POLICY NOTE 12 • February 2015

SUMMARY OF MozSSP WORKING PAPER 4

Synopsis : MARKET INTEGRATION IN A non-parametric extension to the threshold model

Bjorn Van Campenhout Since well-functioning markets are important for economic development, policymakers need to make sure that the right policies that promote a vibrant market system are in place. This policy note is a synthesis of a technical paper that assesses the degree of maize mar- ket integration in Mozambique using a new technique—an iterative process between parametric threshold modeling and kernel smooth- ing—that allows one to estimate transaction costs in a more flexible way. Market integration is the most important measurement of how well a commodity market is functioning. A well-integrated agricul- tural market system linking production areas to consumer centers is important, especially for agricultural societies. It ensures that the right incentives (that is, higher prices than would be the case under autarky due to high demand from consumer centers) are in place for farmers, while at the same time keeping basic needs affordable (that is, lower prices than would be the case under autarky due to high supply from supply areas) in consumer centers. In particular, staple food market integration is extremely important from a food security perspective. This study finds that maize markets in Mozambique are far from fully integrated. As expected, the paper finds that maize markets that are closer to each other are better integrated. For instance, seems to be rather well integrated, particularly with other markets in the northern and the eastern parts of Mozambique. The least integrated market pair is the – trade route. Moreover, the somewhat isolated Lichinga market is found to be less integrated with the rest of Mozambique. Generally, findings sug- gest that there is substantial room for the improvement of maize market integration. The first way to improve maize market integration in Mozambique is to better physically link markets to each other by improving the road infrastructure. The second, but at least equally important, way is to increase the flow of information between different markets. Third, the government should devise mechanisms that increase competition among middlemen.

CONTEXT annually, making it the country’s most important crop. About half of the maize production occurs in central Mozambique In Mozambique, newspapers regularly report instances of pock- (Manica, Zambezia, and Tete provinces). Another 40 percent ets of hunger due to droughts and floods. Pockets of hunger are takes place in the North (especially ). essentially caused by adverse shocks from which the conse- quences cannot be spread over a large enough area due to a lack Maize is also the preferred crop for consumption in Mozam- of market integration. Different market integration studies indi- bique, and the region as well. The largest deficit consumption ar- cate that agricultural markets in Mozambique are far from fully eas are in the southern part of the country. Maputo is the largest integrated. In addition, previous studies underline the depend- city and the capital, which, together with its twin city, , ence of Mozambique’s capital, Maputo, on for its hosts a population of about 2.5 million. Despite the considerable food supplies, instead of on its own hinterland. volumes of maize produced in central and northern Mozam- bique, the southern part of the country consistently imports Mozambique is an interesting case for the study of market maize from South Africa (Traub et al. 2010). integration for several reasons. It is a vast country with a rela- tively low population density, and so the provision and mainte- There have been several maize market integration studies on nance of infrastructure is a challenge. Similarly, because of a long Mozambique in the past. Generally, those studies indicate that civil war, road rehabilitation and investments picked up only re- the country’s maize markets are far from fully integrated and cently. In addition, recent climate shocks (excess rainfall and un- that there is significant variation in the level of integration of dif- usual droughts) have affected the supply and demand patterns of ferent pairs of maize markets across the country. agricultural producers. Moreover, pockets of hunger are fre- For instance, one study tests maize market integration in quently recorded in Mozambique, suggesting that markets are Mozambique in the 1990s using the parity bounds model (Pen- insufficiently integrated to deal with the aforementioned charac- zhorn & Arndt 2002). Using weekly price data, the study looked teristics. only at two markets: Maputo and in in Maize is an important crop in Mozambique both from a pro- west-central Mozambique. The study estimates that about 25 duction and food security point of view. According to the report percent of the time the price difference between the two mar- on the 2010 Food and Agriculture Organization and World Food kets is above the parity bounds, and concludes that during some Programme Crop and Food Security Assessment Mission to periods the markets were not integrated at all. Mozambique, most maize in Mozambique is produced by small- Another study looks at 13 market pairs in Mozambique (Tos- holder farmers, who make up 81 percent of the total population. tão & Brorsen 2005). This study also employs the parity bounds Mozambique produces just under 2 million metric tons of maize method on a monthly price series that runs up to 2001. It finds

that markets within the South are efficient more than 55 percent the market), extreme prices (both low and high) will be less ex- of the time, and markets within the central region are efficient treme and less common (as a price increase will attract more more than 84 percent of the time. However, it also finds that the traders from further away and price decreases will lead to ex- North is completely isolated from the Center and the South. An- ports to places further away). When markets become better inte- other study investigates whether the substantial road rehabilita- grated, price risk in a particular location will be spread over a tion begun at the end of the 1990s had an effect on market inter- larger geographical area. connectedness in Mozambique (Cirera & Arndt 2008). This study Market integration is also important as a driver for produc- also uses the parity bounds method, but it allows the probabili- tivity-enhancing technology adoption and structural transfor- ties of each regime to be a linear function of time. It finds that mation. In better-integrated markets, returns to increased out- maize markets tend to be segmented most of the time due to put diminish less rapidly than in locally segmented markets char- high transaction costs. But the impact of the road rehabilitation acterized by more price-inelastic demand—the change in quan- seems to be small and not statistically significant for most market tity demanded of a good or a service in response to a change in pairs considered. its price is small. Thus, the more segmented the market, the Two studies use a threshold vector error correction ap- more price inelastic the demand and the lesser a share of the proach to analyze the integration of four major maize market gains accrue to the productive producers. Based on this underly- pairs in Mozambique (Alemu & Biacuana 2006; Alemu & Van ing reality, some argue that households that have linkages to Schalkwyk 2009). In line with expectations, those studies find markets have a better chance to escape from poverty. that estimated transaction costs were higher between distant Methodology markets and when markets are connected by poor roads. The studies then compare actual price differences with the estimated Markets are said to be integrated if they are connected by a pro- transaction costs and find that price differences are mostly out- cess of arbitrage, which is the practice of taking advantage of a side the band formed by transaction costs for Chimoio–Beira and price difference between two or more markets to make a profit. Ribaue–Nampula, which are only 204 kilometers and 147 kilome- This will be reflected in the price series of commodities in spa- ters apart, respectively. For the market pairs that have the small- tially separated markets. Thus, as a measure of market integra- est observed differences outside the band, the estimated adjust- tion, the extent of co-movement between prices in different lo- ment speed also seems to be higher. cations has been suggested. While the first studies looked at cor- relation coefficients between prices of a product in different mar- CONCEPTUAL FRAMEWORK, kets, with the genesis of nonstationary time series analysis in the METHODOLOGY, AND DATA 1980s, market integration essentially became synonymous with co-integration. Conceptual Framework The realization that the existence of transaction costs cre- The basic idea behind spatial market integration is discussed in ates a nonlinearity in the adjustment process prompted the de- the classic work of Takayama and Judge (1971). This theory velopment of two competing models: the parity bounds model states that in a setting with free flow of goods and information, and the threshold auto-regressive model. Both of these models prices of a homogeneous good in two spatially separated mar- have been extended in various ways. kets should differ only by the transaction costs. This is so because if the price in one market is higher than the price in another mar- One of the main problems of the threshold auto-regressive ket plus the transaction costs—that would be involved if one had model is that it assumes constant transaction costs; however, to move the product from the market with the low price to the transaction costs are not constant—rather they change depend- market with the high price—unexploited profits would exist. ing on other factors or variables, and it is straightforward to Profit-seeker traders would therefore enter the market and capi- model transaction costs as a function of another variable. For in- talize on those profit opportunities, increasing demand in the stance, the author has modeled transaction costs as a simple lin- market where prices are low and increasing supply in the loca- ear function of time on Tanzanian maize markets. Nonparametric tion where prices are high. These latter two forces will, ceteris methods do not necessitate assumptions about the functional paribus, drive up the price in the market that initially had a low form, but they let the data inform and determine the shape of price and reduce the price in the market that initially had a high the relationship. price. The end result will be that prices adjust up to the point This paper proposes an iterative process between paramet- where trade becomes unprofitable again, that is, until the price ric threshold modeling and kernel smoothing to come to a more difference becomes equal to the transaction costs. flexible characterization of the evolution of market integration in A well-integrated market system is central to a well-func- Mozambique over time. tioning market economy. As production decisions are based on Data observed prices, the most efficient allocation of resources would This study assesses the degree of market integration in five dif- come about when prices represent scarcity conditions. In other ferent maize markets—Maputo, Maxixe, Chimoio, Nampula, and words, a large network of markets connected by fast and effi- Lichinga—in Mozambique over the past decade. The data are cient arbitrage is needed in order to exploit spatial comparative from a monthly price observation series of the Sistema de In- advantages (Fackler and Goodwin 2001). formação de Mercados Agrícolas de Moçambique (SIMA) running Apart from that general reason, well-connected markets are from January 2000 to February 2011. also important for food security. Indeed, the answer to the ques- Maize is chosen because it is a homogeneous commodity tion of how long an initially localized scarcity can be expected to with relatively low variability in quality that is traded widely in persist entirely depends on how well this market is integrated eastern and southern Africa. This allows one to make statements into the wider economy (Ravallion 1986). Although a better-inte- on which markets are better integrated (in terms of speed of ad- grated market may experience more volatility (since now price justment and transaction costs) and when. changes in more distant markets will also influence the price in

The five markets are chosen to reflect the geographical di- Table 2 presents the estimation results of different models versity of the country (Table 1). The first market, Maputo, is the used by the study. The two markets closest to each other are largest city in the country and, by far, the major consumption Maputo and Maxixe—they are less than 500 kilometers apart. area, with a strong demand pull for white maize and with low- Both are located directly along Mozambique’s main north-south potential agricultural land nearby. Lichinga is probably the most arterial roadway, the EN1. The simple auto-regressive (AR) model remote market in the dataset, tucked away in the northwestern that regresses the change in the price difference between Ma- corner of Mozambique. Chimoio lies somewhat halfway between puto and Maxixe on (the absolute value of) the lagged level of Maputo and Lichinga. The other two markets considered are the price difference yields an estimate of -0.1154. The interpreta- Nampula and Maxixe, which are in the northern and southern tion of this result is that a price difference (excluding the transac- parts of the country, respectively. tion costs) observed between these two markets would tend to disappear at a rate of 11.5 percent per year. It is known that the Main Findings AR models underestimate the true adjustment parameter. This is Table 1 presents descriptive summary statistics of maize price se- because AR models aggregate the adjustment inside and outside ries of the five markets for the period running from January 2000 the band formed by transaction costs into a single parameter. to February 2011. Thus, this study also considers the nonlinear estimate and finds  The descriptive summary statistics of the maize price series an adjustment speed that is significantly higher (in absolute suggest that on average prices are higher in the South. This is value) than the estimate of the AR model. In other words, when especially true for the capital city, Maputo. we use a threshold auto-regressive model (TAR) and impose an adjustment speed of zero inside the band formed by the transac-  The study finds that the standard deviation is largest in Ma- tion costs, we find the adjustment speed to be -0.2105. The puto, while it is moderated in more remote markets. study also estimates the speed of adjustment that measures the market interconnectedness between Maxixe and Maputo using Table 1—Descriptive statistics of maize prices, MT/kg the iterative flexible threshold auto-regressive method (FlexTAR) Standard Maxi- and finds a -0.2358 speed of adjustment, which is also higher Region Market Mean deviation Minimum mum than the estimate of the AR model (-0.1154) in absolute value South Maputo 7.48 3.50 2.55 13.14 terms. Maxixe 5.95 3.00 1.45 14.29 Apart from the speed of adjustment, the study also esti- Central Chimoio 5.21 2.97 1.37 14.00 mates the transaction costs that have to be considered if one North Nampula 5.26 2.77 1.00 13.26 wants to assess the degree of market integration. Since the Lichinga 4.96 2.81 1.14 18.63 transaction costs are estimated in a nonparametric way, the re- sult is presented in Figure 1. The graph shows that transaction costs were higher in the beginning of the period of our price se- Despite Maputo having the largest standard deviation, it has ries, and went down until about 2007. Transaction costs drop the smallest range. The range is highest for Lichinga, which is the from about 11 percent of the average maize price to about 7 per- most remote market. cent. Using the averages reported in Table 1, the study finds that The overall implication of the descriptive analysis is that bet- transaction costs between Maputo and Maxixe have dropped ter-integrated markets (such as Maputo) may face more volatile from 738 meticais per metric ton to 470 meticais per metric ton. prices, since the price level in such markets can easily be influ- Taking distance into consideration, this means from US$48 per enced by prices in other markets in a larger area. Poorly inte- 1,000 kilometers per metric ton to US$30 per 1,000 kilometers grated markets (such as Lichinga), on the other hand, experience per metric ton, assuming an exchange rate of US$0.03 per meti- more extreme prices, as shortage and excess supply signals are cal. After 2007, transaction costs seem to increase again. only slowly and partially transmitted to other markets.

Table 2—Market integration estimation results

AR TAR FlexTAR Distance Market pair (km) Adj Adj TC Adj Maputo – Maxixe 472 -0.1154** -0.2105** 0.0844 -0.2358** Maputo – Chimoio 1,149 -0.0568* -0.1376** 0.1314 -0.1389** Maputo – Nampula 2,178 -0.0579* -0.0795* 0.1246 -0.1001** Maputo – Lichinga 2,007 -0.0546+ -0.0958** 0.1785 -0.0728* Maxixe – Chimoio 677 -0.1603** -0.1855** 0.0833 -0.1720** Maxixe – Nampula 1,706 -0.1422** -0.1359** 0.0486 -0.2081** Maxixe – Lichinga 1,535 -0.1403** -0.1880** 0.1204 -0.1892** Chimoio – Nampula 1,154 -0.2671** -0.2929** 0.0586 -0.3462** Chimoio – Lichinga 898 -0.2335** -0.3177** 0.0889 -0.2865** Nampula – Lichinga 696 -0.2124** -0.2320** 0.0669 -0.2243**

Figure 1—Maputo–Maxixe, maize marketing transaction roadway, which links the north of Mozambique to the south, is a costs, 2000 – 2011, as a proportion of price of maize major step in the right direction. However, the government should also invest in secondary roads that link eastern and west- ern locations to the EN1. For a commodity like maize, with a rela- tively low value-to-weight ratio, railways are often a cheaper way of transportation. The railroads presently operating in Mozam- bique run only through three different trade corridors (north from Nacala to Malawi, center from Beira to Malawi, and south from Maputo to South Africa). Second, it is equally important to increase the flow of infor- mation between different markets. Arbitrage involves the ex ante comparison of prices from one market to prices from sev-

eral other markets. Based on that information, traders can start moving goods from low-price markets to high-price markets. The two markets that seem to be least integrated according These search costs are thus entirely part of the transaction costs. to the results presented in Table 2 are Maputo and Lichinga. In addition, the speed at which information travels will also af- They are 2,007 kilometers apart by road. Using the simple AR fect how quickly arbitrage happens. So, the speed at which infor- model, the study estimates an adjustment speed of -0.0546, mation (price, road conditions, quality requirements, and so on) which is a very small adjustment speed. Apart from the fact that travels will also influence the adjustment speed. The mobile this trade route has such a low (in absolute value) adjustment phone revolution is certainly contributing to increased market in- speed, the threshold model also estimates very high transaction tegration. One study found that the adoption of mobile phones costs. The iteratively estimated flexible threshold model by Indian fishermen and wholesalers was associated with a dra- (FlexTAR), which is pictured in Figure 2, converges to an estima- matic reduction in price dispersion, the complete elimination of tion speed of -0.0728. This result means that it would take more waste, and near-perfect adherence to the law of one price (Jen- than nine months for a shock away from the transaction cost to sen 2007). Similarly, another study found that the introduction of return to half its initial value. Again using the average reported in mobile phone service in Niger between 2001 and 2006 explained Table 1, the study finds that the cost of transacting a metric ton a 10 to 16 percent reduction in grain price dispersion (Aker of maize between these two markets is almost 1,000 meticais, 2010). but if we express this per 1,000 kilometers, the figure reduces to Third, it is important to devise policies that promote compe- about US$14 per metric ton. This suggests that in the Mozam- tition among traders. A core assumption in spatial equilibrium bique grain trade a large part of the transaction costs are fixed models, such as we employed here, is that all markets are suffi- costs. ciently competitive. If there is too little competition among trad- Figure 2—Maputo–Lichinga, maize marketing transaction ers, those traders will be able to engage in monopolistic price costs, 2000 – 2011, as a proportion of price of maize setting. While information and rural infrastructure will increase the number of traders operating in remote areas, there are poli- cies that can increase competition among middlemen. Govern- ments should appreciate the important role played by a suffi- ciently competitive trade sector for food security and agricultural development, and should encourage institutions that cater to its interests.

REFERENCES Fackler, P.L., and B.K. Goodwin. 2001. Spatial Price Analysis. In Handbook of Agricultural Economics, vol. 1, part 2. B.L. Gardner & G.C. Rausser, eds. Amsterdam: Elsevier. 971–1024. Ravallion, M. 1986. Testing market integration. American Journal of Agri- CONCLUSION AND POLICY IMPLICATIONS cultural Economics. 68 (1): 102-109. Generally, the findings from our analysis suggest that maize mar- Takayama, T., and G.G. Judge. 1971. Spatial and Temporal Price and Allo- ket integration in Mozambique has substantial room for im- cation Models. Amsterdam: North-Holland. provement. Several approaches merit consideration. Traub, L.N., R.J. Myers, T.S. Jayne, and F.H. Meyer. 2010. Measuring inte- A first way to improve market integration is to better physi- gration and efficiency in maize grain markets: The case of South Africa and Mozambique. Paper 96644, AAAE Third Conference/AEASA 48th cally link markets to each other. The rehabilitation of the EN1 Conference, Cape Town. Sept. 19–23.

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This publication has been prepared as an output of the Mozambique Strategy Support Program (MozSSP), which is facilitated through funding from the United States Agency for International Development (USAID) mission office in Mozambique. It has not been peer reviewed. Any opinions stated herein are those of the author(s) and are not necessarily representative of or endorsed by the International Food Policy Research Institute or USAID/Mozambique. Copyright © 2015 International Food Policy Research Institute. All rights reserved. To obtain permission to republish, contact [email protected]