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

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)

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

Empirical Model

1. 𝑝𝑖,𝑡 = 𝛼0,𝑗 + ∑ 𝛼𝑘,𝑗𝑝𝑗,𝑡−𝑘𝑚1𝑘=0 + ∑ 𝛼𝑘,𝑤𝑝𝑤,𝑡−𝑘

𝑚2𝑘=0 +

𝜇𝐶(𝐵𝑗,𝑡)𝑖𝑗 + 𝜀𝑡 , 𝑖 ≠ 𝑗, 𝜀𝑡~𝑁(0, 𝜀2)

2. Pr(𝐵𝑗,𝑡 𝑝𝑗,𝑡−𝑘 ,𝑝𝑤,𝑡−𝑘 , 𝑧𝑡 =

Ф 𝛽0,𝑗 + ∑ 𝛽1,𝑘𝑝𝑖,𝑡−𝑘𝑚3𝑘=1 + ∑ 𝛽2,𝑘𝑝𝑤,𝑡−𝑘 + ∑ 𝜏𝑧𝑡

𝑚5𝑡=1

𝑚4𝑘=0 + 𝑒𝑡

zt = instruments (futures prices; temperature levels in surplus producing regions)

Empirical Model

3. 𝐶𝑖𝑗,𝑡 = (1 + 𝜃𝐵𝑖,𝑡)𝑑𝑖𝑗,𝑡

𝑑𝑖𝑗,𝑡 = 𝑑𝑖𝑑𝑑𝑑𝑑𝑑𝑒 × 𝑝𝑝𝑖𝑑𝑒 𝑜𝑜 𝑜𝑓𝑒𝑓𝑡 × 𝑑𝑝𝑑𝑡𝑒𝑓 𝑑𝑖𝑡𝑒𝑡

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

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

Estimation

Treatment-effects Vector Error Correction Model

First estimate equation 2, followed by equation 1

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

Results

99.

510

10.5

log

price

of m

aize

ln(T

sh/1

00kg

)

Jan 2004 Jan 2006 Jan 2008 Jan 2010 Jan 2012Jan 2005 Jan 2007 Jan 2009 Jan 2011Month and year

Arusha Dar es SalaamIringa Mbeya

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 0.4721 Prob>F

0.9200 0.021 0.114 Bartlett's Chi2 test for equal variances

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

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)

Export Ban Effect on Nairobi Maize Prices

Log Price and Location

Lags 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

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

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

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

Thank you!