rising tractor use in sub-saharan africa: evidence …...agricultural & applied economics...

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

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

3Bureau of Food and Agricultural Policy

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

e o

f im

po

rts

in U

S$

th

ou

san

d

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%

Causes of Rising Tractor Use in SSA

Conceptual Framework

5Bureau of Food and Agricultural Policy

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)

Data & Methods

7Bureau of Food and Agricultural Policy

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

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

• 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

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)

11Bureau of Food and Agricultural Policy

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

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a

Kil

iman

jaro

Ku

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i U

ngu

ja

Moro

goro

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ga

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yan

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pa

% o

f H

H u

tili

sing

tra

cto

r re

nta

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rvic

es

2014/15

2008/09

World Bank online data: Tanzania National Panel

Survey, 2008/09, 2010/11, 2012/13 & 2014/15

Estimation Results

• Pooled GLM probit

• Mundlak-Chamberlain (MC) indicator / CRE model

• Predicted Probabilities

13Bureau of Food and Agricultural Policy

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

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

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edia

n

20

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5th

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cen

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20

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5th

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cen

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20

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edia

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20

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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

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

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

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

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)

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

Conclusions

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

THANK YOU

www.bfap.co.za

+27 82 420 6964

divan.vanderwesthuizen@up.ac.za

Acknowledgement: Ayala Wineman for her supportive role &

advice in the research

Supporting Slides

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)

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%

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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