An Assessment of the Impact of Wheat
Market Liberalization in Egypt: A MultiMarket Model Approach
Gamal M. Siam and André Croppenstedt
ESA Working Paper No. 07-15
May 2007
Agricultural Development Economics Division
The Food and Agriculture Organization
of the United Nations
www.fao.org/es/esa
ESA Working Paper No. 07-15
www.fao.org/es/esa
An Assessment of the Impact of Wheat Market
Liberalization in Egypt: A Multi-Market Model Approach1
May 2007
Gamal M. Siam
André Croppenstedt
Department of Agricultural Economics
Faculty of Agriculture,
Cairo University, Giza,
Egypt
e:mail: [email protected]
Agricultural Development
Economics Division
Food and Agriculture Organization
Italy
e-mail: [email protected]
Abstract
Wheat is central to the government of Egypt’s food security policy which is influenced by a
concern for overdependence on imports and the need to provide subsidized bread for the
poor. This paper uses a multi-market approach to assess the impact of complete wheat
market liberalization, an international wheat price increase, the value of strategic stocks and
the impact of investment to generate higher yields and lower transaction costs for wheat
producers. Results show that wheat market liberalization implies very substantial costs for
consumers and producers. The estimated income losses that these groups suffer would
appear to be below the current total subsidy costs and hence a cash transfer program would,
in principle, be feasible. The results show that wheat price movements impact strongly on the
supply and/or demand side in particular of berseem, rice, maize, cotton and livestock which
has significant implications for their net imports as well as input use. Results indicate that
strategic stocks can be useful to neutralize the impact of a wheat price spike. Increasing
wheat yields and reducing transportation boosts wheat self-sufficiency but does not dampen
the impact of removing the wheat subsidy system on household’s welfare.
Key Words: Egypt, agriculture sector, wheat, multi-market model, bread subsidy, policy
scenario impact analysis.
JEL: Q11, Q18.
The designations employed and the presentation of material in this information product do not imply the
expression of any opinion whatsoever of the part of the Food and Agriculture Organization of the United Nations
concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation
of its frontiers or boundaries.
1
This paper was prepared as part of the “Linking Agriculture Policies to Poverty and Food Security” module of
the Roles of Agriculture Project. The designations employed and the presentation of material in this information
product do not imply the expression of any opinion whatsoever on the part of the Food and Agriculture
Organization of the United Nations concerning the legal status of any country, territory, city or area or of its
authorities, or concerning the delimitation of its frontiers or boundaries. Content and errors are exclusively the
responsibility of the author and do not necessarily reflect the position of the Food and Agriculture Organization of
the UN or of Cairo University. Content and errors are exclusively the responsibility of the authors and not the FAO
or the authors’ institution. The multi-market model developed for this study builds on Lundberg and Rich (2002)
and Stifel and Randrianarisoa (2004). We are grateful to these authors for sharing their GAMS code with us.
2
1.
INTRODUCTION
Wheat, the key staple food crop in Egypt, occupies about 33 percent of the total winter crop
area, accounts for 9 percent of water resources and contributes 17 percent of the total value
added in Egyptian agriculture. Consumed mainly as bread it provides, on average, one-third
of the daily caloric intake of consumers and 34 percent of their total daily protein
consumption. Because it is such an important component of the daily diet in particular, but
not only, for the poor, and because Egypt is only 51 percent self-sufficient in wheat
production it follows that wheat policy is central to food security in Egypt.
The primary driver of wheat policy at the national level reflects a concern that a high
exposure to international markets implies an unacceptably high risk to the country’s wheat
supply. In part this reflects the political concern of being overly dependent on foreign wheat
supplies and the concern (real or perceived) about Egypt becoming too exposed to political
pressure from foreign governments and/or foreign-based multinational corporations. This
concern is based on the country’s past experience with aid and imports. In 1956 Egypt
received US $ 70 million in Title I concessional food aid but political differences led to a
suspension of US economic assistance in 1957 and 1958. US policy changed again during the
Eisenhower and Kennedy administrations and by 1963 Egypt was the world’s largest percapita consumer of American food aid. Then, with relations deteriorating, the US withheld
payments on a 3 year PL 480 agreement and in 1966 President Nasser rejected US food aid
altogether (Dethier, 1991). Adding to political uncertainties was the country’s experience with
wheat price fluctuations in the early 1970s. The international price of wheat rose dramatically
from US $ 60 to US $ 250 a ton by 1973 nearly trebling the country’s import bill. Within this
context the government of Egypt (GOE) gives support to domestic production with an eye to
increasing self-sufficiency in wheat production and hence reducing dependency on imports.
At the individual level wheat policy is aimed at providing access for the poor to baladi
bread and wheat flour in order to help create a more equitable society. The poverty rate stood
at 16.7 percent in 1999/2000 (United Nations and Ministry of Planning, 2004) and its
reduction as well as the provision of support to the poor is one of the key goals of
development policy in Egypt. Wheat policy is seen as an important component of the safety
net for the poor. However policy makers continue to look for ways to improve targeting and
cost-effectiveness of the food subsidy system.
This study aims to quantify the effects of complete wheat market liberalization on
cropping patterns, producer and consumer prices, household income and calorie intake and
other variables related to wheat policy. We further assess the impact of a 20 percent increase
in the import price of wheat as well as replacing imports by strategic stocks to mitigate
against such a significant increase in the world price of wheat. Finally, we simulate the
impact of the 10 percent increase in wheat yields accompanied by a 10 percent decrease in
wheat margins together with complete liberalization. We use a multi-market model to provide
ex-ante guidance with regard to the implications of different policies in terms of output,
demand, water use, price, income and calorie intake. An assessment of the impact of the
policy changes on the desired objectives is important from the point of view of helping to
shape the policy debate on the reform alternatives.
3
2
WHEAT POLICY IN EGYPT
2.1
Supply Side Policy
In the early 1960’s the GOE intervened in the production of many major crops – including
cotton, wheat, rice, sugar cane and onions - by specifying output and area to be farmed. The
argument for area control of crops was based on the concept that the agricultural sector was
interrelated with other sectors of the economy and a shortage in supply of cotton would lead
to considerable losses in the industrial sector (MALR and FAO, 2003).
Farmers were required to participate in agricultural cooperatives which meant the
obligatory delivery of all or part of their production to the government at a price fixed by the
government and lower than the free market price. Marketing and processing were handled by
the government which also specified the quantity and type of fertilizers and pesticides to be
provided to farmers by the Principal Bank for Development and Agricultural Credit (PBDAC),
which supplied all agricultural inputs.
There is general consensus that these policies had a strong negative effect on
agricultural sector performance overall. This is also reflected in wheat production which saw
modest annual growth of about 1.5 and 1.9 percent in the 1960s and 1970s, respectively (see
tables 1 and 2). Wheat output rose from 1.51 to 1.87 million tons between 1961-63 and 198486 (3 year averages). Concomitantly, over this time period, production in per-capita terms fell
from 51 kg/capita in 1961-63 to 38 kg/capita in 1984-86 (table 1).
Table 1: Wheat Production, Yield, Area, Imports: Three year averages for 1961-2003.
58
Per-capita
wheat
production
(kg/capita)
51
Ratio of
production to
production +
imports
0.50
86
45
0.42
98
40
0.40
1.73
93
45
0.49
0.54
2.28
278
48
0.46
3.38
0.56
3.90
503
47
0.33
1.84
3.20
0.58
5.01
618
43
0.27
1981-1983
1.98
3.46
0.57
5.75
947
43
0.26
1984-1986
1.87
3.74
0.50
6.50
763
38
0.22
1987-1989
2.91
4.80
0.61
6.87
833
55
0.30
1990-1992
4.46
5.09
0.88
6.03
728
78
0.43
1993-1995
5.00
5.24
0.95
5.81
713
83
0.46
1996-1998
5.89
5.74
1.03
6.22
956
92
0.49
1999-2001
6.39
6.35
1.01
4.57
662
94
0.58
2002-2003
6.74
6.47
1.04
4.83
711
95
0.58
Years
Production
(Million Mt*)
Yield
(Mt/ha)
Area
(Million
Hectare)
Imports
(Million Mt)
Imports
(Million US $)
1961-1963
1.51
2.57
0.59
1.55
1964-1966
1.41
2.70
0.52
1.97
1967-1969
1.37
2.46
0.55
2.08
1970-1972
1.62
2.96
0.55
1972-1974
1.81
3.35
1975-1977
1.90
1978-1980
* Mt = Metric tons. Source: FAOSTAT
4
Table 2: Production, Yield Area, Imports: Average Annual Growth rates (%) over selected periods
Category/Years
Production
1962-70
1971-1980
1981-86
1987-1995
1996-2003
1.95
13.91
2.31
2.33
1.54
1.88
Yield
1.44
1.56
3.38
4.37
Area
-0.19
0.39
-1.42
8.94
0.03
Imports (volume)
2.28
21.58
2.74
0.14
-1.63
Imports (value)
5.04
58.55
-4.94
8.40
-1.39
Population
Per-capita wheat
production
Ratio of production to
production + imports
Source: FAOSTAT
2.38
2.21
2.49
2.15
1.95
-0.82
-0.32
-0.52
11.50
0.36
1.37
-6.84
-0.01
9.88
2.97
The inefficiencies in resource allocation were severe and the GOE started to take
action by gradually reducing the scope of government intervention. The first period of reform
(1986-1990) saw the implementation of the Economic Reform and Structural Adjustment
Program (ERSAP). This included the partial liberalization of prices of the ten main crops;
eliminating the obligatory deliveries of the strategic crops; reducing the subsidy on farm
inputs. The second stage (1990-97) included the expansion of the procedures initiated in the
first stage in addition to reforms at the macro-economic level. The reforms have consolidated
and interacted with those of the earlier agricultural sector reforms. As a result most domestic
farm input prices are now international prices. This stage included eliminating the
government monopolization of the main farm inputs and strategic crops in addition to
encouraging and expanding the market for private investment. Liberalization of farm input
marketing included the removal of governmental constraints on the private sector in importing,
exporting, and distribution of farm inputs to compete with the Principal Bank for
Development and Agricultural Credit (PBDAC). Farmers are now free to grow whatever
crops they want with the constraints that the rice area at the national level should not exceed 1
million feddan in the north Delta and that the area allocated to sugar cane is fixed at about 300
thousand feddan in Upper Egypt. During the reform period - after 1987 and after the removal
of mandatory marketing - guaranteed floor prices were offered for wheat and rice. The floor
price of wheat is announced at the time of planting making it possible for farmers to respond
during the current season.
Agricultural performance responded to the agricultural and economy wide reforms
(figure 1). Starting in 1987 production rose strongly to reach 5 million tons in 1993-95 and
then 6.88 million tons by 2002-03. In per-capita terms production increased to 83 kg/capita in
1993-95 and 96 kg/capita in 2002-03 (table 1). Increased wheat supply after 1986 was due to
large yield and area increases. Area increases were in part due to the fact that the government
procurement price since the start of the ERSAP in 1986 was kept close to the domestic free
market price – mostly set above the import price. Government procurement is typically
around 2-3 million tons annually (about 30-40 percent of production) at prices that are mostly
higher than world equivalent prices. For 2005 the estimated nominal protection coefficient
was about 1.05 (Siam, 2006). A further important contributing factor were rising yields after
1986 due to the diffusion of high-yielding long-spike varieties within the National Campaign
for Wheat Improvement.
5
Figure 1: Wheat Production, Area, Yield and Imports: 1961-2003
8000000
7
Production (Mt) - LHS
7000000
Area (Ha) - LHS
6
6000000
Value of Imports (1000 US$) - LHS
5
Yield (Mt/ha) - RHS
5000000
4
4000000
3
3000000
Metric tons/hectare
Tons, 1000 US$ or hectare
Imports (Mt) - LHS
2
2000000
1
1000000
0
2003
2001
2002
2000
1999
1998
1997
1995
1996
1994
1992
1993
1991
1990
1989
1988
1986
1987
1985
1983
1984
1982
1981
1980
1979
1977
1978
1976
1974
1975
1972
1973
1971
1970
1968
1969
1967
1966
1965
1963
1964
1962
1961
0
Source: FAOSTAT
2.2
Consumer Policy
Wheat consumption has risen from 82 kg per-capita per-year in 1961-63 to 135
kg/cap/yr in 1981-83 (three year averages taken from FAO’s Food Balance Sheets). The next
ten years saw a rise to 146 kg/cap/yr for 1991-93 but since then per-capita consumption has
flattened and indeed slightly to 137 kg/cap/yr for 2001-03 (see also figure 2). Wheat
accounted for 30 percent of the proportion of the per-capita daily energy supply in 1961-63
and 1971-73. This proportion rose to 36 percent in 1981-83 and 1991-93 but has since fallen
to a still high 33 percent in 2001-03.
Figure 2: Production, Imports and Food Availability of Wheat: 1961-2003
12,000,000.00
Production
10,000,000.00
Imports
Food Quantity
6,000,000.00
4,000,000.00
2,000,000.00
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
1973
1971
1969
1967
1965
1963
0.00
1961
Metric tons
8,000,000.00
Year
Source: FAOSTAT
6
Wheat is the key staple food crop and the government’s food policy system can be seen as a
reflection of its efforts to promote social equity and political stability. It’s roots can be traced
to rising food prices after World War II which led to the GOE to become involved in
importing large quantities of wheat and flour and selling them at a loss in government owned
shops. The system of food subsidies continued to grow into an elaborate system of subsidies
and rationing. Over time subsidized bread has become a powerful symbol of the broader
social contract between the Egyptian government and the population (Ahmed et al, 2001).
Subsidized wheat through baladi bread is about 6 million tons (for 2003) which
constitute about 50 percent of the total consumption of wheat. Following the dramatic world
price developments of the early 1970s Egypt’s food subsidies bill rose from 3 million
Egyptian Pounds (L.E.) in 1970/71 to L.E. 1.4 billion in 1980/81 (Ahmed et al, 2001). Indeed,
the early 1980s saw the unsustainable peak in subsidy expenditures which by then covered
nearly 20 items.2 Despite a gradual reduction in the coverage to only four items – baladi
bread, wheat flour, oil and sugar - the food subsidy system is still considered a major
component of the social safety net (which also includes water, energy, housing, education,
health and transportation) for the poor. It is credited with helping to reduce infant mortality
and malnutrition (World Bank, 1995). In urban areas the baladi bread subsidy adds nearly 8
percent of total expenditure for the poorest 20 percent of households.
For a number of reasons the food subsidy system has and continues to receive
considerable attention. This is mainly because of the costliness of the system on the one hand
and it’s political sensitivity on the other. The total subsidy for baladi bread and wheat flour
represents about 5.1 percent of government expenditure (down from 14 percent in 1980/81)
and about 1.3 percent of GDP for 2004/05. For baladi bread alone – and baladi bread
accounts for about 60 percent of the total (with another 15 percent accounted for by wheat
flour) - the cost of the subsidy was about L.E. 6 billion in 2004 (Siam (2006)). However,
Adams (2000) notes that subsidies on water and electricity incur a significantly higher cost to
the government than do food subsidies (even without including rural areas in his
calculations – as data is difficult to obtain). Food receives more attention also because in the
case of wheat it adds significantly to the import bill and thus has an impact on the external
debt of the country and the balance of payments. Egypt is consistently one of the world’s
largest (top ten) wheat importers with a wheat import bill averaging 711 million US $ in
2002-03 (table 1).
A number of important issues continue to be of concern to policy makers. One is that
of targeting. With regard to baladi bread all households have equal access and about 75
percent of the non-poor and 66 percent of the poor receive subsidized bread. The per-capita
monthly benefit to consumers from baladi bread is quite evenly distributed across income
groups and this is especially true for rural areas. In effect 1 percent of the population receive
more or less 1 percent of the subsidy benefits, regardless of their income level (Ahmed et al,
2001).
While in principle access is equal in reality the location of bakeries means that
Egyptians living in rural areas – about 58 percent of the total - receive only 30 percent of the
total food subsidies in 1996/97. Apart from this bias the self-targeting nature means that in
urban areas where baladi bread is considered an inferior good, the urban poor receive more in
terms of income transfers from the food subsidies than the rich. In rural areas baladi bread is
not an inferior good and only baladi wheat flour is consumed more in absolute terms by the
poor. Overall Ahmed et al (2001) find that the poor benefit slightly less than the rich from
income transfers (see also Adams, 2000).
2
For more detail on the evolution and description of the food subsidy system see Gutner (1999), Ahmed et al
(2001) and Kherallah et al (2000).
7
Apart from targeting another issue concerns the considerable leakage of benefits. This
amounts to about 12 percent for baladi bread and 28 percent for wheat flour, or 16 percent of
the total cost of food subsidies. System leakage combined with poor targeting meant that only
about one-third of the benefits actually accrued to the poorest 40 percent of the population
(Ahmed and Bouis, 2002). While the cost of income transfers for baladi bread is calculated as
L.E. 1.16 for every L.E. 1.00 transferred the poor targeting and leakages mean that the actual
cost of the transfer of L.E. 1.00 is L.E. 2.98.
The wheat subsidy for consumers through subsidizing baladi bread is a policy
which has a number of economic implications. With respect to the public budget the wheat
subsidy (L.E. 6 billion annually) constitutes a major (and increasing in recent years) part of
government expenditure. Consumers generally enjoy subsidized prices of baladi bread;
however, because of differential access to this commodity, the main benefit goes to urban
consumers. Also, because the subsidy system is not targeted the non-poor acquire part of the
subsidized baladi bread which leads to some distributional inefficiency in the system.
Reform of the system has been undertaken gradually. The experience of the sweeping
reforms of 1977 that had led to rioting and deaths and where then immediately reversed led to
great caution in tackling the issue. Nevertheless the cost of food subsidies, in real terms has
been reduced This has been achieved through: a) reducing the number of subsidized food
items such that by 1996/97 only four food items remained subsidized – down from almost
twenty in 1980; b) a reduction in the number of people who hold ration cards; c) better
targeting of the ration card system; d) the gradual reduction in subsidies through various
techniques.
The government has also gradually increased the price of subsidized bread of which in
1980 there were three types: baladi, shami and fino of 82, 76 and 72 percent extraction
respectively. For all these types of bread the price increased from L.E. 0.01 to L.E. 0.02 in
1983/84 and then to L.E. 0.05 in 1988/89 (which compares to a cost of production for baladi
bread of L.E. 0.121). In 1992 fino bread stopped being subsidized and in 1996 shami bread
was no longer subsidized. At some time in the 1990s the government also started to reduce
the size of a loaf of subsidized bread from 150 to 130 grams.
The government continues to look for ways to improve the system. Improved targeting
and cost reduction are key points while at the same time maintaining the value of the system
for the poor. Recent experience in Egypt shows that the cash transfer system has been very
well accepted since it explicitly targets poor households in rural and urban areas; it does not
affect the preferences of producers and consumers, and most importantly, it does not distort
bread prices. This serves the national plans for the economic policies in Egypt, which are
based on a move towards an open economy and limiting price distortions.
3
MODELLING AGRICULTURAL POLICY REFORM
3.1
Overview
For a number of reasons it is important to be able to provide an ex ante analysis of proposed
agricultural policy changes in developing countries. Many governments intervene directly in
agricultural product, in particular food, markets through taxation and subsidization. Key
objectives are to redistribute income, generate public revenues, correct market failures and
provide incentives to producers (Braverman, Ahn and Hammer, 1983). An assessment of the
impact of the policy changes on the desired objectives is important from the point of view of
helping to shape the policy debate on the reform alternatives.3
3
Braverman and Hammer (1986) argue that quantitative assessments to evaluate policies are important for the
sake of public accountability.
8
Starting in the 1990s, the focus shifted to poverty and hunger reduction. These issues
are still predominantly rural based: the rural poor make up about 70 percent of the world’s
total poor population. Nevertheless, urbanisation processes have recently increased the
number of urban poor and food insecure. Increasingly, therefore, the role of agricultural based
growth in promoting rural development, slowing down the pace of urbanisation and
contributing to an equitable and sustainable overall development is recognised and
emphasised. A focus on the welfare of the rural population and its interdependencies with the
welfare of urban areas, as well as the interlinkages between agricultural and off-farm
activities is essential to foster greater understanding of the links between agricultural policies,
rural development and poverty and food insecurity.
In this study we apply one tool that has been used to analyze ex ante the impact of
agricultural policy reforms, i.e. a multi-market model. Discussion of other types of measures
and models is limited to a brief comparison.4 The most commonly used tool to quantitatively
assess agricultural pricing policies are the domestic resource cost (DRC) and the effective
protection rate (EPR). Both are modified ratios of domestic prices to international prices, the
latter assumed as efficiency benchmarks for the domestic economy. These measures are often
calculated at different levels of the value chain of specific commodities and reported as
summary indicators of the so called “Policy Analysis Matrix” (PAM) (Monke and Pearson,
1989). These measures can only partially address the issues of interest outlined above. In
particular income distribution, public revenue and the impact of taxes or subsidies on
production and consumption are not evaluated by such measures.
Another popular method is that of single-market calculations of consumers’ and
producers’ surplus. This type of analysis ignores the interaction among markets, i.e.
substitution effects in consumption and production, and provides only limited information
with regard to income distribution. The rural labor market is not included and hence the
potentially important direct and indirect effects on wages are ignored. Ignoring the direct and
indirect effects on wages, prices and incomes means that the estimates of welfare changes will
be biased in unknown directions (Arulpragasam and Conway, 2003).
The most sophisticated solution to incorporating direct and indirect effects in several
markets has been to prepare computable general equilibrium models (CGE). These model
goods and factors markets in all sectors and allow for wages, prices and incomes to be
determined endogenously. The main drawbacks of these types of models are their large data
requirements and their high degree of complexity.5
3.2
Multi-market models6
Multi-market models fall short of the complexity of CGEs but do include direct and indirect
effects in a small number of markets. In that sense they are an improvement over single
market partial equilibrium analysis. They typically consist of a producer and consumer core
and allow for the analysis of the impact of price and non-price policies on production, factor
use, prices (for non-tradables), incomes, consumption, government revenues and expenditures
and balance of trade (Sadoulet and de Janvry, 1995). The analysis focuses on those markets
which are assumed to be strongly interlinked, either on the demand or the supply side. Prices
in those markets included in the analysis are endogenous. The bias in estimating welfare
changes as a result of policy reforms is diminished, but remains. It follows that multi-market
4
A more detailed discussion of the various tools to analyse policy change can be found in World Bank (2003).
For a somewhat more detailed exposition on the DRC, EPR and CGE models see Sadoulet and de Janvry
(1995).
6
Multi-market models are sometimes referred to as “limited general equilibrium” (for example in Quizón and
Binswanger, 1986) or “multi-market partial equilibrium” (as in Arulpragasam and Conway, 2003) models.
5
9
models will generate reliable results when the reforms being analysed affect commodities or
factors for which the set of close substitutes and complements are well defined (Arulpragasam
and Conway, 2003).
Multi-market models have proven particularly popular for work on agriculture sector
analysis. In the 1980s the World Bank developed multi-market models for Senegal, South
Korea and Cyprus to analyse how the impact of changes in price policies would affect
production, demand, income, trade and government revenues (Lundberg and Rich, 2002).
Braverman, Ahn and Hammer (1983) and Braverman and Hammer (1986) extended the single
market surplus method to include income distribution and some general equilibrium
considerations. Their analyses cover the agricultural sector and includes an exogenous urban
sector.7 This is important as urban consumption may have an important impact on government
revenue/deficits. Moreover staple food price changes are important for the urban poor. They
note the trade-off between complete information on the consequences of policy and the need
for simplicity in operational work.
Braverman, Ahn and Hammer (1983) use a multi-market model to evaluate
quantitatively the impact of alternative pricing policies aimed at reducing the deficits in the
Grain Management Fund and the Fertilizer Fund in Korea. In particular they measure the
impact of the various alternatives on: i) Production and consumption of rice and barley, ii)
real income distribution, including the income distribution in both rural and urban sectors, iii)
import levels of rice, iv) self-sufficiency in rice and v) the public budget.
On the supply side Braverman and Hammer (1986) assume a Cobb-Douglas
technology. Land and labor are fixed to the region but can be shifted between crops within the
region. Their allocation is determined by equating their value marginal products between uses.
Incomes are determined by profits and non-agricultural receipts held exogenous. The analysis
of demand is based on the Almost Ideal Demand System (AIDS).
Quizón and Binswanger (1986) use a multi-market model to analyse the impact of
agricultural policies as well as technical and economic changes on growth and equity in India.
Their model covered four outputs and three inputs, including labor. Households are divided
by income into four urban and four rural groups. They note that the distributional outcomes
from general equilibrium models depend crucially on labor market assumptions.8 Accurate
modelling of wage formation is therefore central to obtaining meaningful results.
More recently multi-market models have been used for agricultural sector Poverty and
Social Impact Analysis (PSIA).9 Murembya (1998) uses a multi-market model along the lines
of Braverman and Hammer (1986) to study the impact of loosening agricultural price controls
on agricultural production in the smallholder sector, the government budget deficit and on
household welfare in Malawi. Dorosh and Bernier (1994) construct a multi-market model in
the tradition of Braverman and Hammer (1986) that includes yellow and white maize, rice,
wheat and bread, export crops and vegetables, meat and non-agriculture. For vegetables and
meat trade is thin and is fixed exogenously in the model. Three household groups are included:
urban poor, urban non-poor and rural. Demand side parameters are estimated using an AIDS
model while supply side elasticities are based mainly on data from other countries. Dorosh et
al (1995) addresses the question of whether open market sales of yellow maize food aid is an
effective means of poverty alleviation in Maputo and whether such a policy has any negative
effects on the rural poor.
7
Lau et al (1981) is an earlier example of an application of the farm household model in a policy simulation
study. They did not include the urban sector.
8
Quoting Taylor (1979).
9
For detailed information on PSIA see the so dedicated World Bank website [ www.worldbank.org/psia ]. For a
detailed overview of analyses on agricultural market reforms on poverty and welfare see Lundberg (2005).
10
Minot and Goletti (1998) use a spatial multi-market analysis which focuses on market
liberalization of the rice sector in Vietnam.10 Their model is innovative in the sense that it
allows for differences in impact across regions. Building on their work (and also using the
Viet Nam Agricultural Spatial-Equilibrium Model) Goletti and Rich (1998a) study alternative
policy options for agricultural diversification in Viet Nam and Goletti and Rich (1998b) use
the Madagascar multi-market spatial-equilibrium model to analyse agricultural policy options
for poverty reduction.
Srinivasan and Jha (2001) analyze the effect of liberalizing foodgrain trade on
domestic price stability using a multi-market model. In their model the direction of trade is
determined endogenously.
Lundberg and Rich (2002) built a multi-market model to look at agricultural reforms
in Madagascar. This was meant to be a generic model that could be adapted to policy analysis
in a number of African countries. On the product side this model includes fine and coarse
grains, roots and tubers, cash crops, livestock, other food products and non-agricultural
production. On the input side fertilizer, feed and land were included. Labor was not included
as the authors surmised that this input was more appropriately studied through the use of a
CGE model. Stifel and Randrianarisoa (2004) built on Lundberg and Rich (2002) and
included a seasonal dimension to analyze the impact of agricultural reforms, such as tariff
changes, but also going beyond price changes by looking at infrastructure improvements and
yield increases, in Madagascar.
4
THE EGYPT MULTI-MARKET MODEL
4.1
Product categories
The product categories are: 1) food items, 2) non-food items, 3) animal-feed commodities,
and; 4) agricultural inputs. More specifically, these items include:
Wheat (both subsidized and un-subsidized): Wheat is the backbone of the food
security policy in Egypt. About half of the total consumption of wheat is baladi bread which
is subject to subsidy. Subsidized baladi bread it treated as autonomous wheat consumption, i.e.
independent of market prices. Consumer demand is taken as demand for un-subsidized wheat
which is a function of prices and income but is conditional on the level of subsidized wheat.
Maize: This product is the second cereal crop after wheat in terms of cultivated area.
It competes with rice and cotton as well as some other summer crops. Locally produced maize
is used partially for food and partially for animal feed. Imported maize is used as feed in
poultry production. We include maize twice, once the maize used for human consumption and
once for animal consumption. To simplify the model prices are assumed to be linked and
identical for locally produced and imported maize.
Rice: Rice is the only exportable cereal crop and it is the third crop after wheat and
maize in terms of cultivated area. While cotton declined rice cultivation expanded,
encouraged also by incentives to wheat as the two crops are complementary in the crop
rotation. Rice cultivation is very water intensive and uses about 20 percent of the total water
used by agriculture which explains why, in theory, the area cultivated to rice is meant to be
restricted. Yet free water subsidizes rice the most.
Berseem: This product (Egyptian clover) is used entirely as animal feed. It is nontradable.
Cotton: Cotton is the major non-food cash crop in Egypt that also contributes the
largest share to the value of the country’s agricultural exports. It is a summer crop that is
10
See also Minot and Goletti (2000).
11
usually grown after short-season berseem so while it is complementary to short season
berseem it competes with both wheat and long-season berseem as well as with summer crops.
Around 60-70 percent of domestic production is used as an input to the local cotton industry
and the remainder is exported. In this study we assume that industry demand for cotton
represents the final demand.
Livestock (meat): Livestock production contributes about one third of the value
added originating in agriculture. Meat and dairy are the main products of this sub-sector. In
this study, we assume that livestock production is only for meat.
Onions: Included as representative of vegetable commodities with export potential.
Dry onions were the sixth most important agricultural export commodity in 2004, by value.
Oranges: Included as representative of fruits with export potential. Oranges were the
third most valuable agricultural export item in 2004.
Potatoes: This product is the key starchy root product with significant domestic
consumption as well as being an important export crop (4th most valuable agricultural export
item in 2004).
Two agricultural inputs are modelled explicitly:
Fertilizer: This covers the main fertilizer used, nitrogen. Fertilizer consumption levels
are based on recommended use levels (from FAO, 2005) for the crops included. Supply of
fertilizer is assumed exogenous. It is treated as a tradable commodity.
Mechanical traction: This is the number of tractors in use (from FAOSTAT). At the
household level traction use is based on the value of farm equipment (excluding water pumps)
owned by household group and is obtained from the Egypt Integrated Household Survey
(more details given below in section 4). It is treated as a non-tradable commodity.
Land, labor and water: Land is included as a variable input but is not incorporated into
the model as a traded commodity. The land shares are restricted to sum to 1 or more. Labor
and water are included in the model through fixed coefficients and their respective levels are
derived from the level of land allocated to a particular crop.11
4.2
Households
Production and consumption patterns are distinguished among nine broad types of household
groups: urban top, urban middle, urban bottom, rural non-farming top, rural non-farming
middle, rural non-farming bottom, rural farming top, rural farming middle, rural farming
bottom. Where top, middle and bottom refer to the top 20 percent, the middle 50 percent and
the bottom 30 percent of households on the basis of per-capita income. Only rural farming
households are involved in agricultural production activities.
4.3
Structure of the model
The multi-market model is an adaptation of Stifel and Randrianarisoa (2004) and consists of
six blocks of equations: prices, supply, input demand, consumption, income and equilibrium
conditions.12 Unlike Stifel and Randrianarisoa (2004) we do not include seasonality nor an
aggregate for all other food as well as non-food commodities in the model. Below we detail
the different sets of equations, present the data used and explain which are their sources.
Prices: Consumer prices (PC) are higher than producer prices (PP) due to the
domestic marketing margin (MARG) which can proxy, for example transportation costs due
11
Water and Labor coefficients are taken from Nassar and Khaireldin, 2005, "Cropping pattern alternatives for
Egypt," mimeo, Center for Agricultural Economic Studies, Cairo University.
12
The model described below is different to the one used in Siam (2006) but generates broadly similar results.
12
to infrastructure improvements. A possible subsidy to producer prices is allowed for by
including PSUB.
PC c ,h, r = PPc ,h, r • (1 + MARGc, r ) •
(1 − PSUBc )
(1)
where the subscripts c, h, and r refer to commodity, household type and region, respectively.
The border price (PM) of the importable products (im) wheat and maize are linked to
the world price by the exchange rate (er), import tariffs (tm), and the international marketing
margin (RMARG).
PM im = PW im • er • (1 + RMARGim ) • (1 + tmim )
(2)
The border price (PX) of the exportable products (ix) rice, onions, cotton and
fertilizer13 are linked to the world price by the exchange rate (er), import tariffs (tm), and the
international marketing margin (RMARG).
PX ix =
PWix • er
(1 + RMARGix ) • (1 + teix )
(3)
Consumer prices for the importable items are related to the border price by the
commodity specific border-to-market marketing margin and by a possible consumer subsidy
(CSUB):
PC im ,'urban' = PM im • (1 + IMARGim ) • (1 − CSUBim )
( 4)
where IMARG is the border-to-market marketing margin, specific to commodities.
Consumer prices for the exportable items are related to the border price by the
commodity specific market-to-border marketing margin:
PCim,'urban' =
PX ix • (1 + MARGix )
(1 + IMARGix
(5)
where IMARG is the market-to-border marketing margin, specific to commodities.
Rural consumer prices differ from urban consumer prices by an internal marketing
margin (INTMARG) that reflects transportation and marketing costs.
PC c,'urban' = PC c ,'rural ' • (1 + INTMARG c )
( 6)
The internal marketing margin is positive for products which are primarily exported from
rural to urban areas. Products that are assumed not to move from rural to urban or vice-versa
have a zero INTMARG).
This particular set-up allows one to distinguish between farm to rural (MARG), rural
to urban (INTMARG) and urban to border (IMARG).
We assume that households in the different income groups face the same prices but
that these vary by region. We include a price index for each household group to reflect
changes in prices weighted by their shares of consumption:
13
Oranges and potatoes are also exported in sizeable quantities but it is assumed that domestic demand
determines their price. We return to this issue in the section on the equilibrium conditions.
13
∑
PINDEX h =
i

w
 h, r ,i

 PC 1h,r ,i
•
 PC 0
h, r ,i





(7 )
where w is the budget share for each commodity. The superscript on the PC terms refers to
periods 0 and 1 (not the seasons) and denote starting prices and end of simulation prices.
Since we do not include all consumption items on which households spend money the weights
in the PINDEX must be adjusted by the actual weight of the consumption commodities
included in the model.14
Supply: Rural household’s supply of wheat, maize rice, berseem, onions, oranges,
potatoes and cotton are determined by: a) the total amount of land available to each household;
b) the share of that land allocated to the specific crops, and; c) the associated yield for the
crops. The share of land (SH) allocated to a particular crop by household group h is a function
of all crop prices:
(
)
log SH h, f = α s +
∑β
s
(
• log PPh , f
)
(8)
f
where f refers to farmed commodities. The sum of the shares may or may not be restricted to
sum to 1. If not restricted to 1 the assumption is that land is endogenously determined even
though land is not explicitly traded. If shares add up to more than one following a simulation
then extensification is practiced. The realism of this assumption will depend on the particular
setting. The land substitution and expansion elasticities will reflect how easy it is to switch
between crops and/or to bring new land into production.
Yields (YLD) for crops f by household groups h are a function of output and input
prices as well as land. The log-log equations are based on an underlying translog profit
function.
(
)
log YLDh, f = α y +
∑β
y
) ∑ γ y • log(PCh,in )
(
• log PPh, f +
f
(9)
in
where the coefficients represent the price elasticities..
The total household supply to the market is then determined as the product of the
initial area under cultivation, the share of land devoted to the crop, and the yield. Adjustments
are made for losses and use of the output for seed (loss), and for any related conversion
factors (conv).15
(
) (
HSCRh, f = AREA • SH h, f • YLDh , f 1 − loss f • 1 − conv f
)
(10)
The total supply of each of the commodities is the sum of household supply:
SCR f =
∑ HSCR
h, f
(11)
h
Household livestock supply is modelled as a function of livestock prices and input prices of
animal feed products, i.e. berseem and maize.
14
The share of the consumption bundle included in this model in total expenditure is taken from Fayyad et al
(1995) and combined with information from the EIHS to generate group specific shares (Top = 47%, Middle =
52% and Bottom = 57%).
15
Losses and conversion factors are taken from FAO’s Food Balance Sheets and are assumed as given.
14
(
HSLVSTK h = α hlvstk + β lvstk • log (PPh,lvstk ) + γ lvstk • log PC h ,af
)
(12)
where the subscript af refers to animal feed products. Total livestock supply is given by:
SL =
∑ HSLVSTK
(13)
h
h
Input Demand: Household h’s demand for input (HDIN) is a function of output
prices (PP) and input prices (PC).
log (HDIN h ,in ) = α in +
∑β
in
(
) ∑ γ in • log(PC h,in )
• log PPh , f +
f
(14)
in
where the subscript in refers to fertilizer, traction, berseem and maize for animal feed
(berseem and maize). Total demand for the inputs is given by:
DIN in =
∑ HDIN
(15)
h
h
Consumption Block: Demand for the consumption items (HC) by the household
groups in urban and rural locations is modelled as:
log (HCh ,i ) = α hd,i +
∑β
d
h ,i
• log (PCh ,i ) + γ hd,i • log(YH h )
(16)
f
where the i refer to commodities households purchase, i.e. wheat, maize, rice, livestock, onion,
oranges and potatoes. YH is household income (defined below), PC are consumer prices, P is
the stone geometric price index defined as:
log (Ph , r ,i ) =
∑ w • log(PC )
(17)
h ,i
i
Total demand is:
TCON i =
∑ HC
h ,i
(18)
h
We note that cotton demand is not modelled at the household level. Rather cotton
demand (final) is modelled as aggregate industry demand.
Income Block: Agricultural income (YHAG) for rural households is the sum of crop
revenue minus input costs:
YHAGh =
∑ (PP
h, f
)
• SCR h , f + PPh ,lvstk • HSLVSTK h − (PC h,in • DIN h ,in )
(19)
f
And total household income (YH) is the sum of agricultural income and the exogenously
determined non-agricultural income. The latter component is adjusted by a price index:
YH h = YHAGh + YHNAG h • PINDEX h
(20)
15
and the price index is as defined in equation (7).
Equilibrium Conditions: All commodity markets clear, i.e. the sum of quantity
supplied (domestic production plus net imports) is equal to the amount demanded for human
and animal consumption.
SCR f + M f + STOCK∆ f = CONS f + FEED f
LVSTK + M lvstk = DIN lvstk
SDIN in + M in = DIN in
(21)
(22)
(23)
where M equals imports and CONS and FEED denote human and animal consumption
respectively. For products not traded imports are fixed at zero. Animal feed from maize and
berseem is endogenous but feed from other commodities is treated as exogenous.
Wheat and maize (for human and for animal consumption) are treated as importable
commodities while rice, onions, cotton and fertilizer are treated as exportable commodities.
For the commodities onions, potatoes and oranges exports are allowed to fluctuate within 10
percent of the baseline level. Net livestock imports are constrained to be non-negative, i.e.
there can be no exports.
4.4
Data requirements
Three types of data are needed to calibrate the model to a baseline solution. These are:
Levels: production, consumption, income, and input levels must be defined for all
commodities and household groups. Aggregate levels are typically taken from FAOSTAT.
Household level land allocation shares, production levels and consumption and input
demand are based on Egypt Integrated Household Survey (EIHS) data.16
Prices: consumer, producer, user, and border prices must be defined for all
commodities. They also define the marketing margins. Producer prices for wheat, maize,
livestock, berseem and rice are taken from Siam (2006). Cotton prices are taken from the
Ministry of Agriculture and Land Reclamation. Rural consumer prices for berseem, maize and
livestock are also taken from Siam (2006). Other prices are from the EIHS as are rural to
urban and producer to consumer price margins. The price of traction is not an actual price per
tractor but rather that price which translates the value of farm equipment at the household
level (from the EIHS) to the national level # of tractors. It is of relevance within the model
only.
Parameters: these are the demand and supply elasticities, nearly all of which are best
guesses. 17 The own-price elasticities of the land share equations for wheat, maize, rice,
berseem and cotton were estimated from EIHS data. We give a short overview of the
elasticities: Land-share elasticities – equation 8: the own-price elasticities are between 0.24 0.31 (wheat=0.31); the cross-price elasticities are positive and between 0.1 and 0.3 for wheat
and rice, maize and berseem, maize and onion, and maize and potatoes. For all other
combinations of commodities they are negative and range between -0.1 and -0.5. For oranges
all cross-price effects are assumed equal to zero. Crop yield elasticities – equation 9: the own16
The 1997 Egypt Integrated Household Survey (EIHS) was undertaken by the International Food Policy
Research Institute in collaboration with the United States Agency for International Development (USAID), the
Ministry of Agriculture and Land Reclamation and the Ministry of Trade and Supply. The EIHS survey was
funded under USAID Grant No. 263-G-00-96-00030-00.
17
These best guesses are based on the first named author’s long experience with agricultural sector models for
Egypt.
16
price elasticities are between 0.3 and 0.5 (0.4 for wheat) except for cotton which is 1.2. The
cross-price elasticities are all assumed equal to zero. Crop yield elasticities with respect to
input prices are -0.7 for fertilizer and -0.9 for traction (in line with Antle and Aitah’s (1986)
results for maize, rice and cotton). Livestock output supply elasticities – equation 12: The
own-price elasticity is 0.6 and the elasticity of livestock supply with regard to animal feed
prices are -0.5 for both maize and berseem. Input demand elasticities – equation 14: The ownprice elasticity for fertilizer and animal-feed maize is -0.4 and for traction and berseem it is
-0.5. The price elasticities of fertilizer and traction with regard to the price of the crop to
which they are applied is 0.1 for all crops except for berseem for which it is 0.05 (for
traction). All cross-price effects are assumed to be zero. The elasticities for animal-feed maize
and berseem with respect to livestock price is 0.2 and 0.7 respectively. The cross-price effects
for the two animal feed products is 0.2. Consumer demand elasticities – equation 16: The own
price demand elasticity -0.3 for all commodities except for maize for which it is -0.2 and
livestock for which it is -0.7. Cross-price effects are between -0.1 and -0.3 except for wheat
and rice for which it is 0.2. Demand elasticities with respect to income are -0.1 for wheat and
maize, 0.1 for rice, onion, oranges and potatoes and 0.2 for livestock. Demand for cotton is
modeled separately as aggregate industry demand with a own price elasticity of 0.5. An
assessment of the sensitivity of the model results with regard to the parameters is given in
section 5.1.1
4.5
Baseline solution18
The baseline solution corresponds to wheat production of 5.5 million tons per year.
This corresponds to 2.5 million feddan of land, and about 4 billion m3 of water, and imports
of 5.2 million tons of wheat per year. Total availability, including imports, losses and animal
feed is about 10.7 million tons. The subsidized producer and urban and rural consumer prices
are 1,175, 1,507 and 1,117 L.E./ton whereas the respective unsubsidized prices are 1,056,
2,412 and 1,788 L.E./ton. The combined producer and consumer subsidy is estimated as 7.9
billion L.E. This figure is based on the assumptions with regard to quantities and prices made
for this model and is appropriate for comparisons among the baseline and scenarios. It is not
necessarily accurate in an absolute sense.
Berseem, as a winter crop, is highly competitive with wheat (cultivated on a similar
amount of land). Production is over 65 million tons a year, all for local consumption, and
there are no imports. Wheat, maize, rice, berseem and cotton account for 26, 20, 15, 29 and 6
percent of total land where total land is the sum of land cultivated to the eight commodities
included in the system. Finally, caloric intake levels are calculated on the basis of calorie
conversion factors based on the FAO Food Balance Sheets.
5
POLICY SIMULATION RESULTS
Four policy scenarios are analyzed using the multi-market model: 1) Complete liberalization;
2) increasing the import price of wheat by 20 percent; 3) replacing imports with strategic
stocks; 4) increasing wheat yields by 10 percent accompanied by a reduction in marketing
costs of 10 percent. Results are shown in table 3.
18
The baseline data set is calibrated using interlinked excel sheets that may be useful to others [even if
considerable adaptation will inevitably be required] in simplifying this kind of exercise. The excel file for this
Egypt multi-market baseline is available from André Croppenstedt at [email protected].
17
5.1
Complete liberalization
Complete liberalization of wheat implies a large increase in wheat consumer prices
and a drop in wheat producer prices. Consequently wheat consumption demand (-6.5%) and
output supply (-4.2) are depressed. On the supply side the biggest impact is on cotton (+5.7%),
onion (+4.7%), livestock (+2%), berseem (+1.9%) and maize production (+1.6%). On the
consumption side livestock (-6.1%), maize (-4.3%) and rice (+5.4%) are strongly impacted on.
Imports for wheat drop by about 378 thousand tons while rice exports fall by about 156
thousand tons due to the large increase in domestic demand. Cotton exports are up by 34
thousand tons. Wheat self-sufficiency is not much affected. Water and labor use increase
marginally, in both cases due to increases in berseem followed by cotton production.
With regard to household welfare changes we consider: a) changes in caloric intake; b)
the change in the value of the original consumption bundle when valued at old versus new
prices19 , and; c) per-capita income changes. First, we find that caloric intake is generally
down by between 3 and 4% and the change is quite uniform across the household groups.
Comparing the value of the original consumption bundle at old versus new prices we note that
the price changes have different effects on the different household groups. All household
groups are worse off but the bottom and middle income groups more so: this reflects the fact
that the rise in the CPI is always larger for middle and bottom income groups. Finally, turning
to the income changes we note that on an income-group-by-income-group basis the losses are
quite similar for urban and rural non-farming households. They are however substantially
larger for rural-farming households. The overall payment required to compensate households
for the income effects of the liberalization is estimated at 8.6 billion L.E.
5.1.1 Sensitivity analysis
To gauge how sensitive the results are to the elasticities used we consider how they change
when we halve or double the various elasticities. It emerges that the results are affected
moderately with the key relationships between price and output changes preserved. However,
results are sensitive to a change in the elasticities if such a change impacts in particular on the
bounded commodities. This highlights the fact that the model is sensitive to the bounds
imposed on the export and imports of some of the commodities. In particular, for this scenario,
the results are sensitive to the upper limits imposed for exports of onion, oranges and potatoes
as well as the lower limit of imports for livestock. It is a limitation of this model that the
bounds are central to the model results and need to be considered carefully.
5.2
Complete liberalization with a 20 percent increase in wheat import prices
A jump in the import price of wheat stimulates supply of wheat (+15.6%), rice
(+12.1%), onion (+5.1%) and cotton (+4.8%) but significantly reduces supply of berseem
(-5.6 %) and livestock (-3.9%) and less so of maize (-1.7%). Imports of wheat drop by over
1.4 million tons while rice exports increase by 159 thousand tons as compared to the baseline.
At the same time consumption of wheat falls (-6.2%) due to the large increase in consumer
prices. Maize and livestock consumption are down 3.9% and 3.1% respectively while input
demand for berseem falls by 5.6%. Self-sufficiency of wheat rises to 63 percent due to
increased production coupled with reduced consumption. Total water consumption is similar
19
Calculated as: (value of original bundle at new prices minus value of original bundle at original prices)/value
of original bundle at original prices. The bundle includes the food commodities covered in the model, i.e. wheat,
maize, rice, livestock, onion, potatoes and oranges.
18
to that found for scenario 1 but the increase over the baseline is now mainly due to increased
water use for rice and to a lesser extent wheat. Labor use is a little lower than in scenario 1
with the increase over the baseline due, more or less equally, to wheat and rice.
The implied price increases as measured by the CPI are relatively large and, except for
the top income group for which livestock represents a relatively larger proportion of
consumption, larger than for scenario 1. Caloric intake falls but the impact is weaker than in
scenario 1. The difference lies in part in the greater responsiveness of rice consumption and
the smaller fall in livestock consumption combined with the more moderate reductions in the
consumption of the other commodities. Moreover although the wheat prices increase more
livestock prices fall quite strongly in this scenario. The change in the value of the original
consumption bundle from old to new prices shows that the urban top group is better off while
for the rural farming top the change is neutral. For urban middle and rural non-farming top the
change is very small. Most severely affected are rural non-farming middle, followed by rural
farming bottom and rural non-farming bottom. The impact is much milder than in scenario 1
due to the larger shift to rice consumption and the larger fall in particular of consumer
livestock prices (and livestock weighing more heavily in consumption the higher the income).
The per-capita income changes are negative for all households and are in the range of 5 to 20
percent. In all regions the middle and bottom groups are worst affected. The rural farming
households are again worst affected by this measure with both middle and bottom groups
being significantly worse off than the top group which itself sees per-capita income fall by
13.5 percent. The overall payment required to compensate households for the income effects
of the liberalization is estimated at 26.8 billion L.E.
5.3
Substituting imports with strategic stocks
Sudden upward shocks to the world price of wheat can be mitigated through the use of
strategic stocks (replacing imports). Not surprisingly, because the level of strategic stocks
chosen coincides with the level of imports for scenario 1, the implied production and
consumption changes are similar to scenario 1. The benefit to households, both urban and
rural, are very substantial when this scenario is compared to scenario 2. This is true even if
one assumes storage costs of between 10 and 20% of the value of wheat stocks, i.e. 0.5 and 1
billion L.E., respectively at 1,070 L.E./ton. One must, however, bear in mind that the storage
costs accrue every year while the frequency of price spikes is not known. Finally we note that
the overall payment required to compensate households for the income effects depends on the
level of strategic stocks with higher levels implying lower overall payments.
5.4
A 10 percent increase in wheat yields plus a 10 percent drop in transportation
costs.
The scenario of complete liberalization accompanied by wheat yield increases of 10%
(simulating increased R&D spending) and a drop in wheat margins of 10% (perhaps due to
lower transportation costs as a results of infrastructure improvements) differs from scenario 1
mainly in that wheat production rises strongly, by 9% when compared to the baseline and by
14% when compared to scenario 1. Wheat imports drop significantly over scenario 1 – and by
1.1 million tons over the baseline - but not consumption which is virtually the same as in
scenario 1. Hence the self-sufficiency ratio for wheat jumps to 59%. The other differences to
scenario 1 are that rice production rises by 1.8% (2% over the baseline) and rice exports
increase 22% (although they are still about 91 thousand tons below the baseline).
The impact on household welfare are indicated by caloric intake, changes in
purchasing power and changes in income are nearly identical to scenario 1. It follows that the
19
overall payment required to compensate households for the income effects of the
liberalization is estimated at 8.3 billion L.E.
6
CONCLUSIONS
Wheat policy in Egypt has been gradually reformed from one of massive government
intervention to a much more market-oriented one. Nevertheless, food security concerns and
the concern for an excessive dependency on imports mean that the GOE does continue to
intervene in several markets, including the wheat market. At the same time policy makers try
to look ahead to design new policies which aim to achieve greater food security. Given the
importance of wheat and the potential costliness of failure it is important to have accurate exante information on the impact of such potential reforms. This study contributes to this effort
by developing a multi-market model for Egypt which does provide pertinent and timely
information on policy reform scenarios to policy makers.
The multi-market model generates the level of detail that allows for clearer
understanding of the trade-offs policy makers face on the production and consumption side as
well as with regard to household welfare. Results show that wheat market liberalization
implies very substantial costs for consumers and producers, with the latter group always
experiencing larger income drops. The estimated income losses that these groups suffer would
appear to be below the current total subsidy costs and hence a cash transfer program would, in
principle, be feasible. We also note the positive impact on labor use that we estimate which
helps in particular the rural poor who generate a significant proportion of their household
income through work as casual labor (Croppenstedt, 2006). The results show that wheat price
movements impact strongly on the supply and/or demand side in particular of berseem, rice,
maize, cotton and livestock which has significant implications for their net imports as well as
input use.
The need for some type of safety net system is even more obvious when including the
scenario of a significant upward fluctuation of international wheat prices. The cumulative
effect on household income is very severe. Strategic stocks are shown to be able to isolate
from such an effect and given the magnitude of the income effects might even be justified.
Finally we find that yield and transportation improving investments for wheat have a strong
impact on wheat production but little overall impact, in terms of the household welfare
measures used, in dampening the effect of removing the current wheat subsidy system.
20
7
REFERENCES
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System,” Policy Research Working Paper 2322, The World Bank, Washington, D.C.
Ahmed, A.U. and H.E. Bouis, 2002, “Weighing what’s practical: proxy means tests for targeting food subsidies in
Egypt,” Food Policy 27: 519-540.
Ahmed, A.U., H.E. Bouis, T. Gutner and H. Löfgren, “The Egyptian Food Subsidy System: Structure, Performance,
and Options for Reform,” Research Report 119, International Food Policy Research Institute, Washington, D.C. 2001.
Antle, J.M. and A.S. Aitah, 1986, “Egypt’s Multiproduct Agricultural Technology and Agricultural
Policy,” Journal of Development Studies, 22(4): 709-23.
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22
Table 3: Baseline solution and results for the 4 policy reform scenarios
Scenario 1
Complete
liberalization:
no
consumer or producer
subsidies
Variable
Baseline
Wheat
Maize
Rice
Berseem
Onion
Oranges
Potatoes
Cotton
Livestock (meat)
5,484,000
4,433,997
3,620,008
65,214,019
585,000
2,020,000
1,576,000
596,000
1,378,000
Scenario 2
As scenario 1 and a
20% increase in the
import
price
of
wheat
Scenario 3
No imports – strategic
stocks replace imports
such that they balance
demand and supply
Scenario 4
As scenario 1 with a
10% increase in
wheat yields and a
10% drop in MARG
Domestic production (tons)
5,256,058
6,338,352
4,504,132
4,359,216
3,625,135
4,056,729
66,472,172
61,560,760
612,257
614,702
2,008,458
2,029,882
1,581,435
1,543,167
630,118
624,386
1,405,453
1,324,135
5,371,496
4,428,222
3,684,547
63,865,032
611,363
2,018,177
1,583,492
632,058
1,363,439
5,979,284
4,512,054
3,691,194
66,462,759
612,332
2,011,105
1,582,013
627,506
1,404,766
4,816,000
-145,002
-323.434
0
5,287,600
-349,345
-184,000
-252,000
-233,058
81,723
-157,132
0
4,092,137
-266,331
-356,535
0
5,461,780
-352,000
-184,000
-252,000
-228,506
0
-73,712
0
Net imports (tons)
Wheat
Maize
Rice
Berseem
Maize – animal feed
Onion
Oranges
Potatoes
Cotton
Livestock (meat)
Fertilizer
Traction
5,193,820
0
-447,008
0
5,511,000
-320,000
-167,000
-229,000
-197,000
118,000
0
0
4,815,656
-259,003
-291,411
0
5,452,285
-352,0000
-184,000
-252,000
-231,118
0
-85,656
0
3,760,635
-98,440
-605,762
0
5,175,130
-352,000
-184,000
-208,000
-225,386
126,933
-27,756
0
23
Berseem
Maize – animal feed
Fertilizer (N)
Traction (# of tractors)
Wheat
Maize
Rice
Onion
Oranges
Potatoes
Cotton
Livestock (meat)
Wheat
Maize
Rice
Berseem
Onion
Oranges
Potatoes
Cotton
Livestock
65,214,019
5,564,000
4,171,235
89,700
9,412,820
4,434,000
2,986,000
265,000
1,853,000
1,347,000
399,000
1,496,000
1,175
800
1,120
153
271
565
395
10,940
20,000
Input demand (tons)
66,472,172
5,505,285
4,085,579
89,700
61,560,760
5,228,130
4,143,480
89,700
63,865,032
5,340,600
4,014,103
89,700
66,462,759
5,514,780
4,097,523
89,700
Consumption - final demand (tons)
8,805,534
8,832,807
4,245,128
4,260,775
3,146,724
3,263,967
260,257
262,702
1,824,458
1,845,882
1,329,435
1,335,167
399,000
399,000
1,405,453
1,451,067
8,921,316
4,283,221
3,174,113
262,018
1,834,177
1,331,492
399,000
1,445,161
8,805,242
4,245,723
3,147,659
260,332
1,827,105
1,330,013
399,000
1,404,766
1,070
800
1,120
138
271
559
376
10,940
18,046
1,097
800
1,120
147
271
545
375
10,940
19,946
Producer prices (L.E./ton)
1,056
800
1,120
146
271
551
375
10,940
19,877
1,267
800
1,120
134
271
539
362
10,940
16,743
24
Wheat
Maize
Rice
Onion
Oranges
Potatoes
Cottoni
Livestock
1,507
1,138
2,109
920
884
680
13,800
39,000
Urban consumer prices (L.E./ton)
2,412
2,894
1,138
1,138
2,109
2,109
920
920
863
843
645
623
13,800
13,800
38,760
32,649
2,444
1,138
2,109
920
875
647
13,800
35,190
2,412
1,138
2,109
920
853
645
13,800
38,895
Wheat
Maize
Rice
Berseem
Maize – animal feed
Onion
Oranges
Potatoes
Fertilizer
Tractionii
Livestock (meat)
1,117
980
2,032
167
980
886
815
632
1,000
9,080
30,000
Rural consumer/input prices (L.E./ton)
1,788
2,145
980
980
2,032
2,032
159
146
980
980
886
886
795
777
600
579
1,000
985
8,990
8,851
29,815
25,114
1,812
980
2,032
151
980
886
807
606
1,046
8,717
27,069
1,788
980
2,032
160
980
886
786
600
1,000
8,908
29,919
25
Urban top
Urban middle
Urban bottom
Rural non-farming top
Rural non-farming middle
Rural non-farming bottom
Rural farming top
Rural farming middle
Rural farming bottom
Wheat
Maize
Rice
Berseem
Onion
Oranges
Potatoes
Cotton
Wheat
Maize
Rice
Berseem
Onion
Oranges
Potatoes
Cotton
Total
100
100
100
100
100
100
100
100
100
26
20
15
29
1
2
2
6
4,036,561
5,520,968
8,375,505
7,357,738
97,441
1,097,473
532,607
1,926,000
28,944,293
CPI
104.8
106.3
108.0
105.6
108.4
108.5
104.9
106.8
109.2
Land share (%)
25
20
15
29
1
2
2
6
Water use (m3)
4,002,056
5,558,438
8,312,799
7,538,262
101,075
1,089,458
538,121
2,018,148
29,158,356
104.9
107.6
110.7
106.7
112.0
112.9
105.4
109.0
113.1
102.5
104.1
105.9
103.6
116.8
106.7
102.8
104.9
107.3
104.9
106.4
108.1
105.7
108.4
108.6
104.9
106.9
109.3
28
19
17
27
1
26
20
16
29
1
26
20
16
29
2
2
6
2
2
6
1
2
2
6
4,377,407
5,248,560
9,075,890
6,990,701
99,006
1,081,831
517,810
1,951,079
29,342,285
4,082,969
5,484,615
8,479,735
7,390,373
101,294
1,094,073
540,219
2,031,716
29,204,993
4,042,900
5,522,279
8,394,452
7,463,489
100,253
1,085,669
533,829
1,993,202
29,139,072
26
Wheat
Maize
Rice
Berseem
Onion
Oranges
Potatoes
Cotton
Total
Wheat
Maize
Rice
Berseem
Onion
Oranges
Potatoes
Cotton
Urban top
Urban middle
Urban bottom
Rural non-farming top
Rural non-farming middle
Rural non-farming bottom
Rural farming top
Rural farming middle
Rural farming bottom
106,555,191
94,333,077
105,637,000
170,654,752
4,096,957
31,045,629
30,970,119
37,450,000
580,742,725
Labor use (man-days)
105,644,328
94,973,318
104,846,110
174,841,809
4,249,729
30,818,897
31,290,746
39,241,764
585,906,700
115,552,667
89,678,640
114,470,689
162,141,733
4,162,770
30,603,147
30,109,689
37,937,656
584,656,992
107,780,230
93,711,953
106,951,606
171,411,688
4,258,943
30,949,438
31,412,745
39,505,594
585,982,199
106,722,506
94,355,485
105,875,976
173,107,532
4,215,189
30,711,7253
31,041,136
38,756,708
584,786,255
51
44
114
100
221
109
117
149
Self-sufficiency ratios (%)iii
52
46
109
100
235
110
119
160
63
46
118
100
234
110
116
157
53
46
110
100
233
110
119
158
59
46
111
100
235
110
119
159
2,360
2,292
1,997
2,506
2,371
1,979
2,343
2,369
2,044
Caloric intake (Kcal/capita)
2,280
2,214
1,935
2,429
2,290
1,920
2,292
2,291
1,966
2,309
2,240
1,959
2,460
2,314
1,943
2,330
2,323
1,989
2,307
2,239
1,956
2,456
2,314
1,940
2,319
2,320
1,991
2,280
2,214
1,935
2,429
2,290
1,920
2,292
2,291
1,965
27
Percentage change in value of original consumption bundle when evaluated at new and old pricesiv
Urban top
0
6.8
-1.4
Urban middle
0
8.1
1.7
Urban bottom
0
9.6
4.7
Rural non-farming top
0
8.0
1.5
Rural non-farming middle
0
11.1
8.1
Rural non-farming bottom
0
10.3
6.8
Rural farming top
0
6.8
-0.03
Rural farming middle
0
8.9
3.6
Rural farming bottom
0
11.1
7.3
Urban top
Urban middle
Urban bottom
Rural non-farming top
Rural non-farming middle
Rural non-farming bottom
Rural farming top
Rural farming middle
Rural farming bottom
Total cost of subsidy system (within
model calculation) L.E.
Total cost of compensation based on
change in real per-capita income
Change in real per-capita income (%)
0
-1.6
0
-2.0
0
-2.3
0
-1.8
0
-2.4
0
-2.4
0
-5.1
0
-5.4
0
-7.4
0.1
2.1
4.1
1.9
6.3
5.4
0.9
3.3
5.8
7.0
8.3
9.8
8.2
11.3
10.4
7.0
9.1
11.4
-5.3
-6.3
-7.2
-5.6
-6.9
-7.2
-13.5
-20.8
-18.6
-2.4
-2.9
-3.4
-2.6
-3.3
-3.4
-9.7
-13.6
-13.4
-1.6
-1.9
-2.3
-1.8
-2.3
-2.4
-4.6
-4.9
-6.8
7,859,599,000
0
0
0
0
--
8,635,500,000
26,820,000,000
13,729,000,000v
8,273,200,000
i
Cotton demand is not modelled at the household level but rather only in aggregate. The price is given as urban consumer price is the price of cotton faced by industry.
The cost of traction is not an actual price per tractor. Rather the price is that level which translates household level data on the value of farm equipment to the national level.
It is of relevance within the model only.
iii
Refers to domestic production out of total availability, i.e. domestic production + net imports.
iv
Calculated as: (value of original bundle at new prices minus value of original bundle at original prices)/value of original bundle at original prices. The bundle includes the
food commodities covered in the model, i.e. wheat, maize, rice, livestock, onion, potatoes and oranges.
v
Does not include cost of storage which is estimated at between 0.5 and 1billion L.E., i.e. between 10 and 20% of the value of the wheat stock.
ii
28
ESA Working Papers
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thought on hunger and poverty alleviation through its economic studies publications which
include this working paper series as well as periodic and occasional publications.
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29