1. Maize Price Differences and Evidence of Spatial Integration in Malawi: The Case of Selected Markets BY LOVEMORE NYONGO ICAS VI: RIO DE JANEIRO, BRAZIL. 23-25 OCTOBER 2013

2. PRESENTATION OUTLINE Introduction Motivation and study Objectives Literature review Sample description and data sources Estimation Techniques  Co-integration Analysis and Error Correction Model Results and interpretation Conclusion and policy implications

3. INTRODUCTION Efficient markets are key to achievement of food security in countries where many people are net food buyers. Spatial market integration (SMI) becomes a useful tool in allocating food within the economy. SMI refers to a measure of the extent to which demand and supply shocks in one location are transmitted to another location As such, competition among arbitragers ensures a unique equilibrium where local prices in regional markets differ by no more than transportation costs. In more integrated markets, farmers specialize in their production, consumers pay less and the society benefits from economies of scale.

4. MOTIVATION AND STUDY OBJECTIVES In their study, Chirwa and Zakeyo (2003) reported that 93.2 percent of farming households cultivated maize. The country’s CPI is dominated by maize Specifically, the study had the following objectives:  To investigate the price transmission mechanism across selected maize markets in the economy.  To establish if there are central maize markets in the economy.  To assess the impact of transaction costs on maize market integration.

5. LITERATURE REVIEW In a competitive market economy, markets transmit information that is useful in decision-making to economic agents. Pricing signals regulate production, consumption and marketing decisions over time, form and place (Kohls and Uhl, 1998). The price relationships between spatially separated markets are generally analyzed within the framework of spatial price equilibrium theory developed by Enke (1951), Samuelson (1964) and Takayama and Judge (1964). The key assumption underpinning the theory is that price relationships between spatially separated competitive markets depend on the size of transaction costs. When the price difference between markets exceeds transaction costs, arbitrage opportunities will be created.

6. SAMPLE DESCRIPTION AND DATA SOURCES The study analyzed monthly retail maize prices for 6 geographically separated markets from January 2000 At least one commercial center (Mzuzu, Lilongwe and Limbe) and one rural area in each region (Chitipa, Ntchisi and Muloza) were included in the study.

7. ANALYTICAL FRAMEWORK Cointegration Analysis Procedure If I(k) If I(0) Accept Reject Notes: k>0, k is the order of integration. Unit root test to determine order of integration (ADF) Test null of no cointegration btwn prices at different markets (Johansen or Engle and Granger) Estimate VAR model in 1st differences, perform Granger Causality tests and innovation accounting Specify and estimate (V) ECM to assess dynamics and speed of adjustment, conduct Granger Causality tests and innovation accounting. Evaluation

8. Co-integration Analysis and Error Correction Model The long-run equilibrium, according to the theory of law of one price (LOP), is specified as: P i t = β 1 + β 2 P j t + ε t (1) If ε t is stationary and β 2 is unity, then the markets are completely integrated. In the study, equation 1 was modified to include – variables found or assumed to influence market integration – natural logarithms within VA framework – transaction costs (TC).

9. Co-integration Analysis and Error Correction Model Cont’d…. (2) If co-integration is established, the relationship can be expressed in an ECM which depicts the process of adaptation in the short run. Johansen Co-integration test was conducted. To determine the number of co-integrating relations in the system, the study invoked the Johansen Trace test and Maximum Eigenvalue. Failure to accept the null hypothesis of no co-integration confirmed the need to re-specify equation 2 as a VECM as in equation 3 below: (3)

10. Co-integration Analysis and Error Correction Model Cont’d…. To appreciate the impact of transaction costs, equation 2 and 3 were estimated with and without transaction costs. The Granger causality test was conducted to determine the direction of price adjustment. Wald F-test was conducted for linear restrictions to find out if one market’s lagged prices and transaction costs jointly contribute to predictability of maize prices in another market.

11. RESULTS AND INTEPRETATION Cont’d… Johansen Co-integration Test Results – 9 market pairs had 1 co-integrating relationship – 6 market pairs had no co-integrating relationships – Out of 5 market pairs involving Ntchisi, 4 indicate the absence of a long-run relationship – Out of 5 pairs involving Chitipa, 3 are not integrated. – a VEC model (equation 3) with 1 co-integrating relationship was estimated for the 9 co-integrated market. – a VAR model (equation 2) was estimated for the market pairs without cointegrating relationships.

12. RESULTS AND INTEPRETATION Cont’d… Impact of Transaction Costs – have a significant impact on market integration, especially on equations involving the border markets and those markets with poor road network. – short-run speed of adjustment between market pairs ranges from 10 percent to 72 percent if transaction costs are considered and 21 percent to 66 percent when transaction costs are not considered. – Government policies, licensing procedures, delays in accessing price information and capacity constraints pertaining to storage are important factors to consider.

13. RESULTS AND INTEPRETATION Cont’d… Granger Causality Tests – No market is causing all other markets without being caused by any of them. – However, Muloza and Limbe seem to granger cause 5 and 4 other markets, respectively and, therefore, can be good markets for policy intervention. – Lilongwe seems to be a major supplier of maize to all three regions because it is Granger caused by Mzuzu, Ntchisi, Limbe and Muloza.

14. CONCLUSION AND POLICY IMPLICATIONS Short run integration is very low implying that it takes a longer period for maize markets to respond to localized shocks. Policy makers should consider market infrastructure development as a key priority to ensure linkages of maize markets. Maize marketing in Malawi is complex and dynamic hence the need to continuously study it.

15. END OF PRESENTATION THANK YOU FOR YOUR ATTENTION!!!