Processors pricing and the response of the milk supply in Malawi

Transcription

1 Processors pricing and the response of the milk supply in Malawi Cesar Revoredo-Giha 1, Irina Arakelyan 1,2 and Neil Chalmers 1,2 1 Land Economy and Environment Research Group, SRUC 2 University of Edinburgh 29th International Conference of Agricultural Economists Agriculture in an Interconnected World, Milan, Italy, August 9-14, 2015.

2 Outline of the presentation 1. Introduction 2. Purpose and research questions 3. Background 4. Data for the analysis 5. Econometrics 6. Estimation results and discussion 7. Conclusions 8. Acknowledgements 2 2

3 Introduction This paper is part of the Dfid-ESRC funded project Assessing contribution of Dairy Sector to Economic Growth and Food Security in Malawi ( ). Dairy is a key investment sector for the Government of Malawi. Nevertheless, consumption of milk products in Malawi remains very low, estimated at 4-6 kg/capita/year. Most dairy (smallholder) farmers are situated around the three large cities in Malawi: Blantyre (the Southern Region), Lilongwe (Central Region) and Mzuzu (the Northern Region). Based on the recent information received from sources at Bunda College of Agriculture, there are currently around 9,584 dairy farmers in three milk producing regions of Malawi, with 61% of them located in the Southern region. 3 3

4 Background Number of dairy cows. Several estimations. The Malawi Food Security Bulletin (2009) reported a total of 35,594 dairy cattle in the formal and informal sectors. Milk production. According to the figures from the 3 main milk producing associations in Malawi in 2012, smallholders produced around 13.5 million litres of milk of which, approximately 90% of which was produced in the Southern region. Marketing channels. Two channels: formal and informal. The two channels differ in the way milk reaches the final consumer. In the formal sector, milk is processed and sold to the consumer via retail outlets, whereas in the informal sector milk is sold raw. Note that informal vendors procure their milk in several forms. 4 4

5 Background Milk bulking groups (MBGs). These are local farmer associations and belong to the regional milk producers association and are around the three major cities (Blantyre, Lilongwe and Mzuzu). They have cooling centres where farmers within a radius of 8-10 km deliver their milk to keep it cool. According to the most recent data there are currently approximately 54 registered MBGs in Malawi selling milk in bulk to the dairy processors. The Shire Highlands Milk Producers Association (SHMPA) in the Southern Region has 25 mbgs (46%). The Central Region Milk Producers Association (CREMPA) has 17 mbgs (32%). The Mpoto Dairy Farmers Association (MDFA) (North)12 mbgs (22%). 5 5

6 Background Processors. The formal milk processing sector is currently dominated by 3 main dairy processing plants Lilongwe Dairies, Dairibord Malawi and Suncrest Creameries, situated around the cities of Blantyre and Lilongwe. In 2012 Lilongwe Dairies represented about 54.3% of the milk collections, Dairiboard Malawi (30.7%) and Suncrest Creameries (11.9%). Their main products are pasteurised milk, flavoured and plain yoghurt (chambiko), cream, butter and cheese. As of 2012 estimations indicate that processors operate with about 50% idle capacity. 6 6

7 Responsiveness of milk deliveries to changes in the prices A statistical analysis was carried out which indicated that farmers supply of milk is price responsive. The price elasticity of the supply in the short term is equal to 0.6 whilst in the long term is This indicates that farmers revenues not only benefit from an increase in the price of milk but also from the increase in the quantity produced. An increase in 1% in the real price would increase revenues by 2.45%. In practice, the nominal price of milk is infrequently adjusted by processors and the its value in real terms is eroded until the next adjustment. 7 7

8 Sep-08 Dec-08 Mar-09 Jun-09 Sep-09 Dec-09 Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11 Dec-11 Mar-12 Jun-12 Sep-12 Dec-12 Mar-13 Jun-13 Sep-13 Dec-13 Mar-14 Jun-14 Sep-14 Dec-14 Mar-15 Nominal price in Kwachas Real price in 2000 Kwachas Nominal and real weighted average price of milk paid to farmers by processors Nominal price Real price Linear (Real price) Source: Own elaboration based on Shire Highlands Milk Producers Association (SHMPA) data. Based on estimates by Revoredo-Giha et al (2013), in the long-run a 1% real price increase gives 1.4% milk delivery increase and 2.45% in farm revenue. 8 8

9 Purpose and research questions Given the evolution of the real prices for milk, an statistical analysis was carried out to study its effects on the collection of milk by processors. Purpose of the paper To study the responsiveness of the deliveries of milk at the milk bulking groups to changes in the price paid to farmers by processors (i.e., the elasticity of supply faced by each processor). Specific research questions Is the supply of milk at the MBG price responsive? Are there differences between the response that each processor face? Is there any asymmetric effect in the supply response to increases and decreases in the real price received by farmers? 9 9

10 Data for the analysis The dataset used for the analysis was constructed based on the monthly reports produced by the Shire Highlands Milk Producers Association (SHMPA). SHMPA provides information of farmers deliveries to milk bulking groups (MBGs) associated to the main Malawian dairy processors: Dairibord Malawi Limited, Lilongwe Dairies Limited, Suncrest Creameries Limited and Sable Farming Company and to small processors (named as Others ). They cover the period September 2008 until July The analysis was focused only on the Southern region due to the fact that prices paid to farmer were only available for this region. This is not so important for providing an analysis of the Malawian milk supply because as according USAID (2012c), SHMPA represents 89.2 per cent of the total

11 Data for the analysis The dataset comprised: The monthly quantity of milk delivered by farmers to the different milk bulking group; The prices paid by processors to the milk bulking group; The price received by farmers and the total discounts applied to milk prices

12 Real price paid to farmers and monthly milk quantities delivered to Dairibord Malawi / Litres Sep-08 Dec-08 Mar-09 Jun-09 Sep-09 Dec-09 Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11 Dec-11 Mar-12 Jun-12 Sep-12 Dec-12 Mar-13 Jun-13 Sep-13 Dec-13 Mar-14 Jun-14 Sep-14 Dec-14 Mar-15 Real price recevied by farmers 700, , , , , , , Quantity Price Source: Shire Highlands Milk Producers Association Notes: 1/ Prices are in 2000 Kwachas and the milk deliveries are litres

13 Real price paid to farmers and monthly milk quantities delivered to Lilongwe Dairies / Litres Sep-08 Dec-08 Mar-09 Jun-09 Sep-09 Dec-09 Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11 Dec-11 Mar-12 Jun-12 Sep-12 Dec-12 Mar-13 Jun-13 Sep-13 Dec-13 Mar-14 Jun-14 Sep-14 Dec-14 Mar-15 Real price recevied by farmers 900, , , , , , , , , Quantity Price Source: Shire Highlands Milk Producers Association Notes: 1/ Prices are in 2000 Kwachas and the milk deliveries are litres

14 Real price paid to farmers and monthly milk quantities delivered to Suncrest Creameries / Litres Sep-08 Dec-08 Mar-09 Jun-09 Sep-09 Dec-09 Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11 Dec-11 Mar-12 Jun-12 Sep-12 Dec-12 Mar-13 Jun-13 Sep-13 Dec-13 Mar-14 Jun-14 Sep-14 Dec-14 Mar-15 Real price recevied by farmers 700, , , , , , , Quantity Price Source: Shire Highlands Milk Producers Association Notes: 1/ Prices are in 2000 Kwachas and the milk deliveries are litres

15 Econometrics The model used to estimate the supply response was the nonlinear autoregressive distributed lag model (NARDL), which follows the work of Shin et al. (2011). The starting point is asymmetric the long-run regression: 15 15

16 Econometrics As shown in Shin et al (2011) from a nonlinear ARDL(p,q) model such as (5): It can be derived a nonlinear error correction model such as (6): Where 0, + and - are the asymmetric long-run parameters, the others are parameters characterizing the short term dynamics. Equation (6) can be estimated by ordinary least squares (OLS) and cointegration tested using a Wald F test of the null hypothesis = + = - =0 and using the bounds testing approach of Shin et al. (2011)

17 Results of the estimations Final equations by company Dairibord - ΔLog(MDEL1) Lilongwe Dairies - ΔLog(MDEL2) Suncrest Creameries - ΔLog(MDEL3) Sable Farming - ΔLog(MDEL4) Other - ΔLog(MDEL5) Coeff. Std. err. Sig. Coeff. Coeff. Std. err. Sig. Coeff. Coeff. Std. err. Sig. Coeff. Coeff. Std. err. Sig. Coeff. Coeff. Std. err. Sig. Intercept Intercept Intercept Intercept Intercept Log(MDEL1(-1)) Log(MDEL2(-1)) Log(MDEL3(-1)) Log(MDEL4(-1)) Log(MDEL5(-1)) LP1+(-1) LP2+(-1) Log(MDEL3(-2)) LP4+(-1) LP5+(-1) LP1+(-2) LP2+(-2) LP3+(-3) LP4+(-5) LP5-(-1) LP1-(-2) LP2-(-1) LP3+(-4) LP4-(-2) Trend Trend Trend LP3-(-4) LP4-(-3) Squared trend Squared trend Squared trend Trend LP4-(-4) Dummy - February Dummy - February Dummy - February Squared trend LP4-(-5) Dummy - May Dummy - March Dummy - September Cubed trend Trend Dummy - April Dummy - April Dummy - November Dummy - February Squared trend Dummy - February Dummy - May Dummy - June Dummy - April Dummy - July Dummy - August Dummy - November Sig. Sig. Sig. Sig. Sig. Observations 69 Observations 69 Observations 67 Observations 47 Observations 67 Adjusted R squared 0.53 Adjusted R squared 0.65 Adjusted R squared 0.48 Adjusted R squared 0.62 Adjusted R squared 0.86 Log-likelihood Log-likelihood Log-likelihood Log-likelihood Log-likelihood 7.88 Residuals' tests Residuals' tests Residuals' tests Residuals' tests Residuals' tests Jarque-Bera Jarque-Bera Jarque-Bera Jarque-Bera Jarque-Bera Breusch-Godfrey Breusch-Godfrey Breusch-Godfrey Breusch-Godfrey Breusch-Godfrey ARCH ARCH ARCH ARCH ARCH Ramsey RESET Ramsey RESET Ramsey RESET Ramsey RESET Ramsey RESET

18 Long term parameters and cointegration test Long term parameters Dairibord Lilongwe Suncrest Sable Other Dairies Creameries Farming Error correction term ** ** ** ** ** Intercept ** ** ** ** ** Increases in price ** ** ** ** Decreases in price ** * ** ** Cointegration test ** ** ** ** ** Cointegration tested using a Wald F test of the null hypothesis of = + = - =0 and using the bounds testing approach of Shin et al. (2011)

19 Conclusions Four factors should be noted from the outset: 1. Each company seems to face their own dynamic (differences in their MBGs). 2. All the estimated supply showed strong trends. 3. The presence of some seasonal effect. 4. There are some specific shocks that affect the supplies of milk from MBGs. For most of the supply equations these values fluctuate around , i.e., the first period (month) 45 per cent of the disequilibrium is corrected (Dairiboard being -0.43, Lilongwe Dairies -0.46, Sable Farming and Others -0.47) being the exception Suncrest Creameries which is The response of the milk deliveries to changes in the price were found asymmetric for all the companies

20 Conclusions Dairibord - The long term effect of prices indicates little impact of an increase in price and a strong elastic effect to a decrease in price (i.e., 1.13). For Dairibord is better by avoid its price to drop in real terms due to the effect on its milk supply. Lilongwe Dairies - An increase in the real price by 1% improves the supply by 1.2%, whilst the decrease in the price in real terms has a minor effect on the supply. This indicates that Lilongwe Dairies can actually adjust its price sporadically without major effect (ceteris paribus). Suncrest Creameries - The effect of an increase in the price by 1% duplicates the milk supply (the coefficient is equal to 2) and the effect of a decrease is smaller but also elastic (i.e., 1.68). The company benefits by not letting the real price paid dropping

21 Acknowledgements We are particularly grateful to Mr. Brian Lewis, advisor from the Shire Highlands Milk Producers Association (SHMPA) for providing the data information used in the analysis, the Bunda College of Agriculture in Malawi for background information All the opinions in the paper are sole responsibility of the authors

22 Thank you for your attention! 22

23 Additional materials 23 23

24 Descriptive statistics of variables Statistics Monthly deliveries (Litres) Real prices paid to farmers (2000 MK) Dairibord Lilongwe Suncrest Sable Other Dairibord Lilongwe Suncrest Sable Other Dairies Creameries Farming Dairies Creameries Farming Mean 421, , , , , Median 416, , , , , Maximum 590, , , , , Minimum 261, , , , Standard deviation 86, , , , , Skewness Kurtosis Jarque-Bera 1/ Probability Observations Notes 1/ Normality test (null hypothesis that the variable is normal). Probability stands for the probability of accepting the null hypothesis

25 Unit root tests Variables in levels Phillips-Perron unit root tests Dairibord Lilongwe Suncrest Sable Other Dairies Creameries Farming Test Sig. Test Sig. Test Sig. Test Sig. Test Sig. Monthly deliveries * * Real prices * Variables in first differences Monthly deliveries * * * * * Real prices * * * * * Notes: All the variables are in logarithms. Unit root test considering intercept and trend. * indicates that the null hypothesis of unit root is rejected at 5 per cent significance