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Primary agricultural product demand and economic development
Lin, Chi-Yuan, Ph.D.
The Ohio State University, 1990
UMI 300 N. Zeeb Rd. Ann Arbor, MI 48106
PRIMARY AGRICULTURAL PRODUCT DEMAND AND ECONOMIC DEVELOPMENT
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
the Degree Doctor of Philosophy in the Graduate
School of the Ohio State University
By
Chi-Yuan Lin, B.S., MBA
* * * -k
The Ohio State University
1990
Dissertation Committee: Approved by
Norman Rask
Dennis R. Henderson Advisor Scott Irwin Department of Agricultural Economics and Rural Sociology ACKNOWLE DGEMENTS
I would like to give my appreciation first to my advisor, Dr. Norman Rask, for the guidance, encouragement and patient understanding given to me during the course of my studies at The Ohio State University. His idea and suggestions have been invaluable to the development of the concept embodied in this dissertation.
In addition, I wish to thank Dr. Dennis Henderson and
Dr. Scott Irwin for their constructive comments and suggestions.
Finally, I am very deeply indebted to my wife, Chen
Chan-O, for her supports and encouragement during these years. VITA
April 19, 1951 ...... Born, Tainan, Taiwan
1973 ...... B.S. Department of Agricultural Economics, National Taiwan University, Taipei, Taiwan
1979 ...... MBA, Graduate School of Business Administration, National Chengchi University, Taipei, Taiwan
1980 - 1983 ...... Specialist, Euro-Asia Trade Organization, Taipei, Taiwan
1984 - 1986 ...... Specialist, Division of Economic Research, Council of Agriculture Taipei, Taiwan
1988 - 1990 ...... Research Associate, Department of Agricultural Economic and Rural Sociology, Ohio State University, Columbus, Ohio
FIELDS OF STUDY
Major Field: Agricultural Economics and Rural Sociology TABLES OF CONTENTS
ACKNOWLEDGEMENTS...... ii
VITA ...... iii
LIST OF T ABLES ...... vi
LIST OF FIGURES ...... viii
CHAPTER > Page
I. INTRODUCTION
1.1 Dynamic Process of Food Demand ...... 1 1.2 Statement of Problem ...... 4 1.3 Objectives of Study ...... 8
II. REVIEW OF LITERATURES
2.1 Measurement of Food Consumption...... 10 2.2 Functional Forms and Explanatory Variables . . 14 2.3 Results of Previous Studies ...... 18 2.4 Limitations of Previous Studies ...... 22
III. THEORETICAL CONSIDERATIONS
3.1 Introduction...... 26 3.2 Theoretical Considerations of Food Demand under Alternative Measurements ...... 28 3.3 Characteristics of The Demand of Food Groups Measured by Cereal Equivalents...... 34 3.4 The Constancy of Food Consumption-Income Relationship over T i m e ...... 43
IV. RESEARCH PROCEDURES AND METHODOLOGIES
4.1 The Variables and Model Specification .... 48 4.2 The Moving Regression Method for Testing Constancy of Engel Functions over Time .... 57 4.3 D a t a ...... 59 V. EMPERICAL RESULTS AND IMPLICATIONS
5.1 Introduction ...... 64 5.2 Estimation of Engel Functions of Food Groups with Income Converted by Exchange Rates . . . 67 5.3 Estimation of Engel Functions of Food Groups with Income Converted by Purchasing Power P a r i t y ...... 75 5.4 Estimation of Demand Functions for Food G r o u p s ...... 82 5.5 Comparison of Income Elasticities of Food Group Demand Measured by Alternative Methods. .91
VI. SUMMARY AND CONCLUSIONS
6.1 Summary ...... 97 6.2 C o n c l u s i o n ...... 100 6.3 I m p l i c a t i o n...... 102
VII. A P P E N D I X ...... 105
Table 14. Cereal Equivalent Conversion Ratio for Individual Food Item by Food Groups . .106 Table 15. Per Capita Income for Alternative Sample Composition with 108 and 56 Country Observation for 1980 ...... 110 Table 16. Food Consumption for Alternative Sample Composition with 108 and 56 Country Observation for 1980 ...... 113 Table 17. Demand Functions of Total Food, Animal Products Measured by Calories, 56 Countries in 1980 D a t a ...... 116 Table 18. Demand Functions of Total Food, Animal Products Measured by Expenditures, 56 Countries in 1980 D a t a ...... 117
VIII.REFERENCE S ...... 118
V LIST OF TABLES
Table 1. Per Capita Cereal Consumption in 1985 ...... 2
Table 2. Engel Functions of Total Food, Animal Products and Starchy Staples, 108 Countries 1961-1985 Data, Income Converted by Exchange Rates .... 68
Table 3. Per Capita Annual Consumption of Total Food, Animal Products and Starchy Staples in Terms of Cereal Equivalents at Various Income Levels. .71
Table 4. F Statistics of Moving Regression Test on Engel Functions, 108 Countries 1961-1985 Data, Income converted by Exchange Rates . . . .75
Table 5. Engel Functions for Total Food, Animal Products and Starchy Staples, 108 and 56 Countries 1961- 1985 Data, Income Converted by Exchange Rates . .77
Table 6. F Statistics of Chow Test on The Similarity of Engel Functions Estimated from Samples with 108 and 56 Countries, Income Converted by Exchange Rates ...... 78
Table 7. Engel Functions of Total Food, Animal Products and Starchy Staples, 56 Countries 1961-1985 Data, Income Converted by Purchasing Power Parity and Exchange Rates ...... 80
Table 8. F Statistics of Moving Regression Test on Engel Functions, 56 Countries 1961-1985 Data, Income Converted by Purchasing Power Parity . . .82
Table 9. Demand Functions for Total Food, Animal Products and Cereal Products, 56 Countries 1980 Data, Income Converted by purchasing Power Parity ...... 84
Table 10. Income and Own Price Elasticities of The Consumption of Total Food, Animal Products and Cereal Products at Various Income Levels . . 88 Table 11. Income Elasticities for Alternative Measures of Food Consumption at Various Income Levels . .92
Table 12. Income Elasticities for Alternative Measures of Animal Products at Various Income Levels . .94
Table 13. Income Elasticities for Alternative Measures of Cereal Products at Various Income Levels . .96
vii LIST OF FIGURES
Figure 1. The Shift of Engel Curves over Time from Systematic Difference in Tastes ...... 45
v m CHAPTER I
INTRODUCTION
1.1 Dynamic Process of Food Demand
Food demand is a dynamic concept when viewed across all
stages of economic development. At early ‘stages (low
income), diets are quite simple and composed largely of
cereals and/or tubers with only small quantities of
livestock products [29]. As development proceeds and incomes
grow, consumers rapidly change diet composition to include greater proportions of livestock products which are more
efficient sources of protein in consumption, but require more production resources than do proteins from crops [44].
Hence, there is a dramatic increase in the indirect demand
for primary agricultural products for food purpose, especially cereals for feeding livestock.
Global cereal direct and indirect consumption (feed use) by level of development illustrates this dynamic process. In 1985 the per capita consumption of cereals in developed economies was 554 kilograms. Direct food consumption was 139 kilograms which accounted for 25% only, the other 75% or 415 kilograms was for feed purposes. On the
1 2
Table 1 Per Capita Cereal Consumption in 1985
Weight (Kilograms) ______Percentage
Total Direct Indirect Total Direct Indirect
World 319 175 144 100 55 45
Developing 239 187 52 100 78 22 Economies i
Developed 554 139 415 100 25 75 Economies
Note: Cereals include wheat, rice, barley, rye, oats,
millet, brans and other cereals.
Source: Calculated from FAO Food Balance Sheet Tape. contrary, the per capita consumption of cereals in developing economies was 239 kilograms, or about 43% as much as in developed economies. Seventy-eight percent of the cereal consumption or 187 kilograms in the developing economies was for direct food use. Only 22% or 52 kilograms was for feed use. Thus, indirect consumption of cereals in developing countries is only one ninth of that in developed countries.
Because primary agricultural product demand increases rapidly in high growth developing countries, their production growth may not keep pace with consumption needs.
In many countries, though not all, natural resource limitation also impose constraints on the output growth of primary agricultural products. Therefore, food self- sufficiency ratios in most developing countries show a declining trend, particularly for feed grains [39]. This decline in food self-sufficiency means that in the developing countries with poor resource endowments, feed grains or livestock product imports will be necessary.
Today, about 35% of the world's population lives in countries that are already in the rapid food consumption growth stage of development, where a significant proportion of any increase in per capita income will be translated into increased demand for a changing food diet.. Another 50% are in the very early stages of development [37]. Thus, a careful understanding of the dynamics of this process will be important to agricultural export countries, as well as to countries in early and middle development stages. Since income growth triggers diet changes, and at the same time food prices play a critical role in determining the effective income or purchasing power level, the estimation of both income and price elasticity of food demand are important for policy analysis.
*
1.2 Statement of Problem
It is generally understood that income growth is the major factor that triggers diet change in the low and middle income economic development stages. Although there are many studies that have investigated the relationship between per capita income and food consumption, the studies are generally partial and three problems related to studying food consumption behavior in the full range of development still exist. They are: selection of an appropriate measure of food demand, non-availability of country specific consumption and income data over all levels of development, and the understanding of the difference of tastes in food across countries.
Measure of Food Demand
Food is a composite commodity including various heterogeneous food items. In a practical sense, it is necessary and convenient to aggregate various food items
into total food or food subgroups to make empirical study possible and also to obtain results meaningful for policy making. From an agricultural view point, we would like to know the relationship of the effect of changing diet composition on the demand of primary agricultural products at various stages of economic development in turn the change in demand for primary agricultural products can be used to approximate the demand for agricultural resources, especially farm land which is generally the most limited resource in agricultural production.
Most studies, however, have used calories
[2,3,12,17,29,44] or food expenditures [12,17,20,21,49] to measure the magnitude of food demand changes associated with income growth. Calorie and expenditure measures are used to aggregate food items for determining the relationship of food consumption pattern changes to economic development or to per capita income. Neither measures can reflect the changing demand for primary agricultural products. Using calories contained in the final food items to measure food demand always underestimates the demand for primary agricultural products since the calories provided by livestock are significantly less than the calories used to produce the livestock. On the other hand, using expenditures to measure food demand will overestimate or underestimate the demand for primary agricultural products because food expenditures include services from the non-agriculture sector and food expenditure levels may be affected, positively or negatively, by food price policies in a country.
To determine adequately the effect of changing diet composition on primary agricultural product demand throughout the economic development process, it is convenient to convert various food items into a common unit which can reflect the demand for primary’agricultural products (in this study cereal equivalents) and investigate its relationship to income changes.
Non-availability of Income and Price Data
No single country has income time series data which spans a time period that includes both low and very high per capita incomes. Therefore, pooling data from different countries for a year or several years becomes necessary.
From pooled data, we have the great advantage of a large variation in the independent variables to secure the correctness of estimation of income and price sensitivity of food demand, but problems of converting incomes and prices of different countries into common units remain.
Normally exchange rates are used to adjust income across countries to a common unit. Usually, a large sample of countries from low to high income can be obtained to estimate the generalized relationship of food consumption to economic development. However, exchange rates are influenced
only by the prices of internationally traded goods. They do
not reflect the purchasing power of income on the
consumption bundle, including non traded goods within a
country. This bias will be large especially in converting
the incomes of low income countries into a common unit
because the relative cheapness of labor intensive non
tradable goods is greater in low income countries [14].
Furthermore, because food is a composite commodity and
consumers in each country purchase different combinations of
food items, it is much more difficult to convert food prices
in different countries to a common unit. Although food price
is an important determinant in influence food consumption,
especially at low and middle developing stages, most studies
omit food prices as an explanatory variable and leave the
price effect on food consumption during the economic
development process unknown.
Difference in Food Tastes Across Countries
It is usually believed that habit, culture and geographic location affect food consumption behavior. Hence,
the tastes in food consumption are different among
countries.
The difference in tastes will not bias the estimation of the relationship of food consumption to income if the taste differences can be accounted for by randomly distributed error terms in the regression estimation. However, if there exists systematic differences in tastes among countries at different stages of development and these differences remain unchanged throughout the development process, then the relationship of food demand to economic development will not be constant over time. No studies have investigated the constancy of the relationship of food consumption to income when cross country data are used in estimation.
t
1.3 Objectives of Study
The major purpose of this study is to estimate the relationship of economic development (represented by per capita income) to the consumption of total food and selected food subgroups by using a measure which accounts for diet upgrade and completely reflects final consumption change on primary agricultural product demand at various levels of economic development.
The specific objectives of this study are:
1. Using unweighted pooled data, i.e. 1961-1985 time series data from countries at various economic development stages, to estimate the relationship of per capita annual consumption of total food and food subgroups to per capita annual income level when income level serves as the only explanatory variable and test the constancy of this relationship over time; 2. To determine the effects of per capita annual income and food price level on the consumption of total food and food subgroups;
3. To measure and contrast income elasticities of food consumption using three alternative food consumption measures; calories, expenditures, and cereal equivalents. CHAPTER II
REVIEW OF LITERATURE
In this chapter, studies using cross country data to measure the relationships of total food or selected food subgroup consumption to economic development (per capita income or per capita total expenditures) are reviewed.
Because food is a general name which includes various heterogeneous food items, different measures are used to aggregate food items together in various studies to achieve their purposes. Therefore, the measures of food consumption used in pervious studies are discussed first. The functional forms used are discussed second. Finally, the results of selected studies are summarized.
2.1 Measurement of Food Consumption
Studies investigating the relationship of food consumption to per capita income can be divided into four categories by the measure used to aggregate food items. The first category uses calories or other nutrients to measure food demand and looks at the quantity changes of nutrients
10 or the changes of diet composition in nutrient terms with
respect to per capita income levels. The second category
uses expenditures to measure food consumption and
investigates the relationship of food expenditures to per
capita total expenditures or income. The third category uses
the weight units to measure the consumption of a food group
and relates the weight units of a food group to per capita
total expenditures or income. The last category uses cereal
equivalents or primary food energy concepts to measure food demand and investigates their change as incomes change.
The study by Bennett [2] is one of the earliest to
investigate how the composition of food in caloric terms changes following income changes. The relationship of per capita income in US dollars to the proportion of calories derived from starchy staples (starch staple ratio) is investigated. Blandford [3] investigated the relationship of per capita income to total calorie consumption, and to the calorie share of animal products, which is equal to one minus the starchy staple ratio, country by country for 21
OECD countries in 1960-1980. Jureen [17] used 16 European countries' data to estimate the relationship of calorie consumption from total food, animal products, and vegetable products to per capita income. Thomson [44] estimated the relationship of per capita total expenditures to per capita total calorie consumption, protein consumption, percentage of protein from animal sources, and to the starchy staple 12 ratio. Goreux [12] estimated the income elasticities of the per capita consumption of calorie, fat, and animal protein by using the pooled time series data from different countries. Marks et. al. related caloric percentage of three food groups - coarse grains (maize, millet, barley, sorghum), wheat and rice, and meat to per capita income.
Expenditure is another way to aggregate food items into food groups. Houthakker [21], Weisskoff [49], Kravis et. al.
[24], and Theil [43] used food expenditures at constant prices (food expenditure divided by food price index) to measure food consumption. On the other hand, Goreux,
Houthakker [20] also used food expenditures to measure the relationship of food consumption to per capita total expenditure.
In addition to estimating the relationship of calorie shares of selected food subgroups to per capita income,
Marks et. al. estimated the relationship of income to consumption in weights of three food subgroups - coarse grains (maize, millet, barley and sorghum), wheat and rice, and meats (beef and buffalo, pork, poultry, sheep and goat).
To permit direct comparison among cereal consumption in their study, the edible primary and secondary products of wheat, rice, maize, millet, barley and sorghum were converted to a wheat equivalent unit. This conversion procedure was to calculate the total calories contained in each individual cereal product and then divide by 2.53 13 million calories to arrive at a nation's annual consumption of that commodity in wheat equivalent units. This conversion method is similar to the concept of cereal equivalents or primary food energy. Although the Marks et. al. study converted different cereal products into wheat equivalents, they did not convert the different products of meat into a common unit which is comparable with wheat equivalent units.
They merely summed up the weight of different meats.
In order to appropriately measure the demand for primary agricultural products, N. Rask [37] applied the concept of cereal equivalents to measure the food demand across various diets. A sample of 65 countries, representing various geographic, income and resource situations was selected for a preliminary investigation of the food consumption-income relationship. On the other hand, F. M.
Sanderson et. al. [39] used the primary food energy concept to measure food demand.
In the cereal equivalents category, food commodities which can be consumed directly are converted into cereal equivalents by conversion ratios and then are aggregated together to measure total food or food group consumption.
For the primary food energy concept, primary agriculture products used directly and indirectly for food purposes are converted into calorie units and then are aggregated together to measure the consumption of total food. Although the primary food energy concept also uses calories to 14 measure food demand, it measures the calories contained in the primary agricultural products which are consumed directly and indirectly for food purpose. This concept is totally different from the concept of using calories contained in the final food to measure food consumption.
The concepts of cereal equivalents and primary food energy are similar. The former concept converts backward food commodities consumed directly to a common unit. The latter converts forward the primary agricultural products consumed directly or indirectly for food purpose to a common unit.
2.2 Functional Forms and Explanatory Variables
Income and food price are the two factors used commonly to explain changes in food consumption. However, owing to the fact that income data are easier to secure, using income only to explain food consumption deviation across countries is also very common.
There are two approaches that can be adopted for food demand analysis. They are the pragmatic approach in which the demand functional form does not refer to any utility function, and the theoretical plausible approach in which the demand functional form refers to a utility function.
There are numerous functional forms that can be used to regress food consumption on income in the pragmatic 15 approach. Reciprocal, hyperbolic and log-inverses functional forms are the most common forms used to regress the food consumption in terms of calories to per capita income or total expenditures, because these three functional forms have the property that consumption will increase at a decreasing rate as incomes rise. This property is consistent with the physiological limitation of food consumption in terms of calories.
For estimating the relationship of total food consumption in terms of calories contained in final food to per capita income, Blandford and Jureen used reciprocal and hyperbolic functional forms respectively. Goreux used a log- inverse functional form. In Mark et. al. study, they used polynomial functional forms to estimate the relationship of calorie share of selected food subgroups to per capita income in order to trace the economic stages of diet composition change in calorie terms.
In order to avoid the income conversion problem among countries, Thomson applied the first difference of double logarithmic functional forms. Because the first difference of the logarithmic value of a variable is the growth rate of that variable, Thomson, in fact, regressed the growth rate of the food consumption indicator to the growth rate of per capita total expenditures. For picking up the long term effect of per capita total expenditure changes, Thomson took the average of the first difference of the logarithmic value 16
for each country, and used these average values to estimate
the food demand and per capita total expenditure
relationship. In order to catch the effect of per capita
total expenditure levels and age distribution, two more variables are used. A dummy variable which divides all of
the sample countries into developing and developed groups
and a variable of population growth rate which is a proxy
for age distribution are included as explanatory variables
in addition to the growth rate of income:
When the relation of food expenditure to per capita total expenditure is investigated, double logarithm, first difference of double logarithm, and semi-logarithm
functional forms are used by different studies.
Houthakker is the first one to apply the first difference of the double logarithmic functional form to regress food expenditures at constant prices to total expenditure and food prices. In order to catch both the short and long term effect, Houthakker separated regressions
into "within" and "between" countries. These regressions can be shown as follows:
"within" countries regression
(1) ^log qjt - *iog = /?w ( ^log ujt - ^log Uj )
+ rw ( a log pjt - a log p i )
"between" countries regression
(2) ^ l o g qj = j0b^log Uj + rb ^log pj
Where qjt is per capita expenditure at constant food 17 prices in country j in year t . ujt is total consumer's expenditure at constant price per capita in country j in year t. pjt is the relative price of food (that is, the implicit deflator for food divided by the implicit deflator for total consumption) in country j in year t. ^'s indicate the first difference (thus ^-log ujt = log ujt - log Uj t_1 ) .
Barred terms are the means over all the years of the first differences for each country.
Weisskoff applied the same methodology as Houthakker to estimate total expenditure and price elasticities of food expenditure at constant prices among a group of developing or low-income countries. Meanwhile, to test if population changes exert an independent influence, aggregated data of food expenditure at constant price and income are used to do the analysis and population level is added as one of the explanatory variables.
Houthakker and Kravis et. al. also used purchasing power parity data to convert cross country data of per capita total expenditure and food price level to estimate the relationship of food expenditures at constant price to per capita total expenditures and food price levels through double logarithmic functional forms.
On the estimation of the relationship of food expenditure to per capita total expenditure by using household survey data from various countries, Houthakker used double logarithm functional forms, but Goreux applied a 18
semi-logarithm functional form.
When weight units of three food groups are regressed on per capita income, Mark et. al. applied polynomial
functional forms again to find the economic stages of income elasticity changes for these three food groups.
Like the pragmatic approach, there are also many different functional forms that can be used for the theoretically plausible demand functional forms. Kravis et. al. used the Linear Demand System to estimate the demand equations for food and the other three consumer's commodity groups. Theil used Almost Ideal Demand System to estimate the demand equations for food and the other nine consumer's commodity groups. Both studies assumed that tastes among different countries are the same and there exists a two- stage budget process in consumer's consumption decision.
2.3 Results of Previous Studies
When food demand is measured by calories, the major factor affecting consumption is per capita income (GDP, GNP) level . However, at high income levels, the consumption of calories from total food is largely stable [3,12,44]. This relationship of calorie consumption to income exist not only within countries through time, but also between countries, despite differences with respect to geographic location, consumption habits, etc. [17]. While total calorie intake increases initially as incomes grow from low levels, diet composition changes also occur at the same time. This leads to an increase in the percentage of calories from animal products as incomes rise
[3,12,44]. At the same time, the percentage of calories from starchy staples (starchy staple ratio) declines [2,44].
However, the relationship of the starchy staple ratio to per capita income is not clear between nations with the lowest per capita income [2]. In addition to the problem of using exchange rates to convert incomes in different currencies into a common unit, Bennett attributed the deviation to preferences or habits and to food price differentials among countries.
When food demand is measured by expenditures, per capita income level is still the major factor affecting food expenditures. Whether a pragmatic or theoretically plausible approach is used, the income elasticity of food expenditure is diminished as incomes rise [12,20,43]. The continuous increase of food expenditures across all income levels is attributed to a food quality improvement as incomes grow
[20]. Furthermore, income elasticity of food expenditure is less than one whether cross country data or cross section data from a country are used. This result is consistent with
Engel's Law. However, the income elasticity of expenditure on some food groups, such as meat, milk products, and sugar are greater than one at low income levels and then decrease 20 gradually following income growth.
In contrast to the above, the income elasticity of food expenditure in Houthakker's studies [20,21] is constant over different income level. This constant income elasticity of food expenditure results from the use of the first difference double logarithmic functional form which is criticized by Russel based on demand theory [38]. Similarly, the income elasticity of food expenditure in Kravis's et. al. study approaches one as incomes change. This unreasonable result is because they used a Linear
Expenditure Demand System Model which has a linear Engel curve.
Although Houthakker argued that to specify the period of adjustment in demand analysis is important, he did not obtain a consistent relationship of income elasticity of food expenditures between long term and short term.
Houthakker hypothesized that the income elasticity of food expenditures in the long term is greater than in the short term. He used a stock adjustment model to justify his hypothesis. The results from using a first difference double logarithmic functional form support his hypothesis, but he later contradicts his own hypothesis when using a double logarithmic functional form. In addition, the results in
Weisskoff's study which used the first difference double logarithmic functional form were also contrary to
Houthakker's hypothesis. 21
When food demand is measured by the weight unit, Mark et. al. found that the quantity demanded (not including non- agricultural services) and the quantity income elasticity for cereals (wheat and rice) and meats rise at low income levels then fall as incomes increase. Coarse grain quantities demanded decrease in a nonlinear manner as income rises, but its quantity income elasticity remains constant.
When cereal equivalents and primary food energy are used to measure food demand, their relationship with respect to income are relatively similar. The increase in food consumption is rapid at low income levels and becomes moderate at middle income levels and finally is quite stable at high income levels.
From the scatter diagram of per capita income and food consumption in terms of cereai equivalents, it was found in
Rask's study that annual per capita consumption of food is generally between 0.3 - 0.5 metric tons of cereal equivalents for low income countries. But in high income countries, he found that food consumption will increase to over 2.0 metric tons of cereal equivalents. There exist substantial deviation in some countries from the generalized relationship. Rask attributed this to three reasons: income distribution, food prices, and resource endowment.
In F. M. Sanderson et. al. study, food demand in terms of primary food energy (including feedstuffs needed for conversion into livestock products) increases from 2,000 - 22
3,000 calories a day in the least developed countries to as much as 12,000 calories a days in the most affluent countries. Both studies come to similar conclusion concerning the change in per capita food demand (up 4 - 7 times) from low to high income countries.
2.4 Limitations of Previous Studies
All of the above studies, except those by Rask and
Sanderson et. al., do not measure the demand for primary agricultural products. Calorie is a physical measure, and can be assumed to be reasonable common across countries for a given food item and to be invariant through time.
Therefore, a calorie measure can simplify cross-country comparisons of diet changes in food consumption. But the use of calories in aggregation means that greater emphasis is given to food items which are high in calories per unit e.g. cereals and starchy food items, and less weight is given to most fruits, vegetables which are low in calories per unit
[2]. At the same time, using calories to measure livestock product demand will substantially under estimate the demand for primary agricultural products because the calories provided by livestock products are significantly less than the calories contained in the feed grains used to produce the livestock. Hence, using calories to aggregate food demand will underestimate the primary agricultural product 23 demand.
Using expenditures is not an appropriate way to measure the demand for primary agricultural products either, because the total expenditure or income elasticities of food expenditure include not only the effect of diet composition changes but also the increase of non-agricultural services
(quality change). Even though the total expenditure elasticity of food expenditure at constant prices still has a part of the increase of non-agricultural services because the food price index, which is calculated based on a constant food composition, cannot capture all these quality changes. This quality change effect on total expenditure elasticity of food expenditure at constant prices is discussed in detail in section 3.3.
Furthermore, food price levels in different countries are usually influenced by price policy in that country.
Therefore, higher food expenditures do not absolutely represent more demand on primary agricultural products.
Although income elasticities of food expenditure give us some idea of what happens to the demand for food in terms of money as income rises, they also include increased non- agricultural services purchased along with the food and the effect of food price policy. Hence, using food expenditures to measure food demand will estimate the demand for primary agricultural products incorrectly. Therefore, the relationship between food consumption and income level in 24 the above-mentioned studies cannot help us to identify how the demand for primary agricultural products will change following income growth and how food price levels will affect this demand.
Both cereal equivalents and primary food energy measures more closely approximate primary agricultural product demand. However, up to the present time, there exists no quantitative analyses relating primary agricultural product consumption to economic development.
It is also a problem, that most previous studies did not appropriately convert income from different countries into a common unit. Because of the non-availability of purchasing power parity data, most studies have used official exchange rates to convert per capita income or total expenditure from different countries to a common unit.
However, there always exists a gap between official exchange rates and purchasing power parity, especially at low income levels. This inappropriate income conversion approach also affects the precision of estimation.
Almost all of the studies pointed out that food price is an important factor affecting food consumption. However, when we convert prices from different national currencies into a common unit, the problem is the same as that for converting income. Therefore, most of the previous studies omitted the food price variable when they estimated food demand with respect to income. Although using the first difference of double logarithmic functional form can avoid the conversion problem of income or total expenditure, and food price from different countries, this functional form makes income or total expenditure elasticity constant. This characteristic of the double logarithmic functional form is in contrast to the finding in most of the food demand studies, that income elasticity will decline as income increase. Especially, when international data are used, the variation of income is so large that we cannot neglect the fact of declining income elasticity.
Thus to appropriately estimate the demand of primary agricultural products during economic development, it is necessary to convert various food items into groups by using a measurement which can approximate the demand for primary agricultural products. It is also necessary to use functional forms which allow income and food price elasticities to change as income levels change. CHAPTER III
THEORETICAL CONSIDERATION
3.1 Introduction
In estimating a food consumption-income relationship, various food items need to be aggregated■into food groups by a common unit. Hence, the problems of commodity aggregation need to be addressed. That is, are the commodity groups selected and the measure chosen in this study consistent with economic theory. A discussion of the cereal equivalents measure and its application as it relates to conventional theory is given in section 3.2.
Calories and food expenditures are the two most common measures used to aggregate non-homogeneous food items into food groups as noted earlier. Many studies have investigated the quantitative relationships of level of economic development to the consumption of total food or food subgroups measured by calories or expenditures. As contrasted with these two common measures, cereal equivalents is an intermediate measure which includes the diet changing effect on food demand but excludes the non- agricultural services included in an expenditure measure. It 27 quantifies the diet upgrade by giving allowance to food items which need more primary agricultural products to produce.
From a physiological point of view, per capita food consumption measured either by cereal equivalents or calories has a maximum limitation. On the other hand, from a quality view point, per capita food consumption measured by either cereal equivalents or expenditures includes the diet upgrade effect. In section 3.3, the similarities and differences of these three food consumption measures are discussed.
Usually, per capita income is used to represent the level of economic development in a country. Because income growth is the major reason causing diet upgrade, income elasticity of food consumption is an important indicator for policy making. Using different measures to aggregate non- homogeneous food items into food groups will produce different income elasticities of food consumption. Thus, one of the objectives in this study is to contrast the income elasticities of the consumption of food groups for each of the three measures: cereal equivalents, calories and food expenditures. Further, since these differences in income elasticities can be expressed as a mathematical relationship, this relationship is discussed also in section
3.3. In order to explore the generalized per capita consumption-income relationships, pooled time series and cross country data are used for estimation. However, estimating food consumption-income relationships from pooled time series data from different countries may introduce measurement problems not generally encountered when using survey data from a single country. Differences in tastes and food prices between countries and years, for example, might not give a random effect, but instead exert a systematic influence upon this relationship. Therefore, in section 3.4, the influences of taste and price differences between countries and years on the constancy of the food consumption-income relationship are discussed.
3.2 Theoretical Consideration of Food Demand under
Alternative Measurements
In demand theory, commodities are regarded as well- defined and distinct. This, however, in a practical sense, is a simplifying abstraction about real phenomena. Empirical demand studies generally retreat from this restrictive constraint and focus on a selected commodity group from which consumers can obtain the same services rather than a single well defined commodity. There are two practical reasons why empirical studies generally estimate the demand of a commodity group. First, almost all of the published 29
consumption data under a commodity name is a group label of
a more or less vague aggregate of different items and
qualities; second, from the view point of government policy,
studies of the demand for a commodity group such as meat,
food, etc., have greater policy application than do studies
of a distinct commodity. In this study, Cereal equivalents
is used to aggregate food items into food groups. This
raises the question: Can the restrictive assumptions of
demand theory still apply to the demand of food groups which
are aggregated using cereal equivalents measure ?
Usually there are three alternative measures for commodity aggregation, (1) a direct sum of the sub-item quantities such as summing up different qualities of corn by weight units; (2) a weighted sum of the sub-item quantities such as using calorie or cereal equivalent content in each food item as a weight to do the aggregation; (3) a sum of the sub-item expenditures such as using the price of each sub-item as a weight. The first two aggregations drop the assumption that the demand functions belong to a preference field [50]. The third aggregate in procedure, using price as a weight, will connect to a preference field and be consistent with demand theory if the prices in that group move parallel, or consumers' preference can fulfill the two- stage utility maximization process.
When prices are used as a weight to aggregate non- homogeneous commodities and if the prices in that commodity group move in parallel, then the corresponding group of
commodities can be treated as a single good as defined in
demand theory by the expenditure at constant prices. In this
case, all commodities with prices moving parallel can be
grouped together. This is the Composite Commodity Theorem
[8]. This theorem allows the application of demand theory to
a commodity group in low level aggregation. But, the
usefulness of this theorem in constructing commodity
groupings at higher aggregation levels for empirical
analysis is likely to be limited, because commodity groupings are usually made in which the individual
commodities have a close substitution relationship with each other, while their relative prices are largely independent.
When prices are used as a weight to aggregate non- homogeneous commodities and consumers assign their budget by a two- stage utility maximization process then commodity groups can be established based on consumers' utility functions. The sufficient conditions for two-stage utility maximization are that there exists weak separability of utility for each commodity group and either (1) there are only two commodity groups, or (2) each group quantity index can be written in direct form as a homogeneous function of the quantities in the group, or (3) the quantity indices for all groups but one (say the first) are homogeneous, and total utility may be written as y = y (x1# H(x2,..... z5^)) where y is a total utility function and xi is the utility level of commodity group i, or (4) the quantity indices for all groups beyond the dth are homogeneous, and y may be d written m the following additive form as y = y ( ^Gpfx,.) +
H(xd+1,...... z5^)) where y is total utility and x1 is the utility level of commodity group i. In this case, the commodity groups should be established on the basis of consumer utility functions and cannot be established arbitrarily. The process of two-stage utility maximization is that the decision at each stage can be thought of as corresponding to a utility maximization problem of its own.
In the first stage, a full utility function would determine the allocation of a budget to each commodity group. In the second stage, the sub-utility function would determine the allocation to each particular commodity.
There are two very restrictive conditions that allow commodity group aggregation to be consistent with demand theory: Price of each commodity in a commodity group must move in parallel or consumers must allocate their budget through the two-stage utility maximization process. When these conditions are not met, even if prices are used as weights to do the aggregation, the demand function of a commodity group will not be a theoretical plausible demand function. It will be just a statistical relationship between the expenditure on that commodity group and its explanatory variables. In this case, the four properties for a demand system, including homogeneous, adding up, symmetry and negativity, will not be necessary for this non-theoretical plausible demand function. For simplicity, almost all the demand studies use prices as weights to aggregate food items at different level into groups based on their study purpose and assume that consumers allot their budget through the two- stage utility maximization process.
As mentioned earlier, using calorie'or cereal equivalents to aggregate food items into food groups drop the assumption that the demand function of a food group belongs to a preference field. Therefore, when the cereal equivalents measure is used to aggregate the non-homogeneous food items into food groups, the regression of food group demand on food price and income will not be a theoretical plausible demand equation but simply a statistical relationship without utility theory [43]. For simplicity in the terminology, it is also called a demand function (or
Engel function when food price is not included as an explanatory variable) for food groups in this study. It is important to note that the use of this relationship is not intended to imply that consumers base their food consumption decision on the common unit value of that food group. The estimated functions should be viewed as descriptive devices to deal with the problem of aggregating non-homogeneous food items with multiple attributes [3]. One way to estimate the relationship of food group demand in terms of cereal equivalents to the level of economic development is to estimate, based on demand theory, the quantity demand of each distinct food item as the economy develops and then convert the demand of each distinct food item into cereal equivalents measure, add them together and finally relate the food group demand measured by cereal equivalents to the level of economic development in a single country.
But in fact, the above method is impossible to do. The data of per capita food consumption and per capita income do not exist for a single country across all levels of economic development. Therefore, we have to pool data from different countries at various stages of economic development.
However, the composition of food consumption between countries is different. Many food items consumed in one country are not consumed in other countries. Hence, it is nearly impossible to estimate the relationship of food groups demand measured by cereal equivalents to economic development through estimation of the demand of each distinct food items first. Although the demand of food groups measured by cereal equivalents is not theoretical plausible, it can be estimated and used to trace the influences of diet composition changes on primary agricultural product demand during economic development stages. 34
3.3 Characteristics of The Demand of Food Groups Measured by
Cereal Equivalents
As mentioned in section 3.1, calories and expenditures
are the two most common measures used to aggregate various
food items into food groups. Cereal equivalents is an
intermediate measure between calories and expenditures.
The calorie measure of final food consumption does not
account for the quality difference among calorie sources.
The cereal equivalent measure is similar to the calorie measure for basic vegetable, fruit, and grain food
commodities consumed directly as it relates calorie
difference per unit of these food items to cereals. However,
the cereal equivalent measure incorporates diet upgrade by
applying a primary agricultural product concept to measure
the total input of grain equivalents necessary to produce
the livestock products consumed. Due to the production
inefficiency in converting grains to livestock products, the
consumption changes from low to high income levels will be
several times higher by using the cereal equivalent measure than by using a calorie measure. However, the consumption of
food either in terms of cereal equivalents or in terms of
calories will have a physical limitation.
Different from caloric and cereal equivalents measures,
food expenditures include not only the diet changing effects 35 as measured by higher costs for livestock products and other exotic food items but also the non-agricultural services which are not subject to physiology consumption limitations.
Based on the characteristics of these alternative measures, the demands of total food and selected food subgroups measured by cereal equivalents are discussed below.
It is well known that annual per capita caloric consumption from total food will increase at a decreasing rate when per capita incomes rise. On the other hand, based on Engel's Law, annual per capita expenditures for total food also increase at a decreasing rate when per capita incomes rise. Because the cereal equivalents measure is an intermediate measure between calories and expenditures, it is reasonable to expect that the consumption of total food measured by cereal equivalents increases at a decreasing rate also. However, the implicit diet upgrade effect in the cereal equivalents measure will make the maximum consumption quantity of cereal equivalents occur at higher income levels than the total calorie consumption. Therefore, per capita total food consumption measured by calories reaches a physiological satiation consumption level first, then followed by cereal equivalent measure, while total food expenditure will continue to increase without limitation.
Animal products are the major component of total food at middle and high income levels when food consumption is measured by cereal equivalents. It is also well known that 36 the consumption of animal products measured by calories will increase at a decreasing rate. Like the consumption of total food measured by cereal equivalents, it also reasonable to expect that the consumption of animal products measured by cereal equivalents increases at a decreasing rate. Because animal products are preferred goods in the economic development process, their income elasticities will be higher than income elasticities of total food demand which includes starchy staples, the least preferred food items.
Starchy staples are the major food source at low income levels and barring a significant level of country wide malnutrition the consumption of starchy staples measured by cereal equivalents should decline as incomes rise. That is, the income elasticity of starchy staple demand should be negative.
In the above, we discussed the relationships of per capita income level to the consumption of total food and selected food subgroups measured by cereal equivalents. The magnitude relations of income elasticities of each food group measured by expenditures, cereal equivalents and calories are compared below through their mathematical decomposition equations.
The income elasticity of a food group measured by expenditures can be written as follows:
(3) S Pi q, = f ( I, Z ) 37
ASp, q, Al 3SP1 q, I (4) e = / ---- =------Sp, q, I 31 Sp, q, where p, : price of food commodity i
q, : per capita consumption of food commodity i
Z : other variables affecting food consumption
I : per capita income
eE: income elasticity of a food subgroup measured by
expenditures (or expenditure income elasticity). t ASp,q, Al AP iQi AP2q2 APnqn (5) e = / = ( + + + ) / ---- Sp^, I Sp^, Sp^, Sp,q,
api qx I ap2 q2 I a Pn qn I = + + + --- ai sp^, ai sp^, a i sp^,
aqx I q, I aq2 I q2 I — — — Pi + — — — p2 +. ai qj I Sp^, ai q2 I Sp,q,
5q n i q n I + — Pn ---- ai qn I sp,q,
apx i Pj i ap2 i p2 i + -- — q, + q2 +, ai Pl i sPiqi ai p2 i sPigi
5Pn 1 Pn + ------— 9n ai Pn I Sp,q,
denote — --- = e, ai q, 38
aPl i 31 Pi
E SPi q,
(6) eE = elp w:E + e2p w2E + + e,e2 w+ Wj + e2 w2 +
Where ei : quantity income elasticity of food commodity
i i eip : price elasticity with respect to income
w^ : expenditure share of food commodity i
From the above expansion of the income elasticity of a food group measured in expenditure, it is clear that income elasticity of a food group is the sum of the weighted quantity income elasticity of each item and the weighted price elasticity with respect to income. The weights are the expenditure shares of each food commodity.
The income elasticity of a food group measured by calories can be written as
(7) Sk, q, = f ( I, Z )
ASki qi Al 3Sk1 qi I (8) ek Sk, q / I a I Sk, where k1 : calories contained in food commodity i
qi : per capita consumption of food commodity i
I : per capita income
Z : other variables affecting food consumption 39
ek : income elasticity of a food subgroup measured by
calories (or calorie income elasticity).
By the same procedures used to expand the income elas ticity of a food group measured by expenditure, the income elasticity of a food group measure by calories can be decom posed as
(9) ek = ej Wjk + e2 w2k + ...... + en wnk where ek : income elasticity of a food subgroup measured by
calories.
ei : quantity income elasticity of food commodity i
w^: caloric share of food commodity i
From the above expression, income elasticity of a food group measured by calories is the weighted average of quantity income elasticity of each food commodity. The weights are the caloric shares of each food commodity. Because the calorie conversion factors are constant, the terms of caloric conversion factor elasticity with respect to income vanish.
By the same procedures used to expand the income elas ticity of a food group measured by calories, the income elasticity of a food group measured by cereal equivalents can be written as
(10) SCi q, = f ( I, Z )
A Sciqi AI 3Sci qi I (11) ec = ------/ -- = ------^c1qi I 31 Sci q, 40 where ci : cereal equivalent conversion ratio for food
commodity i
qi : per capita consumption of food commodity i
Z : other variables affecting food consumption
I : per capita income
ec : income elasticity of a food subgroup measured by
cereal equivalents (or cereal equivalent income
elasticity).
Similar to the income elasticity of’a food group measured by calories, the income elasticity of a food group measured by cereal equivalents can be expanded as
(12) ec = e: Wj0 + e2 w2c + ...... + en wnc where ec : income elasticity of a food subgroups measured by
cereal equivalents
w^ : cereal equivalent share of food commodity i
ei : quantity income elasticity of food commodity i
From the above expansion equation, the income elasticity of a food group measured by cereal equivalents is the weighted average of the quantity income elasticity of each food item. The weights are the cereal equivalent share of each food item. As in the expansion of caloric income elasticity, the term of cereal equivalent conversion factor elasticity with respect to income vanish in the cereal equivalent income elasticity expansion equation.
When the cereal equivalents measure is used, animal products account for a greater share in total consumption 41
than when a calorie measure is used. The relatively higher
quantity income elasticity and high shares of animal
products make the income elasticities of total food
consumption measured by cereal equivalents higher than
income elasticities of total food measured by calories at
any specific income level.
However, the relative magnitude between calorie income
elasticity and cereal equivalent income elasticity of total
food demand cannot be analogized to the relative magnitude
between calorie income elasticity and cereal equivalent
income elasticity of animal product demand, since each item
in the animal product group has a positive quantity income
elasticity. If the cereal equivalents measure gives greater
shares to some animal product items which have lower
quantity income elasticities, then the cereal equivalents
income elasticity of animal product demand is less than the
calorie income elasticity and vice versa.
The relative magnitude of cereal equivalent income
elasticity and expenditure income elasticity of total food
demand is more complex. From the expenditure income
elasticity expansion equation, the magnitude of expenditure
income elasticity is decided not only by the expenditure
shares of each food item but also by the price elasticity with respect to income. Under the following two conditions the expenditure income elasticity of total food demand is
equal to the cereal equivalent income elasticity : (1) the cereal equivalent conversion ratio of each food commodity
can consistently reflect the production cost difference, and the retail price of each food commodity has the same percentage markup on its agricultural production cost; then the cereal equivalent share of each food item is equal to expenditure share. (2) food prices paid by consumers are the same at different income levels, then the price elasticity with respect to income is equal to zero. However, the quality of food consumed by different income levels is usually different. This quality difference is reflected by food price. Therefore, expenditure income elasticity of total food demand, in fact, is the sum of the quantity elasticity and the quality elasticity (or price elasticity with respect to income). The latter is shown to be considerable in many cases [20]. The quality difference of food items among different income level cannot be captured by a price index since a food price index is calculated based on a basket of food items with constant quality.
Therefore, even though total food expenditure at current price is deflated by a food price index, part of the quality content still remain in the food expenditures at constant price. Hence, the income elasticity of food expenditure at constant price includes two portions : quantity income elasticity and part of quality income elasticity. If the quality income elasticity is large, then the income elasticity of total food demand measured by expenditure will r
43
be higher than the income elasticity of total food demand
measured by cereal equivalents. The above relative magnitude
between expenditure income elasticity and cereal equivalent
income elasticity of total food can also be applied to
animal products and starchy staples.
3.4 The Constancy of Food Consumption-Income Relationship
over Time
Pooled time series data from various countries are used
in this study to investigate the generalized per capita food
consumption-income relationship by regressing annual per
capita consumption of food on annual per capita income. This
raises a question of constancy of these relationships over
time, since several factors including difference in tastes
and food prices in different countries and years may result
in instability of this food consumption-income relationship.
In the following, conditions which result in instability of
food consumption-income relationships are discussed.
The food consumption level for an individual is
determined by his income, and prices prevailing in the
market in conjunction with his tastes. Tastes, as distinct
from income and prices, are non-observable. Therefore, when
time series data of a country are used to estimate demand
functions, tastes are assumed to be the same in different years. If tastes are different over time, then demand 44
functions will change over time. When cross section data of
a country are used to estimate Engel functions, tastes and
prices among each observation are assumed to be the same or
to give a random effect on the demand of a commodity.
When time series data from different countries are
pooled together and used to estimate food consumption-income
relationship, both the same trend change in tastes or food price over time and the systematic difference of tastes or
food price among countries will result in the changes of this relationship over time. The effect of systematic difference of tastes or food prices on this relationship can be shown in Figure 1.
Assume there exist no same trend change in tastes in each country over time, the food price levels are the same in all countries over time, and consumers in low, middle and high income countries have the Engel curves of I, II and III respectively based on their individual preferences. Then, at time T, we will obtain an Engel curve DT and, at time T+l when the per capita income of each country increases, we will obtain an Engel curve of DT+1. It is clear that the
Engel curve will shift over time when low income countries enter middle income stages and middle income countries enter into high income stages.
In this study, the food consumption-income relationship is estimated by using pooled time series data from different countries. Food prices are different among countries and 45
A' T+l Per Capita III
Consumption II
Per capita income
Figure 1. The Shift of Engel Curves over Time from
Systematic Difference in Tastes 46 over time. This food price differences may also cause instability of food consumption-income relationship over time. The possibility of food consumption-income relationship change from the systematic difference in food prices among countries at different stages of economic development has been mentioned in Rask's study [37].
" Many of these developing countries will need to
import corn or other feed grains, at costs
significantly greater than those that presently exist
in the exporting countries of North America and Europe.
These exporting countries currently constitute the high
consumption phase of the sample countries. Thus, as
more countries pass through the development phases, the
income-consumption curve will probably plateau at
a slightly lower level."
From the above discussion, It is clear that there are several possible reasons for food consumption-income relationship change over time. However, if this relationship remains stable over time, then we can conclude that the differences in tastes and food prices in different countries and years result in random effects on the food consumption.
If stability is confirmed, this will offer valuable support for the use of cross country time series data to estimate the food consumption-income relationship. Furthermore, the random effects of tastes will support the use of cross country data to estimate the demand function of food groups. 47
The difference of tastes among different countries will not result in the instability of food demand function but merely reduce the estimation precision.
t CHAPTER IV
RESEARCH PROCEDURES AND METHODOLOGIES
In this chapter, dependent and independent variables are discussed first and a number of different functional forms are fitted for Engel and demand functions of total food and selected food subgroups. The functional forms which can best fit the empirical data and correspond to the consumption behavior of food measured by cereal eguivalents are chosen. Then, a moving regression test method is introduced in section 4.2 for testing the constancy of the
Engel functions of total food and selected food subgroups.
Finally, the principles of converting various food items into cereal equivalent units and the way to convert per capita income and food prices in local currency into constant 1980 U.S. dollars are shown in section 4.3
4.1 The Variables and Model Specification
In this study, food consumption is used to represent the changing demand placed on primary agricultural products at various stages of economic development. Diet upgrading to include increased quantity of livestock products will
48 49 increase rapidly the need for livestock feed which is an important component of the increased demand placed on primary agricultural products as incomes rise with development. However, consumers also demand food related services such as product preparations, packing, etc. that become part of the cost of food, but are not related to primary agricultural product demand.
In order to include the diet composition change but exclude the non-agricultural service contribution to final food consumption, cereal equivalents is used to aggregate the consumption of various food items into total food and into selected food subgroups including starchy staples and animal products which are the two most important food subgroups (The food items included in each food group can be found in Table 14 in the Appendix. All food items are grouped as total food; cereals and roots and tubers are grouped as starchy staples? livestock products and fish are grouped as animal products). The principles for converting each food item into cereal equivalents is discussed in section 4.3.
The explanatory variables used in this study include: annual per capita income, prices of total food, prices of animal products, prices of starchy staples, and the price ratio between animal products and starchy staples. The definition of these explanatory variables are discussed in the following. 50
Within the economic literature, per capita income is used frequently as the proxy measure for level of economic development. This same procedure is followed in this study
In most studies using cross country data, exchange rates are used to converted per capita income in different currencies into a common unit. However, exchange rates reflect only the purchasing power of per capita income on internationally traded goods and do not reflect the purchasing power level of per capita income on all domestically traded goods. The practice of using purchasing power parity to substitute for exchange rates can avoid the biased income comparison [14]. However, when exchange rate are used to convert per capita income to common units more countries can be included in the sample. Hence, in this study, both exchange rates and purchasing power parity are used to convert per capita income in different countries into a common unit.
Food price is a second important factor affecting food group consumption, especially at low income levels. Usually the demand for a commodity will be affected not only by its own price level but also by the price levels of its substitutes or complements. Because the functions of food are totally different from other commodities, the substitute and complement relationships between food and non-food commodities are relatively small. At the same time, parallel movement of food and non-food prices raise the collinearity 51
problem in the regression estimation process [45].
Therefore, food price level is used as the only price factor
in the demand function of total food.
Because total food is composed of a mix of commodities,
some studies [3,17,21] have proposed that differences in the
relative prices between individual item in a commodity group
will result in a change of consumption in that commodity
group. Because starchy staples and animal products are the
most important components in the total food group, their
relative price ratios are probably an important factor
affecting the consumption of total food [2,3]. Therefore,
the price ratio of animal products to starchy staples is
chosen as one of the explanatory variables for estimating the demand functions of total food. On the other hand, in the diet upgrade process, consumers will shift food consumption from starchy staples to animal products.
Therefore, both starchy staple and animal product prices are
important factors that affect the demand of these two food subgroups and are included as explanatory variables in the demand functions.
Using average figures of national aggregated income and food consumption data to estimate an individual demand function is not appropriate. This is commonly referred to as the "aggregation problem" [8]. ( The aggregation problem here is usually referred to as the aggregation among consumers. The aggregation problem mentioned earlier in section 3.2 is usually referred to as aggregation among commodity items). The aggregation among consumers can be made without error only under very restrictive assumption :
(1) income distribution remain constant and , (2) demographic composition (sex, education, age distribution etc.) remains constant. If income distribution varies then an exact linear aggregation requires the Engel functions to be linear. But the income elasticity of a linear Engel function will approach one. This property of a linear Engel function is opposite to the fact of physiological limitation on food demand. However, Houthakker and Taylor stated that
"of all of the errors likely to be made in demand analysis, the aggregation (among consumers) error is one of the least troublesome" [22]. Hence, even if the above two assumptions of exact aggregation do not hold, the aggregation error among consumers is always neglected. In this study, we adopt the same view point, and do not use any variable to capture the difference of income distribution and demographic composition among sample countries.
The choice of mathematical forms for total food and selected food subgroup demand functions is a matter of great concern. Usually income elasticity values depend on the types of functions that have been fitted. Therefore, different functional forms will result in different income elasticities from the same data.
For total food and each food subgroup, the following 53
Engel functional forms were estimated.
(13) q = f ( I , I2 )
(14) q = f ( Lnl )
(15) q = f ( Lnl , (Lnl)2 )
(16) Lnq = f ( Lnl , (Lnl)2 )
(17) q = f ( 1 / I )
(18) Lnq = f ( 1 / I ) where Ln : natural logarithm;
q : annual per capita consumption;
I ; annual per capita income.
The semi-log Engel functional forms specified in the following sections fit the empirical data the best (high R2 in regression) and correspond to the characteristics of the demand of total food and selected food subgroups.
Total food, animal products:
(19) q = Oj + a2(Ln I) where Ln: natural logarithm;
q : annual per capita consumption of total food,
animal products measured by cereal equivalent in
kilograms;
I : annual per capita income in U. S. dollar at 1980
U.S. price.
Previous discussion leads to the expectation that per capita consumption of total food, animal products will increase as income increases. Under the above specification, it is expected that 54
3q (20) = olz > 0 3Ln I
The income elasticity is determined by
d q I d q 3Ln I I I I a2
1 31 q 3Ln I 3 I q 2 I q q
Starchy staples
(22) q = + /?2(Ln I)
Where q is the annual per capita consumption of starchy staples measured by cereal equivalents in kilograms. Under the above specification, it is expected that
3 q (23) = p z < 0 3Ln I
The income elasticity is determined by
3q I 3 q 3Ln I I I I (3Z
1 " 31 q 3Ln I 3 I q 2 I q q
When own price, relative price ratio, and income distribution are included as explanatory variables, the following demand functional forms are estimated for the consumption of total food, animal products and starchy staples
Total food demand:
(25) q = f ( Lnl, LnTP, LnRELP )
(26) q = f ( Lnl, TP, RELP )
Animal product demand or starchy staple demand:
(27) q = f ( Lnl, LnAP, LnCP ) 55
(28) q = f ( Lnl, AP, CP ) where Ln : natural logarithm
q : annual per capita consumption of total food,
animal products or starchy staples measured by
cereal equivalents in kilograms.
I : annual per capita income measured in constant
1980 U.S. dollars.
TP : price index of total food.
AP : price index of animal products.
CP : price index of starchy staples.
RELP : relative price ratio of animal product price
index divided by cereal product price index.
The functional forms specified in the following were found to best fit the empirical data and to correspond to the characteristics of the demand of total food, animal products and starchy staples.
Total food demand:
(29) q = Qt1 + a2 Lnl + a3 LnTP + a4 LnRELP
Under the specification of the above functions, the income and price elasticity will be
f a 2 (30) Ej = -----
<*3 (31) EpF=p q It is also expected that the following properties hold in 56
3 q 3 q d q (32) > 0 < 0 < 0 3Ln I 3Ln TP dLn RELP
Animal product demand:
(33) q = (3l + f3z Lnl + jS3 LnAP + jS4 LnCP
Under the specification of above functions, the income and price elasticity will be
A ^2 A ^3 (34) Ej = ------(35) Ep = ---- q ’q It also expected that the following properties hold
3 q 3 q 3 q (36) > 0 < 0 > 0 3Ln I 3Ln AP 3Ln CP
Starchy staple demand:
(37) q = r2 + r2 Lnl + r3 LnCP + r4 LnAP
Under the specification of above functions, the income and price elasticity will be
r2 r3 (38) EjC= ------(39) Epc = -- q q It is also expected that the following properties hold
3 q 3 q 3 q (40) — 7 r- < 0 ------r- < 0 t > 0 3Ln I 3Ln CP 3Ln AP 57
4.2 The Moving Regression Method for Testing Constancy of
Engel Functions over Time
Numerous varying parameter models have been developed
for testing the constancy of regression parameters over
time, such as the Hildrett and Houck model, switching
regression model [28] . In this study the "moving regression"
method proposed by Brown et al [4] and the conventional
method of partitioning sample periods with a priori
information are used. This moving regression method has been
used by Chern et. al. in their residential electricity
demand study [6].
A basic regression model can be written in a compact
form as
(41) Yt = Xt* Bt + ut t = 1,...... T
where at time t, Yt is the observation on the dependent
variable and Xt is the column vector of observations on k
regressors. Column vector of parameters, Bt is written with
the subscript t to indicate that it may vary with time.
Error terms ut are assumed independent and normally
distributed with zero means and variances a 2 , t = 1,....,T.
The hypothesis of constancy over time, which will be denoted
by H0 is
(42) Bi = B2 = ...... = Bt = B
Brown et. al. suggested that the way to investigate the time variation of B is to fit the regression on a short segment
of n successive observations and to move this segment along 58 the time series.
A significant test for constancy based on this approach is derived from the results of regressions based on non overlapping time segments, using the analysis of variance.
The time segments used for a moving regression of length n, are ( 1, n ), (( n+1), 2n ),..... / ((p-2)n+l/ (p-l)n), ((p- l)n+l, T), where p is the integral part of T/n, and the variance ratio considered which is called by Brown et al the homogeneity statistic is
(T-kp) s(l,T) - {s(l,n)+s(n+l,2n)+...... (43) ----- .------(kp-k) {s(l,n)+s(n+l,2n)+......
+s(pn-2n+l,pn-n) + s(pn-n+l,T)}
...... +s(pn-n+l,T)} where S(r,sjis the residual sum of squares from the regression calculated from observations from t = r to s inclusive. This is equivalent to the usual "between groups over within groups" ratio of mean squares. Under H0, it is distributed as F(kp-k, T-kp).
When each segment contains only the observations of one country from different years, then the method proposed by
Brown et. al. is identical to the Chow test commonly known in the econometric literature [22]. 59
4.3 Data
The food consumption model specified in the previous chapter contains six variables. They are : per capita annual food consumption as measured by cereal equivalents, per capita annual income, total food price levels, animal product price levels, cereal product price levels and price ratio between animal products and cereal products. The definition, calculation and source of each variable are discussed below.
The food consumption values are secured from The Food and Agriculture Organization's (FAO) Food Balance Sheet
Tapes. The data cover the period 1961-1985 for about 150 countries. In order to permit direct comparison on per capita food consumption among different countries, each food commodity is converted to cereal equivalents. The table of cereal equivalent convertors for each food commodity is adapted from N. Rask's method (Table 14 in the Appendix).
The principles of his method are as follows:
1. The cereal equivalent convertors for starchy staples such as wheat, rice, barley, rye, oats, millet and tubers etc., are estimated based on their caloric content relative to the caloric content of corn.
2. The cereal equivalent convertors of animal products
(excluding fish) such as poultry, hogs and cattle are determined by the quantity of feed units used in the production of a unit of livestock. A feed unit is the 60 quantity of any feed that is equivalent to the feed value of corn. In order to avoid bias, ten year aggregated data from
1963-1973 in the United States are used to calculate the cereal equivalent converters for animal products.
3. The cereal equivalent convertors for fish are estimated as the same as that for chicken. There are two reasons for this. First, fish is primarily extracted rather than produced; therefore, feed conversion ratio are not available. Second, an opportunity cost concept is used in which the prices of fish are relatively close to the prices of chicken.
Per capita disposable income is the most appropriate definition for the income variable in our model. However, the data of per capita disposable income is not available for many of the developing countries, forcing this study to use estimates of per capita gross domestic product (GDP). To the extent disposable income remains a constant proportion of GDP, justification exists for substituting GDP for disposable income in consumption analysis. The GDP series is then converted to per capita terms by dividing by population. Both the data of GDP and population are obtained from the Financial Statistical Book published by the
International Monetary Fund. Then, the real per capita GDP at 1980 constant prices are adjusted by exchange rate and by purchasing power parity to a 1980 U.S. dollar equivalent.
The exchange rate data are obtained also from the 61
Financial Statistical Book. The purchasing power parity data and the local currency expenditure for total food and each food subgroups are obtained from the United Nations' " World
Comparisons of Purchasing Power and Real Product for 1980."
The data of purchasing power parity cover sixty countries.
i Fifty six out of the 60 countries with purchasing power parity data are used as sample countries in this study. The procedures to calculate the real per capita GDP in constant
1980 U.S. dollars are shown as follows [7],
Symbolically, the purchasing power parity in 1980 between country j and U. S. is
,
1980.
Define the consumer price indices for country j and U. S. as
4 (Pj)t 4 4 4 (45) (IJ), = . ------:----- (46) (pJ)t =(IJ)t * (pJ), (PJ)so
(pus) (47) (Ius)t = ----- — ^--- (48) (pus)t =(Ius)t * (pus)t (Pus)so where (IJ)t = consumer price index in country j in year t with 1980 as base year. Then the purchasing power parity between country j and U.S. in year t is
, (PJ)t (l3)t * (PJ)ao (49) (PPPJ)t = — ------(pus)t (Ius)t*(pus)80 62
(IJ)t * (PPPj)8o (Ius)t To convert nominal GDP in local currency to U. S. dollars we use
(NGDPj)t (Ius)t (50) (NGDPJ)t $ = (NGDPJ)t * ------(PPPj)t (IJ)t*(PPPJ)80 where (NGDPJ)t = nominal GDP in local currency in country j in year t. (NGDPj)t $ = nominal GDP in U.S. dollars in country j in year t.
To further convert nominal GDP in U. S. dollar to real GDP in 1980 U.S. dollar, we use
(NGDPj)t $ (Ius)t (51) (RGDPJ)t = ------= (NGDPJ)t* (Ius)t (Ij)t*(PPPj)80
1 (NGDPj)t - j -/ (PPPJ)80 (I )t
The food price index of total food, animal products, and cereal products are calculated through dividing the purchasing power parity of total food, animal products, and 63 cereal products by the purchasing power parity of all consumer's goods.
t CHAPTER V
EMPIRICAL RESULTS AND IMPLICATION
5.1 Introduction
It was pointed out previously that in order to estimate the relationships of total food, animal product, and starchy staple consumption to economic development, it is necessary to use data from countries at different development stages.
Because data for all determinant affecting food consumption are not always available for each country in the world, samples with different number of countries are used in the analysis.
When per capita income (GDP) in each country is converted by exchange rates into U.S. dollars, the income data of 108 countries from 1961 to 1985 are available. These
108 countries include most of the population in the world except Russia and some eastern block countries. Four oil exporting high income countries, Libya, Saudi Arabia, Kuwait and United Arabia Emirate, are excluded from these 108 sample countries because their high per capita income does not result from a process of general economic development but rather represents sudden wealth from high oil prices.
64 65
The excluding of these four countries can avoid the bias on the estimation of the food consumption-income relationship caused by the possibly highly skewed income distribution.
Purchasing power parity data are available in 56 countries only. Hence, when purchasing power parity is used to convert income into U.S. dollars, the country number in our sample reduces to 56. When the price index of each food group is calculated based on the purchasing power parity of that food group, then the demand functions are estimated from a sample further reduced to 56 countries in 1980 only.
Countries in alternate samples used in this study are listed in Table 15 in the Appendix. The per capita annual consumption of total food, animal product, and starchy staples measured by cereal equivalents, and the per capita annual income in U.S. dollars converted by exchange rates and by purchasing power parity for 1980 are also shown in
Table 15 and Table 16 in the Appendix. The procedures of quantitative analysis on food consumption in this study are as follows.
In the second section, the generalized relationships of food consumption to per capita income are estimated (for simplicity, this relationship is also called Engel function thereafter). Per capita consumption of total food, animal products, and starchy staples are regressed on per capita income adjusted by exchange rate by using the pooled data of
108 countries from 1961-1985. Then the Brown et. al.'s 66
moving regression method is applied to test the constancy of
this consumption-income relationship.
In the third section, the generalized relationship of
food consumption to per capita income is estimated again by
using income converted by purchasing power parity as
explanatory variable. Observations of 56 countries from
1961-1985 are used. However, in order to understand the
representation of the sample with 56 countries, per capita
consumptions of each food group are regressed first on per
capita income converted by exchange rate and then a Chow
test is applied to test if the Engel functions of food
consumption estimated from samples with 56 countries and
with 108 countries are similar. Then, the Engel functions of
food consumptions with per capita income converted by
purchasing power parity are estimated and compared with the
Engel functions with per capita income converted by exchange
rates. Furthermore, the Brown et. al.'s moving regression method is used again to test the constancy of the Engel
functions of food consumption with per capita income
converted by purchasing power parity.
In the fourth section, the demand function of each food group is estimated by using the data of 56 countries in 1980
only with per capita income converted by purchasing power parity, and food price as explanatory variables.
In the last section, we compare the income elasticity of each food group consumption when food consumption is 67
measured by different methods including cereal equivalents,
calories, and expenditures. The data of 56 counties in 1980
are used to estimate the demand functions and to calculate
the income elasticities.
5.2 Estimation of Engel Functions of Food Groups with Income
Converted by Exchange Rates
t
Estimation of Engel Functions
In this section, three semi-log Engel functions are
estimated by the ordinary least square method for total
food, animal products, and starchy staples by using the data
from 108 countries in 1961-1985 (Table 2). Per capita income
is converted to U.S. dollars at 1980 constant prices by
exchange rates.
Despite differences between countries with respect to
geographical position, tradition, consumption habits, and price levels, certain relationships are apparent between per
capita annual income and per capita annual consumption of total food, animal product, and starchy staples. The consumption levels of total food and animal products
increase in a non-linear manner as incomes rise. On the other hand, the consumption level of starchy staple declines but also in a non-linear manner. These imply that total food and animal products are normal goods and starchy staples is 68
Table 2 Engel Functions of Total Food, Animal Products and Starchy Staples, 108 Countries 1961-1985 Data, Income Converted by Exchange Rates
Dependent Coefficients of Explanatory Variable Variable
Constant Ln I R2
Total Food -1752.6* 381.5* 0.65 (42.1) (5.8)
Animal Products -1946.4* 373.1* 0.64 (42.7) (5.8) ★ Starchy Staples 226.0* -9.5 0.10 (4.5) (0.6)
1. Ln = natural logarithm.
1= per capita GDP measured by U.S. dollar from dividing
per capita GDP at 1980 constant price in local
currency by 1980 exchange rate.
2. In each equation, dependable variable is the per capita
annual consumption in kilograms of cereal equivalents.
3. Figures in parentheses are standard errors.
4. Coefficients with represent significant at 5% level. an inferior good in the economic development process when their consumption levels are measured in cereal equivalents.
Because one unit of animal product production needs a several fold greater unit of cereal equivalents, and one unit production of starchy staples needs only about one unit of cereal equivalents, the consumption of total food in terms of cereal equivalents continues to increase in the early and middle stages of economic development despite the decreasing consumption of starchy staples.
The explanatory power of per capita income in the Engel function of starchy staples is merely 0.10. Therefore, per capita income is not an important determinant influencing the consumption level of starchy staples. There should be other determinants, especially the prices of starchy staples, to affect the consumption behavior of starchy staples. However, because the consumption of animal products measured by cereal equivalents not only measures the quantity aspect but also measures the diet composition change and, in addition, because the higher price of animal products relative to starchy staple need to be sustained by higher income level, per capita income is an important explanatory variable in the Engel function of animal products. The Rz of the Engel function of animal products is as high as 0.64. Meanwhile, when total food consumption is measured by cereal equivalents, a large portion of total food consists of animal products. Hence, the consumption 70
variation of total food among different countries can also
be explained largely by per capita income levels. From the
above discussion, it is clear that the growth of per capita
income is a major factor causing the rapid increase in the
demand for primary agricultural products.
Based on the estimated Engel functions, per capita
consumptions of total food, animal products, and starchy
staples at various income levels are given in Table 3.
There exists a six fold increase in>total food
consumption in terms of cereal equivalents between very low and very high levels of income. Total food consumption
increases very fast as income grows from low levels. At very low level of income ( 200 US dollars) annual per capita consumption of total food is 266 kilograms in cereal equivalents. Total food consumption increases sharply to 879 kilograms when per capita income level reaches 1000 US dollars. In this stage, there exists a 280% increase in per capita total food consumption. When per capita incomes rise from 1000 to 3 000 US dollars, total food consumption increase 50%. Then the next increase of 50% of total food consumption requires per capita incomes to rise 11,000 US dollars from 3000 to 14,000 US dollars.
In the consumptions of different food subgroups, starchy staple consumption declines as income increases but at a slow rate. The per capita consumption of starchy staples at very high income level (14,000 US dollars) is 71
Table 3 Per Capita Annual Consumption of Total Food, Animal Products, and Starchy Staples in Terms of Cereal Equivalents at Various Income Levels
Per Capita Animal Starchy Other Total Income Products Staples Products Food (1980 U.S. (Kg.) (Kg.) (Kg.) (Kg.) dollars)
200 30 176 60 266 (0.11) (0.66) (0.23) 400 289 169 72 530 (0.55) (0.32) (0.13) 600 440 165 79 684 (0.64) (0.24) (0.12) 800 547 163 84 794 (0.69) (0.21) (0.10) 1000 631 160 88 879 (0.72) (0.18) (0.10) 1500 782 157 94 1033 (0.76) (0.15) (0.09) 2000 889 154 100 1143 (0.78) (0.13) (0.09) 2500 972 152 104 1228 (0.79) (0.12) (0.09) 3000 1040 150 107 1297 (0.80) (0.12) (0.08) 4000 1148 147 112 1407 (0.82) (0.10) (0.08) 5000 1231 145 116 1492 (0.83) (0.10) (0.08) 6000 1299 143 119 1561 (0.83) (0.09) (0.08) 8000 1406 141 124 1671 (0.84) (0.08) (0.08) 10000 1489 139 128 1756 (0.85) (0.08) (0.07) 12000 1557 137 132 1826 (0.85) (0.08) (0.07) 14000 1615 135 134 1884 (0.86) (0.07) (0.07)
1. Per capita annual consumption is calculated based on the Engel functions in Table 2. 2. Figures in parentheses are the shares of the consumption of each food subgroups. still maintained at 76% of the consumption at very low income level (200 US dollars). On the other hand, the consumption of animal products increase dramatically between very low and very high income levels, increasing from 30 kilograms of cereal equivalents per capita at an income level of 200 US dollars to 1615 kilograms at an income level of 14,000 US dollars. The sharpest increase in animal product consumption, like total food, appears in the income ranges between 200 and 3,000 US dollars.-Although the consumption of other products also increases as incomes rise, there is about a one fold increase only between very high and very low income levels.
Because the consumption of animal products increases so fast, its share in total food consumption rises from 11% at very low income levels to 80% at middle income levels and then rises gradually to 86% at very high income levels.
Clearly, the dramatic increase in animal product consumption is the main source of the rapid increase in total food consumption as incomes grow. Livestock products are the major component in the animal product group in most countries in the world. Meanwhile, the rapidly rising demand for livestock products in developing countries has been met by increased production of pork and poultry meat because the production cycle for pig and poultry are short and they are more efficient converters of grains calories into meat calories than are cattle [52,29]. At initial stages of development, wastes and residues of crops are the main sources of feed for producing pork and poultry meat.
However, these feed sources are limited and when an economy begins to develop, the main source of feed will shift to feed grains. Thus the per capita demand for feed grains will rise rapidly as an economy passes through the early and middle income stages. Furthermore, both economic growth rates and population growth rate are high at early economic development stages. These two factors combined with a rapid increase in per capita feed grain demand at low and middle income levels will raise the aggregate demand of feed grains very rapidly.
Most developing countries do not have ample feed resource endowments to produce sufficient feed grains to meet these new demands. Usually, surplus, low cost labor dictates the importing of feed grains, not animal products directly.
Constancy Test of Engel Functions
The moving regression method is used to test the constancy of the Engel functions of total food, animal products, and starchy staples estimated previously. Data used are annual observations for 108 countries from 1961 to
1985. Segments of five and ten years are chosen as a priori for conducting the moving regression test. When each segment is five years, there are five pairs of segment. When each segment is ten years, there are only two non-overlapping
time segments. For computing the test statistics for the
moving regression with ten years in each segment, the Engel
function is estimated for a twenty year internal for the
following six periods: 1961-1980, 1962-1981, 1963-1982,
1964-1983, 1965-1984, 1966-1985.
Whether five or ten year segments are used in the
moving regression test, the F statistics are all
insignificant at 5% and 1% significant levels (Table 4). It
implies that in the observed time periods, there are no
trend changes in tastes and food prices in the sample
countries, and no systematic difference in tastes and food prices among developing and developed countries. Therefore,
it can be concluded that if there exists systematic difference in the agricultural prices between developing and developed countries because of difference in natural resource endowment, this systematic difference does not carry through to the consumer food prices in each countries
in our sample. 75
Table 4 F Statistics of Moving Regression Test on Engel Functions, 108 Countries 1961-1985 Data, Income Converted by Exchange Rates
Function Sample Degree < Period Total Animal Starchy Freedom Food Products Stacies
1961-1985 0.006 0.001 0.028 8, 2308 t 1961-1980 0.027 0.037 0.048 2, 1834
1962-1981 0.022 0.031 0.050 2, 1865
1963-1982 0.015 0.023 0.053 2 / 1893
1964-1983 0.008 0.015 0.051 2, 1914
1965-1984 0.003 0.009 0.051 2, 1919
1966-1985 0.001 0.006 0.053 2, 1918 76
5.3 Estimation of Engel Functions of Food Groups with Income
Converted by Purchasing Power Parity
Estimation of Engel Functions
In this section, three semi-log Engel functions are estimated for total food, animal products,, and starchy staples by ordinary least square method with per capita income converted by purchasing power parity, using the observations of 56 countries from 1961 to 1985. However, in order to understand if this sample of 56 countries can be used to represent the 108 countries, the Engel functions of food consumption estimated from the 56 country sample with per capita income converted by exchange rates is estimated first and compared with the Engel functions estimated from the 108 country sample.
The coefficients of Engel functions estimated from samples with 108 and 56 countries are given in Table 5. The coefficients in the Engel functions of total food, animal products, and starchy staples from both samples are very similar. In order to assure their similarity, the Chow test is used to do the statistical test.
F statistics in Table 6 are all insignificant at 5% and
1% significant levels. It implies that the Engel functions estimated from either the sample with 56 or the sample with
108 countries are the same. Therefore, the 56 countries with purchasing power parity data can be used to represent the 77
Table 5 Engel Functions for Total Food, Animal Products and Starchy Staples, 108 and 56 Countries 1961-1985 Data, Income Converted by Exchange Rates
Dependent Country Coefficients of Explanatory Variable Number Variable R2 Constant Ln I
Total food 108 -1752.6* . 381.5* 0.65 56 -1810.4 389.4 0.68
Animal products 108 -1946.4* 373.1* 0.64 56 -1984.3 378.5 0.66 ★ Starchy staples 108 226.0* -9.5, 0.10 56 224.3 -9.3 0.10
1. Ln = natural logarithm
1= per capita GDP measured by U.S. dollar from dividing
per capita GDP at 1980 constant price in local
currency by 1980 exchange rate.
2. In each equation, dependable variable is the per capita
annual consumption in kilograms of cereal equivalents
3. Coefficients with represent significant at 5% level. 78
Table 6 F Statistics of Chow Test on The Similarity of Engel Functions Estimated from Samples with 108 and 56 Countries, Income Converted by Exchange Rates
Function F Statistics Degree of Freedom
Total Food 1.13 2, 2314
Animal Products 0.641 ' 2, 2314
Starchy Staples 0.069 2, 2314 79
108 countries.
Table 7 listed the Engel functions of each food group with per capita income converted by both exchanges and purchasing power parity. There are no great changes in the explanatory power of per capita income on each food group consumption no matter which conversion method is used.
However, the coefficients of per capita income in each Engel function are higher when income is converted by purchasing power parity.
It is understandable that purchasing power parity reflects not only the price level of internationally traded goods but also the price level of non-internationally traded goods such as services in a country. Because services are cheap in low income countries and are expensive in high income countries, the purchasing power parity of all consumption goods is usually lower than the exchange rate in low income countries and higher than the exchange rates in high income countries, (see Table 2 in the appendix).
Thus, the per capita income range from low to high income countries becomes smaller when per capita income is converted by purchasing power parity than when converted by exchange rates. The small per capita income range between low and high income countries leads to higher coefficients of income in Engel functions higher when per capita income levels are converted by purchasing power parity.
However, whether per capita incomes are converted by 80
Table 7 Engel Functions of Total Food, Animal Products, and Starchy Staples, 56 Countries 1961-1985 Data,Income Converted by Purchasing Power Parity and Exchange Rates
Coefficients of Explanatory Dependent Method of Variable Variable Income R2 Adjustment Constant Ln I
t Total Food E.R. -1810.4* 389.4* 0.68 PPP -2587.4 473.9 0.63
Animal Products E.R. -1984.3* 378.5* 0.66 PPP -2722.0 458.4 0.60 ★ Starchy Staples E.R. 224.3* 0.10 - 9 * 3* PPP 248.9 -12.1 0.10
1. E.R.: per capita income is adjusted by exchange rate
PPP : per capita income is adjusted by purchasing power
parity
2. Figures with •*' represent significant at 5% level exchange rates or purchasing power parity the signs of the
income coefficient are consistent in the Engel function of each food group. The consumption of total food and animal products increase as incomes rise, while the consumption of starchy staples declines as incomes rise.
Constancy Test of Engel Functions
The moving regression method is used again to test the constancy of the Engel functions of total food, animal products and starchy staples with per capita income converted by purchasing power parity. Segments of five and ten years are chosen for conducting the moving regression test as in the previous section.
In the moving regression test, for both five or ten year segments, the F statistics are all insignificant at 5% and 1% significant levels (Table 8). It implies that in the observed time periods, there are no trend changes in tastes and prices in the sample countries, and no systematic difference in tastes and prices among developing and developed countries.
5.4 Estimation of Demand Functions for Food Groups
In the previous section, Engel functions are estimated for the per capita consumption of total food, animal products, and starchy staples to obtain the generalized 82
Table 8 F Statistics of Moving Regression Test on Engel Functions, 56 Countries 1961-1985 Data Income Converted by Purchasing Power Parity
______Function______Sample Total Animal Starchy Degree of Period Food Products Staples Freedom
1961-1985 0.026 0.032 0.048 8, 1289
1961-1980 0.091 0.110 0.150 2, 1031
1962-1981 0.085 0.104 0.150 2, 1041
1963-1982 0.071 0.089 0.145 2, 1050
1964-1983 0.058 0.075 0.132 2, 1057
1965-1984 0.051 0.067 0.119 2, 1059
1966-1985 0.041 0.056 0.101 2, 1059 consumption-income relationship with per capita income converted by exchange rates and by purchasing power parity separately. However, in addition to per capita income, there are several other factors which can affect per capita food consumption. In this section, demand functions of total food, animal products, and cereal products are estimated
(Cereal product subgroup is used to substitute for starchy staple subgroup in this and next section because of the unavailability of price data for starchy•staples). Per capita income converted by purchasing power parity, prices of total food, and relative prices ratio between animal products and cereal products are introduced as explanatory variables to estimate the demand function of total food. On the other hand, per capita income converted by purchasing power parity, prices of animal products, and price of cereal products are used as explanatory variables to estimate the demand functions of animal products and cereal products. The regression method used in estimating the demand functions is the ordinary least square method.
Demand functions of total food, animal products, and cereal products in Table 9 are estimated by using the data form 56 countries in 1980 only.
Per capita annual income and own price are the two important factors affecting the per capita consumptions of total food and animal products. The R2 for the demand function of total food and animal products are 0.73 and 0.76 Table 9 Demand Functions for Total Food, Animal Products, and Cereal Products, 56 Countries 1980 Data Income Converted by Purchasing Power Parity
Coefficients of Explanatory Variable Dependent R2 Variable Constant Ln I Ln TP Ln AP Ln CP Ln RELP
Total Food -1864.8* 424.9* -953.3* -324.7 0.73 (430.61 (48.51 (302.1) * (179.1) Animal Products -2271.2 441.4 -1070.7 -6.2 0.76 (362.;) (42.7; (230.9)* (192.1)* Cereal Products 197.4 -10.5 63.3 -72.5 0.28 (41.6) (4.9) (26.5) (22.1)
1. The definitions of variables are as following: Ln = natural logarithm I = annual per capita income adjusted by purchasing power parity in U.S. dollars at 1980 price TP = price index of total food AP = price index of animal products CP = price index of cereal products RELP = relative price ratio of animal product price index divided by cereal product price index 2. In each equation, dependable variable is the per capita annual consumption in kilograms of cereal equivalents. 3. Figures in parentheses are standard errors. 4. Coefficients with are significant at the 5% significant level. respectively. In the demand function of cereal products, in addition to per capita annual income and own price, the price of animal products is also an important factor affecting the per capita consumption of cereal products. But the R2 of cereal product equation is only 0.28. The low explanatory power of income and price on cereal product consumption suggests that there exist other factors affecting cereal product consumption behavior. Some non economic factors such as physical activity, body size, climate and habits are usually mentioned.
In the total food demand function, all variables have signs consistent with our expectations in section 4.1. Per capita Income has a positive effect on total food consumption. On the other hand, food price and relative price ratios have negative effects on total food consumption. However, only the coefficients of per capita income and food price are significant at the 5% level.
In the animal product demand function, all variables have the signs consistent with our expectations except the variable of cereal product price. Per capita income has a positive effect on animal product consumption. On the other hand, the prices of animal products and cereal products have a negative relationship with animal product consumption. In this function only the coefficients of per capita income and animal product price are significant at the 5% level to explain the variation of animal product consumption across 86 countries.
The insignificance of cereal product price in animal product demand function implies that in the diet upgrade process, even if the cereal product price is maintained at low levels, the consumption of animal products will continue to increase as incomes rise. Maintaining a low price of cereal products can only save consumers' expenditure on cereal products, but cannot stop the diet upgrade from cereal products to animal products in the economic development process.
In the cereal product demand function, all the variables have signs consistent with our expectation and are significant at the 5% significant level. Both per capita income and cereal product price have a negative relation with cereal product consumption. The animal product price has a positive effect with cereal product consumption. The negative relationship between per capita income and the consumption of cereal products implies that cereal products is an inferior good in the economic development process.
This result is the same as the Engel equation estimated for starchy staples in section 5.3.
In the cereal product demand function, an increase of animal product price will result in more consumption of cereal products, but in the animal product demand function, an increase of cereal product price will not raise the consumption of animal products. This suggests that there 87
exists price asymmetries in animal product and cereal
product demand in the economic development process. This
phenomena also exists in Jureen's study [17] which used
calories to measure the consumption of animal products and
cereal products.
Table 10 lists income and own price elasticities of the
consumption of total food, animal products and cereal
products. The income elasticities for animal products are
the highest at any income levels. Animal•products is a
luxury good at income levels less than 1000 US dollars and
then becomes a necessary good at income level above 1,000
U.S. dollars.
The income elasticities of total food are less than for
animal products at any income level because total food
includes animal products and cereal products and the latter
is an inferior good whose consumption declines as incomes
rise.
Income elasticities of both total food and animal
products decline as incomes rise. The rate of decline is
very fast at the developing stages. The income elasticity of
total food demand at the income level of 3000 US dollars is
only one third of the income elasticity at the level of 500
US dollars. Similarly, the income elasticity of animal product demand at the level of 3000 US dollars is around one
seventh of income elasticity at the level of 500 US dollars.
The high income elasticities of animal products or total 88
Table 10 Income and Own Price Elasticities of The Consumption of Total Food, Animal Products, and Cereal Products at Various Income Levels
Income Elasticity Own Price Elasticity Income Levels Total Animal Cereal Total Animal Cereal Food Product Product Food Product Product CO in 0 o 1 I • 500 1.02 3.52 t -2.29 -0.47 0 o 1 l • 1000 0.60 1.02 -1.34 to « 03 -0.50
1500 0.48 0.72 -0.07 -1.08 -1.75 -0.51 0 o 00 1 2000 0.42 0.60 • -0.95 -1.45 -0.52 0 o CO 1 I l • H • to 00 2500 0.39 0.53 o 00 -0.53 0 o CO 1 3000 0.36 0.48 • -0.81 -1.17 -0.54
4000 0.33 0.42 -0.08 -0.73 -1.03 -0.55 0 o CO 0 VO CO 1 1 • 5000 0.30 0.39 • -0.94 -0.56 0 o CO 1 6000 0.29 0.36 • -0.65 -0.88 -0.57 in CO 0 VO CM 0 1 1 1 • • 7000 0.28 0.34 o • o 03 -0.83
8000 0.27 0.33 -0.08 -0.60 -0.79 -0.58 0 o CO 0 in CO 1 1 • 9000 0.26 0.32 • -0.76 -0.59 0 o cn 1 10000 0.25 0.30 • -0.56 -0.74 -0.59 in o CM 0 in 1 « 1 • •
11000 0.25 0.30 o • o VO -0.60 in 0 o cn 0 1 1 I • • 12000 0.24 0.29 o • ^4 O -0.60
1. Income and price elasticities are calculated based on
the demand equations in Table 9 at the mean values of
each variables. 89
food demand at low income levels will push the world food
demand up rapidly as more lower income countries enter into
developing stages.
Although the income elasticity of cereal product demand
is negative and increasing, the value of its income
elasticity is relatively small. The low income elasticity
and low share of cereal products in total demand means that
the decline of cereal product consumption has no great
impact on the increase of total food demand.
On own price elasticity, there exists large difference among total food, animal products, and cereal products. The price elasticities of animal product demand are higher than total food demand at any income level. At low income levels, the own price elasticities of total food and animal product demand are elastic. The own price elasticities of total food consumption becomes inelastic when per capita income level is over 2000 US dollars, the own price elasticity of animal product consumption, however, becomes inelastic only at a higher income level of 5000 US dollars.
The high price elasticities of total food and animal product consumption suggests that lower food price will accelerate the increase in total food and animal product consumption. If the feed import price can be lowered and result concurrently in a lower animal product price, more export opportunities will be created for feed grain exporting countries. The high price elasticity of animal product demand can be used to explain the rapid growth of U.S. feed grain exports in recent years. The Food Security Act of 1985 in the U.S. sharply dropped market price supports for export grains. In addition, the legislation also included an Export
Enhancement Program that provides export subsidies in kind
[9]. The result of this legislation sharply dropped the feed grain export price in U.S. and world markets. For example, corn prices in U.S. dollars dropped by one third.
Furthermore, this price decline was reinforced by sharp decline in the dollar exchange rates since 1985.
The price elasticity of cereal product demand is lower than animal products or total food at any income level.
Furthermore, its price elasticity increases as incomes rise but very slowly. It is understandable that at high income levels, the price elasticity of cereal products is higher than at low income level. At low income levels, cereal products are the main sources of food, consumers can cut consumption only very little as prices rise. But at high income levels, consumers have the ability to shift consumption from cereal products to animal products, resulting in a higher price elasticity of cereal product demand at high income levels. 91
5.5 Comparison of Income Elasticities of Food Group Demand
Measured by Alternative Methods
In this section, income elasticities of food consumption measured by expenditures and calories are compared with cereal equivalents. Food price is included as a second explanatory variable for estimating the demand functions. Data from 56 countries in 1980 are used.
A semi-log functional form is selected a priori for estimating food demand functions measured by expenditures.
Since the physiological limitation suggests that per capita consumption of calories will not increase indefinitely as per capita incomes grow, the log-inverse form is chosen for estimating food demand functions measured by calories. The demand functions of total food, animal products, and cereal products measured by calories and expenditures are listed in Table 17 and 18 in the Appendix.
The income elasticities of total food demand measured by different methods are given in Table 11. The relative magnitude of income elasticities of total food demand measured by different measures are consistent with the expectation in section 3.3. The income elasticities of total food demand measured by expenditure are always the highest at any income level. This can be attributed to the continuous increase of demand on services from non- agricultural sectors as incomes grow.
Income elasticity of total food demand measured by 92
Table 11 Income Elasticities for Alternative Measures of Food Consumption at Various Income Levels
Measure Income Level Cereal Equivalents Calories Expenditures
1000 0.60 0.19 0.82
2000 0.42 0.09 > 0.52
3000 0.36 0.06 0.43
4000 0.33 0.04 0.38
5000 0.30 0.03 0.35
6000 0.29 0.03 0.33
7000 0.28 0.02 0.32
8000 0.27 0.02 0.30
9000 0.26 0.02 0.29
10000 0.25 0.02 0.28
11000 0.25 0.02 0.28
12000 0.24 0.01 0.27
1. Income elasticities of food consumption measured by
calories and expenditures are calculated based on the
demand functions in Table 4 and 5 in the Appendix. cereal equivalents ranks second, while those measured by
calories are the lowest. Income elasticities of total food
demand measured by calories and cereal equivalents are the
weighted quantity income elasticity of each food item
demand. In the cereal equivalent measure, greater weights
are given to animal product demand because the production of
animal products needs more primary agricultural resources.
The greater weights and the higher quantity income
elasticity of animal products make the income elasticity of
total food demand measured by cereal equivalents greater
than measured by calories.
The fact suggests that using the expenditure measure
will overestimate the income effect and using calorie
measure will understate the income effect on demand for
agricultural resources in the economic development process.
The order of relative magnitude of income elasticities
of animal product demand measured by cereal equivalents,
calories, and expenditures are the same as total food (Table
12). Income elasticities of animal product demand measured by expenditures are still the highest at any income level.
Again this can be attributed to the increased costs from
non-agricultural services.
Therefore, using expenditures and calories to measure animal product consumption will overestimate and underestimate the increase demand respectively on primary agricultural products in economic development. 94
Table 12 Income Elasticities for Alternative Measures of Animal Products at Various Income Levels
Measure Income Level Cereal Equivalents Calories Expenditures
1000 1.02 0.83 1.31
2000 0.60 0.42- 0.69
3000 0.48 0.28 0.54
4000 0.42 0.21 0.47
5000 0.39 0.17 0.42
6000 0.36 0.14 0.39
7000 0.34 0.12 0.37
8000 0.33 0.10 0.35
9000 0.32 0.09 0.34
10000 0.30 0.08 0.33
11000 0.30 0.08 0.32
12000 0.29 0.07 0.31
1. Income elasticities of animal product consumption
measured by calories and expenditures are calculated
based on the demand functions in Table 4 and 5 in the
Appendix. The income elasticities of cereal product demand measured by cereal equivalents or calories are negative, but both are relatively small and similar (Table 13). On the other hand, the income elasticity of cereal product demand measured by expenditure are positive as income increases.
This is the most striking case reflecting the effect of a continuously increasing demand for non-agricultural services. The quantity income elasticities of each item in cereal product group are generally negative, therefore, the income elasticity of cereal product demand is negative whether demand is measured by calories or cereal equivalents. The only source making the income elasticity of cereal product demand measured by expenditures positive should come from the continuous increase of demand on service from non-agricultural sectors, such as packing, processing etc. 96
Table 13 Income Elasticities for Alternate Measures of Cereal Products at Various Income Levels
Measure Income Level Cereal Equivalents Calories Expenditures
1000 -0.07 -0.10 0.24
2000 -0.08 -0.11. 0.21
3000 -0.08 -0.12 0.19 0 o 00 1 4000 • -0.12 0.18
5000 -0.08 -0.13 0.17
6000 -0.08 -0.13 0.17
7000 -0.08 -0.13 0.16
8000 -0.08 -0.13 0.16 0 o CO 1 9000 • -0.14 0.16
10000 -0.09 -0.14 0.16
11000 -0.09 -0.14 0.15
12000 -0.09 -0.14 0.15
1. Income elasticities of cereal products measured by
calories and expenditures are calculated based on the
demand equations in Table 4 and 5 in the Appendix. CHAPTER VI
SUMMARY AND CONCLUSION
6.1 Summary
The direct and indirect (feed uses) consumption of cereals for food purposes across a broad However, per capita indirect consumption of cereals in developed countries is several fold higher than in developing countries and makes the per capita direct and indirect consumption of cereal products higher in developed countries than in developing countries. Hence, consumer diet composition change from cereals or starchy staples to animal products in the economic development process brings a rapid growth in the demand for primary agricultural products, especially feed grains for livestock. The principal purpose of this study is to estimate the relationship of economic development (represented by per capita income) to the consumption of total food and selected food subgroups by using a measure which accounts for diet 97 98 upgrades and completely reflects final consumption change on primary agricultural resource demand at various levels of economic development. Cereals are the basic food building blocks. A single food consumption measure, cereal equivalents is chosen in this study to aggregate various food items into total food and selected food subgroups. In this measure, animal product consumption is valued according to the grain equivalent necessary in the livestock production process and thus represent the changing food demand placed on primary agricultural resources at various stages of economic development. The cereal equivalents measure is a new concept. A discussion of this measure and its application as related to conventional demand theory is made first. Then the similarities and differences between cereal equivalent demand and the two most common food consumption measures, calories and food expenditures, are discussed. The income elasticities of food demand in terms of these three measures are also related. Pooled data of 108 countries from 1961-1985 (FAO Food Balance Sheet Tape) are used to estimate the generalized relationship of per capita income to food consumption by regressing food consumption in terms of cereal equivalents to per capita income which is converted by official exchange rates. In order to avoid the bias from adopting official 99 exchange rates to convert different currencies into common units, purchasing power parity is also introduced for a subset of 56 countries where purchasing power parity data is available to convert income from different countries to a common unit. Pooled data of this subset of 56 countries from 1961-1985 is used to estimated the generalized relationship of food consumption to per capita income. A moving regression method is applied to test if the generalized relationship of food consumption to economic development is stable over time. Based on this result we confirm that difference in tastes and food prices among countries do not exert a systematic effect on food consumption and thus the relationships estimated from cross country, time series data are stable over time. To refine the generalized relationship of food consumption to income growth, food prices in addition to per capita income are included as explanatory variables to estimate the demand functions of total food and selected food subgroups. Finally, the income elasticities of each food group demand are compared when three alternative measures of food demand are used : cereal equivalents, calories, and expenditures. 6.2 Conclusion 100 1. Total food demand measured in cereal equivalents grows rapidly as economic development (represented bv per capita GDP) proceeds, especially at low income levels. There exists a six fold increase in total food consumption in terms of cereal equivalents between very low and very high levels of income. The growth in food consumption is especially rapid at low income level where total food consumption nearly triples as income grows from $ 200 U.S. GDP/per capita to $ 1,000 U.S. At high income levels (about $ 10,000 GDP/per capita), food consumption measured in cereal equivalents is largely stable. 2. Increased consumption of animal products is the principal reason for the increase in per capita total food consumption associated with economic development. Annual consumption of animal products grows from 30 kilogram per capita in cereal equivalents at very low income levels to 1615 kilogram at very high income levels. The sharpest increase in animal product consumption appears in the income ranges between $ 200 U.S. and $ 3,000 U.S. GDP/per capita. 3. The consumption of starchy staples declines as incomes rise. This fact implies that starchy staples is an inferior good. But the decline of starchy staple consumption is slow. Per capita annual consumption of starchy staples at very high income level ($ 14,000 U.S.)is still maintained at 76% of the consumption at very low income level ($ 200 U.S.). 4. The generalized relationships of food consumption to per capita income is stable over time and across countries. Although food prices and tastes may be different among countries and through times, no same trend or systematic difference in food prices and tastes across countries were noted in the pooled time series data from 108 and from 56 countries. Therefore, if there exist systematic difference in the agricultural price, this systematic difference does not carry through to consumer food prices in each country. 5. Own price is an important factor affecting food consumption behavior. The own price elasticity of total food is elastic when per capita income is less than $ 2,000 U.S. GDP/per capita. However, the own price elasticity of animal products continuous to be elastic until per capita income reach $ 4,000 U.S.. Although the own price elasticity of cereal products is not elastic, it maintains a narrow range of -0.5 to -0.6 at all income levels. 6. Income elasticities of total food and animal product demand measured bv cereal equivalents are intermediate between income elasticities measured in calories and food expenditures. Because the calorie measure count the final calories only, it underestimates the need of primary agricultural products. On the other hand, the expenditure measure includes not only the diet upgrade effect but also services from non-agricultural sectors, hence the relationship of per capita income to total food and to animal product expenditure will overestimate the demand for primary agricultural products in the economic development process. 7. Both the income elasticities of cereal product demand measured in calories and cereal equivalents are negative while the income elasticity of cereal product demand measured in expenditures is positive. This is the most striking case as the continuous demand in service from non- agricultural sectors make the expenditure income elasticity of agricultural products increase despite the decrease in demand from the agricultural sector. Therefore, the relationship of per capita income to cereal product consumption in terms of expenditures will overestimate the demand in the agricultural sector in the economic development process. 6.3 Implication The very positive relationship identified in this study between income growth, rapid diet change and the resulting substantial increase of per capita demand on agriculture as economic development occurs holds important implications for developing countries, food and feed grain exporting countries and for further research refinement. These implications and further research needs are detailed below. 1. Most developing countries do not have sufficient resources to increase agricultural production rapidly enough to supply domestically the newly demanded animal products or feed grains and thus will need to import to fill the consumption-production gap. 2. The few developing countries with ample agricultural resources will need to plan the production of more feed grains or the increased production of alternative feed crops which they can produce efficiently for raising livestock. 3. For exporting countries, the lowering of export prices of animal products and feed grains is an effective way to promote increased exports to developing countries, especially if the decline in export price is carried through to a concurrent decline in the retail price of animal products in developing countries. 4. A more accurate measure of aggregate food consumption is needed to reflect the demand on the agriculture sector during the economic development process. In this study, cereal equivalents is used to represent the demand on the agriculture sector. The cereal equivalents measure is an improvement over the more common measures of calories and expenditures. However, in this study, the conversion ratio for animal products is based on ten year aggregated data from 1963-1973 in the United States. This conversion ratio neglects not only the feed source difference between low and middle income countries and the U.S., but also the difference in production technology. Furthermore, the principle to convert fish consumption is still a very troublesome problem. Refinement in the cereal equivalents measure to reflect these difference would make it more relevant to developing countries needs. 5. Per capita income and food price levels explain a large part of variation in total food consumption across countries. However, when total food consumption is divided into animal products, and cereal/tuber products, per capita income and price still explain a large part of the variation on animal product consumption but explain only 28% of the variation in cereal/tuber product consumption across countries. Hence, non-economic factors may be helpful to explain the variation in cereal/tuber product consumption across countries. 6. Within the consumption sub-category of animal products, additional study of the sequence of composition change, i.e. poultry, pork, and beef would be helpful since each requires a different quantity and mix of feed inputs. 7. Also, the sub-category of cereal/tuber product while declining in consumption will experience composition change to greater quantities of wheat, rice etc. Therefore, a better understanding of this change is also useful for both exporting and importing countries. APPENDIX Table 14. Cereal Equivalent Conversion Ratio for Individual Food Item by Food Groups Conversion Ratio I Crops A. Cereals Wheat and products 1.00 Rice and products (husked equivalents) 1.13 Barley and products 1.00 Maize and products . 1.00 Rye and products 1.25 Oats and products 1.00 Millet and products 1.00 Sorghum and products 1.00 Cereals, Others and products 1.00 Brans 0.90 B. Roots and tubers Potatoes and products 0.27 Cassava and products 0.35 Sweet potatoes and products 0.35 Roots and tubers, other and products 0.3 2 C. Sugar and honey Sugar, non centrifugal 1.35 Sugar and products (raw equivalents) 1.35 Sweeteners, other and products 1.35 Molasses 0.86 Honey 1.20 D. Pulses Beans, dry and products 1.30 Peas, dry and products 1.30 Pulses, other and products 1.3 0 E. Total vegetable and tree nut oils Nuts and products 0.95 Soybeans and products 1.60 Groundnut (shelled equivalents) 2.10 Sunflower seed 1.30 Rape, mustard seeds 1.30 107 (Tablel4 Con't.) Cottonseed 1.30 Coconuts and copra 1.50 Sesame seed 2.10 Palm kernels 2.00 Olives 0.40 Oil crops, others 1.30 Soybean, oil 3.30 Groundnut, oil 3.30 Sunflower seed, oil 3.30 Rape and mustard, oil 3.30 Cottonseed, oil 3.30 Palm kernel, oil 3.30 Palm, oil 3.30 Copra, oil 3.30 Sesame seed, oil 3.30 Olive and olive residue, oil 3.30 Rice, bran oil 3.30 Maize, germ oil 3.30 Oil crops, other oil 3.30 Soybean, cake 1.20 Groundnut, cake 1.20 Sunflower seed, cake 1.20 Rape and mustard, cake 1.10 Cottonseed, cake 1.20 Palm kernel, cake 1.00 Copra, cake 1.10 Sesame seed, cake 1.15 Oilseed, others, cake 1.10 Total fruits and vegetables Tomatoes and products 0.07 Onions, dry 0.15 Vegetables, other and products 0.09 Oranges, tangerines, mandarins and products 0.11 Lemons, lines and products 0.05 Grapefruit and products 0.08 Citrus fruit, other and products 0.18 Bananas 0.24 Plantains 0.24 Apples and products 0.18 Pineapples and products 0.25 Dates 0.70 Grapes and products (excluding wine) 0.24 Fruit, other and products 0.15 Aquatic plants and products 0.10 G. Total spices and stimulants Coffee and products 0.20 Cocoa beans and products 1.90 108 (Table 14 Con't.) Tea 0.15 Pepper 0.20 Pimento 0.20 Cloves 0.20 Spices, other 0.20 II. Livestock products A. Beef Meat and products, bovine 19.8 Offal, edible 12.8 B. Lamb and goat i Meat and products, sheep and goat 19.8 Offal, edible 12.8 C . Pork Meat and products, pig 8.5 Offal, edible 12.8 D. Poultry Meat and products, poultry 4.7 offal, edible 12.8 E . Other meats Meat and products, other animals 12.0 Offal, edible 12.8 Meat and blood meal 1.15 F. Animal fats and oils Fats, animals, raw 3.10 Tallow 3.00 Lard 3.00 Fish body oil 3.05 Fish liver oil 2.25 G. Total milk and products Milk, whole 1.20 Milk, skim 0.37 Butter, ghee 21.00 Cheese 8.80 Whey and products 0.08 Cream 10.00 109 (Table 14 Con11.) H. Eggs Eggs and products 3.80 III. Fish Freshwater fish and products 3.82 Demerseal fish and products 2.25 Pelagic fish and products 4.50 Marine fish, other and products 4.50 Crustaceans and products 2.25 Cephalopods and products 3.15 Mollusks, other and products 0.68 Aquatic mammals meat ■ 4.50 Aquatic animal, others 4.50 Fish meal 1.30 110 Table 15. Per capita Income Values for Alternate Sample Composition with 108 And 56 Country Observation for 1980 Country CYCD* No. of Income Income Sample Adjusted by Adjusted Country Exchange by PPP ------Rates 108 56 (1980 U.S. dollar) ETHIOPIA 62 * * 107 220 NEPAL 149 * 139 BANGLADESH 16 * 144 BURMA 28 * 174 MALAWI 130 * * 205 416 BURKINA FASO 233 * 209 RWANDA 184 * 225 BURUNDI 29 * 231 ZAIRE 250 * 233 CHINA 41 * 250 INDIA 100 * * 256 597 SRI LANKA 38 * * 273 1197 TANZANIA 215 ** 277 392 HAITI 93 * 287 PAKISTAN 165 ** 287 907 LESOTHO 122 * 311 MALDIVES 132 * 319 BENIN 53 * 336 MADAGASCAR 129 * * 367 566 SIERRA LEONE 197 * 373 KENYA 114 * * 426 642 CYPRUS 50 * 426 ANTIGUA & BARBU 8 * 463 NIGER 158 * 471 ST. VINCENT 191 * 473 YEMEN ARAB REP. 246 * 481 LIBERIA 123 * 495 INDONESIA 101 ** 495 1108 EGYPT 59 * 517 SENEGAL 195 * * 521 701 ZAMBIA 251 * * 667 751 GUYANA 91 * 680 HONDURAS 95 * * 689 1231 THAILAND 216 * 719 PHILIPPINES 171 * * 729 1721 ZIMBABWE 181 * * 750 917 CAMEROON 32 * * 755 825 (Table 15 Con't.) NICARAGUA 157 * 758 EL SALVADOR 60 * * 791 1509 DOMINICA 55 * 842 PAPUA NEW GUINE168 * 861 BOLIVIA 19 * * 874 1476 MOROCCO 143 * * 889 1220 ST. LUCIA 189 * 945 PERU 170 * * 996 2217 JORDAN 112 * 1092 BOTSWANA 20 * * 1114 1567 NIGERIA 159 * * 1132 1020 GUATEMALA 89 * * 1139 2438 BELIZE 23 * 1142 MARUITIUS 137 * 1217* DOMINICAN REPUB 56 * * 1219 2052 JAMAICA 109 * 1252 COTE dIVOIRE 107 * * 1282 1373 COLOMBIA 44 * * 1290 2773 TURKEY 223 * 1312 GHANA 81 * 1350 TUNISIA 222 ** 1356 1976 GRENADA 86 * 1375 PARAGUAY 169 * * 1412 2121 ECUADOR 58 * * 1445 2551 SYRIAN ARABIC 212 * 1501 ROMANIA 183 * 1550 REP. OF KOREA 117 * * 1637 2590 MALAYSIA 131 * 1780 PANAMA 166 * * 1873 3321 FIJI 66 * 1973 BRAZIL 21 ** 1977 3204 HUNGARY 97 ** 2057 4968 COSTA RICA 48 ** 2147 3178 ALGERIA 4 * 2274 POLAND 173 ** 2286 4398 CHILE 40 * * 2475 3619 PORTUGAL 174 * * 2540 4015 IRAN 102 * 2558 SOUTH AFRICA 202 * 2785 YUGOSLAVIA 248 * * 2796 3586 MEXICO 138 * 2940 MALTA 134 * 3154 BARBADOS 14 * 3443 URUGUAY 234 ** 3459 4180 VENEZUELA 236 * * 3943 5390 112 (Table 15 Con't.) GABON 74 * 4038 GREECE 84 ** 4165 5011 SINGAPORE 200 * 4862 ARGENTINA 9 ** 5009 3848 SPAIN 203 * * 5645 6365 ISRAEL 105 ** 5648 6990 IRELAND 104 * * 5666 5972 TRINIDAD & TOBA220 * 5721 NEW ZEALAND 156 * 7226 ITALY 106 * * 8080 9117 JAPAN 110 ** 9061 8560 AUSTRALIA 10 * 9382* UNITED KINGDOM 229 * * 9578 8463 AUSTRIA 11 * * 10183 8560 FINLAND 67 * 10800 CANADA 33 ** 11064 11976 UNITED STATES 231 * * 11787 11787 NETHERLANDS 150 ** 11979 9413 BELGIUM 15 ** 12175 9725 FRANCE 68 * * 12334 9947 DENMARK 54 ** 12954 9826 WEST GERMANY 78 ** 13217 10137 ICELAND 99 * 13802 NORWAY 162 ** 14110 11314 SWEDEN 210 * 14940 SWITZERLAND 211 * 15904 1. CYCD is the FAO country code. 2. PPP is the purchasing power parity. 113 Table 16. Consumption Values for Alternate Sample Composition with 108 and 56 Country Observation for 1980 Country CYCD Per Capita Food Consumption (Kilograms in cereal equivalents) Total Animal Starchy Food Product Staples ETHIOPIA 62 450 285 119 NEPAL 149 428 191 210 BANGLADESH 16 323 92 208 BURMA 28 467 164 266 MALAWI 130 411 132 • 214 BURKINA FASO 233 334 176 92 RWANDA 184 313 116 117 BURUNDI 29 343 103 115 ZAIRE 250 347 119 181 CHINA 41 438 161 240 INDIA 100 319 95 160 SRI LANKA 38 392 127 168 TANZANIA 215 542 270 204 HAITI 93 397 194 106 PAKISTAN 165 522 289 167 LESOTHO 122 600 375 180 MALDIVES 132 '511 245 169 BENIN 53 435 218 174 MADAGASCAR 129 748 448 251 SIERRA LEONE 197 395 178 170 KENYA 114 633 415 169 CYPRUS 50 1574 1272 158 ANTIGUA & BARBU 8 1148 939 99 NIGER 158 671 423 202 ST. VINCENT 19 721 438 160 YEMEN ARAB REP. 246 488 333 93 LIBERIA 123 464 197 226 INDONESIA 101 406 108 228 EGYPT 59 725 370 238 SENEGAL 195 601 331 214 ZAMBIA 251 519 264 206 GUYANA 91 633 345 194 HONDURAS 95 602 388 128 THAILAND 216 519 226 219 PHILIPPINES 171 587 304 208 ZIMBABWE 181 532 284 174 CAMEROON 32 466 250 140 NICARAGUA 157 747 499 126 114 (Table 16 Con 't) EL SALVADOR 60 549 338 119 DOMINICA 55 735 467 143 PAPUA NEW GUINE168 711 456 153 BOLIVIA 19 847 627 144 MOROCCO 143 663 330 228 ST. LUCIA 189 935 696 113 PERU 170 706 473 148 JORDAN 112 794 485 184 BOTSWANA 20 796 608 121 NIGERIA 159 448 233 161 GUATEMALA 89 601 366 140 BELIZE 23 887 636 131 MARUITIUS 137 706 385 186 DOMINICAN REPUB 56 688 432 109 JAMAICA 109 798 513 139 COTE dIVOIRE 107 639 362 216 COLOMBIA 44 955 690 130 TURKEY 223 838 469 231 GHANA 81 404 219 147 TUNISIA 222 663 341 203 GRENADA 86 750 523 104 PARAGUAY 169 1394 1116 166 ECUADOR 58 742 519 102 SYRIAN ARABIC 212 923 590 181 ROMANIA 183 1380 1067 211 REP. OF KOREA 117 590 295 233 MALAYSIA 131 701 421 195 PANAMA 166 1061 823 131 FIJI 66 893 576 169 BRAZIL 21 936 639 161 HUNGARY 97 1645 1371 170 COSTA RICA 48 1020 762 122 ALGERIA 4 651 333 211 POLAND 173 1834 1509 225 CHILE 40 941 669 174 PORTUGAL 174 1090 789 179 IRAN 102 855 526 221 SOUTH AFRICA 202 1074 790 189 YUGOSLAVIA 248 1376 1021 236 MEXICO 138 991 679 176 MALTA 134 1357 1095 133 BARBADOS 14 1462 1165 139 URUGUAY 234 2315 2081 144 VENEZUELA 236 1140 874 137 115 (Table 16 Con't) GABON 74 705 474 159 GREECE 84 1698 1341 159 SINGAPORE 200 1025 776 141 ARGENTINA 9 2556 2290 150 SPAIN 203 1374 1069 148 ISRAEL 105 1213 902 153 IRELAND 104 2211 1937 150 TRINIDAD & TOBA220 955 648 161 NEW ZEALAND 156 2691 2461 122 ITALY 106 1693 1343 196 JAPAN 110 993 711 168 AUSTRALIA 10 2223 1967 - 122 UNITED KINGDOM 229 1661 1429 120 AUSTRIA 11 1899 1639 113 FINLAND 67 1840 1615 125 CANADA 33 1964 1695 114 UNITED STATES 231 2136 1844 105 NETHERLANDS 150 1689 1462 99 BELGIUM 15 1995 1755 125 FRANCE 68 2113 1870 130 DENMARK 54 1895 1665 113 WEST GERMANY 78 1901 1660 117 ICELAND 99 2668 2452 103 NORWAY 162 1805 1545 126 SWEDEN 210 1669 1432 109 SWITZERLAND 211 1947 1699 115 1. CYCD is the FAO country code. Table 17. Demand Functions of Total Food, Animal Products and Cereal Products Measured by Calories, 56 Countries in 1980 Data Dependent Coefficients of Exolanatorv Variable R2 Variable Constant 1/1 Ln I Ln TP Ln RELP Ln AP Ln CP Total Food 7.1* -171.1* -0.4* 0.03 0.52 (0.1) (27.6) (0.1) (0.08) Animal Products 6.2* -831.3* -1.8* 0.2 0.61 (0.2) (104.4) (0.4) (0.4) * Cereal Products 665.0* -44.0 202.2* -168.8* 0.35 (113.1) (13.3) (72.6) (60.3) 1. The definition of independent variables are as following: Ln = natural logarithm 1= annual per capita income adjusted by purchasing power parity in U.S. dollar at 1980 price TP = price index of total food AP = price index of animal products CP = price index of starchy staples RELP = relative price ration of animal product price index divided by starchy staple price index 2. In total food and animal product equations, dependent variable is the logarithmic value of per capita annual calorie consumption from each food group. In cereal product equation, dependent variable is the per capita annual calorie consumption from cereal products. 3. Coefficients with ' *' are significant at 5% significant level. Table 18. Demand Functions of Total Food, Animal Products and Cereal Products Measured by Expenditures, 56 Countries in 1980 Data Dependent Coefficients of Explanatory Variable R2 Variable Constant Ln I Ln TP Ln RELP Ln AP Ln CP Total Food -1293.3* 249.3* -485.8* 4.1 0.87 (153.5) (17.2) (108.3) (64.1) ■k Animal Products -695.9* 124.7 -217.7* -6.6 0.81 (81.1) (9.5) (52.1) (43.3) * Cereal Products -104.4* 24.2* 109.7* -124.4 0.44 (47.8) (5.6) (30.7) (25.5) 1. 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