Determinants of gross margin

Gross margin analysis is one way in which marketing efficiency can be assessed. According to Timmer et al., (1983) large gross margin may co-exist with inefficient use of resources;

low labor, capital and management productivity, poor coordination and consumer satisfaction;

and disproportionate profit elements. Conversely, higher margins result from increased services, including better coordination, may leave producers and consumers better off, and low margins may be due to low productivity. Therefore, in using market margin analyses to assess the marketing efficiency, it is preferable to analyze factors that determine the gross margin.

Variables that can determine gross margin can be observable or unobservable. Only the observable variables; i.e., asset variables (human, financial and social capital), variables representing trading practices and other general variables were used for this analysis. Natural logarithm of the gross margin as the dependent variable was applied to have direct estimates of elasticity (Jabbar et al., 2006) which is given by:

ln GM = X +

 

Asset variables include education, business age or experience, occupation other than livestock trading, number of workers engaged in livestock trading including family and hired hands, working capital, number of other traders and brokers in purchase and sale markets known who could be relied up on for information, advice or credit, access to credit, access to information and variable costs incurred during transaction.

Variables related to trading practices include trading system, use or non-use of brokers for selling livestock, whether conducted purchase and/or sale transactions with regular suppliers and buyers and number of days between purchase and sale transactions which included travel time between markets as well as temporal arbitrage behavior. Risk associated with loss of sheep was also included as an explanatory variable.

Definition of variables and hypothesis

In determining factors that affect gross margin, the first step is to analyze factors that influence gross margin and how. Thus, potential variables which are expected to have an effect on gross margin need to be explained. Accordingly, the major variables expected to have influence gross market margin are explained as follows:

Dependent Variable

Gross margin: which is a measure of return on own labor and capital, is a continuous variable which represents dependent variable; the actual gross margin per animal is selected for regression analysis takes of positive value.

Independent Variables

Asset variables

Education level of trader: refers the formal schooling of a respondent during the survey period. Those traders who had higher level education have better skill and know how on how to handle sheep trading than those traders with lower level education. It was hypothesized that as schooling year of traders’ increases, it is expected to have positive influence on gross margin than the base variable - illiteracy.

Experience or business age in sheep trading: a study conducted by Jabbar et al., (2008) indicated that experience or business age in animal trading has a significant effect on gross margin. That is as number of years increased, the more experience traders have in conducting sheep trading business which makes them to increase their profit by raising their gross margin. Hence it is hypothesized that experience of trader has positive effect on gross margin.

This variable is a continuous variable measured in number of years, but it is categorized in different years so as to see the difference in years and to compare with base variable.

Occupation other than livestock trading: It is a dummy variable that show income obtained by the trader other than livestock selling. This income may strength traders to enlarge gross margin expecting higher profit. On the other hand, traders depending only on livestock trade for livelihood may need quicker turnover of transactions and be less able to cope with market uncertainties and use trading practices that will reduce transaction costs hence settle with lower profit margins. Thus, it is hypothesized that this variable is positively related with gross margin.

Number of workers engaged: this is a continuous variable which shows the number of workers engaged in livestock trading including family and hired hands. It is obvious that as the number of workers increases, it will increase the cost of trading, hence positive relationship is hypothesizes among the variable and gross margin.

Working capital: Theoretically large working capital would normally be expected to permit larger volume of business and economy of scale and specialization in livestock trade would be expected to generate better profits as well as higher gross margin due to better knowledge and skills in trade negotiations. Therefore, the coefficient of this variable is expected to have positive sign.

Known traders and brokers: this variable refers to the number of traders and brokers known in purchase and sale, who could be relied up on for information advice or credit (representing social capital). According to Jabbar (2006), large network of traders\brokers enables traders to minimize cost and maximize profit and gives the space to raise gross margin so as to earn better price. Therefore, it is expected that as known traders\brokers and gross margin are positively related. It is a continuous variable measured in number of persons.

Access to credit: it is hypothesized that accesses to credit and gross margin have positive relationship, as access to credit would enhance the financial capacity of the trader to make business transaction. The variable is entered in the model as a dummy variable (it takes a value 1 if the trader has access to credit service and 0 otherwise).

Access to information: this is a dummy variable. Traders’ marketing decisions are based on market price information, and poorly integrated markets may convey inaccurate price information, leading to inefficient product movement. Access to market information helps traders to minimize their transaction cost which leads to reduce gross margin. On the other hand, when information is asymmetric; it makes some traders (who have access to information) better off by raising gross margin so as to earn higher profit. Hence, access to information is assumed to have direct or inverse relation with gross margin.

Trading practices

Trading system: this variable entered the model in two categories; private and partnership trading system. It is hypothesized that traders who perform their trading business privately have lower gross margin than those in partnership.

Use of brokers for selling livestock: It is a dummy variable taking a value 1 if traders use broker and 0 otherwise. It is expected that using the service of brokers lowers gross margin by reducing transaction costs through providing better information and lesser time for contract negotiation and enforcement. Hence, there is negative relationship between the variable and gross margin.

Sale to regular customer: It is a dummy variable that refers to traders whether they conduct their transaction with regular customer or not. Lower margin per animal is normally expected for those traders who undertake all or most of transactions with regular buyers as compared to those who do not do so; as long-term business relations based on trust, reliable information on price and supply, and assured delivery of products in a timely manner usually reduce transaction costs and minimize unit gross margin. Thus, negative relationship between sale to regular customer and gross margin is hypothesized.

Days between purchase and sale: longer arbitrage days have positive effect on gross margin, as increase in numbers of days between purchase and sale usually raises costs of feeding, watering and keeping the animals. This variable is continues and measured in number of arbitrage days.

Other variable

Loss of sheep: which shows risk associated with loss of sheep in the form of theft or during transportation is a continuous variable; expected to have positive relation with gross margin.

As the number of sheep lost increases, traders will increase gross margin by raising selling price so as to compensate the loss.

Marketing channels

The analysis of marketing channels is intended to provide a systematic knowledge of the flow of the goods and services from their origin (producer) to their final destinations (consumers) (Mendoza, 1995).

The efficiency of alternative marketing channels was judged on the basis of the following indicators following Ramakamar:

1. Net price received by producers 2. Total cost of marketing

3. Price mark-up

Price mark-up is the difference between the price paid in purchasing the commodity and price received when the commodity is sold. It is expressed as a percentage of the price paid while purchasing.

Based on each efficiency indicator, the marketing channels have been ranked and finally efficiency of different channels has been judged on the basis of a composite index:

i i

R R

 N (11)

where R is an overall rank of a channel (all performance indicators), Ri is rank of a channel per a single indicator and Ni is the number of performance indicators (volume handled, rate of return, producers’ share, and marketing margin).