FAO COMMODITY AND TRADE POLICY RESEARCH WORKING PAPER

No. 7

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FAO Commodity and Trade Policy Research Working Papers are published by the Commodities and Trade Division of the Food and Agriculture Organization of the United Nations (FAO). They are working documents and do not reflect the opinion of FAO or its member governments. Also available at http://www.fao.org/es/ESC/ Additional copies of this working paper can be obtained from Olwen.Gotts@fao.org The designations employed and the presentation of material in this information product do not imply the expression of any opinion whatsoever on the part of the Food and Agriculture Organization of the United Nations concerning the legal or development status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. All rights reserved. Reproduction and dissemination of material in this information product for educational or other non-commercial purposes are authorized without any prior written permission from the copyright holders provided the source is fully acknowledged. Reproduction of material in this information product for resale or other commercial purposes is prohibited without the written permission of the copyright holders. Applications for such permission should be addressed to the Chief, Publishing Management Service, Information Division, FAO, Viale delle Terme di Caracalla, 00100 Rome, Italy or by e-mail to copyright@fao.org.

© FAO 2004

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ABSTRACT The paper is aimed at providing evidence on price transmission in a number of agricultural markets, both per se and in support of analytical efforts in the area of agricultural trade policy analysis. Work is based on a price database collected from various sources in sixteen countries - Argentina, Brazil, Chile, Costa Rica, Egypt, Ethiopia, Ghana, India, Indonesia, Mexico, Pakistan, Senegal, Thailand, Turkey, Uganda, and Uruguay - primarily for basic food commodities. Both spatial and vertical price relations are considered, as the database includes prices at the producer, wholesale and retail levels. These are supplemented with information from FAOSTAT. Data are analyzed with an econometric framework based on the estimation of Autoregressive Distributed Lag models, and of the corresponding Error Correction specification. Tests for Granger causality and for asymmetric transmission are also performed. Results indicate that the African markets included in the sample are characterized by more incomplete transmission compared to Latin American and Asian markets.

RÉSUMÉ Cette étude vise à apporter un éclairage sur la transmission des prix dans plusieurs marchés agricoles, à la fois pour étudier cet aspect en tant que tel et pour contribuer aux efforts analytiques menés dans le domaine de l’analyse des politiques commerciales en matière d’agriculture. Ce travail s’appuie sur une base de données relatives à des prix obtenus de plusieurs sources dans les 16 pays suivants : Argentine, Brésil, Chili, Costa Rica, Egypte, Ethiopie, Ghana, Inde, Indonésie, Mexique, Ouganda, Pakistan, Sénégal, Thaïlande, Turquie et Uruguay, et ce, dans un premier temps, pour les produits alimentaires de base. L’étude envisage les relations de prix du point de vue spatial et amont-aval. En effet, la base de données inclut les prix observés aux stades du producteur, du grossiste et du détaillant. Cette information a été complétée par celle de la FAOSTAT. La méthodologie utilisée pour étudier ces données s’appuie sur les techniques économétriques basées sur l’estimation des modèles autorégressifs à retards échelonnés, ainsi que le modèle à correction d’erreurs. Des tests de causalité de Granger et de transmission asymétrique ont également été menés. Les résultats font apparaître que les marchés africains compris dans l’échantillon se caractérisent par des transmissions relativement plus incomplètes.

RESUMEN La finalidad de la presente publicación es poner en evidencia la transmisión de precios en una cantidad de mercados agrícolas, tanto de manera intrínseca, como una forma de asistir a los esfuerzos de investigación en el área del análisis de las políticas de comercio agrícola. Esta labor se sustenta en una base de datos recopilada a partir de diversas fuentes de 16 países - Argentina, Brasil, Chile, Costa Rica, Egipto, Etiopía, Ghana, India, Indonesia, México, Pakistán, Senegal, Tailandia, Turquía, Uganda y Uruguay – fundamentalmente respecto de productos alimentarios básicos. Se consideran relaciones de precio tanto espaciales como verticales, ya que la base de datos registra precios de productores, comerciantes mayoristas y minoristas, información que fue complementada por datos de FAOSTAT. Los datos se analizan en un marco econométrico basado en la estimación de los modelos de los rezagos distribuidos autorregresivos y en la correspondiente especificación de corrección de error. Asimismo, se realizaron pruebas destinadas a evaluar la causalidad de Granger y la transmisión asimétrica. Los resultados indican que los mercados africanos incluidos en la muestra se caracterizan por transmisiones en cierto medida menos completas.

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CONTENTS

ABSTRACT/RÉSUMÉ/RESUMEN....................................................................................................... i 1 INTRODUCTION.......................................................................................................................... 1 2 PRICE TRANSMISSION AND MARKET INTEGRATION....................................................... 1 3 PRICE TRANSMISSION PARAMETERS IN STRUCTURAL MODELS ................................. 4 4 THE ESCB PRICE DATABASE................................................................................................... 6 5 AN ECONOMETRIC FRAMEWORK FOR ANALYSING PRICE TRANSMISSION.............. 8 6 THE RESULTS............................................................................................................................ 10

6.1 The annual price series ..................................................................................................... 10 6.2 The monthly price series................................................................................................... 14

6.2.1 Costa Rica ......................................................................................................... 14 6.2.2 Egypt ................................................................................................................. 15 6.2.3 Ethiopia ............................................................................................................. 16 6.2.4 Ghana ................................................................................................................ 17 6.2.5 Indonesia ........................................................................................................... 18 6.2.6 Senegal .............................................................................................................. 18 6.2.7 Turkey ............................................................................................................... 19

7 CONCLUDING REMARKS ....................................................................................................... 20 REFERENCES..................................................................................................................................... 22 FIGURES .......................................................................................................................................... 25 TABLES .......................................................................................................................................... 33 APPENDIX: Results of the Unit Root tests ......................................................................................... 72

ACKNOWLEDGEMENTS

The author is indebted to Merritt Cluff, Adam Prakash and George Rapsomanikis for useful comments and suggestions on earlier versions of this paper. Responsibility for errors lies only with the author.

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1 INTRODUCTION A key premise of several arguments in economics is that markets allow for price signals to be transmitted both spatially and vertically. An obvious example is the assessment of the relative merits of alternative trade and/or policy environments: potential losses for a country or a group of economic agents and benefits crucially depend, inter alia, upon markets receiving price signals, which, in turn, depends upon a number of markets’ features, including their very existence. The extent to which a price shock at one point affects a price at another point can broadly indicate whether efficient arbitrage exists in the space that includes the two points. At two extremes, one may assume that a full transmission of price shocks can indicate the presence of a frictionless and well functioning market, while at the other extreme a total absence of transmission may make the very existence of a market questionable. Therefore, the degree of price transmission can provide at least a broad assessment of the extent to which markets are functioning in a predictable way, and price signals are passing-through consistently between different markets. For this very reason, the topic has attracted a considerable amount of theoretical and empirical work. The aim of this paper is to provide evidence on price transmission in a number of agricultural markets for a wide set of countries. The idea is to employ available information both per se, and in support of other analytical efforts, particularly in the field of agricultural trade policy analysis. This work is based on a price database collected from various sources in sixteen countries - Argentina, Brazil, Chile, Costa Rica, Egypt, Ethiopia, Ghana, India, Indonesia, Mexico, Pakistan, Senegal, Thailand, Turkey, Uganda, and Uruguay - mostly from local statistics. Based on the content of the database, focus is primarily on basic food commodities, although some important cash crops are also included. Both spatial and vertical price relation are considered, since the database includes prices at the producer, wholesale and retail levels, although not regularly. Available data was also supplemented with comparable information drawn from FAOSTAT. The analysis consists of a set of econometric applications. Annual price information was analyzed by testing mostly for the existence of a long run equilibrium between the price series; whereas monthly information, which is available for seven of the countries included, was analyzed by paying more attention to the dynamics of the relation between prices, to their causality, and to the symmetry of transmission. The study is organized as follows. The next section reviews some of the most important contributions in the literature, in order to locate the evidence proposed within the existing work. Section 3 proposes some insights into the possible use of the results of this work for policy analysis, while section 4 describes the major characteristics of the database. Section 5 describes the methods employed to analyze the data, while the results are reported in section 6. This is divided into two parts, dealing with the results of the analysis of annual and monthly information respectively. Finally, some concluding remarks are in section 7.

2 PRICE TRANSMISSION AND MARKET INTEGRATION A wide economic literature has studied the relationship between prices, either spatial or vertical. Concerning the former, a wide recent critical review is in Fackler and Goodwin (2001). The premises of full price transmission and market integration correspond to those of the standard competition model: in a frictionless undistorted world, the Law of One Price (LOP) is supposed to regulate spatial price relations, while pricing along production chains will depend exclusively on production costs, with all firms producing on the highest isoquant compatible with their isocost lines.

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In fact, however, the literature on price transmission indicates that there are at least six groups of factors affecting it1. Transport and transaction costs: where the latter can be classified, following Williamson, into the three groups of information, negotiation, and monitoring and enforcement costs. These can act as wedges between prices in different markets, which need to be overcome by the total price differences between two locations or industries to allow for arbitrage and integration to take place between two markets. Their treatment is simple if they can be assumed to be stationary, proportional to traded quantities rather than fixed, and if they can be assumed to be additive rather than multiplicative. If this is not the case, modelling price transmission and integration requires non linear models, or linear models including thresholds (McNew, 1996; Barrett and Li, 2002; Brooks and Melyukhina, 2003). Market power: along production chains some agents may behave as price makers while some other as price takers, depending on the degree of concentration of each industry. It may be the case that e.g. input price increased in an industry may be passed over to consumers, while input price decreases can be captured in the mark-ups of the industry (Wohlgenant, 1999; Azzam, 1999; Goodwin and Holt, 1999; Dhar and Cotterill, 1999; Mc Corriston et al., 2001). Increasing returns to scale in production: along the same lines, they may be at the origin of market power, although as has been shown, their effect on vertical price transmission is different from that of market power (Mc Corriston et al., 2001). Product homogeneity and differentiation: the degree of substitutability in consumption between similar goods produced in different countries may affect market integration and price transmission. This type of evidence can be addressed through the introduction of the so-called Armington assumption, of less than infinite substitutability in consumption between goods produced in different countries. Exchange rates: the extent to which changes in the exchange rates are “passed through” on output prices has been studied in relation to the ability of firms to discriminate prices across destinations (pricing-to-market behaviour), to market structure, to product non-homogeneity, and the adjustment costs of firms (Dornbush, 1987; Froot and Klempeter, 1989; Knetter, 1993). Border and domestic policies: those that directly affect spatial price transmission are trade policies, although domestic policies affecting price formation do also affect both vertical and spatial price relations (Mundlak and Larson, 1992; Zanias, 1993; Baffes and Ajwad, 2001; Thompson et al., 2002; Sharma, 2003). Among border measures, non tariff barriers may have strong effects on price transmission: this is the case of variable tariffs; tariff rate quota, prohibitive tariffs, and technical barriers. Ad valorem and fixed tariffs, instead, should behave exactly like proportional and fixed transaction costs respectively. All these elements can affect both spatial and vertical price relations; nonetheless, the second, the third and the fourth have been mostly investigated with reference to vertical price transmission, while the last one has been mostly studied with reference to spatial price transmission. A general distinction may be drawn in the literature in terms of the extent to which contributions deal explicitly with the deviations from the competitive model. Many papers are aimed at checking the consistency of the empirical evidence with the competitive framework, without proposing explicitly an alternative behavioural model, rather attempting to infer behavioural evidence from the data. The relatively more recent among such contributions focus their attention on the dynamics of the transmission process, using the properties of co-integrated time series, and the related econometrics. In

1 Aside from the literature specifically dealing with this issue, imperfections in the degree of price transmission occur in a number of analytical frameworks in economics. Most of these are reviewed in Prakash (1999), chapter two).

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such type of work, which may be considered to be following a “non structural” approach, factors determining transmission are treated as external prior information – rather than the outcome of a theoretical framework – to be confirmed by the results. In other words, the data are the starting point, while the tests indicate the extent to which prices adjust toward an equilibrium. Evidence showing that this is not the case is interpreted in terms of one or more of the highlighted factors that can affect price transmission depending on the specific context. From this point of view, testing for price transmission can be interpreted as an exercise to check the degree of efficiency of the markets, in terms of their being close to the competitive model, or as a tests for market integration, following the definition offered by Barrett and Li (2002). Comprehensive analytical framework for this econometrics approach can be found in Balcombe and Morrison (2002), and Rapsomanikis et al. (2003). Co-integration between the price series analyzed implies that two prices may behave in a different way in the short run, but that they will converge toward a common behaviour in the long run. If this property is verified, the characteristics of the dynamic relationship between the prices can be described by an Error Correction Model (ECM). Despite a number of caveats (Barrett and Li, 2002; Rapsomanikis et al., 2003), the short-run adjustment parameter of this type of model can be interpreted as a measure of the speed of price transmission, while the long run multiplier can be interpreted as a measure of the degree of price transmission of one price to the other (Prakash, 1999). The properties of co-integrated series also imply the existence of a causality relation, as defined by Granger, that can be tested by assessing if the past observations of one of the two prices (fail to) predict those of the other. Therefore, most analyses start by investigating the dynamic properties of the price series, through tests for the presence of unit roots, and then proceed with co-integration tests, and with the specification of ECMs. Among econometric applications, some were directly aimed at verifying the LOP for commodity prices. Examples are Ravallion (1986), Ardeni (1989), Baffes (1991), Mundlak and Larson (1992), Gardner and Brooks (1994), Goletti and Babu (1994),Mohanty et al. (1998b), Yang et al. (2001), Baffes and Ajwad (2001), Barrett (2001). Results appear controversial, and sensitive to the techniques employed. Interesting extensions of the econometric approach allow for transmission to be affected by the presence of asymmetric response, by thresholds, and for a fractional order of integration of the price series. Threshold models were introduced by Enders and Silkos (1999) and quite widely applied to agricultural price series (Goodwin and Piggott, 1999; Thompson and Bohl 1999; Goodwin and Harper, 2000; Mainardi, 2001; Abdulai, 2002; Meyer, 2002; Sephton, 2003). This type of model is aimed at testing for the presence of non linear transaction costs, and in general for the existence of price bands within which there is no transmission. Models with asymmetric adjustment have been frequently employed to test for the presence of market power, drawing on the idea that agents holding market power will pass-through only (or mostly) positive input changes. Examples include Morissett, (1998), based on a static framework for analyzing annual data. In dynamic applications, the short-run adjustment term is substituted by two separate coefficients accounting, respectively, for negative and positive deviations from the long run equilibrium. This allows testing for asymmetry in terms of rejection of the restriction that the two coefficients are equal. Applications of this type can be found, among others, in Goodwin and Holt, (1999); Abdulai (2000), Meyers and von Cramon, (2000), Kuiper et al. (2002), Rapsomanikis et al. (2003). A simpler method is adopted by Prakash et al. (2001), based on the significance of a dummy variable accounting for positive residuals in the static regression between the two price series involved. The idea is that if this variable is significantly different from zero, and if the ECM coefficient of the model including this variable is greater than the one without the dummy variable, transmission is asymmetric, since positive shocks are passed through faster than negative ones. Alternative to the econometric approach, there are a (relatively limited) number of contributions that, instead of testing for transmission and market integration, attempt to derive explicitly an alternative behavioural rule, different from those implied by the simple equalization of price behaviour.

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Contributions following this “structural” approach are relatively less frequent in the analysis of spatial transmission, and more frequent in the analysis of vertical transmission; in this field there are a number of industrial economics applications, addressing the presence of market power and/or of increasing returns to scale in production. Examples are McCorriston et al. (2000), showing, on the one hand, how market power is expected to reduce the degree of price transmission compared to competitive markets, given that producers will be able to gain extra profits by holding prices higher than in competitive market conditions; while increasing returns to scale, on the other hand, are able to increase the degree of price transmission beyond the level of perfect competition. In the same vein, Dhar and Cotteril (1999) propose a structural model with strategic behaviour to estimate price transmission along a dairy supply chain; and Acharya (2000) shows how asymmetric price transmission behaviour can be explained by the existence of market power along the food chain. Concerning spatial relations, interesting work has been devoted to specify conditions allowing for market integration and price transmission to take place within competitive models. McNew (1996) provides an example of this approach; an ad hoc spatial trade model for an homogenous product, driven by transport costs, is employed to estimate the probability of trade between markets on the basis of a competitive market structure in which the LOP holds. In the same line, Barrett and Li (2002) develop a spatial model, also driven by transaction cost, in which, among other things, they highlight two issues: firstly, the possibility that price transmission occurs in absence of trade (segmented equilibrium), and that trade takes place in absence of price transmission (imperfect market integration); and, secondly, that most econometric applications are in fact aimed at testing the most restrictive condition, in which both market integration and a competitive equilibrium are verified. These applications, therefore, would not be capable of fully capturing the “messy” character of market relationships (Barrett and Li, 2002), arising from treating price transmission mostly as a linear phenomenon, and from neglecting to consider the erratic nature that transaction costs can assume. As usual, each approach has its own merits and drawbacks. The econometric applications, and especially the most recent ones, have analyzed mostly the dynamics of price transmission, while elaborating less from a theoretical point of view. At the same time, models attempting to develop behaviour rules governing pricing and market relations - either in a competitive environment with transaction costs or under imperfect competition and increasing returns to scale - appear more specific and more demanding in terms of data. Given the purpose of this paper – which is that of providing evidence of transmission for a wide set of prices, both within the same areas along the production chains, and between local and world reference prices – it is chosen a simple econometric framework. As seen, despite its limitations, this approach can provide at least an useful starting point for more in-depth investigations to be conducted on specific cases, together with a set of background information to be used in the development of structural models. This latter topic is discussed in next section.

3 PRICE TRANSMISSION PARAMETERS IN STRUCTURAL MODELS

Given that most data employed in the econometric analysis is expressed in logarithms to reduce data variability, the estimated parameters can directly be interpreted as “transmission elasticities” of one price with respect to another. The dangers involved in this interpretation are clearly highlighted in the literature, particularly by the critics of the econometric approach: in fact such parameters can be • affected by factors that do not prevent market integration or the transmission of price signals (Mc

New, 1996; Barrett and Li, 2002; Brooks and Melyukhina, 2003), so that a low parameter may arise between two markets which are in fact integrated;

• smaller than one even if price transmission and market integration are complete: a transmission elasticity will be equal to one only if there are no fixed elements involved in the transaction, but only ad valorem ones (Sharma, 2003).

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Given these caveats, still the value of the parameters and their significance level provides information about the extent to which markets share the same price shocks or, conversely, the extent to which they are “messy” according to Barrett and Li’s (2002) expression. In other words, a transmission parameter summarizes the overall effect of a set of factors affecting price signals, including transaction costs that may be stationary, the existence of market power among the agents involved in transactions, the existence of non-constant returns to scale, the degree of product homogeneity, the changes of the exchange rates, and the effects of border and domestic policies. Since most estimations include a constant term, they should include only the effects of those elements that change proportionally with prices, without accounting for the interaction between the effects of each of those elements. Can transmission parameters be safely plugged in projection and simulation models to reproduce the functioning of the involved markets? At least, in the price transmission equations the coefficient of the constant term may be included together with the transmission elasticity, to account for the fixed effects separately from the proportional ones. But a more complete answer depends on the aim of the projection or simulation model involved; and the more these models are capable of reproducing explicitly factors affecting price transmission, the higher may be their usefulness for projections and simulations. For instance, in multi-markets models aimed at policy simulation, the inclusion of a set of variables representing a policy tool should be preferred to the inclusion of a simple transmission elasticity, which summarizes the effect of many factors, since in the first case it is possible to assess the effect of a change in the policy itself on the transmission of prices, while in the second it will not be possible to separate the effect of the policy change from one taking place in the other factors affecting transmission. As an example, suppose that a country changes the level at which it operates the floor price for wheat. If this policy is represented with an equation that triggers stock accumulation when the price falls below the floor level, while transport costs are dealt by within a different constant term that determines a wedge between the domestic price and the world price, it will be possible to assess separately the effect of a 10 percent reduction in the floor price from that of a 10 percent reduction in transport costs; whereas, if all is summarized in a spatial price transmission elasticity, it will only be possible to represent both changes as some percentage change in the elasticity. Despite this limitation, however, transmission elasticities can serve as background information for policy modellers, to understand how specific factors, and their interaction, are affecting transmission. In the above example, an estimate of the price transmission elasticity between a world reference price and a domestic price for wheat can help understanding if the adopted modelling strategy is in fact reproducing the functioning of that particular market, or if, e.g. transmission is in fact very incomplete and asymmetric due to, for example, a combination of high transaction costs, and a highly concentrated market. The calibration of policy analysis models could benefit from information on transmission parameters: it will be possible to check the extent to which the results generated by the structural model are consistent with the overall price transmission observed in the econometric exercise. In fact, large size equilibrium models currently employed in agricultural market projection and policy simulation tend to use policy variables rather than transmission elasticities, and/or to supplement policy variables with the transmission elasticities where less information is available, or where domestic prices are not defined. A review of the spatial price transmission mechanism in such models can be found in Cluff (2003). That paper shows how, for example, in the FAO WFM model price transmission for countries with no WTO commitments is modelled without qualifying the cause affecting it, simply with a price transmission elasticity derived in some cases from estimation, but often from expert judgments or calibration. In the same model, policies are assumed to be the major determinant of price transmission for countries that have undertaken WTO commitments, together with a residual term accounting for transaction costs. In the FAPRI modelling system, policies are kept

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separated from the other elements that affect price transmission, which are included in a constant term. Even this term, however, includes policies affecting prices as a fixed - rather than proportional - element. A more interesting approach is proposed by the USDA CCLS model, which allows for a variety of factors to explicitly affect price transmission, such as transport costs, the exchange rate, and trade policies, depending on the specific market. AGLINK, the OECD agricultural model, also takes a similar approach. As a matter of fact, even in well researched and known markets such as, for example, wheat in the EU and the United States the degree of price transmission hypothesised by different models is widely variable (Cluff, 2003), reflecting the relative importance attached by each researcher to different elements, if not measurement errors. Each of these models seems to be setting price transmission according to the main variables of interest: where policy analysis are the major objective, price transmission is mainly affected by policy measure, while other elements assume a less important role: either generic transmission measures are utilized to handle cases in which no information is available (e.g. an average for similar countries), or elements different from policies are included as fixed terms, or as calibration residuals. Apart from the size of the parameters and their form, price transmission in large-size equilibrium models depends also on the nature of the trade component. Where trade is modelled as a residual of domestic supply and demand, and only net positions are generated endogenously – as is the case for models quoted above – price transmission for one market will defined by a single set of parameters for each product. If the trade component, instead, generates a set of bilateral trade flows, then a specific set of transmission parameters are to be defined for each of such bilateral flows. This is the case of spatial equilibrium policy models (Anania, 2001), in which transmission is affected separately by transaction costs and policies, allowing the effects of changes in both groups of variables to be simulated separately. Another example is that of models in which bilateral flows are defined through the elasticity of substitution between imported and domestic products - following the so-called Armington assumption – in which case transmission is affected by the size assigned to this parameter. The implementation of this approach can be problematic, as it may be difficult to assign credible values to the elasticities of substitution, especially for a wide set of markets; in the case of the GTAP model, for instance, these are assumed to be homogenous across products, to keep the modelling simple (Hertel, 1997). Market structure as a factor affecting price transmission appears very rarely in transmission equations of large size equilibrium models. Examples can be found among the GTAP application (Francois et al, 2003) where the assumption of increasing returns to scale allows for spatial transmission to be governed by a non competitive pricing rule. Another example can be found in the partial model proposed by Moro et al. (2002), in which price transmission include policies as price wedges, while wedges between producer and consumer prices, representing vertical transmission, are driven by Herfindhal indexes, related to the degree of concentration of that particular market. Within the limitations implied by such simple modelling, the representation allows for a separate simulation of the effects of a change in the market structure of an industry and those in the policy setting. To sum up, rather than directly providing parameters to be inserted in policy analysis models, evidence on price transmission may be employed to check the consistency of the results of such models. Ideally, by including an explicit modelling of those factors that affect transmission – such as policies, the exchange rate, transaction costs, quality differentials, the degree of concentration etc. - equilibrium models should be able to reproduce a degree of transmission consistent with the one found in econometric exercises; the inclusion of a transmission elasticity appears mostly as a measure of the lack of interest of the modeller in the factors affecting transmission.

4 THE ESCB PRICE DATABASE The database employed in this study was compiled by consultants in the countries involved, working in coordination with FAO Representatives. At present the database includes data for major food

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staples in 16 countries. In more than one country, however, data for cash crops and agricultural exportables which are important in the local context are not considered. For some countries data is very scarce: e.g. for Ethiopia and Argentina, for which only few annual data points are available. Monthly prices series are also available for seven countries (Costa Rica, Egypt, Ethiopia, Ghana, Indonesia, Senegal, Thailand, and Turkey), with a variable number of observations, ranging in most cases from 100 up to more than 300. All prices have been reported in US dollars. Annual data for five countries (Brazil, Costa Rica, India and Indonesia) and monthly data for all countries were converted from local currencies to US dollars using the current average period exchange rates reported by the IMF (2003) International Financial Statistics database. Annual data were supplied in US dollars by the local consultants for the remaining ten countries (Chile, Egypt, Ghana, Mexico, Pakistan, Senegal, Thailand, Turkey, Uganda, and Uruguay). For few countries data are available at all the three stages of the food chain (producer, wholesale, retail).2 For the analysis presented in this paper it was necessary to select: (i) annual price series including at least 30 observations; this excluded the annual prices reported for Ethiopia, and all data for Argentina; (ii) monthly price series showing an acceptable degree of continuity. Most price series in fact showed considerable gaps. Missing observations were replaced through interpolation, and the consistency of the new data points was checked on the basis of the parameter of the underlying AR (1) model.3 The very nature of the “prices” included in the database is variable. Firstly, included border “prices” from FAOSTAT are indeed import unit values. Secondly, “wholesale”, “retail” and “producer” prices may inevitably refer to a different price in each market, depending on their specific characteristics. The local consultants have chosen those reported as being representative prices at the three levels. Their level of accuracy, therefore, depends on the variability of market features, and on the size of the countries. The same inevitably applies also to product definitions.4

2 Particularly

• producer prices are available on a relatively regular basis for seven countries (Brazil, Egypt, Indonesia, Mexico, Turkey, Uganda, and Uruguay); few producer prices are available for 6 other countries (Chile, Costa Rica, India, Pakistan, Senegal and Thailand; these are mainly cereal prices); no producer prices are available for Ghana;

• wholesale prices are available on a (relatively) regular basis for nine countries (Chile, Costa Rica, Egypt, India, Indonesia, Pakistan, Thailand, Turkey and Uganda); no wholesale data are available for the other five (Brazil, Ghana, Mexico, Senegal and Uruguay);

• retail prices are available on a (relatively) regular basis for five countries (Egypt, Ghana, Pakistan, Turkey and Uganda); few retail data are available for five others (Chile, India, Thailand, Senegal and Uruguay); and no retail data are available for the remaining four (Brazil, Cost Rica, Indonesia and Mexico).

3 In practice, data were first replaced through interpolation, and then replaced with the fitted data of the underlying AR(1) model until the parameters of the model stabilized. 4 Particularly, for Costa Rica, data come from the Instituto Nacional de Estadística y Censos (INEC);

• for Egypt, price sources are i) the Central Agency for Public Mobilization and Statistics, Consumer & Wholesale Price Bulletin; ii) Ministry of Agriculture & Land Reclamation, Agricultural Statistic Bulletin; iii) Ministry of Supply;

• for Indonesia, producer, wholesale and retail prices results from a set of prices collected in different provinces and cities and published respectively in the Statistik Harga Produsen Sektor Pertanian di Indonesia, Statistik Harga Perdagangan Besar Beberapa Propinsi di Indonesia and in the Harga Konsumen Beberapa Barang dan Jasa di Indonesia, of the Central Bureau of Statistics;

• for Thailand, data was collected from: the Quarterly Bulletin of Statistics, the Statistical Yearbook of Thailand, the Trade and Economic Indices Bureau, the Department of Internal Trade, the Economic and Financial Statistics, and the Office of Agricultural Economics, and the Quarterly Bulletin, the Monthly Bulletins and the reports of the Departments of Business Economics and of Internal Trade;

• for India, the reported “prices” are averages of the prices in two or three main markets;

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5 AN ECONOMETRIC FRAMEWORK FOR ANALYSING PRICE TRANSMISSION

Slightly different analysis frameworks were adopted for monthly an annual data. But in both cases the first step taken was the analysis of the dynamic properties of the price series, aimed at understanding if price pairs are integrated to the same order, by testing for the presence of unit roots. For the monthly data, two different tests were applied: the Augmented Dickey-Fuller (ADF) test, and the Phillip-Perron (PP) test.5 These were run with and without a time trend and a constant term6, for a number of lags varying from two to twelve, both on the log-level series and the series in first differences. On the basis of the properties, the test for unit roots was applied also to the residuals of the static regression between each pair of prices, in order to test for co-integration following the Engle and Granger (1987) procedure. Where co-integration arose, a set of Auto Regressive Distributed Lag (ARDL) models were specified and estimated as follows:

1 0

J K

t j t j k t k tj k

pd a T pd pw eτ β γ− −= =

= + + + +∑ ∑ (1)

where pd are the countries’ (logarithm of the) import unit values in time t, pw is the (log) world reference price, a is an intercept, T is a time trend, e is the error term, and t is the period index. A key issue in estimating this type of model is the identification of the correct number of lags to be included, given that both under- and over-parametrization can create problems, respectively of misspecification and of unnecessary reduction in the degrees of freedom. The relevant J and K were chosen here through the minimization of the Akaike information criterion, supplemented by the Schwartz-Bayesian, the Hannan-Quinn, and Log-Likelihood tests mainly to check for the consistency of the results. Given the lag structure, the presence of a long run relationship between pd and pw can be tested by considering the parameters of the relation 0 1t t tpd pw uλ λ= + + in which, under the assumptions that t t kpd pd k−= ∀ and t t jpw pw j−= ∀ it is

0 1 1

j j

a Tτλβ β

= +− −∑ ∑

1 1

kk

jj

γλ

β=

−

∑∑

and 1

tt

jj

euβ

=−∑

• for Uganda data was collected through a set of institutions including: The Agricultural Secretariat (AGSEC), the Market News Services (MNS), the International Institute of Tropical Agriculture (IITA), the Uganda Oils Producers and Processors Association (UOSPPA), the Investment in Developing Export Agriculture (IDEA) Project, the Uganda Bureau of Statistics (UBOS), the Bank of Uganda (BOU), the Dairy Corporation (DC), the Ministry of Agriculture, Animal Industry and Fisheries (MAAIF), and the Uganda Ginners and Cotton Exporters Association (UGCEA).

5 As is known, the first is a parametric test, based on the estimation of an AR(n) model, in which the null hypothesis that the coefficients of the lagged dependent variables are unitary is tested against a one sided alternative that they are strictly smaller than one; where the former identifies a random walk, while a coefficient higher than one would imply an explosive behaviour. The Phillips-Perron test is conceptually similar to the ADF, but it is based on an AR(1) model, in which the same test on the coefficient of the lagged variable is performed by correcting the usual t-statistic with a (non parametric) estimate of the spectrum of the error term. 6 For brevity, only the tests including the constant and the trend are reported in the tables.

9

Where the null of absence of co-integration is rejected in the Engle and Granger (1987) procedure, the adjustment taking place around the long run equilibrium can be modelled through an Error Correction (ECM) specification, such as:

* *1 1 1

1 0[ ]

J K

t t t j t j j t k tj k

pd a T pd pw pd pw hδ ρ λ β γ− − − −= =

∆ = + + − + ∆ + ∆ +∑ ∑ (2)

in which the coefficient )1( ∑−= jβρ usually named “ECM coefficient”, indicates the short run

adjustment of prices toward the long run equilibrium, and λ1.is the same as the one calculated from the ARDL model in (1). Results reported here include for each commodity the parameters and the t statistics for the long run equilibrium, together with the results of the estimation of the corresponding ECM specifications. In order to test for Granger non-causality between the pairs of prices, model (1) and its reverse form have been estimated by dropping the contemporaneous coefficients, according to

1 1

J K

t j t j k t k tj k

pd a T pd pw eτ β γ− −= =

= + + + +∑ ∑ and

' '

1 1

J K

t j t j k t k tj k

pw a T pd pw zτ β γ− −= =

= + + + +∑ ∑ (3)

Both equations were tested for γk βj γ'k β'j significantly different from zero for any j, k. Acceptance of the null implies that past values of the series on the right hand side are not adding information on the actual values of the series on the left hand side, on top of what is provided by its own past values. If this happens in both equations, then neither of the two series is Granger-causing the other, while if the null can be rejected in one of them, the price appearing on the left hand side will be Granger-causing the other. Given that a co-integrating relation must exist between the two series involved if Granger-non-causality is rejected in at least one of the two equations, this test has been used here firstly, as a confirmation of the test for the long run equilibrium; secondly, to understand which of the two price acts as a source of information for the other; and thirdly, to gain qualitative elements to understand the results, in terms of the causality direction. Rejection of the null in both the equations is to be considered as indicating a model misspecification or incompleteness, as it implies that both series are being Granger-caused by some third unknown variable. This test was performed, on monthly data, for those pairs of prices showing the presence of a long run equilibrium. Moreover, on these same price pairs the symmetry of transmission is tested drawing on Prakash, Oliver and Balcombe (2002). A dummy variable is added to the ARDL model (1), assigning a value of 1 to the observations showing positive residuals in the static regression between each pair of prices. Rejection of the t test on this variable allows the series between which transmission is not symmetric to be identified When this is the case, comparison of the short and the long run parameters of the ECM specifications with and without the dummy, allows it to be understood if positive price shocks are passed on the other price series to a greater or smaller extent. In other words, if the model that includes the dummy variable shows a higher speed and a higher degree of price transmission, this means that positive shocks are transmitted more and faster than negative ones. This procedure for testing asymmetry was applied only to those pairs of prices for which the results indicate the presence of co-integration and of a significant long run equilibrium, thereby precluding non-spurious cases, together with a meaningful result of the Granger tests for non-causality.

10

Concerning annual data, they were also analyzed by testing for co-integration with the Engle and Granger (1987) procedure - after running the Unit Root tests - and for long run equilibrium; but in the specification of model (1) a simple structure including a maximum of two lags, without selection criteria, was assumed. The significance of the long parameter calculated from model (1) was tested while no evidence was reported for the ECM coefficients, which were considered less interesting, given that in annual data most of the short run variability is already averaged out. For this same reason, no tests were applied for causality and asymmetry on annual data.

6 THE RESULTS Results are reported first for annual information, for which the pairs of price series that show a long run equilibrium, and then for monthly data in a more detailed fashion. Given the scope of the analysis and the amount of information to be processed, much of the evidence has be considered as the basis for more detailed investigations on specific case studies, possibly employing more sophisticated analytical tools. It is also worthwhile mentioning that for several pairs of prices, a priority area for more in-depth analysis is the presence of structural breaks, which in this work are dealt with only for those series in which they appeared more evident, and more directly connected with changes in the economic and policy framework of the countries involved. Given the purpose of the paper, which is to provide evidence on price transmission also in support of policy analysis work, an attempt has been made to comment the results with reference to the policy framework. This has been possible especially in section 6.2, on the basis of the relatively more accurate indications yielded through monthly data. 6.1 The annual price series The number of observations available for each series (about 32 on average) prevents us from obtaining fully conclusive evidence from the tests on the dynamic properties of the series, and therefore also from the co-integration tests. This is somehow confirmed by the fact that the ADF and the PP test – whose results are not reported in the tables for brevity – show non homogeneous results in more than one case. Where the dynamic properties of the series could not be clearly identified, the evidence on transmission had to be presented as being less clear cut. Results are organized by country in alphabetical order. They are reported for both the relation of each price with the reference world market reference prices, and for vertical relations within each country. The latter are analyzed only in those cases in which they appeared likely to convey additional information compared to the former, i.e. where a significant difference emerged among domestic prices in the relation with the world reference price, while they where not considered when either all prices behave in a similar way in relation to world prices, or the different responses could be explained by differences in the order of integration of the series. The key to the names of variables is reported in Table 1. Results of the Unit root tests are reported in Tables A1 to A15 in the Appendix. These were firstly applied to the series of world reference prices; from the ADF test these all resulted I(1), since all regressions in level accept the null of absence of unit roots, while the same test run on the first differences rejects it at least at 5 percent confidence level for the specific distribution. Annual price data for Brazil covers only one of the major exportable cash crops in the country, which is soybeans, together with bovine meat, and a set of relatively less important products, such as wheat, maize and rice. In general, these prices show a high degree of transmission with world prices, especially for soybeans (Table 2), as can be expected given the importance of the country in that market. A long run equilibrium emerges between the border prices and the world reference price, as the Engle and Granger procedure shows the presence of co-integration, and the long run parameters are both significant. The same does not apply to the domestic producer price reported for the same product, despite a significant long run coefficient. However, this is not a firm indication of absence of

11

transmission, given both the limited number of observations on which the ADF test was performed, and the fact that critical values are not far from the usual significance levels. Also the Brazilian border price for maize provides evidence of co-integration with the world reference, whereas for the export price this same evidence is not supported by a significant long run parameter (Table 2). Available prices for Chile do not cover horticultural products, which are among the most important in local agriculture, while it includes some of the livestock products. In general, transmission of world price changes on local prices appears fairly complete, as can be expected given the liberal attitude of the economic policy framework, and the long standing attempt to liberalize foreign trade. Results show a significant long run equilibrium with the world reference prices for the rice and pig meat (Table 3). For rice, all the three prices available – the import unit value, the wholesale and the producer prices reported in FAOSTAT - are co-integrated with the world reference price and show significant coefficients. The same applies to the retail price for pork meat, and to the import and producer price for the same product. For wheat, the Chilean wholesale price shows a high degree of common movement with the world reference, whereas evidence is less firm for the retail price (Table 3). For poultry meat the domestic producer price shows a significant long run parameter, but co-integration is rejected. Although this price is strongly related to the import price, it appears not to be significantly related with the wholesale price: in other words, shocks in the world price appear to be transmitted at the producer level, but not at the import price level, although producers are affected by changes in the import price. The result for milk powder (Table 3) is most probably a spurious one, and/or the outcome of an incorrect model specification, given that the domestic milk powder price results I(0) from the ADF test. Also the results for dairy products, indicating a significant degree of integration for butter and cheese with the world reference export unit values, appear questionable for the same reason. For Costa Rica few price series are available which do not include the main products of local agriculture, and especially the main cash crops such as bananas, coffee, pineapples, oranges, and other horticultural products. Results obtained from the annual data are not directly comparable with those of the monthly data, reported in section 6.2.1, apart from the case of pork meat. The tests on bovine meat border unit values accept co-integration with the world reference price, especially on the export side (Table 4), while for the import unit value the test rejects the null. The FAOSTAT domestic price for this same product, however, shows no evidence of a significant relation either with the world reference prices, or with the border prices. For pig meat as well a long run equilibrium emerges between the export price and the world reference, and between the domestic producer price and export price. The Engle and Granger test for these price pairs, however, indicates absence of co-integration. In the case of Egypt, available prices are referred to some of the most important products in the local market, including cereals, cotton, and some of the major livestock products. Cotton and rice, however, had to be excluded from the analysis, since the database only reported administered prices, which were kept fixed through time or changed only few times over the sample period; this prevented a meaningful application of the analysis mentioned in section 5. Also for Egypt, most results from the annual data are not directly comparable with those described in section 6.2.2 for the monthly data; they appear consistent however, in the case of wheat and bovine meat. Results for the annual series indicate in general a fairly incomplete transmission between domestic and world reference prices (Table 5). The few products whose price shows a significant long run equilibrium are trade unit values, and particularly the export price of rice, the import price of wheat and maize, and the retail price of butter. In these four cases, the evidence of co-integration is consistent with the estimates of a significant long-run parameter; for wheat and butter, however, such evidence is less firm, as co-integration is rejected. Among the other products, there is only evidence of vertical transmission, particularly for wheat, between the export, producer, wholesale, and retail price series, and for maize, sorghum and bovine meat between the producer and the wholesale levels. Estimated coefficients are not far from unity in all cases, apart from those relative to spatial transmission between maize import prices and rice export prices with international reference prices, which are significantly higher then one.

12

Available monthly data for Ghana include prices of some important food crops, like maize and sorghum, which constitute the bulk of cereal consumption, but exclude important cash crops, and primarily cocoa. Evidence of significant price transmission arises for the import price of maize and for the import and the wholesale prices of imported rice. For this latter product, also the producer price reported by FAOSTAT for the locally produced varieties, which are priced differently from the imported ones, show a long run equilibrium with the world reference price, although co-integration is rejected. Relatively less firm evidence of transmission also arises for sorghum, maize, and cassava both at the producer and the wholesale level. As it will be clear from section 6.2.4, part of these results are not confirmed by the more detailed analysis of monthly data, particularly for the local price of maize. Available price data for India show a considerable degree of linkage with world reference prices, despite the overall agricultural policy attitude of the country, which for a long period has been characterized by a relatively high degree of public regulation. At the same time, for this country in particular the average data employed are inevitably inaccurate, given its size and diversity. Estimates indicate evidence of long run equilibrium in the spatial transmission between the domestic and the world reference prices of wheat, maize, cassava, milk powder, and to some extent rice, while coefficients are mostly not significant for meats (Table 7). For wheat, the producer price shows the highest degree of transmission with the world reference price, while results are less clear cut for the wholesale, the retail and the import prices, and for the FAOSTAT producer price: in these cases co-integration is rejected. For rice, the import price shows a common movement with the world reference price, but the relation with the domestic wholesale and producer price is less clear. Nonetheless, within the domestic market transmission appears to be fairly complete between the wholesale and the retail prices, and between the producer price and the export price. For maize, the relation between the world price and the wholesale price shows a long run equilibrium, while, for other prices, the co-integration is rejected. In the case of Indonesia, prices showing evidence of a long run equilibrium with the world reference are mainly the import unit value of maize and rice, the producer price of cassava, the FAOSTAT producer price of soybeans, and the import unit value of bovine meat (Table 8). For maize and cassava, evidence is quite clear concerning both the import and the producer price reported in FAOSTAT. For rice, however, evidence appears clear for the import unit value, while less so for the FAOSTAT price, for which co-integration is rejected. Of the two meat products reported, bovine meat shows a long run equilibrium with the world reference price, while the same does not apply to the domestic price reported by FAOSTAT. Most of these results are consistent with those of the analysis based on monthly data, reported in section 6.2.5, particularly for rice. Annual price data available for Mexico cover some of the most important food crops, such as maize, wheat, sorghum, and some livestock products, which show in general a considerable degree of common movement with world reference prices. Important cash crops such as coffee and horticultural products, however, are not included in the dataset. The tests on the annual price data report significant long run relations primarily between local prices of wheat and bovine meat and the corresponding world reference prices (Table 9), both for the domestic and the border prices. For wheat, the export price shows a clearer relationship than the import price, while the opposite is true for bovine meat. Import prices of soybeans and pork meat also show significant long run equilibrium with the world price, while in the case of poultry, the significance is questionable, as the series result I(0) from the unit root test. Annual prices for Pakistan include major food staples grown in country, such as wheat, maize sorghum and rice, but it excludes important crops, such as cotton. In general, local price appear to be connected to world reference prices to a significant extent. Results of the test indicate the presence of a long run equilibrium between the domestic and the world reference prices primarily for wheat, rice, maize and bovine meat. A long run relation emerges for the export price of the Basmati rice, and for the domestic wholesale price of the Irri rice. For wheat, maize and bovine meat, a long run equilibrium with world prices emerges for the wholesale and the retail prices, although the estimate for the latter is

13

not confirmed by the result of the co-integration tests. A significant long run coefficient also arises for pig meat and for poultry, although the tests reject co-integration with world reference prices. Estimates for Senegal (Table 11) are referred to products which are not very important in the country’s agricultural sector. For some more important locally produced goods – like groundnuts, rice, maize – a considerable amount of monthly information is available, whose results are described in section 6.2.7. Concerning annual price series, estimates show only one pair of prices for which a significant long run equilibrium emerges in the presence of firm evidence of co-integration; this is the export price of bovine meat, which is definitely a minor item in the country. For the other products, estimates of significant coefficients are obtained in absence of a clear outcome of the co-integration tests. This is the case for the producer price of maize, and for the import and the producer prices of milk powder, which appear somehow tied to the corresponding world reference prices. Similar evidence also arises for poultry and pork meat. In the case of the import price of rice, however, the result is most probably a spurious one, as the corresponding price series results I(0) in level. For Thailand, available annual data include some the most important products of local agriculture, primarily rice. Most price series show a high degree of transmission with the corresponding world market reference, a result which is consistent with the policy attitude of the country, traditionally pointing towards openness to foreign trade. As expected, for rice and cassava transmission in the long run is almost complete with the corresponding world reference prices (Table 12), as the prices of these two products – respectively the FAOSTAT export unit value and the producer prices – are in fact almost coincident with the world reference reported by the IMF. The degree of integration is high both at the border and at the domestic producer and retail levels. Similar results also emerge at the wholesale level, although in this case the test rejects co-integration with the world reference price. Maize prices also appear to be related to the world reference price at the wholesale and at the producer level, and the same applies to cassava at the producer level, to the retail price of poultry, and to the producer price of pork meat. For bovine meat, the significance of the long run equilibrium is less clear-cut, due to rejection of the test for co-integration. Transmission parameters for Turkey (Table 13) indicate rice, wheat, maize and poultry as products whose price is moving similarly to the world market reference. For these products, a long run equilibrium emerges at the wholesale, retail and producer levels, and also with the import price in the case of rice. For wheat, evidence is clear at the wholesale level, while for the export price the difference in the order of integration of the series prevents the possibility of estimating a meaningful coefficients. For bovine meat, only the export unit value appears to be significantly related to the world reference price, while the domestic market appears to receive fewer signals from world markets, despite vertical integration arises both between the producer and the wholesale prices, and between the latter and the retail price. For sunflower seeds, transmission between the producer prices and the world reference appears to be fairly complete. In the case of butter, both the wholesale and the retail prices show a common movement with the world reference unit value in the long run, while no transmission appears to take place at the producer level. For crops, these results are consistent with those generated by the analysis of available monthly price data, reported in section 6.2.7. Despite being quite detailed and comprehensive, the annual price dataset for Uganda does exclude some important agricultural exportables of the country, such as coffee, tea, and cash crops such as cotton. The degree of price transmission observed appears particularly low: parameters are mostly not significant, especially in the relations between the domestic and the world reference prices. Exceptions are wheat, whose producer price reported by the FAOSTAT shows a long run equilibrium with the world price, the producer price of sorghum, and the import unit value of milk powder. Given the Unit roots tests showing that a number of the series involved are I(0), moreover, some of the significant spatial transmission coefficients are most probably spurious: this is the case for the import price of maize, and for the producer price of soybeans. Vertical transmission between domestic prices, instead, appears quite effective: this is the case of sorghum, maize, rice, and cassava, while results for wheat, soybean, poultry and pork meat appear questionable, due also in this case to the series being I(0). Visual inspection, however, indicates quite clearly the existence of common trends between the

14

domestic price series at different levels. Reasons that can explain these results range from the relatively low degree of tradability of most of the products involved, which are probably traded mostly in local markets, to the relatively high degree of self sufficiency of the country, which also may contribute to explain the poor linkages with the world reference prices. Finally, annual data for Uruguay, include bovine meat among the main export products, and a number of other important items in local production, primarily cereals. The analysis yields significant transmission parameters for the export price of bovine meat, the producer prices of sorghum and sunflower seeds, and for rice, despite co-integration is rejected. These results appear generally consistent with the long standing liberal attitude of the country in the area international trade policy. Results for wheat and soybeans, pointing to a significant long run equilibrium, instead, are more questionable: they may either to be considered spurious, or deriving from incorrect model specifications, given that some of the corresponding series are I(0) in the levels. 6.2 The monthly price series Also in this case results are reported by country in alphabetical order. Monthly data do not overlap frequently with the annual information, since there are no border or domestic prices available from FAOSTAT on this time scale, and because in for many products monthly data correspond to too few annual observations to run meaningful analysis. Unit root tests for world reference prices and domestic price series are reported in the Appendix, in Tables A16 to A23. The former are mostly I(1) according to the PP test; inconsistency with the results of the ADF tests arises for wheat and palm kernel oil, which appear to be I(0) from this latter test, and for pig meat and poultry, which appear to be I(2). In order to save space, the Tables attached to the following sections only report coefficients that resulted statistically significant at least at 5 percent level. 6.2.1 Costa Rica Few monthly price series are available for Costa Rica: palm oil, bovine, pork and poultry meats. As already pointed out for annual data, the prices of major cash crops produced in the country – such as bananas, coffee, pineapples, oranges - do not appear in the dataset, which contains information for few food staples. The Unit root tests indicate that most series are I(1), apart from the poultry price which is stationary, and from the bovine meat price, for which the results are inconsistent between the ADF and the PP tests (see Table A.17 in the Appendix). Following the Engle and Granger (1987 procedure, the Unit roots test was applied to the residuals of the static regression between each series and the corresponding world reference price, and for pork meat only, to residuals of the relation between the wholesale and the retail prices (Table 16). Co-integration emerged for pork meat, both within the domestic market and with the world reference price, and for palm oil. The long run parameter, however, is not significant for the latter product; therefore, despite the existence of some common movement in the two price series, they appear not to be driven by a long run equilibrium. This is not the case for pork meat, whose transmission parameters are significant in both the short and the long run. As can be expected by looking at the series (Figure 1), transmission is far more complete and quicker within the domestic market compared to that with the world market reference price. Given the minor role played in commercial agriculture by the products whose prices were considered here, part of the explanation for the poor transmission observed may arise from the limited amount of foreign trade; maize, beef and poultry are mostly exchanged in local markets. In any case, reasons for these results appear not to be found in the trade policy setting, as at least in recent years pork meat shows one of the highest applied tariffs, far higher than the one for bovine meat and palm oil. The causality tests indicate that the world reference pork meat price is Granger-caused by the Costa Rican wholesale price (Table 17), whereas it is inconclusive between the wholesale and the retail pork meat prices. This result can only be explained by the existence of some expectation mechanism by

15

which the local wholesale price is sensitive to expectations about the future evolution of the world reference price. Finally, concerning asymmetry – which was analyzed only for the relatively firmer relation found between the pig meat wholesale and world reference price – the dummy variable for the positive residuals of the static regression resulted significant; but the ECM coefficient is smaller in size compared to that of the model without such variable. This indicates that positive price shocks are transmitted in the domestic market at a slower pace compared to negative ones. In turn, this can be explained by the presence of some policy provision aimed at smoothing price effects in the local market, stabilizing the domestic market. 6.2.2 Egypt Results for monthly information are generally consistent with those yielded with annual data. Transmission between the local and the world reference prices appears generally poor, apart from a few products including wheat, but only over the period following the economic reforms. On the contrary, transmission results generally high within the domestic markets. As it was the case for annual data, also for monthly data rice and cotton prices had to be excluded from the analysis, as only the administrative price where reported, showing insufficient variability in the sample period. Unit root tests indicate that all series are I(1) (see Table A.18 in the Appendix). The same test applied to the residuals of the static regression (Table 19) accepts co-integration in five out of the nine pairs of prices. For wheat there appears to be no evidence of a long run relationship between the wholesale price and the world reference price. Both a visual inspection of the series (Figure 2), and the test for the stability of parameters based on the Cumulative Sum of Recursive Residuals (Figure 3), however, show a structural break in August 1989. In the data, this period corresponds to a change in the exchange rate reported by the IMF International Financial Statistics, employed to convert the local currency prices. As confirmed by FAO (1999), in the late 1980s, the Egyptian Government moved progressively toward restructuring and reorganizing the economy, adopting, among other provisions, the liberalization and unification of the exchange rate regime that was previously set at different levels for different transactions7, together with a major reduction in trade barriers, and the liberalization of marketing channels for several commodities, that were previously operated solely under State control. Accordingly, the sample was divided into two parts, and two separate models were estimated.8 The results for the 1969-1989, which are not reported in the Tables, again failed to show evidence of co-integration, while this is not the case over the period 1989-2001 (Table 19), a result confirmed by the significant long run parameter of model (1). This appears consistent with the qualitative notion that transmission has improved after the economic reform, directly affecting the exchange rate. It is worth observing that the wheat market in Egypt is still a relatively administered one: the Government operates a floor price both at the producer and at the consumer level (for bread). The transmission result found here from the late 1980s on indicates that prices are transmitted and markets are integrated at least to some extent despite the presence of a minimum guaranteed level. In turn, this is an indication that the floor price is probably only affecting the level of risk for farmers, by truncating the probability distribution of price outcomes, rather than directly affecting price formation. Contrary to the previous case, co-integration emerges between the domestic wholesale and retail wheat prices (Table 19); this is a fairly predictable result, given the plots of the series (Figure 2). For maize and sorghum co-integration with the world reference price is rejected, as already indicated by annual price series (Table 19). However, a long run relationship between the wholesale and retail prices emerges (Figures 4 and 5), and appears to be confirmed by the significance of the parameters of the

7 Products imported by the Public Authority for Commodities Supply, including wheat, were denominated in a specific exchange rate, which was lower than the free market one (in local currency for US dollars). 8 The introduction of a dummy variable did not affect the result obtained with the full sample.

16

ECM model. Both these are minor products in the Egyptian market, characterised by low public involvement compared to the wheat market; therefore one would expect a more direct relationship of domestic prices with the world reference prices. The same applies to bovine meat, whose co-integration test is rejected (Table 19). For this product too the mentioned change in the exchange rate of the late 1980s could have had an effect. Therefore model (1) was also estimated on the two separate sample period employed for wheat. Co-integration, however, emerges for the period prior to the change in the exchange rate regime (Table 19) – something which is difficult to link qualitatively to the change in the policy environment - and moreover, the long run coefficient shows a negative sign, which is meaningless. The world reference price appears to Granger-cause the domestic wholesale price for wheat – after 1989 – and for bovine meat (Table 20). Within the domestic market, instead, the tests are inconclusive between the wholesale and the retail prices, both for wheat and bovine meat. For sorghum, the retail price appear to be Granger caused by the wholesale price. The test for asymmetry indicates that a higher speed and a higher degree of transmission arises for all the three pairs of series showing a long run equilibrium with the world reference price: the wheat wholesale price in the 1989-2001 period, and sorghum retail and wholesale prices for the whole sample (Table 21). In all three cases, the dummy variables for the positive residuals in the static regression are significant, and the ECM parameter are larger in size than the corresponding parameters in the models without the dummies (reported in Table 19). This indicates that the domestic markets smoothed the price reductions taking place in the world reference price, while they fully passed through the increases in world prices. To some extent, this may also be due to the operation of the floor price at the producer level. 6.2.3 Ethiopia Available prices for this country include some of the main staples, particularly maize and sorghum, together with a few other food crops, but exclude the main cash crops of the country, such as coffee. Monthly data resulted mostly I(1) from the Unit root tests (see Table A19 in the Appendix) with the two tests yielding almost fully consistent results. The Engle and Granger (1987) procedure indicated that co-integration is verified in four pairs of prices out of seven. Notably, a long equilibrium emerged between the wheat retail price and the corresponding world reference price, for the retail price of sorghum, and for both the retail and the producer prices of maize. No meaningful relation arose, instead, for sunflower seeds, bovine meat, and the rice retail price, rejecting both co-integration and tests for a long run equilibrium. For maize, the producer price appears to react to changes in the world reference price with a four months delay, whereas two more months are required for the wholesale price. For wheat and sorghum, instead, transmission is faster, taking place within two and three months respectively. The currently applied tariffs appear small and homogeneous among cereals, rice, and sunflower seeds, and therefore they do not seem to provide any explanation for the results. Rather, the size of the market may be more explicative, since wheat, sorghum and maize are respectively the three most important commodities, at least in terms of current import volume. At the same time, a relatively higher tariff applied in recent years may contribute to explain the lack of transmission observed for bovine meat. Results of the Granger non-causality tests are inconclusive for wheat and for the producer price of maize, which appear not to be caused by the corresponding world reference price, whereas the world reference prices of maize and sorghum are Granger-causing the local retail prices for these products (Table 23). For maize, the discrepancy between the results of the two prices appears somewhat

17

puzzling, given also the very close behaviour of the two series that can be observed from the plots (Figure 8). Based on these results, the test for asymmetry was only performed for the maize and sorghum retail prices (Table 24). Both products show a higher speed in the response of prices to positive price shocks, given that the coefficient of the model with the dummy variable is higher compared to the corresponding ones without it, reported in Table 22. Whatever the reason lying behind this evidence, it should be noted that it may be harmful in terms of food security, insofar as the two crops are relevant food staples. 6.2.4 Ghana Also monthly information for this country include prices of basic food crops like maize and sorghum, while exclude important cash crops, primarily cocoa. Results of the Unit root tests indicate that most of them are I(1) (see Table A20 in the Appendix), with the exception of the retail price of cassava, that results I(0) from the ADF test, and I(1) in the PP test. Co-integration and the related estimation of ARDL models shows the presence of a reliable long run equilibrium with the corresponding world reference prices only for the wholesale prices of groundnut (Table 25). For maize and sorghum, despite the long run parameter is significant, co-integration is rejected, and for maize also the size of the coefficient appears too large to be credible. For imported rice, cassava, palm oil and palm kernel oil, however, co-integration is rejected, and the long run parameter is no significant. More evidence of transmission arose within the domestic market, where long run parameters are significant between the wholesale and the retail prices of maize, sorghum, palm oil, imported rice, and cassava, whereas this is not the case for groundnut. For maize, these results are consistent with the conclusions of other studies (Badiane and Shively, 1998; Abdulai, 2000), indicating that the domestic market is relatively well integrated. In principle, the lack of transmission with the world price may be related to the policy framework that the government was operating until the 1980s, which included price support through intervention. After that period, several changes in the institutional framework have been taking place in country, particularly with the implementation of the Economic Recovery Programme 1983, and with the related change in the exchange rate regime that took place over the 1983 to 1985 period (Badiane and Shively, 1998). The inclusion of a dummy variable accounting for a break in that period, and also the break of the sample into two periods did not yield improvements in the relation with the world reference prices, neither before nor after the mid 1980s. Results of the test for Granger causality also are puzzling for maize (Table 26), as lagged observations of the Ghanaian wholesale price explain the behaviour of the world reference price. Once more, this could be explained by assuming that price formation in the country effectively follows some world price forecasts. For sorghum and groundnuts, instead, the reverse is true, and the domestic price is Granger-caused by the world reference price. Within the domestic market, the maize wholesale price is caused by the retail price, and the same applies to imported rice. Results for sorghum, palm oil and cassava, instead, are inconclusive. The picture obtained here partially confirms the one derived from the annual data. Particularly, maize and rice import unit values from FAOSTAT showed in that case a significant relation with the world reference price, that appeared not to apply to the domestic wholesale price of both products, and also to the domestic price of cassava. For sorghum, however, the long-run relation with the world reference price emerged in both cases. Given the results of the previous tests, the ECM specification with the dummy variable for positive residuals was implemented only for three pairs of prices: the domestic and world reference prices of

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groundnuts, and the relation between the retail and the wholesale prices of maize and imported rice in the domestic market (Table 27). For these last two products, given the evidence that the retail price is causing the wholesale, the model is considered with the wholesale price as a dependent variable. For the groundnut price, the ECM coefficient of the model including the dummy appears slightly larger in size compared to the one without it; both coefficients, however, are very small, indicating in both cases a slow transmission of (positive and negative) shocks. Within the domestic market, instead, this difference results more substantive for rice, while is quite small in the case of maize.9 In other words, the degree of asymmetry is fairly strong for the former, and weaker for the latter product. 6.2.5 Indonesia Monthly price series for this country are mostly local wholesale prices, whose behaviour is analyzed in relation to the world reference prices. Some of the results obtained from the annual data are partially confirmed, particularly those concerning the wholesale prices of rice, while this does not apply to maize and soybeans: annual import and export unit values reported by FAOSTAT for these products show a common movement with the world reference price which is not confirmed by the monthly data for the corresponding wholesale prices. Most local price series result I(1) from both Unit root tests (see Table A21 in the Appendix), with the exceptions of local rice and sorghum. Evidence on co-integration with the world reference prices arises only for coffee (Table 28). This is a major crop in the country, whose importance as a foreign currency source is still considerable among agricultural commodities, despite reduction of its overall importance since the growth of oil exports in the 1970s. The long run parameter for coffee appears close to unity, and the results of the test for co-integration – both the ADF and the PP which is not reported in the table – are consistent with it. Moreover, the indications of a common movement in prices is also confirmed by the plots of the series, showing a lagged relation between the two prices (Figure 18). Such qualitative evidence, however, appears also for local rice (Figure 19), although the dynamic properties of this second series do not allow running meaningful correlation analysis. Drawing on this qualitative evidence, the Granger test is run also for local rice: this shows that for both local rice and coffee the world reference price is Granger-causing the local wholesale price, as one would expect (Table 29). The test for asymmetry, that could be run only for coffee, shows that positive price shocks are transmitted to the domestic market to a different extent compared to negative ones, since the dummy variable for positive residuals is significant, and that positive shocks are transmitted more rapidly than negative ones, since the size of the ECM coefficients is almost double compared to the one of the model without the dummy (Table 30). It is interesting to note that the results indicate the presence of transmission in the rice sector, although from the causality test only. Being by far the major food staple in the country, the rice market has been extensively regulated by the BULOG, a State trading enterprise which has been holding monopoly power for a long period, and is still in charge of managing important domestic policies, such as floor prices, and foreign trade. The results obtained are indicating that in the long run the BULOG has been following, at least to some extent, the world market price trends. 6.2.6 Senegal The monthly price dataset for this country covers some of the major crops, particularly maize, rice, sorghum and groundnut. The series for this last product, however, are stationary, therefore they could not be meaningfully studied in relation with the world price. In general, transmission with the

9 Estimation of the ECM model with the wholesale price as a dependent variable and without the dummy for positive residuals in the static relation (not shown in the Tables) yielded ECM coefficients of 0.29 and 0.33 for maize and rice respectively.

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reference world prices appears limited, an observation that arose also from the analysis of the annual price series. Over the 1990s, the country underwent substantial changes in its commodity policy framework. Particularly, the privatization programme reduced the role of parastatal enterprises in the trade of some major cash crops, such as groundnuts and cotton, and the overall level of public intervention in agricultural markets. From the available data, however, these changes do not appear to have caused systematic changes. The 1994 devaluation of the CFA Franc, instead, is shown by the data as a sudden fall in the local price expressed in US dollar; nonetheless the effect of the devaluation seems to vanish within a short period, and prices appear to go back to about their previous trends (Figures 20 and 21). The unit root tests indicate that maize and sorghum prices are I(1), that groundnuts prices, both those of the oilcrop and for direct consumption, are stationary, while they diverge between the PP and ADF for cassava, palm oil and rice (see Table A22 in the Appendix). For this latter product, the ADF test only indicates that the price series are I(1). Co-integration tests yield significant results only for the retail price of rice in relation to the world reference price (Table 31); this is consistent with the importance that this commodity gained in recent years among food imports. For maize, despite significant long run and ECM parameters, and although the plots appears to confirm the existence of a relation (Figure 21), the Engle and Granger procedure rejects co-integration, indicating the risk of spurious t tests results. For rice, the response to a shock in the world market price appears to take place over a far longer time period compared to maize. Lack of transmission arises from both the co-integration tests and the estimation of the ARDL models for the other products for which monthly prices are available, including sorghum, palm oil, and cassava. Concerning causality (Table 32), for both maize and rice the world reference price is Granger-causing the local price, as can be expected given the relatively small size of the Senegalese markets for these products. The ECM with the dummy variables for positive price shocks (Table 33) also yields significant results. For rice, the size of the ECM coefficient is almost four times bigger than in the model without this variable, and more than three times bigger for maize. Dealing mostly with food crops, this evidence - telling that positive price changes affect local price far faster than negative ones - indicates a potential danger in terms of food insecurity for the net buyers of these goods. 6.2.7 Turkey Results for this country appear consistent with those found in the analysis of annual data: most crop prices appear to be connected with the corresponding world reference prices, despite the relatively long periods of high inflation included in the sample – particularly the beginning and the end of the 1980s and the early 1990s - the repeated devaluations of the lira, and the crop specific support which is operated in the country for some of the major crops. As it is the case for other countries, the dataset does not cover some important products of Turkish agriculture, such as hazelnuts, cotton, olives, sugar, tea, and tobacco. The Unit root tests indicate that most monthly series are I(1). Exceptions are the wholesale price of maize, soybeans and bovine meat, and the producer price of soybeans, which are stationary (see Table A23 in the Appendix). Co-integration with the world reference prices arises for the wholesale and the producer prices of rice, and for the producer price of soybeans (Table 34). For some other products, although co-integration is rejected, a long run equilibrium with the world reference price emerges, in accordance with the results of the analysis based on annual data; this is the case of wheat - both for the producer and the wholesale prices – and of the producer price of maize and sunflower seeds. Within the domestic market, co-integration arises for wheat and rice (Table 34); for this last product, the long run parameter results significant both between the producer and the wholesale prices, and between the retail and the wholesale prices.

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Granger tests for causality indicate that the world reference prices of wheat and sunflower seeds are sources of information for the corresponding domestic wholesale prices (Table 35), while the result is inconclusive both for the wholesale and the retail prices of rice, for the wholesale prices of maize and wheat, and for the producer price of soybeans. A high degree of transmission emerges within the domestic market, instead, for rice, whose wholesale price is found to Granger-cause the corresponding retail price, and also for wheat and soybeans, whose producer prices appears to cause the corresponding wholesale ones. Finally, the ECM models with the dummy for asymmetric transmission show that the speed is on average higher for positive price shocks than for negative ones (Table 36). The model was estimated in this setting also for the transmission between the prices of wheat and sunflower seeds and the respective world references, as the results of the tests for co-integration were close to the usual level of significance. The same evidence is confirmed within the domestic market, between the producer and the wholesale prices of wheat, and between the retail and wholesale prices of rice.

7 CONCLUDING REMARKS Given the amount of information presented and its uneven composition in terms of countries, products and types of price, it is difficult to draw definite generalizations. A summary overview of the results is presented in Table 37, in which “yes” and “no” summarize the overall conclusion about the presence or absence of price transmission between the world market and the domestic markets, and within the domestic markets, while empty cells indicate that either the results did not allow for a firm conclusion, or the absence of the relevant data for a product in a country. In general, considering the results altogether, there are at least three regular features that can be identified, and few unexpected insights. Firstly, there is a geographical regularity. Results for African countries generally tend to show a lower degree of price transmission compared to that of other countries. This is partly the result of the amount of information available, which is relatively little for Senegal, Ghana, and Ethiopia; but at least for Uganda and Egypt a considerable amount of information is available, and the generalized low level of price transmission with world reference prices appears quite clearly in both countries. Physical barriers, infrastructural gaps, together with remoteness and limited market sizes, are all elements to be further investigated in order to gain a wider understanding of the specific cases. Among the other areas, instead, transmission appears relatively complete in Asian countries, while the picture is somehow more mixed in Latin America for which, however, information available in the data set is far more limited. A second regular feature of the results is that vertical transmission between the producer, the wholesale, and the retail level within the countries appears generally higher than the transmission of changes in the world reference prices. This is reasonable, given that geographical and infrastructural distances are likely to imply more substantive stationary transaction costs. However, this applies far less to the relation between the retail, wholesale and domestic prices included in the database, and the trade unit values retrieved from FAOSTAT. In other words, transmission between the domestic and the border prices is fairly incomplete in many countries in which the domestic markets appears to be fairly integrated. A third regularity that appears in the results is that high and fast transmission is relatively more frequent for cereals, followed by oilseeds, while it is generally poorer for livestock products. This is a direct consequence of the overall character of these markets, and of their degree of integration and product homogeneity, which appears to be reflected in the results. Coming to the less predictable outcomes, the main ones have to do with policies. In more than one case evidence of price transmission emerged for products and countries that are known to be deeply regulated by public intervention. This is the case of the evidence on wheat in Egypt, on cereals in India and Pakistan, and on local rice in Indonesia. Although more in depth investigations may be required in these markets to better qualify the results and to increase the

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confidence in their firmness, they still indicate that in the long run also an interventionist policy environment cannot prevent domestic prices from following world price trends and signals, and/or that that policy makers were taking into account world market trends in managing of domestic markets. These results may also provide insights for policy modelling. In this area, for instance, floor prices and other main price polices have been often represented as simple wedges between the domestic and the world prices, typically the market price components of the OECD Producer Support Estimates. Depending on the floor price level relatively to the world price level, this may overstate significantly the distortionary impact of such measure if, as it appeared from this work for the Egyptian wheat and the Indonesian rice, world price signals are transmitted in the domestic market.

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FIGURES

Graph 1 pork meat prices in Costa Rica

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Ghraph 2. Wheat prices in Egypt

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Figure 2. Wheat prices in Egypt

Figure 1. Pork meat prices in Costa Rica

Figure 3. CUCSUM test for the relation between EGWHWS and EGWHRP

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Ghraph 4 . Maize prices in Egypt

Ghraph 5. Sorghum prices in Egypt

Ghraph 6. Bovine meat prices in Egypt

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Figure 4. Maize prices in Egypt

Figure 5. Sorghum prices in Egypt

Figure 6. Bovine meat prices in Egypt

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Graph 7. wheat price in Ethiopia

Graph 8. Maize prices in Ethiopia

Graph 9. Sorghum price in Ethiopia

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M5

2000

M9

2001

M1

2001

M5

ETMZRTWRMZRP ETMZPR

-3-2-10123456

1993

M9

1994

M1

1994

M5

1994

M9

1995

M1

1995

M5

1995

M9

1996

M1

1996

M5

1996

M9

1997

M1

1997

M5

1997

M9

1998

M1

1998

M5

1998

M9

1999

M1

1999

M5

1999

M9

2000

M1

2000

M5

2000

M9

2001

M1

2001

M5

ETSHRTWRSHRP

Figure 7. Wheat prices in Ethiopia

Figure 6. Maize prices in Ethiopia

Figure 6. Sorghum prices in Ethiopia

Figure 7. Wheat prices in Ethiopia Figure 7. Wheat prices in Ethiopia

28

Graph 10. Wholesale maize price in Ghana

Graph 11.wholesale and retail maize prices in Ghana

Graph 12. Wholesale sorghum price in Ghana

Graph 13.wholesale and retail sorghum prices in Ghana

0123456789

1965

M1

1966

M7

1968

M1

1969

M7

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M1

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M7

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M1

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M1

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M7

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M1

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M7

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M1

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M7

2001

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GHMZWSWRMZRP

2

2.5

3

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M1

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M7

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M1

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M7

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M1

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GHMZRTGHMZWS

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M7

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GHSHWSWRSHRP

00.5

11.5

22.5

33.5

44.5

5

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M1

1990

M7

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M1

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M7

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M1

1998

M7

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M1

1999

M7

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M1

2000

M7

2001

M1

GHSHRTGHSHWS

Figure 10. Wholesale maize prices in Ghana

Figure 11. Wholesale and retail maize prices in Ghana

Figure 12. Wholesale sorghum prices in Ghana

Figure 13. Wholesale and retail sorghum prices in Ghana

29

Graph 14. Wholesale and retail palm oil prices in Ghana

Graph 15. Groundnut price in Ghana

Graph 16: imported rice wholesale and retail prices in Ghana

Graph 17. Cassava retail and wholesale prices in Ghana

02

46

1990

M1

1990

M7

1991

M1

1991

M7

1992

M1

1992

M7

1993

M1

1993

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M1

2000

M7

2001

M1

GHPORTGHPOWS

1

2

3

4

5

6

7

8

9

1965

M1

1966

M8

1968

M3

1969

M10

1971

M5

1972

M12

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M7

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M2

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M9

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M4

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M11

1982

M6

1984

M1

1985

M8

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M3

1988

M10

1990

M5

1991

M12

1993

M7

1995

M2

1996

M9

1998

M4

1999

M11

GHGRWSWRGRRP

33.23.43.63.8

44.24.44.6

1990

M1

1990

M6

1990

M11

1991

M4

1991

M9

1992

M2

1992

M7

1992

M12

1993

M5

1993

M10

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M3

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2000

M11

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M4

GHRI1RTGHRI1WS

1

1.5

2

2.5

3

3.5

4

1990

M1

1990

M7

1991

M1

1991

M7

1992

M1

1992

M7

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M1

1993

M7

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M1

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M7

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M1

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M1

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M7

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M1

1998

M7

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M1

1999

M7

2000

M1

2000

M7

2001

M1

GHCSRTGHCSWS

Figure 14. Wholesale and retail palm oil prices in Ghana

Figure 15. Groundnut prices in Ghana

Figure 16. Imported rice wholesale and retail prices in Ghana

Figure 17. Wholesale and retail cassava prices in Ghana

30

Graph 18. Coffee price in Indonesia

Graph 19. Rice price in Indonesia

34

567

89

Jan-79

Jan-81

Jan-83

Jan-85

Jan-87

Jan-89

Jan-91

Jan-93

Jan-95

Jan-97

Jan-99

Jan-01

ISCFWSWRCFRP

4

4.5

5

5.5

6

6.5

Jan-79

Jan-81

Jan-83

Jan-85

Jan-87

Jan-89

Jan-91

Jan-93

Jan-95

Jan-97

Jan-99

Jan-01

ISRIWSWRRIRP

Graph 20. Rice price in Senegal

Graph 21. Maize price in Senegal

3.5

4

4.5

5

5.5

6

6.5

Jan-

90

Jul-9

0

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91

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92

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96

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98

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99

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9

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00

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0

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01

SNRIRTWRRIRP

2.8

3.3

3.8

4.3

4.8

5.3

5.8

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88

Jan-

89

Jan-

90

Jan-

91

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92

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94

Jan-

95

Jan-

96

Jan-

97

Jan-

98

Jan-

99

Jan-

00

Jan-

01

SNMZRTWRMZRP

Figure 18. Coffee prices in Indonesia

Figure 19. Rice prices in Indonesia

Figure 20. Rice prices in Senegal

Figure 21. Maize prices in Senegal

31

Graph 22. Wheat prices in Turkey

Graph 23. Maize prices in Turkey

44.24.44.64.8

55.25.45.65.8

6

Jan-

82

Jan-

83

Jan-

84

Jan-

85

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86

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93

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94

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95

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96

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97

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99

J an-

00

Jan-

01

TKWHPRTKWHWSWRWHRP

4

4.2

4.4

4.6

4.8

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5.2

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5.6

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82

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83

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95

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96

Jan-

97

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98

Jan-

99

Jan-

00

Jan-

01

TKMZPRTKMZWSWRMZRP

Graph 24. Rice prices in Turkey

Graph 25. Soybeans prices in Turkey

5

5.5

6

6.5

7

7.5

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82

Jan-

83

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84

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85

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01

TKRIPRTKRIWSTKRIRTWRRIRP

4

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82

Jan-

83

Jan-

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Jan-

95

Jan-

96

Jan-

97

Jan-

98

Jan-

99

Jan-

00

Jan-

01

TKSYPRTKSYWSWRSYRP

Figure 22. Wheat prices in Turkey

Figure 23. Maize prices in Turkey

Figure 25. Soybean prices in Turkey

Figure 24. Rice prices in Turkey

Figure 22. Wheat prices in Turkey

32

Graph 26. Sunflowerseed prices in Turkey

Graph 27. Bovine meat prices in Turkey

5.5

6

6.5

7

7.5

8

Jan-

82

Jan-

83

Jan-

84

Jan-

85

Jan-

86

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91

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94

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96

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97

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99

Jan-

00

Jan-

01

TKSFWSWRSFRP

4

5

6

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8

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82

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Jan-

84

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85

Jan-

86

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88

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91

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95

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96

Jan-

97

Jan-

98

Jan-

99

Jan-

00

Jan-

01

TKBMWSTKBMRTWRBMRP

Figure 26. Sunflowerseed prices in Turkey

Figure 27. Bovine meat prices in Turkey

33

TABLES

Table 1. Key to names of variables Variables' names are made up of three parts: country code+product code+type of price code Country code Product code Type of price code Brazil BR barley BY world market reference price (or proxy for) RP Chile CH bovine meat BM import unit value from FAOSTAT IM CostaRica CR butter BT export unit value from FAOSTAT EX Egypt EG cassava CS producer ESCB PR Ethiopia ET cheese CE producer FAOSTAT PF Ghana GH coffee CF wholesale ESCB WS India IN copra CP retail ESCB RT Indonesia IS maize MZ Mexico MX milk powder MP Examples Pakistan PK pig meat PM THRIWS =wholesale price of rice in Thailand Senegal SN poultry meat PL MXWHIM = import price of wheat in Mexico Thailand TH palm oil PO Turkey TK palm kernel PK Uganda UG rice RI Uruguay UY sorghum SH World WR soybeans SY sunflower seeds SF wheat WH Notes D without country specification is the dummy variable for positive residuals of the static regression employed in the ECM models to test for asymmetry in transmission All series are natural logarithms of nominal prices in US$ per tonne RI1 in Ghana is imported rice RI1 in Indonesia is local rice RI1 in Pakistan is IRRI rice; RI is Basmati rice GR1 in Senegal is groundnut for direct consumption Import and export unit values of milk powder are referred to FAOSTAT Condensed+evaporated WRWHRP = US Gulf Port (source: IMF IFS) WRMZRP = US Gulf port (source: IMF IFS) WRRIRP = Thailand (Bangkok) (source: IMF IFS) WRSYRP = US Gulf Ports (source: IMF IFS) WRBMRP = all origins US ports90=100 Australia-NZ (US Ports) (source: IMF IFS) WRPLRP = FAOSTAT world export unit value WRPMRP = FAOSTAT world export unit value WRMPRP = FAOSTAT skim cow milk powder world export unit value WRCSRP = export price for Thailand (source IMF, IFS) WRSHRP = US Gulf Port (source IMF, IFS)

34

Table 2. Brazil. Co-integration tests and ARDL long run coefficients (annual data)

Dependent variable Regressors Cointegration* Dependent

variable Regressors Cointegration*

BRWHPR = WRWHRP BRSYEX = WRSYRP 1.07 -0.29 1.14 -0.94 1.53 -1.97 9.93 -4.07

BRRIPR = WRRIRP BRSYPR = WRSYRP 0.85 -0.56 1.58 -0.40 1.91 -2.66 1.91 -2.49

BRMZIM = WRMZRP BRBMIM = WRBMRP 1.69 -1.97 -0.22 -1.44 5.27 -6.67 -0.66 -4.03

BRMZEX = WRMZRP BRBMEX = WRBMRP 0.13 -1.38 0.89 -1.20 0.28 -7.14 2.88 -2.93

BRSYIM = WRSYRP 0.45 -0.95 2.44 -3.86

see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration.

35

Table 3. Chile. Co-integration tests and ARDL long run coefficients (annual data)

Dependent variable Regressors Cointegration* Dependent

variable Regressors Cointegration*

CHWHIM = WRWHRP CHPMIM = WRPMRP 0.62 -0.88 0.63 -0.88 1.66 -3.51 2.78 -3.76

CHWHPF = WRWHRP CHPMPF = WRPMRP 0.76 -0.99 1.00 -1.14 1.51 -3.58 2.07 -3.74

CHWHWS = WRWHRP CHPMRT = WRPMRP 0.49 -0.71 1.09 0.87 5.01 -4.23 2.10 3.99

CHWHRT = WRWHRP CHPLIM = WRPLRP 0.53 -0.55 1.66 -0.39 2.39 -3.18 1.39 -2.40

CHWHWS = CHWHPF CHPLPR = WRPLRP 0.37 -0.61 0.95 -0.71 2.43 -2.79 4.09 -3.27

CHRIIM = WRRIRP CHPLWS = WRPLRP 1.12 -1.09 -0.43 -1.11 7.57 -5.26 -0.63 -3.53

CHRIPF = WRRIRP CHPLIM = CHPLWS 0.64 -1.62 -0.06 -0.39 6.11 -4.48 -0.09 3.05

CHRIWS = WRRIRP CHPLPR = CHPLIM 0.54 -1.64 0.27 -0.69 4.25 -4.56 2.28 3.79

CHMZIM = WRMZRP CHMPIM = WRMPRP 1.40 -1.08 0.22 -0.80 3.98 -2.84 0.65 -4.05

CHMZPF = WRMZRP CHMPWS = WRMPRP 0.77 -0.88 0.77 -0.84 4.33 -2.92 4.60 -3.63

CHBMIM = WRBMRP CHBTWS = WRBTRP 0.47 -0.56 0.97 -0.41 1.58 -2.74 3.88 -5.23

CHBMPF = WRBMRP CHCEWS = WRBTRP 1.26 -0.77 0.62 -1.45 2.74 -2.58 3.28 -4.90

CHBMWS = WRBMRP CHCERT = WRBTRP 0.12 -0.51 0.63 -0.45 0.22 -2.75 2.13 -4.60

CHBMWS = CHBMPF 1.02 -0.72 172.08 -2.74 see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration.

36

Table 4. Costa Rica. Co-integration tests and ARDL long run coefficients (annual data)

Dependent variable Regressor Cointegration* Dependent

variable Regressor Cointegration*

CRMZIM = WRMZRP CRBMPF = CRBMEX 1.20 -0.93 -0.14 -0.24 5.71 -3.85 -0.16 2.21 CRMZPR = WRMZRP CRPMIM = WRPMRP

0.43 -0.23 0.50 -0.75 1.15 -1.77 2.54 -3.26 CRMZPR = CRMZIM CRPMEX = WRPMRP

0.21 -0.30 0.75 -0.67 1.63 -2.02 3.08 -3.26 CRMPIM = WRMPRP CRPMPF = WRPMRP

1.16 -0.24 1.39 -0.33 1.95 -2.17 1.70 -1.91 CRBMIM = WRBMRP CRPMPF = CRPMIM

0.54 -0.75 0.34 -0.08 1.87 -3.53 0.21 -0.68 CRBMEX = WRBMRP CRPMPF = CRPMEX 1.15 -0.85 1.29 -0.57 18.12 -3.88 4.30 -2.57 CRBMPF = WRBMRP CRPLIM = WRPLRP

0.16 -0.23 1.34 -0.27 0.17 -1.51 0.71 -1.47 CRBMPF = CRBMIM CRPLEX = WRPLRP

0.39 -0.27 0.59 -1.02 1.78 -1.82 2.81 -2.99 CRBMEX = CRBMPF 0.61 -0.10 0.13 -0.59

see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration

37

Table 5. Egypt. Co-integration tests and ARDL long run coefficients (annual data)

Dependent variable Regressor Cointegration* Dependent

variable Regressor Cointegration*

EGWHIM = WRWHRP EGMZPR = EGMZIM 0.95 -0.52 1.13 -0.25 4.68 -2.66 1.54 -2.04

EGWHPR = WRWHRP EGMZWS = EGMZIM 0.91 -0.22 1.34 -0.22 0.95 -1.76 1.52 -1.79

EGWHPF = WRWHRP EGMZWS = EGMZPR 0.72 -0.25 1.00 -1.48 0.85 -1.57 42.87 -4.73 EGWHWS = WRWHRP EGMZRT = EGMZWS

0.64 -0.22 0.95 -0.49 0.80 -1.64 10.07 -2.75

EGWHRT = WRWHRP EGSHPR = WRSHRP 0.83 -0.18 1.59 -0.20 0.85 -1.51 1.26 -1.70

EGWHPR = EGWHIM EGSHPF = WRSHRP 0.96 -0.24 0.81 -0.18 1.30 -1.91 0.56 -1.22 EGWHWS = EGWHIM EGSHWS = WRSHRP

0.80 1.28 -0.17 1.21 0.80 -1.51 EGWHWS = EGWHPR EGSHRT = WRSHRP

1.04 -1.60 1.19 -0.16 70.38 -5.99 0.70 -1.53

EGWHRT = EGWHWS EGSHWS = EGSHPR 0.94 -0.52 1.01 -1.01 13.02 -2.57 43.67 -3.64

EGRIEX = WRRIRP EGSHRT = EGSHWS 1.22 -0.65 1.00 -0.76 5.48 -3.86 23.54 -3.23

EGRIPF = WRRIRP EGBUPR = WRBTRP 0.41 -0.29 1.42 -0.21 0.74 -2.11 1.44 -2.39

EGRIWS = WRRIRP EGBURT = WRBTRP 0.73 -0.32 1.54 0.47 1.14 -1.57 1.52 2.90

38

Dependent variable Regressor Cointegration* Dependent

variable Regressor Cointegration*

EGRIRT = WRRIRP EGBURT = EGBUPR 0.64 -0.13 0.99 -0.76 0.50 -1.42 37.65 -3.63

EGRIWS = EGRIPR EGCEPR = WRBTRP 1.36 -0.39 1.15 -0.24 1.28 -2.02 1.50 -2.63

EGRIRT = EGRIWS EGBMPR = WRBMRP 0.53 -0.23 0.42 -0.34 0.53 -2.21 0.42 -2.74

EGRIWS = EGRIEX EGBMPF = WRBMRP 0.20 -0.33 0.09 -0.25

0.45 -1.47 0.06 -2.17

EGMZIM = WRMZRP EGBMWS = WRBMRP 1.26 -1.05 0.29 -0.33 11.99 -4.20 0.30 -2.93

EGMZPR = WRMZRP EGBMRT = WRBMRP 0.45 -0.23 0.64 -0.35 0.44 -1.90 0.66 -2.91

EGMZPF = WRMZRP EGBMRT = EGBMWS 0.66 -0.19 1.04 -1.20 0.55 -1.27 48.33 -5.54

EGMZWS = WRMZRP EGBMWS = EGBMPR 0.92 -0.19 0.96 -1.26 0.81 -1.43 33.42 -5.04

EGMZRT = WRMZRP EGPLPF = WRPLRP 1.02 -0.19 -1.81 -0.33 0.78 -1.64 -0.86 -2.33

see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration

39

Table 6. Ghana. Co-integration tests and ARDL long run coefficients (annual data)

Dependent variable Regressors Cointegration* Dependent

variable Regressors Cointegration*

GHMZIM = WRMZRP GHRIIM = WRRIRP 1.02 -1.27 0.94 -0.80 2.78 -4.81 7.23 -3.68

GHMZPF = WRMZRP GHRIPF = WRRIRP 5.17 -0.47 3.81 -0.39 4.03 -2.68 6.62 -2.86

GHMZWS = WRMZRP GHRI1WS = WRRIRP 4.04 -0.43 3.77 -0.29 4.22 -3.09 4.83 -3.10

GHSHPF = WRSHRP GHCSPF = WRCSRP 4.34 -0.42 4.57 -0.50 4.26 -2.65 6.69 -2.78

GHSHWS = WRSHRP GHCSWS = WRCSRP 3.95 -0.40 2.62 -0.32 4.85 -3.33 2.96 -2.34 see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to

the Engle and Granger (1987) test for cointegration.

40

Table 7. India. Co-integration tests and ARDL long run coefficients (annual data)

Dependent variable Regressor Cointegration* Dependent

variable Regressor Cointegration*

INWHIM = WRWHRP INMZWS = WRMZRP 1.06 -0.49 0.55 -0.92 2.38 -2.47 5.18 -4.22

INWHPR = WRWHRP INMZRT = WRMZRP 0.25 -0.89 0.51 -0.69 3.20 -5.09 3.23 -3.23

INWHPF = WRWHRP INCSPR = WRCSRP 0.48 -0.37 0.25 -0.30 4.31 -1.73 1.90 -1.62 INWHWS = WRWHRP INCSPF = WRCSRP

0.42 -0.55 1.25 -0.62 3.76 -2.95 3.80 -3.23

INWHRT = WRWHRP INCSWS = WRCSRP 0.47 -0.34 0.31 -0.84 3.60 -1.50 3.33 -3.78

INRIIM = WRRIRP INBMEX = WRBMRP 0.67 -1.24 -0.06 -0.35 3.16 -4.83 -0.12 -2.64

INRIEX = WRRIRP INBMPF = WRBMRP 0.63 -0.31 3.57 -0.20 0.94 -2.36 1.33 -1.14

INRIPR = WRRIRP INBMWS = WRBMRP 0.21 -0.67 0.58 -0.57 1.83 -4.28 1.60 -2.72

INRIPF = WRRIRP INBMWS = INBMPF 0.64 -0.27 -0.18 0.35 2.14 -1.56 -0.55 1.54

INRIWS = WRRIRP INPMPF = WRPMRP 0.59 -0.30 4.22 -0.08 2.87 -2.31 0.49 -0.81

INRIRT = WRRIRP INPMWS = WRPMRP 0.21 -0.78 -0.09 -0.51 1.20 -3.40 -0.20 -3.82

INRIEX = INRIPR INPMRT = WRPMRP 2.75 -0.53 0.71 -0.33 4.62 -3.44 2.82 -1.67

41

Dependent variable Regressor Cointegration* Dependent

variable Regressor Cointegration*

INRIRT = INRIWS INPMWS = INPMPF 0.66 -1.19 -0.13 -0.46

2.55 -4.40 -0.66 -2.41

INMZIM = WRMZRP INPMRT = INPMWS 0.81 -0.62 0.88 -0.33 1.94 -3.28 1.10 -1.67

INMZPR = WRMZRP INMPIM = WRMPRP 0.60 -0.58 0.84 -0.77 3.32 -3.06 2.17 -3.58

INMZPF = WRMZRP INSYPF = WRSYRP 0.54 -0.79 -4.71 -0.50 2.89 -3.27 -0.20 -3.32 see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration

42

Table 8. Indonesia. Co-integration tests and ARDL long run coefficients (annual data)

Dependent variable Regressors Cointegration* Dependent

variable Regressors Cointegration*

ISBMIM = WRBMRP ISMZPF = WRMZRP 2.22 -1.29 1.17 -0.86 3.23 -4.78 8.71 -4.92

ISBMPF = WRBMRP ISCSPR = WRCSRP 1.63 -0.36 0.65 -1.12 1.44 -2.46 2.92 -3.73

ISPMIM = WRPMRP ISCSPF = WRCSRP 1.25 -0.61 1.14 -0.85 1.87 -3.44 3.41 -3.98

ISPMPF = WRPMRP ISSYIM = WRSYRP 3.18 -0.46 1.51 -0.47 3.56 -2.65 1.40 -3.12

ISPLIM = WRPLRP ISSYEX = WRSYRP 0.65 -0.37 3.58 -0.42 0.75 -1.90 2.07 -2.24

ISPLPF = WRPLRP ISSYPR = WRSYRP 2.53 -0.35 0.04 -0.59 2.63 -2.11 0.06 -1.95

ISRIIM = WRRIRP ISSYPF = WRSYRP 0.80 -0.78 1.23 -0.81 6.72 -4.67 8.32 -4.81

ISRIPF = WRRIRP ISMPIM = WRMPRP 1.13 -0.51 1.00 -0.47 4.86 -3.03 2.60 -2.70

ISMZIM = WRMZRP 0.86 -0.99 7.73 -5.57 see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration

43

Table 9. Mexico. Co-integration tests and ARDL long run coefficients (annual data)

Dependent variable Regressors Cointegration* Dependent

variable Regressors Cointegration*

MXWHIM = WRWHRP MXBMIM = WRBMRP 0.65 -0.46 1.00 -0.63 2.63 -3.29 2.85 -3.81 MXWHEX = WRWHRP MXBMEX = WRBMRP

1.18 -0.86 0.89 -0.57 3.39 -4.33 3.77 -2.90 MXWHPF = WRWHRP MXBMPF = WRBMRP

0.35 -1.01 1.35 -0.94 2.24 -3.01 3.57 -4.12

MXMZIM = WRMZRP MXPMIM = WRPMRP

0.41 -0.71 0.79 -0.75 1.74 -3.33 4.47 -3.88 MXMZEX = WRMZRP MXPMEX = WRPMRP

0.94 -0.33 0.32 -0.55 0.53 -2.01 0.89 -2.87

MXMZPF = WRMZRP MXPMPF = WRPMRP 0.72 -0.71 0.77 -0.89 2.01 -2.25 3.01 -2.62

MXSHPF = WRSHRP MXPLIM = WRPLRP 1.55 -0.33 0.41 -1.32 2.28 -1.84 2.12 -5.26

MXSYIM = WRSYRP MXPLPF = WRPLRP 0.67 -0.69 1.26 -0.53 3.34 -3.60 1.29 -2.11

MXSYPF = WRSYRP MXMPIM = WRMPRP 1.07 0.05 0.80 -0.30 2.01 0.10 1.28 -2.12 see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration

44

Table 10. Pakistan. Co-integration tests and ARDL long run coefficients (annual data)

Dependent variable Regressors Cointegration* Dependent

variable Regressors Cointegration*

PKWHIM = WRWHRP PKMZPF = WRMZRP 1.04 -0.64 0.48 -0.54 6.46 -3.18 2.69 -3.09

PKWHPR = WRWHRP PKMZWS = WRMZRP 0.35 -0.58 0.48 -0.76 2.54 -3.27 2.09 -3.54

PKWHPF = WRWHRP PKSHPF = WRSHRP 0.48 -0.48 0.10 -1.25

1.97 -2.99 1.22 -4.06

PKWHWS = WRWHRP PKSHWS = WRSHRP 0.32 -0.71 0.52 -0.74 2.41 -3.91 1.84 -3.76

PKWHRT = WRWHRP PKSHWS = PKSHPF 0.52 -0.71 1.61 -0.65 4.44 -3.51 1.89 -2.87

PKRIIM = WRRIRP PKBMIM = WRBMRP 2.73 -0.36 0.43 -0.71 1.78 -2.02 0.95 -3.53

PKRIEX = WRRIRP PKBMPF = WRBMRP 0.76 -0.92 0.84 -0.53 7.41 -4.38 1.14 -3.16

PKRIPR = WRRIRP PKBMWS = WRBMRP 0.57 -0.55 0.76 -0.73 2.26 -3.26 4.44 -4.46

PKRI1PR = WRRIRP PKBMRT = WRBMRP 0.95 -0.55 0.58 -0.86 5.16 -3.26 3.80 -4.51

PKRIPF = WRRIRP PKPMPF = WRPMRP 0.60 -0.39 1.57 -0.42

3.41 -1.99 3.86 -2.31

PKRIWS = WRRIRP PKPLPF = WRPLRP 0.52 -0.49 2.20 -0.32

2.55 -2.54 2.90 -2.10

PKRI1WS = WRRIRP PKPLRT = WRPLRP 0.85 -0.47 2.15 -0.38 3.35 -2.73 2.72 -2.14

PKRIRT = WRRIRP 0.87 -0.45 3.42 -3.24

see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration

45

Table 11. Senegal. Co-integration tests and ARDL long run coefficients (annual data)

Dependent variable Regressors Cointegration* Dependent

variable Regressors Cointegration*

SNMZPR = WRMZRP SNBMEX = WRBMRP 0.87 -0.45 1.67 -0.59

2.13 -2.59 2.37 -3.78

SNMZPF = WRMZRP SNBMPF = WRBMRP 0.73 -0.46 0.67 -0.82 1.04 -1.89 1.47 -2.66

SNRIIM = WRRIRP SNPMIM = WRPMRP

0.79 -0.70 0.68 -0.62 9.48 -3.24 2.83 -3.36

SNRIEX = WRRIRP SNPMEX = WRPMRP 1.07 -0.53 0.32 -1.10 1.16 -2.46 1.11 -4.41

SNRIPR = WRRIRP SNPMPF = WRPMRP 0.11 -0.45 0.95 -0.69

0.28 -2.52 4.66 -2.52

SNRIPF = WRRIRP SNPLIM = WRPLRP 0.32 -0.68 0.01 -0.55

1.37 -2.86 0.01 -3.27 SNMPIM = WRMPRP SNPLEX = WRPLRP

1.19 -0.55 1.46 -0.64 2.33 -2.89 5.04 -3.12

SNMPPF = WRMPRP SNPLPF = WRPLRP 0.82 -0.54 1.37 -1.07 2.36 -2.20 4.94 -3.22

SNBMIM = WRBMRP

-0.10 -0.94 -0.48 -2.96 see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration

46

Table 12. Thailand. Co-integration tests and ARDL long run coefficients (annual data)

Dependent variable Regressors Cointegration* Dependent

variable Regressors Cointegration*

THWHIM = WRWHRP THRIPF = WRRIRP 0.83 -1.04 0.75 -1.16

13.27 -3.70 8.38 -5.02 THMZIM = WRWHRP THRIWS = WRRIRP

2.32 -0.43 1.19 -0.49 2.94 -2.29 5.81 -2.99 THMZEX = WRWHRP THRIRT = WRRIRP 0.64 -1.04 0.66 -0.52 5.14 -3.75 5.64 -3.48

THMZPR = WRWHRP THCSPR = WRCSRP

0.69 -1.18 0.72 -0.95 8.58 -4.67 5.17 -4.72 THMZPF = WRWHRP THCSPF = WRCSRP

0.70 -1.27 0.43 -0.97 8.29 -4.94 1.47 -3.69 THMZWS = WRWHRP THBMPF = WRBMRP 0.74 -1.10 1.72 -0.56

11.88 -3.86 3.98 -3.29 THSHPR = WRSHRP THPLPR = WRPLRP

0.99 -0.60 1.10 -0.49 7.12 -2.89 4.99 -3.15 THSHWS = WRSHRP THPLWS = WRPLRP 1.01 -0.66 0.90 -0.57

11.75 -2.84 5.43 -3.24

THRIEX = WRRIRP THPLRT = WRPLRP 1.03 -1.02 0.62 -0.59 24.50 -4.44 3.09 -3.40

THRIPR = WRRIRP THPMPF = WRPMRP

1.15 -0.39 0.88 -0.85 5.23 -2.15 4.25 -3.80 see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration

47

Table 13. Turkey. Co-integration tests and ARDL long run coefficients (annual data)

Dependent variable Regressors Cointegration* Dependent

variable Regressors Cointegration*

TKWHEX = WRWHRP TKBMRT = TKBMWS 0.25 -0.83 1.01 -0.70 0.77 -4.42 12.09 -3.09 TKWHPR = WRWHRP TKPLPR = WRPLRP

0.48 -0.47 2.38 -0.57 2.53 -2.39 3.84 -2.46 TKWHPF = WRWHRP TKPLWS = WRPLRP

0.48 -0.49 1.05 -0.74 1.89 -2.53 7.25 -3.48 TKWHWS = WRWHRP TKPLRT = WRPLRP 0.50 -0.67 1.01 -0.56 3.07 -3.07 4.14 -2.92

TKRIIM = WRRIRP TKSYPR = WRSYRP 0.87 -0.82 0.60 -0.69 4.91 -3.93 1.61 -3.30

TKRIPR = WRRIRP TKSYPF = WRSYRP 0.66 -0.66 0.83 -0.37 3.76 -3.55 2.89 -1.17

TKRIPF = WRRIRP TKSFPR = WRSFRP 0.62 -0.48 0.43 -0.74 1.50 -1.88 3.64 -3.75

TKRIWS = WRRIRP TKBUPR = WRBTRP 0.89 -0.72 0.18 -0.65 6.30 -4.28 1.09 -2.70

TKRIRT = WRRIRP TKBUWS = WRBTRP 0.75 -0.64 0.60 -0.39 4.67 -3.66 1.99 -3.20

TKMZPR = WRMZRP TKBURT = WRBTRP 0.80 -0.57 0.57 -0.45

3.84 -3.14 2.16 -3.40

TKMZPF = WRMZRP TKBUWS = TKBUPR 0.76 -0.50 0.80 -0.21 2.66 -2.66 0.48 -2.11

TKMZWS = WRMZRP TKBURT = TKBUWS

0.76 -0.56 0.80 -0.61 3.35 -3.47 5.96 -3.27

48

Dependent variable Regressors Cointegration* Dependent

variable Regressors Cointegration*

TKBMEX = WRBMRP TKCEPR = WRBTRP 0.56 -1.01 8.32 -0.09

2.60 -4.23 0.17 -0.66

TKBMPF = WRBMRP TKCEWS = WRBTRP 0.65 -0.18 1.43 -0.36 0.36 -1.26 2.37 -2.43 TKBMWS = WRBMRP TKCERT = WRBTRP 0.22 -0.36 0.75 -0.45 0.45 -2.30 2.90 -3.23 TKBMRT = WRBMRP TKCEWS = TKCEPR

0.41 -0.32 -0.87 -0.08 0.82 -2.13 -1.77 -0.65 TKBMWS = TKBMPF TKCERT = TKCEWS

0.29 -0.42 0.61 -0.22 2.96 -2.21 1.87 -2.30 see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration

49

Table 14. Uganda. Co-integration tests and ARDL long run coefficients (annual data)

Dependent variable Regressors Cointegration* Dependent

variable Regressors Cointegration*

UGWHIM = WRWHRP UGBMPR = WRBMRP 0.70 -0.68 -0.06 -0.94 4.49 -3.09 -0.36 -4.43 UGWHPR = WRWHRP UGBMWS = WRBMRP

-0.11 -0.61 -0.07 -0.78 -0.40 -3.60 -0.45 -3.22 UGWHPF = WRWHRP UGBMRT = WRBMRP

1.09 -0.65 -0.02 -0.80 2.28 -3.66 -0.14 -4.33 UGWHWS = WRWHRP UGBMWS = UGBMPR -0.10 -0.63 1.15 -0.84 -0.39 -3.70 15.16 -4.47 UGWHRT = WRWHRP UGPMWS = UGPMPR -0.08 -0.71 1.12 -0.29 -0.34 -2.92 7.91 -1.54 UGWHWS = UGWHPR UGPMPR = WRPMRP 0.94 -1.17 0.00 -1.00 46.06 -3.94 0.03 -5.37 UGWHRT = UGWHWS UGPMWS = WRPMRP

0.95 -1.07 -0.11 -0.92 38.44 -3.93 -0.82 -5.54

UGRIPR = WRRIRP UGPMRT = WRPMRP 0.19 -0.37 -0.07 -0.83 0.32 -2.44 -0.44 -5.22

UGRIPF = WRRIRP UGPMRT = UGPMWS 1.02 -0.67 1.14 -0.78 1.89 -3.39 27.53 -2.84

UGRIWS = WRRIRP UGPLPR = WRPLRP 0.26 0.64 0.12 -0.87 0.54 -2.84 0.56 -5.13

UGRIRT = WRRIRP UGPLWS = WRPLRP 0.23 -0.50 0.12 -0.82

0.61 -2.78 0.56 -4.96

UGRIWS = UGRIPR UGPLRT = WRPLRP 0.93 -0.44 0.12 -0.82 20.98 -2.69 0.60 -5.32

UGRIRT = UGRIWS UGPLWS = UGPLPR 0.87 -0.95 1.00 -0.99

47.45 -3.64 48.56 -3.81 UGMZIM = WRMZRP UGPLRT = UGPLWS

0.73 -0.91 0.97 -1.83 2.55 -4.78 73.51 -6.15

UGMZPR = WRMZRP UGMPIM = WRMPRP 0.57 -0.81 0.53 -1.00

2.55 -3.30 2.32 -3.66

50

Dependent variable Regressors Cointegration* Dependent

variable Regressors Cointegration*

UGMZPF = WRMZRP UGSYPR = WRSYRP 1.30 -0.80 0.62 -0.94 2.37 -3.23 2.87 -3.93 UGMZWS = WRMZRP UGSYPF = WRSYRP 0.49 -0.84 1.49 -0.64 1.94 -3.50 2.06 -2.83 UGMZRT = WRMZRP UGSYWS = WRSYRP

0.22 -0.78 0.40 -0.79 0.67 -3.39 1.32 -3.60 UGMZWS = UGMZPR UGSYRT = WRSYRP

0.78 -0.89 0.19 -0.64 7.13 -3.39 0.54 -3.44 UGMZRT = UGMZWS UGSYRT = UGSYWS

0.96 -1.04 0.97 -1.00 27.28 -3.41 21.60 -3.72

UGSHRT = UGSHWS UGCSPR = WRCSRP 0.92 -0.94 -0.66 -0.31 21.86 -3.86 -0.71 -2.02 UGSHWS = UGSHPR UGCSPF = WRCSRP

0.87 -1.40 1.63 -0.70 18.32 -4.76 4.16 -2.92

UGSHPR = WRSHRP UGCSWS = WRCSRP 0.48 -0.83 -0.71 -0.84 2.29 -3.59 -0.78 -4.47

UGSHPF = WRSHRP UGCSRT = WRCSRP 1.50 -0.69 -0.65 -0.31 2.49 -2.89 -0.78 -1.97 UGSHWS = WRSHRP UGCSWS = UGCSPR

0.27 -0.75 0.97 -0.79 0.90 -3.53 21.83 -3.37

UGSHRT = WRSHRP UGCSRT = UGCSWS 0.26 -0.79 0.94 -1.30 0.92 -3.56 63.30 -5.86 UGBMIM = WRBMRP

0.32 -0.48 0.62 -2.98 see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration

51

Table 15. Uruguay. Co-integration tests and ARDL long run coefficients (annual data)

Dependent variable Regressors Cointegration* Dependent

variable Regressors Cointegration*

UYWHIM = WRWHRP UYPMPF = WRPMRP 1.14 -1.42 0.96 -1.20

6.72 -6.12 0.67 -3.91 UYWHPR = WRWHRP UYSHPR = WRSHRP

-0.03 -0.54 0.81 -1.01 -0.02 -3.43 6.33 -4.19 UYWHPF = WRWHRP UYSHPF = WRSHRP

0.43 -0.54 0.91 -0.95 0.41 -2.62 5.56 -3.46 UYWHPR = UYWHIM UYSYIM = WRSYRP

0.14 -0.33 0.16 -1.19 0.35 -2.44 0.61 -5.98

UYRIEX = WRRIRP UYSYPF = WRSYRP 1.25 -0.31 0.50 -1.24 4.62 -2.18 4.90 -6.25

UYRIPR = WRRIRP UYSFPR = WRSFRP 0.33 -0.92 0.68 -1.21 2.27 -3.32 10.93 -4.62

UYRIPF = WRRIRP UYSFPF = WRSFRP 1.09 -0.60 0.88 1.38 5.02 -2.32 7.94 -5.44 UYMZIM = WRMZRP UYBMEX = WRBMRP

-0.22 -0.73 0.72 -0.80 -0.29 -3.02 4.60 -3.64

UYMZPF = WRMZRP UYBMPF = WRBMRP 0.69 -0.61 2.67 -1.16 2.35 -2.82 1.59 -3.90 UYPMEX = WRPMRP

0.51 -1.50 1.64 -5.64 see Table 1 for variables' names *figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration

52

Table 16. Costa Rica. Co-integration tests, ARDL long run coefficients, and ECM representations

(montlhy data)

Dependent variable Regressors

sample: Jan 1995 - May 2001 CRPOWS = WRPORP cointegration *

0.47 -0.41 0.85 -3.56

sample: Jan 1995 - May 2001 CRPMWS = WRPMRP cointegration *

0.16 -0.39 3.05 -4.51

dCRPMWS = dCRPMWS1 dCRPMWS4 dWRPMRP3 dWRPMRP5 dWRPMRP9 ecm(-1) 0.91 0.50 -0.17 -0.21 -0.17 -0.81 5.11 2.83 -2.05 -2.68 -2.59 -4.09

sample: Jan 1995 - May 2001 CRPMRT = CRPMWS cointegration *

0.80 -0.91 10.23 -4.82

dCRPMRT = dCRPMWS ecm(-1) 0.65 -0.81

7.53 -8.73

see Table 1 for variables' names d = differences; 1, 2, 3,… = differences lagged 1, 2, 3,… * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration source: own calculation on ESCB price data

53

Table 17. Costa Rica. Tests for Granger causality (monthly data)

Dependent variable Regressors

sample: Jan 1995 - May 2001 CRPMWS = CRPMWS(-1) CRPMWS(-2) CRPMWS(-5) WRPMRP(-4) WRPMRP(-5) WRPMRP(-6) 1.04 -0.66 -0.54 0.25 -0.26 0.20 7.78 -3.28 -3.06 2.13 -2.29 2.02

WRPMRP(-8) WRPMRP(-9) WRPMRP(-

10) 0.24 -0.28 0.24 2.45 -2.93 2.47 WRPMRP = CRPMWS(-1) CRPMWS(-3) WRPMRP(-1) WRPMRP(-2) WRPMRP(-3) 0.55 0.82 1.03 -0.53 0.71 2.00 1.93 6.98 -2.43 3.20 sample: Jan 1995 - May 2001 CRPMRT = CRPMWS(-1) CRPMRT(-1)

0.31 0.44 2.09 2.77 CRPMWS = CRPMWS(-1) CRPMRT(-1) 0.44 0.37 2.86 2.23 see Table 1 for variables' names source: own calculation on ESCB price data

Table 18. Costa Rica: ECM model with dummy variables for positive residuals of the static model (monthly data)

Dependent variable Regressors

sample: Jan 1995 - May 2001 dCRPMWS = dWRBMRP dDPMWS ecm(-1)

0.07 0.95 -0.18 2.41 76.74 -2.71 see Table 1 for variables' names d = differences; 1, 2, 3,… = differences lagged 1, 2, 3,… source: own calculation on ESCB price data

54

Table 19. Egypt. Co-integration tests, ARDL long run coefficients and ECM representations (monthly data) Dependent

variable Regressors

sample: Aug 1989 - May 2001 EGWHWS = WRWHRP cointegration * 1.24 -0.09 2.10 -3.47 dEGWHWS = dEGWHWS1 dEGWHWS4 dEGWHWS6 dEGWHWS8 dWRWHRP ecm-1 -0.21 -0.17 -0.23 0.20 0.09 -0.07 -2.50 -2.13 -3.05 2.67 2.81 -3.45 sample: Jan 1969 - May 2001 = WRWHWS cointegration * 0.93 -0.19 10.62 -4.95 dEGWHRT = dEGWHRT1 dEGWHRT2 dEGWHRT3 dEGWHRT4 dEGWHRT5 dEGWHRT6 dEGWHRT7 dEGWHWS dEGWHWS1 -0.23 -0.17 -0.18 -0.16 -0.22 -0.22 -0.09 0.66 0.21

-3.83 -2.80 -3.12 -2.89 -4.10 -4.05 -1.62 21.40 3.96 dEGWHWS2 dEGWHWS3 dEGWHWS4 dEGWHWS5 dEGWHWS6 dEGWHWS7 dEGWHWS8 ecm-1

0.15 0.15 0.17 0.20 0.19 0.10 0.09 -0.07 2.79 2.86 3.35 3.89 3.78 2.05 2.99 -1.93 sample: Jan 1969 - May 2001 EGSHRT = EGSHWS cointegration * 1.00 -0.22 -23.21 -5.27 dEGSHRT = dEGSHRT1 dEGSHRT4 dEGSHRT6 dEGSHRT7 dEGSHWS dEGSHWS1 dEGSHWS2 dEGSHWS3 dEGSHWS4 -0.21 -0.14 -0.11 -0.08 0.77 0.19 0.15 0.13 0.13 -3.48 -2.62 -2.18 -2.53 -23.94 -3.24 -2.69 -2.35 -2.31 dEGSHWS5 dEGSHWS6 ecm-1 0.12 0.13 -0.12 2.26 2.62 -2.77

sample: Jan 1969 - Jul 1989 EGBMWS = WRBMRP cointegration *

-0.71 -0.11 -3.26 -3.50

dEGBMWS = dWRBMRP ecm-1 -0.06 -0.09 -2.58 -3.45

sample: Jan 1969 - May 2001 EGBMRT = EGBMWS cointegration * 1.03 -0.25 62.95 -5.24 dEGBMRT = dEGBMRT1 dEGBMRT11 dEGBMWS dEGBMWS1 ecm-1

-0.16 0.14 0.82 0.18 -0.28 -3.11 4.42 24.77 3.34 -6.93

see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration source: own calculation on ESCB price data

55

Table 20. Egypt. Tests for Granger causality (monthly data)

Dependent variable Regressors

sample: Aug 1989 - May 2001 EGWHWS = WRWHRP-1 EGWHWS-1 0.10 0.71 3.08 8.36 WRWHRP = WRWHRP-1 0.97 34.63 sample: Jan 1969 - Jul 1989 EGBMWS = EGBMRP-1 WRBMWS-1 -0.06 0.91 -2.45 36.26 WRBMRP = EGBMRP-1 0.95 43.03 sample: Jan 1969 - May 2001 EGWHWS = EGWHRT-1 EGWHRT-8 EGWHWS-9 0.84 0.18 -0.24 10.70 2.25 -5.43 sample: Jan 1969 - May 2001 EGWHRT = EGWHRT-1 EGWHWS-1 EGWHWS-2 EGWHWS-9 0.22 0.79 0.28 -0.23 2.45 9.89 2.91 -4.61 sample: Jan 1969 - May 2001 EGSHRT = EGSHWS-1 EGSHRT-1 0.24 0.77 2.96 9.26 EGSHWS = EGSHWS-1 0.92 11.02 sample: Jan 1969 - May 2001 EGBMRT = EGBMWS-1 EGBMRT-1 0.20 0.80 3.15 13.10 EGBMWS = EGBMWS-1 EGBMRT-1 0.84 0.14 13.83 2.45 see Table 1 for variables' names source: own calculation on ESCB price data

56

Table 21. Egypt. ECM models with dummy variables for positive residuals of the static model

(monthly data)

Dependent variable Regressors

sample: Aug 1989 - May 2001 dEGWHWS = dEGWHWS6 dEGWHWS8 dEGWHWS9 dWRDWH ecm(-1) -0.14 0.15 0.16 0.20 -0.24 -2.85 2.82 3.08 6.42 -4.00 sample: Jan 1969 - Jul 1989 dEGBMWS = dWRBMRP dDBM ecm(-1) -0.15 0.10 -0.23 -5.30 9.54 -5.95 sample: Jan 1969 - May 2001 dEGSHRT = dEGSHRT2 dEGSHWS dDSH dDSHR1 ecm(-1) 0.05 0.87 0.07 -0.01 -0.50 2.07 34.94 16.38 -2.24 -11.44 see Table 1 for variables' names Bold = significantly different from zero at 5 percent or less * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration

57

Table 22. Ethiopia. Co-integration tests, ARDL long run coefficients and ECM representations

(monthly data) Dependent variable Regressors

sample: Sept 1993 - May 2001 4 = WRWHRP cointegration * -0.43 -0.31 -4.25 -3.53 dETWHRT = dETWHRT1 dETWHRT3 dWRWHRP ecm(-1) 0.32 0.26 -0.14 -0.33 2.84 2.29 -3.41 -4.06 sample: Sept 1993 - May 2001 ETMZRT = WRMZRP cointegration * -0.86 -0.43 -3.35 -3.83 dETMZRT = dWRMZRP dWRMZRP2 dWRMZRP3 ecm(-1) -0.53 -0.50 0.60 -0.32 -2.03 -1.90 2.46 -3.71 sample: Sept 1993 - May 2001 ETMZPR = WRMZRP cointegration * -1.48 -0.26 -2.14 -3.70 dETMZPR = dETMZPR1 dWRMZRP3 ecm(-1) 0.37 0.55 -0.25 2.74 2.54 -3.02 sample: Sept 1993 - May 2001 ETSHRT = WRSHRP cointegration * -0.83 -0.56 -2.28 -3.50 dETSHRT = ecm(-1) -0.20

-2.54 see Table 1 for variables' names d = differences; 1, 2, 3,… = differences lagged 1, 2, 3,… * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration source: own calculation on ESCB price data

58

Table 23. Ethiopia. Tests for Granger causality (monthlydata)

Dependent variable Regressors

sample: Sept 1993 - May 2001 ETWHRT ETWHRT(-1)

0.82 12.16 WRWHRP WRWHRP(-1) WRWHRP(-2) 1.21 -0.42 10.99 -2.46 sample: Sept 1993 - May 2001 ETMZRT WRMZRP(-3) WRMZRP(-4) ETMZRT(-1)

1.05 -0.50 0.71 2.60 -2.15 8.82 WRMZRP WRMZRP(-1) WRMZRP(-2) 1.36 -0.45 12.48 -2.46 sample: Sept 1993 - May 2001 WRMZPR WRMZPR(-1) WRMZPR(-3)

0.76 -0.28 4.54 -1.40

ETMZRP WRMZPR(-1)

1.23 7.61

sample: Sept 1993 - May 2001

ETSHRT ETSHRT(-1) WRSHRP(-1) 0.65 -0.14 6.00 -1.91 WRSHRP WRSHRP(-1)

0.95 23.13 see Table 1 for variables' names source: own calculation on ESCB price data

59

Table 24. Ethiopia. ECM model with dummy variables for positive residuals of the static model

(monthly data) Dependent

variable Regressors

sample: Sept 1993 - May 2001 dETMZRT = dWRMZRP dWRMZRP3 dWRDMZRT1 ecm(-1) -0.75 0.40 0.19 -0.58 -3.77 2.02 7.51 -7.25 sample: Sept 1993 - May 2001 dETSHRT = dWRSHRP1 dWRSHRP2 dWRSHRP3 dWRSHRP4 dWRSHRP5 0.72 0.65 0.72 0.63 0.73 2.27 2.04 2.43 2.56 2.94 dWRSHRP7 dWRSHRP9 dWRDSHRT ecm(-1) 0.58 0.67 0.13 -0.59 2.33 2.70 4.01 -5.43 see Table 1 for variables' names d = differences; 1, 2, 3,… = differences lagged 1, 2, 3,… source: own calculation on ESCB price data

60

Table 25. Ghana. Co-integration tests, ARDL long run coefficients and ECM representations (monthly data)

Dependent

variable Regressors

sample: Jan 1965 - May 2001 GHMZWS = WRMZRP cointegration *

2.89 -0.04 2.72 -2.99

dGHMZWS = dGHMZWS1 dGHMZWS7 dGHMZWS8 dGHMZWS11 dWRMZRP8 ecm(-1) 0.16 0.09 -0.12 0.26 -0.73 -0.04 3.46 1.99 -2.57 5.57 -3.65 -2.97

sample: Jan 1967 - May 2001 GHSHWS = WRSHRP cointegration *

2.62 -0.06 1.92 -2.57

dGHSHWS = dGHSHWS1 dGHSHWS2 ecm(-1) -0.37 -0.12 -0.05 -7.41 -2.36 -2.66

sample: Jan 1965 - May 2001 GHGRWS = WRGRRP cointegration *

2.54 -0.07 2.87 -4.18

dGHGRWS = dGHGRWS5 dGHGRWS9 dGHGRWS11 dWRGRRP1 ecm(-1) -0.34 -0.17 0.15 -0.29 -0.03 -6.39 -2.99 2.44 -2.31 -2.46

sample: Jan 1990 - May 2001 GHMZRT = GHMZWS cointegration *

0.71 -0.31 8.78 -3.60

dGHMZRT = dGHMZRT1 dGHMZRT4 dGHMZRT5 dGHMZWS dGHMZWS4 dGHMZWS5 0.28 0.21 0.23 0.73 -0.18 -0.15 3.08 2.23 2.48 15.34 -2.29 -1.97

sample: Jan 1990 - May 2001

GHSHRT = GHSHWS cointegration * 0.62 -0.34 5.88 -3.80

dGHSHRT = dGHSHWS ecm(-1)

0.09 -0.22 3.13 -4.50

sample: Jan 1990 - May 2001 GHPORT = GHPOWS cointegration *

0.66 -0.49 4.97 -5.14

61

Dependent variable Regressors

dGHPORT = dGHPORT3 dGHPORT4 dGHPOWS ecm(-1) 0.17 -0.22 0.11 -0.37 1.97 -2.58 2.34 -4.81

sample: Jan 1990 - May 2001

GHRI1RT = GHRI1WS cointegration * 0.69 -0.40 3.88 -4.64

dGHRI1RT = dGHRI1RT1 dGHRI1WS ecm(-1) -0.27 0.19 -0.27 -2.92 2.19 -3.09

sample: Jan 1990 - May 2001 GHCSRT = GHCSWS cointegration *

0.73 -0.49 6.53 -3.71

dGHCSRT = dGHCSRT1 dGHCSWS ecm(-1) -0.19 0.35 -0.48

-2.13 4.41 -5.35 see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration source: own calculation on ESCB price data

62

Table 26. Ghana. Tests for Granger causality (monthly data)

Dependent variable Regressors

sample: Jan 1965 - May 2001

GHMZWS GHMZWS(-1) GHMZWS(-2) GHMZWS(-7) GHMZWS(-8) GHMZWS(-11) GHMZWS(-12) 1.15 -0.18 0.16 -0.22 0.32 -0.28

24.58 -2.52 2.32 -3.15 4.56 -5.92

WRMZRP GHMZWS(-1) WRMZRP(-1) 0.03 0.98

2.27 85.58

sample: Jan 1967 - May 2001

GHSHWS GHSHWS(-1) GHSHWS(-2) GHSHWS(-3)

0.58 0.25 0.12

11.81 4.54 2.43

WRSHRP WRSHRP(-1) 0.96

69.58

sample: Jan 1965 - May 2001

GHGRWS GHGRWS(-1) GHGRWS(-6) GHGRWS(-12) WRGRRP(-2) 1.02 0.31 -0.16 0.24 20.91 4.07 -2.51 2.05

WRGRRP WRGRRP(-1) WRGRRP(-2) 1.23 -0.30

26.19 -6.30

sample: Jan 1990 - May 2001

GHMZRT GHMZRT(-1) GHMZRT(-2) 1.56 -1.00

9.26 -4.04

GHMZWS GHMZWS(-1) GHMZRT(-1) GHMZRT(-2) 0.73 0.72 -0.98

4.35 3.83 -3.53

sample: Jan 1990 - May 2001 GHSHRT GHSHWS(-1) GHSHRT(-1)

0.08 0.82 2.33 16.17

GHSHWS GHSHWS(-1) GHSHRT(-1) 0.32 0.59 3.79 4.52

sample: Jan 1990 - May 2001 GHPOWS GHPORT(-1) GHPORT(-3) GHPORT(-4)

0.77 0.47 -0.59 4.73 2.49 -3.07

GHPORT GHPORT(-1) GHPORT(-3) GHPORT(-4) GHPOWS(-1) 0.69 0.24 -0.42 0.15 7.84 2.39 -4.00 3.03

63

Dependent variable Regressors

sample: Jan 1990 - May 2001

GHCSRT GHCSRT(-1) GHCSRT(-2) GHCSWS(-1) 0.32 0.25 0.28 3.66 3.00 3.76

GHCSWS GHCSRT(-2) GHCSWS(-1) 0.24 0.61 2.85 8.03

sample: Jan 1990 - May 2001 GHRI1RT GHRI1RT(-1) GHRI1RT(-2)

0.52 0.32 6.02 3.58

GHRI1WS GHRI1RT(-1) GHRI1WS(-1) 0.17 0.69

2.71 11.55 see Table 1 for variables' names source: own calculation on ESCB price data

64

Table 27. Ghana. ECM model with dummy variables for positive residuals of the static model (monthly data

Dependent variable Regressors

sample: Jan 1965 - May 2001 dGHGRWS = dGHGRWS5 dGHGRWS9 dGHGRWS11 dWRGRRP dWRGRRP1 dDGRWS ecm(-1)

-0.28 -0.13 0.14 0.26 -0.26 0.32 -0.07 -5.64 -2.54 2.46 2.20 -2.30 8.26 -3.58

sample: Jan 1990 - May 2001 dGHMZWS dGHMZWS4 dGHMZWS5 dGHMZRT dGHMZRT4 dGHMZRT5 dD1MZWS ecm(-1) 0.24 0.18 0.93 -0.28 -0.23 0.07 -0.33 2.96 2.15 16.17 -2.89 -2.37 4.41 -4.69 sample: Jan 1990 - May 2001 dGHRI1WS = dGHRI1RT dD1RI1WS ecm(-1) 0.30 0.09 -0.36 6.51 8.46 -6.49

see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger 1987 test for cointegration d = differences; 1, 2, 3,… = differences lagged 1, 2, 3,… source: own calculation on ESCB price data

Table 28. Indonesia. Co-integration tests, ARDL long run coefficients and ECM representation (monthly data)

Dependent variable Regressors

sample: Jan 1979 - May 2001 ISRIWS = WRRIRP cointegration *

0.81 -0.14 1.83 -4.21

sample: Jan 1979 - May 2001 ISCFWS = WRCFRP cointegration *

1.00 -0.11 3.40 -3.59

dISCFWS = dWRCFRP ecm(-1) 0.10 -0.10

3.87 -4.01

see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration source: own calculation on ESCB price data

65

Table 29. Indonesia. Tests for Granger causality (monthly data)

Dependent variable Regressors

sample: Jan 1979 - May 2001 ISCFWS = WRCFRP(-1) ISCFWS(-1) ISCFWS(-2) ISCFWS(-4)

0.08 0.69 0.29 -0.16 3.01 10.48 3.69 -2.50

sample: Jan 1979 - May 2001 WRCFRP = WRCFRP(-1) 1.00 63.12 sample: Jan 1979 - May 2001 ISRIWS WRRIRP(-1) ISRIWS(-1) -0.10 0.31 -2.71 12.38 sample: Jan 1979 - May 2001 WRRIRP WRRIRP(-1) 0.97 62.53 see Table 1 for variables' names source: own calculation on ESCB price data

Table 30. Indonesia. ECM model with dummy variables for positive residuals of the static model

Dependent variable Regressors

sample: Jan 1979 - May 2001 dISCFWS dISCFWS1 dISCFWS3 dWRCFRP dDCF ecm(-1)

-0.21 0.19 0.36 0.25 -0.21 -3.22 3.19 3.36 7.16 -4.69

dISRI1WS dDRI ecm(-1) 0.13 -0.21 8.06 -5.32 see Table 1 for variables' names source: own calculation on ESCB price data

66

Table 31. Senegal. Co-integration tests, ARDL long run coefficients and ECM

representation (monthly data) Dependent

variable Regressors

sample: Jan 1990 - May 2001 SNRIRT = RIRP cointegration *

0.46 -0.31 2.51 -3.82

dSNRIRT = dSNRIRT1 dWRRIRP2 dWRRIRP6 ecm(-1) 0.25 -0.57 -0.59 -0.31 2.85 -3.76 -4.15 -4.93

sample: Jan 1988 - May 2001 SNMZRT = WRMZRP cointegration *

0.59 -0.19 2.15 -3.01

dSNMZRT = dWRMZRP ecm(-1)

-0.28 -0.15 -2.01 -3.71

see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration source: own calculation on ESCB price data

Table 32. Senegal. Tests for Granger causality (monthly data)

Dependent variable Regressors

sample: Jan 1990 - May 2001 SNRIRT WRRIRP(-12) SNRIRT(-1) SNRIRT(-10)

0.22 0.48 0.42 2.15 2.37 1.96 WRRIRP WRRIRP(-1)

1.00 4.59 sample: Jan 1988 - May 2001 SNMZRT WRMZRP(-1) SNMZRT(-1)

0.08 0.86 2.06 22.29 WRMZRP WRMZRP(-1) 0.95 35.48 see Table 1 for variables' names source: own calculation on ESCB price data

Table 33. Senegal. ECM model with dummy variables for positive residuals of the static model (monthly data)

67

Dependent variable Regressors

sample: Jan 1990 - May 2001 SNRIRT WRRIRP DRIRT 0.50 0.11 8.11 17.67 dSNRIRT dSNRIRT1 dSNRIRT2 dSNRIRT3 dWRRIRP dWRRIRP1 dDRIRT dDRIRT1 dDRIRT2 dDRIRT3 dDRIRT4 ecm(-1) 0.71 0.47 0.33 0.25 -0.40 0.05 -0.07 -0.04 -0.03 -0.03 -1.33 3.03 2.61 2.08 3.71 -3.22 8.36 -2.78 -2.37 -2.81 -3.12 -4.35sample: Jan 1988 - May 2001 SNMZRT DMZRT 0.20 5.44 dSNMZRT dMZRP1 dDMZRT ecm(-1) -0.41 0.10 -0.51 -2.27 3.90 -4.93 see Table 1 for variables' names d = differences; 1, 2, 3,… = differences lagged 1, 2, 3,… source: own calculation on ESCB price data

68

Table 34. Turkey. Co-integration tests, ARDL long run coefficients and ECM representation (monthly data)

Dependent variable Regressors Dependent

variable Regressors

sample: Jan 1982 - May 2001 TKWHWS TKWHRP dTKWHWS dTKWHWS1 dTKWHWS3 dWRWHRP6 ecm(-1)

0.77 -0.13 0.17 -0.15 -0.32 -0.20 4.08 -3.25 2.57 -2.11 -2.39 -4.75 sample: Jan 1985 - May 2001

TKWHPR WRWHRP dTKWHPR dWRWHRP ecm(-1) 0.94 -0.11 0.08 -0.08 2.52 -3.05 2.70 -2.82 sample: Jan 1985 - May 2001

TKMZPR WRMZRP dTKMZPR dWRMZRP ecm(-1) 0.61 -0.11 0.05 -0.09 2.17 -2.89 1.93 -2.70 sample: Jan 1982 - May 2001

TKRIWS WRRIRP dTKRIWS dTKRIWS8 dWRRIRP ecm(-1) 0.56 -0.18 0.22 0.08 -0.14 2.78 -4.30 3.11 2.46 -3.33 sample: Jan 1985 - May 2001

TKRIPR WRRIRP dTKRIPR dWRRIRP ecm(-1) 0.51 -0.22 0.07 -0.15 2.76 -4.28 2.24 -3.61 sample: Jan 1985 - May 2001

TKSYPR WRSYRP dTKSYPR dTKSYPR2 dWRSYRP2 dWRSYRP6 ecm(-1) 0.45 -0.23 0.18 -0.34 -0.48 -0.34 2.63 -4.06 2.51 -1.94 -2.86 -5.99 sample: Jan 1982 - May 2001

TKSFWS WRSFRP dTKSFWS dWRSFRP5 dWRSFRP6 ecm(-1) 0.55 -0.11 0.14 0.13 -0.06 2.02 -3.27 2.33 2.18 -2.39 sample: Jan 1985 - May 2001

TKWHWS TKWHPR dTKWHWS dTKWHWS1 dTKWHWS2 dTKWHWS3 dTKWHPR ecm(-1)

0.85 -0.39 0.20 0.15 -0.16 0.45 -0.34

8.36 -5.22 2.88 2.11 -2.22 4.83 -5.58

sample: Jan 1985 - May 2001 TKRIWS TKRIPR dTKRIWS dTKRIWS4 dTKRIWS8 dTKRIPR ecm(-1)

0.87 -0.30 0.14 0.34 0.47 -0.33 8.36 -4.63 1.98 4.79 6.45 -5.74 sample: Jan 1982 - May 2001

TKRIRT TKRIWS dTKRIRT dTKRIWS ecm(-1) 0.71 -0.25 0.46 -0.28 11.32 -3.72 11.77 -6.09 see Table 1 for variables' names * figures reported are the ADF coefficients and t values for the level of the residual of the static regression between the two variables reported on the left. This corresponds to the Engle and Granger (1987) test for cointegration source: own calculation on ESCB price data

69

Table 35. Turkey. Tests for Granger causality (monthly data)

Dependent variable Regressors Dependent

variable Regressors

sample: Jan 1982 - May 2001 TKWHWS TKWHWS(-1) WRWHRP(-6) TKWHRP WRWHRP(-1) WRWHRP(-2) 0.98 0.30 1.28 -0.39 sample: Jan 1982 - May 2001 TKWHPR TKWHPR(-1) TKWHRP TKWHRP(-1) WRWHRP(-2) 0.89 1.28 -0.37 12.11 17.29 -3.09 sample: Jan 1982 - May 2001 TKMZPR TKMZPR(-1) TKMZRP WRMZRP(-1) 0.92 0.95 28.27 40.02 sample: Jan 1982 - May 2001 TKRIWS TKRIWS(-1) TKRIWS(-8) WRRIRP(-1) 0.93 0.32 0.09 13.93 3.37 2.68 TKRIRP TKRIWS(-1) TKRIWS(-3) WRRIRP(-1) 0.11 -0.18 0.95 2.07 -2.51 37.20 sample: Jan 1982 - May 2001 TKRIPR TKRIPR(-1) WRRIRP(-1) TKRIRP TKRIPR(-1) WRRIRP(-1) 0.87 0.07 0.09 0.92 22.20 2.03 2.58 32.94 sample: Jan 1983 - May 2001 TKSYPR TKSYPR(-1) WRSYRP WRSYRP(-1) WRSYRP(-2) 0.69 1.30 -0.41 9.57 17.72 -3.40 sample: Jan 1982 - May 2001 TKSFWS TKSFWS(-1) WRSFRP(-1) WRSFRP(-5) TKSFRP WRSFRP(-1) WRSFRP(-2) WRSFRP(-3) 0.93 0.14 0.26 1.31 -0.59 0.28 35.59 2.32 2.68 19.42 -5.32 2.40sample: Jan 1982 - May 2001 TKWHWS TKWHWS(-1) TKWHPR(-1) TKWHPR TKWHPR(-1) 0.75 0.36 0.92 14.01 3.57 12.11 sample: Jan 1982 - May 2001 TKRIWS TKRIWS(-1) TKRIPR(-1) TKRIPR TKRIWS(-1) TKRIWS(-8) TKRIPR(-1) 0.83 0.16 0.15 0.15 0.82 10.58 2.82 2.09 2.12 15.52sample: Jan 1982 - May 2001 TKRIRT TKRIWS(-1) TKRIRT(-1) TKRIWS TKRIWS(-1) 0.10 0.81 0.81 2.32 15.00 13.53 sample: Jan 1982 - May 2001 TKSYWS TKSYWS(-1) TKSYPR(-1) TKSYPR TKSYPR(-1) 0.93 0.09 0.79 12.63 2.15 16.05 see Table 1 for variables' names source: own calculation on ESCB price data

70

Table 36. Turkey. ECM model with dummy variables for positive residuals of the static model (monthly data) Dependent

variable Regressors

sample: Jan 1982 - May 2001 dTKWHWS dTKWHWS1 dTKWHWS3 dWRWHRP dDWHWS ecm(-1)

0.14 -0.12 0.12 0.14 -0.28 2.43 -2.01 3.86 8.14 -5.62

sample: Jan 1982 - May 2001 dTKSFWS dWRSFRP2 dWRSFRP6 dDSFWS ecm(-1)

0.11 0.17 0.08 -0.19 1.97 3.27 7.70 -5.14

sample: Jan 1982 - May 2001 dTKWHWS dTKWHWS1 dTKWHWS2 dTKWHWS5 dTKWHPR dTKWHPR2 dD1WHWS dD1WHWS1 dD1WHWS2 ecm(-1)

0.41 0.37 0.15 0.62 -0.24 0.11 -0.05 -0.05 -0.75 4.77 4.60 2.32 7.61 -2.45 7.30 -2.37 -2.99 -8.16

sample: Jan 1985 - May 2001 dTKRIRT dTKRIWS dD1RIRT ecm(-1)

0.57 0.08 -0.66 18.41 12.92 -14.41

see Table 1 for variables' names d = differences; 1, 2, 3,… = differences lagged 1, 2, 3,… source: own calculation on ESCB price data

71

Table 37. Summary of results

annual data BR CH CR EG GH IN IS MX PK SN TH TK UG UYwheat world yes no yes yes yes yes yes no domestic yes yes yes yes yes yes nomaize world yes yes no yes yes yes yes yes yes no domestic no yes yes yes yes yes yesrice world yes no yes yes yes yes yes yes yes no yes domestic yes yes yes yes yes yessorghum world no yes no yes domestic yes yesbovine meat world yes no yes no no yes yes yes yes no no yes domestic no yes no yes yespig meat world yes yes no yes yes yes no domestic no no yespoultry meat world yes yes no no yes yes no domestic yes yessoybeans world yes yes domestic yescassava world yes yes yes no domestic yes yessunflower world yes domestic milk powder world yes yes domestic butter world yes no yes domestic yes cheese world yes yes domestic no monthly data

CR EG ET GH IS SN TK sym asym sym asym sym asym sym asym sym asym sym asym sym asym

wheat world yes yes yes yes domestic yes yes yes yesmaize world yes yes yes yes yes domestic yes yes yes yesrice world no no yes yes yes yes yes domestic yes yes yes yessorghum world no yes yes no domestic yes yes yes bovine meat world no no domestic yes yes pig meat world yes yes domestic soybeans world yes yes domestic cassava world no no domestic yes sunflower world yes yes domestic groundnut world yes yes domestic coffee world yes yes domestic palm oil world no no domestic yes palm kernel world no domestic sym = from symmetric ECM; asym= from asymmetric ECM other symbols as in Table 1

72

APPENDIX: RESULTS OF THE UNIT ROOT TESTS

Table A1. World reference prices: results of the unit root tests (annual data)

WRWHRP level -0.36 -2.73first differences -1.18 -4.89

WRMZRP level -0.28 -2.35first differences -1.17 -5.09

WRRIRP level -0.43 -1.97first differences -1.11 -5.60

WRSHRP level -0.25 -2.37first differences -1.06 -4.63

WRBMRP level -0.19 -1.99first differences -1.15 -4.67

WRPMRP level -0.12 -1.40first differences -1.07 -4.46

WRPLRP level -0.12 -1.41first differences -0.78 -3.56

WRCSRP level -0.11 -1.03first differences -1.49 -6.74

WRSYRP level -0.25 -2.37first differences -1.06 -4.62

WRSFRP level -0.32 -2.62first differences -1.42 -7.06

WRMPRP level -0.34 -2.04first differences -1.40 -4.89

WRBURP level -0.22 -1.98first differences -0.93 -4.15

see Table 1 for variables' names Bold = significantly different from zero at 5% or lesssource: own calculation on ESCB price data

ADFcoefficient (-1) t- values

73

Table A2. Brazil. Results of the unit root tests (annual data)

BRWHPR level -0.10 -0.92first differences -1.53 -4.97

BRRIPR level -0.40 -2.12first differences -1.75 -5.52

BRMZIM level -0.41 -2.44first differences -1.37 -4.59

BRMZEX level -0.42 -2.28first differences -1.50 -4.34

BRSYIM level -0.57 -1.73first differences -1.97 -4.02

BRSYEX level -0.17 -1.39first differences -1.37 -4.90

BRSYPR level -0.14 -1.01first differences -1.60 -5.65

BRBMIM level -0.81 -3.65first differences

BRBMEX level -0.26 -2.09first differences -1.07 -4.51

see Table 1 for variables' namesBold = significantly different from zero at 5% or lesssource: own calculation on ESCB price data

ADFcoefficient (-1) t- values

Table A3. Chile. Results of the unit root tests (annual data)

CHWHIM level -0.84 -3.63 CHPLIM level -0.40 -2.39first differences first differences -1.45 -4.98

CHWHPF level -0.79 -3.25 CHPLPR level -0.38 -2.11first differenc -1.54 -4.50 first differences -1.11 -4.33

CHWHWS level -0.46 -2.31 CHPLWS level -1.09 -3.49first differenc -1.37 -4.04 first differences -1.98 -5.44

CHWHRT level -0.23 -3.14 CHMPIM level -0.51 -3.31first differenc -0.36 -1.96 first differences -1.37 -5.72

CHRIIM level -0.28 -2.13 CHMPWS level -0.51 -3.58first differenc -1.25 -5.35 first differences

CHRIPF level -1.11 -3.47 CHBMIM level -0.45 -2.39first differenc -2.14 -6.08 first differences -1.56 -5.17

CHRIWS level -0.92 -3.11 CHBMPF level -0.78 -3.03first differenc -1.94 -6.00 first differences -1.66 -4.64

CHMZIM level -0.78 -3.37 CHBMWS level -0.41 -2.90first differenc -1.81 -6.30 first differences -0.95 -4.18

CHMZPF level -1.55 -4.53 CHBUWS level -0.33 -2.25first differences first differences -0.87 -3.07

CHPMIM level -0.63 -3.27 CHCEWS level -0.35 -2.73first differenc -1.77 -5.83 first differences -0.77 -3.04

CHPMPF level -0.94 -3.32 CHCERT level -0.30 -2.56first differenc -1.94 -5.42 first differences -0.66 -2.75

CHPMRT level -0.37 -2.82first differenc -0.89 -3.98

see Table 1 for variables' namesBold = significantly different from zero at 5% or lesssource: own calculation on ESCB price data

t-valuest-valuesADF

coefficient (-1)ADF

coefficient (-1)

74

Table A4. Costa Rica. Results of the unit root tests (annual data)

CRMZIM level -0.37 -1.97first differences -1.95 -6.27

CRMZPR level -0.22 -1.65first differences -1.45 -4.90

CRMPIM level -0.04 -0.64first differences -0.63 -3.01

CRBMIM level -0.58 -2.95first differences -1.64 -5.72

CRBMEX level -0.14 -1.68first differences -1.17 -4.80

CRBMPF level -0.33 -2.38first differences -1.26 -4.26

CRPMIM level -0.64 -3.01first differences -1.54 -4.89

CRPMEX level -0.41 -2.36first differences -1.93 -7.07

CRPMPF level -0.07 -0.61first differences -1.73 -5.47

CRPLIM level -0.23 -1.24first differences -1.53 -4.33

See Table for variables' namesBold = significantly different from zero at 5% or lesssource: own calculation on ESCB price data

ADFcoefficient (-1) t- values

Table A5. Egypt. Results of the unit root tests (annual data)

EGWHIM level -0.41 -2.80 EGMZPF level -1.22 -1.22first differenc -1.47 -4.14 first differences -0.98 -3.37

EGWHPR level -0.22 -1.73 EGMZWS level -0.18 -1.51first differenc -1.08 -3.98 first differences -1.08 -3.70

EGWHPF level -0.26 -1.73 EGMZRT level -0.16 -1.48first differenc -1.11 -3.93 first differences -0.98 -3.45

EGWHWS level -0.22 -1.62 EGSHPR level -0.20 -2.15first differenc -1.10 -3.79 first differences -0.72 -3.14

EGWHRT level -0.18 -1.46 EGSHPF level 0.50 1.99first differenc -1.07 -3.75 first differences 1.27 3.99

EGRIEX level -0.38 -3.12 EGSHWS level -0.21 -2.28first differenc -1.07 -5.27 first differences -0.77 -3.69

EGRIPF level -0.17 -2.21 EGSHRT level -0.20 -2.14first differenc -0.98 -3.62 first differences -0.82 -3.56

EGRIWS level -0.34 -1.53 EGBUPR level -0.16 -2.02first differenc -1.10 -3.53 first differences -0.74 -3.26

EGRIRT level -0.13 -1.39 EGBURT level -0.15 -1.89first differenc -0.69 -2.96 first differences -0.75 -3.20

EGMZIM level -0.31 -2.33 EGCEPR level -0.19 -2.11first differenc -1.16 -4.42 first differences -0.86 -3.81

EGMZPR level -0.20 -1.78 EGBMPR level -0.19 -1.90first differenc -1.05 -3.82 first differences -0.81 -3.34

EGBMWS level -0.19 -2.13 EGBMPF level -0.13 -1.90first differenc -0.81 -3.64 first differences -0.58 -2.53

EGPLPF level -0.35 -2.60 EGBMRT level -0.13 -2.08first differenc -0.96 -3.69 first differences -0.85 -3.77

see Table 1 for variables' namesBold = significantly different from zero at 5% or lesssource: own calculation on ESCB price data

t- valuesADF ADF

coefficient (-1) coefficient (-1)t- values

75

Table A6. Ghana. Results of the unit root tests (annual data)

t- valuesGHMZIM level -0.93 -3.72

GHMZPF level -0.11 -1.11first differences -1.04 -3.71

GHMZWS level -0.16 -1.75first differences -1.07 -4.66

GHSHPF level -0.10 -1.08first differences -0.78 -3.23

GHSHWS level -0.15 -1.88first differences -0.99 -4.52

GHRIIM level -0.47 -2.95first differences -1.49 -5.86

GHRIPF level -0.15 -1.56first differences -0.90 -3.55

GHRI1WS level -0.14 -2.03first differences -0.78 -4.11

GHCSPF level -0.12 -1.61first differences -0.83 -3.84

GHCSWS level -0.17 -1.87first differences -1.10 -4.63

see Table 1 for variables' namesBold = significantly different from zero at 5% or lesssource: own calculation on ESCB price data

ADFcoefficient (-1)

Table A7. India. Results of the unit root tests (annual data)

INWHIM level -0.14 -1.18 INMZPF level -0.37 -2.27first differences -1.46 -4.83 first difference -1.33 -5.29

INWHPR level -0.50 -3.30 INMZWS level -0.49 -2.80first differences -1.22 -6.02 first difference -1.57 -6.02

INWHPF level -0.23 -1.43 INMZRT level -0.56 -2.99first differences -1.33 -4.77 first difference -1.23 -4.30

INWHWS level -0.33 -2.18 INCSPR level -0.45 -2.56first differences -1.32 -4.78 first difference -1.37 -5.09

INWHRT level -0.27 -1.65 INCSPF level -0.06 -0.55first differences -1.34 -4.71 first difference -1.08 -3.44

INRIIM level -0.88 -3.65 INCSWS level -0.68 -3.04first differences first difference -1.91 -7.40

INRIEX level -0.28 -2.19 INBMEX level -0.34 -2.91first differences -1.38 -5.75 first difference -1.20 -4.38

INRIPR level -0.54 -3.63 INBMPF level -0.05 -0.60first differences first difference -0.75 -3.41

INRIPF level -0.17 -1.44 INBMWS level -0.40 -2.27first differences -1.29 -4.69 first difference -1.46 -4.63

INRIWS level -0.22 -2.06 INPMWS level -0.44 -3.67first differences -1.47 -5.53 first differences

INRIRT level -0.74 -3.34 INPMRT level -0.19 -1.23first differences -1.87 -5.95 first difference -1.31 -4.15

INMZIM level -0.40 -2.97 INMPIM level -0.59 -2.94first differences -0.94 -3.62 first difference -1.74 -6.19

INMZPR level -0.29 -1.83 INSYPF level -0.11 -1.69first differences -1.26 -4.54 first difference -1.26 -5.38

see Table 1 for variables' namesBold = significantly different from zero at 5% or lesssource: own calculation on ESCB price data

t- valuesADF ADF

coefficient (-1) t- values coefficient (-1)

76

Table A8. Indonesia. Results of the unit root tests (annual data)

ISBMIM level -0.17 -1.45 ISMZPF level -0.24 -2.59first differences -1.66 -6.59 first differences -1.06 -4.40

ISBMPF level -0.12 -1.50 ISCSPR level -0.88 -3.23first differences -0.85 -5.71 first differences -1.62 -4.63

ISPMIM level -0.48 -2.81 ISCSPF level -0.31 -2.39first differences -1.45 -5.67 first differences -1.67 -5.60

ISPMPF level -0.13 -1.69 ISSYIM level -0.36 -3.05first differences -0.60 -2.55 first differences -1.50 -9.09

ISPLIM level -0.34 -1.73 ISSYEX level -0.23 -1.42first differences -1.68 -4.86 first differences -1.79 -5.76

ISPLPF level -0.12 -1.23 ISSYPR level -0.39 -1.64first differences -1.23 -5.13 first differences -1.64 -4.37

ISRIIM level -0.29 -2.50 ISSYPF level -0.24 -2.73first differences -1.45 -5.90 first differences -1.21 -5.33

ISRIPF level -0.16 -1.83 ISMPIM level -0.30 -2.17first differences -0.94 -3.11 first differences -1.13 -4.56

ISMZIM level -0.29 -2.50first differences -1.45 -5.90

see Table 1 for variables' namesBold = significantly different from zero at 5% or lesssource: own calculation on ESCB price data

t- valueADF ADF

coefficient (-1) t- value coefficient (-1)

Table A9. Mexico. Results of the unit root tests (annual data)

MXWHIM level -0.57 -4.00 MXBMIM level -0.47 -3.01first differences -1.00 first differences -1.48 -6.57

MXWHEX level -0.46 -2.85 MXBMEX level -0.27 -1.97first differences -1.57 -6.59 first differences -1.42 -4.81

MXWHPF level -0.60 -2.71 MXBMPF level -0.27 -1.07first differences -1.25 -4.39 first differences -1.52 -4.68

MXMZIM level -0.76 -3.45 MXPMIM level -0.57 -3.10first differences first differences -1.35 -5.21

MXMZEX level -0.24 -1.96 MXPMEX level -0.54 -2.83first differences -1.28 -4.60 first differences -1.84 -6.30

MXMZPF level -0.31 -1.61 MXPMPF level -0.27 -1.07first differences -1.06 -3.95 first differences -1.51 -4.63

MXSHPF level -0.13 -1.08 MXPLIM level -1.13 -4.50first differences -1.07 -3.67 first differences -1.91

MXSYIM level -0.32 -2.22 MXPLPF level -0.36 -1.53first differences -1.11 -4.15 first differences -1.15 -3.43

MXSYPF level -0.05 -0.20 MXMPIM level -0.15 -1.17first differences -1.42 -3.79 first differences -1.69 -5.65

see Table 1 for variables' namesBold = significantly different from zero at 5% or lesssource: own calculation on ESCB price data

t- valueADF ADF

coefficient (-1) t- value coefficient (-1)

77

Table A10. Pakistan. Results of the unit root tests (annual data)

PKWHIM level -0.29 -2.39 PKRIRT level -0.17 -1.63first differences -1.26 -5.15 first differences -1.07 -4.73

PKWHPR level -0.54 -3.21 PKMZPF level -0.21 -1.64first differences -1.45 -5.99 first differences -1.71 -5.48

PKWHPF level -0.45 -2.82 PKMZWS level -0.64 -2.92first differences -1.14 -4.14 first differences -1.86 -7.42

PKWHWS level -0.65 -3.53 PKSHPF level -1.24 -3.99first differences -1.73 -7.35 first differences

PKWHRT level -0.50 -2.67 PKSHWS level -0.63 -3.18first differences -1.55 -5.58 first differences -1.63 -6.28

PKRIIM level -0.28 -1.84 PKBMIM level -0.67 -3.49first differences -1.46 -5.21 first differences

PKRIEX level -0.42 -2.58 PKBMPF level -0.13 -1.17first differences -1.89 -7.34 first differences -1.11 -3.78

PKRIPR level -0.45 -2.69 PKBMWS level -0.34 -2.52first differences -1.57 -6.25 first differences -1.03 -7.62

PKRI1PR level -0.48 -2.45 PKBMRT level -0.32 -1.92first differences -1.24 -6.10 first differences -1.42 -5.78

PKRIPF level -0.28 -1.66 PKPMPF level -0.15 -1.32first differences -1.52 -4.96 first differences -1.18 -4.19

PKRIWS level -0.37 -2.22 PKPLPF level -0.11 -0.99first differences -1.46 -5.01 first differences -0.98 -3.31

PKRI1WS level -0.33 -2.06 PKPLRT level -0.01 -0.04first differences -1.38 -5.24 first differences -1.41 -4.32

see Table 1 for variables' namesBold = significantly different from zero at 5% or lesssource: own calculation on ESCB price data

t- valueADF ADF

coefficient (-1) t- value coefficient (-1)

Table A11. Senegal. Results of the unit root tests (annual data)

SNMZPR level -0.22 -1.58 SNBMEX level -0.38 -2.85first differences -1.30 -4.89 first differences -1.07 -4.31

SNMZPF level -0.11 -0.63 SNBMPF level -0.51 -1.58first differences -0.83 -2.46 first differences -1.43 -2.86

SNRIIM level -0.58 -4.30 SNPMIM level -0.40 -2.68first differences first differences -1.35 -5.32

SNRIEX level -0.40 -1.93 SNPMEX level -1.05 -4.26first differences -1.71 -5.72 first differences

SNRIPR level -0.37 -2.37 SNPMPF level -0.37 -1.91first differences -1.05 -3.68 first differences -0.93 -2.80

SNRIPF level -0.50 -2.44 SNPLIM level -0.55 -3.21first differences -1.22 -3.52 first differences -1.29 -4.73

SNMPIM level -0.25 -1.45 SNPLEX level -0.44 -2.54first differences -1.84 -5.43 first differences -1.39 -4.90

SNMPPF level -0.42 -2.13 SNPLPF level -0.37 -1.87first differences -0.96 -2.90 first differences -0.92 -2.76

SNBMIM level -0.88 -2.88first differences -2.08 -6.18

see Table 1 for variables' namesBold = significantly different from zero at 5% or lesssource: own calculation on ESCB price data

t- valueADF ADF

coefficient (-1) t- value coefficient (-1)

78

Table 12. Thailand. Results of the unit root tests (annual data)

THWHIM level -0.32 -2.29 THRIPF level -0.43 -3.25first differences -1.21 -4.66 first differences -1.05 -4.75

THMZIM level -0.30 -1.86 THRIWS level -0.22 -1.99first differences -1.70 -5.86 first differences -0.95 -4.68

THMZEX level -0.60 -2.92 THRIRT level -0.30 -2.53first differences -1.83 -6.42 first differences -0.84 -3.64

THMZPR level -0.40 -3.06 THCSPR level -0.92 -3.79first differences -1.27 -6.28 first differences

THMZPF level -0.41 -2.84 THCSPF level -1.24 -4.71first differences -1.17 -5.35 first differences -1.94 -6.81

THMZWS level -0.28 -2.55 THBMPF level -0.25 -1.85first differences -1.06 -5.91 first differences -0.94 -3.60

THSHPR level -0.35 -2.35 THPLPR level -0.16 -1.71first differences -1.26 -4.47 first differences -0.98 -4.60

THSHWS level -0.28 -2.18 THPLWS level -0.31 -2.11first differences -1.23 -5.40 first differences -1.10 -4.14

THRIEX level -0.48 -3.49 THPLRT level -0.38 -2.35first differences -1.15 -5.48 first differences -1.36 -5.39

THRIPR level -0.27 -2.34 THPMPF level -0.51 -2.88first differences -0.91 -4.43 first differences -1.37 -4.90

see Table 1 for variables' namesBold = significantly different from zero at 5% or lesssource: own calculation on ESCB price data

t- valueADF ADF

coefficient (-1) t- value coefficient (-1)

Table A13. Turkey. Results of the unit root tests (annual data)

TKWHEX level -0.89 -4.77 TKBMWS level -0.32 -2.11first differences first differences -1.22 -4.24

TKWHPR level -0.43 -2.47 TKBMRT level -0.26 -1.84first differences -1.29 -4.72 first differences -1.00 -3.51

TKWHPF level -0.43 -2.65 TKPLPR level -0.26 -2.01first differences -1.09 -3.74 first differences -1.12 -3.61

TKWHWS level -0.38 -2.47 TKPLWS level -0.15 -1.21first differences -1.07 -4.01 first differences -1.23 -4.31

TKRIIM level -0.44 -2.76 TKPLRT level -0.17 -1.34first differences -1.34 -5.56 first differences -1.05 -3.77

TKRIPR level -0.30 -1.99 TKSYPR level -0.62 -3.12first differences -1.40 -5.11 first differences -1.64 -5.01

TKRIPF level -0.45 -2.01 TKSYPF level -0.33 -1.53first differences -1.39 -4.70 first differences -1.52 -3.89

TKRIWS level -0.31 -2.43 TKSFPR level -0.43 -2.50first differences -1.27 -6.07 first differences -1.28 -4.47

TKRIRT level -0.32 -2.49 TKBUPR level -0.63 -2.43first differences -1.29 -5.58 first differences -1.34 -4.10

TKMZPR level -0.23 -2.01 TKBUWS level -0.28 -2.54first differences -0.99 -4.14 first differences -0.82 -3.34

TKMZPF level -0.32 -2.35 TKBURT level -0.32 -2.50first differences -1.03 -3.79 first differences -1.18 -4.72

TKMZWS level -0.18 -2.00 TKCEPR level -0.15 -1.37first differences -0.91 -3.95 first differences -0.89 -3.12

TKBMEX level -0.67 -3.11 TKCERT level -0.18 -1.44first differences -2.18 -8.81 first differences -0.85 -3.26

TKBMPF level -0.15 -1.57 TKCEWS level -0.09 -0.73first differences -0.79 -2.67 first differences -1.35 -4.69

see Table 1 for variables' namesBold = significantly different from zero at 5% or lesssource: own calculation on ESCB price data

t- valueADF ADF

coefficient (-1) t- value oefficient (-1

79

Table A14. Uganda. Results of the unit root tests (annual data)

UGWHIM level -0.44 -2.41 UGBMPR level -0.91 -4.31 first differences -1.59 -5.29 first differences

UGWHPR level -0.59 -3.60 UGBMWS level -0.83 -4.40 first differences first differences

UGWHPF level -0.37 -1.98 UGBMRT level -0.81 -4.34 first differences -1.84 -6.14 first differences

UGWHWS level -0.61 -3.72 UGPMWS level -0.92 -5.50 first differences first differences

UGWHRT level -0.63 -3.69 UGPMPR level -0.98 -5.33 first differences first differences

UGRIPR level -0.36 -2.52 UGPMRT level -0.83 -5.15 first differences -1.05 -3.85 first differences

UGRIPF level -0.41 -2.49 UGPLPR level -0.87 -5.13 first differences -1.68 -5.19 first differences

UGRIWS level -0.42 -2.75 UGPLWS level -0.82 -4.93 first differences -1.13 -4.21 first differences

UGRIRT level -0.47 -2.87 UGPLRT level -0.81 -5.31 first differences -1.23 -4.50 first differences

UGMZIM level -0.69 -3.87 UGMPIM level -0.69 -3.05 first differences first differences -2.02 -6.00

UGMZPR level -0.55 -2.74 UGSYPR level -0.62 -3.48 first differences -1.51 -4.41 first differences

UGMZPF level -0.50 -2.48 UGSYPF level -0.39 -2.12 first differences -1.62 -4.85 first differences -1.76 -5.59

UGMZWS level -0.63 -3.15 UGSYWS level -0.58 -3.43 first differences -1.51 -4.60 first differences

UGMZRT level -0.60 -3.15 UGSYRT level -0.48 -3.39 first differences -1.45 -4.55 first differences

UGSHRT level -0.62 -3.38 UGCSPR level -0.33 -2.15 first differences -1.42 -5.00 first differences -1.14 -3.78

UGSHWS level -0.63 -3.51 UGCSPF level -0.40 -2.34 first differences -1.41 -5.27 first differences -1.27 -4.13

UGSHPR level -0.63 -3.28 UGCSWS level -0.32 -2.11 first differences -1.46 -5.04 first differences -1.16 -3.87

UGSHPF level -0.36 -2.07 UGCSRT level -0.33 -2.10 first differences -1.70 -5.24 first differences -1.21 -3.96

UGBMIM level -0.42 -2.77first differences -1.45 -6.19

see Table 1 for variables' names Bold = significantly different from zero at 5 percent or lesssource: own calculation on ESCB price data

t- value ADF ADF

coefficient (-1) t- value coefficient (-1)

80

Table A15. Uruguay. Results of the unit root tests (annual data)

UYWHIM level -0.79 -3.65 UYPMPF level -1.04 -3.53first differences first differences -3.15 -6.35

UYWHPR level -0.17 -1.93 UYSHPR level -0.33 -2.00first differences -1.05 -4.08 first differences -1.49 -5.09

UYWHPF level -0.20 -1.77 UYSHPF level -0.46 -2.54first differences -1.09 -4.07 first differences -1.32 -4.15

UYRIEX level -0.28 -2.32 UYSYIM level -1.10 -5.78first differences -1.10 -5.04 first differences

UYRIPR level -0.69 -2.79 UYSYPF level -0.98 -4.53first differences -1.42 -4.21 first differences

UYRIPF level -0.34 -2.01 UYSFPR level -0.41 -2.44first differences -1.13 first differences -1.59 -6.11

UYMZIM level -0.74 -3.21 UYBMEX level -0.48 -2.63first differences -2.26 -7.52 first differences -1.21 -4.44

UYMZPF level -0.25 -1.79 UYBMPF level -0.92 -3.26first differences -1.03 -3.57 first differences -3.12 -6.13

UYPMEX level -1.39 -5.07 UYMZPR levelfirst differences first differences

see Table 1 for variables' namesBold = significantly different from zero at 5% or lesssource: own calculation on ESCB price data

t- valueADF ADF

coefficient (-1) t- value coefficient (-1)

81

Table A16. World reference prices: results of the unit root tests (monthly data)

WRWHRP level -0.04 -0.02-3.54 -2.05

first differences -0.72-14.65

WRMZRP level -0.04 -0.02-3.06 -2.33

first differences -0.76 -0.67-5.01 -13.96

WRSHRP level -0.04 -0.03-3.11 -2.72

first differences -0.88 -0.74-4.84 -15.02

WRRIRP level -0.04 -0.03-2.40 -1.74

first differences -0.91 -0.73-4.50 -12.26

WRCSRP level -0.06 -0.04-3.35 -2.95

first differences -0.77 -0.82-4.38 -14.33

WRSYRP level -0.07 -0.05-3.11 -2.59

first differences -0.88 -0.71-4.25 -12.01

WRSFRP level -0.10 -0.08-3.11 -2.92

first differences -1.05 -0.73-6.30 -8.83

WRCPRP level -0.05 -0.03-3.12 -2.02

first differences -0.73 -0.90-5.94 -14.66

WRPORP level -0.07 -0.06-2.51 -2.30

first differences -1.07 -1.28-3.78 -20.53

WRBMRP level -0.04 -0.04-2.09 -2.24

first differences -0.99 -0.80-3.62 -12.50

WRPMRP level -0.17 -0.12-1.80 -2.08

first differences -1.32 -7.53-2.35 -7.53

WRPLRP level -0.14 -0.08-2.55 -2.01

first differences -0.97 -0.41-2.96 -4.63

WRGRRP level -0.03 -0.03-2.32 -2.30

first differences -1.12 -0.72-7.42 -15.63

WRCFRP level -0.04 -0.02-2.72 -1.31

first differences -0.58 -0.72-3.56 -12.17

WRPKRP level -0.05 -0.02-4.38 -3.24

first differences -0.77-17.58

see Table 1 for variables' namesBold = significantly different from zero at 5% or lesssource: own calculation on ESCB price data

ADFcoefficient (-1) coefficient (-1)

PP

82

Table A17. Results of the unit root tests for Costa Rica (monthly data)

CRPOWS level -0.06 -0.07-1.23 -1.41

first differences -1.23 -0.82-6.19 -6.95

CRPMWS level -0.05 -0.78-1.44 -0.03

first differences -0.90 -0.70-6.31 -6.17

CRPMRT level -0.04 -0.03-1.13 -0.75

first differences -0.92 -0.94-4.37 -7.99

CRPLRT level -0.43 -0.42-3.81 -4.75

CRBMRT level -0.08 -0.12-2.55 -5.45

first differences -1.24-4.72

see Table 1 for variables' namesbold = significant at 5% or moreSource: calculations n ESCB and IMF data

ADF PPcoefficient (-1) coefficient (-1)

Table A19. Results of the unit root tests for Ethiopia (monthly data)

ETBMRT level -0.13 -0.09-2.89 -2.65

first differences -1.03 -1.10-2.75 -13.47

ETMZPR level -0.18 -0.08-2.71 -2.01

first differences -0.91 -0.77-4.64 -6.32

ETMZRT level -0.23 -0.23-1.89 -3.34

first differences -1.09 -1.19-1.49 -11.54

ETRIRT level -0.14 -0.17-1.73 -2.16

first differences -1.58 -1.11-8.95 -9.37

ETSFRT level -0.14 -0.16-2.21 -2.50

first differences -1.41 -1.29-6.37 -12.84

ETSHRT level -0.12 -0.16-1.83 -2.77

first differences -1.42 -1.24-6.06 -12.25

ETWHRT level -0.13 -0.11-2.42 -2.38

first differences -0.83 -0.83-4.89 -7.92

see Table 1 for variables' namesbold = significant at 5% or moreSource: calculations n ESCB and IMF data

ADF PPcoefficient (-1) coefficient (-1)

83

Table A20. Results of the unit root tests fopr Ghana (monthly data)

GHMZWS level -0.02 -0.02-2.33 -2.23

first differences -0.76 -0.84-4.33 -17.74

GHRI1WS level -0.03 -0.05-1.89 -2.61

first differences -1.72 -1.37-5.66 -30.82

GHRI1RT level -0.09 -0.17-1.88 -3.03

first differences -1.87 -0.25-6.04 -17.65

GHRIRT level -0.11 -0.11-2.49 -2.86

first differences -1.24 -1.21-5.30 -14.15

GHGRWS level -0.01 -0.02-1.26 -2.26

first differences -1.23 -0.95-5.28 -19.73

GHGRRT level -0.19 -0.26-2.13 -4.43

first differences -2.36 -1.23-3.47 -14.26

GHPOWS level -0.28 -0.03-5.51 -1.76

first differences -1.56 -1.25-5.75 -28.78

GHPORT level -0.23 -0.21-3.25 -3.96

first differences -1.48 -1.19-3.32 -15.79

GHPKWS level -0.02 -0.04-1.42 -2.05

first differences -1.90 -1.39-11.08 -34.60

GHSHWS level -0.03 -0.05-1.73 -3.22

first differences -1.67 -1.35-5.74 -30.02

GHCSWS level -0.03 -0.05-2.24 -3.24

first differences -1.19 -1.20-8.67 -25.55

GHCSRT level -0.18 -0.28-2.52 -4.54

first differences -1.50-4.83

GHSHRT level -0.09 -0.11-2.38 -2.78

first differences -1.15 -1.19-5.65 -13.97

see Table 1 for variables' namesbold = significant at 5% or moreSource: calculations n ESCB and IMF data

ADF PPcoefficient (-1) coefficient (-1)

84

Table A21. Results of the unit root tests for Indonesia (monthly data)

ISMZWS level -0.05 -0.05-2.16 -2.41

first differences -1.18 -1.07-4.86 -17.52

ISCFWS level -0.06 -0.07-2.65 -2.92

first differences -0.99 -1.21-6.34 -20.07

ISCPWS level -0.04 -0.03-1.83 -1.87

first differences -1.03 -0.97-4.43 -14.90

ISRIWS level -0.05 -0.04-2.20 -2.14

first differences -1.00 -0.82-4.48 -13.44

ISRI1WS level -0.14 -0.12-3.45 -4.10

ISSHWS level -0.14 -0.18-3.51 -5.14

ISSYWS level -0.06 -0.07-2.05 -3.03

first differences -1.53 -0.85-6.36 -13.98

ISBM1WS level -0.08 -0.04-2.45 -2.28

first differences -1.30 -0.92-5.19 -14.24

ISBMWS level -0.05 -0.04-2.46 -2.40

first differences -1.01 -0.88-5.22 -14.37

see Table 1 for variables' namesBold = significantly different from zero at 5% or lesssource: own calculation on ESCB price data

ADF PPcoefficient -1 coefficient -1

85

Table A22. Results of the unit root tests for Senegal

SNMZRT level -0.14 -0.11-2.81 -3.23

first differences -1.30 -1.01-6.53 -12.66

SHRP level -0.11 -0.09-2.92 -3.06

first differences -1.09 -0.99-6.27 -12.38

SNRIRT level -0.27 -0.23-2.55 -4.17

first differences -2.32-3.92

SNGRRT level -0.26 -0.18-4.14 -4.12

SNGR1RT level -0.18 -0.13-3.54 -3.73

SNPORT level -0.94 -0.62-1.68 -4.66

first differences -3.81 -1.08-1.68 -6.76

SNCSRT level -0.57 -0.13-2.34 -1.35

first differences -0.37 -1.08-0.61 -6.46

see Table 1 for variables' namesbold = significant at 5% or moreSource: calculations n ESCB and IMF data

ADF PPcoefficient (-1) coefficient (-1)

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