"Tanzania’s Maize Export Ban and Heterogeneous Impacts on Regional Food Prices", presented by Athur Mabiso, Postdoctoral Fellow, IFPRI at the Agricultural & Applied Economics Association’s AAEA & CAES Annual Meeting, Washington DC, August 4-6, 2013

1. INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
Tanzania’s Maize Export Ban
and Heterogeneous Impacts
on Regional Food Prices
Athur Mabiso
Postdoctoral Fellow, IFPRI
August 6, 2013

2. Background & Motivation
 In recent years, periodic export restrictions, incl.
export bans (Martin and Anderson, 2012; Bouët and
Laborde Debucquet, 2012)
 High food prices and domestic shortfalls in production
believed to engender the export restrictions
(Hernandez, Robles and Torero, 2010)
 Often modeled at global levels with the exception of a
few studies (World Bank, 2009; Jayne, Zulu and
Nijhoff, 2006; Chapoto and Jayne, 2009; Porteous,
2012)
 Export restrictions rarely modeled as endogenous
(Headey, 2011; Porteous, 2012)

3. Research Objective
 Test effect of export ban on regional price
levels
 Test for endogeneity of export ban
• If endogenous, model accordingly as an
endogenous variable using treatment effects
vector autoregression models

4. Empirical Model
1. 𝑝𝑖,𝑡 = 𝛼0,𝑗 + ∑ 𝛼 𝑘,𝑗 𝑝𝑗,𝑡−𝑘
𝑚1
𝑘=0 + ∑ 𝛼 𝑘,𝑤 𝑝 𝑤,𝑡−𝑘
𝑚2
𝑘=0 +
𝜇𝐶(𝐵𝑗,𝑡)𝑖𝑖 + 𝜀𝑡, 𝑖 ≠ 𝑗, 𝜀𝑡~𝑁(0, 𝜀2
)
2. Pr(𝐵𝑗,𝑡 𝑝𝑗,𝑡−𝑘, 𝑝 𝑤,𝑡−𝑘, 𝑧𝑡 =
Ф 𝛽0,𝑗 + ∑ 𝛽1,𝑘 𝑝𝑖,𝑡−𝑘
𝑚3
𝑘=1 + ∑ 𝛽2,𝑘 𝑝 𝑤,𝑡−𝑘 + ∑ 𝜏𝑧𝑡
𝑚5
𝑡=1
𝑚4
𝑘=0 + 𝑒𝑡
zt = instruments (futures prices; temperature
levels in surplus producing regions)

5. Empirical Model
3. 𝐶𝑖𝑖,𝑡 = (1 + 𝜃𝐵𝑖,𝑡)𝑑𝑖𝑖,𝑡
𝑑𝑖𝑖,𝑡 = 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 × 𝑝𝑝𝑝𝑝𝑝 𝑜𝑜 𝑓𝑓𝑓𝑓 𝑡 × 𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡𝑡
 Travel time helps capture differences in
transaction costs due to road infrastructure
quality differences and customs/border delays
 Also accounts for potential asymmetric travel
cost structure e.g. due to altitude – driving
uphill may be more expensive

6. Data
 Tanzanian export ban data and monthly
prices of maize and rice (2004-2011) from
Min. of Industry and Trade.
 Regional prices from FEWS NET (Kenya,
Uganda, Malawi, Tanzania, DRC)
 Distance and travel time (Local traders;
transport companies)
 Exchange rates from Min. of Finance
 World Prices from World Bank Pink Sheets
 Futures prices from CME Group

7. Estimation
 Treatment-effects Vector Error Correction
Model
 First estimate equation 2, followed by
equation 1

8. Periods of Export Ban by Tanzania
 January 2004 to December 2005
 March 2006 to December 2006
 March 2008 to February 2011
 March 2008 to February 2011

9. Results
99.51010.5
logpriceofmaizeln(Tsh/100kg)
Jan 2004 Jan 2006 Jan 2008 Jan 2010 Jan 2012Jan 2005 Jan 2007 Jan 2009 Jan 2011
Month and year
Arusha Dar es Salaam
Iringa Mbeya

10. Differences in mean prices (Dar es
Salaam)
Export ban regime Free trade (no export ban) regime
Maize price
(Tsh/100kg
)
Rice price
(Tsh/100kg)
Beans
price
(Tsh/100kg
)
Maize price
(Tsh/100kg
)
Rice price
(Tsh/100kg
)
Beans price
(Tsh/100kg)
Mean 29,550 89,042 79,761 24,781 78,790 75,149
Standard
deviation 9,756 28,151 26,140 9,573 17,459 19,045
F-test of differences in mean
Maize Rice Beans
0.0585 0.1335 0.4721Prob>F
0.9200 0.021 0.114
Bartlett's Chi2 test for
equal variances

11. Endogeneity Test from Treatment
Effects Model)
Sample: 2004m5 -
2011m12 No. of obs = 92
Log likelihood =
1090.951 AIC = -17.62937
FPE = 4.21e-18 HQIC = -14.53167
Det(Sigma_ml) =
6.92e-21 SBIC = -9.954358
rho 0.264
sigma 3.061
lambda 0.808
Sample: 2004m5 - 2011m12 No. of obs = 92
Log likelihood =
1351.761 AIC = -19.2441
rho 0.422 HQIC = -15.93117
sigma 2.723 SBIC = -10.024817
lambda 1.149
Sample: 2004m5 - 2011m12
No. of
obs =
92
Log likelihood =
2811.339 AIC = -29.49722
rho 0.504 HQIC = -23.64201
sigma 5.687 SBIC = -18.33785
lambda 2.866

12. Results: Unit Root tests
Market
Location Unit roots test:
Maize
price lags(ᵨ) Rice price lags(ᵨ)
Beans
price lags(ᵨ)
Arusha DF-GLS tau statistic -2.999 3 -3.267* 7 -3.151* 1
KPSS 0.156* 3 0.0691 7 0.585** 1
Phillips-Perron (Rho
statistic) -16.132 4 -16.655 7 -17.385 1
Dar es Salaam DF-GLS tau statistic -2.870 2 -3.109* 2 -1.925 7
KPSS 0.11 0.215* 0.178
Phillips-Perron (Rho
statistic) -17.716 3 -20.416 2 -18.335 1
Dodoma DF-GLS tau statistic -2.757 9 -2.835 0 -3.420* 11
KPSS 0.0566 0.330 0.0699
Phillips-Perron (Rho
statistic) -16.131 4 -18.793 1 -17.726 14
Iringa DF-GLS tau statistic -2.819 1 -2.956 1 -1.988 9
KPSS 0.256 0.682 0.0979
Phillips-Perron (Rho
statistic) -16.668 2 -16.967 2 -32.411** 3
Mbeya DF-GLS tau statistic -3.002* 11 -2.812 1 -2.985 1
KPSS 0.0496 0.284 0.550
Phillips-Perron (Rho
statistic) -17.716 2
Morogoro DF-GLS tau statistic -3.097* 3 -2.055 11 -4.122** 1
KPSS 0.103 0.114 0.479**
Phillips-Perron (Rho
statistic) -16.702 2 -20.151 2
Moshi DF-GLS tau statistic -2.643 1 -2.980 4 -2.807 8
KPSS 0.316 0.0977 0.0665
Phillips-Perron (Rho statistic)

13. Export Ban Effect on Nairobi Maize
Prices
Log Price and
Location
Lag
s Coefficient Standard error P-value
Maize, Dar es
Salaam L1. 0.25 0.13 0.06
L2. 0.30 0.15 0.04
Maize, Arusha L1. 0.18 0.12 0.13
L2. -0.06 0.16 0.69
L3. 0.23 0.17 0.18
L4. -0.13 0.14 0.36
Maize, Iringa L1. 0.15 0.08 0.06
L2. 0.32 0.09 0.00
L3. 0.11 0.10 0.28
L4. 0.13 0.09 0.16
Maize, Mbeya L1. 0.92 0.11 0.00
L2. -0.13 0.15 0.38
L3. -0.01 0.13 0.93
L4. -0.07 0.10 0.50
Maize, Morogoro L1. 0.01 0.12 0.93
L2. 0.07 0.12 0.57
L3. -0.08 0.13 0.53
Maize, USA L1. 0.12 0.04 0.00
L2. 0.26 0.09 0.00
Export ban 0.10 0.03 0.00

14. Export Ban Effect on Dar es Salaam
Maize Prices
Log Price
and Location Lags Coefficient
Standard
error
P-value
Maize, Dar
es Salaam L0. 0.27656 0.12551 0.028
L1. 0.17204 0.74583 0.024
L2. -0.07212 0.14028 0.607
L3. -0.05478 0.13312 0.681
Maize,
Arusha L0. 0.07006 0.01892 0.071
L1. -0.50342 0.20227 0.013
L2. 0.79244 0.20933 0.000
Maize, Iringa L0. 0.76921 0.01297 0.000
L1. 0.06511 0.01523 0.067
L2. -0.11050 0.14981 0.461
L3. -0.04944 0.12663 0.696
Maize,
Mbeya L0. 0.899271 0.11939 0.000
L1. -0.464881 0.16004 0.004
Maize, USA L0. 0.425578 0.16607 0.006
L1. 0.023667 0.10325 0.001
L2. 0.127660 0.49870 0.000
Export ban -0.06215 0.02763 0.000

15. Export Ban Effect on Mbeya Prices
Log Price
and Location Lags Coefficient
Standard
error
P-value
Maize, Dar
es Salaam L0. 0.011 0.004 0.028
L1. 0.064 0.028 0.024
L2. -0.012 0.140 0.607
Maize,
Arusha L0. 0.082 0.017 0.000
L1. -0.307 0.200 0.013
L2. 0.791 0.213 0.000
Maize,
Mbeya L0. 0.021 0.013 0.000
L1. -0.370 0.11 0.000
Maize, USA L0. -0.168 0.061 0.306
L1. -0.231 0.103 0.001
L2. 0.125 0.009 0.000
Export ban -0.147 0.036 0.000

16. Conclusion
 Export bans appear endogenous
 Export bans have some effect in reducing
domestic maize prices in Dar es Salaam and
in surplus producing areas like Mbeya
 Does not seem to have an impact on other
commodities like rice and beans
 But Tanzania’s maize export ban likely comes
at a cost of increasing transaction costs of
trade (bribes, etc.)
 This may further increase prices in
neighboring country markets that rely on
imports from Tanzania such as Nairobi

17. Thank you!