2014/15
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15Bureau of Food and Agricultural Policy
26.3%
55.1%
62.6%
48.5%
55.6%
61.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
Dodoma Arusha Kilimanjaro Morogoro Pwani Manyara
Predicted probabilities for regions where land size = 4-9.99 ; year =2014 ; head type = male & concentration of
0-5 ha producers = 2014 median
Pro
bab
ilit
y P
erce
nta
ge
Model prediction Overall model prediction
Tractor adoption – Regionally concentrated within specific groups
Predicted probabilities for land size group = 5-9.99; year = 2014, head type = male & concentration of medium-scale producers per district = 2014 median
World Bank online data: Tanzania National Panel Survey,
2008/09, 2010/11, 2012/13 & 2014/15
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16Bureau of Food and Agricultural Policy
0%
50%
100%
150%
200%
250%
300%
2008/09 2010/11 2012/13 2014/15
Ind
ex:
20
09
= 1
00
Rental tractor use Wage rate Tractor rental cost
Induced Innovation Hypothesis
Mean of median changes in district-level factor prices
World Bank online data: Tanzania National Panel Survey,
2008/09, 2010/11, 2012/13 & 2014/15
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17Bureau of Food and Agricultural Policy
Induced Innovation Hypothesis
Relative change in factor prices vs. change in share of farms renting tractors
(0.25)
(0.20)
(0.15)
(0.10)
(0.05)
-
0.05
0.10
0.15
0.20
0.25
-25% -15% -5% 5% 15% 25%
Change: FP 2008-2010
(0.25)
(0.20)
(0.15)
(0.10)
(0.05)
-
0.05
0.10
0.15
0.20
0.25
-25% -15% -5% 5% 15% 25%
Change: FP 2010 - 2012
(0.25)
(0.20)
(0.15)
(0.10)
(0.05)
-
0.05
0.10
0.15
0.20
0.25
-25% -15% -5% 5% 15% 25%
Change: FP 2012-2014
(0.25)
(0.20)
(0.15)
(0.10)
(0.05)
-
0.05
0.10
0.15
0.20
0.25
-25% -15% -5% 5% 15% 25%
Change: FP 2008-2014
Y axis: Change in factor price ratio
X axis: % change in
# of farms renting
tractors
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18Bureau of Food and Agricultural Policy
Induced Innovation Hypothesis Testing
Relative change in factor prices vs. change in share of farms renting tractors
World Bank online data: Tanzania National Panel Survey,
2008/09, 2010/11, 2012/13 & 2014/15
Model Specification to test the induced innovation hypothesis:
Y = % change in the number of HH renting tractors: ∆ 2008-2010; ∆ 2010-2012 ∆ 2012-2014
X1 = ∆ in factor price (FP) ratio: ∆ 2008-2010; ∆ 2010-2012 ∆ 2012-2014 where FP ratio = wage
rate divided by tractor rental cost
X2 = Household asset wealth (lagged)
X3 = Market distance from household to closest market (lagged)
X4 = Quantity maize harvested in kilograms per hectare (lagged)
X5 = Maize price in TZS per kilogram sold (lagged)
X6 = Fertilizer cost in TZS per kilogram of fertilizer (lagged)
X7 = Concentration of 5-10 hectares farming households per district (lagged)
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19Bureau of Food and Agricultural Policy
Induced Innovation Hypothesis Testing
Relative change in factor prices vs. change in share of farms renting tractors
World Bank online data: Tanzania National Panel Survey,
2008/09, 2010/11, 2012/13 & 2014/15
OLS Regression Statistics for Change: % change in the number of HH renting tractors: ∆ 2008-2010; ∆ 2010-2012 ∆ 2012-2014
F-test 2.620 Prob(F) 0.012 Unrestricted Model
MSE1/2 0.080 CV Regr 560.623 F-test 2.620
R2 0.050Durbin-Watson
2.065 R2 0.050
RBar2 0.031 Rho -0.036 RBar2 0.031
Akaike Information Criterion
-5.034Goldfeld-Quandt
0.354Akaike Information Criterion
-5.034
Schwarz Information Criterion
-4.958Schwarz Information Criterion
-4.958
95% InterceptChange in
Factor Price Ratio
Lag_asset_wealth
Lag_market_dist
Lag_qty_harvested
Lag_maize_price
Lag_fert_cost
Lag_hh_5_10_ha
Beta 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.10S.E. 0.01 0.03 0.00 0.00 0.00 0.00 0.00 0.12t-test -0.32 2.98 0.29 -0.97 2.56 1.02 0.50 0.88Prob(t) 0.75 0.00*** 0.77 0.33 0.01*** 0.31 0.61 0.38
Elasticity at Mean -0.026 0.023 -0.348 0.547 0.685 0.270 0.186Partial Correlation 0.157 0.015 -0.052 0.136 0.056 0.025 0.047
OLS regression indicates significant & positive signs for the change in factor price ratio &
quantity of maize harvested on the % change in the number of HH renting tractors
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Conclusions
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21Bureau of Food and Agricultural Policy
Conclusions
• Concentration of medium-scale farms per district coupled with increasedtractor rental use by smallholders
• Landholding size coupled with increased tractor rental use
• Increase in # of households using tractors not limited to larger-scaleproducers
• Largest increase in tractor rental use was observed in the 2-4.99 and 5-9.99 hectares’ land size groups
• Significant regional variation in tractor rental use & adoption
• Estimation results uphold the importance of relative changes in factorprices consistent with the induced innovation hypothesis
• Although overall tractor rentals remain low, it is rising particularly in ruralareas experiencing economic transformation
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THANK YOU
www.bfap.co.za
+27 82 420 6964
Acknowledgement: Ayala Wineman for her supportive role &
advice in the research
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Supporting Slides
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24Bureau of Food and Agricultural Policy
Tanzania Maize Market
Area increase & increase in real maize prices
Renapri, 2018
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Area PlantedDomestic Maize Price (Arusha)Domestic Maize Price (Arusha) - Real 2005 termsLinear (Area Planted)Linear (Domestic Maize Price (Arusha) - Real 2005 terms)
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25Bureau of Food and Agricultural Policy
Induced Innovation Hypothesis
Change in wage rate vs. tractor rental costs
0%
50%
100%
150%
200%
250%
300%
350%
2008/09 2010/11 2012/13 2014/15
Wage rate (index: 2008/09 = 100)
Wage rate index: 10 highest rental use regions
Kilimanjaro Arusha Morogoro
Pwani Dodoma Manyara
Mbeya Iringa Mara
Mwanza
0%
50%
100%
150%
200%
250%
300%
350%
400%
450%
2008/09 2010/11 2012/13 2014/15
Tractor rental cost (index: 2008/09 = 100)
Tractor rental cost index: 10 highest rental use regions
Kilimanjaro Arusha Morogoro
Pwani Dodoma Manyara
Mbeya Iringa Mara
Mwanza