An analysis of Livestock market integration in Kenya: A case of Pastoral Beef Cattle

Abstract

Studies on price transmission are used to examine relation between price series at spatially separated markets since price transmission integrates markets. Such studies provide important information on the extent to which markets function efficiently and how shocks in one market are transmitted to another. Economic actors' decisions on output mix and resource allocation is dependent on prices in free market economies. In the livestock industry in Kenya, this is particularly useful in the markets for ruminant livestock whose production system is characterized by regions of production surplus and regions of deficit. In addition, for pastoral production, this is even more critical since they depend on the markets for their livelihood. Policies to support market development for improving resilience of pastoralists depend on market integration and incorrect policies can be costly to the country. Livestock Market integration information has the potential of contributing to policy and interventions aimed at improving market access of ASAL cattle. Unfortunately, literature provides inadequate information on market integration of pastoral livestock markets. Thus, the status of price transmission and market integration for livestock markets is not well understood in Kenya. This study was designed to analyze the level of market integration in the livestock markets in Kenya. This is an important area of concern because it touches on the livelihood of the beef cattle producers and the availability of beef for the city dwellers. The study used weekly beef cattle price data for the January 2006 to December 2010 period from 10 spatially separated markets. Data processing and analysis used Descriptive statistics, Co-integration and an Error Correction Model (ECM) and Granger causality tests. The study noted that typical of agricultural products, beef cattle prices varied both in space and over time. The markets in the clitt)e producing areas reported lower mean cattle prices while terminal markets posted higher mean prices as the major demand centres. In additional the study noted that beef cattle prices seem to be correlated with

all price series showing similar trend. This implies that the price movements are following a similar stochastic trend. Spatial beef cattle market integration analysis was used to establish co-movement of prices between markets. The stationarity test using ADF and Phillip-Perron unit root tests shows that all the price series are non-stationary at levels. On differencing once, stationarity was attained indicating that the prices are integrated of order one. Cointegration test results shows that there is more than one stochastic process for beef cattle markets in Kenya, one for northern rangelands and one for southern rangelands. Granger Causality test through weak exogeneity test revealed that Nairobi market acts as a single central market in the beef cattle market in Kenya. In conclusion, although the markets for pastoral beef cattle and specifically for those in the northern rangelands are integrated, the price relationships are not that strong. This could be attributed to the fact that beef cattle goods are not perfect substitutes, or that there exists market imperfections like barriers to entry and exit, insufficient market information and transportation costs preventing markets from being perfectly integrated. Policy recommendations emanating from this study include use of market prices as an early warning tool and pastoral beef cattle price stabilization initiatives to improve resilience of the pastoralists. In the area of improving price transmission, policies to increase information flow and continued support to physical infrastructure to livestock marketing taking cognizance of existence of more than one beef cattle market system in the country in program and project design. Further research is also proposed that will quantify the trade-flows and transaction costs between different livestock markets to enable use of more robust market analysis tools.