Regional price transmission in Southern African maize markets

Transcription

1 Regional price transmission in Southern African maize markets T. Davids, K. Schroeder, F.H. Meyer, and B. Chisanga Invited paper presented at the 5th International Conference of the African Association of Agricultural Economists, September 23-26, 2016, Addis Ababa, Ethiopia Copyright 2016 by [authors]. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.

2 Regional price transmission in Southern African maize markets By Ms T Davids 1, Dr. K Schroeder 2, Prof FH Meyer 3 and Mr. B Chisanga 4 1 Reasearch Analyst and 3 Director - Bureau for Food and Agricultural Policy Department of Agricultural Economics, Extension and Rural Development Faculty of Natural and Agricultural Sciences University of Pretoria 2 Research Scientist - Food and Agricultural Policy Research Institute, University of Missouri 4 Research Associate Indaba Agricultural Policy Research Institute 1 tracy.davids@up.ac.za / tracy@bfap.co.za 3 ferdi.meyer@up.ac.za 2 schroederkg@missouri.edu 4 brian.chisanga@iapri.org.zm 1

3 REGIONAL PRICE TRANSMISSION IN SOUTHERN AFRICAN MAIZE MARKETS ABSTRACT In light of the importance of maize as a staple crop in Southern Africa, as well as its prioritisation from a policy perspective, this study evaluates the extent of price transmission between selected maize markets in the region. It employs secondary data of weekly white maize prices in seven markets in the region to quantify the long and short run price relationships between relevant markets based on geographic proximity and expected trade patterns. While several authors have noted the isolation of white maize markets in Southern Africa from the global market, this study finds evidence of co-integration between multiple maize markets within the Southern African region. By implication, policy decisions affecting prices in any single country will influence price levels in multiple surrounding markets, impacting on both producer and consumer welfare not only in the country of application but also in the region as a whole. 1. INTRODUCTION Maize represents the principal food staple in Southern Africa and on average between 2013 and 2015, per capita consumption was more than four times the global average at 87kg per capita (OECD-FAO, 2016). Consequently its availability and affordability is concomitant with food security in the region and its nature as a strategic political crop has also prioritised the maize sub-sector from a policy perspective. Despite the international drive towards liberalization, the perceived need to stabilise prices and supply has been offered as justification for the continued government intervention in maize markets across Eastern and Southern Africa (ESA) (Jayne and Tschirley 2009, Minot 2014). It has been argued that in Africa in particular, interventions that manage volatility will reduce price risks for multitudes of consumers that spend a large share of their incomes on food products, whilst also prioritising the sustainability of the substantial share of the population that depends on agriculture for their livelihood (Minot 2014). Historically, however, such interventions have been highly discretionary and unpredictable, often characterised by the sudden implementation of trade controls, unanticipated changes to tariff policy and inconsistent pricing policies for government purchases. The unpredictability and ad-hoc nature of government activity in markets has resulted in additional risks and costs for the private sector, impeding investments that would improve access to markets and services for multitudes of small scale producers. Consequently, it has not been effective in supporting agricultural productivity growth in the region (Jayne & Tschirley, 2009) and contrary to the stabilisation objectives, observed volatility over the past decade has been 2

4 higher in markets where governments intervene most actively (Chapoto and Jayne 2009, Jayne 2012, Minot 2014). Abundant literature evaluating the impact of different policies have been focused on specific countries, whilst the prevalence of intra-regional trade suggests that quantitative analysis considering any single country in isolation would provide an incomplete picture, ignoring impacts on neighbouring countries. A simple graphical representation of wholesale white maize prices in Zambia and Zimbabwe in Figure 1 suggests a significant impact on Zimbabwean prices when export bans are implemented in Zambia and empirical evidence suggests that the implementation of export bans have a significant impact on price differences between countries in ESA (Porteous 2012) USD / ton ZAM_LUSAKA ZIM_HARARE BAN Figure 1: Wholesale white maize prices in Zambia and Zimbabwe during periods of open borders and export control: January October 2014 Source: FEWSNET (2015), Porteous (2012) and various press releases. Despite the need to consider the wider regional effect of policies, the extent of market integration between different countries in the region has not been well quantified. Quantification of the extent to which interventions in a single country impact affordability across the rest of the region requires an understanding of price formation and therefore the extent of price transmission between the different markets in the region is essential. This study aims to identify the magnitude, speed and direction of regional price transmission between selected markets in five countries within Southern Africa (Malawi, Mozambique, South Africa, Zambia and Zimbabwe) 1. The choice of markets is based on the availability of price data, geographic proximity of surplus and deficit regions, as well as the consequent expectations related to trade-flows. 1 The markets included in the analysis are Lusaka (Zambia), Ranfontein (SAFEX South Africa), Harare (Zimbabwe), Maputo (Mozambique), Nampula (Mozambique), Beira (Mozambique), Lilongwe (Malawi) 3

5 Million tons 2. OVERVIEW OF THE SOUTHERN AFRICAN MAIZE MARKET Maize production in Southern Africa has expanded rapidly over the past decade and notwithstanding the drought conditions experienced in 2015, which reduced crops markedly, aggregate production from the five countries included in the study have increased by an annual average of 3.4% 2 p.a. since 2000 (Figure 2). South Africa is the largest producer in the region, accounting for almost 60% of total production between 2013 and Growth has however been particularly rapid in Zambia and Malawi, where maize production expanded by 10.2% p.a. and 5.9% p.a. respectively. Growth in these countries is attributed to both area expansion and significant improvements in yield levels. Underpinned by strong population growth and rising income levels, total consumption across the five countries has also expanded by an annual average of 3.1% (Figure 2). While the aggregated net trade position of the five countries has therefore only increased marginally, significant shifts have been evident at individual country level Aggregate Consumption Production South Africa Production Zambia Production Mozambique Production Zimbabwe Production Malawi Figure 2: Production and Consumption of maize in 5 countries in Southern Africa Source: ReNAPRI, 2015 South Africa remains the largest and most consistent maize exporter in the region, but in Zambia, production growth has exceeded demand, inducing a shift from a net importing position in the early 2000 s to a fairly consistent exporter since 2007 (Figure 3). Consistency of exports have however been influenced by export controls through periods of 2008/09 and 2013/14, aimed at ensuring availability and reducing price volatility. 2 Growth rate calculated using the least squares method. 4

6 Million Tons South Africa Zambia Mozambique Zimbabwe Malawi Figure 3: Net trade for maize in 5 countries in Southern Africa Source: ITC (2016) and FEWSNET (2015) Whilst much of the historic policy focus has been centred on improved availability and reduced volatility, food security also relates to affordability and with few exceptions, maize prices in the ESA region remain high in the global context. Figure 4 illustrates that domestic prices tend to be lower in countries that export relatively consistently, such as South Africa and in recent years Zambia. These net exporting countries also present the only cases where domestic prices have dropped below the representative world price for an extended period of time. In consistent deficit markets, prices have tended to be much higher; over the past decade prices in Zimbabwe have averaged almost 50% above South African levels, reaching a high of 131% in High transportation costs associated with the region are an important factor that drives high price differences between markets Figure 4: Maize wholesale price levels: Source: FEWSNET (2015) and FAO-GIEWS (2015) South Africa, Randfontein Zambia, Lusaka Zimbabwe, Harare Mozambique, Maputo Mozambique, Manica Mozambique, Tete Malawe, Lilongwe World, US Gulf

7 3. MARKET INTERACTION IN SOUTHERN AFRICA With the exception of South Africa, maize markets in the region are somewhat isolated from the global market (Minot 2011, Baquedano and Liefert 2014, Baffes et al. 2015), partly due to high transportation costs and the preference for white maize that is free of genetically modified (GM) technology, which limits procurement options in the global market. Consequently, intra-regional trade becomes increasingly important and apart from South Africa, which imports significant volumes of yellow maize for use in the animal feed market in deficit periods, on average less than 10% of total imports have originated from outside of the region over the past five years (ITC, 2015). Given the prevalence of informal trade within the region (FEWSNET, 2015), the actual share is likely even smaller than that computed from the officially reported trade data. Figure 5 presents typical intra-regional trade-flows, as well as the intensity of production within ESA. Since 2008, South Africa and Zambia have supplied consistent exports into the region, whilst Zimbabwe and Mozambique have remained in deficit. Mozambique presents a very regional market however; the Southern region is typically in deficit, relying on imports from South Africa, whereas the Central and Northern regions such as Beira and Nampula often produce a surplus. High transport costs inhibit maize shipments from the Northern surplus regions to the deficit markets in the South (Tostao & Brorsen, 2005), resulting in a net import position at national level. Malawi has traded closer to self-sufficiency, often switching between net imports and net exports based on weather conditions. Whilst integration with world markets is accepted as weak, the prevalence of intra-regional trade suggests that the extent of market integration within the region is higher. 6

8 Figure 5: Maize trade-flow in Eastern and Southern Africa average 2012 to 2014 Source: ITC (2015), FEWSNET (2015) and GAEZ (2015) Literature related to price transmission in Southern Africa has been focused on quantifying transmission from world to domestic markets, as well as the extent to which South African prices, as the largest surplus producer, are transmitted to trade partners in the region. Traub et al. (2010) found evidence of price transmission from South Africa to Maputo in periods of high import volumes, whilst Myers and Jayne (2012) highlighted a co-integrated relationship between South African maize prices and those in Lusaka under low and medium import regimes, but no co-integration under high import regimes, due to the tendency for governments to intervene in the market. Myers and Jayne (2012) based the analysis on monthly data from 1994 to 2009, however Zambia has since moved from a net importer to a net exporter, suggesting that more recent price relationships may differ. Given the emergence of Zambia as a key exporter in the region, this study will consider its impact on other prices in the region through wider country coverage. 7

9 4. DATA AND METHODOLOGY The notion of price co-integration and consequent market integration lies in the Law of One Price (LOP), according to which uniform goods in an efficient market must have only one price once transportation costs have been accounted for - assuming the absence trade restrictions (Isard, 1977). This strong version of the LOP holds on the condition of spatial arbitrage, which suggests that if the prices of two identical goods have different prices in different locations, the higher prices will attract the arbitrageurs to take advantage of the existing profits until the point when the prices equalize across the different locations. Thus, in the short-run prices can deviate from one another, but in the long run they will be the same after accounting for the transportation costs. In reality, however, there are a number of factors that could affect the efficiency of the markets and/or price relationships between different goods, such as, transaction costs, market power, exchange rates, quality differences, etc. This results in a failure of most of the empirical tests to support the hypothesis of the LOP, which might depend both on the strong assumptions underpinning it and on the inherent features of the empirical models used (Listorti, 2008). Therefore, most economists tend to focus on testing market integration, or the weak LOP, as opposed to the strong LOP. In particular, under the weaker condition of LOP two spatially separated markets are considered to be integrated for a particular good if there is a long-run relationship between the prices for this good in different markets. A large number of empirical models have been used for spatial price analysis, however, time series analysis, and particularly co-integration models are the most widely used for the analysis of price transmission. Their popularity is underpinned by a number of benefits: firstly co-integration models allow for the analysis of both short- and long-run price dynamics. Second, they are able to provide reliable results when the only data available are prices. However, the interpretation of such results needs to be conditional on the assumption that there exist continuous and unidirectional trade linkages among the analysed countries. Therefore, the conclusions need to be carefully drawn with the specifics of the particular maize market in mind. For example, the absence of co-integration might not necessarily be a guarantee of lack of market integration, but of a need to research other factors that could affect price co-integration and market efficiency. Finally, use of co-integration models is beneficial in that they do not require the assumption of the exogeneity for the analysed price series. In order to quantify the long and short run dynamics, this study employs the residual based Engle-Granger (1987) procedure. To be able to test two price series for the co-integrating vector, the presence of a unit root within each series must first be confirmed. For this purpose, Augmented Dickey Fuller (ADF), Phillips Peron (PP) and Kwiatkowski, Phillips, Schmidt, and Shin (KPPS) tests are used. The null hypothesis of both the ADF and PP test assume non-stationarity, whereas the KPPS test is based on the null hypothesis of stationarity. Owing to the differences in design, the tests are good compliments and when the 8

10 ADF and PP tests fail to reject the null hypothesis whilst the KPPS rejects it, strong evidence of the presence of a unit root can be assured. The Engle and Granger (1987) procedure consists of two steps. First, the long run relationship between the pairs of log-prices is estimated as per equation 1, which is an example of the relationship between Zambia (Lusaka) and Zimbabwe (Harare) maize prices: Where: lnp t ZIM = α 0 + β i lnp t ZAM + ε t (1) ZIM P t is the price in Zimbabwe ZAM P t is the price in Zambia β i is the long run price transmission elasticity is the associated error term ε t Second, unit root tests are employed to evaluate the residuals (ε t ) for stationarity. The ADF, PP and KPPS tests are used for this purpose. Two non-stationary series that are integrated of the same order are co-integrated if they have a long-run relationship and a linear combination of the series is stationary, even if they diverge in the short run. If two price series are co-integrated, their short-run dynamics can be analysed using an error correction model (ECM), represented below: Where: 9 lnp ZIM n ZAM n ZIM t = α 0 + i=1 β i lnp t i + j=1 θ j lnp t j + α 1 ε t 1 + μ t (2) ZIM P t is the price in Zimbabwe in first difference form ZAM P t is the price in Zambia in first difference form ε t 1 is the lagged residual from (1), representing the error correction term is the associated error term μ t The coefficient on the error correction term gives an indication of how long a shock that causes dis-equilibrium needs to move through the system. The negative coefficient indicates that the system converges back to equilibrium status following an external shock, while the magnitude of the coefficient indicates the time required for the system to return to equilibrium. Following estimation of the ECM, the Breusch-Godfrey test is performed to test for autocorrelation. In the presence of auto-correlation, additional lagged variables of both the dependant and independent can be added until the residual if free from autocorrelation. Following the procedure used by Ghoshray (2002), the number of weeks required for the Zimbabwean series to adjust back to the equilibrium following a change in the Zambian price. The formula to use is as follows:

11 n = log(1 p) log (1 a 1 ) (3) where p is a given proportion of the disequilibrium to be corrected, and a 1 is the short-run adjustment speed coefficient from (2). This analysis is based on secondary data of weekly nominal white maize wholesale prices form different cities in Malawi, Mozambique, Zambia, Zimbabwe and South Africa, presented in US dollars. The time span of the analysis is from November 2011 till December Weekly data provides 195 observations, whilst starting the analysis after the emergence of Zambia as an important exporter. The relevant cities, source, and summary statistics of the data series are presented in Table 1. Table 1: Summary statistics, source and period of price data used in the analysis Mean Min Max CV Source Period South Africa: SAFEX Randfontein South African commodities exchange Nov 2011-Dec 2015 Zambia: Lusaka Commodity Insight Africa Nov 2011-Dec 2015 Zimbabwe: Harare Commodity Insight Africa Nov 2011-Dec 2015 Mozambique: Nov 2011-Dec 2015 Maputo Commodity Insight Africa Mozambique: Nov 2013-Dec 2015 Nampula Commodity Insight Africa Mozambique: Beira Commodity Insight Africa Nov 2013-Dec 2015 Malawi: Lilongwe Commodity Insight Africa Nov 2011-Dec EMPIRICAL RESULTS Prior to the model estimation, the order of integration of all the analyzed series is determined using the unit-root tests described in Section 4. The results of the stationarity tests in levels are presented in Table 2 and in first difference form in Table 3. The table presents the test statistic related to each test, which are compared to the critical values at 1%, 5% and 10%. The number of lags is determined by the Hannan-Quinn (1979) criterion. 10

12 Table 2: Results of stationarity tests in levels South Africa: SAFEX Randfontein Nr. Lags Zambia: Lusaka 0 Zimbabwe: Harare 0 Mozambique: Maputo 5 Mozambique: Nampula 1 Mozambique: Beira 1 Malawi: Lilongwe 0 0 Model Test Statistics ADF PP KPSS Drift *** Trend ** Drift * Trend *** Drift * Trend *** Drift ** Trend ** Drift Trend ** Drift Trend ** Drift Trend ** Asterisks denote the level of significance (*10%, **5%, ***1%). The 5% and 10% critical values for ADF and PP tests with a drift are and respectively; for the tests with a drift and a trend are and respectively. Critical values were obtained from MacKinnon (1991). The 5% and 10% critical values for the KPSS test in levels are and respectively; for the KPSS tests with a trend they are and respectively. Table 3: Results of stationarity tests in first difference South Africa: SAFEX Randfontein Nr. Lags Zambia: Lusaka 3 Zimbabwe: Harare 13 Mozambique: Maputo 5 Mozambique: Nampula 2 Mozambique: Beira 5 Malawi: Lilongwe 10 3 Model Test Statistics ADF PP KPSS Drift *** *** 0.14 Trend *** *** 0.19** Drift *** *** 0.14 Trend *** *** 0.18** Drift *** *** 0.10 Trend -5.83*** *** 0.27*** Drift -7.96*** *** 0.09 Trend -9.56*** *** 0.07 Drift -7.95*** -7.88*** 0.27 Trend *** *** 0.13* Drift -7.91*** -7.90*** 0.30 Trend -7.56*** *** 0.32*** Drift *** *** 0.06 Trend -7.90*** *** 0.31*** Asterisks denote the level of significance (*10%, **5%, ***1%). The 5% and 10% critical values for ADF and PP tests with a drift are and respectively; for the tests with a drift and a trend are and respectively. Critical values were obtained from MacKinnon (1991). The 5% and 10% critical values for the KPSS test in levels are and respectively; for the KPSS tests with a trend they are and respectively. The price series in levels are non-stationary, while the tests conducted in first difference form are conclusively stationary for all series. As such, we conclude that the analyzed price series of are I(1). The tests were conducted both with and without a trend present. The trend was not found to be significant in any of the series and did not affect the outcome. 11

13 Given that all series are I (1), co-integration tests were conducted based on the Engle Granger procedure presented in section 4. Results, as well as the estimated long run transmission elasticities are presented in Table 4. Table 4: Results of the Engel-Granger co-integration procedure Dependent Independent Lag β 1 Engel and Granger Procedure ADF PP KPSS Conclusion Lusaka Randfontein * -1.98** 0.34 Co-integrated Randfontein Lusaka ** -2.12** 0.78*** Not co-integrated Lilongwe Nampula *** -2.20** -2.27** 0.15 Co-integrated Nampula Lilongwe *** -2.45** -2.45** 0.11 Co-integrated Harare Beira *** Not co-integrated Beira Harare *** -2.45** -1.99** 0.26 Co-integrated Lusaka Harare *** -3.55*** -3.85*** 0.18 Co-integrated Harare Lusaka *** -3.71*** -4.08*** 0.14 Co-integrated Nampula Harare *** -2.08** -2.08** 0.29 Co-integrated Harare Nampula *** Co-integrated Maputo Randfontein *** -3.01*** -3.86*** 0.22 Co-integrated Randfontein Maputo *** -3.00*** -3.76*** 0.47** Not co-integrated Harare Randfontein ** -2.08** -2.34** 0.32 Co-integrated Randfontein Harare ** -2.08** -2.21** 0.76*** Not co-integrated Lusaka Lilongwe *** -2.49** -2.51** 0.34 Co-integrated Lilongwe Lusaka *** -2.89*** -2.75*** 0.24 Co-integrated Lusaka Beira *** -1.65* -1.67* 0.07 Co-integrated Beira Lusaka *** -1.93* -1.93* 0.37* Not co-integrated Asterisks denote the level of significance (*10%, **5%, ***1%) Table 4 indicates that five of the 18 pairs of series (Randfontein-Lusaka, Harare-Beira, Randfontein-Maputo, Randfontein-Harare and Beira-Lusaka) are not co-integrated. In six instances, the ADF and PP test contradicted the KPSS test, in which case it was concluded that series are not co-integrated. The only exception is for Harare-Nampula, where the ADF and PP tests are significant at 11% and the KPSS test points to stationarity, leading to the acceptance of co-integration. For the co-integrated series, the results of regressions that analyze the relationship between variables are consistent and therefore β 1 represents the long-run price transmission elasticity. This price transmission elasticity is indicative of the percentage change in the price of the dependent market arising from a 1% change in the price of the independent market. Such an interpretation needs to be treated with caution however as it is based on a pure price relationship and therefore assumes that transaction costs remain proportional to the price, which is a strong assumption that will seldom hold, particularly in Africa. Nonetheless, it provides an indication of co-integrated relationships between different markets in the long run. As an example, a 10% change in the price in Lusaka will result in a 5.9% change in the price in Harare. Similarly a 10% change in the SAFEX price in South Africa will typically result in only a 12% change in the price in Harare. In the instance of South Africa Harare, the negative sign seems counter intuitive, as it suggests that higher prices in South Africa will be accompanied by lower prices in Zimbabwe in the long run. 12

14 The coefficient of long run price transmission was significant in all co-integrated pairs, except for Lusaka-Randfontein, where trade has been very limited in recent years. The long run price transmission elasticity was the highest between Lusaka and Harare, as well as Maputo and Randfontein, both instances where trade is frequent over the period investigated. Long-run price transmission is the least elastic between Harare and Randfontein. This would indicate that in the long run, the price in Zimbabwe is more responsive to price movements in Zambia than in South Africa, which is in line with the fact that Zambia has a preferable transport differential and apart from periods where Zambian exports are controlled, it represents the preferred market for imports into Zimbabwe. On average across the entire sample, approximately 50% of price shocks are transmitted between price pairs, which would indicate that in addition to neighbouring markets, many other factors influence prices in the region. Having assessed the long run relationships, the next step relates to the construction of an error correction model for the co-integrated series that quantifies the short term dynamics around the long run relationships. Such results are able to indicate the number of weeks required for a shock that causes disequilibrium to dissipate through the system, whilst also providing useful indications of directional relationships. A negative sign on the error correction term (α 1 ) indicates that the system converges back to equilibrium following an external shock, while the magnitude of the coefficient indicates the share of the disequilibrium that will be corrected every week and can therefore be used to compute the time required for the system to return to equilibrium. The results of the parameter estimates, as well as the Breucsh- Godfrey test for serial correlation are presented in Table 5. Table 5: Error correction model parameter estimates Weeks to correct LM Test F-Test Dependent Independent Lag α 1 90% of Test Stat P-value Test Stat disequilibrium Lusaka Randfontein 0,1-0.03* * Lilongwe Nampula 2,2-0.12* ** Nampula Lilongwe 1,1-0.09** ** Beira Harare 1,1-0.07* * Lusaka Harare 0, Harare Lusaka 0,0-0.14*** *** Nampula Harare 1,1-0.07* * Harare Nampula 0,1-0.08* * Maputo Randfontein 2,2-0.11*** *** Harare Randfontein 0,0-0.04* ** Lusaka Lilongwe 0,1-0.04** * Lilongwe Lusaka 1,1-0.07*** ** Lusaka Beira 0,0-0.06* *** Asterisks denote the level of significance (*10%, **5%, ***1%). Table 5 indicates that the error correction terms are significant for all pairs tested except for Lusaka-Harare. Given that the error correction term is significant for the Harare-Lusaka price pair, this would indicate that a shock in Lusaka causes price corrections in Harare, but not vice versa, indicating that Lusaka prices lead in this instance, which is consistent with the fact that significant quantities are imported to Harare from Lusaka, its closest surplus market. 13

15 While the error correction terms are significant in all other price pairs, complicating conclusions related to the direction of causality, the rate of adjustment tends to differ based on where the shock occurred. Within the system as a whole, South Africa can be considered weakly exogenous, given that co-integration is not found when the South African price is the dependent variable, but South African prices are co-integrated to Lusaka, Maputo and Harare when used as an explanatory variable. Adjustments back to equilibrium following shocks are slow in the region (Table 5), averaging 45 weeks across the entire sample. This could be indicative of inefficient trade, as non-tariff barriers are a significant problem in the region, as well as poor information related to neighbouring markets. Within the markets where the short term adjustment parameter was significant, adjustments are the fastest in Harare in response to a shock in Lusaka at 18 weeks. Prices in Lilongwe take 20 weeks to dissipate 90% of the disequilibrium caused by a shock in Nampula, while prices in Maputo also took 22 weeks to correct 90% of the disequilibrium caused by price movements in South Africa. By contrast, the slowest adjustment was found in Lusaka in response to price shocks occurring in South Africa. Given that the distance between these two markets is the greatest of all the price pairs tested, combined with the low frequency of trade between these two markets, this finding seems consistent with prior expectation CONCLUDING REMARKS Given the importance of maize as a staple in the Southern African region, the purpose of this study was to evaluate long and short term price relationships between different maize markets in Southern Africa. This allows quantification of the impacts that policy actions impacting maize markets in a single country can have on neighbouring markets and other trade partners. The residual based Engel-Granger procedure was employed to test long run co-integration between 9 different price pairs in selected markets in South Africa, Zambia, Malawi, Mozambique and Zimbabwe. All pairs were tested in both directions; hence 18 tests were run to evaluate the 9 market pairs. The isolation of Southern African maize markets from the world market both in terms of trade-flow and price transmission has been noted by several researchers, but the prevalence of intra-regional trade across the region would suggest that different prices in the region may be co-integrated. Results confirmed this priori expectation, with 13 of the 18 price pairs reflecting long run co-integrated relationships. Estimation of an error correction models for the co-integrated price pairs allowed quantification of short run dynamics around the long term relationships and directional inference. While rates of adjustment differed depending on which market shocks occurred in, the significance of the error correction terms in the majority of price pairs indicate that causality tends to be bi-directional. With few exceptions, the adjustment back to equilibrium conditions were found to be fairly slow, with an average of 45 weeks required for 90% of the

16 disequilibrium to dissipate through the system. This would indicate that policies aimed at improving the efficiency of both trade and information across borders would benefit the region. The study considered only price relationships, implying that transaction costs are assumed at a fixed proportion of prices. Furthermore it does not account for different regimes which may be relevant in the region. This suggests that there is room for improvement in terms of quantifying transmission elasticities, however it presents a good starting point in understanding long and short term price relationships in the region. The finding that most of the regional markets are co-integrated is important from a policy perspective. It implies that any policy affecting prices in a single market could potentially influence prices and welfare in multiple other markets, suggesting a far-reaching impact for often ad-hoc policy decisions taken in Southern African maize markets. 15

17 7. REFERENCES Baffes, J., Kshirsagar, V., and Mitchell, D., What Drives Local Food Prices? Evidence from the Tanzanian Maize Market. Baquedano, F.G. and Liefert, W.M., Market integration and price transmission in consumer markets of developing countries. Food Policy, 44, Breusch, T.S Testing for autocorrelation in dynamic linear models. Australian Economic Papers 17: Chapoto, A. and Jayne, T.S., The Impact of Trade Barriers and Market Interventions on Maize Price Unpredictability: Evidence from Eastern and Southern Africa. Engle, R.F. and C.W.J. Granger Cointegration and error correction: representation, estimation and testing. Econometrica 55(1): Famine Early Warning Systems Network (FEWSNET) Unpublished data. Food and Agricultural Organisation of the United Nations (FAO) Global Agro Ecological Zones database. Accessed October Ghoshray, A Asymmetric price adjustment and the world wheat market. Journal of Agricultural Economics 53(2): Global Information and Early Warning System on Food and Agriculture (GIEWS) Food price monitoring and analysis tool. Food and Agriculture Organisation of the United Nations (FAO). Rome. [Online] Available at: Goychuk, K Three essays on Black Sea grain markets. A Dissertation presented to the Faculty of the Graduate School at the University of Misourri. Hannan, E.J. & Quinn, B.G The determination of an order of an Autoregression. Journal of the Royal Statistical Society B (41): International Trade Council (ITC) Trademap database. [Online] Available at: < Isard, Peter, (1977), How Far Can We Push the "Law of One Price"?, American Economic Review, 67, issue 5, p , Jayne, T.S., Managing food price instability in East and Southern Africa. Global Food Security, 1 (2),

18 Jayne, T.S. and Tschirley, D.L., Food price spikes and strategic interactions between the public and private sectors: Market failures or governance failures. Listorti, G Testing international price transmission under policy intervention. An application in the soft wheat market. PhD dissertation. Italy: Universita Politecnica delle Marche. MacKinnon, J.G Critical Values for Cointegration tests. In Long-run Economic Relationships: Readings in Cointegration edited by R.F. Engle and C. W.J. Oxford University Press. Minot, N., Transmission of World Food Price Changes to Markets in Sub-Saharan Africa. IFPRI Discussion Paper Series, (January), Minot, N., Food price volatility in sub-saharan Africa: Has it really increased? Food Policy, 45, Myers, R.J. and Jayne, T.S., Multiple-Regime Spatial Price Transmission with an Application to Maize Markets in Southern Africa. American Journal of Agricultural Economics, 94 (1), Organisation for Economic Coordination and Development (OECD) and Food and Agriculture Organisation of the United Nations (FAO) OECD-FAO Agricultural outlook Forthcoming. Porteous, O., Empirical Effects of Short-Term Export Bans : The Case of African Maize. Working paper. [Online] available at: ReNAPRI Anticipating the Future of Agriculture in the Region: Outlook for Maize, Wheat, Sugar and Rice. Lusaka, Zambia. Tostao, E. and Brorsen, B.W., Spatial Efficiency in Mozambique s Post-Reform Maize Markets. Agricultural Economics, 33 (2), Traub, L., Myers, R., Jayne, T.S., and Meyer, F., Measuring Integration and Efficiency in Maize Grain Markets: The Case of South Africa and Mozambique. Joint 3rd African Association of Agricultural Economists (AAAE) and 48th Agricultural Economists Association of South Africa (AEASA) Conference. 17