This report analyzes the impact of country of education on the income level of individuals. It also examines the influence of gender and region of residence on income. The data used in the analysis is related to 35,475 respondents in Italy. The regression analysis shows a low correlation between country of education and income level.
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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
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. 1|P a g e
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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 reportï‚·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.ï‚·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.ï‚·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.ï‚·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.ï‚·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. 2|P a g e
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 toNoble 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, onemust with due care must select education institute for taking education so that good amount of earning can be made at job. Contrary to thisHiza 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 3|P a g e
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 toChevalier 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 thisRodrÃguez-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. 4|P a g e
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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 analysisisanothermethodthatisusedtoanalyzerelationshipbetweendependentand 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. 5|P a g e
Empirical evidence Descriptive statistics DESCRIPTIVES VARIABLES=sex region income /STATISTICS=MEAN STDEV MIN MAX. Descriptive Statistics NMinimu m Maximu m MeanStd. Deviation Sex34394121.53.499 Region of residence3547519911.0114.282 Monthly income22495033331199.16553.397 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. 6|P a g e
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 ModelVariables Entered Variables Removed Method 1Region of residenceb.Enter a. Dependent Variable: Monthly income b. All requested variables entered. Model Summary ModelRR SquareAdjusted R Square Std. Error of the Estimate 1.104a.011.011550.404 a. Predictors: (Constant), Region of residence ANOVAa ModelSum of SquaresdfMean SquareFSig. 1 Regression74615975.855174615975.855246.302.000b Residual6814139797.34 922493302944.907 Total6888755773.20 322494 7|P a g e
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a. Dependent Variable: Monthly income b. Predictors: (Constant), Region of residence Coefficientsa ModelUnstandardized CoefficientsStandardized Coefficients tSig. BStd. ErrorBeta 1(Constant)1156.8814.553254.119.000 Region of residence4.125.263.10415.694.000 a. Dependent Variable: Monthly income Model 1 Region of residence4.125 Constant1156.88 Observations35475 R squared0.011 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 ofsignificanceis 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 8|P a g e
/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 ModelVariables Entered Variables Removed Method 1Region of residenceb.Enter 2Sexb.Enter a. Dependent Variable: Monthly income b. All requested variables entered. Model Summary ModelRR SquareAdjusted R Square Std. Error of the Estimate 1.105a.011.011552.867 2.199b.039.039544.898 a. Predictors: (Constant), Region of residence b. Predictors: (Constant), Region of residence, Sex 9|P a g e
ANOVAa ModelSum of SquaresdfMean SquareFSig. 1 Regression74842429.804174842429.804244.854.000b Residual6668006769.08 221815305661.553 Total6742849198.88 621816 2 Regression265973456.7702132986728.385447.897.000c Residual6476875742.11 521814296913.713 Total6742849198.88 621816 a. Dependent Variable: Monthly income b. Predictors: (Constant), Region of residence c. Predictors: (Constant), Region of residence, Sex Coefficientsa ModelUnstandardized CoefficientsStandardized Coefficients tSig. BStd. ErrorBeta 1(Constant)1157.5354.639249.513.000 Region of residence4.190.268.10515.648.000 2 (Constant)1441.85812.103119.130.000 Region of residence3.952.264.09914.964.000 Sex-187.3247.383-.168-25.372.000 a. Dependent Variable: Monthly income Excluded Variablesa 10|P a g e
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ModelBeta IntSig.Partial Correlation Collinearity Statistics Tolerance 1Sex-.168b-25.372.000-.169.999 a. Dependent Variable: Monthly income b. Predictors in the Model: (Constant), Region of residence Model 1Model 2 Region of residence4.1903.952 Sex-187.32 Constant1156.881441.85 Observations3547535475 R squared0.0110.039 Interpretation Value of R is 0.105 for model 1 and same is 0.199 for model 2 and value of R square for model 1 is 0.011 and 0.039 which means that independent variable only explain 0.1% and 3% change in dependent variable which is monthly income. Value of level ofsignificancefor model 1 is 0.00<0.05 and same results are obtained in second model. On this basis it can be said that either single variable region of residence is taken or both gender and region of residence similar results are obtained and significent difference is observed in monthly income due to change in these variables CONCLUSION Brief summary of findings On basis of finding it can be said that gender and region of residencehave significent impact on income level of individuals. Monthly income standard deviation is high which reflect that same is changing at fast rate. Along with this, region of residence of individuals is also changing at moderate rate. It can be said that both independent variables are affecting dependent variable. 11|P a g e
Hypothesis testing Hypothesis testing is done and value of level of significence is 0.00<0.05 which is reflecting that significent difference present between income level and region of residence as well as gender group. If both variables are taken in to consideration then also same result of value of level of significance of 0.00<0.05 is obtained. Hence, in all situation altenrative hypothesis is accepted. Further steps Ressearch can be further carried out and new variables can be added and under this waiting time can be recorded which will reflect duration during which one after completing education was unemployed. This thing will help one in conducting research in better manner. It can be identified whether waiting time or gap between education end time and job starting time have impact on individual salary. 12|P a g e
REFERENCES Books and journals Noble, K.G. and et.al., 2015. Family income, parental education and brain structure in children and adolescents.Nature neuroscience.18(5). pp.773-778. Hiza, H.A. and et.al., 2013. Diet quality of Americans differs by age, sex, race/ethnicity, income, and education level.Journal of the Academy of Nutrition and Dietetics.113(2). pp.297-306. Chevalier, A. and et.al., 2013. The impact of parental income and education on the schooling of their children.IZA Journal of Labor Economics.2(1). p.8. RodrÃguez-Pose, A. and Tselios, V., 2010. Inequalities in income and education and regional economic growth in western Europe.The annals of regional science.44(2). pp.349-375. Online The connection between education, income inequality and unemployment, 2017. [Online]. Availablethrough:<https://www.huffingtonpost.com/steven-strauss/the-connection- between-ed_b_1066401.html>. 13|P a g e