Regression Analysis of Education, Gender, and Income in Italy

This report uses regression analysis to investigate the relationship between education, gender, and income levels in Italy, utilizing data from 35,475 respondents. The research aims to determine the impact of the country of education on individual income, considering gender and region of residence as independent and control variables. Descriptive statistics and regression models are employed to analyze the data, revealing insights into how these factors influence income. The report includes a literature review, data description, methodology, empirical evidence, and a conclusion, contributing to the understanding of the determinants of income in the context of Italian education and employment.

REPORT

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................1
Research aim and research question............................................................................................1
Contributions...............................................................................................................................1
Theoritical or empirical evidence...............................................................................................1
Structure of report........................................................................................................................1
Theory and literature review............................................................................................................1
Theory..........................................................................................................................................1
Literature review..........................................................................................................................1
Research hypothesis.........................................................................................................................1
.........................................................................................................................................................1
Data..................................................................................................................................................2
Information on variables for research..........................................................................................2
Data used in research...................................................................................................................2
Variables..........................................................................................................................................2
Dependent variables.....................................................................................................................2
Independent variables..................................................................................................................2
Control variables..........................................................................................................................2
Nominal or ordinal categories.....................................................................................................2
Method and analytical strategy........................................................................................................2
Statistical tools used in analysis..................................................................................................2
Analytical strategy and model specification................................................................................2
Empirical evidence..........................................................................................................................3
Descriptive statistics....................................................................................................................3
Regression models.......................................................................................................................3

CONCLUSION................................................................................................................................6

INTRODUCTION
Statistics is the one of the area that is greatly used to understand business problems and
identifying solution of same. In the current report regression analysis technique is used to
understand relationship between dependent and independent variables. In this report data related
to education of students of Italy and income they earned is analyzed. By doing so analysis is
done in proper manner and at end of the report conclusion is formed.
Research aim and research question
Research aim: The present research aims at identifying impact that country of education have on
the income level of individuals
Research objectives
To identify impact that country of education have on the income level of individuals.
To find out impact the sex and region of residence collectively have on the income level of
respondents.
Research question
What is the impact that country of education have on the income level of individuals?
What is the impact the sex and region of residence collectively have on the income level of
respondents?
Contributions
Current research study mainly focused on identifying impact that country of education or
education taken from different regions have on the income level of an individual. In past
research studies it is only identified that what sort of impact education have on income level of
an individual. Few research studies are carried out relevant research topic. Present research study
contribute a lot to past research because in this number of variables are taken in to account like
gender and country of edcuation and it will be identified that which of these factor have huge
impact on income level. In past researches focus was only on specific factor like country of
education. Current research study give extension to past researches and identify impact of both
education and country of education on income level. Thus, scope of study is wide and help one
in developed good understanding of research topic.
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Theoritical and empirical issues to make research innovative
Theoritical issue that make research innovative is that in impact of both country of
education and gender is identified on income level and will help in identifying that which of
these factors play decisive role in determining income of individual.
Theoritical or empirical evidence
Research is interesting because extent to which gender and country of education affect
income level will be identified in present research study. If it is identified that country of
education have more impact then gender on income level then in that case students can plan to
take education from specific location. This research outcome will to great extent will reflect
employers perception and their thinking pattern which make research more interesting.
Structure of report Introduction: It is important section of the report and under this research aim and
objectives are given and ways are explained in which current research study contribute to
existing literature. Apart from this, points which will make research interesting are also
explained in this section of report. Literature review: In literature review section all literatures will be reviewed and
perspectives of different writers in respect to research topic will be analyzed tha are
reflected by past year research paper. Data, variable and method: In this section of report variables that will be analyzed using
specific approach will be discussed in detail. This section is very important part of the
report. Empirical evidence: In this section of report statistical modeling will be done and on that
basis it will be identified which independent variables and to wha extent will affect
dependent variable. Conclusion: In this part conclusion will be formed on basis of results analyzed and
literature review section. Thus, it is final part of the report.
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Theory and literature review
Theory
Education and income are interrelated to each other and it can be observed that income is
heavily affected by education level that individual have. Education determine qualification and
by considering same salary is given to the individuals (The connection between education,
income inequality and unemployment, 2017). Some times busienss firms also consider location
because in some locations there are large number of educational institutions which deliever high
quality of education. Hence, employers have special attention towards candidates that take
education from location or city that is known for well named colleges and also pay high salary to
them.
Literature review
According to Noble and et.al., (2015) there are number of factors that determine income
level of individual and education is one of them. Usually, it is observed that if one have high
level of education then employers think that it is well qualified person and offer high amount of
salary to it. On this basis it can be said that education factor play vital role in determining income
level of an individual. Apart from this, country of education is the another factor that have heavy
influence on the determination of income level. Country of education basically means nation
from which one take education or states of the nation from which education is taken by an
individual. It is observed that state of the nation from which education is taken is widely
considered by the employers while giving salary to individuals. This is because there are specific
locations or states where ther are number of highly technical education institutes and due to this
reason employers think that individual coming from that areas are well qualified and good
amount of salary is offered to them. On this basis, it can be said that education and location from
which education taken both have impact on income level of individuals. Thus, one must with
due care must select education institute for taking education so that good amount of earning can
be made at job.
Contrary to this Hiza and et.al., (2013) state that income level and country of education
does not have any relationship. Business firms usually give importance to the knowledge level
that individual have in respect to its domain. College from which an individual do study does not
have any sort of relationship with its income level. If an individual completed its study from well
known college then it does not mean that it also possess good knowledge. It is possible that
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individual completed its study from college that is now so prestigious but it may have good
knowledge or vice verse. Due to this reason firms does not make any difference among
candidates in terms of salary on basis of education from specific area. Thus, it is the knowledge
level which is respected by employers and accoridingly salary is given to employees.
According to Chevalier and et.al., (2013) gender is the factor that is considered by most
of employees while deciding salary of employees. It is observed that low amount of salary is
given to women then men. This gender descrimination is observed across all sorts of firms and
more commonly in European nations. Woemes salary is significently lower then same of male
and it can be said that this is wrong thing that is happeneing across globe.
Contrary to this Rodríguez-Pose and Tselios, (2010) state that usually it is observed that
womens are not involved in highly technical work and this is the reason due to which low salary
is given to them irrespective of geographic location from which they start their study and type of
qualification they have in respect to performance of the job. Thus, on this ground salary
descrimination can not be considered completely wrong.
Research hypothesis
H0: There is no significent impact of education that individual take from specific country of
education on income it earned on job.
H1: There is significent impact of education that individual take from specific country of
education on income it earned on job.
H0: There is no significent impact of gender on income it earned on job.
H1: There is significent impact of gender on income it earned on job.
Data
Information on variables for research
Variable for research are education that individual taken from different regions and other
variable is income that individuals earned onn their job. Apart from this, third variable take in
research is gender. By using different methods relationship between these variables is identified.
Data used in research
Data used in research is related to 35,475 respondents that live in different regions of
Italy and education background is different. Out of entire number of variables only specific
variables which are gender, education and income level are taken in to consideration.
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Variables
Dependent variables
Dependent variable is only income that is earned by total number of respondents which
are 35,475. In research only single variable is taken for analysis purpose.
Independent variables
Multiple independent variable is taken in to account for analysis purpose which are
gender and country of education. Linear regression models will be used to data analysis.
Control variables
Gender is the control variable in present research study
Nominal or ordinal categories
Nominal variables are gender which are classified in to male and female category. Apart
from this, region is another categorical variable in present research study.
Method and analytical strategy
Statistical tools used in analysis
Statistics used in analysis are descriptive staitstics and regression analysis. By using
descriptive statistics detailed information about variable is gathered. Apart from this, regresion
analysis is another method that is used to analyze relationship between dependent and
independent variables. It can be said that by using relevant statistics analysis will be done in
proper manner.
Analytical strategy and model specification
As part of analytical strategy first of all for specific variables descriptive statistics will be
run. Apart from this, regression analysis will be run and under this model will be developed
under which there will be one independent and other will be dependent variable. As part of
model specification it is clear that for first objective independent variable will be country of
education and dependent variable will be income level. On other hand, for second objective there
will be same dependent variable and two independent variable which are country of education
and gender.
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Empirical evidence
Descriptive statistics
DESCRIPTIVES VARIABLES=sex region income
/STATISTICS=MEAN STDEV MIN MAX.
Descriptive Statistics
N Minimu
m
Maximu
m
Mean Std.
Deviation
Sex 34394 1 2 1.53 .499
Region of
residence 35475 1 99 11.01 14.282
Monthly income 22495 0 3333 1199.16 553.397
Valid N (listwise) 21817
Interpretation
In case of sex mean value and standard deviation is (M=1.53, SD=0.499) and same for
region of residence mean value is (M=11.01, SD=14.282). It can be said that region of residence
is changing at fast rate then sex. Majority of respondents are female. Monthly income mean
value is (M=1199.16, SD= 553.397). This means that on average basis monthly income of
individuals is 1199 and it is deviating at moderate rate which is 553.397.
Regression models
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT income
/METHOD=ENTER region.
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H0: There is no significent impact of education that individual take from specific country of
education on income it earned on job.
H1: There is significent impact of education that individual take from specific country of
education on income it earned on job.
Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
1 Region of
residenceb . Enter
a. Dependent Variable: Monthly income
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
1 .104a .011 .011 550.404
a. Predictors: (Constant), Region of residence
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 74615975.855 1 74615975.855 246.302 .000b
Residual 6814139797.34
9 22493 302944.907
Total 6888755773.20
3 22494
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a. Dependent Variable: Monthly income
b. Predictors: (Constant), Region of residence
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 1156.881 4.553 254.119 .000
Region of residence 4.125 .263 .104 15.694 .000
a. Dependent Variable: Monthly income
Model 1
Region of residence 4.125
Constant 1156.88
Observations 35475
R squared 0.011
Interpretation
R square value is 0.011 and R value is 0.104 which reflect that there is low coorelation
between both independent and dependent variables. Apart from this, R square value is 0.104
which means that only 10% deviation in dependent variable is explained by independent
variable. Value of level of significance is 0.00<0.05 which means that there is significent mean
difference between dependent and independent variable. This means that with change in
independent variable which is region of residence have significent impact on education level of
individuals. Hence, alternative hypothesis accepted.
Regression
REGRESSION
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/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT income
/METHOD=ENTER region
/METHOD=ENTER sex.
H0: There is no significent impact of gender on income it earned on job.
H1: There is significent impact of gender on income it earned on job.
Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
1 Region of
residenceb . Enter
2 Sexb . Enter
a. Dependent Variable: Monthly income
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
1 .105a .011 .011 552.867
2 .199b .039 .039 544.898
a. Predictors: (Constant), Region of residence
b. Predictors: (Constant), Region of residence, Sex
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