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 What Drives Local Food Prices?
Evidence from the Tanzanian Maize Market∗
John Baffes†
Varun Kshirsagar‡
Donald Mitchell§
June 4, 2015
Abstract
We quantify the relationship between Tanzanian and external maize markets while
also accounting for domestic influences. We conclude that external influences on domestic prices originate from regional, rather than global, markets. We also show that,
compared to external factors, domestic factors exert a greater influence on Tanzanian
maize markets. Further, the mechanisms through which trade policies influence maize
markets involve interactions with both external market shocks and domestic weather
shocks. Overall, we provide evidence that the intermittent imposition of export bans
in Tanzania has had adverse impacts on its maize markets, and consequently, on the
development of its agrarian economy.
JEL CLASSIFICATION: E31, O13, Q02, Q13, Q18
KEY WORDS: Export ban, Food prices, Weather anomalies, Price transmission, Tanzania
∗ This
paper was funded by the USAID Tanzania SERA Policy Project and was prepared by Booz Allen
Hamilton and the World Bank’s Development Prospects Group. We would like to thank Karen Brooks,
Gary Eilerts, David M. Johnson, Charles Knoeber, John McIntire, Kateryna G. Schroeder and Sergiy Zorya
for comments and suggestions on earlier drafts, Rachel Weaving for editorial services, Aneth Kayombo
for data assistance, and Alex Mkindi for valuable insights on the Tanzanian maize market. The views
expressed here are our own, and do not necessarily reflect those of the United States Government or the
World Bank.
† Senior
Economist at the World Bank. Email: [email protected]
‡ Independent
§ Senior
consultant. Email: [email protected]
Advisor and former Chief-of-Party of the Tanzania SERA Policy Project.
[email protected]
Email:
1
Introduction
What drives food price changes in developing countries? This question has received considerable attention in the aftermath of the post-2005 spikes in international food prices.
Much of that attention has focused on the influence of world markets.1 In contrast, we
develop a tractable empirical framework that measures and accounts for the influence of
both external and domestic drivers of maize prices in Tanzania.2
We show that all 18 Tanzanian maize markets (for which we examine monthly price
changes between August 2002 and July 2012) adjust to changes in maize prices in Nairobi
with a lag, but significantly faster than they do to changes in the U.S. Gulf and South
African prices. This suggests that the impact of external markets is not as strong as typically assumed and comes mostly from regional, rather than global, markets. These findings are consistent with the view that African and world food markets are weakly integrated (e.g. Minot (2011)).
We also find that export bans delay the adjustment towards long-run equilibrium in
local maize markets. Weather shocks and harvest cycles also influence the adjustment
process and are a major source of short-term price variability. However, we show that the
magnitude of their influence is contingent on whether an export ban is imposed. Further,
the impacts of weather shocks and harvest cycles are less pronounced in markets that are
connected to regional and international trade hubs. Together, our results support the idea
that restrictive trade policies exacerbate the impact of domestic weather shocks.
Though the welfare consequences of increases in food price levels are still the subject of policy debate, the welfare consequences of price uncertainty are less ambiguous:
greater price uncertainty discourages smallholder farming households from incurring investments that raise their agricultural productivity. Our analysis points to two mechanisms that may reduce price uncertainty arising from domestic sources. First, investments
that engender a shift away from primitive agrarian techniques and towards more modern production and marketing methods may partially mitigate the impact of domestic
weather shocks. Second, and perhaps more importantly, a more open trade policy regime
will lessen the influence of domestic shocks.
Several features of Tanzania’s agrarian economy lend themselves to a study of food
staple markets that may be of broader interest. First, Tanzania is a large country with
five distinct geographic and agroecological zones. Second, food self-sufficiency varies
1 While
Ivanic and Martin (2008) and von Braun (2008) have suggested that the 2008 food price spike had a
strong impact, Aksoy and Hoekman (2010) and Headey (2013) note that the impact was muted. Swinnen
(2011) reviews this literature.
2 Tanzania comprises the mainland and Zanzibar.
The mainland and Zanzibar follow different food policies.
They also have different food staples: maize on the mainland and rice in Zanzibar. This paper is specifically
focused on maize policies, in the mainland of Tanzania.
1
widely across different parts of the country - notably, the remote and fertile Southern
zone is the major food surplus area.3 Third, trade routes (potentially) involve coastal and
inland water transport as well as road transport, both within the country and the region.
Fourth, the eight countries that border Tanzania vary in terms of their net food import
needs. Fifth, while the government influences prices through export bans, prices within
the country are determined by market forces. Consequently, the 18 Tanzanian markets we
study cover several different types of food staple markets that exist in Sub-Saharan Africa,
rendering our analysis relevant to many types of local food markets across the continent.
The paper proceeds as follows. Section 2 presents an overview of the Tanzanian maize
market, including its physical and policy aspects. Section 3 describes the framework relevant to the estimation of long-term trends and short-term dynamics. Sections 4 and 5
discuss results on the price and non-price factors affecting domestic maize prices. Section
6 examines the mechanisms through which the export ban influences domestic prices. Section 7 focuses on the 2011 export ban by estimating the path that domestic prices would
have followed had Tanzania not imposed the ban. Section 8 concludes.
2
The Tanzanian Maize Market
Tanzania, Sub-Saharan Africa’s sixth most populous nation, borders eight countries, three
lakes, and the Indian Ocean. It has two major ports, Dar es Salaam and Tanga, and two
smaller ones, Lindi and Mtwara (Figure 1). Nairobi, East Africa’s largest city and major
commercial center, is a key destination of Tanzania’s maize exports. Other major destinations include Malawi and Mozambique, and occasionally other countries in the region.
Physical Characteristics of the Tanzanian Maize Market
Maize has been cultivated in East Africa since the 17th century as a garden crop. The early
20th century saw a major shift in the types of maize cultivated, reflecting the introduction
of better-performing varieties from the United States, initially in South Africa and later
in Southern and Eastern Africa including Tanzania (McCann (2005)). Two policy changes
in the first half of the 20th century incentivized the expansion and commercialization of
maize: The 1911 introduction by the London Corn Exchange of grading standards for
African imports, and the 1925 Rhodesian Maize Act, which codified the primary cultivation of white dent corn into law.
Today, maize is Tanzania’s most important food staple, contributing almost half of the
country’s total calorie intake. It accounts for 40 percent of the country’s cropped area, with
3 USAID’s
Famine Early Warning System Network (FEWS NET) provides a clear quantitative description of
Tanzanian food markets : http://www.fews.net/east-africa/tanzania.
2
an estimated 85 percent of farmers cultivating the crop on plots of less than one hectare.4
It is a rainfed crop produced with limited use of modern inputs, mostly cultivated by
hand hoe, and vulnerable to weather shocks; productivity is low, at less than one ton per
hectare compared to a world average of more than 5 tons per hectare.5
During 2010-14, Tanzania’s annual maize production averaged 4.5 million tons, up
from 2.5 million a decade earlier (Figure 2). Global maize production averaged about
800 million tons during this period, rendering Tanzania a small country in terms of its
relevance in the global maize market.
Maize is grown in all five of Tanzania’s agroecological zones and sold in 18 markets
(Figure 1). Nearly one third of national output is produced in the Southern zone (Table 1),
whose four surplus markets — Iringa, Mbeya, Songea, and Sumbawanga — are relatively
isolated, being more than 500 kilometers away from Dar es Salaam (a major consumption
market) and Nairobi (an export destination) and having no convenient access to a port.
Mbeya and Songea export maize to Malawi and Mozambique, most notably to Nampula
in northern Mozambique; this trade often goes unrecorded (FEWSNET (2014); Burke and
Myers (2014)). Tanzania’s Northern zone markets — Arusha, Moshi, and Tanga — account
for almost 15 percent of maize output and are well connected to other major food-deficit
markets: Arusha and Moshi are close to Nairobi while Tanga is close to the Kenyan port
of Mombasa, in addition to having Tanzania’s second largest port.6 The Central zone markets of Dodoma, Singida, and Tabora account for about 20 percent of maize production.
They are food-deficit markets but have good access to transport infrastructure. The four
Lake zone markets — Bukoba, Musoma, Mwanza, and Shinyanga — are all food-deficit
markets as well, with convenient access to the markets of Kenya and other neighboring
countries. Last, in the Coastal zone, which includes the large market of Dar es Salaam
along with Lindi, Mtwara, and Morogoro, only the latter is an important maize producer.
The relative isolation of the Southern zone surplus markets, combined with the long
distances from markets to consumption centers and ports, and poor transport and storage
infrastructure, subjects Tanzania’s maize sector to seasonal influences.
The Policy Environment
The increased importance of maize in the local diet after World War II attracted government intervention. To secure food grain supplies during WWII, Kenya and Tanganyika
(now mainland Tanzania) established a Cereals Board to control food grain trade. Gov4 Government
of Tanzania.
5 USDA,
psdonline : https://apps.fas.usda.gov/psdonline/.
6 Arusha
has historically been one of Tanzania’s most important transport hubs (Iliffe (1979)).
3
ernment intervention continued well after WWII under the aegis of a Cereals Pool jointly
operated by Kenya, Uganda, and Tanganyika. Tanganyika withdrew from the pool in 1949
and established a Grain Storage Department that not only became the sole marketer and
trader of food crops but also instituted a pan-seasonal and pan-territorial pricing mechanism. This arrangement lasted until 1955, when, after a number of good harvests, the
government’s intervention in the food sector diminished (Suzuki and Bernard (1987)).
For the next seven years, maize was freely traded in Tanzania, but that changed following two successive poor harvests in 1961 and 1962; the National Agricultural Products
Board was established in 1964 with responsibilities very similar to those of the post-WWII
Grain Storage Department. Almost a decade later, the National Milling Corporation took
over marketing, trade, and storage aspects for most food commodities, while the responsibility for price setting stayed with the government.7 Although some changes were introduced in marketing and pricing policies in the early 1980s, the government continued to
intervene in the maize sector through ad hoc export bans, which were typically imposed
in response to food security and price volatility concerns, but some were attributable to
political pressures (Edwards (2014)).
Several authors have highlighted the negative long-term consequences of restrictive
trade policies in the Tanzanian context. For example, Lofchie (1978) linked the collapse of
the agrarian economy to the high taxes on agricultural production and exports imposed
during the two decades following the (1967) Arusha Declaration. Even President Julius
Nyerere, the chief architect of the Arusha Declaration, acknowledged (in 1979) that weak
economic incentives may have been responsible for the decline in agricultural productivity.8 Suzuki and Bernard (1987, p 87) made a compelling case in favor of more open trade
policies, arguing that: "Tanzania will not be completely self-sufficient in subsistence food
for some years, but it can move towards that goal by producing surplus food in the areas
that can support it. The most efficient policy would be to sell that surplus to neighboring
countries to buy much needed inputs ...".
Despite these warnings and assessments, export bans continued to be imposed as recently as 2011. During the past decade five bans were imposed on maize exports, with
an average duration of 13 months. The first and second of these spanned January 2005 to
January 2007, with a three-month hiatus at the beginning of 2006. A five-month ban was
in place in 2008 and another during 2009-10. The duration of the most recent ban was less
7 The
policy changes of the early 1970s were undertaken in conjunction with the campaign to replace the
traditional system of rural settlements with large villages. This villagization policy was introduced in 1963
and within a decade had been expanded to the entire countryside. Between 1973 and 1976 as many as 11
million people moved, either voluntarily or forcibly (Mapolu et al. (1990)).
8 Edwards
(2014) discusses the broader policy context under which Tanzania’s restrictive agricultural policies were implemented.
4
clear: it was announced in March 2011 and became effective in July; likewise, its removal
was announced in October 2011 but did not take effect until December that year.
Consistent with the government’s concerns about food security and price volatility, the
bans were introduced when maize prices were high and removed when prices were low
(Figure 3). The inflation-adjusted price of maize has been, on average, 28 percent lower
in the last month of each ban than in the first (Table 2). Further, prices fall more rapidly
when export bans are in effect, especially during the harvest season (Figure 4). In the next
section, we outline a framework that quantifies both the overall impact of export bans
on price changes, and the mechanisms through which export bans engender these price
changes.
3
The Estimation Framework
Seasonality, weather shocks, and export bans, above and beyond the effects of external
market conditions, are all likely to have important effects on the price determination process of maize. Moreover, the effects of these factors may differ across markets. We develop
a model that captures the response of domestic prices to external market signals and also
accounts for the factors mentioned above as well as fuel costs and inflation.
Early studies measured the strength of the relationship between external and domestic
commodity markets through the following equation9
pit = µ + βptE + eti
(1)
where pit and ptE denote the logarithm of the nominal price in domestic market i and the
external market at time t, while µ and β are parameters to be estimated and eti is the error
term. Full price transmission requires that β = 1, a hypothesis that could be tested and
would also imply that ( pit − ptE ) ∼ N (µ, σ2 ).
However, such an approach has two shortcomings. First, nonstationary prices may
overstate the strength of the price transmission estimates. Second, in primary commodity
markets such as those in Tanzania, with high trade costs, seasonality, policies, and other
domestic influences, it is unlikely that external and domestic prices will differ only by an
i.i.d. N (µ, σ2 ) term.
9 Although
our framework builds on estimation techniques used in the market integration and law of one
price literature, our focus is on what causes price changes in local markets - not whether these markets are
"efficient". These are related but not identical concepts. For example, a low degree of market integration
could reflect trade frictions engendered by a host of factors, including geography, poor infrastructure,
or policies. For a comprehensive literature review on market integration see Fackler and Goodwin (2001).
Meyer and Cramon-Taubadel (2004) survey the literature on asymmetric adjustment. Goodwin and Piggott
(2001) employ a threshold cointegration technique that estimates market integration while adjusting for
unobserved transport costs.
5
With respect to the nonstationarity concern, one could examine the order of integration
of the error term in equation (1). Under nonstationary prices, the existence of a stationary
error term implies co-movement between the two prices. However, if β 6= 1, the uniqueness of the co-integration parameter in the bivariate case implies that the corresponding
price differential would be growing and that such growth would not be accounted for,
even though prices may move in a seemingly synchronous manner. Hence the stationarity of the error term of equation (1), while establishing co-movement, should not be
considered as a testable form equivalent to β = 1. In fact, a number of authors have
warned, in a non-stationary context, against interpreting a non-unity slope coefficient as
a sign of market integration (e.g. Baffes (1991); Barrett (1996); Baffes and Gardner (2003)).
To account for the non-unity slope coefficient we impose β = 1, in which case the
problem is equivalent to testing for a unit root in the following univariate process:
( pit − ptE ) ∼ I (0)
(2)
Stationarity as defined in equation (2) implies that external price signals are transmitted
to domestic markets in the long run. The assumption (or finding) that the co-integration
parameter is unity is crucial, because it ensures that no other non-stationary component
is influencing domestic prices. The absence of co-integration (with unity slope coefficient
in the present setting) can be attributed to omitted non-stationary variables. Therefore,
equation (2) cannot serve as a substitute for β = 1 in equation (1); it can only serve as an
intermediate step in establishing its validity.
In order to model a more realistic price dynamic process, we generalize equation (1) in
three ways. As a first step, we introduce an auto-regressive structure by appending one
lag for each price as follows:
pit = µ + β 1 ptE + β 2 ptE−1 + β 3 pit−1 + uit
(3)
where ut is i.i.d. N (µ, σ2 ) and β 3 < 1. Hendry et al. (1984) discuss a number of testable
hypotheses resulting from corresponding restrictions on the parameter space of equation
(3). The most important of which is long-run proportionality, which ensures that external
price movements will eventually be transmitted to domestic markets. Under long run
proportionality, which can be tested through the restriction, ∑i β i = 1, equation (3) can be
reparameterized as follows:
( pit − pit−1 ) = µ + (1 − β 3 )( ptE−1 − pit−1 ) + β 1 ( ptE − ptE−1 ) + µit
(4)
Because of the equivalence of the existence of co-integration and the error correction
model, stationarity of the price differential in equation (2) implies the existence of an error
6
correction mechanism as defined in equation (4), and vice versa.10 On the other hand, the
restriction | β 3 | < 1 implies that 0 < 1 − β 3 < 2.11
In equation (4), β 1 indicates how much of a given change in the external price will
be transmitted to domestic markets within the first period; (1 − β 3 ) indicates how much
of the world-domestic price spread will be eliminated in each subsequent period. The
closer to unity is (1 − β 3 ), the more rapidly prices will converge. It is worth emphasizing
that (1 − β 3 ) being different from zero is a necessary and sufficient condition for long-run
convergence. By contrast, a β 1 significantly different from zero is neither a necessary nor
a sufficient condition for long-run price convergence.
As a second step, we allow for the (potential) influence of domestic factors by extending equation (4) as follows:
∆pit = µ + γ1 ( ptE−1 − pit−1 ) + γ2 ∆ptE + Ft [] + uit
(5)
To simplify the notation, we employ the difference operator (∆) and also set γ1 = (1 − β 3 )
and γ2 = β 1 . Ft [] is defined as follows:12
Ft [] =
γ3 ∆ptF
+
γ4 ∆ptI
+
γ5S sin
2πt
12
+
γ5C
cos
2πt
12
+ γ6 NDV It + γ7 IBANt (6)
ptF and ptI denote the (logarithm of the) price of fuel and the urban consumer price index, respectively. The trigonometric terms capture the periodic influence of harvest cycles.13 As a proxy for disturbances in weather conditions, NDV It represents the monthly
anomaly at time t in the Normalized Difference Vegetation Index and varies by month and
agroecological zone. Finally, IBAN is the export ban, taking the value of one when a ban is
in effect and zero otherwise.
The parameter estimates of the lagged price difference between external and domestic
markets are expected to be positive (or not significantly different from zero in the absence
of co-integration). The price of fuel, a key cost of production and transport, has a positive
impact on maize prices. The consumer price index, which captures other cost pressures
10 This
follows the Engle-Granger representation theorem (Engle and Granger (1987)).
11 The
sign of β 3 , or alternatively whether (1 − β 3 ) falls within [0, 1] or [1, 2] intervals, signifies the type of
convergence: monotonic in the former and oscillatory in the latter.
12 The
explanatory power of the model increases by almost four-fold when we include these variables: the
R-squared of a panel fixed model (for all 18 markets) increases from 0.059 to 0.199.
13 In contrast to a dummy variable specification, this specification minimizes the potential impact of outliers,
which is an especially useful feature in view of the relatively small number of harvest cycles (either 12 or
24) in our sample. Further, this specification uses a priori information that seasonal influences are smooth
and cyclical. More than two trigonometric functions could be used to capture seasonality (e.g. Shumway
and Stoffer (2011)). However, we use two, so that specification remains parsimonious.
7
such as increases in rural wages and costs of intermediate materials, is also expected to
have a positive impact on maize prices. An export ban is expected to exert downward
pressure on domestic prices since it increases the availability of domestic supplies. The
trigonometric variables capture seasonal influences on food prices arising from the interaction of harvest cycles and inadequate storage and transport capacity. Finally, a positive NDVI anomaly during the growing season, is expected to have a negative impact on
prices, and vice versa.
As a third step, we allow for different impacts of domestic and external factors under
two trade policies (IBAN = 1 when an export ban is in affect, INO_BAN = 1 otherwise), by
restating equation (6) within a panel framework as follows:
∆pit
=
µ + γ3 ∆ptF
+ γ4 ∆ptI
+ γ5CTR cos
+ ∑ ITRt ∗
h
γ1TR ( ptE−1
−
pit−1 ) + γ2TR ∆ptE
TR
2πt
12
i
+ γ6TR NDV It + ui + eti ,
+ γ5STR
sin
2πt
12
TR ∈ { BAN, NO_BAN }
(7)
ui denotes the market fixed effect, eti the idiosyncratic error clustered by market i, and ITR
the trade regime, as defined above. The part of equation (7) in square brackets can be
viewed as the trade regime dependent process ( RDt ), and as such, it sheds light on the
channels through which export bans influence domestic prices:
∆pit
=
γ3 ∆ptF + γ4 ∆ptI
|
{z
}
Price Controls
+
i
+
RD
|{z}t
Regime Dependent Process
u
|{z}
Market Fixed Effect
eti
|{z}
+
Idiosyncratic Error
where,
Domestic (Non-Price) Drivers
External (Price) Drivers
z
}|
RDt = γ1 ( ptE−1 − pit−1 ) +
|
{z
}
Adjustment
{z
γ2 ∆ptE
| {z }
Short-Run Effect
+ γ5S sin
|
2πt
12
}|
2πt
C
+ γ5 cos
+
12
{z
}
Harvest Cycles
{
γ6 NDV It
| {z }
Weather Anomalies
In the following three sections we employ the first two steps (i.e. the identification of
an appropriate external market and estimation of the impacts of domestic and external
shocks) in order to estimate maize price dynamics for each of the 18 markets. We employ
the panel specification (i.e. the third step) to identify the channels through which export
bans affect domestic price dynamics in each agroecological zone.14
14 We
employ a panel specification in order to gain statistical power. However, this increased statistical
power comes at the cost of assuming that parameters within an agroecological zone are homogenous.
8
4
Price Transmission
We begin by applying unit root tests to price levels (without and with a trend) and first
differences using the Augmented Dickey-Fuller (ADF, Dickey and Fuller (1979)) and the
Phillips-Perron (PP, Phillips and Perron (1988)).15 Unit root results are reported in Table
B1. Stationarity in log levels without a trend (first two columns) is overwhelmingly rejected in all cases. When a trend is included, the PP test indicates stationarity for some
prices but the ADF does not. Taking first differences (last two columns) induces stationarity in all cases. Consequently, the long-term relationship between domestic and external
prices should be examined on the basis of co-integration statistics, while short-run dynamics should be examined through an error correction model (equation 5) rather than an
autoregressive structure (equation 3).
Selecting the Appropriate External Market
The first step of the analysis involves identification of the appropriate maize price anchor.
The most commonly used world price indicator for maize is the U.S. Gulf price. It is the export price of the United States, the world’s largest maize exporter, comes from the world’s
most liquid market; but it pertains to yellow maize, which is used primarily for animal
feed. Another commonly used price benchmark in the context of Eastern and Southern
Africa is the Randfontein (South Africa) white maize price, which is also associated with
a liquid market and serves as a price-discovery mechanism in the region (Traub and Jayne
(2008)). A third choice would be the price in Nairobi, which is the main destination for
Tanzania’s maize exports; it comes from a less liquid market, but is in a net maize deficit
area and in physical proximity to Tanzania (Figure 5 depicts all three prices in US Dollar
nominal terms).16
We begin the analysis by examining the long-run relationship between the Tanzanian
maize markets and each of the three external price indicators. The full results are available
in Appendix B, while Table 4 provides a summary of these results.
Using the U.S. Gulf price, the parameter estimates we obtain are significant for all 18
markets (all slope parameter estimates are significantly different from zero at the 1 percent
level), with a median R-square of 0.69. However, the parameter estimate of equation (1)
is well below unity (the median across markets is 0.78). And while 30 of the 36 unit root
statistics of equation (1) support co-integration at the 5 percent level, only 20 support
15 The
data description and sources can be found in the Appendix A. Table 3 presents summary statistics on
maize prices in domestic Tanzanian markets as well as key external markets.
16 As
Table 3 documents, average maize prices in Nairobi over the sample period were between 10 percent
and 30 percent higher than prices in local Tanzanian markets (Figure 6 compares the Nairobi and Dar es
Salaam price time series). In contrast, prices were lower in U.S. Gulf and South African markets than in
Tanzanian markets.
9
stationarity of the price differential.
Using the South African maize price, we obtain estimates for β in equation (1) that are
much closer to unity (the median across the 18 markets is 0.9) than those based on the U.S.
Gulf price. All are significantly different from zero at the 1 percent level but the median
R-square is 0.60 — lower than with the U.S. Gulf price. Furthermore, about half of the unit
root statistics of equation (1) do not confirm a long-run relationship at the 5 percent level.
The evidence based on the price spread is more supportive of the existence of a long-run
relationship between the Tanzanian markets and the South African.
Using the Nairobi maize price, we find that all the parameter estimates are highly significant and much closer to unity (the median across 18 markets is 0.97) than their counterparts based on either U.S. Gulf or South African prices, and with a median R-square of
0.78 — much higher than with the other two prices. The unit root statistics of equation
(1) confirm co-integration. The price differential is stationary as well, in most cases at the
1 percent level (the only exception is the ADF test for Bukoba): this is expected since the
co-integration parameter is very close to unity. Based on these results, we choose the price
in Nairobi as our reference for the external market influence.
Quantifying Price Relationships
Table 5 reports parameter estimates consistent with the error correction specification (6).
This model explains about 30 percent of domestic price changes, with the R-square ranging from 0.20 in Songea, a surplus market in the Southern zone, to 0.37 in Arusha, a deficit
market in the Northern zone. The error correction term, γ1 , is significant in 17 of the 18
markets: in 15 markets at the 1 percent level and in 2 markets at the 5 percent level (Mbeya
the exception). However, the estimates vary widely, from a low of 0.11 in Iringa (t-statistic
= 2.64) to a high of 0.31 in Bukoba (t-statistic = 4.60). The error correction term averages a little more than 0.13 across all 18 markets, implying that, on average, 13 percent of
the price gap between Tanzania and Nairobi prices will be eliminated in the second (and
every subsequent) period.17
Results on the short-run impact of the external price shocks are even more heterogeneous. The parameter estimate for γ2 differs significantly from zero at the 5 percent level
in 13 markets. The short-run effect in these 13 markets averages 0.25, implying that only
one quarter of the adjustment to external price shocks is transmitted instantaneously.
To better understand the adjustment process, we combined the short-run and feedback
effects into a single summary statistic.18 Figure 8 depicts the cumulative adjustment for
17 Figure
7 shows prices in Dar es Salaam, Songea (lowest average real prices for our sample) and Mwanza
(highest average real prices). Although domestic prices move together, there are differences in terms of
price levels, the influence of harvest cycles as well as anomalous movements (also see Table 3).
18 The
cumulative adjustment is calculated as follows. Let, k be the amount of adjustment that takes place
10
the first three months and shows that the adjustment exceeds two thirds in only one market (Bukoba), but exceeds 50 percent in another seven markets. The median adjustment
across all markets is 0.40. Of the markets that adjust more quickly to external price shocks,
some represent surplus and some represent deficit regions, but most are either close to
Nairobi (Bukoba and Musoma) or have access to a port (Tanga and Mtwara) - suggesting
that geography plays a key role in the price adjustment process. The importance of geographic features in explaining market behaviour is consistent with a growing literature on
the determinants of spatial differences in food prices. Several studies, including Versailles
(2012) and Brenton et al. (2014), find significant border effects as well as large trade costs
across space.
We also control for changes in fuel prices and the consumer price index. Although
the cost of energy is expected to be captured by fluctuation in the external maize price, the
large distances among Tanzanian markets make it likely that domestic fuel prices also play
a role in domestic price determination.19 We find that the effect of changes in fuel costs
is mixed. In contrast to coastal markets and food surplus markets, food deficit markets
that are in the interior are typically influenced by changes in fuel prices. Market prices in
Dodoma and Morogoro, both in Tanzania’s interior and with reasonable road connectivity,
are the most sensitive to changes in fuel prices. In addition, Musoma, Mwanza, and Moshi
are also influenced by changes in fuel prices in the short run.
The inclusion of the Consumer Price Index (CPI) to control for inflation ensures that
the influences of the other drivers do not merely reflect co-movement due to general inflationary pressures.20 The CPI parameter estimate is statistically different from zero in
several markets. Maize prices are more strongly associated with inflation in the Northern, Lake, and Central zones, and less so, or not at all, in the Southern and Coastal zones.
in n periods. In the current period, n = 0, k takes the value of γ2 [also equal to 1 − (1 − γ2 )] which is the
short-run effect of the external price on the domestic price. In the next period, n = 1, k takes the value
of γ2 + (1 − γ2 )γ1 , which is the effect of the previous period, γ2 , plus the feedback effect, (1 − γ2 )γ1 .
It can also be written as (1 − (1 − γ2 )(1 − γ1 )γ1 . For n = 2, k takes the value of the previous period,
[γ2 + (1 − γ2 )]γ1 , plus γ1 (1 − γ2 − (1 − γ1 )γ2 ) [which can be written as 1 − (1 − γ2 )(1 − γ1 )2 ]. The terms
of the second parenthesis form a geometric sequence with the ratio equal to (1 − γ1 ) and the nth term
equal to (1 − γ1 )n . Hence, the adjustment at period n will be given by k = 1 − (1 − γ2 )(1 − γ1 )n . Figures
8 and 9 report the three-month cumulative adjustment with Nairobi and the US Gulf, respectively, i.e.,
n = 2. For k = 0.5, n gives the half-life of a shock.
19 We
also examined whether world crude oil prices influence the domestic fuel prices. While we find that
the world fuel price and the Dar es Salaam fuel price are cointegrated, the short-run magnitude of the
external influence is quantitatively small. In the short run, a 10 percent increase in global crude prices is
associated with a 0.9 percent increase in the local fuel prices in Dar es Salaam. After 3 months, just 35
percent of the shock is dissipated. Consequently, even after considering the impact of global crude price
changes, we conclude that global commodity markets still exert a weak short-run influence on domestic
markets in Tanzania.
20 We
also estimated the specifications by deflating all prices with the CPI, and the results are quantitatively
very similar.
11
The implication is that maize prices are more detached from general inflation in the foodsurplus Southern markets and the better connected Coastal markets.
We also calculated the three-month cumulative adjustment to the U.S. Gulf price. The
results, shown in Figure 9, confirm that all Tanzanian maize markets adjust to changes in
the Nairobi price much more quickly than they do to changes in the U.S. Gulf price.21 This
suggests that although external shocks influence domestic price movements, their impact
is not as strong as typically assumed and comes mostly from the regional, not the global,
market. These results are consistent with several studies that have previously examined
Tanzanian maize markets.22
Yet, full price transmission, especially in the context of the 2008 and 2011 price spikes,
is widely assumed. For example, Ivanic and Martin (2008) assume full transmission from
world market prices to domestic prices, which in turn drives their assessment of the
poverty impact of the 2008 food price spike. Nicita et al. (2014) also assume full price
transmission in Sub-Saharan Africa in their analysis of the distributional biases of agricultural trade policies. The empirical relevance of the full price transmission assumption
has been questioned in a broader context by Headey (2013) and Swinnen and Squicciarini (2012). Given that domestic maize prices in Tanzania (and, perhaps, many parts of
Sub-Saharan Africa) are only weakly influenced by external prices in the short run, any
explanation of food price movements must include the influence of domestic drivers. The
next section identifies such drivers and quantifies their impact.
5
Non-Price Determinants
The error correction model includes three non-price factors: export bans, seasonality, and
weather. The rest of this section discusses the influence of these drivers.
Export Bans
The parameter estimate for the export ban dummy is negative and significantly different
from zero at the 10 percent level in 11 of the 18 markets, with estimates ranging from -2.52
21 The
parameter estimates of the error correction model are not reported here.
22 Suzuki and Bernard (1987) and Minot (2010) provide insightful descriptive analyses, while several authors
have provided econometric analysis. Kilima et al. (2008) employ an ARCH-m model to link remoteness
with greater maize price volatility. The World Bank (2009) provides econometric evidence on the poor
linkages between four Tanzanian markets and world markets, as well as compelling descriptive evidence
on the marketing and transport inefficiencies that plague Tanzania’s maize sector. Dillon and Barrett
(2014) also find that world maize price movements do not influence domestic maize price movements
in Tanzania. Although they find that world crude prices have an influence, they do not account for the
influence of any domestic factor. To the best of our knowledge, no previous study has simultaneously
measured the influence of external and domestic factors or has shown that the magnitudes of their impacts
depend on trade policies.
12
(t-statistic = 1.81) in Dodoma to -5.36 (t-statistic = 1.71) in Lindi. The export ban is associated with a 3.29 percent monthly price decline in Dar es Salaam, which translates into a 20
percent cumulative price decline if a ban is in effect for six months. Of the seven markets
whose prices are not affected by the ban, four are in Tanzania’s Southern zone (Mbeya,
Songea, Iringa, and Morogoro); Tabora is in the Central zone; and the other two are the
southernmost port (Mtwara) and the northernmost port (Tanga). Markets in the Southern
zone are remote maize surplus markets with prices considerably lower than elsewhere;
these features are likely to attenuate the impact of an export ban. In the southern ports,
as well, prices are unlikely to reflect the full impact of official trade restrictions, because
market participants can circumvent formal trade channels (FEWSNET (2008),FEWSNET
(2014)).
Export bans have been studied elsewhere, both in the context of domestic-world price
linkages and as a cause of the 2008 and 2011 food price spikes. Ihle et al. (2009) examined
the influence of export bans on Tanzanian food prices and concluded that, in contrast
to our results, the influence on markets was larger in the Southern than in the Northern
zone. Our results may differ from theirs because we use observed export ban dummies
instead of estimating the latent influence of unobserved export bans, or alternatively, because we account for weather shocks and harvest cycles. Götz et al. (2013) examined the
effects of Russia’s and Ukraine’s export grain policies during 2007/08 and 2010/11 using
a Markov switching model, concluded that such policies reduced the degree of integration
with world markets and also increased domestic price variability. Further, numerous authors have noted that restrictive export policies were key contributors to the price spikes
of 2008 and 2011 (see, among others, Timmer (2008); Abbott (2012); and Martin and Anderson (2011)).
Seasonality
We measure and control for the likely impact of harvest cycles on maize prices by employing a trigonometric specification that captures periodicity in the price time series
(Shumway and Stoffer (2011)). We find that in most markets at least one of the two seasonality parameter estimates differs significantly from zero at the 5 percent level. Yet the
magnitude of the seasonal changes differs across markets and zones. Even for Southern
zone markets that exhibit strong seasonal patterns, the magnitude of the seasonal influence differs across markets. Prices in Songea begin increasing in September and reach
their peak in February. As the harvest approaches, prices increases first moderate, and
then prices fall rapidly from April through June (they decline nearly 6 percent during
these months). On a cumulative basis, harvest cycles cause prices to be 20 percent lower
in June than in February, and 20 percent higher in December than in August. This is consistent with a 40 percent gap between the lean season peak and the harvest season bottom.
13
While Mbeya is also a food surplus market in the Southern highlands with harvest cycles
that occur in similar months, in contrast to Songea, Mbeya is less remote. As a result,
compared to Songea, the influence of harvest cycles on local food prices in Mbeya is less
pronounced (with a peak of a little more than 4 percent and with less than a 30 percent
gap between the lean season peak and harvest bottom).
Evidence in support of seasonal influences on food prices, especially in the context of
Sub-Saharan Africa, where it appears to be more pronounced than in other parts of the
world, has been reported elsewhere. Sahn et al. (1989) highlighted the influence of seasonality in the context of developing-country agriculture. More recently, Kaminski et al.
(2014) have shown that the seasonal component represents a significant share of total food
price variability. Indeed, in Tanzania’s case, the share is up to 20 percent (see also FEWSNET (2008),FEWSNET (2014), and Tschirley and Jayne (2010)).
Weather Anomalies
Vegetation anomalies provide estimates for domestic supply shocks which are, in turn,
inversely related to local price changes.23 The NDVI anomaly parameter estimate differs
significantly from zero (at a 10 percent confidence level) in 14 markets, including several
food-deficit markets. Exceptions are Mwanza, Musoma, and Shinyanga, which are located
in the Lake zone and better connected, and Iringa, in the Southern Highlands.24
The other relatively isolated maize-surplus markets in the Southern Highlands —
Songea, Sumbawanga, Morogoro, and Mbeya — exhibit the strongest price response to
weather shocks. In Songea a 10 percent increase in the NDVI index, as experienced in December 2012, is associated with a 10.6 percent decline in prices. In contrast, Mbeya’s price
decline is only 5.1 percent; although Mbeya is a food-surplus area it has a better connected
and developed market than does Songea and hence it benefits from greater absorption of
surplus production by other markets. Consistent with this, seasonal price changes are
smaller in Mbeya than in Songea (Figure 10).
Similarly, prices in Dar es Salaam are less responsive to local weather anomalies. Dar
es Salaam is much better connected than Mbeya (in addition to being food-deficit) and
consequently its vulnerability to weather shocks are muted. A 10 percent increase in the
23 The weather disturbances in this study were estimated using satellite-derived Normalized Difference Veg-
etation Index (NDVI) imagery over cultivated areas as a proxy (c.f. Tucker (1979)). Brown (2014) reviews
the literature that has examined the relationship between weather anomalies that are detected using satellite imagery and food prices. Johnson (2014) provides evidence in support of the informativeness of NDVI
anomaly signals in the context of predicting the impacts of shocks to (county and state-level) grain yields
in the United States.
24 The reasons behind the non-significant result for the Iringa market are unclear; it is possible that transport,
storage and food processing facilities may be relatively better in Iringa (since it connects the Southern
Highlands to Dodoma and Arusha to the North and Dar es Salaam to the East), or this may be caused by
measurement noise.
14
NDVI index is associated with just a 2.7 percent decline (t-statistic = 4.51) in prices in Dar
es Salaam — about half Mbeya’s magnitude.
The relationship between weather vulnerability and market connectivity also helps
explain why restrictive trade policies exacerbate the impact of rainfall shocks, as the next
section notes.
6
Price Dynamics under Export Bans
The previous two sections provide strong evidence that export bans exert downward pressure on local prices. In this section we examine the mechanisms through which export
bans depress local prices. In particular, we show that export bans dampen adjustments to
changes in the price in Nairobi and exacerbate responses to favorable weather shocks.
Table 6 reports results from a panel benchmark model that is similar to the specification in Table 5. The effects of the export ban are more muted in the Southern zone, but
the effects on price changes are significantly different from zero across all zones and in
the aggregate. For Tanzania as a whole, holding other factors constant, an export ban is
consistent with price changes that are 3.1 percent (z-statistic = 12.7) lower than with no
ban. Weather anomalies influence price dynamics across all zones, producing the largest
impacts in the major maize producing areas. In the Southern zone, a 10 percent decline in
the NDVI (during the growing season) is associated with a 6.5 percent increase in prices
— an impact that is more than twice as large as the national average of 3.1 percent. In the
Northern zone, too, weather impacts are above average: a 10 percent decline in the NDVI
is associated with a 4.8 percent increase in prices. Price dynamics in all zones are also
influenced by the lagged price difference from Nairobi. On average, if Nairobi prices are
10 percent higher than Tanzanian prices, the latter will increase by 1.6 percent (z-statistic
= 9.23) to move towards equilibrium. All zones except for the Coastal zone are also influenced by changes in Nairobi prices in the short run. The largest short-run influence is felt
in the Northern zone where a 10 percent change in the Nairobi price translates into a 3.3
percent change in the local maize price.
The results on adjustment differences under the two trade regimes are reported in
Table 7. For the country as a whole, a 10 percent average difference with the Nairobi price
is associated with a 2.1 percent increase in the local price under no ban and a 1.3 percent
increase under a ban regime. These estimates are statistically different from zero and from
each other at the 1 percent significance level.
The adjustment magnitudes under a ban, implied by Table 7, also point to the presence
of substantial informal trade between countries. This is consistent with Tschirley and
Jayne (2010) and FEWSNET (2014), who also suggest that export bans delay, but do not
15
eliminate, price arbitrage between countries. Taken together, the results suggest that after
three months, 52 percent of a given shock is dissipated when there is no ban – compared
to 42 percent under a ban (Figure 11).
As expected, the influence of weather anomalies on local price changes is exacerbated
during an export ban.25 A 10 percent decline in the NDVI is associated with a 3.8 percent
increase in prices during a ban and only a 2.7 percent increase when there is no ban.
These estimates are significantly different from zero and also from each other at the 1
percent level. The Southern and Northern zones, the main maize export areas, experience
the largest differential impacts of weather disturbances under the two trade regimes. It
is worth noting that the mechanisms through which export bans influence market prices
involve changes in the impacts of both external and domestic shocks and not merely lower
price levels (as shown in table 5).
In the case of the Northern and Southern zones, which are the main export areas, bans
exacerbate weather shocks and consequently engender greater price uncertainty during
the harvests. Consequently, maize producers are adversely affected by both lower prices
at harvest as well as greater uncertainty due to the larger impacts of domestic shocks.
7
The Impact of the 2011 Export Ban
What path would maize prices have taken in the absence of export bans? This question is
important because it constitutes an essential first step to understand the welfare impacts
of trade policies. Answering this question precisely is difficult because of the likely endogeneity of export bans. First, ad-hoc agricultural trade policies may exert an adverse
long-run influence on economies through mechanisms that disrupt market institutions
and engender sub-optimal investments along the supply chain.26 Indeed, the majority of
Tanzanian maize markets analyzed in this study are characterized by price volatility that
is significantly higher than external markets (Table 3), suggesting that these mechanisms
may indeed be at work. This type of endogeneity is systemic, and has been a feature
of Tanzania’s maize sector all along, so it is not expected to bias our results. It would,
however, be a more relevant concern in the context of cross-country comparisons.
Second, the timing of the imposition of a particular ban may be endogenous as well.
As noted earlier, bans are more likely to be imposed when prices are elevated. Though
our econometric analysis accounts for harvest shocks and consequently removes one (po25 These
results are similar to those of Burgess and Donaldson (2010), who found (based on 1875-1915 data
for India) that the tendency of rainfall shortages to cause famines diminished as trade increased (as measured by railroad expansion).
26 Tschirley
and Jayne (2010) discuss the adverse long-term consequences of discretionary (and unpredictable) trade policy regimes.
16
tential) source of omitted-variable bias, this type of endogeneity may exert an influence
through other channels. We leave further examination of these channels to future research.
Because the circumstances under which export bans are imposed are different, and
have complex interactions with other variables affecting domestic price movements, we
isolate the impact of the last export ban from earlier ones by providing a separate estimate
(Table 8).27 For the country as a whole, the 2011 export ban caused the monthly price to be
8.7 percentage points lower, for every month that it was in effect, than they would have
been without the ban. While all five zones experienced a large and significant impact,
the effect of the ban was weakest in the Southern zone, most likely because of its poor
transport and market infrastructure.
To better understand the impact of the 2011 ban, with the caveats above, we use the
results in Table 8 to estimate counterfactual maize price changes in Dar es Salaam and
Songea under a no-ban scenario. The counterfactual price path is estimated in the following manner. First, we assume that the counterfactual price change in the first period will
differ from the actual price change by the size of the export ban coefficient and by the
adjustment coefficient adjusted by the last period’s price difference with Nairobi.28 Then,
we use the counterfactual change to calculate the counterfactual level for the first period.
Last, we repeat these steps recursively to generate the counterfactual price path.
Figures 12 and 13 show the actual and counterfactual maize prices in Dar es Salaam
and Songea. Actual maize prices began to diverge from counterfactual prices at the start of
the ban in July 2011. By December 2011, the last month of the ban, the estimates suggest
that maize prices in Dar es Salaam would have been 38 percent higher than they were
under the ban, while in Songea they would have been 31 percent higher.29 After the ban’s
removal, actual and counterfactual prices took several months to converge.
27 The
difference in the magnitude of the impacts is statistically significant both across the full sample and
across all zones. There are two reasons for the relatively larger impact of the 2011 export ban. First,
prices in Nairobi were especially elevated during this ban. Second, there were significant investments in
transport infrastructure during the late 2000s and this may have resulted in lower natural trade costs. As a
consequence, the last export ban may have exerted a larger influence on both maize trade flows and local
maize markets.
28 The
results for the specification with a separate export ban (in 2011) are very similar to those reported
Table 6. The adjustment parameter is 0.15 (instead of 0.13) in the Northern zone and 0.13 (instead of 0.12)
in the Southern zone. Similarly, the short run effect is 0.33 (instead of 0.32) in the Northern zone and the
same (0.21) in the Southern zone. All these parameters for both specifications are significantly different
from zero at a 1 per-cent level of significance.
29 Although
the estimated coefficients for Dar es Salaam and Songea are similar, the export ban does in fact
have a much larger impact on levels in Dar es Salaam than in Songea. These differences reflect the fact that
Dar es Salaam is better connected and has better functioning markets. There are two mechanical reasons
why (apparently) similar coefficients translate into large level differences. First, because Dar es Salaam
prices (without the ban) are higher than Songea prices, percentage changes translate into larger level
changes. Second, our analysis is in terms of percentage changes (i.e. log differences) and the differences
are compounded every month.
17
As the previous section shows, the impacts of weather shocks are more pronounced
during a ban. Given that weather anomalies across Tanzania were large and positive for
the maize planting and growing season at the end of 2011, had the export ban remained
in place in 2012, maize prices would have fallen sharply with the 2012 maize harvests.
This would have lowered farm incomes, and more importantly, weakened smallholders’
incentives to make investments that improve their agricultural productivity.
8
Conclusions
We have shown that, in the long run, Tanzanian maize price movements are influenced by
price movements in Nairobi. We also document that the impacts of movements in South
African and U.S. Gulf prices, both commonly used in the literature on the subject, are far
more muted. However, in the short run, maize prices are governed by a constellation of
domestic factors. Export bans exert downward pressure on domestic maize prices. Both
weather shocks and harvest cycles also have a strong short-run influence on local prices.30
Together, these results underscore the importance of measuring and accounting for the
influence of domestic drivers of local food prices.
We also find that the mechanisms through which trade policies influence maize markets involve interactions with both external market shocks and domestic weather shocks.
Responses to weather shocks are less pronounced in markets that are connected to regional and international trade networks—suggesting that trade mitigates the influence of
local shocks. Consistent with this, an export ban amplifies domestic weather disturbances.
Restrictive trade policies delay, but do not eliminate, the adjustment towards a long-run
equilibrium with external markets. These results complement attempts to identify local
and global food crises (see, for example, Cuesta et al. (2014)). In this context, our study
may be viewed as an attempt to better understand the mechanisms that lead to domestic
food price spikes.
In addition, there is substantial variation in market linkages and behaviour across
the 18 markets we study. This large sub-national variation has two important implications. First, although export bans are often imposed with the goal of protecting (betterconnected) urban consumers, as transport costs decrease, the adverse impacts on rural
producers will be larger, thus exacerbating the tradeoffs implicit in agricultural trade policies. Second, given the large differences in price differentials and adjustment speeds with
external markets, measures of agricultural protection (e.g. the ones reported by Anderson and Valenzuela (2008)) gloss over large sub-national differences. Consequently, our
results speak to the importance of incorporating sub-national differences into the design
30 The
strength of these relationships are consistent with poor storage and transport infrastructure.
18
of any policy that impacts domestic food markets.
Further, a better understanding of the relationship between weather shocks and food
markets may improve our understanding of the mechanisms behind the well documented
causal relationship between climate shocks and conflict (e.g. Sambanis (2002), Burke et al.
(2015)).31 Perhaps more fundamentally, Brückner and Ciccone (2011) document a causal
relationship between adverse rainfall and transitions to democracy in Africa, while Dell et
al. (2012) link positive temperature anomalies to lower growth rates in developing countries. Our study contributes to this literature by documenting a relationship between
weather shocks and food prices, and by extension, food availability and rural incomes,
both of which are related to conflict and economic growth.
These results also have a bearing on the role policies could play in mitigating the impacts of climate change on food supply. There is evidence of an increase in climate variability in tropical Sub-Saharan Africa (e.g. Thornton et al. (2009), Feng et al. (2013) and
Field et al. (2014)). As a result, food markets with pronounced seasonality and greater
sensitivity to weather anomalies are likely to be those more seriously affected if climatic
changes intensify. For example, Rowhani et al. (2011) have shown that maize yields in
Tanzania are affected by shifts in the growing season as well as by greater intra-seasonal
variability. Cross-border food trade flows are an important mechanism through which
some of the impacts associated with greater climate variability may be mitigated. It is
therefore important to deepen our understanding of the influence that agricultural trade
policies have on local food prices. Such an understanding will inform policies that aim
to reduce the impact that climate change will have on the most vulnerable people in the
developing world.
31 Related,
Brückner and Ciccone (2010) show that negative shocks to commodity export demand may engender conflict in (the relevant countries) in Sub-Saharan Africa. In addition, Nunn and Qian (2014) show
that US food aid extends the duration of civil conflict in Africa. However, they do not examine the channels through which food aid impacts local food markets.
19
References
Abbott, Philip C, “Export restrictions as stabilization responses to food crisis,” American
Journal of Agricultural Economics, 2012, 94 (2), 428–434.
Aksoy, Ataman and Bernard Hoekman, Food prices and rural poverty, Centre for Economic
Policy Research, 2010.
Anderson, Kym and Ernesto Valenzuela, “Estimates of global distortions to agricultural
incentives, 1955 to 2007,” World Bank, Washington, DC, 2008.
Baffes, John, “Some further evidence on the law of one price: The law of one price still
holds,” American Journal of Agricultural Economics, 1991, 73 (4), 1264–1273.
and Bruce Gardner, “The transmission of world commodity prices to domestic markets
under policy reforms in developing countries,” Policy Reform, 2003, 6 (3), 159–180.
Barrett, Christopher B, “Market analysis methods: are our enriched toolkits well suited
to enlivened markets?,” American Journal of Agricultural Economics, 1996, 78 (3), 825–829.
Becker-Reshef, Inbal, Chris Justice, Mark Sullivan, Eric Vermote, Compton Tucker, Assaf Anyamba, Jen Small, Ed Pak, Ed Masuoka, and Jeff Schmaltz, “Monitoring global
croplands with coarse resolution earth observations: The Global Agriculture Monitoring (GLAM) project,” Remote Sensing, 2010, 2 (6), 1589–1609.
Brenton, Paul, Alberto Portugal-Perez, and Julie Régolo, “Food prices, road infrastructure, and market integration in Central and Eastern Africa,” World Bank Policy Research
Working Paper 7003, 2014.
Brown, Molly E, Food security, food prices and climate variability, Routledge, 2014.
Brückner, Markus and Antonio Ciccone, “International commodity prices, growth and
the outbreak of civil war in sub-saharan Africa,” The Economic Journal, 2010, 120 (544),
519–534.
and , “Rain and the democratic window of opportunity,” Econometrica, 2011, 79 (3),
923–947.
Burgess, Robin and Dave Donaldson, “Can openness mitigate the effects of weather
shocks? Evidence from India’s famine era,” The American Economic Review, 2010,
pp. 449–453.
Burke, Marshall, Solomon M. Hsiang, and Edward Miguel, “Climate and Conflict,” Annual Review of Economics, 2015.
Burke, William J and Robert J Myers, “Spatial equilibrium and price transmission between Southern African maize markets connected by informal trade,” Food Policy, 2014,
49, 59–70.
Cuesta, José, Aira Htenas, and Sailesh Tiwari, “Monitoring global and national food
price crises,” Food Policy, 2014, 49, 84–94.
20
Dell, Melissa, Benjamin F Jones, and Benjamin A Olken, “Temperature shocks and economic growth: Evidence from the last half century,” American Economic Journal: Macroeconomics, 2012, 4 (3), 66–95.
Dickey, David A and Wayne A Fuller, “Distribution of the estimators for autoregressive
time series with a unit root,” Journal of the American Statistical Association, 1979, 74 (366a),
427–431.
Dillon, Brian M and Christopher B Barrett, “The impact of world oil price shocks on
maize prices in East Africa,” IMF/OCP/NYU International Conference on Food Price Volatility, Rabat, Morocco, 2014.
Edwards, Sebastian, Toxic aid: economic collapse and recovery in Tanzania, Oxford University
Press, 2014.
Engle, Robert F and Clive WJ Granger, “Co-integration and error correction: Representation, estimation, and testing,” Econometrica, 1987, pp. 251–276.
Fackler, Paul L and Barry K Goodwin, “Spatial price analysis,” Handbook of agricultural
economics, 2001, 1, 971–1024.
Feng, Xue, Amilcare Porporato, and Ignacio Rodriguez-Iturbe, “Changes in rainfall seasonality in the tropics,” Nature Climate Change, 2013, 3 (9), 811–815.
FEWSNET, “Informal cross border food trade in southern Africa,” 2008.
, “East Africa crossborder trade bulletin,” 2014.
Field, Christopher B, Vicente R Barros, KJ Mach, and M Mastrandrea, “Climate change
2014 : impacts, adaptation, and vulnerability,” Contribution of Working Group II to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 2014.
Goodwin, Barry K and Nicholas E Piggott, “Spatial market integration in the presence of
threshold effects,” American Journal of Agricultural Economics, 2001, 83 (2), 302–317.
Götz, Linde, Thomas Glauben, and Bernhard Brümmer, “Wheat export restrictions and
domestic market effects in Russia and Ukraine during the food crisis,” Food Policy, 2013,
38, 214–226.
Headey, Derek D, “The impact of the global food crisis on self-assessed food security,”
The World Bank Economic Review, 2013, 27 (1), 1–27.
Hendry, David F, Adrian R Pagan, and J Denis Sargan, “Dynamic specification,” Handbook of Econometrics, 1984, 2, 1023–1100.
Ihle, Rico, Stephan von Cramon-Taubadel, and Sergiy Zorya, “Markov-switching estimation of spatial maize price transmission processes between Tanzania and Kenya,”
American Journal of Agricultural Economics, 2009, 91 (5), 1432–1439.
Iliffe, John, A modern history of Tanganyika, Cambridge University Press, 1979.
21
Ivanic, Maros and Will Martin, “Implications of higher global food prices for poverty in
low-income countries,” Agricultural Economics, 2008, 39 (s1), 405–416.
Johnson, David M, “An assessment of pre-and within-season remotely sensed variables
for forecasting corn and soybean yields in the United States,” Remote Sensing of Environment, 2014, 141, 116–128.
Kaminski, Jonathan, Luc Christiaensen, and Christopher L Gilbert, “The End of Seasonality? New insights from sub-Saharan Africa,” World Bank Policy Research Working
Paper 6907, 2014.
Kilima, Fredy, Chanjin Chung, Phil Kenkel, and Emanuel R Mbiha, “Impacts of market reform on spatial volatility of maize prices in Tanzania,” Journal of Agricultural Economics, 2008, 59 (2), 257–270.
Lofchie, Michael F, “Agrarian crisis and economic liberalisation in Tanzania,” The Journal
of Modern African Studies, 1978, 16 (3), 451–475.
Loveland, TR, BC Reed, JF Brown, DO Ohlen, Z Zhu, LWMJ Yang, and JW Merchant,
“Development of a global land cover characteristics database and IGBP DISCover from
1 km AVHRR data,” International Journal of Remote Sensing, 2000, 21 (6-7), 1303–1330.
Mapolu, Henry, HA Amara, B Founou-Tchuigoua et al., “Tanzania: imperialism, the state
and the peasantry,” African Agriculture: The Critical Choices., 1990, pp. 138–148.
Martin, Will and Kym Anderson, “Export restrictions and price insulation during commodity price booms,” American Journal of Agricultural Economics, 2011, p. aar105.
McCann, James, Maize and grace: Africa’s encounter with a New World crop, 1500-2000, Harvard University Press, 2005.
Meyer, Jochen and Stephan Cramon-Taubadel, “Asymmetric price transmission: a survey,” Journal of Agricultural Economics, 2004, 55 (3), 581–611.
Minot, Nicholas, “Staple food prices in Tanzania,” Comesa policy seminar on ’Variation in
staple food prices: Causes, consequence, and policy options’, Maputo, Mozambique: African
Agricultural Marketing Project (AAMP), 2010.
, “Transmission of world food price changes to markets in Sub-Saharan Africa,” Washington DC, International Food Policy Research Institute, 2011.
Nicita, Alessandro, Marcelo Olarreaga, and Guido Porto, “Pro-poor trade policy in SubSaharan Africa,” Journal of International Economics, 2014, 92 (2), 252–265.
Nunn, Nathan and Nancy Qian, “US food aid and civil conflict,” The American Economic
Review, 2014, 104 (6), 1630–1666.
Phillips, Peter CB and Pierre Perron, “Testing for a unit root in time series regression,”
Biometrika, 1988, 75 (2), 335–346.
22
Rowhani, Pedram, David B Lobell, Marc Linderman, and Navin Ramankutty, “Climate
variability and crop production in Tanzania,” Agricultural and Forest Meteorology, 2011,
151 (4), 449–460.
Sahn, David E et al., Seasonal variability in Third World agriculture: the consequences for food
security., Johns Hopkins University Press, 1989.
Sambanis, Nicholas, “A review of recent advances and future directions in the quantitative literature on civil war,” Defence and Peace Economics, 2002, 13 (3), 215–243.
Shumway, Robert H and David S Stoffer, Time Series Analysis and its Applications: with R
examples, Springer, 2011.
Suzuki, Yuriko and Andrew Bernard, “Effects of panterritorial pricing policy for maize
in Tanzania,” Washington DC, International Food Policy Research Institute, 1987.
Swinnen, Johan, “The right price of food,” Development Policy Review, 2011, 29 (6), 667–
688.
and Pasquamaria Squicciarini, “Mixed messages on prices and food security,” Science,
2012, 335 (6067), 405–406.
Thornton, Philip K, Peter G Jones, Gopal Alagarswamy, and Jeff Andresen, “Spatial
variation of crop yield response to climate change in East Africa,” Global Environmental
Change, 2009, 19 (1), 54–65.
Timmer, C Peter, Causes of high food prices, Vol. 128, Asian Development Bank, 2008.
Traub, Lulama Ndibongo and Thomas S Jayne, “The effects of price deregulation on
maize marketing margins in South Africa,” Food Policy, 2008, 33 (3), 224–236.
Tschirley, David L and Thomas S Jayne, “Exploring the logic behind southern Africa’s
food crises,” World Development, 2010, 38 (1), 76–87.
Tucker, Compton J, “Red and photographic infrared linear combinations for monitoring
vegetation,” Remote Sensing of Environment, 1979, 8 (2), 127–150.
Versailles, Bruno, “Market integration and border effects in eastern africa,” International
Monetary Fund, 2012.
von Braun, Joachim, “The food crisis isn’t over,” Nature, 2008, 456 (7223), 701–701.
World Bank, Eastern Africa: A study of the regional maize market and marketing costs, World
Bank, Washington DC, 2009.
23
Table 1
Key Physical Characteristics of Tanzanian Maize Markets
FEWS NET
Categorization
----------------------Maize statistics on -----------------------Production
Area
Yield
Share
(000m MT)
(000 Ha)
(MT/Ha)
(Percent)
----------- Time and distance via major roads to -----------Dar
Nairobi
Dar
Nairobi
(Hours)
(Hours)
(Km)
(Km)
Central
Dodoma
Minor deficit
351
339
1.0
74
7:33
8:45
502
682
Singida
Minor deficit
190
150
1.3
53
na
na
na
na
Tabora
Minor deficit
376
292
1.3
68
12:51
11:35
886
873
4
6
0.7
55
0:00
11:43
0
913
Coastal
Dar es Salaam
Deficit
Lindi
Minor deficit
63
76
0.8
59
5:57
17:31
457
1,362
Morogoro
Minor deficit
238
232
1.0
44
2:34
11:22
194
889
Mtwara
Minor deficit
63
78
0.8
67
7:17
18:51
562
1,467
Lake
Bukoba
Deficit
121
100
1.2
71
18:29
12:30
1,415
960
Musoma
Minor deficit
117
65
1.8
69
15:25
6:44
1,149
495
Mwanza
Deficit
250
263
0.9
56
na
na
na
na
Shinyanga
Deficit
672
516
1.3
65
13:17
10:28
1,017
761
Arusha
Deficit
210
124
1.7
95
8:25
3:33
647
272
Moshi
Minor deficit
150
108
1.4
94
7:21
4:32
567
349
Tanga
Surplus
273
188
1.5
na
4:39
8:32
356
619
Iringa
Surplus
384
247
1.6
91
6:29
11:41
503
946
Mbeya
Surplus
495
271
1.8
72
10:32
15:23
829
1,165
Songea
Surplus
237
149
1.6
79
11:51
19:10
924
1,374
Sumbawanga
Surplus
351
224
1.6
70
18:51
19:00
1,215
1,466
Northern
Southern Highlands
Sources: FEWS NET categories from USAID’s www.fews.net. Data for regional maize production from Tanzania’s Agricultural Sample Census 2007/08 (2012). Road
distances and time from Google Maps (estimated times assume no traffic). Road travel is less relevant for markets in the lake and coastal zones.
Table 2
Maize Prices during Export Bans
--------------- Export ban’s duration --------------Last
month
First
January 2004
December 2005
24
457
302
-34%
Second
March 2006
December 2006
10
423
200
-53%
Third
January 2008
April 2008
4
445
442
-1%
Fourth
January 2009
September 2010
21
457
296
-35%
Fifth
July 2011
December 2011
6
447
373
-17%
13
446
323
-28%
Average
Duration
(months)
------- Price (Tsh/kg, CPI-deflated, 2010) -------
First
month
First
month
Last
month
Percent
change
Notes: Prices refer to the Dar es Salaam market. Export bans were in effect for 65 months (out of 144 months in the
sample).
25
Table 3
Summary Statistics of Maize Prices in Tanzania (June 2002 –July 2014)
-------- Average Price -------Tsh/kg, Real, Sep 2010 = 100
Full
No ban
Ban
-------------------- Volatility -------------------Standard deviation of logarithmic change
Full
No ban
Ban
U.S. Gulf
248
272
219
6.9
7.1
6.6
South Africa
282
297
264
8.1
8.1
8.1
Nairobi
400
394
409
9.6
9.9
9.3
Tanzania, average
332
333
332
12.9
12.0
13.6
Dodoma
356
351
362
10.1
8.8
11.1
Singida
333
330
336
11.2
10.2
12.0
Tabora
343
351
334
13.2
13.6
12.5
Dar es Salaam
357
362
349
10.2
9.4
10.7
Lindi
356
361
350
20.0
19.1
21.0
Morogoro
349
342
357
13.3
13.6
12.6
Mtwara
353
351
355
21.9
20.5
23.5
Bukoba
359
361
357
11.3
12.0
10.2
Musoma
378
384
370
11.4
11.3
11.2
Mwanza
385
390
380
10.3
10.0
10.3
Shinyanga
353
353
352
10.2
8.4
11.5
Arusha
334
324
346
9.6
8.7
10.0
Moshi
348
337
362
10.3
9.0
11.1
Tanga
333
330
336
14.0
12.3
15.4
Iringa
279
277
282
13.2
12.8
13.7
Mbeya
281
292
267
10.2
9.6
10.9
Songea
242
237
247
17.3
13.3
21.2
Sumbawanga
247
259
232
14.1
13.1
15.2
Central
Coastal
Lake
Northern
Southern Highlands
Notes: Both Tanzanian and external prices are expressed in real Tanzanian shillings (September 2010 = 100, deflated by the
total CPI). Volatility is defined as standard deviation of logarithmic change, multiplied by 100. Out of the 144 monthly
observations, 64 correspond to Ban and 80 to No ban regimes.
26
Table 4
Summary Indicators of the Relationship between Domestic and External Prices
U.S. Gulf
South Africa
Nairobi
Median R2
0.69
0.60
0.78
Lowest R
0.59
0.50
0.64
Highest R2
0.78
0.72
0.88
24
12
3
Conventional statistics, equation (1)
2
Absolute deviation of β1 from unity (%)
Stationarity statistics of equation (1) (count out of 18)
ADF <5%
15
8
17
PP <5%
15
12
15
ADF <1%
1
0
14
PP <1%
3
4
14
6
14
17
Stationarity statistics of equation (2) (count out of 18)
ADF <5%
14
13
18
ADF <1%
0
2
15
PP <1%
0
4
13
PP <5%
Notes: This table summarizes conventional and stationarity statistics reported in tables B2 (U.S. Gulf), B3 (South Africa), and B4 (Nairobi), reported in Appendix B.
27
Table 5
Parameter Estimates for Error Correction Model, Nairobi
Arusha
Bukoba
Dar es Salaam
Dodoma
μ
-0.02
(1.57)
-0.03*
(1.83)
-0.01
(1.01)
-0.02
(1.25)
-0.04*
(1.79)
-0.00
(0.19)
-0.02
(1.08)
-0.03
(1.59)
-0.02
(1.22)


(−1
− −1
)
0.12***
(2.97)
0.31***
(4.60)
0.13***
(3.53)
0.09***
(3.20)
0.11***
(2.64)
0.30***
(3.11)
0.05
(1.44)
0.15****
(3.05)
0.13***
(3.13)

0.32**
(4.75)
0.37***
(3.93)
0.21**
(2.50)
0.22***
(3.49)
0.37***
(3.19)
-0.07
(0.37)
0.17**
(1.99)
0.41***
(2.75)
0.32***
(5.05)

0.21
(1.40)
0.21
(1.14)
0.21
(1.05)
0.42***
(2.69)
0.20
(0.81)
-0.31
(1.03)
-0.12
(0.67)
0.36*
(1.83)
0.28*
(1.82)

3.03***
(3.07)
1.96*
(1.69)
1.98**
(2.24)
2.90***
(3.26)
1.04
(0.97)
-0.22
(0.12)
1.90**
(1.99)
2.75**
(2.10)
2.48***
(3.33)

-3.31*
(1.73)
-3.48**
(2.16)
-3.29**
(2.13)
-2.52*
(1.81)
-1.46
(0.73)
-5.36*
(1.71)
-1.24
(0.74)
-1.58
(0.75)
-2.68*
(1.75)
2
 (
)
12
0.03**
(2.19)
0.03
(1.58)
-0.03**
(2.37)
0.04***
(2.95)
0.05***
(2.98)
0.10***
(3.36)
-0.04**
(2.58)
0.05**
(2.61)
0.02
(1.53)
2
 (
)
12
0.01
(1.15)
-0.05***
(-4.00)
0.00
(0.05)
0.01
(0.57)
0.01
(0.38)
-0.02
(0.53)
0.03***
(3.05)
0.00
(0.30)
0.03***
(2.97)

-0.46***
(2.79)
-0.38***
(-4.46)
-0.27**
(4.51)
-0.29***
(-5.45)
-0.43
(1.55)
-0.62***
(2.75)
-0.54***
(2.70)
-1.02***
(4.74)
-0.29**
(2.47)
R-square
0.37
0.31
0.29
0.39
0.21
0.29
0.24
0.33
0.34
Notes: See next page.
Iringa
Lindi
Mbeya
Morogoro
Moshi
Table 5 (continued)
Parameter Estimates for Error Correction Model, Nairobi
Mtwara
Musoma
Mwanza
Shinyanga
Singida
μ
-0.01
(0.36)
-0.00
(0.31)
-0.00
(0.11)
0.01
(1.04)
-0.02*
(1.74)


(−1
− −1
)
0.30***
(3.72)
0.24***
(3.69)
0.20***
(4.06)
0.13***
(3.37)

-0.03
(0.16)
0.31***
(3.50)
0.22**
(2.44)

-0.27
(0.90)
0.44**
(2.12)

-1.06
(0.52)

Songea
Sumbawanga
Tabora
Tanga
-0.09**
(2.26)
-0.05*
(1.75)
-0.03
(1.48)
-0.02
(0.94)
0.14**
(3.01)
0.16**
(2.52)
0.14***
(3.02)
0.15***
(2.88)
0.15***
(2.98)
0.15**
(2.11)
0.14
(1.53)
0.14
(0.98)
0.16
(1.30)
0.26**
(2.79)
0.36***
(3.15)
0.49**
(2.33)
0.17
(0.89)
0.24
(1.00)
0.21
(0.77)
0.08
(0.27)
0.34
(1.14)
0.29
(1.44)
2.27*
(1.69)
2.21**
(2.42)
0.35
(0.37)
2.77***
(3.03)
1.03
(0.59)
1.03
(0.69)
2.74***
(2.62)
1.72
(1.10)
-3.52
(1.09)
-4.58**
(2.51)
-3.65**
(2.43)
-4.19***
(2.66)
-3.03*
(1.84)
-0.52
(0.19)
-4.42*
(1.98)
-3.03
(1.56)
-3.32
(1.58)
2
 (
)
12
0.12***
(3.59)
-0.03*
(1.73)
0.03*
(1.93)
0.05***
(3.37)
0.04**
(2.34)
0.07**
(2.32)
0.04*
(1.78)
0.05**
(2.32)
0.08***
(4.34)
2
 (
)
12
-0.01
(0.25)
-0.02
(1.48)
-0.02**
(2.15)
0.02*
(1.78)
0.00
(0.24)
-0.03*
(1.72)
-0.06***
(4.07)
-0.04**
(2.49)
0.02
(0.98)

-0.56**
(2.16)
-0.22
(1.14)
-0.16
(1.36)
-0.01
(-0.06)
-0.23***
(3.12)
-1.06***
(-2.75)
-0.61*
(1.94)
-0.16*
(1.80)
-0.71***
(3.30)
R-square
0.26
0.22
0.28
0.25
0.28
0.20
0.23
0.26
0.34
Notes: Each regression has 144 monthly observations. The dependent variable is the change in the logarithm of the nominal price in market i. Absolute (robust) tstatistics in parentheses, significance level, * = 10 percent, ** = 5 percent, *** = 1 percent.
Table 6
Parameter Estimates for Panel Specification with Simple Dummy, Nairobi
Central
Coastal
Lake
Northern
Southern
National
μ
-0.02***
(8.38)
-0.02***
(3.20)
-0.00
(0.68)
-0.02***
(7.54)
-0.05**
(4.66)
-0.02***
(5.33)


(−1
− −1
)
0.13***
(8.26)
0.23***
(5.76)
0.20***
(4.93)
0.13***
(19.82)
0.12**
(7.02)
0.16***
(9.23)

0.21***
(6.90)
0.12
(1.52)
0.26***
(5.23)
0.33***
(21.13)
0.21***
(4.52)
0.21***
(6.75)

0.33***
(8.00)
0.01
(0.11)
0.32***
(5.01)
0.26***
(14.92)
0.09
(1.55)
0.20***
(4.37)

2.78***
(67.95)
0.77
(1.09)
1.70***
(4.33)
2.40***
(7.81)
1.25***
(7.30)
1.68***
(6.61)

-2.78***
(15.77)
-3.49***
(6.93)
-3.83***
(17.23)
-2.75***
(11.13)
-2.07***
(2.75)
-3.09***
(12.72)
2
 (
)
12
0.04***
(15.43)
-0.07***
(4.30)
0.03***
(7.14)
0.04***
(2.64)
0.05***
(9.68)
-0.05***
(8.20)
2
 (
)
12
-0.01
(0.81)
-0.01
(1.62)
-0.03***
(4.63)
0.02***
(4.54)
-0.03**
(2.35)
-0.01**
(2.32)

-0.23***
(8.13)
-0.38***
(2.95)
-0.19***
(2.66)
-0.48***
(4.64)
-0.65***
(6.30)
-0.31***
(7.42)
R-square
0.28
0.22
0.24
0.32
0.18
0.20
Notes: The dependent variable is the change in the logarithm of the nominal price in market i. All regressions employ
a (market) fixed effects methodology with bootstrapped standard errors (1,000 replications). Robust absolute z-statistics in parentheses, significance level, * = 10 percent, ** = 5 percent, *** = 1 percent; significance levels are different than
typical due to clustering adjustment of the standard errors. The bootstrapped standard errors are clustered at the market level.
30
Table 7
Parameter Estimates for Panel Specification with Interaction Dummies, Nairobi
Central
Coastal
Lake
Northern
Southern
National
μ
-0.04***
(9.10)
-0.03***
(5.83)
-0.02***
(4.01)
-0.04***
(18.49)
-0.05**
(4.34)
-0.04***
(8.80)


(−1
− −1
)*
0.10***
(6.46)
0.18***
(5.40)
0.14***
(7.09)
0.12***
(12.58)
0.10**
(4.11)
0.13***
(7.98)


(−1
− −1
)*_
0.17***
(10.80)
0.31***
(5.89)
0.25***
(5.03)
0.19***
(70.44)
0.15***
(7.62)
0.21***
(9.30)
 *
0.22***
(6.56)
0.15
(1.58)
0.26***
(5.65)
0.34***
(27.60)
0.23***
(4.69)
0.23***
(7.60)
 *_
0.22***
(6.73)
0.14
(1.51)
0.26***
(5.68)
0.35***
(27.00)
0.22***
(4.71)
0.23***
(7.51)

0.36***
(9.60)
0.04
(0.29)
0.35***
(4.70)
0.29***
(15.11)
0.17**
(2.27)
0.23***
(4.82)

2.89***
(9.99)
0.40
(0.43)
1.75***
(3.84)
2.76***
(8.46)
0.65***
(3.15)
1.61***
(4.97)
)*
0.04***
(13.09)
0.08***
(3.43)
0.03***
(3.84)
0.05***
(2.72)
0.06***
(5.95)
0.05***
(7.45)
)*_
0.04***
(6.23)
0.09***
(5.24)
0.03***
(5.58)
0.04**
(2.57)
0.06***
(8.10)
0.05***
(7.54)
)*
0.00
(0.10)
-0.02
(1.24)
-0.02***
(3.35)
0.04***
(7.22)
-0.03**
(2.33)
-0.01
(1.07)
)*_
-0.02***
(3.66)
0.00
(0.36)
-0.03***
(5.56)
0.00
(1.14)
-0.03**
(2.58)
-0.02**
(3.47)
 *
-0.28***
(8.16)
-0.37***
(3.07)
-0.27*
(1.74)
-0.59***
(5.55)
-0.86***
(3.98)
-0.38***
(6.18)
 *_
-0.19***
(3.17)
-0.40**
(2.37)
-0.13**
(2.01)
-0.43***
(3.73)
-0.21
(1.36)
-0.27***
(5.03)
R-square
0.27
0.23
0.24
0.30
0.21
0.20
 (
 (
 (
 (
2
12
2
12
2
12
2
12
Chi-square tests
Difference in adjustment
0.07
0.14
0.11
0.07
0.05
0.08
Diff in Adj-Chi
29.56***
20.92***
11.16***
70.93***
21.05***
47.58***
Difference in NDVI
0.09
0.03
0.14
0.17
0.65
0.11
Diff in NDVI-Chi
1.31
0.05
0.95
6.39***
3.36**
3.30*
Notes: The dependent variable is the change in the nominal price in market i. All regressions employ a (market) fixed
effects methodology with bootstrapped standard errors (1,000 replications). Robust absolute z-statistics in parentheses,
significance level, * = 10 percent, ** = 5 percent, *** = 1 percent; significance levels are different than typical due to
clustering adjustment of the standard errors. The bootstrapped standard errors are clustered at the market level. The
Diff in Adj-Chi and Diff in NDVI-Chi provide the chi-squared statistics from a Wald test of the difference in the values
taken by the adjustment coefficient and the NDVI anomaly, respectively, under Ban and No ban regimes.
31
Table 8
Separating the Impact of the 2011 Export Ban
Central
Coastal
Lake
Northern
Southern
National
-2.75***
(11.13)
-2.07***
(2.75)
-3.09***
(12.72)
Estimate reported in Table 6 (sixth row)

-2.78***
(15.77)
-3.49***
(6.93)
-3.83***
(17.23)
Specification separating the impact of the 2011 export ban
 , excluding 2011
-2.27***
(14.43)
-2.80***
(4.27)
-3.30***
(18.22)
-2.01***
(9.48)
-1.62***
(2.36)
-2.52***
(10.46)
2011 BAN
-7.94***
(10.28)
-10.57***
(4.09)
-9.00***
(10.92)
-9.17***
(12.47)
-7.28***
(3.31)
-8.87***
(12.25)
Notes: The dependent variable is the change in the logarithm of the nominal price in market i. All regressions employ
a (market) fixed effects methodology with bootstrapped standard errors (1,000 replications). Robust absolute z-statistics
in parentheses; significance level, * = 10 percent, ** = 5 percent, *** = 1 percent; significance levels are different than
typical due to clustering adjustment of the standard errors. The bootstrapped standard errors are clustered at the market level. The top row shows the parameter estimates of the export ban reported in Table 6. The second and third rows
provide estimates of the bans prior to 2011 and the 2011 ban, respectively, which were estimated together with a similar
specification to that of the model reported in Table 6.
32
Figure 1
Maize Markets in Tanzania
U G A N D A
Nairobi
Bukoba
Musoma
Lake Victoria
R W A N D A
K E N Y A
Mwanza
Lake
B U R U N D I
Arusha
Shinyanga
Moshi
Northern
Singida
Tabora
Central
Tanga
Dodoma
Morogoro
Lake Tanganyika
Dar es Salaam
Indian Ocean
Sumbawanga
Iringa
Southern Highl ands
Coastal
Mbeya
Lindi
Z A M B I A
Songea
Mtwara
Lake Nyasa
M A L A W I
M O Z A M B I Q U E
33
Figure 2
Maize Production in Tanzania
Thousand of metric tons
6,000
5,000
4,000
3,000
2,000
1,000
0
1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012
Source: U.S. Department of Agriculture
Figure 3
Maize Price in Dar es Salaam and Export Bans
TZ Shillings/kg (real 2010, CPI-deflated)
700
600
500
400
300
200
100
Jul-02 Jul-03 Jul-04 Jul-05 Jul-06 Jul-07 Jul-08 Jul-09 Jul-10 Jul-11 Jul-12 Jul-13 Jul-14
Source: FAO GIEWS, newspaper articles, and interviews with industry representatives
34
Figure 4
Average Price Changes during Ban and No Ban Periods
Average price change across the harvest cycle
15%
No Ban
Ban
May
Jul
10%
5%
0%
-5%
-10%
-15%
Jan
Feb
Mar
Apr
Jun
Aug
Sep
Oct
Nov
Dec
Source: Authors’ calculation based on unadjusted price data.
Figure 5
Nominal Maize Prices, International Comparison
USD/kg
0.6
Nairobi
South Africa
US Gulf
0.5
0.4
0.3
0.2
0.1
0.0
Jul-02 Jul-03 Jul-04 Jul-05 Jul-06 Jul-07 Jul-08 Jul-09 Jul-10 Jul-11 Jul-12 Jul-13 Jul-14
Source: Ministry of Industry and Trade, Tanzania, FAO GIEWS, World Bank
35
Figure 6
Nominal Maize Prices, Dar es Salaam and Nairobi
TZ Shillings/kg
1,000
Dar es Salaam
Nairobi
800
600
400
200
0
Jul-02 Jul-03 Jul-04 Jul-05 Jul-06 Jul-07 Jul-08 Jul-09 Jul-10 Jul-11 Jul-12 Jul-13 Jul-14
Source: Ministry of Industry and Trade, Tanzania
Figure 7
Nominal Maize Prices, Domestic Comparison
TZ Shillings/kg
1,000
Songea
Dar es Salaam
Mwanza
800
600
400
200
0
Jul-02 Jul-03 Jul-04 Jul-05 Jul-06 Jul-07 Jul-08 Jul-09 Jul-10 Jul-11 Jul-12 Jul-13 Jul-14
Source: Ministry of Industry and Trade, Tanzania
36
Figure 8
Price Adjustment Achieved within 3 Months (percent), Nairobi
Bukoba
70%
Musoma
60%
Morogoro
57%
Tanga
54%
Mtwara
51%
Lindi
51%
Iringa
50%
Mwanza
50%
Moshi
49%
Arusha
47%
Tabora
47%
Dar
40%
Singida
36%
Shinyanga
36%
Dodoma
35%
Songea
29%
Sumbawanga
26%
Mbeya
17%
0%
20%
40%
60%
80%
Source: Authors’ calculation based on parameter estimates
Figure 9
Price Adjustment Achieved within 3 Months (percent), US Gulf
Mtwara
42%
Lindi
36%
Songea
26%
Sumbawanga
23%
Tanga
19%
Tabora
17%
Musoma
15%
Bukoba
15%
Singida
14%
Mwanza
14%
Mbeya
14%
Iringa
14%
Dar
14%
Shinyanga
12%
Morogoro
12%
Dodoma
8%
Moshi
Arusha
6%
0%
0%
20%
40%
Source: Authors’ calculation based on parameter estimates
37
60%
80%
Figure 10
Seasonal Influence on Maize Price Changes: Songea and Mbeya
Change in maize price (percent)
8.0%
Songea
Mbeya
4.0%
0.0%
-4.0%
-8.0%
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Source: Authors’ calculation based on parameter estimates
Figure 11
Price Adjustment Achieved within 3 Months (percent), Asymmetric
58%
Northern
52%
58%
Lake
45%
No export ban periods
44%
Southern
Export ban periods
38%
47%
Central
38%
52%
Coastal
33%
20%
30%
40%
50%
Source: Authors’ calculation based on parameter estimates
38
60%
70%
Figure 12
Impact of the 2011 Export Ban in Dar es Salaam
TZ Shillings/kg
900
600
300
Dar es Salaam
Nairobi
Counterfactual
0
Jan-11
Mar-11
May-11
Jul-11
Sep-11
Nov-11
Jan-12
Mar-12
May-12
Source: Authors’ calculations based on parameter estimates
Figure 13
Impact of the 2011 Export Ban in Songea
TZ Shillings/kg
900
600
300
Songea
Nairobi
Counterfactual
0
Jan-11
Mar-11
May-11
Jul-11
Sep-11
Source: Authors’ calculations based on parameter estimates
39
Nov-11
Jan-12
Mar-12
May-12
Appendix A : Data Description and Sources
Domestic Prices
Wholesale white maize prices in Tanzania are monthly averages, expressed in TZ shillings
per kg, collected by Tanzania’s Ministry of Industry and Trade in 18 markets. These markets are grouped into five zones and cover the entire country. Data on domestic fuel prices
and CPI are collected by Tanzania’s National Bureau of Statistics (NBS).
External Prices
Wholesale white maize prices in Nairobi (Kenya), Randfontein (South Africa), and Nampula (Mozambique) are sourced from FAO GIEWS (http://www.fao.org/giews/english/
index.htm); U.S. Maize prices represent no. 2, yellow, f.o.b. Gulf ports. Crude oil price
is the average of Brent (U.K. 380 API), West Texas Intermediate (WTI 400 API), and Dubai
(Fateh 320 API) equally weighted. World prices for maize and oil were taken from the
World Bank’s pink sheet (http://go.worldbank.org/4ROCCIEQ50).
Export Ban
An export ban is represented by a dummy variable taking the value of 1 when the ban was
effective and zero otherwise. The ban was effective during the following months: Jan—
Dec 2005, Mar 2006—Jan 2007, Jan—May 2008, Jan 2009—Oct 2010, Jul 2011 (introduction
was announced informally in Mar 2011)—Dec 2011 (lifting was announced informally in
Oct 2011). This was sourced from FAO GIEWS, newspaper articles, and interviews with
industry representatives.
Normalized Difference Vegetation Index anomaly
Weather disturbances in this study were estimated using satellite-derived Normalized
Difference Vegetation Index (NDVI) imagery over cultivated areas as a proxy. NDVI is derived from the visible and near-infrared portions of the electromagnetic spectrum and the
contrast between the two provides a strong indication of vegetative health and plant productivity (c.f. Tucker (1979)). In an agricultural context, NDVI captured during the heart
of the growing season, and thus anomalies of it from normal, have been shown related
to crop productivity (Becker-Reshef et al. (2010); Johnson (2014); Brown (2014)). In other
words, higher than expected values of NDVI during the growing season would be consistent with weather conditions that have been favorable to crop yields and, conversely,
below normal NDVI values suggestive of lower than typical yields.
The NDVI data used in this research was collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the NASA polar orbiting satellite Aqua
from July 2002 to July 2014. MODIS provided an NDVI estimate every eight days at an
approximately 250 meter ground sample (pixel) resolution across the entire study area. To
isolate the NDVI signal to only those areas where crops are expected the time series was
‘masked’ by the Croplands and Cropland/Natural Vegetation Mosaic categories found
within the IGBP land cover classification (c.f. Loveland et al. (2000)). Next, to reduce potential pixel-level noise (both in time and space) and simplify the modeling efforts, the
measurements were aggregated to monthly values at a 5 kilometer grid cell size. And finally, the values were spatially averaged further to each of study areas in order to directly
link them with the economic data.
40
Figure A1 depicts the NDVI values across the harvest cycle for the Southern zone (multiplied by 10,000 as per convention). Each bar represents the median NDVI value for the
relevant month for the 12 years of the sample. The median NDVI values range from a
low of 3,910 in October (the last month of the harvest season) to a high of 6,761 in March
(which is towards the end of the lean season that ends in April. For example, the November median value of 4505 reported in Figure A1 ranges between a low of 3,866 in 2003
to a high of 5,247 in 2011. Figure A2 depicts Southern zone’s NDVI anomaly for the
month of November. The (positive) anomaly equals 14.2 percent in 2006, calculated as the
deviation of the NDVI value of 5,148 from the median of 4,506. Note that of the 12 observations shown in Figure A2, only five values lie outside the [-10 percent, +10 percent]
interval, which are the ones which are assigned non-zero values for the NDVI anomaly
used in the estimation; the rest are set to zero. Figures A3 and A4 depict the values of the
NDVI anomaly used for the Northern and Southern zone, respectively. In the Northern
zone, twenty months are assigned a non-zero NDVI anomaly, seven of which are negative
(representing worse-than-normal weather conditions) while 13 are positive (representing
better-than-normal weather conditions). In contrast, only seven months are assigned nonzero values in the Southern zone, three corresponding to better and four corresponding to
worse than normal weather conditions.
41
Figure A1
NDVI Values across the Harvest Cycle in the Southern Zone
Median NDVI*10,000 across the harvest cycle (Southern Zone), 2002-14
Harvest
Lean Season
Lean Season
7,000
6,000
5,000
4,000
3,000
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Source: Authors’ calculation based on NDVI data
Figure A2
NDVI Anomalies for November in the Southern Zone
NDVI anomaly for November, Southern zone
20%
15%
10%
5%
0%
-5%
-10%
-15%
-20%
2002
2003
2004
2005
2006
2007
Source: Authors’ calculations based on original NDVI data
42
2008
2009
2010
2011
2012
2013
Figure A3
NDVI Anomaly, Northern Zone
Percent
20
10
0
-10
-20
Jul-02
Jul-03
Jul-04
Jul-05
Jul-06
Jul-07
Jul-08
Jul-09
Jul-10
Jul-11
Jul-12
Jul-13
Jul-14
Jul-13
Jul-14
Source: NASA and authors’ calculations
Figure A4
NDVI Anomaly, Southern Zone
Percent
20
10
0
-10
-20
-30
Jul-02
Jul-03
Jul-04
Jul-05
Jul-06
Jul-07
Jul-08
Source: NASA and authors’ calculations
43
Jul-09
Jul-10
Jul-11
Jul-12
APPENDIX B: Stationarity Properties and Parameter Estimates of Equation (1) and (2) for the three External Price Indicators
Table B1: Stationarity Properties
-- Log-levels without trend -ADF
PP
-- Log-levels with trend -ADF
PP
-- First differences of logs -ADF
PP
U.S. Gulf
-1.59
-1.46
-1.83
-1.76
-3.04
-9.65***
S. Africa
-2.00
-1.70
-2.58
-2.21
-3.54**
-8.88***
Nairobi
-1.14
-2.01
-3.25*
-3.22*
-3.91***
-10.04***
Arusha
-1.66
-2.23
-3.51**
-3.01
-4.53***
-7.94***
Bukoba
-1.10
-1.83
-3.91**
-3.53*
-4.01***
-11.24***
Dar es Salaam
-1.23
-1.30
-3.37*
-3.03
-4.28***
-8.87***
Dodoma
-1.57
-1.96
-3.90**
-2.89
-4.47***
-6.81***
Iringa
-1.35
-2.12
-3.45**
-3.13*
-4.47***
-8.72***
Lindi
-1.16
-2.45
-3.92**
-4.38***
-4.19***
-14.77***
Mbeya
-1.11
-1.37
-3.78**
-2.87
-4.23***
-8.09***
Morogoro
-1.85
-2.05
-3.36*
-2.78
-4.07***
-7.50***
Moshi
-1.47
-1.70
-3.35*
-2.79
-3.67***
-8.91***
Mtwara
-1.41
-2.99
-3.17*
-4.74***
-3.22**
-16.58***
Musoma
-0.89
-2.21
-4.20***
-3.52*
-4.56***
-9.34***
Mwanza
-1.24
-2.27
-4.28***
-3.57*
-4.52***
-10.65***
Shinyanga
-1.26
-2.91
-4.18***
-4.16***
-4.04***
-8.70***
Singida
-1.41
-2.48
-4.19***
-3.17*
-4.43***
-9.83***
Songea
-1.65
-2.11
-3.83**
-3.59**
-4.19***
-11.08***
Sumbawanga
-1.22
-1.65
-3.36*
-3.23*
-4.18***
-9.98***
Tabora
-1.54
-2.34
-4.23***
-3.30*
-3.98***
-11.09***
Tanga
-1.62
-2.39
-3.22*
-2.91
-3.94***
-7.58***
Notes: All variables are expressed in logarithms. ADF and PP denote the Augmented Dickey-Fuller and Phillips-Perron
statistic for unit roots, respectively. The ADF statistic was based on 12 lags, while the spectral estimation for the PP statistics
was based on the Bartlett kernel method. Significance level of stationarity: * = 10 percent, ** = 5 percent, *** = 1 percent.
44
Table B2: Parameter Estimates of Equation (1) and Stationarity Properties of Equation (2)
between Tanzanian Markets and U.S. Gulf
------------------------ Regression results, eq. (1) -----------------------R-square
μ

ADF
PP
--- Price spread, eq. (2)--ADF
PP
Arusha
2.72***
(7.28)
0.76***
(20.55)
0.68
-2.90**
-2.91**
-2.53
-2.67*
Bukoba
2.79***
(9.60)
0.76***
(26.00)
0.75
-3.00**
-3.21**
-2.85*
-2.99**
Dar es Salaam
2.44***
(7.91)
0.79***
(25.56)
0.73
-2.64*
-2.85*
-2.61*
-2.92**
Dodoma
2.03***
(4.81)
0.83***
(19.84)
0.67
-3.07**
-2.76*
-2.95**
-2.72*
Iringa
2.38***
(6.02)
0.77***
(19.60)
0.67
-3.03**
-3.20**
-2.86*
-3.03**
Lindi
2.80***
(5.94)
0.76***
(16.22)
0.62
-3.27**
-3.96***
-3.26**
-3.46**
Mbeya
1.38***
(4.32)
0.87***
(27.39)
0.78
-2.96**
-2.99**
-2.07
-3.05**
Morogoro
2.86***
(7.09)
0.75***
(18.69)
0.65
-3.18**
-2.97**
-2.58*
-2.83*
Moshi
2.71***
(6.64)
0.76***
(18.94)
0.65
-2.70*
-2.51
-2.53
-2.49
Mtwara
3.30***
(6.67)
0.70***
(14.43)
0.59
-2.92**
-4.42***
-2.65*
-3.99**
Musoma
2.93***
(8.40)
0.75***
(21.66)
0.73
-2.93**
-3.28**
-2.78*
-2.95**
Mwanza
2.14***
(5.72)
0.83***
(22.52)
0.77
-3.17**
-3.24**
-2.96**
-3.09**
Shinyanga
2.51***
(5.63)
0.78***
(17.84)
0.70
-2.86*
-3.59***
-2.61*
-3.24**
Singida
2.03***
(4.49)
0.83***
(18.65)
0.71
-3.27**
-3.24**
-2.99**
-3.08**
Songea
2.80***
(6.56)
0.72***
(16.74)
0.60
-3.27**
-3.40**
-2.92**
-3.19**
Sumbawanga
2.49***
(6.23)
0.75***
(18.62)
0.69
-2.90**
-3.23**
-2.73*
-3.12**
Tabora
2.51***
(6.32)
0.78***
(19.85)
0.73
-3.57***
-3.43**
-2.90**
-3.16**
Tanga
2.48***
(5.60)
0.78***
(17.85)
0.63
-3.01**
-3.13**
-2.74*
-2.96**
Notes: ADF and PP denote the Augmented Dickey-Fuller and Phillips-Perron statistic for unit roots, respectively. The
numbers in parentheses are absolute t-statistics. Significance levels: * = 10 percent, ** = 5 percent, *** = 1 percent.
45
Table B3: Parameter Estimates of Equation (1) and Stationarity Properties of Equation (2)
between Tanzanian Markets and Randfontein (South Africa)
------------------------ Regression results, eq. (1) -----------------------R-square
μ

ADF
PP
--- Price spread, eq. (2)--ADF
PP
Arusha
1.59***
(2.87)
0.86***
(15.72)
0.57
-2.85*
-2.77*
-3.10**
-2.79*
Bukoba
1.19***
(3.16)
0.91***
(24.00)
0.69
-2.43
-3.03**
-2.70*
-3.09**
Dar es Salaam
1.25***
(2.82)
0.90***
(20.06)
0.60
-2.63*
-2.48
-2.88**
-2.65*
Dodoma
0.87
(1.42)
0.93***
(15.36)
0.55
-2.83*
-2.62*
-2.95**
-2.65*
Iringa
1.21**
(2.37)
0.88***
(17.17)
0.55
-2.79*
-2.97**
-3.08**
-3.01**
Lindi
1.49**
(2.50)
0.87***
(14.91)
0.53
-3.32**
-3.72***
-3.68***
-3.74***
Mbeya
-0.19
(0.47)
1.02***
(24.50)
0.68
-2.53*
-2.66*
-2.48*
-2.64*
Morogoro
1.74***
(3.19)
0.85***
(15.62)
0.53
-2.98**
-2.86*
-3.16**
-2.92**
Moshi
1.61**
(2.56)
0.86***
(13.92)
0.53
-2.73*
-2.34
-3.00**
-2.43
Mtwara
20.7***
(3.31)
0.82***
(13.34)
0.51
-3.10**
-4.18***
-3.52***
-4.10***
Musoma
1.52***
(2.96)
0.88***
(17.23)
0.64
-2.62*
-3.11**
-3.03**
-3.14**
Mwanza
0.29
(0.58)
1.00***
(20.33)
0.72
-2.79*
-3.11**
-2.79*
-3.11**
Shinyanga
0.76
(1.31)
0.95***
(16.61)
0.66
-2.49
-3.59***
-2.63*
-3.58***
Singida
0.54
(0.86)
0.96***
(15.51)
0.62
-2.82*
-3.12**
-2.90**
-3.12**
Songea
1.68***
(3.08)
0.82***
(14.87)
0.50
-3.11**
-3.19**
-3.43**
-3.23**
Sumbawanga
0.95**
(1.99)
0.89***
(18.48)
0.63
-3.14**
-3.08**
-3.50**
-3.16**
Tabora
0.69
(1.35)
0.95***
(18.76)
0.70
-3.16**
-3.60***
-3.27**
-3.62***
Tanga
1.37**
(2.17)
0.88***
(14.02)
0.52
-2.92**
-3.03**
-3.14**
-3.04**
Notes: ADF and PP denote the Augmented Dickey-Fuller and Phillips-Perron statistic for unit roots, respectively. The
numbers in parentheses are absolute (robust) t-statistics. Significance levels: * = 10 percent, ** = 5 percent, *** = 1 percent.
46
Table B4: Parameter Estimates of Equation (1) and Stationarity Properties of Equation (2)
between Tanzanian Markets and Nairobi
------------------------ Regression results, eq. (1) -----------------------R-square
μ

ADF
PP
--- Price spread, eq. (2)--ADF
PP
Arusha
-0.28
(0.84)
1.01***
(32.24)
0.87
-5.38***
-3.73***
-5.36***
-3.72***
Bukoba
0.19
(0.68)
0.97***
(37.49)
0.88
-2.30
-4.20***
-2.45
-4.20***
Dar es Salaam
-0.07
(0.17)
0.99***
(24.56)
0.82
-3.65***
-3.98***
-3.69***
-3.99***
Dodoma
-0.99**
(2.30)
1.08***
(26.28)
0.81
-4.88***
-3.33**
-4.39***
-3.21**
Iringa
0.08
(0.17)
0.96***
(21.70)
0.73
-3.54***
-3.31**
-3.69***
-3.32**
Lindi
0.46
(0.79)
0.94***
(17.34)
0.69
-3.54***
-4.52***
-3.71***
-4.52***
Mbeya
-0.63
(1.34
1.03***
(22.90)
0.77
-3.56***
-3.22**
-3.41**
-3.17**
Morogoro
0.19
(0.43)
0.97***
(23.24)
0.78
-4.50***
-3.46**
-4.54***
-3.49***
Moshi
-0.64*
(1.88)
1.05***
(32.54)
0.88
-4.29***
-3.94***
-4.19***
-3.79***
Mtwara
1.19*
(1.87)
0.87***
(14.49)
0.65
-4.45**
-4.58***
-3.58***
-4.81***
Musoma
0.28
(0.92)
0.97***
(33.51)
0.87
-3.63***
-4.69***
-3.74***
-4.73***
Mwanza
-0.54
(1.55)
1.05***
(31.79)
0.87
-3.34**
-4.44***
-3.18**
-4.35***
Shinyanga
-0.06
(0.14)
0.99***
(22.66)
0.81
-3.84***
-3.94***
-3.86***
-3.94***
Singida
-0.66
(1.46)
1.04***
(24.42)
0.81
-5.38***
-3.71***
-5.15***
-3.68***
Songea
0.76
(1.46)
0.88***
(17.61)
0.64
-3.90***
-3.77***
-4.15***
-3.80***
Sumbawanga
0.90*
(1.66)
0.87***
(16.79)
0.66
-3.25**
-3.38***
-3.76***
-2.46**
Tabora
0.61
(1.35)
0.93***
(21.52)
0.74
-4.07***
-3.50***
-4.24***
-3.52***
Tanga
-0.45
(0.98)
0.93***
(23.40)
0.78
-4.76***
-3.49***
-4.67***
-3.48**
Notes: ADF and PP denote the Augmented Dickey-Fuller and Phillips-Perron statistic for unit roots, respectively. The
numbers in parentheses are absolute (robust) t-statistics. Significance levels: * = 10 percent, ** = 5 percent, *** = 1 percent.
47
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