Trade Policy Inconsistency and Maize Price ... - Wiley Online Library

African Development Review, Vol. 25, No. 4, 2013, 607–620

Trade Policy Inconsistency and Maize Price Volatility: An ARCH Approach in Kenya Elodie Maître d’Hôtel, Tristan Le Cotty and Thom Jayne

Abstract: The 2007–2008 food crisis and current food price swings led economists to re‐evaluate the potential for policy instruments to manage food price volatility, including tariff policy. The use of tariffs in importing countries to stabilize price is theoretically not recommended because of its domestic and international costs but in practice many countries use import tariffs with the intention to stabilize their domestic prices. Among them, some achieve price stabilization, some do not. We address the reason why it sometimes works, sometimes not. In the context of Kenya, we show that while domestic price levels are mainly explained by seasonal cycles, and international prices, domestic price volatility is mainly explained by inconsistent moves of trade policy. Thus, the ability of a policy regime to lower food price volatility does not depend on the nature of the policy instrument only, but also on the government ability to implement it. We define a consistent policy adjustment as a tariff decrease when world price increases and a tariff increase when world price is decreasing. We use an autoregressive conditionally heteroscedastic model of price determination in which prices and prices volatility are jointly estimated, using monthly data over the 1994–2009 period in Kenya.

1. Introduction The 2007–2008 food price surge and the crisis that followed in some developing countries brought agricultural and food prices volatility to the heart of political debates, even though such a price increase does not necessarily imply increased volatility. The welfare effects of price volatility and the merits of policies devoted to mitigate volatility continue to be debated by economists (Barrett and Bellemare, 2011; Prakash, 2011). For instance, price stabilization policies can have positive welfare effects for some agents and negative effects for some others, depending on the source of price shift (Turnovsky, 1974). Nevertheless, for Turnovsky, as well as for Aizenman and Pinto (2005), price volatility results in an overall welfare loss. Economic consequences seem to be more serious for producers than for consumers, since price volatility generates income variations for producers (Newbery and Stiglitz, 1981) as well as under‐investment (Sandmo, 1971). For some authors, volatility can have adverse effects for consumers (Byerlee et al., 2006), whereas some authors argue that consumers can often mitigate the impacts of volatility (Bellemare et al., 2010). The economic consequences of negative effects of volatility for producers are potentially more important in African countries, where farmers face many uncertainties, where markets are spatially segmented and where food expenditures account for most of household expenditures. This study does not address the debate whether managing price volatility is desirable or not. Our motivation is to analyze the efficiency of existing ways of stabilizing food prices for countries that have committed themselves to doing so. Many developing countries have recently reinforced their efforts to stabilize food prices since the events of 2007/8, through tighter control of trade and marketing functions (Demeke et al., 2008). Whether welfare enhancing or not, the food crises indeed provided an invigorated context for stabilization policies in developing countries and many policies have been pursued, notably trade policies (changes in import tariffs, restriction and prohibition of exports) and marketing policies (reduction of taxes, release of public stocks, and price controls). However, all these policies have not led to lower price volatility. Indeed, a given trade policy instrument can either lower or increase price volatility, depending on whether it responds in a countervailing way to changes in world prices (e.g.,

Elodie Maître d’Hôtel and Tristan Le Cotty, Centre International de Recherche en Agronomie pour le Développement (CIRAD); e‐mail: elodie. [email protected]. Thom Jayne, MSU Michigan State University.

© 2014 The Authors. African Development Review © 2014 African Development Bank. Published by Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

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import tariffs are raised when there is a fall in world prices and vice versa), and how quickly and frequent these adjustments are made. The ideal way to manage food price volatility is another long‐standing debate among economists. Many support the idea that trade openness is compatible with price stabilization (Martin and Anderson, 2011; Anderson and Nelgen, 2012), and that protectionist measures are not first best policies for price stabilization. Some authors further find that trade liberalization has positive effects on price stabilization, especially after a destabilizing transition period (Shively, 1996), while other studies find that liberalization has resulted in higher price volatility (Barrett, 1997; Karanja et al., 2003). Our hypothesis is that a tariff policy effect on price stability depends on the frequency and timeliness of tariff rate adjustments to shifts in the margin between world and domestic prices. This is why, according to us, an average decrease in tariffs (often called trade liberalization) can generate volatility increase or volatility decrease, depending on the rules governing how tariff rates adjust to international and domestic price dynamics. This study tests this hypothesis based on the case of maize in Kenya, a country that has used variable tariff rates to stabilize food prices for many decades. In our framework as in several recent analyses, stabilizing food prices does not mean making prices as constant as possible. Instead, it means removing as much of the unpredictable nature of price variations. A sudden unpredicted price decrease followed by a sudden price increase itself followed by a sudden price decrease at high frequency is not a significant market signal, that producers could interpret and use for price prediction or production strategy. By contrast, a slow and sustained price decrease of one commodity due to improved technology can be interpreted and used for prediction and production strategy. The predicted versus unpredicted components of price movements is typically estimated through forecast errors of price formation models based on information known in advance (Shively, 1996; Barrett, 1997). An efficient stabilization policy lowers the unpredictable part of price variations, not the absolute measure of price variation. This is consistent with most common definitions of price volatility, like OECD’s (1982) ‘price variations of such a frequency and scale’ that instead of constituting market signals to agents, ‘they exceed agents’ capacity to adapt’ (OECD, 1982). Being able to predict a price shift and being able to adapt to it is not synonymous though. We stand in line with Barrett or Shively’s view of volatility, which does not consider whether volatility is necessarily detrimental to economic agents or not. A more precise definition is given later in the paper. We test this intuition that for a policy to be effective in reducing price unpredictability for market participants, the appropriate use of the chosen tool — here trade policy — is critical. We define a tariff policy as consistent with the public objective of domestic price stabilization if the tariff declines when the world price increases and conversely. Since tariff policy addresses specifically the imported source of volatility, not the domestic source, the resulting effect is not expected to be a perfect price stabilization, even when the policy use is fully consistent. We show that periods of higher price predictability are associated with better use of the tariff level. To test this, we build an ARCH model, adapted from Barrett (1997), where we introduce policy inconsistency as an explanatory variable of price level and of price volatility. Apart from lagged price, dummy regional variables, and the ARCH term itself (past values of the error term of the price model), the only source of volatility we can identify is the inconsistent use of trade tariff. Section 2 summarizes expected effects of domestic policies on food price volatility. Section 3 presents price and policy dynamics in Kenya, and describes the ARCH model and data used to estimate the model. Section 4 discusses the empirical importance to put the emphasis on how to use a stabilization tool.

2. Expected Effects of Stabilization Policies on Food Price Volatility 2.1 Price Volatility Definition and Measure The definitions and measures of price volatility are several in the literature (Prakash, 2011). Price volatility is about the variation of prices over time, but ‘not all price variations are problematic: variations in prices become problematic when they are large and cannot be anticipated and, as a result, create a level of uncertainty which increases risks for producers, traders, consumers and governments and may lead to sub‐optimal decisions’ (FAO et al., 2011). Indeed, ‘as households and planning agencies are able to cope better with predictable variation, unpredictable changes are of primary concern’ (Prakash, 2011). In theory, unpredictable price variations are not necessarily problematic (a consumer can take advantage of unexpected prices decreases for instance) and predictable price shifts can be problematic for some agents (post‐harvest price drop is predictable, but still harmful for farmers with liquidity constraints). But it is true that a detrimental price shift is all the more detrimental as it is unexpected. Nevertheless, we will focus only on the unpredictable character of price variation in this paper as the core of volatility, not on the problematic character. It is useful to distinguish between variability in general and unpredictable variability: variability describes the overall © 2014 The Authors. African Development Review © 2014 African Development Bank

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Trade Policy Inconsistency and Maize Price Volatility

Table 1: Price variability versus price volatility

Variability Volatility

Definition

Measure

Unconditional variance: overall price variations Conditional variance: unpredictable component of price variations

Coefficient of variation Error variances from price forecast models

price variations while unpredictable variability refers to the unpredictable part of price variations. The OECD definition of volatility also includes its effect on agents, ‘variations of such a frequency and scale that they exceed agents’ capacity to adapt’ (OECD, 1982). Again, one could argue that this definition relies too much on agents ‘slowness’. It implies that for very fast agents that would be able to adapt instantly to prices shifts, no price would be qualified as volatile. Of course, in reality, agent’s capacity to adapt to price shifts is greater when their capacity to predict price shifts is greater. Nevertheless, we find it more convenient to stick to the unpredictable character of prices, whatever their welfare effects. This definition also has limitations. In particular, it implies that a same price is less volatile for well‐informed agents than for poorly informed agents. We have to choose a particular level of information, which is in our model, probably more important than the average information available to most farmers for instance. A common indicator to measure variability — the overall price variations — is the coefficient of variation, which measures the dispersion of prices around their mean. This coefficient encompasses both the predictable (for example, seasonal price variations) and unpredictable components of price variations. To measure volatility, we isolate the unpredictable component of price variations from the predictable one, relying on price forecast models. To identify predictable price moves, several authors after Engel (1982) have used the conditional variance of price as an indicator of price volatility (see Barrett, 1997). This approach is adapted to the measurement of volatility as we define it, the unpredictable component of price instability. The variance of the residuals of a price formation model typically measures the unpredictable price shifts (at least, what the model does not predict). This variance can be called a ‘conditional’ measure of volatility, that is, conditioned on the known information in the price forecast model. Typical models for this measure of volatility are ARCH models, in which the variance of residuals is allowed to depend on time and other variables By contrast, variability could be defined as the unconditional variance in food prices over a time period, as summarized in Table 1, but this definition does not account for the fact that some portion of the price variability can be predicted in advance by market actors (e.g., seasonality).

2.2 Low Policy Consistency May Entail Policy Ability to Lower Food Price Volatility In the empirical literature, several studies have been considering the effect of policies on food price volatility, and have obtained different results. Barrett (1997) found that policy withdrawal has caused an increase in cereals price volatility in Madagascar, and similar results were obtained by Karanja on maize in Kenya (Karanja et al., 2003), and on corn, soybeans and wheat in USA (Yang et al., 2001). By contrast, Shively found that liberalization tends to reduce maize price volatility in Ghana (Shively, 1996), in line with results on the US wheat market (Crain and Lee, 1996). As we have hinted earlier, this may have to do with the fact that a similar policy tool — in our case the definition of import tariffs — does not always produce the same effect because it is not always used in a same way. The fact to decrease the average level of trade protection on foodstuff may not be enough information to have a robust and unique effect on price predictability. This paper is devoted to the empirical test of the assumption that a same instrument can increase or decrease price volatility, depending on how it is implemented. A growing literature on price stabilization policies deals with the concrete mechanisms through which policies are implemented. Part of this literature points out that the capacity of public intervention to regulate food price volatility may be entailed by governance problems (Poulton et al., 2006), governance failures (Jayne and Tschirley, 2009) or coordination failures (Dorward et al., 2005). Recent empirical studies have demonstrated that, in a context of price jumps, a public intervention aimed at containing the price surge could indeed result in having no effect on it (Galtier, 2010), or worse, in an aggravation of it (Nijhoff et al., 2002; Mwanaumo et al., 2005; Chapoto and Jayne, 2009). Highly discretionary and ad hoc trade policies can lead to this outcome. It has been highlighted that, in some cases, governments could announce a food stabilization policy but fail to effectively implement it, because of deficient management or enforcement control and because of insufficient financial capacities (Gérard et al., 2011). Many examples can be drawn from West African countries: those problems are more likely to occur in low‐income countries because of limited financial resources allocated to stabilization policies (notably buffer © 2014 The Authors. African Development Review © 2014 African Development Bank

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stock policies that require immediate access to funds to effectively smooth price volatility), and in landlocked countries because of practical difficulties to physically control borders (and thus to enforce trade control policies). Furthermore, it has been demonstrated that when governments intervene in a discretionary way, market participants cannot correctly anticipate government actions or may decide not to operate in food markets, and that this could exacerbate price instability (Byerlee et al., 2006). Many examples were given from East African countries of situations where public intervention could produce unanticipated consequences, such as crowding out private trade or imports that otherwise might have occurred, or were indeed expected by the government (Nijhoff et al., 2002; Chapoto and Jayne, 2009; Jayne and Tschirley, 2009). These analyses, applied to price stabilization policies, are consistent with more general analyses on governance forms that prevail in the elaboration and implementation of policies and that insist on the capacity of diverse players (government, private actors etc.) to satisfy their objectives (Kaufmann et al., 2010). Somewhat summarized, these works suggest that ‘the precise policies may be less important than the fact that they exist and that main stakeholders find them credible’ (Tschirley and Jayne, 2010).

3. The Regulation of Maize Market in Kenya (Model and Data) Our aim is to test the effect of maize price stabilization policies pursued by the Kenyan government on maize price volatility, focusing on the consistency of those policies. Kenya is characterized by a long tradition of public intervention to regulate maize prices and by the persistence of volatile maize prices.

3.1 Maize Trade and Marketing Policies Pursued in Kenya Maize prices are a crucial social and political issue in Kenya, maize being the main staple food, accounting for 36 per cent of total food caloric intake in the country (Ariga et al., 2010), and being the most common crop grown by rural poor households (Nyoro et al., 1999). The Kenyan government has been intervening extensively in maize markets through trade and marketing policies, even during the so‐called liberalization period, and has reinforced its intervention in the last ten years. In most years, domestic production is insufficient to fulfil consumption requirements in Kenya and maize imports are required. In the last ten years, maize imports accounted for more than 10 per cent of domestic consumption (World Bank, 2009). Government imposed a variable tariff on maize imports at the Port of Mombasa for decades. This tariff has been subject to frequent and sudden adjustments for several decades (Figure 1). Imports from countries that are not part of either the East African Community (EAC) or the Common Market for Eastern and Southern Africa (COMESA) are typically taxed at the rate of 50 per cent, but this tariff can be waived and re‐imposed without prior notification and is a source of major uncertainty for market participants. Maize markets in Kenya are connected to neighbouring countries such as Uganda and Tanzania: being part of COMESA, imports from those countries have been taxed at the rate of 2.75 per cent only (0 per cent since 2008). But in periods of serious shortage, like in 2000 and 2008, poor harvest situations tend to affect neighbouring countries as well, and there is a need to import from outside COMESA countries, such as the USA and South Africa. In such a context, a tariff waiver is expected to increase imports and put downward pressure on domestic prices. But in some cases, domestic prices have continued to increase even after the tariff was waived. Our interpretation is as follows: if such waivers appear imminent but it is difficult to predict with precision when they will occur, potential importers wait until just after the tariff is actually waived before they arrange for imports. It can take several weeks or even a few months before sufficient volumes are transported to the exporting country harbour, then shipped to the importing country border. It takes a few more days or weeks before these volumes meet the targeted population and eventually push local prices back down to import parity (Jayne and Tschirley, 2009). Transport capacity bottlenecks in the importing country also limit the speed of price adjustment even after imported food lands at the port. In Kenya’s case, problems with the rail system limited traders’ ability to transport maize from Mombasa to Nairobi and other upland markets to roughly 150,000 tons per month in 2009, even though maize consumption requirements were much greater than this. In addition to the unpredictability of tariffs, numerous non‐tariff barriers to regional trade such as food quality and safety standard certificates are additional sources of uncertainty for importers (Ariga et al., 2010). The inconsistency of policies is related to timing. To illustrate that, we present below the chronology of policy events that led to the 2008/2009 crisis in Kenya (Ariga et al., 2010). © 2014 The Authors. African Development Review © 2014 African Development Bank


Figure 1:

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Maize import tariffs as a percentage of CIF price (from the Kenyan Ministry of Trade and Industry)

Early 2008. Early warnings were communicated that maize production would be below average and that imports would be required to satisfy maize needs. Estimates of import requirement were in the range of 1 million tons. Prices began to rise. Mid 2008. Erratic rains reinforced the early warning messages and it became clear that massive imports would be required. Imports from Tanzania and Uganda were believed to be able to satisfy part of the requirements, but those countries experienced poor harvest as well and had an export ban in place. Despite this situation, the government maintained the 50 per cent import tariff. As this high tariff made imports uneconomic, private operators argued for a tariff waiver from mid‐2008. However, they didn’t succeed in their application and the government maintained the 50 per cent import tariff throughout 2008, creating a situation in which the government would need to arrange maize imports from the world market to avert shortages. The uncertainties about tariff waiver and about delays and volumes of public imports added to the fear of private operators to intervene on import activities. In response to the poor harvest, to restrictions on imports and to uncertain public imports, maize prices have risen sharply in 2008, going from US$200 a ton to US$400 a ton in a period of 3 months. End 2008. The government had imported only 135,000 tons from South Africa and despite the existence of trade bans, private informal imports were estimated at 120,000 tons. Because of both delays in public imports and government’s decision to maintain the 50 per cent tariff for private operators, maize prices stayed at very high levels in late 2008, over US $300 a ton, even after the main season harvest hit Kenyan domestic markets. Early 2009. As the Kenyan crisis deepened, the government finally waived the import tariff at the end of January 2009, allowing private operators to place imports from international markets. The first imports became effective by the end of February but the capacity to import was insufficient because of inland transport constraints. The maximum transport capacity was 150,000 tons per month. Maize prices remained high until mid 2009 (on average US$300–400 a ton). Besides its intervention through trade control, the Kenyan government is intervening on maize markets through marketing policies. The National Cereals and Produce Board (NCPB) was created in 1979 to regulate maize markets through the administration of prices, the purchase of domestic maize production and the management of a public buffer stock. With the liberalization reform, between 1995 and 2000, the NCPB scaled back its purchases, providing greater scope for the private sector to operate. However, since 2000, the government has gradually increased NCPB’s purchases, and remains a dominant player in the maize market, purchasing in normal or good years around 25–35 per cent of the total domestically marketed maize, most of it from large‐scale farmers (Jayne et al., 2001). The evolution of maize trade and marketing policies in Kenya has been marked by frequent and usually unanticipated changes in trade tariffs, NCPB prices set and volumes purchased (Ariga et al., 2010). Empirical studies showed that these discretionary © 2014 The Authors. African Development Review © 2014 African Development Bank

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policies raised market uncertainties for private stakeholders and led to crowding out effects (Chapoto and Jayne, 2009; Tschirley and Jayne, 2010).

3.2 Maize Prices in Kenya The evolution of Nairobi maize wholesale prices from January 1994 to December 2009 is depicted in Figure 2. Nominal data were deflated by the traditional consumer price index to construct real price series. At first sight, it appears that prices are quite variable, that this variability does not look constant through time and does not look understandable only by seasonal regularities, cycles, and that prices have a decreasing trend from 1994 to 2008. Policies could be considered as such a structural factor. Indeed, prices seem to be characterized by lower amplitude variations in the recent period that corresponds to reinforcement of maize marketing and trade policies. The year 2000 corresponds to a reinforcement of both maize marketing and trade policies (increase of NPCB purchases and trade control), and we can see indeed a very different pattern of price variation before 2001 (with apparently lower frequency and higher magnitude of price variation) and after 2001 (more frequent changes, lower magnitude). But the coefficients of variation corresponding to the period 1994–1999 (CV ¼ 19 per cent) and to the period 2000–2009 (CV ¼ 24 per cent) do not exhibit a clear difference. However, if the coefficient of variation is a meaningful measure of price variations, it does not tell us much on the ‘seriousness’ of these variations for agents. Figure 3 illustrates the evolution of world and domestic prices as well as tariffs. It is likely that tariff shifts have had an impact on domestic prices, and that this impact has not been constant. For instance, 1994 is characterized by a climb in domestic price, following a historically low harvest in 1993 (2,089,000 tons whereas the average production in the 12 following years is 2,750,000 tons) and relatively low world price. One would expect a zero tariff to be favourable to imports which would mitigate the domestic price peak. It is not possible to check if this actually occurred with our data, but it is possible to say that during that period, the tariff policy was consistent with the objective to stabilize domestic price, or at least to mitigate price peak. By contrast, the increase in tariff at the beginning of the period from 1997 to 2000, also characterized by two peaks of high domestic price compared to world prices, is a priori not consistent with the stabilization objective, since one would expect that lower tariffs may have reduced these peaks. This a priori inconsistency is all the more suspected to create instability that it is not a constant tariff, but a relatively high tariff with sudden cut and increases. Note that if tariff policy is at some period consistent and at some periods inconsistent with the objective of stabilizing domestic food price, it may be consistent with other objectives, whether they are fiscal objectives or protectionist objectives.

Figure 2:

Nairobi maize real and nominal prices (USD/ton)

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Figure 3: World and domestic maize prices, deflated by CPI (US$/ton), import tariffs (%)

3.3 An ARCH Model of Food Price Distributions in Kenya Auto Regressive Conditionally Heteroscedastic (ARCH) models are used to characterize and model observed time series (Engle, 1982). ARCH modelling allows simultaneous estimation of temporal variation in the conditional mean and variance of a dependent variable, namely the deflated maize price. The analysis of the error term of the mean equation at any time t provides useful information to interpret price volatility. In particular, when the variance of the error term of the mean equation is not homoscedastic, this variance can increase with the lagged values of the error term of the mean price equation, and the — conditional — variance is interpreted as a measure of price volatility. This is basically what has been done by Shively and Barrett who wanted to assess the impact of policy reforms on price volatility (Shively, 1996; Barrett, 1997), and this is what is done in this paper as well. Therefore, significant explanatory variables of conditional variance are explanatory factors of price volatility. When the set of explanatory variables is supposed to be the set of information available for agents at the time when the prediction is made, this measure of volatility is also a measure of price unpredictability (it is the variance of the prediction error). The ARCH model general structure is as follows. pit ¼ b0 þ

s X

bk pi;tk þ g 0 X it þ eit

eit

iidNð0; hit Þ

ð1Þ

nit

ð2Þ

k¼1

hit ¼ a0 þ

r X k¼1

ak e2itk þ

q X

vk pi;tk þ l0 X it þ nit

iidNð0; sÞ

k¼1

where the subscripts i and t stand for region and monthly period respectively. Equation 1 is the mean equation that determines the maize price pit process as an autoregressive process of s periods, and a vector Xit of exogenous variables explaining the level of maize price. The error term of this equation eit has typically a non‐constant variance over time, hit, and eventually across region. A least square estimation of such a process would generate a heteroscedastic error term and biased estimates. Equation 2 determines the conditional variance of the error term of Equation 1, hit, by past values of the squared error term, e2itk , with k varying from 1 to r, past prices, Pi,t‐k, with k varying from 1 to q, and by the same vector of exogenous variables explaining price volatility, Xit. © 2014 The Authors. African Development Review © 2014 African Development Bank

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Table 2: Explanatory variables used Variable name Lagged prices Stock levels Exchange rate International price Policy inconsistency

Variable description

Sources

Maize wholesale real prices (ZMK/kg) Annual stock level (MT) ZMK/USD Maize real international price (USD/kg) Import tariffs variability

Kenyan Ministry of Agriculture USDA PSD database Kenyan Bureau of Statistics UNCTAD Ministry of Trade and Industry

3.4 Data Many factors are likely to influence food price volatility and could be introduced in X. Basically, food price volatility is related to supply and demands fundamentals, which are likely to include market‐specific and broader economic factors (Roache, 2010), and changes in these factors may have large effects because the short‐run supply and demand elasticities of food prices are typically low (Balcombe, 2009). The following factors are taken into account in our analysis (see Table 2), and correspond to the ones included in Barrett’s model specification (Barrett, 1997).

Monthly lagged prices. There are periods of relatively high and low price volatility, though the underlying unconditional

volatility remains unchanged. This principle underlines the choice of an ARCH model. Domestic nominal prices, obtained from the Kenyan Ministry of Agriculture, have been converted into US dollars by the application of an exchange rate (extracted from the Kenyan National Bureau of Statistics). Inflation. Inflation has an obvious direct effect on food price volatility: to account for this effect, we have been working on Consumer Price Index deflated price series. International prices have been deflated by using the US CPI base 2007 (extracted from the International Labour Organization database) and domestic prices have been deflated using the Kenyan CPI base 2007 (extracted from Kenyan National Bureau of Statistics) Yearly stock levels. Stocks have an important role in theoretical models of commodity pricing (Williams and Wright, 1991; Deaton and Laroque, 1992). In theory, when stocks are low, volatility is expected to increase; empirical evidence, so far, has been mixed. To account for this potential effect, we used data on Kenyan stock levels from the United State Department of Agriculture database to include the levels of stocks held by the NCPB in our model. Monthly exchange rate. Volatile exchange rates are likely to induce a higher volatility in food prices, as the riskiness of returns increases (Balcombe, 2009). Exchange rate data, obtained from the Kenyan Bureau of Statistics, are included in the first step of our model. Monthly international prices. International prices were calculated as prevailing international market prices, extracted from the database of the United Nations Conference on Trade and Development (UNCTAD): we have been working on real prices expressed in US$/ton base 2007. To do so, the international nominal prices have been deflated by the US CPI.

Other economic factors that potentially influence food price volatility were not included because of lack of data or redundant information, such as weather patterns,1 oil price volatility,2 and speculation.3 Added to these common economic factors, we integrate in our model one additional factor reflecting policy inconsistency. We characterize policy inconsistency by the dynamic analysis of import tariffs policies in Kenya related to international prices. We generate the variable INCONS as a dummy variable that equals one for the event of simultaneously high international price (higher than its average) and high tariff (higher than its average) or of simultaneously low international prices and low tariff. In all other cases, INCONS equals zero. INCONS accounts for the absence of the expected response to an international price change. When INCONS equals one, policy is inconsistent with the objective of removing effects of international price volatility, and as such is hardly predictable by market operators. More elaborate definitions of inconsistency have been tried, taking into account past values of price in particular, but this very simple measure is sufficient to illustrate our point.

3.5 Empirical Model Specification The above data have been collected in six markets in Kenya, and we thus have six time series for price. We introduce a dummy variable (R) for each of these markets (but one to avoid multicollinearity), both in mean and variance. We also introduce a dummy © 2014 The Authors. African Development Review © 2014 African Development Bank

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Table 3: Descriptive statistics Variable Market Price P (price index) World price WP (world price index) Exchange rate STOCK Tariff Trend Inconsistency

N

Mean

Data frequency

Standard deviation

Minimum

Maximum

904 904 904 904 904 904 904 904 904

349.95 1 331.38 1 68.89 1297.29 25.38 96.5 0.48

Monthly Monthly Monthly Monthly Monthly Yearly Monthly Monthly Monthly

692.12 0.31 19.08 0.27 9.76 250.945768 15.14 55.45 0.5

819.28 0.4 44.56 0.64 42.38 997.02 0 1 0

4627.54 2.26 136.65 1.97 81.27 1714.8 50 192 1

variable for the post‐harvest season (S) to capture seasonal regularities in price level and volatility, and a trend in both processes. The level of stock at the beginning of each year (STOCK) is an explanatory factor both for the price level (with a negative effect expected) and for price inconsistency (with a negative effect expected). Mean and conditional variance specific variables are international price level (IP), the yearly exchange rate (ER), the level of stock (STOCK), and the policy inconsistency (INCONS). IP, ER, STOCK and INCONS are used with a one‐month lag to avoid endogeneity problems that may arise. The model specification is as follows. pit ¼ b0 þ b1 pit1 þ g 1 IPt þ g 2 ERt þ g 3 STOCKt þ g 4 TRENDt þ þ g 5 INCONSt þ u3 S3 þ

6 X

fr Rr þ eit

ð3Þ

r¼1

hit ¼ a0 þ a1 e2it1 þ v:pit1 þ l1 IPt þ l2 ERt þ l3 STOCKt þ l4 TRENDt þ l5 INCONSt þ r3 S3 þ

6 X

mr Rr þ nit

ð4Þ

r¼1

Positive coefficient for INCONS, l5 > 0, means that the tariff policy inconsistency tends to favour more volatile prices.

3.6 Data Statistics The descriptive statistics of the variables used are presented in Table 3. Price indexes have been used for computation. Although this is not to change the results for each particular market, it improves the overall data set by removing unobserved sources of heterogeneity due to markets differences. For each market i, the following standardization is applied: Pit ¼

M Pit þ M Pi• M Pi•

where Pit is the price index at time t in market i and M Pi• is the average market price in market i. Unit root tests show that prices series of each single market are single‐mean stationary at the 5 per cent augmented Dickey– Fuller test, with one period lag, with the exception of Nairobi where the hypothesis of stationary is rejected at the 10 per cent threshold. We can either remove or keep the Nairobi price series in our quantitative analysis, without a change in the result. The results proposed below include Nairobi prices.

4. Results Results from the ARCH model estimates from maximum likelihood estimation are found in Table 4. © 2014 The Authors. African Development Review © 2014 African Development Bank

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Table 4: Estimates of ARCH model

Constant Lagged price International price Exchange rate Stock Tariff Pre Harvest Post harvest Trend Inconsistency Region Eld Region Kis Region Kit Region Mom Region Nai

Mean

Conditional variance

0.0691 (1.16) 0.9197 (67.18) 0.0365 (1.64) 0.001457 (2.17) 0.000087 (2.29) 0.000112 (0.27) 0.0364 (3.65) 0.0535 (6.29) 5.118E‐7 (0.00) 0. 000186 (0.02) 0.007703 (0.70) 0.00441 (0.40) 0.001984 (0.17) 0.003669 (0.31) 0.003734 (0.32)

0.000136 (0.02) 0.006681 (5.04) 0.0000858 (0.04) 1.1361E‐6 (0.02) 0 9.747E‐10 () 0.0000435 (0.04) 0 0 0.002564 (3.66) 0.001288 (1.14) 0 0.003524 (3.16) 0 0

ARCH1

0.0584 (2.51) N ¼ 904 R2 ¼ 0.89

Note: Figures in parentheses correspond to associated t‐stats.

4.1 Analysis of the Mean Equation (Price Levels) The mean equation shows that the maize price follows an autoregressive process with a highly significant and strong monthly autocorrelation. The effect of international prices on domestic prices is non‐significant, as might be expected from Figure 1. Most of the maize price dynamic in Kenya is endogenous and responds to domestic drivers. A Johansen cointegration test between world price and domestic price would assess non‐integrated price series. Therefore, it is not surprising to find no effect of the tariff on domestic price, and no effect of the tariff policy consistency. On average, the domestic price is neither determined by the average tariff, nor the average consistency of the tariff policy with the stabilizing policy. The domestic price is not particularly higher when the tariff is higher, and is not particularly higher when the tariff policy is consistent with the stabilizing objective. Recall that a consistent tariff is approximately countercyclical with world prices. By contrast, the exchange rate has a significant impact on domestic prices as well as the stock level, which tends to decrease domestic price. Obviously, the seasonal effects are very strong. Pre‐harvest price is significantly higher and the post harvest price is significantly lower than the rest of the year. These results are consistent with the ones of Shively (1996) and Barrett (1997) and Karanja et al. (2003). © 2014 The Authors. African Development Review © 2014 African Development Bank


Figure 4:

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Price volatility, policy inconsistency and tariffs

4.2 Analysis of the Conditional Variance Equation (Price Volatility) The ARCH1 term confirms that the price process is correctly described by an ARCH model. The variance of the residuals is increased by high recent values of the residuals. Apart from this, three variables only have a significant effect on conditional variance of price residuals: lagged price, inconsistency and the dummy for Kitale region. Our findings do establish a strong linkage between policy inconsistency and price volatility, as stated in the third column of Table 4 (that corresponds to the conditional variance equation of the ARCH model used), where we can read that the coefficient associated to price inconsistency is significantly positive (coefficient of 0.002564 with a t‐statistic of 3.66). This indicates that the more a trade policy is unpredictable, the higher the price volatility is. The significant positive effect of tariff policy inconsistency reveals that the episodes of higher volatility are associated with periods of inconsistent uses of the tariff and conversely, that periods of lower volatility are associated with periods of consistent uses of tariff policy. It might appear unsurprising that policy inconsistency increases price volatility but it is more surprising that price volatility is determined entirely by this inconsistency (and past values of price and of residuals). Lower tariffs or higher stocks have no effect on volatility. Our results on stocks stand in contrast with Jayne et al.’s (2008) observations that NCPB activities have stabilized maize prices in Kenya and with Barrett’s (1997) results. Lower world prices have no effect either, and even more surprisingly, seasonality does not seem to impact volatility. A potential consequence of this result is that the degree of trade openness does not seem to increase or decrease volatility, at least in the case of maize in Kenya, but inappropriate moves of tariff policy can increase volatility. Figure 4 illustrates the idea that some of the volatility episodes correspond to inconsistent tariff policy as it is defined. The inconsistency dummy variable is read on the left vertical axis, and the tariff (in per cent) is read on the right vertical axis. Volatility level (a variance of the index price) has been arbitrarily multiplied by 100 to be read on the left axis. This graph does not mean that the tariff is the cause of volatility, but that tariff policy can take advantage of the price gap between world price and domestic price to smoothen volatility, or it can also ignore volatility. The graph shows sequences of consistent policy, each time the variable inconsistency equals 0. For instance, the high tariff around 2000 seems to have been efficient to reduce volatility, whereas the same 50 per cent tariff around 2008 seems to have produced volatility.

5. Conclusion Drawing from the Kenyan case, we make a case for distinguishing between price shifts that well‐informed market actors could anticipate, and price volatility, that is by essence, erratic and unpredictable. Whereas price formation can be partially explained by past information on prices, inter‐annual cycles and seasonal cycles, a large part of price volatility cannot be explained by these © 2014 The Authors. African Development Review © 2014 African Development Bank

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variables. The hypothesis tested in this study is that trade policy inconsistency accounts for some part of the unexplained residual in standard price forecasting models. To establish this result, we include measures of policy inconsistency in an ARCH model of monthly maize price formation, and find that inconsistent policy increases the unpredictable character of price variation. On the contrary, the degree of trade openness does not affect volatility. In the Kenyan case, the degree of trade openness does not seem to have an impact on volatility; whereas the inconsistent moves of trade policy does produce volatility. The fact that policy inconsistency may increase price volatility does not mean that no public intervention is needed to manage food price volatility but, rather, that government actions should be as transparent and rules‐based as possible, or at least based on transparent advance consultation between the state and the private sector over future policy actions. Our results suggest that the nature of the policy tool and the institutional conditions of its implementation can determine whether they succeed or not in achieving their price stabilization objectives. Given the lack of strong institutions in most African countries, one can easily be concerned about the feasibility of such a recommendation. However, examples of consultations between private operators and government representatives prevent us from sinking into pessimism. This is the case in Madagascar, where a ‘multi‐stakeholders consultation platform’ was created in 2008 to discuss rice policy issues with private importers, representatives of the government and producers. This platform ruled from to 2008 to 2010 and allowed private operators and government representatives to jointly and transparently develop their strategies. Multi‐stakeholder coordination systems can thus reduce the risk of counterproductive policies that could worsen price volatility. Those coordination systems are still in their budding stage in Africa and should receive more attention.

Notes 1. Extreme weather events (drought, floods) are directly affecting agricultural productivity (Haile, 2005). Thus, these events are one important source of food price volatility (Ahmed et al., 2009). 2. Recent empirical work has suggested a transmission of prices between oil and food prices, through the channel of fertilizer prices, mechanized agriculture and freight costs (Balcombe, 2009). To account for this effect, some authors introduce the US CPI deflated petroleum spot price (International Monetary Fund data). 3. The impact of speculation on food price volatility is a controversial debate (Roache, 2010).

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