ANNEX III. ESTIMATES OF SUPPORT TO AGRICULTURE BY COUNTRY, 1991-2001 (continued)
ANNEX 1: AN OVERVIEW OF THE ECONOMETRIC TECHNIQUE FOR QUANTIFYING PRICE TRANSMISSION
7. Policy implications – the context of WTO commitments
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effective interventions to deal with enhanced price transmission especially in the context of the persistence of low world prices. The movement towards trade liberalisation, towards allowing market price signals to determine the use of resources, could be frustrated by the reluctance of governments to expose further their farming constituents, especially those in the import competing sectors, to the risks of price instability and periods of persistent low prices that would result from the enhanced price transmission associated with additional reforms. Obviously, those who believe in the benefits of freer world trade have an interest in facilitating policy adjustments in developing economies. But these adjustments must be designed in such a way as to overcome the potential political resistance likely to result from the exposure of large agricultural sectors, where there is a concentration of a large share of the poor, to the risks of sustained price decreases.
Although there is a variety of instruments presently in use, there is the question of how countries might deal with low prices under the current WTO legal framework and what could be the subject of negotiations of the Doha Round and the future. In a relevant technical note, Konandreas (2000) of the FAO discusses the current framework, classifying permissable policies into two broad categories:
border measures through tariffs (within the tariff ceiling bounds) and domestic support measure (price and non-price programs within the limits of WTO commitments).
With respect to border measures, most countries have bound their tariffs at a relatively high rate, that is, 100%, and sometimes more. Several countries, such as the Philippines, Chile, Thailand, Argentina and Malaysia, have bound their tariffs around 30%. In many countries, the actual rates are lower than the bound tariff (the so-called “dirty tariffication”). In Konadreas’s view, a high bound tariff might be insufficient to avoid the politically difficult situation of very low prices. But in addition, in our view, more significantly the lack of other instruments under the WTO has encouraged governments cynically to set an overly high bound tariff. This permits considerable discretion in the selection of tariff levels, and thus a greater degree of uncertainty with regard to the effective trade regime at any point in time.
And in practical political terms, very few countries have access to the special safeguard clause, an exemption to tariff ceiling levels. From earlier commitments eligibility is limited to the tariffication process, and because the majority of developing countries did not “tariffy,” and instead offered ceiling bindings, very few can presently make use of the SSG clause.
With respect to domestic support policies, there are both bound supports under non-exempt policies subject to commitments of aggregate expenditure ceilings (the AMS established during the Uruguay Round), and non-bound supports, exempt from limits, operating within the “green box.” In addition, developing countries have access to a special category of exempted under “Special and Differential Treatment” – investment subsidies, input subsidies to low-income farmers, etc. Most developing countries (61 of 71) reported zero AMS levels – of the remaining ten countries these levels are very small. In part thus was due to fiscal limitations (Konandreas). The implication is that most developing countries are limited in their support options to action under the de minimis clause and the definitions of the “green box,” and are thus restricted in their use of non-border support policies. In contrast developed countries (and a few developing countries) reported high AMS.
Therefore, this is the situation: developing countries have fewer fiscal resources to aid their farmers through domestic supports, and have fewer alternative market instruments to compensate for the higher variability of domestic prices – and particularly lower prices – that might result from the trend toward trade liberalisation. This leaves many governments in developing countries with the temptation to seek protection of their import-competing sectors through border measures. From an economist’s perspective of the welfare gains from trade, and from a practitioner’s perspective of facilitating the liberalisation process, future WTO negotiations might well consider providing greater flexibility to
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developing and transition economies with respect to well-defined and disciplined tariffs and surcharges. Such flexibility might come through price bands, floor price schemes, modification of the general WTO safeguards, and – a perhaps the most promising – the expansion of the special safeguard clause for countries with low bound tariffs.
There is a technical discussion ongoing in Geneva in other circles on these issues. The specificity of these measures, their legality, their operational implementation and their relative effectiveness are a set of questions which would be major research efforts in and of themselves, and which are certainly worthy of the professional attention of economists and others committed to improving the performance of the international trade system.
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181
ENDOGENOUS MARKETS, PRICES AND TRADE REFORMS: EVIDENCE FROM AFRICAN SECTOR AND COMMUNITY DATA
Donald F. Larson* Abstract
The benefits of trade reform depend on the efficiency of markets. Predictions of benefits rely on empirical measures of market efficiency. Using cross-country data on Africa and using community- survey data from Uganda, this paper finds evidence that markets in Africa effectively transmit price signals, but also finds significant heterogeneity in commodity market efficiencies. The paper identifies estimation challenges that make empirical measures difficult to obtain, which may lead to poor forecasts of trade benefits. The paper also concludes that spatial heterogeneity will affect the distribution of trade benefits. To the extent the poor live near markets that are less efficient, the positive and negative effects of trade reform on poverty will be muted.
Introduction
Exchange is a dominant feature of how economists view the world. Economists depend on exchange to value the activities of households and firms and use incomes, a summary of exchange-measured activities, as a first indicator of development. Economists also look at the characteristics of prevalent forms of exchange in a country as a diagnostic measure of development. Douglas North (1994), for one, argues that, historically, development entails moving from autarky and localised trade to far- reaching and specialised impersonal trade. This path relies on developing more efficient forms of economic organisation that are motivated and rewarded by the gains from trade. Sophisticated forms of economic organisation inevitably depend on collective institutions and usually governments. But tensions between specialised and general benefits from institutions lead to tensions in the process of rule making so that the same process that gives rise to more efficient trade can also place limits on the scope of trade. Government enforced restrictions on trade is an example.
Explaining and measuring the costs of such interventions is the mainstay of trade economics.
However, trade reform is of special interest to development economists as well since expanded trade permits growth through the reallocation of scarce factors, rather than the slower process of factor accumulation. Trade reforms also put in place new incentives that influence a change in fixed factors with implications for longer-term growth. At the same time, experience from recent years indicates that the outcomes from trade reform have been uneven. Especially in the context of commodity market reforms, unexpected difficulties, often related to markets, have placed limits on the potential gains from reform (Akiyama, Baffes, Larson and Varangis, 2000.)
* The World Bank.
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In this paper, I present a simple model of spatial price discovery that depends on fixed factors. An implication from the model is that the benefits of trade will depend, in part, on how well markets function. Since markets of all types are heterogeneous in terms of efficiency, the pace and benefits of reform can be uneven. In short, the supply response expected from firms and households following reform may be limited not only by the productive capacity of households and firms, but also by limited stocks of market-related capital and fixed factors. This has implications for policymakers – since some market capital is provided by the public sector – and it also has implications for how market efficiency is measured.
The point of departure is the spatial equilibrium condition that the difference between two prices for the same good will differ by no more than the full cost of transacting an exchange between the markets. The condition is well known and well studied. Ekelund and Hérbert (1999) trace the foundations of spatial market theory to von Thünen (1826). Samuelson (1952) and Takayama and Judge (1971) credit advances in linear programming by Koopman (1949) to renewed interest in the formalisation of spatial equilibrium theory. In 1951, Enke gave a descriptive solution to the spatial market equilibrium that Samuelson formalised in 1952. Most modern models of spatial integration are derived from Samuelson’s paper and subsequent extensions by Takayama and Judge (1964).
Applied models preceded Samuelson. In their recent review, the earliest study of spatial efficiency noted by Fackler and Goodwin (2001) was Mohendru’s 1937 study of Punjab wheat markets.
Although Samuelson (1957) was careful to distinguish between spatial and temporal market equilibria, most empirical studies rely on time-series data and general dynamic structure appended to spatial conditions. This literature is reviewed in Baulch (1997) and also in the already mentioned chapter by Fackler and Goodwin. Fixed effects in spatial data can be used to avoid the need for imposed- dynamics, but cross-sectional studies are less common. Some recent examples include Baffes (1991);
Mundlak and Larson (1992); and Gardner and Brooks (1994).
The remainder of the paper is organised in the following way. In the next section I discuss the theoretical and statistical framework for the analysis. Following that discussion, empirical results are reported using sector data from FAO and then household and community data from Uganda. The last section draws implications for market reforms.
Framework
I draw on the framework developed in Mundlak and Larson (1993), which is a restatement of the spatial arbitrage condition under proportional transaction costs. The spatial arbitrage condition – or the law of one price – states that, when trade occurs, location-related differences in price are due to transaction costs. Otherwise arbitrage opportunities would induce actions to reinstate the condition.
More explicitly, consider prices (P) for the same commodity (c) for two different markets (l,*) at time t, with transaction costs R(p; s). For continuous trade, the arbitrage condition is given as:
l ct l t ct l
ct P E R
P = * 1)
where E is an exchange rate. Transaction costs depend on the market pairs and are also commodity and time specific. This dependence comes both from the differences in the physical costs of storage and transport, but also potentially includes characteristics peculiar to the commodity markets.
Examples include different policy treatment, market liquidity and the cost of acquiring cost and quality information. Implicit in the transaction costs are several forms of fixed factors, s, – some of which are relevant for many markets (road, communications and port infrastructure) and some of
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which are commodity specific (i.e. quality standards) or even location specific (social capital.) Factors that are fixed in the short-run change over time as will available technologies. Moreover some policies are likely commodity specific, while most policies will vary with time and – especially in the case of international trade – location. It is worth noting that the arbitrage condition itself is atemporal;
however, when explicit measure of time-varying factors that explain transaction costs are unavailable, time-effects must be accounted for.
In anticipation of the analysis to follow, note that P can be decomposed into variations along each dimension of a two-dimensioned panel of observations. Regressions based on a panel of data will attempt to explain the observed total variation of the dependent variable, which is, itself, an average of variations in the dimensions of the panel. Consequently, the regression coefficient produced by pooling observations across both dimensions of the panel will depend on the relative variation in dimension of the panel. To be more concrete, allowing lower case letters to signify natural logs, average over location and time to produce
c.
p and over all observations to produce p.. The following sums-of-squares (SS)—taken over all observations – holds:
(
p p..)
SS(p p.) SS(p . p..)SS ct − = ct − c + c − 2)
that is, the total sum-of-squares will equal the within (first term) and between (second term) sum-of- squares. For the sake of illustration consider the regression pctagainst pct* (pooled),
]
[pct −pc. against [pct* −pc*.] (within), and [pc.−p..] against [p*ct − p..*] (between) to produce the slope coefficients β,β(w)and β(b)respectively, the following relationship holds:
) ( ) 1 ( )
(w φ β b
φβ
β = + − 3)
where φis a weight determined by the relative shares of within and between sums-of-squares of pct. Additional relationships hold as well. See Mundlak (1978) and Mundlak and Larson (1992) for more detail.
Although abstract, these relationships are relevant for trade in the following way. Models that try to measure and anticipate the consequences of trade reform depend on empirical measures of the transmission of price signals from international to domestic markets and also among domestic markets. From theory, we have good reason to suspect that markets themselves depend on attributes that vary according to time, location and commodity. However, panels that allow measurement over all three dimensions are rare. Moreover, studies that consider the relationship between the same commodity in two markets across time are most prevalent in the literature. Since composition of the panel brought to the estimation problem potentially matters, reliance on relatively few datasets and a reliance on time-series measures can result in limited measurement of how markets work.
184 Empirical models and results
Using sector data
Producer prices for this exercise are taken from the on-line database of the Food and Agriculture Organisation of the United Nations (FAO1). The data are annual averages for the period 1966-1995 for 17 commodities in 36 African countries. Not all commodities are produced in all countries. World prices are annual OECD import unit value averages. Exchange rates are annual average dollar rates as reported by the International Monetary Fund.
FAO reports producer prices in local currencies. In some of the analysis producer prices are converted to nominal US dollars. Table 1 reports the decomposed sum-of-squares for both the local and the converted series. The series are converted to logs (indicated by lower case letters), that is,p~ctl = plct −etl.
The sums-of-squares from both series are reported in Table 1. Between variations are differences among averages – for example, for a time-commodity panel, the between commodity sum-of-squares measures the tendency for differences among commodities from their average over time. The within- commodity sum-of-squares measures the remaining variation. The table shows that between variations account for a small portion of total variation for time and commodities, but that the between variation comprises a larger share of the country-variation. This is consistent with the notion that there are large policy and institutional differences among African countries that affect market efficiency.
The table also shows that common and sensible manipulations of the data are not neutral in their effects on the composition of variation. Note that the between-country variation is proportionately larger than the between commodity variation in local currencies. Converting to US dollars enhances the country-share of variation. This result is tautological, since the exchange rate is country, but not commodity specific. Consequently, introducing the country-subscripted variable increases the variation of price in one direction only.
Table 1. Decomposition of the producer prices sums-of-squares
Producer prices Observations Total Year Country Commodity
Within Between Within Between Within Between
Local 11 360 98 030 80 292 17 738 41 727 56 303 83 348 14 683
Share 1 0.82 0.18 0.43 0.57 0.85 0.15
$US 11 360 435 911 432 853 3 059 23 577 412 334 418 310 17 602
Share 1 0.99 0.01 0.05 0.95 0.96 0.04
Written in logs and converted into a common currency and with the addition of a random error term, the data can be pooled to test for a restricted version of the spatial arbitrage condition:
1
*
~ r 1p v
pctl = +β ct+ 5)
1. FAO provides the data on-line at www.fao.org.