Maize Price Differences and Evidence of Spatial Integration in

Malawi: The Case of Selected Markets

BY LOVEMORE NYONGO

ECAMA RESEARCH SYMPOSIUM: LILONGWE.

8-10 OCTOBER 2014

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

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.

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.

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.

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.

ANALYTICAL FRAMEWORK

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)

Unit root test to determine order of integration (ADF)

Test null of no cointegration btwnprices 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

Co-integration Analysis and Error Correction Model

• The long-run equilibrium, according to the theory of law of one price (LOP), is specified as:

Pit = β1 + β2Pj

t + εt (1)

• If εt is stationary and β2 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).

Co-integration Analysis and Error Correction Model Cont’d….

ttilt

n

ll

n

i

jiti

it TCppp

lnlnlnln 2

111

ttijlt

q

ll

ilt

q

ll

it TCppp

lnlnlnln 111

1

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)

(2)

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.

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.

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.

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.

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.

END OF PRESENTATION

THANK YOU FOR YOUR ATTENTION!!!