Econometrics report factors affecting the demand for red meat in european countries

The objective of our essay is to demonstrate the accuracy of Demand Function in Microeconomics and to estimate the change in demand for red meat through the relationship between demand a

FACULTY OF INTERNATIONAL ECONOMICS

Hanoi, September 2021

INDIVIDUAL ASSESSMENT

Evaluator

Nguyễn Quỳnh Anh

Nguyễn Thanh Hằng

Nguyễn Vân Thùy Linh

Phạm Khánh Linh Nguyễn Quỳnh

DECLARATION

The research “Factors affecting the demand for red meat in European countries” was carried out by Group 4, consisting of members Nguyen Quynh Anh, Nguyen Thanh Hang, Pham Khanh Linh and Nguyen Van Thuy Linh

We hereby declare that we have collected the data and conducted the research ourselves We do not use any outside help and support or use information not mentioned at the References of this Research

Hanoi, 29/09/2021

We put all our efforts on completing this essay as results of what we gained after the course Our essay may have shortcomings but we really hope that you will enjoy this essay and give us suggestions and comments so that we can be better for the next time

If we have a chance, we hope that you will accompany us in many subjects in the future Thank you so much for everything you taught us! We will keep these knowledges in mind as long as possible

Have a great day and stay healthy our dear teacher!

Group 4

Instructor: MS Nguyễn Thúy Quỳnh

Faculty: International Economics

Keyword: red meat, demand, factors, Europe

Our essay has the title: “Factors affecting the demand for red meat in European countries” This essay investigated the demand for meat in 41 countries in Europe in

2017 using the econometrics method The objective of our essay is to demonstrate the accuracy of Demand Function in Microeconomics and to estimate the change in demand for red meat through the relationship between demand and other factors such

as price of red meat, price of substitution, chicken meat, population and income in the European market We collected data from valid sources like WORLDBANK, NUMBEO and FAOSTAT The data were analysed using the ordinary least square method and multiple regression analysis Results show strong agreement with theoretical predictions, which is that the demand for red meat is influenced by price

of substitutes, the number of people in each country, and income per head of consumers positively The results of hypothesis testing reveal a reverse relationship between demand for red meat and its price, according to the Demand Function The work presented here recommends that the government should implement policies to increase income per capita to augment demand for red meat

TABLE OF CONTENT

INTRODUCTION 8

SECTION 1 OVERVIEW OF THE TOPIC 9

1.1 Definition and Theory of Demand 9

1.1.1 Demand 9

1.1.1.1 Quantity demanded 9

1.1.1.2 Market demand and individual demand 9

1.1.2 Factors affecting quantity demanded 9

1.1.2.1 The law of demand (The relationship between quantity demanded of a good and its price) 9

1.1.2.2 Income of consumers 10

1.1.2.3 Prices of related goods 10

1.1.2.4 Number of consumers 11

1.1.2.5 Demand Function 11

1.2 Overview of the demand for red meat 11

1.3 Related published researches 11

1.4 Research hypothesis 14

SECTION 2 MODEL SPECIFICATION AND DATA 15

2.1 Methodology 15

2.1.1 Method used to derive the model 15

2.1.2 Method used to collect and analyze the data 15

2.2 Theoretical model specification 15

2.2.1 Proxy to measure 15

2.2.2 Specify the model 17

2.2.3 Theoretical relationship between dependent variable and independent variables 17

2.3 Data description 18

2.3.2 Descriptive statistics and interpretation for each variable 18

2.3.2.1 Demand of red meat 19

2.3.2.2 Price of red meat 19

2.3.2.3 Price of chicken 19

2.3.2.4 Income of consumers 19

2.3.2.5 Number of consumers 19

2.3.3 Correlation matrix between variables 20

SECTION 3 ESTIMATED MODEL AND STATISTICAL INFERENCES 22 3.1 Estimated model 22

3.2 Statistical inferences 24

3.2.1 Hypothesis testing 24

3.2.1.1 Statistical significance of individual coefficients 24

3.2.1.2 Overall significance of the multiple regression 26

3.2.1.3 Joint significance of the price of red meat and the price of chicken 26 3.2.2 Consistency of the regression results with the theories 27

3.2.2.1 Consistency of the regression result with the theories 27

3.2.2.2 Mechanism of the relationships between variables 27

3.3 Recommendation and conclusion 28

3.3.1 Recommendation 28

3.3.2 Conclusion 29

REFERENCES 30

APPENDIX 31

INTRODUCTION

Red meat is a type of meat that has a bright red color and changes to darker color after cooking such as beef, pork, lamb, etc Red meat is known as one of the most popular cooking ingredients in the World It contains many nutrients such as iron, zinc, vitamin B12 and especially high protein, providing energy for human activities Red meat can also be easily made into dishes, from the casual to the high-end and brings delicious taste to people Therefore, it becomes one of the most consumed cooking ingredients in the World

Europe is known as one of the areas with the largest consumption demand of red meat in the World However, the demand for red meat in this area cannot be completely free; it is still affected and limited by factors such as price, related commodity prices, income, number of buyers, etc In order to have an accurate view

of the red meat consumption demand in Europe, we have conducted a research called

“Factors affecting the demand for red meat in European countries”

Our research objective is to describe the factors affecting the demand for red meat and to show the relationship and influence level of each of these factors to the quantity demanded of red meat in European countries

The objects of our research are the demand for red meat in European countries and 4 main factors affecting this, which are: prices, prices of related goods (chicken), people's income and number of consumers

Our research scope includes 3 parts: content scope, time scope and spatial scope The content scope is researching on factors affecting the demand for red meat in European countries The time scope of the data in this research is in 2017 The spatial scope includes 42 European countries

This research consists of 3 main sections In section 1, we present 4 parts: the definition of all research objects mentioned in the research, the economic theories related to the research, other related researches and the research hypothesis In section 2, we mention 3 parts: the methodology to derive the model and analyze the data, the theoretical model specification and describing the data In the last section,

we show the estimation of the model, the hypothesis testing and recommendations

SECTION 1 OVERVIEW OF THE TOPIC

1.1 Definition and Theory of Demand

1.1.1.2 Market demand and individual demand

The quantity demanded in a market is the sum of the quantity demanded by all the buyers at each price Thus, the market demand curve is found by adding horizontally the individual demand curve (N.G Mankiw, 69)

Also according to Pindyck, “the market demand curve is the horizontal summation

of the demands of each consumer” (R.S Pindick, 122) The individual demand curve

is a straight line, but the market demand curve can be kinked because the individual's willingness to buy prices may be different

Based on Pindyck's analysis of individual and market demand (R.S Pindyck, 124),

we can conclude that the factors that influence the demand of many buyers will influence the demand of the market

1.1.2 Factors affecting quantity demanded

1.1.2.1 The law of demand (The relationship between quantity demanded of

a good and its price)

“Other things being equal, when the price of a good rises, the quantity demanded of the good falls, and when the price falls, the quantity demanded rises” (N.G Mankiw, 67)

In the law of demand, to simplify things, it is often assumed that factors such as consumer income, prices of related goods, consumer preferences, are constant factors However, in reality, these factors do not always stand still, they change and affect the demand for a good

In the following we examine the relationship between other factors and the demand for a good

1.1.2.2 Income of consumers

According to N.G Mankiw theory, if a person's income decreases, they will be forced to spend less money on most goods, so their ability to buy is lower and that is the reason why their demand for goods reduces Similarly, if their income increases, their demand for goods also increases So, we say that the relationship between income and demand for goods is positive

However, all quantitíes of goods and income do not have a positive relationship If

an increase in income leads to an increase in demand for a good, the good is normal good But, if an increase in income leads to an increase in demand for a good, the goods are inferior goods Attitude toward any goods depends on the income of the buyer, not on the quality of the goods

1.1.2.3 Prices of related goods

There are two types of related goods: substitute goods and complementary goods (N.G Mankiw, 70)

With two goods considered as substitutes, an increase in the price of one leads to an increase in the demand for the other Therefore, the relationship between the price of one good and the demand for the other is positive

With two goods considered complementary, an increase in the price of one leads to

a decrease in demand for the other Therefore, the relationship between the price of one good and the demand for the other is inverse

1.1.2.4 Number of consumers

According to both Mankiw and Pindick theory, because market demand is the sum

of many individual demands, the more people buy a certain good, the more the total market demand for that good increases Therefore, it can be seen that the number of buyers and the market quantity demanded of a good have a positive relationship

1.1.2.5 Demand Function

Q x =f (P x , P y , I, N)

Where:

Qx: The quantity demanded of good X

Px: The price of good X

Py: The price of good Y (Good Y is the related good of good X)

 Quantity demanded of red meat in European countries

Based on FAO’s data, Europe is one of the regions with the highest demand for meat

in general in the world, with 64.4 kg per capita in 2017 In which, on average, each European consumes 31.3 kg of pork and 10.1 kg of beef per year Considering the population of Europe in 2017 is 745 414 735 people, the total quantity of beef and pork (representing red meat) demanded is 30.8 billion kilograms The total quantity

of meat demanded is 48.1 billion kilograms This shows the huge demand of European countries for red meat compared to other types of meat

1.3 Related published researches

 Factors affecting consumer demand for meat, Webster County, Iowa

(Richard Edgar Lund, Iowa State University, 1967)

The research’s methodology is quantitative research, based on demand theory The research’s findings are: Factors affecting consumer demand associated with the retail market were summarized by the variables (a) retail price, (b) an index of newspaper advertising, and (c) an index of in-store promotion These three variables were quantified in the form of data series pertaining to thirteen meat classes, five store groups, and seven weekly time periods Money paid for meats generally increased with both household income and age of household head but decreased with size of household The estimated elasticities of quantity purchased from a retail store (group) with respect to price, advertising, and in-store promotion are: (a) price: -1.305, (b) advertising: 0.042 and (c) in-store promotion: 0.023 The model indicated that the price elasticity becomes more negative by the amount -0.844 for households with children

Based on the results, we see that income and quantity of meat purchased have a positive relationship while price and quantity demanded have an inverse relationship Based on elasticity, we see that for ordinary households, meat is not a necessary good, because people can replace it with other foods such as eggs, vegetables However, for households with children, meat is a necessary good because children need nutrients from meat in their diets

Research has shown the relationships that factors affect the quantity of meat demanded, but the study still has some limitations First, the number of observations

is 642/779 households, which may be biased The observation period is 7 weeks, this

is a short period of time, so the study is only timed, not showing long-term results

In addition, the collection of data by questionnaires may cause the data obtained to

be skewed due to the dishonesty of the respondents

 Analysis of demand for corn, beans, wheat and rice in Mexico (Mateo

Vazquez-Morales, Iowa State University, 1969)

The research’s methodology is quantitative research, based on demand theory The research’s findings are: The price coefficients for corn, beans, wheat, and rice are -2.7224, -2.6663, -2.2254, -1.8316, respectively This represents an inverse relationship between price and quantity demanded Furthermore, data of price and cross elasticities of demand for four commodities are 4.463, 2.957, 0.611, -4.188 However, the study also has some limitations For example, there is a disparity in figures because statistical information in Mexico is reported mainly by two different agencies which are the Ministry of Industry and Trade and Ministry of Agriculture and Livestock In addition, the study encountered some problems when estimating

the income variable, making it difficult to form a clear picture of the real income elasticity

 An analysis of factors' affecting the demand for milk in Montana (John

Elliott Barrel, Montana State University, 1980)

The research’s methodology is quantitative research, based on demand theory The research’s findings are: Price has a negative effect on quantity demanded of milk, a

10 percent change in price will elicit a 26 percent change in quantity demanded Results of the analysis imply fluid milk is a normal good for young individuals (under

18 years of age) and families and an inferior good for older individuals (over 18 years

of age) or families composed mainly of adults Increases in population size significantly increases the total consumption of fluid milk However, a 10 percent increase in the younger population increases consumption to a greater degree than a

10 percent increase in the older population (4.1 -vs- 2.7 percent) While nonfat dry milk is a substitute for fluid milk, the relationship is quite weak A 10 percent increase in dry milk price led to increased fluid milk consumption of 1 percent From the above results, it can be seen that price has a negative relationship with quantity of fluid milk while the price of substitute goods (dry milk) and quantity of consumers have a positive relationship with quantity demanded of fluid milk

However, the study also has some limitations, for example, the unit of measurement

of milk demand is quite difficult to determine (because if measured by boxes, the types of milk are contained in different volume boxes) When measuring by volume (liters) is selected, it will deviate from the substitute good of fluid milk which is dry milk (milk powder) because dry milk cannot be measured in liters

 The Effect of Price Increases on Fresh Meat Consumption in Turkey

(Bekir Demirtas, Mustafa Kemal University, 2018)

The research’s methodology is quantitative research, based on demand theory The research’s findings are: Price has a negative effect on red meat consumption, on average a unit increase in price reduces red meat and white mead consumption by 0.704 units and 0.505 units respectively

However, the research has some limitations, that is, it only surveyed about 455 observations, a very small number compared to the total population in Turkey, so the research results are not highly representative In addition, due to the use of questionnaires to collect data, false data may be obtained due to dishonest declarants

 Analysis of consumer demand for pork and rice products in Nha Trang

city (Nguyen Manh Tuong, Nha Trang University, 2011)

The research’s methodology is quantitative research, based on demand theory Due

to copyright restrictions, we were not able to read the conclusions of this research However, we still find some limitations of the study, that is: the study only surveyed

430 out of about 100,000 households in the area of Nha Trang city, this is a too small number, not representative The period from April 24 to May 24, 2011 is a short time,

so the research is only for that time

Summary of the results of some other related researches

 A 1971 study of quantity demanded of milk of consumers in the

Gainesville, Florida area by Prato estimated the price elasticity of demand

to be -5.7

Prato points out that this value may be distorted due to the manner in which a weighted average price was constructed Consumers in higher income households did purchase greater quantities of milk than lower

 Estimations of Catch Fish Demand Function of Consumers in Uttar

Pradesh (2015)

Consumer surveys are carried out to estimate the demand function of fish species Total amount of fish consumed per day in kg The prices of different fish species, the consumer's income is collected from the market When preference for fish increased by 1 unit, the quantity demanded for fish increased by 0.16 units The results demonstrate that when the price of substitute goods increases, the demand for the primary good increases As a result, when the price of fish increases, the demand for fish decreases The estimated fish price is -0.39, indicating that when fish prices increase by 1%, the quantity demanded for fish has decreased by 0.39%

1.4 Research hypothesis

Based on existing theories and knowledge, the price of meat, price of chicken, income of consumers and the number of consumers is believed to have an impact on the quantity demanded of meat In detail, the price of meat has a negative effect on the quantity demanded of meat (when price increases, demand will decrease) whereas the other factors are positively related to the meat’s quantity demanded (when these factors increase, demand will also increase)

SECTION 2 MODEL SPECIFICATION AND DATA

2.1 Methodology

2.1.1 Method used to derive the model

Regression analysis is a method of inference, it allows us to make inferences about the whole population out of a representative sample In regression analysis, the sample statistics (or the regression coefficients 𝛽̂) are used to estimate the

population parameters 𝛽 It is our mission to find the best possible estimates for the population model

Ordinary Least Squares (OLS) method is the easiest and the most popular way to estimate the parameters of a linear regression model OLS estimator minimize the sum of all squared residuals 𝑢̂ The OLS estimators is consistent when the model 𝑖satisfies 7 basic assumptions of the classical linear regression model Under those conditions, the method of OLS provides the least-squares estimators, in the class of unbiased linear estimators and have minimum variance Then, according to the Gauss - Markov theorem, OLS is BLUE, which is stated for Best Linear Unbiased Estimator In fact, the Gauss-Markov theorem states that OLS produces estimates that are better than estimates from all other linear model estimation methods when the assumptions hold true

For the above reason, we decided to use the Ordinary Least Squares method to derive the model for our research

2.1.2 Method used to collect and analyze the data

The objective of our research is to clarify which factors among the independent variables truly have a significant impact on the dependent variable, which means that

we have to calculate the differences in the effect of each factor on the demand of red meat by doing cross-country comparisons in the year 2017 specifically Thus, we conclude that there are four factors causing tremendous changes After determining the independent variables, we collect and analyze the data on reliable websites to examine the dependency of those variables

2.2 Theoretical model specification

2.2.1 Proxy to measure

 Population

The number of consumers is proportional to population The population of a certain area is defined as the number of people normally living in that area, measured on

January 1st in a given year Specifically, in our research, the population of a European country is measured by the number of people living there on the 1st of

January 2017, with the unit of 100 billion people

The population source can be the most current population census (a census is when the population is counted) Because such a census is usually conducted every ten years, a yearly adjustment is required to obtain the population number for a certain

year The natural population change and net migration cover this adjustment

Natural population change = Births – Deaths

Net migration = Immigrants (population moving into the country) - Emigrants

(population moving out of the country)

GDP = C + G + I + NX

C = all private consumption/consumer spending within a country’s economy,

including, durable goods (items with a lifespan greater than three years), non-durable goods (food & clothing), and services

G = total government expenditures, including salaries of government employees,

road construction/repair, public schools, and military expenditure

I = the sum of a country’s investments spent on capital equipment, inventories, and

Data is presented as an index, with the unit of USD per kilogram

 Chicken price

The price of red meat represents the price received by processors for chicken Data

is presented as an index, with the unit of USD per kilogram

 Quantity demanded

The quantity demanded of a good shows the amount of goods consumers are willing

to buy at each market price We calculated it in the unit of billion kilograms,

according to the following formula:

Q D = a – b(P)

QD = quantity demand

a = all factors affecting price other than price

b = slope of the demand curve

P = Price of the good

2.2.2 Specify the model

Based on related economic theory and previous research, the quantity demanded of red meat depends on 4 factors: price of red meat, price of chicken, income of consumers and number of consumers Thus, we derive the multiple linear regression model as follow:

Population regression model:

𝐷𝑒𝑚𝑎𝑛𝑑𝑖 = 𝛽0+ 𝛽1𝑃𝑟𝑖𝑐𝑒𝑖 + 𝛽2𝐶ℎ𝑖𝑐𝑘𝑒𝑛𝑖 + 𝛽3𝐼𝑛𝑐𝑜𝑚𝑒𝑖 + 𝛽4𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖+𝑢𝑖

Therein:

- 𝐷𝑒𝑚𝑎𝑛𝑑𝑖: quantity demanded of red meat (billion kilograms)

- 𝑃𝑟𝑖𝑐𝑒𝑖: price of red meat ($ per kilogram)

- 𝐶ℎ𝑖𝑐𝑘𝑒𝑛𝑖: price of chicken ($ per kilogram)

- 𝐼𝑛𝑐𝑜𝑚𝑒𝑖: income of consumers (100 billion $)

- 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖: number of consumers (billion)

- 𝛽0: intercept terms

- 𝛽1; 𝛽2; 𝛽3; 𝛽4: partial regression coefficients

2.2.3 Theoretical relationship between dependent variable and independent

variables

- Dependent variable: quantity demanded of red meat