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thesis entitled
THE EFFECT OF MARKET LIBERALIZATION
ON MAIZE MILLING/RETAIL MARGINS IN
SOUTH AFRICA

presented by

Lulama N. Ndibongo-Traub

has been accepted towards fulfillment
of the requirements for

M . S . degree in Agricultural Economics

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6/01 c:/CIRC/DateDue.p65-p.15

THE EFFECT OF MARKET LIBERALIZATION ON MAIZE MILLING/RETAIL
MARGINS IN SOUTH AFRICA

By

Lulama Nosantso Ndibongo-Traub

A TIESIS
Submitted to
Michigan State University
in partial fiilfillment of the requirements
for the degree of
MASTER OF SCIENCE
Department of Agricultural Economics

2002

ABSTRACT

THE EFFECT OF MARKET LIBERALIZATION ON MAIZE NflLLING/RETAIL
MARGINS IN SOUTH AFRICA

By

Lulama Nosantso Ndibongo-Traub

Maize meal is the most important consumer staple food in South Africa. Studies
of Southern Africa have shown that South African maize-meal milling/retail margins
tended to be high when compared to other countries within the region. In particular, the
milling/retail margin in South Africa was found to be more than twice that of neighboring
Zimbabwe, although both industries faced comparative cost structures and Zimbabwe’s
milling industry was concentrated among fewer millers. The objective of the research,
reported in this paper, is to determine, econometrically, the effect of market liberalization
on the maize milling/retail margins within South Africa. Economic theory of market
liberalization would predict a reduction in the real price margins between processed and
raw agricultural products due to entrance into previously closed markets by the informal
sector, thereby increasing competition among industry players. Feasible General Least
Squares method of estimation is applied to two reduced form linear models of the
milling/retail margins in which a binary explanatory variable has been included to capture
the effect of market liberalization. The period of study covers the marketing years from
1976/77 through 2000/2001. From this study we find that despite market liberalization
the maize milling/retailing margin continues to grow in real terms within South Africa,
indicating a need for further investigation into the concentration of the market and the

possible entry barriers at this stage of the maize marketing system.

This Thesis is dedicated to my father, M. F. H. Ndibongo; who besides fighting for the
abolishment of apartheid continued to work for the freedom of the South African poor, up
until his death on November 9‘“, 2001.

iii

ACKNOWLEDGEMENTS

I would like to thank my committee members, Dr. Jayne, Dr. Staatz, and Dr.
Wooldridge, for their input and suggestions. In particular, I would like to acknowledge
Dr. Jayne, my committee head, for his tireless effort and continued patience throughout
the whole process. Without his encouragement, enthusiasm and direction, this thesis
would never have materialized.

I would also like to thank my family for their continued love and support. I
especially want to thank my mother for being my point research person in South Africa
and uncovering hard-to-find data; as well as my husband, who bore the brunt of my stress

and anxiety.

iv

TABLE OF CONTENTS

LIST OF TABLES ....................................................................................... vi
LIST OF FIGURES .................................................................................... vii
CHAPTER 1: INTRODUCTION

1.1. Context .................................................................................................. 1
1.2. Regional Price Comparison .................................................................... 4
1.3. Regional Market Reform ........................................................................ 7
1.4. Purpose of this Paper .............................................................................. 7

CHAPTER 2: THEORETICAL FRAMEWORK FOR ASSESSING THE EFFECTS OF
MARKET REFORM

2.1. Historical Context ................................................................................ 10
2.2. Theoretical Framework ......................................................................... 1 1
CHAPTER 3: AGRICULTURE AND THE MAIZE SUB-SECTOR

3.1. Overview of South African Agricultural Policies ................................... 15
3.2. Maize Sub-Sector ................................................................................. 22
3 .3. Conclusion ........................................................................................... 30
CHAPTER 4: THEORETICAL BACKGROUND ON MARKETING MARGINS
4.1. Literature Review ................................................................................. 32
4.2. Methodology ........................................................................................ 36
4.3. Data and Variable Discussion ............................................................... 40
CHAPTER 5: RESULTS

5.1. Descriptive Statistical Results ............................................................... 45
5.2. Econometric Results ............................................................................. 51
5.3. Conclusion ........................................................................................... 57

CHAPTER 6: CONCLUSIONS AND POLICY IMPLICATIONS

6.1. Summary ............................................................................................. 59
6.2. Research Findings ................................................................................ 59
6.3. Policy Suggestions ............................................................................... 61
APPENDICES

Appendix A ................................................................................................ 66
Appendix B ................................................................................................ 70
BIBLIOGRAPHY ...................................................................................... 75

LIST OF TABLES

TABLE 1.]
Self-Sufficiency Indices (S SI) of Selected Agricultural Commodities
in South Africa ............................................................................................. 2

TABLE 1.2
South African Food Price Percentage Increases
for Cereal Products ....................................................................................... 3

TABLE 1.3
Comparison of White Maize Marketing and Milling Margins in South Africa
& Zimbabwe; in metric tons (U .S.$) .............................................................. 5

TABLE 1.4
Average Annual Real Prices per ton of Maize Grain and Flour in Mozambique
and South Afiica: 1996 & 1999 (U.S.$, 2000:00) .......................................... 5

TABLE 3.1
White/Yellow PS&D - 1999/2000 to 2000/2001 .......................................... 23

TABLE 3.2
Planting, Production & Yield of White & Yellow Maize:
1995/96 to 1999/00 ..................................................................................... 26

TABLE 5.].
Summary Statistics of Producer, Wholesale, Retail Prices & Marketing
and Milling Margins ................................................................................... 49

TABLE 5.2.
Descriptive Statistics: 1976/77 — 2001/02 .................................................... 51

TABLE 5.3.
Milling Margin Determination of the Wholesale-Retail Market in South Africa:
May 1976 — April 2000 ............................................................................... 52

APPENDIX B.4.
Weighted Average Critical Rainfall ............................................................. 74

LIST OF FIGURES

FIGURE 1.1
Retail Maize Meal Prices in South Africa & Zimbabwe
(constant 1995 USS) .................................................................................... 4

FIGURE 4.1.
Primary and Derived Functions and Marketing Margins ............................... 32

FIGURE 5.1.
Producer, Wholesale, Retail Maize Price Spread
(constant 2000 Rands): 1975/76 - 2001/02 .................................................. 45

FIGURE 5.2.
Movement of Real Milling/Retail Margins (Pr-Pf) in South Africa
(constant 2000 Rands): 197 5/76 — 2001/02 .................................................. 47

APPENDIX A. 1.
Total World Corn Exports: 2000 ................................................................. 67

APPENDIX A.2.
Major and Minor Maize Growing Areas ...................................................... 68

APPENDIX A.3.
Crop Calendar of South Africa .................................................................... 69

APPENDIX B. 1.
Producer Prices Histogram .......................................................................... 71

APPENDIX B.2.
Wholesale Prices Histogram ........................................................................ 72

APPENDIX 33.
Retail Price Histogram ................................................................................ 73

vii

CHAPTER 1: INTRODUCTION
1.1: Context

The maize sector is without a doubt one of the most important sectors within the
South African Economy. During the decade of the 1980’s, 40% to the total land under
cultivation was dedicated to maize production, 75% of the total grain produced during
this period was maize, and maize constituted 56% of all grains consumed domestically
(World Bank, 1994). Maize is a vital earner of foreign exchange for South Africa as well
as an important food source for the majority of the population, primarily low-income
consumers (U SDA-FAS, 2000; Department of Trade and Industry, 1998). Therefore,
maize as a commodity becomes important to issues of food-security within the country.

Through most of the 1980’s and up until the mid-1990’s, South Africa’s national food
policy was directed at ensuring food self-sufficiency (van Rooyen et al, 1997). The
White Paper (RSA, 1984: 8-9) established this policy aim by stating:

For any country, the provision of sufficient food for its people is a vital
priority and for this reason it is regarded as one of the primary objectives
of agricultural policy. Adequate provision in this basic need of man not
only promotes, but is also an essential prerequisite for an acceptable
economic, political and social order and for stability.

This conceptualization of food policy was consistent with general global practices in
most other countries, and was fiirther entrenched by the threat of sanctions fi'om the
international community. Table 1.1 below summarizes the self-sufficiency indices of
selected agricultural commodities. This table indicates that in maize production, South

Africa was self-sufficient and able to produce enough maize to meet domestic demand

for both white and yellow maize. In contrast, red meat commodities with self-sufficiency

indices below 100 were dependent on foreign markets, mainly Namibia, Botswana and
EU to meet domestic shortages (Thirtle et al., 2000).

TABLE 1.1: Self-Sufficiency Indices ($81)
of Selected Agricultural Commodities in South Africa

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ICOMMODITY Self-Sufficiency Index ‘
91-94 85-90 85-94
Wheat 95 115.5 107.4
'ze (White & Yellow 109.5 121.1 116.5
otatoes 100.6 100.3 100.4
Negetables 100.9 101.3 101.1
Sgar 163.5 162.5 162.9
Beef 93.1 89.9 91.2
utton, Goat's meat & lamb 82 93.3 88.8
Pork 96.1 100.9 99
Chicken 99.1 99.4 99.3
Eggs 101.7 101.7 101.7
Deciduous & sub-tropical fruit 156.5 152.3 154
Dairy products NA 101 NA
ondensed milk 123.5 105.5 112.1
Fresh milk 100 100 100
heese 100 100.3 100.8
Butter 100 100 100.7
Sunflower seed oil 60.3 87.5 76.6
Citrus fruits 235.5 254 246.6
Rice 0 0 0
icorvmonrrv quorumz
Grains and Field crops 88.2 97.2 94
Horticultural crops 164.3 169.2 167.2
Livestock products 96 99 99.3
Source: Food balance sheets of the Directorates of Agricultural Trends & Agricultural Statistics of the
Department of Agriculture

Although the objective of food self-sufficiency was largely achieved during the late
1980’s and early 1990’s, this sufficiency was accompanied by occurrences of widespread
poverty and malnutrition (Thirtle et al, 2000). The Committee for Development of a
Food and Nutritional Strategy for Southern Africa, using income and nutritional status of
children and pregnant and lactating women as a means to measure the proportion of the

population who were nutritionally deprived, found that in 1989 malnutrition was a

 

1 Self-Sufficiency Index = (Total production/Total local consumption) x100

problem in South Africa. They found that 2.3 million children and pregnant and/or
lactating women (87% of which were black) were malnourished and could be considered
for nutritional assistance (Thirtle et al, 2000). Another study, conducted by the South
Afi'ican Vitamin A Consultative Group in 1995, found that Vitamin A deficiency was a
serious problem in South Africa. In particular, they found that 30% of the children in
South Afiica had marginal Vitamin A status (Thirtle et al, 2000).

It is highly conceivable that observed malnutrition was related to the high cost of the
primary staple food. Table 1.2 below summarizes the annual percentage increase in the

consumer prices of cereal products (which includes maize meal) for South Afiica.

TABLE 1.2: South African Food Price Percentage Increases for Cereal Products

 

 

 

Cknnnnxfigr 96Incnwwerxujmmutwhencxnnpanxiudflrpnndousyear
1990 1396
1991 1896
1992 1896
1993 1296
1994 896
1995 696

 

 

 

Source: Animal R_emrt of the Director General: Agricultural Economics and
Mg, National Department of Agriculture

Cereal, therefore maize meal, prices were continually increasing in the first half of the
1990’s. An article published in Dialogue, a publication of the National Economic
Development and Labour Council, stated that in 2001, the maize meal price to consumers
more than doubled, while the food price index rose by 11.4%, which is 8.4% higher than
the price increase for non-food items. Increasing food prices have a greater effect on
low-income households than on the high-income population since food makes up a larger

share of spending for the poor. In 2001, the CPI rose between 8% and 9% for households

 

2 Unweighted average figures for all commodities of the same group.

with incomes below R2500 (approximately $250) per month, compared to the 6.0% for
the very high-income households (Dialogue, 2001). In a country such as South Africa
where 85% of all households depend on purchased food, these increasing prices have
serious implications on food security.
1.2: Regional Price Comparison

Previous studies within the region have noted that in general South Afi'ican
consumers have tended to pay higher retail prices for maize meal than their neighboring
countries’ consumers. In a study conducted by TS. Jayne, T. Takavarasha, and J. van
Zyl in 1994, it was found that between 1987 and 1994 South African consumers were
paying more than Zimbabwean consumers for commercially sifted maize meal. Figure

1.1 below summarizes their findings.

FIGURE 1.1: Retail Maize Meal Prices in South Africa & Zimbabwe
(constant 1995 USS)

E South Africa
I Zimbabwe

USS/tonne roller meal

 

1987 1988 1989 1990 1991 1992 1993 1994

Years

From this graph it clear that between 1987 and 1994 South African consumers paid more,
sometimes twice as much as Zimbabweans consumer for commercially sifted maize
meal.

Another study conducted by Lawrence Rubey in 1992, found that not only were

South Afiican retail prices higher than those found in Zimbabwe but also milling/retail

margins in South Afiica were more than double the margins found in neighboring
Zimbabwe. Table 1.3, row F below presents data showing the comparative maize-milling

margin earned by the millers in South Afiica vs. millers in Zimbabwe in 1992.

TABLE 1.3: Comparison of White Maize Marketing & Milling Margins in South Africa &
Zimbabwe, in metric tons (U .S. $)

 

 

 

 

 

 

 

 

 

 

 

April 1992

South Africa Zimbabwe
A. Parastatal Producer Price $115 $110
B. Parastatal Selling Price $166 $138
C. Ex-mi“ Price, w/o govt. Subsidy $342 $210
D. Retail price for 80% extraction rate meal, w/o govt. $370 $233
subsidy
E. Retail price for 80% extraction rate meal, w/ govt. -- $155
subsidy
F. Maize Miller Margin (C-B) $176 $72

 

 

Source: Rubey, 1992

Furthermore, when 1996 and 1999 retail prices of maize meal in South Africa are
compared to neighboring Mozambique, there are similar results. Table 1.4 below
summarizes the findings.

TABLE 1.4: Average Annual Real Prices per ton of Maize Grain and Flour in Mozambique and
South Africa: 1996 & 1999 (US$2000=00)’

 

 

 

 

 

 

 

 

 

 

ear Mozambgue South Africa
Maize Grain Maize Flour Price Spread Maize Grain Maize Flour Price Spread
1996 107.91 186.82 78.90 210.09 620.70 410.61
1999 130.08 203.65 73.57 136.29 502.81 366.52

 

Source: Arlindo, 2001

Comparing the price spread between the two countries, it is clear that in 1996 the

difference between wholesale and retail prices of maize in South Africa was more than

four times the amount of the spread in the case of Mozambique. Although in 1999 there

 

3 Used the exchange rate of MZH 23134.91=US$1 for both 1996 and 1999 calculation for Mozambique
market and R4.30=US$1 for 1996 and R6.12=US$1 for 1999 calculation in the case of South Africa

 

is a reduction in the gap, South Africa’s spread continued to remain high relative to that
of Mozambique’s milling/retail spread.

These finding are pertinent to the issue of food security. Although the agricultural
sector in South Afiica does have the inherent ability to feed the nation, malnutrition
continues to be a problem in South Afiica. It is important that national food policy move
in the direction of recognizing the importance of access to food and the role of the entire
food system in ensuring national food security. As Rukuni and Eicher (1987) note, one
of the primary issues of management of a national food system is identifying the least-
cost method of securing national food requirements. In a country such as South Africa,
where maize meal is a staple food for the majority of the population, it is important that
the government uncover why its consumers were paying substantially more for maize
meal than in most neighboring countries, many of whom were not even self-sufficient in
maize. Since approximately 68% of the maize meal cost is generated in the processing
stage of the maize sub-system, then government policy that focuses on achieving
productivity gains in the marketing system would potentially have a larger effect on food
prices for consumers than policy that only succeeded in raising farm-level productivity.
Although the farm-productivity stage is important to achieving food security within a
nation, it is clear that the entire food industry is a significant and strategic economic
sector. Without an efficient food industry sector, the food system of the nation will
create bottlenecks, with large quantities of agricultural commodities unable to reach

consumers at the end of the food system.

1.3: Rggional Market Reform

Studies that looked at the effects of market reform in neighboring countries, such
as Zimbabwe, and Mozambique, have found that, in general, reform has lead to a
reduction in pricing margins within the effected markets thereby indicating lowered retail
prices. It was found that in each country the ultimate result of deregulation was declining
processing/retail margins in real terms.

The study, conducted by Jayne et al., looked at the effects of grain market reform
on low-income consumers access to maize meal in Eastern and Southern Africa. The
study found that in Zimbabwe, Kenya and Zambia, the removal of selected food
marketing controls such as subsidies on refined meal and controls on private grain
movement resulted in increased demand in urban areas for whole maize meal. They
concluded that the two major benefits of market reform in these countries were: 1)
increased availability of cheaper and more nutritious whole maize meal produced by
hammer mills in urban areas that were formerly banned in the controlled marketing
system; and 2) increased competition from hammer millers that put pressure on large-
scale, refined-meal manufacturers to reduce their margin, given that whole maize meal
and refined maize meal are close substitutes in consumption.

1.4: Pumose of this Paper

The purpose of the analysis in this paper is to understand whether market reform
in South Africa led to reductions in the milling/retail margins. Large marketing margins,
according to Timmer, occur for two reasons: high real marketing costs and/or a
monopolistic element in the marketing process that is earning excessive profits.

Although the government, prior to market reform, found there to be adequate competition

among the maize grain processors, the regional retail price comparison seems to indicate
either high costs or collusive behavior among millers and retailers within South Africa
(Rubey, 1992). If monopoly power does exist, market reform would not be expected to
have much effect on price margins between processed and raw agricultural products, and
might actually increase them. The discrepancy in the retail prices of maize meal in South
Africa compared to neighboring countries leads to the formulation of several questions
that will require research and analysis of the South African maize sub-sector. The
questions that arise are as follows:
1. What were the structural adjustment and market liberalization policies; and
how were they implemented?
2. How has market adjustment affected the retail price of processed maize?
3. Do the same high margins exist in the post-liberalization market as in the
pre-liberalization period?
4. If so, what would be reasonable policy options to address these margins; and
what would be the anticipated effects?
The goal of this paper is to develop answers to these questions and to provide some
guidelines to policy makers for further research. The objective of the research is to
econometrically determine the effect of market liberalization on the maize
milling/retailing margins within South Africa.
The remainder of this paper is divided into five parts. Chapter 2 attempts to put
the topic of this paper within a conceptual framework by looking at market liberalization
and its anticipated affects on prices and margins. Chapter 3 gives an overview of the

evolution of agricultural policies in South Africa and its implications on the maize sub-

sector. Chapter 4 presents the methodology, data and model to be used in the empirical
analysis of the milling/retail margins. Chapter 5 gives the results and interpretation of the
model findings. In Chapter 6 conclusions are drawn regarding the achievement of food

security based on the model’s findings and policy implications of this study.

CHAPTER 2:THEORETICAL FRAMEWORK FOR ASSESSING THE EFFECTS
OF MARKET REFORM

2.1: Historical Context

In general the rationale behind the emergence of controlled marketing systems are
two-fold. Firstly, it was during the Great Depression of the 1930’s that many nations first
introduced government-run programs with the purpose of reducing the negative effects of
deteriorating economic conditions (Essinger, 1998). These programs included subsidies,
which gave governments control over prices, supplies, investments and exports; and led
to the increasing substitution of market forces in agriculture and industry with
government control. For example, in Argentina and other Latin American countries
mining, utilities and other such industries were government owned and operated until
recently (Essinger, 1998). Secondly, each government faces the challenge of keeping
producer prices high enough in order generate an adequate food supply, while keeping
food prices low enough so that the entire population has access to food. This food-price
dilemma faced by governments has historically been addressed with a controlled
marketing system (Jayne et al., 1995). For example, many Eastern and Southern African
governments have used producer and consumer subsidies on staple agricultural
commodities as a means of dealing with this dilemma (Jayne, et al., 1995).

However, in the case of Eastern and Southern Africa a third reason is responsible
for existence of a controlled marketing system, particularly within the maize sub-sector
of these countries. The goal of the former “white” governments in Zimbabwe, Kenya,
Zambia and South Africa was to ensure the viability of the European farmers (Jayne et
a1., 1995). In these countries, as the number of European farmers involved in maize

production grew, so did the perception of the African farmer as a threat. In Kenya,

10

Zambia and Zimbabwe there is evidence that African farmers were able to produce at
costs well below those of the European farmers (Jayne et al, 1995). With the depression
of the 1930’s and the successful lobbying of the European Farmers, the governments of
these countries adopted policy measures aimed at undermining the effectiveness of the
African farmers. These policies led to the creation of government-owned crop buying
board, allowed for a two-tiered pricing scheme, which gave higher prices to European
farmers, and enforced restrictions on grain movements from African growing areas to
urban centers (Jayne et al., 1995).

By the late twentieth century, a combination of fiscal debt and changing ideology
lead to many countries reversing the trend of govemment-controlled markets and
movement towards privatization (Essinger et a1., 1998). For example, Argentina in 1992
privatized almost every industry in attempt to reduce its national debt, while the
disintegration of the USSR in the late 1980’s led to the replacement of government
controls with private enterprises in Russia and its satellite countries (Essinger et al,
1998). Similarly, in Africa, with the external donor and internal fiscal pressures, many
governments began in early parts of the 1990’s to establish structural adjustment
programs, which entailed the removal of subsidies and the role of government as the sole
buyer from within staple food markets.

2.2: Theoretical Framework

The term “Market Liberalization”, like “Free Markets”, is a vague term.
Liberalization can be implemented in very different ways in different countries and the
term does not adequately capture the specifics of the policy changes that actually take

place in a particular country. For this reason, the effects of “liberalization” can vary

11

widely across countries not necessarily because the effects are so indeterminate or varied,
but because the set of policy changes actually implemented vary so much. However, the
theoretical framework used in this study borrows largely from standard Industrial
Organization (10) theory. This theory posits that if there are regulatory barriers in a
market that lead to oligopoly and no controls on pricing (which a was formerly the case
in RSA), firms that enjoy the oligopoly situation may collude and derive rents from
setting marginal cost equal to marginal revenues; prices will be higher than marginal
costs in that case. If the regulatory barriers are removed, i.e. restrictions on trade in
maize and maize meal, and if this really reduces the barriers to entry and investment in
maize trading, milling and maize meal retailing, the 10 theory would indicate that there
should be increased competition in the markets, and that prices would fall as the milling
retailing stages of the market change from an oligopoly one to a competitive market.
Although market liberalization, depending on the structure of a nation’s economy,
addresses the removal of many facets of a controlled market system (such as removal of
government parastatals); one of the core facets addressed are price subsidies. Much of
the literature on market liberalization hypothesizes that with the removal of food
subsidies economies are likely to slip into a trap: the short-term effect of price subsidy
removal will be a sharp increase in food prices and therefore a decrease in real incomes
for the poor. prricing signals between consumers and producers are obstructed then
food supply will be unable to respond accordingly, so instead of increasing will decrease

which will lead to further increases in food p1ices'(Jayne et al,, 1995).

 

1 See Lele (1990), Oyjide (1990), Pinstrup-Anderson (1988), and Comia, Jolly, and Stewart (1987). These
studies conclude that the short-term effects of structural adjustment are severe on the mban poor.

12

The key problem of price liberalization, which leads to this trap, lies with the
unresponsive nature of supply, particularly in the supply of agricultural products (Guba et
al., 1998). Guba, Wei, and Burcroff II, in their 1998 work on the hog-pork sector in
Poland, give two reasons why supply may be slow in responding to increasing food
prices. Firstly, in transition economies where farms were state-owned or collectively
owned, the farming sector may be slow to respond to market signals. Secondly, the food
processing and marketing system may be dominated by monopoly forces, which offer
low prices to farmers for their output. In a marketing system, where the transactions
across vertical marketing chains are not competitive, price transmission between
consumers and farmers is obstructed. Also, if within the sector, there exists high levels of
segmentation across vertical marketing chains, when price liberalization is introduced the
market segmentation and obstructed price transmission will result in ineffective supply
response.

In their study, Guba et al. found that after the 1989 price liberalization, although
the nominal price margin between hog and pork increased substantially, this increase was
driven by inflation. In fact, when properly deflated, both retail and farm prices declined
steadily in real terms. At the same time, the supply of live hogs and pork continued to
increase until the 1992 drought, when consumer preferences shifted towards fruits and
vegetables. They accredit this successful ability of transcending the price liberalization
trap to two reasons; firstly, the restructuring of the pork processing and retailing industry
to a more competitive system and secondly, the willingness of farmers to explore

diversified marketing channels as well as actively reacting to market signals.

13

In the case of the South Africa’s maize sub-sector, since maize production is
dominated by large-scale privately owned farms to avoid the price liberalization trap the
primary challenge for South Afiica lays in the marketing system. As noted in the
previous chapter, although the government, prior to market reform, found there to be
adequate competition among the maize grain processors, the regional retail price
comparison seems to indicate either high costs or collusive behavior among millers and
retailers within South Afiica. If monopoly power does exist, market reform would not be
expected to have much effect on price margins between processed and raw agricultural
products, and might actually increase them. The analysis to follow in the remaining
chapters will establish whether or not South Africa’s maize sub-sector was successfully
able to avoid the liberalization trap. However before this can be done it is important to
look at the market structure of the maize sector and various policies that affect it both

before and after market reform.

14

CHAPTER 3: AGRICULTURE AND THE MAIZE SUB-SECTOR

The purpose of this chapter is to give an outline of the evolution of South Afi'ican
agricultural policies and their impact on the maize sub-sector. The first part of the
chapter looks at the changes enacted in the agricultural policy environment ranging from
the early 1900’s through to the market liberalization and structural adjustment era of the
late 1990’s. The second part focuses exclusively on the maize sub-sector and its
development under the various phases of South African agricultural policy.

3.1: Overview of South African Agricultural Policies

Agriculture is regarded in South Africa as a highly sophisticated and successfirl
sector because of the country’s self-sufficiency with regards to most of its agricultural
commodity requirements (World Bank, 1994). In 1989, the South African GDP was
US$804 billion, with 13% derived from services, 45% from industry, 26% from
manufacturing, 11% from mining and 5% from agriculture. In comparison, 12% of GDP
was derived from the agricultural sector in 1960 (World Bank, 1994); indicating a
decreasing share of national GDP of the agricultural sector.

Agricultural policies in South Africa, as in most countries of the world, tend to be
intertwined with social, economic and political objectives. The policy environment
throughout the 1900’s can be divided into four phases.

Phase 1. 1913 to 1940 - institution of the Land Act
Phase 2. 1940 to 1980 - post war era
Phase 3. 1980-1994 - policy reform & structural adjustment

Phase 4. 1995 onwards — post-apartheid market liberalization

15

Institution of the Land Act: 1913-1940

In this period the basic institutional framework of a dualistic agrarian structure was
established. The overall purpose of the Land Act of 1913 and 1936 was to ensure the
dominance of European settler agriculture and to force African families, who were
formerly independent farmers on sharecropped land, into the labor force in order to meet
the growing demand for labor by the newly emerging mining sector. The long-term goal
of these polices, which were successful, was to end Afi'ican farming above the
subsistence level, to convert African families into a cheap source of labor, and to protect
and strengthen large-scale commercial white farmers (Thirtle, 2000).
Post- War Era: 1940-1980

During this era the agricultural sector was transformed into a highly mechanized

and capital-intensive farm structure (World Bank, 1994). The introduction of the
Marketing Act of 1968 established a pricing and marketing system, which, with the
combination of controlled input and output prices and single-channel marketing systems
for most agriculture commodities, resulted in restricted competition. The Marketing Act
allowed for the development of subordinate legislation called schemes. A scheme was
generally established for a commodity or a group of commodities and a control board was
established to administer the scheme (World Bank, 1994). The duties or function of these
boards, among other things, included: buying the commodity at an approved price, and
the single channel sales of said commodity. Under the Marketing Act there were four
types of schemes established:

1. Single-channel fixed price schemes: Here, the farmers were only allowed to

market their goods through the Board or a licensed agent. The prices were set for

16

the year by the board. This scheme was applied to major domestic crops such as
maize, wheat barely and cats.

2. Single-Channel pool schemes: Under this scheme, the farmers marketed their
goods through a pool organized by the board. There was often a guaranteed
minimum price offered, with actual prices being determined by export prices and
marketing board operating costs. Crops facing this type of scheme tended to be
products meant for exportation.

3. Surplus-removal/Price-support schemes: Here, producers would sell their
products on the open market and the Board would only become involved if prices
fell below a minimum fixed price. In such cases they would buy the surplus
supplies and store it for later distribution. Products such as red meat tended to be
marketed under this scheme.

4. Supervisory schemes: Under this scheme, the Board’s role was that of a
supervisor or mediator between buyer and seller of the product. It would help
supervise the arrangement of the price and purchase contracts. Products such as
fruit and cotton were marketed under this scheme.

The outcome of the above marketing schemes was an agricultural sector that was highly
concentrated and which catered predominately to large-scale commercial white-owned
farmers (World Bank, 1994).
Food Self-Suficiency and Structural Adjustment: [980-1994

Although at times paradoxical, the general policy goals during this period

included food self-sufficiency as well as the pursuit of orderly government-controlled

l7

marketing, while considering the principles of the free-market system (Thirtle et al.,
2000)

The White Paper of 1984 motivated the policy aim of food self-sufficiency as
primary objective for agricultural policy (White Papers, 1984). In order to achieve this
aim, the agricultural bureaucracy within South Afiica was focused on large-scale, white
commercial farmers. The bureaucracy’s involvement ranged from protection of said
producers from international competition through various forms of direct subsidies to the
supply of such producers with state-of-the-art productive mechanical and biological
technology (Thirtle et al., 2000). The result of such a focus materialized not only into
the ability of the nation to meet domestic demand for most agricultural commodities but
also allowed it to maintain its position as a surplus agricultural producer. See Chapter 1
for the table on South African self-sufficiency index measures.

It was within this policy framework of food self-sufficiency that the agricultural
sector faced, in the mid-1980, increasing pressure for deregulation due to changes that
were occurring within the macro-economy (Thirtle et al., 2000). For example, the
extensive liberalization of the financial sector in the late 1970’s led to scaling down of
subsidies on interest rate from the Land Bank, while government subsidies to marketing
boards were phased out in the early 1980’s (Oxford Policy Management, 2000). De-
regulation in the macro-economy coupled with international trends of market
liberalization, South Africa established the Agricultural Marketing Policy Evaluation
Committee (AMPEC) (van Dijck et al., 1995). The goal of this committee was to
evaluate the current market structures and propose guidelines for firture marketing

policies.

18

By the early 1990’s, within the context of political reform, there was an increase
in criticism of the marketing system due to its obvious bias towards large-scale white
commercial farmers, and concerns were also raised about relatively high consumer prices
for many commodities. During this time a number of interest groups within South Africa
appeared to arrive at an agreement on strategic notions with regards to issues of
agriculture and rural development (van Rooyen et al., 1997). The consensus favored
comprehensive rural restructuring programs, with the aim of creating access to land,
support services and other resources for the portion of the population that was previously
denied such access. It was within this socio-economic background that the
Reconstruction & Deve10pment Program (RDP) (1994), the Broadening Access to
Agriculture Thrust (BATAT) initiative and the 1995 White Paper on Agriculture were
drafted and adopted.

The principles set out in 1995 White Paper on Agriculture called for transparency
and all-inclusiveness for all market participants, product marketing to become market
orientated, and price fixing by the government to be limited to certain situations (van
Dijck et al., 1995). The RDP can be defined as:

“. . .an integrated and coherent socio-economic policy

framework that seeks to mobilize all people of the country

as well as the country’s resources towards the final

eradication of apartheid and the building of a democratic,

non-racial and non-sexist South Africa (RDP, 199424)
Within this context the Department of Agriculture developed the BATAT initiative,
which was to serve as a vehicle to achieve the goals of RDP within the agricultural

sector. Under these programs, various aspects of the agricultural sector came under

review or restructuring. Firstly, land reform was seen as being a vital force behind rural

19

reconstruction and development. The aim of land reform was to redress the injustice of
the forced removal and historical denial from land access by redistributing 30% of
agricultural land within the first five years of the program (van Rooyen et al., 1997).
Secondly, the programs expressed support for the commercial farming sector, which was
expected to operate in a market-orientated environment with less government support
than in the past. Government support would be directed to small-scale farms and newly
emerging commercial farms (van Rooyen et al., 1997). Commercial farms were expected
to develop their own support system from the private sector rather than the government.
Finally, although the RDP and BATAT refer only briefly to agricultural marketing, the
AN C policy document on agriculture expanded on this topic (van Rooyen et al., 1997).
The overall goal of this policy document was to ensure affordable and sustainable prices
of basic foodstuffs for low-income groups by broadening the objectives of agricultural
marketing policies as they related to food security issues. In order to achieve this level of
food security, four key goals are established:

1. Removal of most Agricultural Marketing Boards except in cases of strategic
commodities, such as maize, where a state-supported Board would remain to
serve as at buyer of last resort.

2. Removal of uniform national pricing, placing greater emphasis on market forces
to determine commodity prices.

3. Regulation of certain agricultural commodities by government justified only in
cases of the existence of monopoly power, food insecurity, nature of the world

market, or the promotion of agro-industrial linkages.

20

4. Provision of uniform regulatory and legislative system of agricultural marketing

to both small-scale and commercial farmers within South Afiica.

These adopted programs and initiatives served to change the environment in which
agricultural policy was written; whereas in the past agricultural markets were under
stringent government control, the emphasis by the early 1990’s shifted towards a market
oriented system in order to achieve the goals of equity within agricultural marketing.

In consequence to the changing policy environment, the country saw the voluntary
shutting of smaller agricultural marketing boards and the scaling down of some of the
activities of the remaining boards.
Post-Apartheid Market Liberalization: 1995 onwards

Although it was determined that the old agricultural marketing system was to be
terminated in the early months of 1995, by the end of the year the most important control
boards and many of their powers were still intact (Bayley, 2000). The newly elected
government was faced with the choice of either accelerating deregulation or reorienting
the existing boards to promote the interest of consumers and small-scale farmers. It
decided on the latter. The final phase of deregulation was rapid and managed under the
Marketing of Agricultural Products Act, 1996 (0PM, 2000). The primary goal of this act
was to improve market access, agricultural efficiency, and to optimize export earnings
through the creation of a market-driven marketing system. Essentially this act legislated
the closure, within one year, of all schemes and control boards that were established
under the Marketing Act of 1968 (OMP, 2000). Although this act was sweeping in
nature, it allowed for limited intervention by government into the market provided that

such intervention would be as a last resort.

21

The response of the private sector to the new agricultural environment has been
impressive. In a study conducted by the Oxford Policy Management Review (0PM)
(Bayley, 2000), they found that in the years following complete removal of government
parastatals, there was an increase in the number of organizations involved in exportation
of citrus and deciduous hit, an increase in the number of enterprises involved in the
food and agricultural sector, a drop in real land prices, and a recovery in real farm
incomes to approximately two thirds of their level in the mid-1970’s. The most
significant development noted by OPM was the establishment of the South Afiican
Agricultural Future Exchange (SAFEX) in 1995. This organization trades firtures and
options contracts on white and yellow maize as well as sunflower seeds. The authors of
the OPM review see this exchange as being a powerfirl instrument for both producers and
processors to help manage risk.

3.2: Maize Sub-Sector

The maize sub-sector is without a doubt one of the most important sectors within
the South African economy. During the decade of the 1980’s, 40% of the total land
under cultivation was dedicated to maize production, 7 5% of total grain produced during
this period was maize, and maize constituted 56% of all grains consumed domestically by
consumers (World Bank, 1994). Maize, besides being an important food source for the
majority of the population, is a vital earner of foreign exchange for South Afiica through
the export of maize and maize products. According to the USDA/Foreign Agricultural
Services (U SDA-FAS), in the year 2000, 2% of the total world corn exports were
comprised of South African maize (See Appendix A. l), the majority of which was
comprised of white maize grain, which is the preferred maize for human consumption in

Southern Africa. Table 3.1 below summarizes the white/yellow corn Production,

22

Supply & Distribution (PS&D) for the 1999/2000 and the estimates for 2000/2001

marketing years complied by the USDA-FAS.

Table 3.1: White/Yellow PS&D - 1999/2000 to 2000/2001

 

 

 

[May/April 99700 00 Mt. lMY 00/01 00 Mt.
[Com White Yellow trotar White Yellow Total
/Stocks* 543 264 807 510 270 780
roduction 4922 2802 7724 6460 4125 10585
ports 0 569 569 0 0 O
UPPLY 5465 3635 9100 6970 4395 11365
xports 495 35 530 840 360 1200
Cons." 4460 3330 7790 5015 3150 8165
/Stocks 510 270 780 115 885 2000

 

 

 

 

 

 

 

 

*Excludes early new season deliveries; ”Includes farm retention

This table shows that in the marketing year 1999/2000 South Africa exported more white
maize than yellow maize grain. Although the estimated export of yellow maize is
expected increase, white maize is estimated to remain higher relative to yellow maize.

As noted in the section 3.1, market deregulation began in the early 1980’s, which
resulted in the reduction of income supports and government control in the marketing
channel of maize grain. Due to firrther deregulations by the government, in 1987, the
Maize Board allowed for grain sales by producers to other sources besides the Board and
changed from a cost-of-production system to a pool pricing system, which fixed the
selling price based on the interplay of domestic market supply and demand (Essinger,
1998). However, with the crop failures in 1992, the one-channel marketing system
became appealing once again for producers; therefore, the government responded by
establishing a floor price for maize (Essinger et al., 1998). Towards the end of 1993,
amid huge government debt, the new Marketing Bill was drafted. Although rejected in
1994, this bill put the concept of free markets into the maize industry players’ heads. By

1995 further indication of market deregulation was seen as multinational grain companies

23

began exporting maize along with the Maize Board which no longer operated as the only
buyer (Essinger et al., 1998). During the 1994/1995 marketing season, the second draft
of the Marketing Bill was proposed, and it stipulated the elimination of the old marketing
plan on April 30“, 1995. However, since Parliament had not arrived at agreed changes to
the Agricultural Marketing Act, the Maize Board retained the one-channel marketing
method as the buyer of last resort until the end of April 1997 (U SDA—FAS, 1995).

Due to the phasing out of marketing control boards within South Afiica, the
agricultural products markets have changed dramatically over the past five years.
However, in order to appreciate the nature and scope of such change, it is important to
include a brief description of the maize sub-sector within South Africa prior to market
liberalization as well as a description of the market structure after liberalization.
Background Information

Prior to liberalization, South Africa was divided into geographical entities called
Area A, Area B and “Exempted area”(Rubey, 1992). Area A consisted of what was
known as the Transvaal, Orange Fee State, and selected districts of the Cape Province
and Natal. The majority of the nation's maize was produced in this area. The provinces
included in Area B were the remaining districts of the Cape Province and Natal. In both
Areas A and B maize producers were required by law to sell their maize to either the
Maize Board, registered maize traders, registered mill traders or end-users of yellow
maize. Mill traders were commercial millers that were registered with the Maize Board
in order to buy directly from the producers, whereas maize traders were registered traders
that could buy maize from the producers but at prices that could not be less than the

prices set in Area A. In the homeland, or “exempted areas” there were no restriction on

24

trade, so producers could sell to anyone at whatever prices. However, their production of
maize was so minimal that overall the Maize Board had virtually all the control over
marketed maize (Rubey, 1992).

Today, with the changing of the national government and the restructuring of the
various provinces, geographically, maize cultivation can be divided into two categories,
the major and the minor growing areas. Included in the major growing areas are parts of
the Orange Free State, North West Province and Mpumalanga. The minor growing areas
include parts of the Orange Free State, Northern Province, Kawazulu Natal, Gauteng and
the northern-most parts of the Eastern and Northern Cape Provinces (See Appendix A2
for map of area). Depending on the rainfall pattern of a particular geographical area,
maize is usually planted between October and January, with harvesting taking place
anywhere between the beginning of May and the end of June (See Appendix A3 for Crop
Calendar of South Africa).

Maize Production & Marketing

In general, maize production is divided fairly evenly between white and yellow
maize; however, since 1995 there has been a swing towards the production of white
maize. Table 3.2 below shows the plantings, production and yield of white and yellow

maize from 1995/96 to 1999/2000.

25

TABLE 3.2: Planting, Production, & Yield of White & Yellow Maize - 1995/96 to 1999/00

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Seasons 1995/96 1996/97 1997/98 1998/99 1999/00
Plantings (ha)
White 1904000 1794000 1797200 1829700 2223000
Yellow 1403000 1567000 1 158800 1075000 1227440
Production (t)
White 5836000 5183000 4806000 4669000 6154500
Yellow 3858000 4549000 2450000 2642000 39864400
Yield (t/ha)
White 3.07 2.89 2.67 2.55 3.07
Yellow 2.75 2.90 2.11 2.46 3.25
Source: “Field Husbandry.” Branches of lndfly. National Department of Agriculture, Republic of South
Africa. April 2001

As of 2000, the ratio of production was 60% white and 40% yellow (National
Department of Agriculture, South Africa). In the 1999/2000 production season, 2.223
million ha (62%) of the total 3.23 million ha planted to maize were used to plant white
maize; the remaining 1.227 million ha was used for yellow maize. In general, 75% of
the domestic commercial requirement of white maize is used for human consumption,
whereas, 85% of yellow maize total production goes towards animal consumption or as
input in the production of animal feed. However, in years of white maize shortages,
yellow maize is sometimes rrrixed with white maize for human consumption. But in
general, yellow maize appears to be less acceptable by consumers.

Price setting of maize under a controlled marketing system was carried out by the
Maize Board and consisted of a graduated pricing system. In August, a price scenario
was posted for the upcoming season. This scenario linked a given national crop with a
particular producer price. For example in 1991/1992 a 6.5 million metric ton crop was
linked to a producer price of R387 (Rubey, 1992). The Board’s price was based on
variables such as expected demand, projected interest rates, inflation rates, export price
trends, and the Board’s budget. Based on these and predicted weather conditions,

farmers, by October or November, made the decision on how much to plant. By March

26

of the following year, when the actual size of the crop was known, the Maize Board
announced the buying and selling price for that marketing year. These producer prices
were pan-territorial and pan-seasonal.

During the time of gradual market liberalization until firll deregulation in 1997,
although the Maize Board remained active within the marketing system, there were new
rules to govern the operation of the maize market. No longer were prices fixed under
statutory regulations; the board held little control over co-operatives that stored grain, and
by 1996 the maximum levy to be collected as well as the minimum prices to be paid to
producers were lowered (Essinger et al., 1998). However, with the enactment of the
Marketing of Agricultural Products Act of 1996, the start of the 1997/98 marketing
season saw the beginning of a new marketing system. Now no longer are farmers forced
to sell their grain to the maize board at a set price, but rather they had to learn marketing
skills to be competitive and stay in business.

Currently, maize producers deliver the grain to, a cooperative and maintain
ownership of the grain; therefore, the farmer is responsible for storage costs. Under the
new marketing system, farmers are now faced with a variety of methods for selling their
grain. In the study conducted by Stacy Essinger, the following options were discussed.
The first option available allows the farmer to sell his/her grain in a pool and get an
advance payment for the grain before prices are set for the marketing season. The second
option, known as the back-to-back option, is similar to that of a spot price offer, where
the buyer, who wants to take immediate possession of the grain, makes an offer to the
producer. The outside purchase option refers to the situation where a buyer contracts

directly with the farmer for the grain. The farmer then delivers the grain to the

27

cooperative, where storage costs are directly charged to the farmer. Finally, with the
emergence of SAFEX, the farmer can use the marketing tools available to him/her to
manage his/her own risk. The cooperatives do not offer hedging opportunities to the
farmers, but farmers may do so through individual dealings with SAFEX.
Maize Milling

With human consumption of maize being approximately 3.5 million tons per year,
the process of dry milling heavily influences maize processing (Essinger et al., 2000).
There are two types of dry milling technology available to millers: (1) Hammer milling,
which produces whole meal and, (2) roller milling technology, which produces a large
range of partially or fully degermed maize meal (Jayne et al., 1995). Hammer mills
consist of a hopper into which the grain is fed, a milling chamber where the maize is
ground, and a filtering screen, which surrounds the hammers and allows the ground grain
to escape when it reaches desired consistency. This technology does not separate the
bran, germ or endosperm in the maize grain but rather it shears and grinds the whole
kernel (Jayne et al., 1995). Roller mills, on the other hand, are generally large-scale
machines, which involve a continuous process of shattering the kernel and then sifting
out the bran, germ and endosperm (Jayne et al., 1995). The resulting maize meal from
this process can be divided into four types:

a. Super — highly refined, de-germed product, with an extraction rate of 62.5%
b. Special Sifted -— refined product with an extraction rate of 78.7% and which is
enriched with proteins and vitamins. This type of maize meal comprises over half
the market in South Africa.

c. Sifted — less refined product, with an extraction rate of 88.7%.

28

d. Un-sifted/straight-run — unrefined meal with an extraction rate of 98%.
The resulting maize meal is a staple food for the majority of the population in South
Afiica, particularly among black South Afiicans. It is eaten as either as thin breakfast
porridge or a thick, stiff porridge known as Pap or Invubo. According to a study
conducted by the Maize Board in 1992, the per-capita consumption in urban areas was 48
kg in 1991 and 78 kg in rural areas for the same time period.

In South Africa, maize millers are very closely tied to producers and cooperatives
for the procurement of their grain. The reason is twofold. The first reason is related to
the purchasing cost of the maize grain. Between 80%-85% of maize processing costs are
accounted for by the cost of the raw material (Essinger et al, 1998). The second reason is
that processors need a steady supply of raw material to keep the efficiency of processing
high (Essinger et al., 1998). In the early 1990’s, maize milling in South Africa was
dominated by commercial millers that, when counted, amounted to over 60 different
firms in the industry. Based on the number of firms within the market it was assumed
that there was a significant degree of competition among the millers, regardless of the
fact that the two largest firms, Tiger Milling and Premier Milling each held
approximately 20% of the maize meal market (Rubey, 1992). Most millers at this time
operated only one shift and it was therefore a common perception that the milling
industry was operating below capacity, which again reinforced the perception that there
was competition among the millers (Rubey, 1992).

Currently, the milling industry is dominated by seven, large-scale millers who
have the capacity of processing quantities of whole grain maize greater than 100,000 tons

each (Chabane, 2002; Essinger et. al. 1998). These millers process approximately 70%

29

of the total maize meal produced in the market (Hendricks, et. al., 2001). The
concentration ratio of the largest four firms is approximately 42.5% of the total maize
market (Chabane, 2002). Fifty small-scale millers account for about 10% of milled
maize. The remaining 20% of maize meal is produced by an estimated 100 or 150
gristing (hammer) mills (Hendricks et. al. 2001).

With the advent of market reform, large-scale maize processors today use forward
contracts in order to ensure an adequate supply of their raw material. These contracts are
offered to the producers either directly by the millers or through the cooperatives on
behalf of the millers; the major difference between the two methods being the point of
delivery, i.e., the cooperative silos or the millers premises (Essinger, 1998). In 1998,
approximately 80% of all maize procurement was done through the cooperatives with the
use of the back-to—back contracting as previously discussed (Essinger, 1998). Ifa miller
contracts directly with the producer, they are able to either use a specific-variety contract
or a pre-harvest contract. With the first type of contract the millers draw up a contract,
which specifies the specific variety to be used; however, they generally do not specify the
method of production to be used by the farmer. The second type of contract tends to be
more risky for both parties since an agreement is reached before weather dictates final
cr0p yields (Essinger et al, 1998).

3.3: Conclusion

As stated previously, it has been found that market reform in other developing
economies was able to successfully ensure reduced real consumer prices because of an
increase in the level of competition in the processing stage of food production due to the

emergence of small-scale processors (see Chapter 2). From this chapter it is clear that

30

one of the goals of market reform in South Africa was to ensure affordable prices of basic
foodstuffs for low-income groups through the operation of free-market mechanisms, i.e.
mechanisms within a market in which the government sole role is as an institution that
gives private entrepreneurs incentive to trade. In order to establish whether or not this
goal has been attained, the chapters to follow will empirically determine the effects of
liberalization (i.e. the removal of the government Maize Board and restriction of maize
grain purchases by private traders) on the milling/retail margins within the maize sub-

sector through the use of descriptive and econometric modeling.

31

CHAPTER 4: THEORETICAL BACKGROUND ON MARKETIN MARGINS

4.1: Literature Review

Marketing margins, in competitive markets, can be defined as; 1) the difference
between retail prices and producer prices or as 2) the price of a collection of marketing
services which is the result of the demand and supply of these services (Tomek and
Robinson, 1981). Under the first definition, the marketing margin is essentially the
difference between the primary demand and derived demand. Primary demand is defined
as the joint demand for all the inputs that go into the final product and is determined by
the demand characteristics of the final consumer (Tomek and Robison, 1981). The
derived demand is essentially the primary demand function minus the per unit costs of
marketing components. Figure 4.1 graphically illustrates the primary and derived

functions and marketing margins.

FIGURE 4.1: Primary and derived functions and marketing margins

Prices

Retail

 

Margin

Farm

 

 

I erived Demand

 

 

 

Quantity per unit of time

32

The primary supply refers to the relationships at the producer level, whereas the derived
supply is obtained by adding an appropriate margin to arrive at the retail level of supply.
As we see from the graph, the retail prices are found where the derived supply intersects
with the primary demand, while the producer prices are found at the intersection of the
derived demand and the primary supply. The second definition of marketing margins
equates margins with the price of a collection of marketing services. This price (or
margin) is a function of both the supply and demand of these services. These services
include such items as the cost of assembly, processing, transportation and retailing
(Tomek and Robinson, 1981). Both of these definitions imply that marketing margins are
related to underlying supply and demand factors. However, this is true only under the
assumption of competitive markets. Ifthis assumption is relaxed, then market structure
needs to be included as an important determinant of marketing margins. The milling
and/or retailing market structure, for example, is likely to affect many forms of
efficiency, including pricing efficiency and x-effrciency.

Over time, many empirical farm-retail price models have been developed under
the rubric of price determination. In situations where adequate data for a structural
specification is available, a simple markup equation has often been assumed to accurately
reflect the relationship between the farm and retail prices (Lyon and Thompson, 1993).
However, in conditions of limited structural data, single reduced form equations are
generally specified. The marketing margin models that will be reviewed in this section
of the paper will be single-equation reduced-form models. Hence there will be no

explicit link of these models to a particular market structure.

33

Markup Model
This model has a long pedigree in empirical work and can be specified
empirically as:
M=fiP,, Z) M=(P,—Pf) (1)
where M is the farm-to-retail margin, Pr is the retail price, Pf is the farm price and Z is a
vector representing all the marketing input costs (Lyon and Thompson, 1993). This
model allows margins to consist of either absolute or percentage markups or a
combination thereof. The theoretical justification of the model lies in the argument that
consumer demand is the determining factor in the relationship between retail and farm
prices; therefore, food prices are determined at the primary level and farm-prices are
simply retail prices minus marketing costs (Waugh, 1964). Bruce Gardner, in his study
of the farm-retail price spread in a competitive food industry, gave firrther theoretical
justification of this model by using the results of his study to imply the viability of simple
rules of mark-up pricing by marketing firms.
Relative Model
This model is obtained from the inverse derived demand function for farm
commodities that the food processors face @yons and Thompson, 1993). It defines a
marketing margin as a filnction of retail prices, quantity and input costs and can be
expressed as:
M =j(P,, Q, Z) (2) -
where Pr is the retail price, Q is the total quantity marketed, Z represents a vector of
marketing cost. This model is consistent with Gardner’s structural analysis where he

models the determinants of supply and demand at each level of the marketing chain in

34

both a competitive factor and product market using a simultaneous equation system. This
model suggests that depending on the source of change — whether it is retail prices, farm
output or the supply of marketing services — the relative effects on the price margin will
differ. However, because shifts in both demand and supply can cause changes in retail
prices and farmer output, a complete analysis of the marketing margin would require
structural equations for all market participants at all stages of marketing system, fi'om
production to consumption. Such a model of a vertical marketing chain would be very
complicated and require extensive amounts of data. Another problem is that P, is
contained in M since M is defined as P, - Pf.
Marketing C ost Model
Wohlgenant and Mullen derived what is known as the marketing cost model. Here, they
assumed that marketing firms provided services up to the point where the marginal cost
of providing such services equaled the marginal revenue fi'om offering that service.
Therefore they argue that marketing margins are determined solely by the quantity of the
farmers’ output and the retail firms’ cost firnction. This model can be expressed as;

M =flQ, Z) (3)
where Q is the quantity of the farmers’ output and Z is the vector of input costs. This
definition of the farm-retail spread is consistent with Tomek and Robinson’s second
definition of marketing margins, which states that margins are essentially the price of a
collection of marketing services. The problem with this model is that once again there is

a potential endogeneity. M will undoubtedly affect P, and Pf, which effects Q over time.

35

 

4.2 Methodology

The empirical model used in this paper relies on the basic economic justification
of the Marketing Cost Model discussed in the previous section, with some modifications.
Starting from the theory that price margins are the sum of marketing services (Tomek and
Robinson, 1990), the maize marketing margin will be modeled as a function of
processing and marketing cost and will follow the work of Guba et. al., 1998. The
particular feature of the maize-milling regime within South Africa necessitates the need
to redefrne the marketing margin. Since the milling industry in general tends to be
vertically integrated with the retailing of maize meal, retail prices and wholesale prices
(at which millers purchase) are available; maize meal prices ex-mill are not. Hence, the
marketing margins that will be estimated will essentially be a processing plus retailing
margin. Secondly, as mentioned in the previous chapter, in the actual process of milling
the maize whole grain, depending on the level of sitting used, there are by-products that
result and are later sold. For instance, some of the by-products that result are used as an
input to livestock feed, dog food and cooking oil. Therefore, for the purpose of this
study, the formula used to estimate the milling margins will be an adaptation of Jayne and
van Zyl’s miller/distributor margin (1994). The formula is:

MM = PMM/z — PS + [(z-l)*PB] + S2 (1)
where PMM equals the retail price of maize meal, 2 represented the average extraction
rate (i.e. tons of grain required to produce one ton of meal), PS is the wholesale price of
the maize grain, PB the price of the by-product and 82 the direct subsidy given to maize

millers. The difference in this study is that there is no subsidy when calculating the

36

milling-retail price margins since during the period under study there were no
government transfers made to consumers or millers of maize grain.
To model the wholesale to milling/retail margin 8 general, a reduced form data
generating process (DGP) can be formulated. The DGP equation is as follows:
M, = X,*Bi* + U, (2)
Where M, = P,/z — Pw + [(1-z)*PB] is the wholesale-to-milling/retail margin. Here P, is
the retail price of maize meal, 2 the average extraction rate (i.e. tons of grain required to
produce one ton of meal), Pw is the wholesale price of the maize grain, PB the price of the
by-product. In equation (2) X,* includes all the exogenous variables affecting the margin
within the market, and U, is an identically and independently distributed error term.
Not all of the X,* variables can be identified because of the lack of observable
data. Therefore we can re-write X,*Bi* as being composed of two parts;
Xt*l3i* = Xt131+ Htai (3)
where X, contains the observable data and H, the unobservable data. We can now write
the DGP equation as:
M, = x43, + V, (4)
where:
V, = H,or, + U, (5)
is the Wold representation of the stochastic component of Z,0t and U,. Any deterministic

mean, trend, or seasonal component of H,0t can be incorporated in the intercept, trend or
seasonal component of X,.
The variables included in X, are rainfall, marketing and processing costs (e. g.

labor), macroeconomic risk, monthly seasonal dummy variables, time trend and a

37

categorical variable differentiating the period before and after market liberalization.
Assuming that there is a linear relationship between the price margin and the independent
variables, equation 4 becomes:

MM: = 50 + 5114.1 + 52Rt-r + 63w, + 64LIBt + 55Tt + Watson, + v, (6)
L represents labor costs to the millers, R is a measure of macro-economic risk, W is a
rainfall index, LIB is the categorical variable which differentiates the time period before
and after market liberalization, T is the time trend, and D,,,, are 11 months dummy
variables. Equation 6 is estimated on monthly data. In this model, it is assumed that one
month is long enough for farmers or firms at different marketing levels to finish adjusting
to market signals, therefore the labor costs and macro risk variables have been included
as lagged variables.

There are three points that need to be made. Firstly, while other marketing
margin models have used Q (the total quantity marketed or total quantity of farmer’s
output) as an explanatory variable, in this study we have chosen not to include Q because
of potential endogeneity. Instead we have chosen to include an exogenous variable that
affects Q, in particular the rainfall index. Secondly, since the calculation of the
milling/retailing margins contain P, (retail prices) and PW (wholesale prices), to include
them in model would lead to problems with endogeneity. Instead exogenous variables,
such as L (labor costs) and R (macro-economic risk), which affect both retail and
wholesale prices, have been included. Finally, in using time series data to estimate the
model there exists the potential of a unit root in the milling/retail margin series which
could potentially lead to the problem of 1(1) cointegration. However, in conducting an

Augmented Dickey-Fuller test for unit root, it was found that although the value of p =

38

9.02 which is < 1 in the AR(1) model, the t-statistic for the unit root test was —3.80 < -

3 .66 which was the critical value for the 2.5% level of significance. In other words, we
reject the hypothesis of a unit root at the 2.5% level of significance. This indicates that
the time series process is 1(0), in other words, the first difference of the process is weakly
dependent; therefore, nothing needs to be done to the series before using them in the
regression analysis.

When Ordinary Least Squares method of estimation was applied to equation 6, it
was found that the milling/retail margin model exhibited serially correlated error terms.
The p—values = 0.000 of the coefficient p on V,., leads us to reject the null hypothesis of
no serial correlation at the 1% level of significance and conclude that there exists
autocorrelation of the 1’”t degree, i.e. AR(1) serial correlation. Furthermore, after the
model had been corrected for serial correlation using the Cochran-Orcutt method and was
tested for heteroskedasticity using the Breusch-Pagan Test, it is found that the F-
statistic’s p-values < 0.0005 therefore we reject the null hypothesis of homoskedasticity.
In order to address these problems, heteroskedasticity and serial correlation shall be
modeled and corrected for through a combined weighted least squares AR(1) procedure.
The steps involved in this process include (W ooldridge, 2000):

a) Estimating equation (6) by OLS thereby predicting the residuals, V,

b) Calculating log( V2,) on the independent variables and obtain the fitted values, g,

c) Calculate the estimate of G, = exp(g,)

d) Then estimate the transformed equation by standard Cochran Orcutt (CO) or
Prais-Winsten (PW) methods.

Gil/254141 = 014/250 + (ll-”251144 + Gt-UZSZRt-l + Gt-l/253wt + Gfl/254LIB1 + Gt'isTt

39

+ G,'”22“m=,5,D,,,, + v, (7)
The resulting feasible GLS estimators are asymptotically efficient and all the standard
errors and test statistics from the CO or PW methods are asymptotically valid.
4.3 Data and Variable Discussion

The definition and expected sign for each of the right-hand side variables in (6)
are discussed briefly as well as their calculation and source.
Producer Price (Pp)

The source of this pricing data was the “A bstract of Agricultural Statistics: 2001”,
which is published by the National Department of Agriculture in South Africa. The price
schedule is reported in nominal terms, as the gross white maize producer prices. These
are the estimated average prices aggregated over the entire country. The prices recorded
in Appendix B are in real terms, having been deflated using the CPI with the base year
2000 = 1.

Retail Prices (P,)

Between the marketing years 1970/71 and 1993/94, the retail prices were obtained
from the Maize Board annual reports, measured in Rands per ton. However, since the
Board stopped compiling retail price information after 1994, these prices were
constructed for 1995/96 through to 2000/01 by extracting the prices from the retail CPI:
This index is simply calculated by taking the average prices of all the different brands
and qualities from across the entire country and calculating an average index. The retail
price for maize meal in this index was measured in Rands per 5kg bag of meal, which is
the most common sized package. To get this measurement in Rands per ton, we

multiplied the 5kg price by 200 then deflated it using the CPI.

4O

Wholesale Prices (Pw)
The source of the wholesale prices from 1970/71 to 1994/95 marketing season is

the “Abstract of Agmultural Statistics 2001”. The reported figures are the selling price

 

of large quantitiesl. We assume that after 1995, the millers’ primary source of maize
grain the open market via the grain market exchange, SAFEX? Therefore, for the
1995/96 to 2000/01 marketing seasons, SAFEX spot white maize wholesale prices were
used in our data set. As with the producer prices, the wholesale prices have been deflated
using the CPI with the base year in 2000.

Salaries and Wages (L)

To control for the effect of labor costs on the milling margins, a wage variable
was included in the model. Since the information regarding the wages specifically in the
milling industry is not available, the average wage and salary measures for the
manufacturing sector within South Africa was used. The primary sources of information
included Qbour Statistics Employment & Salaries & Wages: Mining and ng’ng,

Manufacturing ConjstructionJand Electricity and Labour Sta_tistics: Survey of

 

Employment & Earnings in Selected Industrigs, both of which are statistical releases
complied by the Statistical Services of South Afiica. For the years 1997-2001, the total
gross salaries and wages, which include severance, termination and redundancy

payments, were divided by the number of fill] and part-time employees to get the average

 

1 Large quantities: 190 tons and more
Prior to 1982/83: 216 tons and more
Prior to 1979/80: 380 tons and more
Prior to 1971/72: 453 tons and more

2 The deregulation of markets led to the establishment of a futures market. Early in 1995 SAFEX
Agricultural Derivative posted its first agricultural commodity on the exchange market The exchange
trades on average 90,000 tons of maize a day. Over 420,000 contracts have traded since 1995, with the
bulk of the trades arising from the white maize contract.

41

quarterly salaries and wages per worker. Then this number was divided by quarterly
payments into the marketing season (May lst - April 30th). Finally, these wages were
deflated, using the CPI(2000), in order to get them into real terms. The coefficients on
labor costs can be either positive or negative. Therefore changes in this input cost can
enlarge or depress the margin.

Macroeconomic Risk (R)

In an economy that undergoes transitions, such as in the case of South Africa,
moving from a controlled agricultural market to a liberalized market-driven sector,
macroeconomic risk will likely affect all markets involved in the sector. Therefore using
price uncertainty in a given market may underestimate the real risk faced by marketing
agents (Guba et al, 1998). Macroeconomic risk can reflect the uncertainty in both input
and financial markets, as well as in output markets. It is for this reason that
macroeconomic uncertainty has been used instead of price uncertainty to reflect the risk
faced by the marketing agents in the maize subsector.

The exchange rate has been used to measure macroeconomic risk in our model.
To calculate this rate, we assume that the maize industry’s perception of macroeconomic
risk is based on the past years’ experiences as well as current observation. In other
words, the macroeconomic risk R variable is measured as the squared value of (E, - E,. 1).
The coefficient of R is expected to be positive. The exchange rate data were compiled by
Statistics South Africa.

Weighted Average Critical Rainfall per Province (W)
South African Weather Service (SAWB) and Weatherscape are the two main

sources for the average monthly rainfall data, measured in millimeters, from 1970-2001.

42

Since maize marketing margins are not ultimately affected by rainfall in every month
within the marketing year but specifically by rainfall in the critical months from October
to April, a rainfall index, which placed higher importance on rainfall during those critical
months, was used. In order to generate this rainfall index, the average rainfall during the
months of October to April for each of the maize growmg provinces was weighted by the
proportion of maize production per province over the entire observation period, then
summed the measurement over the entire country to get the final index for marketing
year. See Appendix B.4 for the table that shows you the calculation of such weights.
There are two potentially countervailing effects of rainfall on the milling/retail margin. If
strong scale economies exist in the market, then increase in output Q could lead to a
reduction in the margin. But also, since the margin can be defined as the difference
between retail and producer prices, one could hypothesize that if output increased, this
could lead to a reduction in producer prices, more so than retail prices, especially if retail
industry is concentrated. Hence marketing margins will increase.

Policy Change (LIB)

Ifmarket liberalization has the effect of leveling the playing field, thereby
allowing small, private processing and marketing firms to compete directly with large-
scale millers, then the marketing margin would be expected to decrease. Then the
coefficient on the dummy variable LIB would be negative if LIB takes on the value 1 in
the post reform periods and 0 otherwise.

Time Trend (T)
Although nothing about trending variables necessarily violates the classical linear

model assumptions of OLS, it is important to allow for the fact that many economic time

43

series have a common tendency to grow over time, and that the unobservable factors that
cause the dependent variable to grow overtime might be correlated to the growth in
explanatory variables. Therefore, it is important that a time trend variable be included in
the model in order to capture this phenomenon or we may find a spurious relationship
between the margins and one or more of the explanatory variables. If the coefficient on
the time trend variable is positive, then we can conclude that over time the milling/retail
margin is growing after netting out other factors in the other explanatory variables, which
themselves might be trending. In other words, the milling/retail margin has an upward
trend. However if the coefficient is less than zero, then we can conclude that M, has a
downward trend, i.e., the marketing margins are shrinking over time.

Seasonal Dummy Variable (D)

Since the data are collected on a monthly basis, it is very possible that they may
exhibit seasonality. The way in which is capture this affect is to allow the expected value
of the series on the dependent variables to vary in each month by including dummy
variables for each of the 11 months in the year. If there exists no seasonality in the
margins once the structural variables have been controlled for, then it would be expected

that 86 through 6,,; would all be zero. This can be easily tested by an F-test.

44

CHAPTER 5: RESULTS

 

5.1: Descriptive Statistical Results
Figure 5.1 below depicts the movement of deflated annual average producer,
wholesale and retail prices in the maize market, starting in the marketing year 197 5/76

through to 1999/2000 for producer prices, and 2001/2002 for wholesale and retail prices.

TABLE 5.1: Producer, Wholesale, Retail Maize Price Spreads (Constant
2000 Rands): 1975I76 to 2001/02

+Producer +Wholesale +Retail

Price(R/ton)

 

mummOergmonmmOv-vamgncamera
NNNNDQQQ mcococococrorciormai marmoo
mmmmmmmmmmararmmmmararmarararmarooo
:::::::F:::::::::F::C::3QQQ
miniscoerce-ngmtorsccorot-RmvmgrsactnOu-
NINNNI‘QQCDID ommmmaraiorarmm marmoo
armmmarmmmmmmararmararararararararmmaroo
1-1-1-1-1-v-I-1-1-1-I-1-1-1-I-I-i-F1-ra-1-FFFNN
Year

From 1975/76 through to 1979/80 marketing years there is a clear upward trend in
producer, wholesale and retail maize prices. During this time frame, producer prices
were above wholesale prices, indicating a negative farm-gate to wholesale margin. This
is not surprising since the maize market was operating under a single-channel fixed price
scheme during this period. Under this scheme, the Maize Board was the sole buyer of
whole grain maize. From the price spreads, it appears that in general the Maize Board

would buy high, then sell at an even lower price.

45

During the period spanning the 1980/81 marketing year through to 1994/95
marketing year there is an overall stabilization of all three prices the first half of the
period, then from 1986/87 onwards there are two things occur that are of interest. Firstly,
from 1985/86 to 1994/95 there is an overall slight decline in real terms of both producer
and wholesale prices while retail prices appear to remain relatively constant. Secondly,
in terms of the wholesale and retail price spread there is a closing of gap between the two
price spreads with an eventual fall in producer prices to below wholesale prices. The
movement of these prices can be explained by looking at the policy environment in which
the sub-sector existed. During the early 1980’s, market deregulation first began with the
reduction in both income supports to farms and government control of the marketing
channel. Furthermore, by 1987, the market was further deregulated with allowance of
grain sales by the producers to sources other than the board. In the graph above, we see
that after the 1986/87 marketing year, producer prices fell below wholesale prices for the
first time during the period under observation. From 1987/88 until 1994/95, both price
series display a downward trend. In the case of deflated retail prices, after the 1986/87
marketing year the average retail price fell for two consecutive years, then despite falling
wholesale and producer prices, this price series shows a positive upward trend, further
widening the gap between retail and wholesale prices, i.e. indicating an increase in the
nrilling/retailing margin.

With the intended firll market deregulation set for the beginning of the 1995/96
marketing season, there is a sudden increase in all three prices after the 1994/95
marketing year. However, since full deregulation did not materialize until the beginning

of the 1997/98 marketing year, there is an almost constant growth in all three prices

46

during the period of market transition. However, after 1997/98 marketing season there is
a clear divergence in the three prices. For instance, in the case of producer prices, there is
a slight increase in prices from 1997/98 to 1998/99 then a sudden drop between the
marketing years 1998/99 and 1999/00. With wholesale prices, there is an initial
decreasing trend until after 2000/01 marketing season, when the wholesale prices
suddenly increase dramatically. In the case of retail prices we see again an upward trend
despite falling wholesale and producer prices, however between 2000/01 and 2001/02
marketing seasons, the annual average price remains constant despite the sharp increase
in wholesale prices.

Looking at the movement of the calculated wholesale to nrill/retail margins in real
terms, it is clear that over the entire period under observation this margin has displayed

an upward and increasing trend.

FIGURE 5.2: Movement of Real Milling/Retail Margins (Per) in South
Africa (constant 2000 Rands): 1975176-ZOO1I02

Price (thon)

 

Figure 5.2 above depicts the movement of monthly real milling/retail margins (P,-Pw) in
the maize sub-sector. From May 1976 through to April 1983 the mill margin showed an

upward trend. Although growth seemed to slow from May 1984 to April 1989, we see

47

that from May 1989 through to April 2000 there was again an upward trend in the
milling/retail margin. However from May 2000 to April 2001 we see a decisive drop in
the milling margin in real terms. This spread appears to indicate the existence of a unit
root, but as we shall see in the next section, p < l in the AR(1) model, indicating that the
time series process is 1(0), i.e., the first difference of the process is weakly dependent;
therefore, nothing needs to be done to the series before using them in the regression
analysis.

As a prelude to econometric analysis, the CPI-adjusted producer, wholesale,
retail, and milling/retail margin summary statistics were calculated for the periods
1976/77 — 1979/80 [Post-war era]; 1980/81 — 1994/95 [Policy reform and structural
adjustment]; and 1995/96 — 2000/02 [Post-apartheid market liberalization]. The results

are summarized in Table 5.1 below.

48

 

 

 

 

 

 

 

 

 

 

TABLE 5.1: Summary Statistics
Producer, Wholesale, Retail Prices 8. Milling Mar ins
1976/77-1979/80 1980/81-1994/95 1994/952000412
IProducer
Mean 1 134.2 962.6 666.2
Std Deviation 84.8 274.7 71 .6
Variancel 7196.1 75470.4 5131.9
Minimum 986.7 421.1 520.7
Maximum 1333.2 1383.4 760.2
C.V. (%) 7.5 28.5 10.8
Wholesale
Mean 934.5 995.5 869.6
Std Deviation 77.1 127.2 217.5
Variance 5940.1 16182.8 47287.2
Minimum 786.7 713.3 500.4
Maximum 1110.3 1287.0 1810.9
C.V. (%) 8.3 12.8 25.0
etail
Mean 2154.2 2445.8 2800.0
Std Deviation 180.4 154.2 234.9
Variance 32561.7 23768.0 55181.9
Minimum 1813.3 1994.5 2295.2
Maximum 2565.2 2798.6 3291 .9
C.V. (%) 8.4 6.3 8.4
iflm Margin
Mean 1696.8 1992.0 2550.5
Std Deviation 143.4 205.4 355.5
Variance 20564.7 42174.8 1263539
Minimum 1428.3 1544.2 1828.3
Maximum 2023.0 2498.3 3265.4
C.V. (%) 8.5 10.3 13.9

 

 

The results indicate that the average real prices for maize at the farm-gate fell from
approximately R1134 per ton to about R666 per ton. In the case of wholesale prices,
although there is slight increase in the average wholesale price from the post-war era to
the policy reform era, during the period of full market liberalization there is a decrease in
the average wholesale price below that of the controlled period price. This occurs despite
the fact that from Figure 5.1 we see a sharp increase in wholesale prices in the 2001/02
marketing season. In the case of retail prices, fi'om each period to the next there is an

increase in the average retail price of maize meal from approximately R2154 per ton in

49

the post-war era to about R2706 per ton in real terms. Not surprisingly, then, the
milling/retail margins increase from approximately R1697 per ton in the post-war era, to
approximately R2550 per ton in the post-apartheid market liberalization era.

The variability in monthly producer, wholesale, and retail prices as well as in
milling/retail margins are also presented in Table 5.1. Moving from the period of partial
liberalization to firll-fledged market liberalization, the coefficient of variation has
declined for the producer prices, from 28.5% to 10.8%. This decline indicates an
increase in stability of producer prices in absolute terms. However, the measures of
variability, i.e. CV, for the wholesale price, retail price and the milling margin have
increased in absolute terms, indicating that the mean level of the price spreads have
declined to a greater degree than the absolute volatility of the spreads. The standard
deviation of each of these spreads gives similar results. It is important to note here that
price instability does not necessarily indicate price unpredictability. For instance, in a
market such as the maize sub-sector, some variation in prices is predictable due to intra-
seasonal price increases after the harvest used to induce incentives for grain storages for
later consumption (Jayne, et al., 1998).

Table 5.2 below provides basic information on the variables in equation 7 of
Chapter 4. Comparing the minimum with the maximum and the standard deviation with
the mean, Wage, Rainfall, Macro Risk, Mill and Marketing Margins all display

significant variation between 1976/77 and 2001/2002.

50

 

 

 

 

 

TABLE 5.2: Descriptive Statistics, 1978/77 - 2001/02
Av . Wa e Rainfall Index Macro Risk Mkt. ”1’91" Mlll Margit;

ean 3901.1 575.1 0.0098979 2111.6 2095.5
tandard Deviation 464.8 1 16.3 0.0450523 461 .8 383.6
ample Variance 2233440 13524.1 0.0020297 2132268 147162]
inimum 2411.8 410.6 0.0000000 1289.2 1428.3
aximum 5234.4 893.6 0.7012890 3487.9 3265.4
.V. (%) 12.1 20.2 455.2 21.9 18.3

 

 

 

 

 

 

 

One point to note is the variation in the measure of macroeconomic risk, R. This high
C.V. is an artifact of the mean being very close to zero; so even a small absolute standard
deviation becomes a large C.V. as the mean approaches zero.
5.2: Econometric Results

The analysis so far has considered the effect of liberalization on marketing
spreads without controlling for changes in other factors that are likely to affect marketing

margins. Table 5.3 presents the results of the FGLS estimation results for the

milling/retail margin.

51

TABLE 5.3: Milling/Retail Margin Determination of the Wholesale-Retail Market in South

Africa; May 76 - April '00

 

 

 

 

 

 

 

 

Dependent Remession 1 Regression 2
Variable FGLS w/o Interaction Term FGLS Piecewise
Constant 4.019 6.58
(07805)“ (1 .049)"
Wages lagged 0.282 0.23
(0.029)” (0.234 “
itiacro Risk lagged 467.93 296.44
(118-17) (115.1):
Rainfall Index -0.08 -0.35
(0.121) (0.148)‘
Liberalization Dummy 148.54 -379.72
(56.461 )“ (97993)“
Time Trend 2.355 2.567
(0.337)“ (0.317)“
Liberalization'fi', - To) - 14.36
- (3.796)“
June 25.464 67.39
(17.630) (17207)“
July -128.26 -51.84
(36.735)“ (23.121 )"
August -154..-105.35
(36.735)“ (28.014)"
lSeptember -125.45 29.62
(31 .249)“ (29.179)
October -1 59.61 -5.88
(34.567)“ (28.863)
November -229.97 -1 52.49
(39.036)“ (30014)“
December -188.82 -273.23
(31 .856)“ (35348)“
January 441.95 456.42
(47.363)“ (45957)“
February -90.1 1 -90.86
(26.237)“ (24.998)“
ilarch -1 1 1 .28 -215.86
(23.1 89)“ (24782)“
April -216.73 -290.12
(20.504 " (23345)“
Diagnostics
of Observations 296 296
2 0.9675 0.9578
djusted R2 0.9656 0.9552
Statistic (16, 279) 518.83 370.75
rob > F 0.0000 0.0000
ho 0.861 0.892
urbin-Watson Statistic 2.25 2.04
‘Signlflcant at 5% level “Significant at 1% level (.)Standard deviations

52

 

Regression 1 : F GLS without an interaction term:

60 = 4.019 is the predicted milling/retail margin that will result when all other
variables are set at zero. However, since no one would work without wages, the
intercept in this equation in not by itself meaningful.

61 = 0.282: The slope coefficient on wages indicate that the lag affect of a R1 increase
in average monthly wages, above its long-run trend, on the milling/retail margin is
an approximate twenty-eight cents increase in the margin, ceteris paribus.

52 = -l62.93: The slope coefficient on the Macro-economic risk variable suggests that
when the macroeconomic risk index increases by one unit above its long-run trend,
the milling/retail margin is expected to decrease by approximately R163 per ton,
ceteris paribus. However, in the regression this coefficient is found to be statistically
insignificant, i.e. it has little explanatory power in the existing model.

53 = -0077: The slope coefficient for rainfall indicates that as the rainfall index
increases by one above its long-run trend, the milling/retail margin is expected
to decrease by approximately R008 per ton, ceteris paribus. This outcome is not
what would be expected, however this coefficient is not statistically significant and
therefore has little explanatory power.

54 = 148.54: The slope coefficient for the market reform dummy indicates the
difference in the monthly milling/retail margin between the pre and post
liberalization periods. In other words, given the same level of wages, rainfall,
macro-risk, the milling/retail margins were approximately R148 per ton higher after
market liberalization.

65 = 2.355: The time trend variable’s coefficient implies an approximate R2 per ton
increase in milling/retail margin per month, on average, ceteris paribus.

87-517 = the test of the joint significance of the 11 monthly dummy variables yields a p
-value < 0.005, therefore leading to the conclusion that the seasonal dummies
are jointly significant at the 5% level of significance.

DW= 2.25: This allows us to assume no further autocorrelation.

p = 0.861: Since this value is < 1, it indicates that the time series process is 1(0), i.e.
the first difference of the process is weakly dependent; therefore, nothing needs to be
done to the series before using them in the regression analysis.

In this model, the R2 measure indicates that approximately 96% of the sample variation in
the milling/retail margin is explained by the independent variables included in our model.
The coefficient on lagged macro-economic risk was found to be statistically
insignificant at both the 1% and 5% level of significance. This finding is surprising since
from theory one would expect changes in the exchange rate to influence milling/retailing

margins since maize is an important export commodity in South Africa.

53

Most notable, the liberalization dummy variable has a very large and highly
significant positive coefficient. This situation can be depicted graphically as an intercept
shift between milling/retail margins over time. In the model, the intercept for milling
margins before market liberalization is 60 = 4.018529, whereas the intercept after
liberalization is 50 + 54 = 152.555829. Although it would be expected that market
liberalization would lead to a decrease in the real milling/retail margin due to increased
competition from the informal sector and other small-to-medium scale millers, our
finding indicate otherwise. Therefore, we conclude that this margin differential is due to
market reform policies or factors associated with market reform that we have not
controlled for in the regression.

Regression 2: Piecewise Regression Allowing for Discontinuity

If we wanted to allow for changes in the slope of the milling/retail margins with
the restriction that the line being estimated not be discontinuous, we would use a
piecewise linear model, which allows us to assume no discontinuity or shift in the
intercept of milling/retail margin from year to year. The model to be estimated is as
follows:

MM = 50 + Bil“ + 55T + 66Lib(T - To) (9)
where Z is a vector containing all the explanatory variables used in the original model, T
is the time trend and To is the month in which the structural change occurred. The
interpretation of this model is as follows:
1. The estimated milling/retail margins years prior to liberalization is found by;
E(MM) = 50 + 632a + 55T (10)

where the slope of the line is 85 and the intercept is 50.

54

2. The estimated milling/retail margins after liberalization is found by;
E(MMO = 50 + SiZh + 65T + 66Lib(T - To) (11)
Where the slope of this line is 55 + 66 and the intercept is 50 - 56. However if we were
interested in estimating the change in the milling/retail margins just afier the regime
change occurs, i.e. allowing for discontinuity, we would include the liberalization dummy
variable in our new model. The resulting model to be estimated that allowed for a

discontinuous slope change would be as follows:
BOWL/It) = 50 + 612a + 54le + 55T + 55le<T - To) (12)
In this equation 64 measures the estimated milling/retail margin differential at the point

when the maize market moves from pre-reform to post-reform period, i.e. from April to

May 1997.
The results of equation 12 are summarized in Table 5.3 above. Below is an

interpretation of the coefficients estimated in the regression.

50 = 6.58: is the predicted milling/retail margin that will result when all other
variables are set at zero, i.e., it is the intercept term when we plot the milling/retail
margins against time.

51 = 0.227: The slope coefficient on wages indicate that the lag affect of a R1 increase
in average monthly wages above its long-run trend, is an expected increase in the
milling/retail margin by about twenty-two cents, ceteris paribus. This estimate is not
much different from the first regression.

62 = 296.44: The slope coefficient on the Macro-economic risk variable suggests that
when the macroeconomic risk index increases by one unit above its long-run trend,
the milling/retail margin is expected to increase by approximately R296 per ton,
ceteris paribus.

53 = -O.347: The slope coefficient for rainfall indicates that as the rainfall index
increases by one above its long-run trend, the milling/retail margin is expected
to decrease by approximately R065 per ton, ceteris paribus.

54 = -3 79.72: This coefficient measures the estimated milling/retail margin differential
at the time when the maize market moves from the pre-reform to post-reform period.
It implies that in the month following market liberalization the milling/retail margin
fell by an approximate R380 tons per ton, ceteris paribus.

55 = 2.567 : The time trend variable’s coefficient is the slope of the line which plots

the milling/retail margin against time. It is essentially the rate that the margin grows

55

over time before market liberalization. It implies an approximate R3 per ton
increase in milling/retail margin per month, on average, ceteris paribus.

66 = 14.361: This coefficient gives us the difference between the slope of the
milling/retail margin before and after market liberalization. This measure implies
that after the structural change occurred, the milling/retail margins rate of growth
increased by an approximate R14 per ton, ceteris paribus.

57-517 = the test of the joint significance of the 11 monthly dummy variables yields a p
value < 0.001, therefore leading to the conclusion that the seasonal dummies
are jointly significant at the 1% level of significance.

The result of this regression raises several interesting points that are worthy of
comment. Firstly, looking at the estimated coefficients on the Macro-Risk and Rainfall
Index, we see that unlike the first regression, they become statistically significant at the
5% level of significance when we model our milling/retail margin as a spline-function.
Particularly in the case of the Macro-Risk parameter, it not only became statistically
significant, it also changed signs.

Secondly, from this regression we see that in the month immediately following
market liberalization, the milling/retail margin fell by approximately R380 per ton,
ceteris paribus. This result is not surprising since it is what we would expect given
Figure 5.2, which depicts the movement of real milling margins throughout the entire
observation period. In this figure it is clear that there was a decline in the milling margin
in the month following May 1997. In fact, according to the graph, the milling margin
does not return to normal levels until approximately two years later. One plausible
interpretation of these results is that the regulatory reforms did introduce greater
competition temporarily, but that over time, firms were able to adjust, somehow restrict

entry by small-scale millers, and reintroduce an oligopolistic pricing structure once again.

However, this is just one interpretation of the results. The results do not indicate the

56

reason for the rise in margins afier a temporary fall; it would require firture research to
discover the reasons behind these findings.

Finally, comparing the coefficient 65 = 2.567 and 85 = 14.361 it clear that there is
a significant difference between the slope of the milling/retail before and after market
liberalization. In essence, what this means is that in the period prior to market reform,
the slope of the milling/retail margin was approximately 2.56, i.e. the milling/retail
margin was growing at an average rate of R256 per ton each month, ceteris paribus.
After market reform, this rate of growth changed to 16.927673, i.e. the milling/retail
margin grew at an average rate of approximately R17 per ton each month, ceteris paribus.

Ifwe were to plot milling/retail margins against time, the results from our second
regression would show an upwards sloping function which, at T=May 1997 had a
discontinuous drop of approximately 379, then continues grow over time with a steeper
slope than before until approximately 25 months later, when milling/retail margins
becomes higher than milling/retail margins in the pre-reform period.
5.3: Conclusion

The results of our empirical analysis lead us to conclude that despite market
liberalization in the maize sub-sector, milling/retail margins have not only continued to
grow in real terms in the post-reform period but appear to be growing at a faster rate.
Although economic theory would tell us that market liberalization should lead to an
increase in competition from the informal sector and other small-to-medium scale millers
and therefore a reduction in the real milling/retail margin, in the case of South Africa, this
has not been the case. Our results indicate that market liberalization had no impact on the

marketing behavior of the milling/retail industry except to see a continued increase in

57

retail prices in real terms, leading us to assume an existence of monopoly power within

the market or at the very least collusion among industry players.

58

CHAPTER 6: CONCLUSIONS AND POLICY INIPLICATQNS

6.1: Summary

Pre-liberalization studies of Southern Africa have shown that South Afi’ican
maize-meal milling/retailing margins tended to be high compared to other countries
within the region. In particular, the miller/retailer margin in South Africa was found to
be more than twice that of neighboring Zimbabwe, although both industries faced
comparable cost structures and Zimbabwe’s milling industry was concentrated among
fewer millers.

Within the context of political reform and international trends of market
liberalization, the South African agricultural sector, throughout the late 1980’s and most
of 1990’s underwent gradual stages of market liberalization. The goals and methods of
market reform are clearly laid out in the Reconstruction & Development Program (RDP)
document, the Broadening Access to Agriculture Thrust (BATAT) initiative, as well as
the 1995 and 1997 White Paper on Agriculture. In particular, the ANC policy document
on agriculture, as the over-reaching goal of market liberalization, explains the need to
ensure affordable and sustainable prices of basic foodstuffs for low-income groups.

The objective of this study was to determine econometrically the effect of market
liberalization on the maize milling/retailing margins within South Africa in order to
evaluate the effectiveness of market reform in attaining its goal of “affordable and
sustainable prices” on maize-meal, a basic food good.

6.2: Research Finding
Standard Industrial Organization theory would predict a reduction in the real price

margins between processed and raw agricultural products due to entrance into previously

59

closed markets by the informal sector, thereby increasing competition among industry
players. Feasible General Least Squares method of estimation was applied to two
reduced form linear models of the milling/retailer margins in which a binary explanatory
variable was included in both to capture the effect of market liberalization. The findings
are summarized below:

1. The first model looked at the difference between the pre and post-reform periods.
The model showed that despite the market change, the milling/retail margin
increased overall by about R148 per ton of maize meal within the post-reform
period.

2. The second model allowed for changes in the slope, with the restriction that the
line being estimated only be discontinuous just after the regime change. The
results of this model indicate that although the milling/retail margin fell just after
the structural change occurred, its rate of growth increased by approximately R14
per ton of maize meal per month following the reforms.

These model findings are clear; despite market liberalization, the maize sub-sector’s
milling/retail margins not only continued to remain high, but grew at an increasing rate.
The overall implication of these margins on the nation’s food security is clear. The
existence of these high margins indicates high real retail prices of maize meal, a staple
food for many South Africans, and therefore an overall decline in the real income of the
poor, resulting in increased food insecurity within the nation. In essence, it appears that
market liberalization has not be successful in realizing the goal of affordable and

sustainable prices of basic foodstuffs for low-income groups.

60

6.3: Policy Suggestions

Over the past two years, the problem of rising food prices, particularly in the case of

maize and other basic foods, has gotten increasing attention in the news and media of

South Africa]. The basic stance is that despite fi'eed control structures within the maize

sub-sector, maize meal prices have continued to increase overtime. In general, three

reasons are given as to why these high maize prices exist.

1.

High producer prices based on unjustified import-parity pricing based in part on
persistent official underestimates of crop yields (Dialogue, 2002). It has been
shown that South Africans are paying 70% more for whole grain maize than if
they used export-parity as a pricing mechanism (Liberty News, 2002). In fact, the
prices paid currently for maize grain reflects the US price plus the shipping costs
and South African import duties.

There is also the concern that the benefit of the VAT zero rating on basic
foodstuffs is not being passed onto the consumers (Dialogue, 2002). This is
assumed since surveys of certain retail stores have found that the cost of brown
bread, which is tax exempt, is equivalent to or at times more than the cost of white
bread. This suggests profiteering by the producers and retailers at the cost of the

poon

. These high food prices have also been associated with the high level of

concentration of ownership in production and processing, as well as in formal

retailing (Dialogue, 2002).

A competitive market is not created by the absence of government regulations, rather

it flourishes when the correct set of regulations is enforced by a public agency (Essinger,

61

2000). Due to high levels of concentration in both production and food processing stages

of the maize sub-sector, it is important that the SA. government establish clear policy

guidelines that outline the government’s objectives and policy instruments. The World

Bank (1994) distinguishes three policy approaches that are available to the government:

1.

Laissez-Faire: Government adopts a passive attitude towards concentration with
the hope that the possible benefits of economies of scale and competitiveness
within the international market to outweigh the potential costs from monopolistic

pricing.

. Active Anti-Trust Approach: Under this policy approach, the mandate and the

power of the Competition Board would be increased in order to reduce the
existing levels of concentration through forced divestment of holdings by major
firms. Implementation of this policy approach would in practice be difficult to
enforce. First, there is a lack of information to pursue legal action against
monopolies. Second, forced sales of subsidiaries are likely to purchased by
existing conglomerates instead of new entrants. Third, the unemployment
implications of possible closures would be difficult to accept.

Indirect Approach through Deregulating the Economic Environment: Under this
approach the government, rather than actively trying to disassemble large
dominant firms, would through various policies aimed at changing the economic
environment to remove the economic bias for concentration and reduce the
barriers to entry for new entrants. The adoption of this approach would require

the acknowledgement that the problem of concentration cannot be resolved in a

 

1 See articles by Oxford Policy Management, 2000; and Dialogue, 2002.

62

short period and that the presence or threat of competition in the long-run is the

most affective method of combating the adverse effect of concentration.

Based on the findings of this paper, it is proposed that the South African government

adopt the third approach. There is a need for polices to be developed that are aimed

specifically at reducing the barriers to entry for small-scale millers. These policies

should include:

ii.

iii.

Regulations that ensure market information that is readily and equally
available to all market participants. Information asymmetry can arises due
to factors such as market concentration and/or imperfect competition and
as a result, lead to higher consumer prices. In a recent study by William
Kalaba, Lesiba Bopape, and Lilian Meyer, evidence was found to support
the conclusion of information asymmetry in farm-retail price transmission
in the wheat and maize sub—sectors of South Africa (2002). Government
programs, for example, that invested in information systems would be
effective in reducing information asymmetry within a market.

Regulations that do not allow for the re-introduction of refined maize meal
price controls. It is believed that since such subsidies would artificially
lower refined maize meal prices. This would discourage consumers from
moving towards a whole grain maize meal, which tends to be produced by
small-scale millers and is cheaper and more nutritionally sound than
refined meal.

Regulations aimed at reducing barriers to entry into the maize

milling/retail sector. With the advent of legislation mandating fortification

63

of basic foodstuffs, the barrier to entry into the maize milling industry is
likely to increase for potential entrants. In a study conducted by the South
African Journal of Clinical Nutrition, 2001, it was found that the total cost
of fortifying maize meal would be R232 million. These costs include the
technology needed (dosifiers, mixers, scales) as well as the cost of
micronutrients, equipment maintenance and additional personnel. It was
found that within the entire industry, only six of the large-scale millers
already had the necessary technology/equipment for the program, leaving
approximately fifty or so small to medium sized millers with additional
cost of acquiring the necessary technology in order to operate.

Food security is defined as “. . .the ability of a country or region to assure, on a
long-term basis, that its food system provides the total population with access to a timely,
reliable and nutritionally adequate supply of food” (van Rooyen, et al., 1997). This study
has found that despite market liberalization, the maize milling/retail margin continues to
grow in real terms within South Africa, threatening the level of food security within the
nation. Although this study clearly shows the effect of market liberalization on
milling/retail margins, it has not clearly identified the reasons behind these high margins
and therefore the reasons why market liberalization has not been successful in decreasing
real maize meal prices. According to Timmer, large marketing margins occur for two
reasons: high real marketing costs and/or there is a monopolistic element in the marketing
process that is earning excessive profits. The question that needs to be addressed is
whether or not the retail price in the maize meal market accurately reflects the value of

the traded commodity, i.e. is it a competitive pricing system? Future study, that looks at

64

the structure, conduct and performance of the maize sub-sector within South Afiica
would be able to adequately answer this question and thereby establish policy outlines
aimed at ensuring access to low maize meal prices in real terms to consumers.
Furthermore, the evidence of an increasing growth rate in the milling/retail
margin after market reform instead of a decline in real terms, suggests that unlike
countries such as Zimbabwe, Mozambique and Kenya, the informal hammer-milling
system has failed to successfully emerge within South Africa, thereby increasing
competition among millers. There is a need for future research to establish why rapid
investment in the informal hammer-milling system did not occur after market reform as
other studies have shown in other countries. Such a study would be beneficial in gauging

the level of competition within the maize sector, a necessary component to food security

within South Africa.

65

APPENDIX A

Appendix A. 1: World Maize Exports ..................................................... 67
Appendix A.2: Major and Minor Maize Growing Areas ......................... 68
Appendix A3: Crop Calendar of South Africa ....................................... 69

66

Appendix A.l:
Total World Maize Exports - 2000

South Africa Ukraine Other
2%

  
 

Argentina
14%

Source: USDA/Foreign Agriculture Service, Grain: World Markets and Trade, Jan 13, 2001

67

Appendix A.2:
Major and Minor Maize Growing Areas

 

 

South Africa: Corn

- Major growing areas
- Minor growing areas

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JOINT AMCULTURAL WEA THER FACILITY (NOANLSDA)

 

Source: USDA-Foreign Agricultural Services.
h :l/www.fas.usda. v/ ' d2/hi ' tsl2001/02/safrica/safcrn. '

68

 

Appendix A.3:

Crop Calendar of South Africa

 

 

1

Meat

IF

Barley

d

Maize (W'es‘t)

lb

1

1

Crop calendar of SOUTH AFRICA

 

 

I

 

 

 

 

 

 

 

Maize (East)

 

di-

 

Rice

 

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Millet

 

 

 

 

 

 

 

Soybean

 

 

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Sorghum 1
I

 

 

 

 

 

 

 

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[ I Sowing El Harvest ]

 

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69

Source: USDA-Foreign Agricultural Services.
' ' " tleOOl/OZ/safrim/safcalle. '

 

 

APPENDIX B

Appendix B. 1: Producer Prices ............................................................. 71
Appendix B.2: Wholesale Prices ........................................................... 72
Appendix B.3: Retail Prices .................................................................. 73
Appendix B.4: Weighted Average Critical Rainfall ............................... 74

7O

The objective of this study is to make an inference about the milling and
marketing margin’s population based on the data contained in our sample and to provide
an associated measure of goodness for the inference. Accomplishing this objective
requires a descriptive summary of the variables that are used in our margin models.
Included in this appendix is a discussion of the various price variables used in the
calculation of the margins and the methods of calculation for the Macro Risk and the

Rainfall index variables.

Appendix 3.]: Producer Prices Histogram

I Relative Frequency

    

0.250 m m . m M ._
.3er 93.?

0200 ‘

5‘

50150

3-

5.0100

0.050

0.000
888888888888
ssssésssssss

Producer Prices (thon)

Based on the relative frequency distribution above, it is clear that our sample data on
maize producer prices tends towards a normal distribution. From this chart it is clear that
approximately 99% of all measurements are contained within [.1 +/- 20' interval. Here (.1 =
929.458 represents the sample mean and III = 267.375 is the sample standard deviation.
The variance (62) in this sample is 71489.52, which is indicates great amount of variation

within this sample set.

71

Appendix B.2: Wholesale Prices Histogram

Frequency

 

8 8. 8. 8. 8. 8. 8. 8 8
§ § 8 § 8 § E E E
Wholesale Prices (It/ton)

The relative frequency distribution for our sample data on maize wholesale prices is
approximately normal (bell-shaped). From this chart it is clear that approximately 95%
of all measurements are contained within it +/- 20' interval. Here n = 952.49 represents
the sample mean and o = 160.37 is the sample standard deviation. The variance (02) in
this sample is 25718.32, which is indicates a smaller amount of variation within this

sample set when compared to the producer price data set.

72

Appendix B.3: Retail Price Histogram

I Relative Frequency

 

1700 00
1800 00
1900 00
2000.00
2100 00
2200 00
2300 00
2400.00
2500.00
2600.00
2700.00
2800.00
2900.00
3000.00
3100.00
3200.00

More

Retail Prices (R/ton)

The relative frequency distribution for our sample data on maize-meal retail prices is
approximately normal (bell-shaped). From this chart it is clear that approximately 96%
of all measurements are contained within it +/- 26 interval. This then satisfies the
empirical rule for a distribution of measurements to be normally distributed. Here p. =
2495.31 represents the sample mean and o = 278.47 is the sample standard deviation.
The variance (0'2) in this sample is 77545.57, which is indicates a greater amount of
variation within this sample set when compared to the producer price and wholesale price

data sets.

73

Appendix B.4: Weighted Average Critical Rainfall

 

 

 

 

 

 

 

 

 

 

 

 

 

Average Critical Rainfall

'Rain (mm) WW F*W C*W N*W Rainfall Index
1970/71 299.97 203.05 14.31 32.01 549.3
1971/72 443.50 176.46 17.77 23.63 661.3
1972/73 229.87 134.38 7.02 32.91 404.17
1973/74 295.37 256.71 26.51 25.37 603.96
1974/75 363.61 241.22 17.75 26.06 648.65
1975/76 417.61 277.79 23.98) 35.62 755.00
1976/77 330.30 223.67 20.22 36.26 610.45I
1977/78 388.71 189.79 17.32 41.00 636.82
1978/79 248.83 119.75 11.86 39.08 419.51
1979/80 391.23 124.56 8.52 25.68 550.00
1980/81 394.57 231.61 29.34 27.78 683.30
1981/82 300.23 151.48i 12.53 28.07 492.31
1982/83 263.40 115.65 5.74 29.36 414.15I
1983/84 307.66 156.70 3.14 135.76 603.26
1984/85 314.7 160.61 23.78I 35.24 534.41
1985/86 291.27 178.85 22.97 45.31 538.40
1986/87 373.40 166.62 19.26 26.22 585.53
1987/88 316.65 296.18 26.74 30.49 670.0
1988/89 310.64 209.81 25.01 24.61 570.0
1989/90 416.13 186.87 27.95 33.24 664.1
1990/91 376.94 139.63 17.11 40.59 574.2
1991/92 218.64 115.74 28.46 47.79 410.6
1992/93 201.86 177.25 20.71 17.16 416.9
1993/94 284.76 254.45 20.00 34.29 593.5
1994/95 368.47 89.26 29.32 46.68) 533.7
1995/96 605.52 238.89 11.61 37.61 893.63
1996/97 373.27 219.97 12.08 32.15 637.4
1997/98 237.21 245.01 2.40 20.48! 505.1
1998/99 326.7 196.80 2.39 18.72 544.6
1999/00 468.18 334.54 4.47 21.95 829.1
2000/01 263.18 189.48 2.26 11.06 465.9

 

Notes: T=Transvaal, @Northem/Westem/Eastem Cape, F=Orange Free State, N=Kwazulu Natal
W = weights= Provincial Production/Total National Production

74

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