Pricing Policy Under Double Market Power: Madagascar and the International Vanilla Market

Pricing Policy Under Double Market Power:Madagascar and the International Vanilla Market

Jaime de Melo, Marcelo Olarreaga, and Wendy Takacs*

Abstract

This paper uses a price-leadership model of the international vanilla market to study the welfare conse-quences of alternative pricing policies for Madagascar, the leader, that also controls domestic productionthrough a single-channel marketing system. Econometric estimates of the model are used for simulations ofwelfare and revenue changes and internal redistribution of income. Results indicate that Madagascar couldhave gained between 0.9% and 2.6% of GDP per year on average over the period 1981–91 by followingoptimal pricing policies, and that producers were overtaxed suggesting that political economy considera-tions played a role in the pricing decisions.

1. Introduction

The policies of the marketing-boards is a conspicuous instance of the dis-parity in political effectiveness between the urban political elites and theunorganized and inarticulate rural population. Bauer (1987, p. 235).

Despite success at export diversification, in many developing countries traditional com-modity exports remain an extremely important source of export and tax revenue. Forsome commodities, certain exporting countries have sufficient market share to influ-ence, or even control, the world market prices of their principal export commodity. Inthese cases the exporting country is in a position to improve its terms of trade andnational welfare through appropriate pricing policies. Many countries that believe theyare in this position tightly control internal production, distribution, and pricing of theexport commodity and give monopoly export rights to state-owned marketing-boards.

Marketing-board control of distribution and prices has crucial implications for thegovernment budget. When marketing-boards control production and distribution ofimportant staples for internal distribution, political pressures often result in high pro-ducer and low consumer prices and a significant deficit that contributes to macroeco-nomic imbalance.When the products in question are exported and there is a significantspread between producer and export prices that exceeds processing, transport andstorage costs, the marketing-board raises revenue for the state through implicit taxa-tion of producers.This implicit taxation has also important implications for income dis-tribution within the exporting country. Where the agricultural sector consists of a largenumber of small producers, heavy implicit taxation of export crops can increaseinequality in income distribution.1

Review of Development Economics, 4(1), 1–20, 2000

© Blackwell Publishers Ltd 2000, 108 Cowley Road, Oxford OX4 1JF, UK and 350 Main Street, Malden, MA 02148, USA

*de Melo: University of Geneva, 102 bl Carl Vogt, 1211 Geneva 4, Switzerland; and CEPR, London, UK.Tel: 22-705-8273; Fax: 705-8293; E-mail: [email protected]. Olarreaga: World Bank, DECRG, 1818 Hstreet, Washington, DC 20037, USA; and CEPR, London, UK. Tel: 202-458-8021; Fax: 522-1159; E-mail:[email protected]: University of Maryland, Baltimore County, Baltimore, MD 21228, USA.Tel: 410-455-2174; E-mail: [email protected]. Without implicating them, we thank Olivier Cadot,Jaya Krishnakumar, Maurice Schiff, James Tybout, Alan Winters, and a referee for suggestions on an earlierdraft. An earlier version was presented at the conference “Recent Developments in International Eco-nomics”, Aix-en-Provence (July 1995), and appeared as CEPR DP #1447.

The pricing policies of marketing-boards for export crops have received heavy criti-cism for overtaxation of agriculture, for overestimating the actual degree of marketpower in international markets, and for lack of pricing flexibility in the face of chang-ing world market conditions.2

A prime example would be OPEC’s underestimation of consumer and producerresponse and its loss of market share and market power over time.

Another example is the international vanilla market, which historically has beendominated by a cartel led by Madagascar. In recent years the cartel has continued torestrict exports and maintain high prices despite drastic loss of market share to its principal competitor, Indonesia. These developments have led observers to questionwhether the vanilla marketing authorities in Madagascar have overestimated theirmarket power and followed a pricing policy that has reduced their country’s welfare.

Outside observers’ arguments for dismantling marketing-boards and a “hands-off”policy are based on the principle that taxation of producers is both inefficient and detri-mental on distributional grounds. But governments with weak fiscal systems need toraise revenue and trade taxes may be the only instrument at hand. A marketing-board(or other parastatal agency) is a way of raising revenue through implicit taxation, whichmay be politically more acceptable than transparent high tax rates.3 In the case ofexport crops these organizations redistribute income from (usually) small farmers toan urban political elite. Not surprisingly, the political economy of pricing policy in suchan environment is likely to lead to excessive rent extraction on the part of the paras-tatal at the expense of the poor.4

The economics of marketing-boards in the context of international trade have beenstudied in several settings. Among the more significant recent contributions, Markusen(1984) compares welfare-maximizing and revenue-maximizing export taxes withprofit-maximizing marketing-boards in a two-good general equilibrium model. Krishnaand Thursby (1992) introduce strategic interaction and concentrate on the nature ofcompetition between two duopolist marketing-boards in the international market.These contributions are concerned with policy design in different environments, aconcern we share as well. However, by not confronting the model’s predictions withactual pricing decisions, these authors cannot address, even indirectly, whether politi-cal economy factors may have been important in explaining actual pricing.5

Our model of double market power differs from theirs and others in the literature,and allows us to contribute an empirical case study of pricing policy in this environ-ment. By comparing several alternative strategies to the actual strategy followed byMadagascar, we obtain some idea of the extent to which the operation of marketing-boards reflects political economy motives of internal redistribution as suggested by theabove quote. More generally, this is a case study of overlapping marketing-board objectives as revenue-raising, welfare-maximizing and internal income redistributioninstruments.

We develop and estimate a price-leadership model of the international vanillamarket which allows us to focus on the welfare and internal distributional implicationsof alternative pricing policies on the part of the vanilla marketing authorities in Mada-gascar. Has Madagascar overestimated its market power, as is usually claimed by theopponents of taxation of primary commodities? Or does the double market power ofthe marketing authorities as a monopsony buyer in their own country and a priceleader internationally give them an incentive to raise prices above the optimal levelfrom the point of view of the country as a whole? Has the operating of the marketing-board gone even further and served primarily as a means to redistributeincome internally?

2 Jaime de Melo, Marcelo Olarreaga, and Wendy Takacs

© Blackwell Publishers Ltd 2000

2. Trends in the Natural Vanilla Market

Natural vanilla6 is a tree crop that is exported after a relatively elaborate preparationthat involves drying and curing.7 In 1990, Madagascar was the largest producer (tonsin square brackets) [1800] followed by Indonesia [600]. Other suppliers were on thefringe: Comoros [140], Reunion [16],Tonga [40],Tahiti [50]. Madagascar, Comoros, andReunion produce bourbon vanilla which is considered high quality both because ofhigh vanillin content and of fragrance which distinguishes it from the vanilla offeredby other suppliers.

In 1960, the international market for natural vanilla was dominated by Madagascar,the world’s lowest-cost producer of the high-quality bourbon vanilla. From its domi-nant position of 60% of world volume of natural vanilla exports, Madagascar orga-nized the bourbon vanilla cartel with Comoros and Reunion, setting high prices andrestricting supply. The cartel’s share exceeded 80% of the world market in 1974 butdropped to 30% starting in the mid 1980s as Indonesia’s market share climbed rapidlyto over 30% of world exports.

These developments in the natural vanilla market are summarized in Figures 1, 2(volume and value trends) and Figure 3 (price and taxation trends). The market hasgrown, with Indonesia increasing significantly its output since the mid-1980s (Figure

PRICING POLICY UNDER DOUBLE MARKET POWER 3


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MADSTOCK WORLD MADAG INDON

Figure 1. Madagascar/Indonesia/World Export Volumes and Madagascar’s Stocks

1), catching up with Madagascar by the late 1980s (Figure 2). However, higher unitvalues for bourbon vanilla (see the price differential in Figure 3)—reflecting qualitydifferentials and the cartel’s pricing policies—have contributed to maintain a highmarket share in value terms for Madagascar, at least until 1987 (also Figure 2).

During the period covered by this study (up to 1991), the domestic vanilla marketwas highly regulated in Madagascar. Licenses (valid for three-year periods) forgrowing were required for producers. Vanilla preparation also required a license(yearly renewable) given by the Ministry of Trade to growers and “processors–stock-ers”, the two participants in the preparation of cured vanilla. Purchase prices from pro-ducers were fixed and guaranteed for the entire crop. A stabilization fund (CAVAGI)stabilized the price received by producers and financed stocking costs with contribu-tions taken from export proceeds, after payment of the export tax. Finally, the Min-istry of Commerce selected each year certain processors—stockers allowed to export(38 in 1989, 13 in 1990), as well as quantities exported by each exporter at the pricefixed by the cartel.



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Figure 2. Export Values (in US$) and Volume Shares of Madagascar and Indonesia

Figure 1 also shows the evolution of Madagascar’s vanilla stocks that resulted fromthe marketing-board behavior which guaranteed the purchase of the entire crop at thefixed price. Stocks grew rapidly to an all-time high of over five years’ of exports in1992. By then the stocked vanilla was deteriorating and it was clear that it could notbe sold on world markets. Besides, by 1990, stocking costs accounted for over 60% ofthe CAVAGI budget and amounted to 0.003% of GDP. Not surprisingly, in 1993, three-quarters of the stock was burned.8

Figure 3 shows the evolution of the average price for the two major suppliers andthe rising tax (including payments for the stabilization fund) by Madagascar. It is strik-ing that the cartel’s price remained insensitive to its loss of market share to Indone-sia.The average price for Madagascar’s high-quality bourbon vanilla has remained wellabove Indonesia’s. Part of the price differential is due to the generally higher qualityof the bourbon vanilla exported by Madagascar. (Towards the end of the period thevanillin content of Madagascar’s vanilla was about twice that of Indonesia.) Part of theprice difference would also be attributable to Madagascar’s rising taxation, whichreached 80% of the unit export price.

In Indonesia, by contrast, the government does not regulate the production process.Indonesian vanilla is supplied by a large number of small producers. There is no stock-



60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93

MADUV MADTAX

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Figure 3. Export Unit Values (in US$) of Madagascar and Indonesia, and Madagascar’sTax

piling on the part of the government, and no government intervention in the exportmarket through either export taxes or quantity controls. Two commonly cited factorscontributing to the rapid growth of the industry in Indonesia are the high-price policyof the bourbon cartel whose alleged goal is to supply the high-quality end of themarket,9 and Madagascar’s internal pricing policies, which have discouraged high-quality production, even though production and opportunity costs (of land especially)are low.10 Other factors that have also contributed to the rise of Indonesia in the marketinclude a vanilla shortage in the late 1970s11 that encouraged vanilla purchasers tosecure other sources of supply, most notably in Indonesia, and great improvements inthe quality of Indonesian vanilla, which contributed especially to increased sales in theUnited States.12

3. Alternative Pricing Policies in a Price-Leadership Model

This structure of the international vanilla market and Madagascar’s tight regulation ofthe internal market raise issues about pricing policy under double market power.

Madagascar’s share of the world market and the control that the authorities exer-cise over both export and domestic farm-gate prices implies that the authorities haveboth market power in the export market and monopsony power relative to the growers.The absence of regulation of the market in Indonesia and the large number of inde-pendent suppliers imply that export supply from Indonesia can be modeled as thesupply curve of a perfectly competitive industry.13 We therefore treat the internationalvanilla market using a price-leadership model, with Madagascar as the price leader andIndonesian producers (and other smaller producing countries) acting as followers.Given differences in the quality of vanilla produced in Madagascar and in Indonesia,we treat the two countries’ products as imperfect substitutes.We develop first a graphi-cal representation of the model, then the corresponding structural model that is usedin the simulations in section 5.

Graphical analysis Figure 4 uses a linear representation (the model, estimations andsimulations are based on constant-elasticity specification) to illustrate the market posi-tion of the vanilla marketing authorities in Madagascar: We use it to investigate thewelfare and distributional consequences of alternative pricing policies. Market powerin the export market implies that Madagascar faces a downward-sloping demand curvefor its vanilla exports, D, with the associated marginal revenue curve MR. Note thatthe demand curve shows the quantity that Madagascar would sell in the world marketat various prices for Madagascar vanilla, given the demand and supply conditions forIndonesian vanilla and the resulting equilibrium price in that market. S is the upward-sloping supply curve of Madagascar’s vanilla producers; the resulting marginal cost ofvanilla to the marketing authorities is shown by MC.

A laissez-faire pricing strategy on the part of Madagascar would result in a marketequilibrium at point a, with a free-trade equilibrium price PF

M and quantity of vanillaexports QF

M. Ignoring processing and transport costs, the producers would receive thefull price paid by buyers in the world market, so the producer price PPF

M would notdiverge from the export price. This laissez-faire pricing strategy represents the optimalpolicy for the world (i.e., free trade) where price to consumers equals the marginal costof production.

A pricing strategy by the vanilla marketing authorities to exploit Madagascar’smarket power in the world market and maximize the country’s profits from vanillaproduction and export would imply an export quantity of Q*M (the quantity at which



marginal revenue from exporting equals the true marginal production cost for vanilla),an export price of P*M, and a producer price at PP*M.14 This pricing strategy representsan optimal policy for the country; i.e., exploitation of the country’s monopoly power inthe world market to improve its terms of trade.

A third marketing-board pricing strategy would exploit simultaneously marketpower in the world market and monopsony power relative to the growers to maximizemarketing-board profits, which is equivalent to maximizing vanilla tax revenue. Theequilibrium quantity under this pricing strategy would be Qm

M, where the marginal costof vanilla to the marketing-board equals its marginal revenue from export sales atpoint c.

Equilibrium export price under this pricing strategy would be PmM and producer price

would be PPmM. This monopoly/monopsony pricing strategy would be optimal for the

marketing-board; i.e., the exploitation of both monopoly and monopsony power whichmaximizes marketing-board profits. Maximizing marketing-board profits is not optimalfrom the point of view of the country as a whole because the exploitation of the board’smonopsony position vis-à-vis the growers involves a loss to domestic producers fromlower producer prices and reduced output.

One other pricing strategy, which is not likely to be chosen, but is included in thediscussion here for the sake of completeness, would be exploitation of monopsonypower over the growers, but without simultaneously taking advantage of market powerin the world market. This monopsony-only equilibrium would be at point d, where MCto the marketing authorities equals price. Export price would be set at PM

ms, and pro-ducer price at PPM

ms. Note that the geometry of Figure 4 indicates that if the S and Dcurves are symmetric around a, then the same solution would be obtained by eitherthe exploitation of monopsony power or by the exploitation of monopoly power.



Figure 4. Madagascar Pricing Strategies

Given the divergence of interests of the marketing-board, the growers, and con-sumers abroad, it is useful to identify gains and losses to the world, to Madagascar asa whole, and to the vanilla producers, of the alternative pricing strategies. From thepoint of view of Madagascar as a whole, Q*M is the optimal export quantity. This pricingstrategy results in marketing-board profits of kebm and a loss to producers of labmcompared with free trade. The world suffers deadweight losses equal to triangle eab inFigure 4 because price exceeds the true marginal production costs represented by theheight of the supply curve S. But from the point of view of Madagascar, any divergenceof export levels below or above Q*M involves losses. Compared with free trade, themonopoly pricing strategy nets Madagascar gains of bah.

When the marketing-board maximizes profits at QmM, tax revenue is ifgr, producer

losses (compared with free trade) are lagr, and triangle cbg represents the loss ofwelfare to the country as a whole (compared with the optimal at Q*M). For export levelsbelow Q*M, MR > supply price implies the extra revenue the country could earn byincreasing exports exceeds actual incremental production costs. But the deadweightlosses from the point of view of the world as a whole of triangle fag are even larger.Because both free-trade and the tax-revenue-maximizing solutions are second-bestfrom the country’s point of view, one cannot tell a priori which would lead to greaterwelfare.

The monopsony-only pricing strategy is not the optimal policy from any particulargroup’s point of view. Marketing-board profits of sdjn would be lower than at Qm

M,Madagascar’s profits from producing and exporting would be lower than at Q*M by anamount tbs, and world losses (compared with free trade) would be das.

Algebraic formulation The graphical model used above has a straightforward alge-braic equivalent. We give its representation in the following equations with the constant-elasticity functional forms used in the simulations in section 5.15

Note however, that this model does not correspond exactly to the reduced-formmodel used in section 4 to obtain the demand and supply elasticities needed for thesimulation analysis. Hence the other variables influencing demand and supply rela-tionships (e.g., the price of substitutes, income, etc.) are subsumed in the constant termin the equations that follow (and in the simulations in section 5).

Assume then that both countries export their entire vanilla crop and face constant-elasticity demand curves of the form:

(1)

(2)

where Pj,j = I,M is the export price, A,B > 0 are constants, and a,b,g,d > 0 representown and cross-price elasticities of demand. Other factors influencing demand (e.g.,income per capita and the price of substitutes) are subsumed in the constant term.16

The supply functions are:

(3)

(4)

where PPj is the producer price, C,D > 0 are constants, and e,f > 0 are supply elastici-ties with other intervening factors again subsumed in the constant term.

S D PPI I= ( )f,

S C PPM M= ( )e,

D BP

PI

M

I

=d

g ,

D AP

PM

I

M

=a

b ,



First, assume there is no intervention by Madagascar and that free trade prevails onthe vanilla market. Madagascar’s export price is obtained as follows:

(5)

where

(6)

is Madagascar’s elasticity of demand under the assumption of price leadership.Under the price-leadership assumption, if Madagascar pursues a national welfare-

maximizing pricing strategy, its optimal export price is given by:

(7)

where

(8)

is the ratio of producer to selling price in the leader–follower model that tends to 1 asl Æ -•.17

Correspondingly, if Madagascar behaves as a monopsonist on the domestic marketbut does not intervene in the export market, the export price is given by:

(9)

where

(10)

is the ratio of producer to selling price in the monopsonist model that tends to 1 as e Æ •.

Finally, if Madagascar behaves as a monopsonist on the domestic market and as aprice leader on the export market, we proceed in the same way but the price wedge isnow given by:

(11)

Note that monopsony power in the domestic market vanishes as e Æ •. Likewise theoptimal export tax is a decreasing function of the value of the elasticity of demand.The optimal price under the price-leader–monopsonist assumptions is:

(12)

It can easily be verified that P*M Æ PFM as l Æ -•; that PM

ms Æ PFM as e Æ •; and that

PmM Æ PF

M as l and |e| Æ •.This is a first model of the international vanilla market.18 To compare Madagascar’s

actual observed pricing policy with the alternative pricing scenarios described here,we need estimates of the crucial supply and demand parameters. We now discuss the econometric model used to obtain the elasticities needed for the simulation analysis.

P P D P S PMm

PM M M M M

F

M

= -( ) = -( )argmax W We e l .

PPP

Mm

Mm

= =+

+W

11

ll

ee

.

PP PMms

Mms = +( )e e1

P P D P S PM

ms

PM M M M M

F

M

= -+

ÊË

ˆ¯ =

+ÊË

ˆ¯

-( )

argmaxe

ee

e

e e l

11

,

PP PM M* * = +( )1 l l

P P D P S PMP

M M M M MF

M

* ,= -+Ê

Ëˆ¯ =

+ÊË

ˆ¯

-( )

argmax1

1l

ll

l

e e l

l ad g f b= +( ) - < 0

P D S D SAC

BDM

F

PM M I I

M

= = =( ) = ÊË

ˆ¯

È

ÎÍ

˘

˚˙

+( ) -( )

arg ,sol anda g f e l1



4. Estimation of the Price-Leadership Model

This section reports the results of our estimation of the model described above. Onthe supply side, econometric tests confirmed that supply decisions depend on laggedprices. Demand parameters were estimated from panel data, so that demand andsupply error terms are not correlated, thereby allowing us to estimate separatelydemand and supply sides of the model. As will be seen from the discussion, resultsfrom the demand estimates are remarkably robust, those for supply estimates less so.

Demand

We estimated import demand equations for each of the four major importing countries of the form DJ

H = f(PHH; PI

H; PVH; GDPH) where (H = Japan, USA, France,Germany) and (J = M for Madagascar and I for Indonesia), DJ

H is quantity importedper capita, PVH is the deflated price of the synthetic substitute vanillin, (PH

M; PIH) are

the CIF export prices of Madagascar and Indonesia vanilla, and GDPH is real GDPper capita for importing country H. We estimated these import demand equationsunder partial equilibrium assumptions. This means that we did not impose cross-equation restrictions taking, as it were, refuge behind the observation that expendi-tures on vanilla are a sufficiently small share of total expenditures to be considered inisolation. Estimation is carried out separately for Madagascar and Indonesia usingpanel data for the period 1964–91. Results for the within estimators are reported inTable 1.

Our estimation strategy was as follows. First, we tested for the inclusion of dummiesfor each importing country through Leamer critical F value for large samples, and hadto reject the null hypothesis of no country dummies (F(12,84) = 4.9 for Madagascarand F(12,84) = 5.0 for Indonesia). Second, we estimated the model with a random andfixed effects estimators. Even though our sample is not random (the four largest vanillaimporters), since fixed-effects models are conditional on the effect prevailing in theobserved sample, it is preferable to treat individual effects as random (Mundlak, 1978).The Hausman test (random effects vs within estimator) rejected the hypothesis of nocorrelation of the individual effects with other regressor for Indonesia but could notreject it for Madagascar. Therefore we settled for a fixed-effects estimator for Indone-sia and a random-effect estimator for Madagascar. Demand for Indonesia is estimatedwith heteroskedastic-robust standard errors. Madagascar demand is estimated with a 2SLS procedure (using the estimated value of Indonesia’s price in Madagascar’sdemand equation) to account for the simultaneity bias due to Madagascar’s price-leader and Indonesia’s price-follower behavior.

The results appear in Table 1. All price coefficients have the expected signs and aresignificant at the 1% (5%) level for Madagascar (Indonesia). Three-quarters of thevariance is accounted for by the price and income variables. Even though no restric-tions were imposed on the cross-price elasticities, they turned out to have practicallythe same values. Under the price-leadership assumption (see definition of l above),the own-price demand elasticity is (-1.39) which is lower than the estimate for Indone-sian vanilla (-1.98). This result is intuitively plausible if, indeed, bourbon vanilla is ina “category of its own.”

For both countries, the price of synthetic substitutes is significant even though theestimated value is much higher for Indonesia than for Madagascar. This is probablycredible in relative terms since Indonesia produces lower-quality vanilla which mightbe considered a closer substitute for artificial products. In sum, even though the stan-



dard error of the regression is on the high side,19 in overall terms these results areremarkably good.

Supply

The estimated supply functions are inspired by the literature on the determinants oftree crops where lags in production are important. The supply functions are estimatedin double log form and take the form: SJ = f(PJ; PSJ; RJ; HJ; T; DUM). As before,(J = Madagascar, Indonesia), PJ are one-period lag unit values for Madagascar andIndonesia, PSJ is the price of other factors affecting supply (e.g., wages or the price ofrice), RJ is rainfall deviations from optimal rainfall quantity to produce vanilla, HJ isharvested area, T is a time trend to capture exogenous factors affecting supply, andDUM is a dummy variable that captures the effects of cyclonic conditions in Mada-gascar. Time subscripts and lags have been omitted. A separate supply function is esti-mated for each country over the period 1964–91 according to the following strategy.In the presence of autocorrelation in OLS estimates, a Wald test was used for select-ing between maximum-likelihood iterative AR1 estimates and lagged endogenous



Table 1. Panel Demand Estimates (1966–91)

Dependent variablea: ln D

Madagascar Indonesia

Constant -40.29(-7.54)**

ln PM -2.53 1.66(-3.54)** (2.23)*

ln PI 1.73 -1.99(2.70)** ( -3.73)**

ln PV 1.01 1.85(4.24)** (4.37)**

ln GDP 3.31 4.79(5.80)** (4.38)**

Adjusted R2 0.79 0.77SER 0.59 1.36F-test H0:no-dummies 4.1** 3.6**Leamer F H0:no-dummies 4.9** 5.3**D–W 1.9b 1.2c

Hausman H0:random eff.d 5.0 4.7+

a Data sources are: CAVAGI, COMTRADE, and FAO. Madagascar’s demand is estimated with a randomeffect technique and Indonesia’s demand is estimated with a White robust within-estimator. In both casesrandom and within-estimators yielded very close estimates. Figures in parenthesis are t-statistics: * is for sig-nificance at the 5% level and ** at the 1% level.b Significant at 5% following Bhargava et al. (1981) tables.c Following Bhargava et al. (1981) we cannot reject the hypothesis of autocorrelation (1.2 is below the lowerbound).d We choose a 20% significance level (noted +) since the Hausman test drops all variables for which thefixed-effect estimator is more efficient than the random-effect estimator and therefore biases the testtowards accepting the null hypothesis (H0). For Madagascar the test follows a chi-squared of order 4 andfor Indonesia a chi-squared of order 3.

variables leading to the selection of the AR1 method to correct for autocorrelation.The results appear in Table 2.

In spite of relatively high significance for the main coefficients (usually around 95%for all coefficients in both equations, and no evidence of heteroskedasticity in the esti-mates as shown by the Breusch–Pagan test), the supply estimates posed problemsowing to a lack of data (e.g., the lack of data on rainfall for Madagascar), and the consequent need to search for the best specification on a trial-and-error basis.20 Forinstance, in the case of Madagascar, a dummy variable (DUM) was used to accountfor the 1976 cyclone and for unfavorable climatic factors during 1966–70. Also, thesearch for best-fitting lag structures yielded generally insignificant results so we optedfor a single lag on prices for both countries.21

The results in Table 2 yield similar long-run supply elasticity estimates (0.48 forMadagascar and 0.52 for Indonesia). Perhaps because of measurement problems, realwages in the vanilla growing regions were insignificant in both supply equations. ForMadagascar though, the price of rice (PRi) was significant and was included as an alter-native variable measuring opportunity costs (being insignificant in the case of Indone-sia, it was not included). The absolute deviation of rainfall from the recommendedamounts, RI, does not enter significantly into Indonesia’s supply function. The timetrend coefficient was positive and significant for Madagascar, but the harvested area



Table 2. Supply Estimates (1964–91)

Dependent variablea: ln S

Madagascar Indonesia

Constant 4.80 -5.50(2.98)** (-3.40)**

ln PPt-1 0.49 0.52(2.00)* (4.92)**

ln PRt-1 -1.44(-3.79)**

ln Rt-1 -0.04(-1.23)

Dummy -0.87(-5.63)**

ln H 1.08(5.38)**

Time 0.10(5.33)**

Adjusted R2 0.84 0.85SER 0.31 0.31Wald H0:AR1 6.9 1.6Breusch/Paganb 9.3 6.7D–W 1.8** 1.9**

a Data sources are: CAVAGI, COMTRADE, and FAO. Estimation by amaximum-likelihood iterative first-order serial correlation (AR1) tech-nique. Figures in parenthesis are t-statistics: * is for significance at the 95%level and ** at the 99% level.b H0:homoskedacity.

was insignificant (probably owing to the significant correlation between the time trendand the harvested area), in contrast to Indonesia where the harvested area was sig-nificant (but the time trend insignificant).

In view of the above, it is not surprising that in contrast with the demand estimates,the supply estimates were sensitive to the selection of explanatory variables, especiallyfor Madagascar (for example, in equations including harvested area or wage insteadof time produced supply estimates around 0.8). However, in all instances, positive andsignificant supply estimates were obtained. Hence, the use of a range of estimates inthe simulations reported in Tables 4 and 5.

5. Evaluating Madagascar’s Pricing Strategy

The demand and supply estimates were used to compute Madagascar’s optimal pricesand quantities under each of the alternative pricing strategies pictured in Figure 4 andexpressed in equations (3)–(12). These “optimal” prices (and quantities) were thencompared with actual observed prices and quantities. Comparisons were for the years1981–91, the period when Indonesia came into the market and, more importantly,the period when Madagascar began to accumulate large stocks and to tax producersheavily. The calculations were carried out using standard calibration techniques.22

Welfare and revenue comparisons are discussed, followed by comparisons of the result-ing internal redistribution between producers and the marketing-board.

Welfare and Revenue

Table 3 compares actual average prices and quantities with the calculated averageoptimal prices and quantities under the alternative scenarios.Actual producer prices arequite close to optimal producer prices under the government revenue-maximizationassumption. However, because optimal selling prices are much lower than actual ones,optimal quantities are correspondingly higher than actual ones.Thus there would appearto be overtaxation.

For welfare calculations, we express the triangular welfare losses (areas tbs, cbg, andbah in Figure 4) generated by deviations from the welfare-maximizing strategy.Welfareand marketing-board revenue changes appear in Table 4 and producer surplus changesin Table 5. Neglecting internal vanilla consumption, welfare change equals the changein the marketing-board revenue plus the change in producers’ surplus:23



Table 3. Calculated Optimal Prices and Quantities

Export pricea Producer price Implicit taxb Quantitiesc

Actual (average 1981–91) 66 5 61 877d (1,569)e

Price-leader 36 11 25 2,447Monopsony 35 12 23 2,578

Price-leader/Monopsony 49 5 44 1,634Free trade 26 26 0 3,860

a All prices are in $/kilo.b The implicit tax is given by the difference between export and producer prices.c In metric tonnes.d Actual exports.e Actual production.

(13)

where DWj is the change in welfare had the marketing-board followed policy j; DPj isthe change in marketing-board revenue; PPac is the actual producer price; PPj is pro-ducer price under policy j, and S is the estimated supply function for Madagascar.Under the assumption that internal general equilibrium repercussions of alternativevanilla pricing policy can be neglected, DWj, is an approximation of welfare change.

Consider first the bold figures (figures in parenthesis and brackets are correspond-ing estimates under high and low elasticities) which compare actual outcomes witheither set of optimal policies, welfare-maximizing (price-leader) or revenue-maximizing (price-leader/monopsony). The estimates show very large welfare losses,almost 50% of the annual value of exports, or 1% of realized annual GDP. Given thatthe cumulated stocks were destroyed, ex post these estimates reflect the interpretationof a misguided policy. If, instead, tax revenue maximization was indeed Madagascar’sintended objective, the profile of quantities exported would have been about equal toactual production which is about twice actual exports, or 70% of exports under nationalwelfare maximization. Then welfare gains would have been 0.8% of actual GDP.

Comparing the alternative policies, in terms of the tradeoff between welfare andrevenue maximization, the elasticity estimates here suggest that, in terms of welfare,the departure from welfare maximization to revenue maximization would be justified.A Pigouvian-type calculation indicates that going for revenue maximization onlylowers average GDP by 0.16% per year while yielding an average yearly revenue gainof 0.3% (i.e., a tradeoff between a 14% higher welfare gain under welfare maximiza-tion and a government revenue 52% as large under tax revenue maximization).

D DPW S PP dPPj j

PP

PP

ac

j

= + ( )◊Ú ˆ ,



Table 4. Welfare and Marketing-Board Revenue Gains (1981–91 average)

Percentage of export revenue (ER) and GDP a

Price-leader/Price-leader Monopsony monopsony Free trade

Walfare (44)b 48c [127]d (42) 48 [118] (43) 41 [100] (22) 26 [114](% ER)

Gains (0.90) 0.99 [2.63] (0.86) 0.98 [2.44] (0.87) 0.85 [2.08] (0.46) 0.53 [2.36](% GDP)

M-B revenue (47) 28 [19] (30) 22 [63] (51) 42 [72] (-83) -83 [83](% ER)

Gains (0.98) 0.57 [0.39] (0.63) 0.46 [1.29] (1.06) 0.87 [1.49] (-1.72) -1.72 [-1.72](% GDP)

a Actual export revenue represents 2.1% of GDP during this period and 715% of producers’ revenue.Valuedat export [producer] price vanilla production was 15.2% [1.5%] of GDP.b Figures in parenthesis are higher-bound estimates obtained by decreasing Madagascar’s elasticity of supplyby one standard deviation and its demand elasticity by 1/2 (to avoid an inelastic demand estimate). e = 0.24,b = 1.7, l = -1.06. Other elasticities take the values in note c.c Figures are for estimated elasticities. a = 1.70, b = 2.53, d = 1.66, g = 1.99, l = -1.39, e = 0.49, f = 0.52.d Figures in square brackets are for lower-bound estimates obtained by taking one standard deviation abovethe mean estimate; i.e. a = 1.05, b = 3.25, d = 0.92, g = 2.51, l = -2.94, e = 0.73, f = 0.62.

What about a hands-off policy, the choice usually advocated in the policy circles ininternational organizations? A noninterventionist policy would have resulted in a totalrevenue loss for the marketing-board of 1.7% of GDP on an annual basis, a loss that,arguably, might be politically hard to sustain in a country with a weak fiscal system.However, in terms of welfare, a non-interventionist policy would have yielded a gainwhich amounts to 0.5% of GDP, which supports the hands-off policy, especially sincein addition it would have been more equitable (see below).

How sensitive are these calculations to variations in elasticities? In a first scenario(values in parenthesis in Tables 4 and 5) we increase Madagascar’s monopoly powerin the export market and monopsony power in the domestic market; and in a secondscenario we do the opposite (values in square brackets in Tables 4 and 5). First, notethat the pattern of demand and supply elasticities is such that there is little differencebetween the monopoly and monopsony solutions, this regardless of the pattern of elas-ticities. If the set of elasticities brackets the extent of potential double market powerover the period, actual pricing resulted in a welfare loss of between about 0.9% and2.6% of GDP in comparison with either welfare-maximizing or revenue-maximizingpolicies. On the other hand, laissez-faire would have increased welfare above the actualoutcome by 0.5% to 2.4% of GDP. If fiscal revenue was the primary objective, revenue-maximizing pricing policy would have yielded between 0.9% and 1.5% (in terms ofGDP) more annual revenue than obtained under the observed outcome.

Internal Redistribution

The above results suggest that pricing policy had objectives other than welfare orrevenue maximization, probably reflecting political-economy considerations. Table 5reports the revenue and surplus producers would have had under each of the sce-narios in relation to their average total revenue over the period and as a percentageof GDP. That laissez-faire would have been more equitable is already clear from theabove, as vanilla growers would not have been taxed so heavily. For example, produc-ers’ gain (in comparison with the actual pricing policy) is estimated at 2.2% = 0.5 +1.7) of GDP (or over 11 times their actual revenue) under laissez-faire. Under a morerealistic welfare-maximizing strategy producers would have gained on average 0.4%(= 1.0 - 0.6) of GDP (or over twice their actual average revenue).



Table 5. Producers’ gains/losses (1981–91 average)

Percentage of producers’ total revenue (TR) and GDPa

Price-leader/Price-leader Monopsony monopsony Free trade

Surplus (% TR) (-25)b 143c [855]d (83) 179 [397] (-65) -6.8 [203] (754) 778 [1411]Gains (% GDP) (-0.07) 0.42 [2.37] (0.24) 0.52 [1.14] (-0.19) -0.02 [0.59] (2.18) 2.26 [4.08]

Revenue (% TR) (-40) 215 [1315] (94) 270 [661] (-89) -7.40 [327] (926) 1,162 [2417]Gains (% GDP) (-0.12) 0.62 [3.81] (0.27) 0.78 [1.91] (-0.26) -0.02 [0.95] (2.68) 3.37 [7.00]

a Actual producers’ revenue represents 0.29% of GDP during this period and 13.98% of export revenue.b See note b in Table 4.c See note c in Table 4.d See note c in Table 4.

As already suggested by the price and quantity comparisons in Table 3, the producer surplus calculations reported in Table 5 indicate that producers obtainedapproximately what they would have if the marketing-board had followed a profit-maximizing strategy. Yet selling price was held about 20% above the profit-maximizing level, despite falling international market share and accumulating vanillastocks. Not surprisingly this high price wedge resulted in cases of under-invoicing andrumours of smuggling.

One explanation of this pricing policy is simply an overestimation on the part of thevanilla market authorities of Madagascar’s degree of market power, or an underesti-mation of the degree of substitutability between Madagascar and Indonesian vanilla.But then the puzzle is why producer prices were not set accordingly.

Another possible explanation, along political-economy lines, is that a high-pricepolicy served the interest of what Findlay (1989) refers to as the “bureaucratic capi-talist” alliance. The high-price policy would then be viewed as reflecting the clearingprice in the power struggle in the urban sector. It is also consistent with the widelyheld view that marketing-boards have developed into rent extraction entreprises (seee.g., Helleiner (1964) and Gilbert (1993)).

Our main objective in this paper is an evaluation of Madagascar’s pricing policiesfor vanilla, but a similar analysis could be applied to other export-oriented marketing-boards or any other arrangements or regulations that give monopsony power to asingle marketing entity. In each case careful attention must be paid to institutionalarrangements that influence the distribution of the gains and losses. For example,Trouillot (1988) and Slocum (1996) describe export marketing arrangements forbananas in St Lucia and Dominica in which banana growers’ associations hold legalmonopsony power, but long-term contractual arrangements provide that all bananasexported from Dominica and St Lucia are sold in the United Kingdom by a singlemultinational banana trading company that effectively sets the price paid to bananagrowers and also accounts for a large percentage of total UK banana sales. This mar-keting arrangement may well give the simultaneous price-leader/monopsony power tothe multinational. The likely result is a pricing structure that maximizes profit for themultinational, rather than revenue for the Caribbean banana growers’ associations.

6. Conclusions

For the last decade, Madagascar has followed a high-price strategy in the naturalvanilla market. This paper has attempted to evaluate the welfare and internal dis-tribution consequences of Madagascar’s vanilla pricing policy. We developed and esti-mated a simple price-leadership model of the international vanilla market that takesinto account both the marketing authority’s buying power relative to growers and theselling power in the international market. On the assumption that there was no eco-nomic rationale for stocking (since stocks were burnt), the simulations indicated a largewelfare loss from the pricing policies followed by Madagascar during the period1981–91. Even though laissez-faire would have been far from optimal and probablynot an option in an economy with a weak fiscal system and no mechanisms for lump-sum transfers, it would have been preferable to the policies actually followed on bothefficiency and distributional grounds.

This said, the model and methods used here are only a starting point in the evalua-tion of alternative pricing policies for countries with weak fiscal systems and doublemarket power. In particular, it leaves out several important factors (e.g., stockholdingmotivated by supply uncertainty, incentive effects for the development of synthetic



substitutes, and quality mix choice) that would have affected optimal pricing strategy.The link between pricing policies and the erosion of market power due to entry of newsuppliers and possible entry-deterrence strategies on the part of Madagascar, and thepossibility of cooperation between the two major exporting countries, were not considered.

Nonetheless, this case study suggests that though prices paid to producers might havebeen compatible with revenue maximization, overly high official selling prices consti-tuted implicit overtaxation of producers. In political-economy terms, this case studysuggests that the pricing policy followed by Madagascar was closer to that predictedby a predatory view of the state than by a revenue-maximizing bureaucratic state.

References

Bauer, Peter T., “Marketing-Boards,” in John Eatwell et al. (eds.) The New Palgrave: A Dictio-nary of Economics, New York: Macmillan (1987).

Bhargava, Alok, Luigi Franzini, and Wiji Narendranathan, “Serial Correlation and the FixedEffects Model,” Review of Economic Studies 49 (1981):533–49.

Cardenas, Mauricio, “Stabilization and Redistribution of Coffee Revenues: A Political EconomyModel of Commodity Marketing-Boards,” Journal of Development Economics 44 (1994):351–80.

Denis, Jean-Emile, “Export Performance of the Marketing-Boards in LDCs—the Case of Cocoaand Coffee in West Africa,” in Michel M. Kostecki (ed.) State Trading in International Markets,New York: St Martin’s Press (1982).

Findlay, Ronald, “Is the New Political Economy Relevant to Developing Countries?” CountryEconomics Dept., World Bank (1989).

Gilbert, Christopher L., “Domestic Price Stabilisation Schemes for Developing Countries,” inStijn Classens and Ronald Duncan (eds.) Managing Commodity Price Risk in DevelopingCountries, Baltimore: Johns Hopkins University Press (1993).

Guillaumont, Patrick and Sylviane Guillaumont, “Dévaluation et Prix Réels Payés aux Produc-teurs agricoles dans les Pays en Developpement: Explication d’un Paradoxe,” in Patrick Guillaumont (ed.) Economie des Politiques Agricoles dans les Pays en Developpement, Paris:Revue Francaise d’Economie (1994).

Helleiner, Gerald, “The Fiscal Role of the Marketing-Boards in Nigerian Economic Develop-ment, 1947–61,” Economic Journal 74 (1964):582–610.

Idachaba, Francis S., “Marketing Boards as Potential Stabilizers of Government Revenue inDeveloping Countries: The Nigerian Experience,” Economic Bulletin of Ghana: Second Series3 (1973):23–30.

Just, Richard E., Andrew Schmitz, and David Zilberman, “Price Controls and Optimal Export Policies under Alternative Market Structures,” American Economic Review 69(1979):706–14.

Kolawole, M. I., “The Reform of Commodity Boards in Nigeria: An Analysis of New ProducerPolicy,” Economic Bulletin of Ghana: Second Series 3 (1973):14–19.

Kostecki, Michel M., “State Trading by the Advanced and Developing Countries: the Back-ground,” in Kostecki, Michel M. (ed.) State Trading in International Markets, New York: StMartin’s Press (1982).

Krishna, Kala and Marie Thursby, “Optimal Policies and Marketing-Board Objectives,” Journalof Development Economics 38 (1992):1–15.

Krueger, Anne, Maurice Sehiff, and Alberto Valdes, The Political Economy of Agriculture PriceInterventions in Latin America, San Francisco: ICS Press (1992).

Lloyd, Peter J., “State Trading and the Theory of International Trade,” in Michel M. Kostecki(ed.) State Trading in International Markets, New York: St Martin’s Press (1982).

Markusen, James R.,“The Welfare and Allocative Effects of Export Taxes vs Marketing-Boards,”Journal of Development Economics 14 (1984):19–36.



Mundlak, Yair, “On the Pooling of Time Series and Cross Sectional Data,” Econometrica 46(1978):69–80.

Nielsen, The Story of Vanilla, Nielsen Massey Vanilla, Illinois (1992).Slocum, Kelly, Producing under a Globalizing Economy: The Intersection of Flexible Production

and Local Autonomy in the Work, Lives and Actions of St Lucian Banana Growers, PhD dis-sertation, University of Florida (1996).

Trouillot, Michel, Peasants and Capital: Dominica in the World Economy, Baltimore: JohnsHopkins University Press, 1988.

Van Kooten, G. C. and John Spriggs, “A Comparative Statics Analysis of the Welfare Impact ofSupply Restricting Marketing-Boards,” Canadian Journal of Agricultural Economics 55(1984):221–30.

Wickens, M.R. and James Greenfield, “The Econometrics of Agricultural Supply: An Applica-tion to the World Coffee Market,” Review of Economics and Statistics 55 (1973):443–40.

Notes

1. Cardenas (1994) argues that the redistribution implications of marketing-boards depend onthe type of institutional agreement. He shows that in the case of coffee marketing-boards, coun-tries which have a more proactive behavior by stabilizing producer prices and heavily taxingexporting have generally followed an industrialization policy at the expense of agriculture.2. For an early assessment of distortions that marketing-boards may introduce between foodand export crops, see Idachaba (1973). Kolawole (1973) examined the potential role of marketing-boards to stabilize government revenue and concluded that, in the case of Nigeria,the strong and positive correlation between government revenue exclusive of marketing-boards’contributions and marketing-boards’ revenue suggested a tendency towards destabilization. Forevidence on lack of flexibility in pricing policy on the part of cocoa and coffee marketing-boardsin West Africa, see Denis (1982).3. Kostecki (1982) and Lloyd (1982) suggest that governments opt for marketing-boards ratherthan taxes and subsidies because the taxation is more covert and less likely to prompt politicalopposition.4. This view is forcibly expressed in the above quote from Bauer. For a political economy modelof the predatory state, see Findlay (1989). For a review of the differing roles of parastatals inagriculture see Krueger (1992, ch. 5), and for indirect evidence at the macroeconomic level ofrent-extraction from agricultural producers via exchange rate devaluations in African countries,see Guillaumont and Guillaumont (1994).5. An exception is Van Kooten and Spriggs (1984). However, they focus on the Canadian marketing-boards and assume perfectly competitive markets where free trade is the first-bestpolicy.6. Natural vanilla beans account for only a small fraction of the vanilla flavouring market. Themarket share for natural vanilla bean production is generally considered to be protected fromthe far lower-cost artificial synthetic substitutes on two grounds: first a shift in tastes towardsnatural and organic foods; and second labeling regulations which allow a “vanilla” label to beused only on products made from natural vanilla (new bioengineered products which derivevanillin from vanilla plant tissues pose little threat because of high costs and low probabilitythat new labeling definitions will be created for the biotechnical food industry in the next tenyears).7. Harvesting begins four years after planting, with peak yields after seven years and an averagetree life of 15 years. Preparation costs are about the same among the major suppliers.Thus Mada-gascar’s comparative advantage (production costs less than half of its competitors, Comoros andIndonesia) derives from climatic conditions and from the lower opportunity cost of land andlabor.8. It could be argued that the accumulation of stocks was partly aimed towards building a cred-ible strategy of limit pricing, as for instance in the case of Brazilian coffee stock policy. However,levels were beyond those required for such a strategy.



9. The Alliance de la Vanille negotiates, each year, prices and quantities exported with a smallgroup of buyers in the three major purchasers of natural vanilla (percentage shares of worlddemand in parenthesis): US (60%), France (15%), Germany (15%). The cartel also distributesquota shares among the three exporting members. In recent years, Madagascar has filled between60% and 70% of its allocated quota share. Until 1993 when it was lowered to $60 per kg, theprice was set at around $73 per kg.10. Madagascar’s production costs (including preparation to the exporting stage) are estimatedat less than half those of its competitors (Indonesia and Comoros). Because of bulging stocks(until they were burnt in 1993), purchase prices received by producers have fallen drastically inrecent years. The barter terms of trade with rice has fallen from 10 to 4.11. Stocks were depleted in 1978 following a world crisis in the vanilla market resulting fromcyclonic conditions among Indian Ocean producers. It could therefore be argued that precau-tionary stocking to face uncertainty was a rational policy, though not to the levels observed inthe 1980s.12. Significant improvements in harvesting, drying, and curing techniques improved the qualityof Indonesian vanilla during the last half of the 1980s, which made bourbon and Indonesianvanilla more readily substitutable.13. The noninterventionist policy followed by Indonesia implies that the international vanillamarket differs from the duopoly marketing-board model developed by Krishna and Thursby(1992). Our model also differs from theirs and Just (1979) in that we treat the parastatal marketing-board as part of the government, rather than assuming an independent marketing-board with its own objectives that the government must influence with tax and/or subsidy poli-cies. These models and Markusen (1984) also assume the existence of domestic demand, so theexistence or lack of domestic price regulation and market segmentation becomes important.14. Here we have assumed that the producer price is set to equate quantity produced with quan-tity exported, which assumes no domestic demand and no accumulation of stocks. Given thevariability of production due to climactic conditions, some positive level of stockholding wouldbe optimal, an issue not addressed here.15. Since our interest is in checking the plausibility of alternative pricing rules followed byCAVAGI rather than in predicting trends in the international vanilla market, we did not test for alternative functional forms (e.g., linear demand and supply functions). Constant-elasticitydemand and supply curves around a trend are also easier to calibrate and interpret.16. One can consider them as constants in the optimization problem as these are clearly notstrategic variables.17. If l ≥ -1, then there is no interior solution to Madagascar’s optimization problem.18. In particular, the model abstracts from the potential effects of Madagascar’s pricing policyon the supply of substitutes by Indonesia which in turn would affect the extent of Madagascar’smarket power.This is inherently a dynamic problem which must address the substitutability issueand the precautionary motive for holding stocks in the face of uncertainty.19. This is mostly because of the large fluctuations in small markets (e.g., Japan). Also becausethe calculated D–W statistic for panel data (see Bhargava et al., 1981) could cast doubts onrobustness—not so much because autocorrelation affects efficiency, but because of the possibil-ity of spurious regressions—we tested for cointegration by applying the Dickey–Fuller test tothe residuals. The significant coefficient suggests that variables are integrated. But, though esti-mation in first differences led to lower t-statistics, signs and values on price elasticities remainedcorrect and close to those reported in Table 1 (except for the cross-price elasticity of Madagas-car vanilla with respect to Indonesia which became insignificant). Similar results were obtainedwith AR1 estimates.20. According to Nielsen (1992), no pesticides or fertilizers are used in growing vanilla beans.We could not obtain information on planting, the number of trees and their average age. Becauseit takes around nine months for the bean to mature from pollination time, current prices are notincluded (this was also confirmed econometrically).21. The search for lag length was on the basis of the best fits after having tried out geometricand Almon lags. For Madagascar, we obtained plausible results with a two-period Shiller lag,



which is not unreasonable given that it takes about three years for a tree to yield harvestablebeans. However this lag structure yielded virtually identical values for the lag coefficients andgave the same long-run elasticity estimate as with a one-period lag. Given that for Indonesia,best results were obtained with a one-period lag, we opted for a one-period lag for both coun-tries, thereby treating them symmetrically.As we are interested in long-run estimates rather thanadjustment paths, this seemed reasonable. Better data over a longer period would have allowedmodeling tree stock response based on forward-looking expectations of future prices as in, forexample, Wickens and Greenfield (1973).22. Each constant in equations (1) and (2) was calculated from the corresponding prices andquantities in each year using the selected estimated elasticity. The constants entering in (1)–(4)are the average values for the period 1981–91 taken by the other factors influencing demandand supply decisions. Thus the estimates are intended to represent long-run estimates.23. This measure accounts for nonlinearity of demand and supply functions.



Pricing Policy Under Double Market Power: Madagascar and the International Vanilla Market

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