Part 4: Data Analysis

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En provenance du cours de Technische Universität München (TUM)

The Economics of Agro-Food Value Chains

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Technische Universität München (TUM)

The Economics of Agro-Food Value Chains

69 notes

Have a look at our trailer for a brief introduction to this course: https://youtu.be/GADAgxTHjgE In this course, you learn how the agro-food value chain approaches the challenge of constantly improving its competitiveness by producing high quality food and products and also aiming to attain greater sustainability. Some of the topics of this course are: • The notion of quality for food and agricultural products and consumer value. • How to conduct market research in this field. • Labelling, branding and pricing strategies, as-well as innovation in the agro-food sector. • How sustainability can be used as a competitive advantage through corporate social responsibility. • The role and different types of industrial standards. • The different forms of coordination in the chain, in particular with regard to the retailing and distribution sector. This course is taught by a group of international experts from Universities in Italy, Germany, Ireland, Scotland and the Netherlands. Each of them will give you insights into a specific topic related to food quality and the organization of the agro-food value chains.

À partir de la leçon

Module 3: Market Research

In Module 3 you will learn about market research. After the module you will know the definition, role and types of market research in addition to the research process and the ethics of market research. In the second lesson of the module you will learn about the different types of data and various methods of data collection. The last lesson of the module then deals with data analysis, where you will learn about the most commonly used statistical and econometric techniques to analyze data.

Part 1: Data Analysis5:10

Part 2: Data Analysis5:27

Part 3: Data Analysis4:29

Part 4: Data Analysis8:24

Rencontrer les enseignants

Hannes Lang
Research Associate
Technical University of Munich
Luisa Menapace
Professor
Technical University of Munich
Faical Akaichi
Research Economist
Scotland’s Rural College
Francesco Bimbo
Research Economist
Wageningen University
Montserrat Costa-Font
Research Economist
Scotland’s Rural College
Carlo Russo
Associate Professor
University of Cassino and Lazio Meridionale
Chenguang Li
Research Economist
University College Dublin

Okay, so let's move on to the last section of this lesson, where I am going to focus on multivariate analysis.

Multivariate analysis is used, to study the relationship between a response variable and multiple explanatory variables.

For example, analyzing the relationship between the response variable 'consumers willingness to pay for strawberries'

and the explanatory variables 'consumption of organic fruit', 'gender', 'education level' and 'income level'.

These variables are called 'explanatory variables' because they help to explain

the variation of the 'response variables'. They are also commonly called independent variables.

The response variable is frequently called a dependent variable.

So note that not all, so in the case of all the exploratory variables,

they must be quantitative or binary variables coded as dummy variables.

Just to give you an idea, look at this table, so it shows,

how the categorical variables we will call it as dummy variables,if you remember,

in a previous table we saw that we have three socio-demographic variables:

gender, education level and income level. For the education level and income level,

each one of them has three levels, okay. 1 and they will call it as 1, 2, 3.

To use them in a multivariate analysis, we had to transform them in dummy variables, ok.

So each values can be transformed in three dummies.

And each dummy variables is called as 0 1 as you can see here in the table.

But for the analysis, you can only include two dummy variables of each one of the categorical variables, ok.

This is to avoid the problem of multicollinearity.

Among all the techniques used in multivariate analysis, I am going just to mention

the linear regression, because it is the simplest technique used in a multivariate analysis.

And as a practical example of the use of linear regression,

let's suppose, that we want to explain consumers' willingness to pay for strawberries,

with lower greenhouse gas emission from production, in terms of - here the explanatory variables are:

consumption level of organic fruits, gender, education level and income level.

This is the mathematical formula of the model. The multivariate analysis in this case consists in estimating a linear regression model,

where respondent willingness to pay is the response variable,

so the response variable or the dependent variable in this case is

respondents willingness to pay for one kilogram of strawberries labeled as having

lower greenhouse gas emission and the explanatory variables or the independent variables are:

organic, education level, income level and gender.

The betas are the coefficient to be estimated. The coefficient corresponding to a quantitative explanatory variable

represents the effect on the response variable of an increase of the explanatory variable by 1 unit.

The interpretation is different in the case of dummy variables or categorical variables.

So, in the case of a dummy variables the coefficient is explained or interpreted as follows:

it represents the effect on the response variable of a change of the explanatory variable from the level coded as 0, to the level coded as 1.

So, in this table, I am displaying the results of the estimation of the linear regression model.

First, you should look at the fit of your model, given by the R-squared value.

It tells you how much of the variation of the response variable is explained by the explanatory variable, considered in the analysis.

Note that the closer the value of the R-squared to 1, the better the explicative power of the model, okay.

In our case here, it is 0.85, so it is a good fit of this model to the data.

The significance of the effect of each explanatory variable on the response variable,

is given by the probability value, that you can find in the fifth column of table 8.

The effect of an explanatory variable is significant, if the probability value of the test, which the t-test in this case, is lower than 0.05.

In this example, the results show, that only the variable consumption of organic foods and gender

have a significant effect on respondents willingness to pay for strawberries with lower greenhouse gas emissions.

So, only two variables have a significant effect on the dependent variables,

in this case, it is the willingness to pay or consumers or respondents willingness to pay for one kilogram of strawberries.

The estimated coefficient are interpreted as follows:

for the variable 'organic', the coefficient value of 1.466 indicates that

when respondents' intake of organic fruits increases by one kilogram, her/his WTP will increase by 1.466 pound.

For the variable 'gender', the coefficient value of 0.540, indicates that female respondents,

they were quoted in that data as 1, are willing to pay 0.540 higher for strawberries then male respondents.

This is how we interpret the coefficient corresponding to a dummy variable.

So, the effect that you can see that the effect of the rest of the explanatory variables

is not significantly different from 0, so they don't have a statistically significant effect on the response variable,

in this case is consumers WTP for strawberries, labelled as having lower greenhouse gas emission.

So, before concluding this lesson, I encourage you to answer this question:

the techniques that are commonly used in multivariate analysis in addition to the

linear regression, that I mentioned earlier, are nonlinear regression,

logistic regression and factorial analysis, okay. So these are three

examples of models, that you can use to conduct the multivariate analysis.

But there are many others, okay. Here just imagining three of them.

So, that's all for this lesson, which is the last lesson in Modul 3.

I hope that you have found the material explained in this lesson helpful and useful.

So, thank you very much for your attention and I wish you the best with the rest of modules.

Many thanks again and goodbye.

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