1. Price Transmission in the Cocoa- Chocolate Chain Catherine Araujo Bonjean CNRS, CERDI Jean-François Brun Université d'Auvergne, CERDI First Conference on Economics and Politics of Chocolate Leuven 16-19 September 2012 CENTRE D’ETUDES ET DE RECHERCHE EN DEVELOPPEMENT INTERNATIONAL CNRS – UNIVERSITE D’AUVERGNE FACULTE DES SCIENCES ECONOMIQUES ET DE GESTION

2. Introduction We explore the channels of transmission of the fluctuations in the world price of cocoa beans to the consumers of chocolate bars in France. Special case of a more general pattern of asymmetric vertical price transmission in the commodity-end product chain (for instance from crude oil to gasoline retail price) Consumers often have the feeling that retail prices respond faster to an increase in the price of raw material than to a decrease. 1. Introduction

3. Cocoa beans and chocolate bar prices 1955 – 2011 constant euros (100 = 1990) The cocoa price fluctuations are transmitted to consumers with a delay of about one year. Pass-through of cocoa fluctuations to chocolate bar price seems to be asymmetric. Sharp increases in the cocoa price are more readily transmitted to consumers than price falls.

4. Two types of asymmetry with different origins First, asymmetry in the transmission of positive and negative shocks may be due to imperfect competition in the processing/distribution chain. The chocolate processing and manufacturing sector is highly concentrated since the second half of the 80s. At the world level six companies control more than 90 % of cocoa processing, manufacturing and distribution. In France, the chocolate industry is dominated by 3 companies (Barry Callebaut, Cargill, Cémoi) In a recent paper we showed that the Cote d’Ivoire loose its market power to the benefit of the chocolate industry 1. Introduction

5. Menu costs Second, asymmetry in transmission of large and small shocks due to “menu costs” Changing price is costly. Adjustment costs in the packaging and distribution stage of the marketing process are a possible cause of asymmetry in price transmission according to the size of the shocks processors and/or distributors respond to “small” input price fluctuations by increasing or reducing their margins. The output price adjust only if the fluctuations in the input price exceed a critical level 1. Introduction

6. Implications Non-competitive markets and adjustment costs may thus result in nonlinear price dynamic. Two hypotheses are tested: the chocolate price only adjust to large shocks in the cocoa price cocoa price increases are more rapidly and fully transmitted to consumers than decreases. 1. Introduction

7. Model of price transmission In the standard cointegration framework: The long run relationship between the two prices is given by: (1) Pc: chocolate price; Pb: beans price The short run dynamic is given by the error-correction model (ECM): (2) is the speed of adjustment of  P t to a deviation of P t from its long- run equilibrium. is constant 2. Econometric model

8. Non linear cointegation model ECM with 3 regimes (3)  1 and  2 : two thresholds. The speed of adjustment differs according to the size of the past disequilibrium (  t-d ). Regime switching occurs, with a delay d, when the error term goes above or below the threshold Hypotheses:  m = 0 and  l <  u < 0 2. Econometric model

9. Testing strategy A two-step approach based on Engle and Granger methodology First step: estimate the long run equilibrium relationship between the price of chocolate and the price of cocoa and apply cointegration tests to the equilibrium error. Second step: test for nonlinear threshold behaviour and identify the best fit model, then estimate the short run model of price adjustment. 2 samples: monthly data on the period January 1960 to February 2003 (518 observations); annual data available on a longer period of time, 1949 – 2011 2. Econometric model

10. Estimation results. Monthly data: 1960.01 – 2003.02 3. Main results FMOLS estimate of the cointegrating equation p-value are in parentheses Excluded in  Pc  Pb Error correction termchi-sq 15.2971.605 (p-value) (0.0001)(0.205) Lagged  Pb chi-sq16.54 (p-value) (0.002) Lagged  Pc chi-sq 2.819 (p-value) (0.589) Exogeneity test from VECM

11. Cointegration tests results The standard Engel-Granger test (no coint vs linear coint) does not reject the hypothesis of cointegration but the Enders-Siklos test (no coint vs nonlinear coint) rejects the null of no cointegration when using the consistent estimate of the threshold Engle-Granger  = 0 a TAR(2; 4,1) Consistent threshold TAR(2; 4, 8) b 11 -0.004 -0.030 (-2.133)(-1.249)(-4.939) 22 --0.005-0.001 (-1.732)(-0.328) 11 0.3900.3920.365 (8.880) (8.348) 22 0.0960.0840.079 (2.036)(1.779)(1.706) 33 0.116 0.1110.105 (2.650)(2.514)(2.426) SC-8.982-8.970-9.001  -2.29912.264 Threshold value 0 -0.089  1 =  2 [p-value] 20.130 [0.000] F 12 20.972 [p-value] (0.042) F heteroskedasticity test 9.476 10.433 [p-value] [0.000] 3. Main results

12. Testing nonlinearity 1. Tsay (1989) non parametric test 2. Hansen sup-F test based on nested hypothesis tests. Test the null of a TAR( i ) model, against the alternative of TAR( j ) model: S i is the sum of squared residuals under the null of i regimes. S j is the sum of squared residuals under the alternative hypothesis of a j -regime TAR( j ) We use Hansen (1996) bootstrap procedure to approximate the asymptotic distribution of F correcting for heteroskedascity 3. Main results

13. Testing nonlinearity the linear model is rejected to the benefit of a TAR(3) model for the error term  t is not stationnary within the band but stationnary in the outer regimes TAR(3). Dependant variable:  t VECM(3). ll mm uu Regimes (obs) Estimated thresholds  l =  u  l =  m =  u F 13 F 23 -0.0310.006 -0.009 lower (27) -0.0894.11910.60932.04810.644 (-2.975)(2.025) (-3.733) middle (403) [-0.0896 ; -0.088](0.043)(0.000)(0.028)(0.086) upper (79) 0.066 [0.06217 ; 0.07598] ll mm uu Adj R² Wald  l =  m =  u =0 Wald  l =  m =  u Wald  l =  u  Pc t -0.0290.003 -0.008 0.3696.669 7.4725.012 (0.001)(0.339) (0.004) (0.000) (0.026)  Pb t -0.025-0.016 -0.009 0.1113.149 0.3960.558 (0.212)(0.027) (0.155) (0.025) (0.673)(0.456) 3. Main results Pc adjusts faster to large negative deviations than to large positive ones

14. Robustness tests: annual sample The analysis is duplicated on a sample of annual prices covering the period 1949 – 2011. Two specifications of the long run relationship between the chocolate and the cocoa prices are considered. The first one is a linear model with two variables corresponding to specification tested on monthly prices The second one is a log linear model including an additional variable: the consumer price index in France 4. Robustness

15. Robustness tests: annual sample The no cointegration hypothesis is rejected and the non linearity tests reject the linear model of adjustment against a TAR(3) model : Cointegration is inactive as long as discrepancies from long term equilibrium lie within the band; the process is mean reverting in the outer regimes. The coefficients size suggests that large disequilibria resulting from an increase in the price of cocoa are corrected more rapidly than large discrepancies resulting from a decrease in the price of cocoa 4. Robustness

16. Timing of regime switching Monthly data Annual data 5. Conclusion

17. Conclusion Most of the time, the price of chocolate did not adjust to changes in the price of cocoa beans. The chocolate price adjusts only to large disequilibrium. This is may reflect the presence of adjustment costs. The chocolate price corrects quickly disequilibrium following historical booms in the cocoa market but reverts back more slowly when cocoa price decline. This is consistent with a non-competitive market structure.

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