The House Hold Earnings of Male and Female Are not Significantly Different - Coursework Example

Introduction The wages or remuneration is considered as a social phenomenon may be looked at from different stand points. From one view point it is seen as being payment for labour services (where application of a person’s skills and abilities are in a specified time is paid for by the person receiving the services) subject to the employer being able to afford within economic production process of goods. The level of remuneration set majorly by the rule of supply and demand. An increase in demand for products and services results to results increase in demand of labour (this being dictated by the economic situation in a country) as well as the productivity of those hired to provide the services. According to Ehrenberg , Smith (2003) the productivity of labour is dependant on technologies, the natural resources, the quality of labour, administration and organization. This implies that demand for labour is fixed by both individual’s characteristics as certain human resources and technical resources and work organization some enterprises undertaken by the head of an enterprise. Demand for labour is also fixed by what is reffred to as macroeconomic ambience in a country. The level of labour supply is dependent on a number of people falling in some age groups, institutional legislation and availability of alternative sources of income (Ehrenberg, Smith 2003). Firstly, in a typical case where there are competitive conditions of wage level where the labour that is being put into use is determined by labour demand and supply (Rutkowski, J., & Scarpetta, S. 2005). Secondly, wage is found to be equal to marginal revenue product, simply stated it can be considered as being the value of one extra unit of produced product by use of one extra man-month or man-hour in a given work place (Ehrenberg, Smith 2003; Samuel, L. 1997 ). Thirdly, the level remuneration is dependent on the level of human capital investment which is compost of education and health protection. At individual level this is manifested in a person who has invested substantially in education having high pay and at enterprise level. Fourthly the level of remuneration is found to be affected by political environment like legislation and labour unions activities as well as social and psychological factors (Sachs, Larrain 1993: 497; Ehrenberg, Smith 2003:). Fifthly, deviation of remuneration from market level is determined by the size of enterprise, labour competition situation as well as human resources management strategy. This therefore means that the wages or remuneration level of an employee has a relationship with their productivity level meaning the level of pay for an employee depends on how much has been accomplished by them. This is in conformation with the primary law of enterprise activity which aims at maximizing profit. any enterprise perform analysis of activities they are involved in by looking for optimal disposition of resources with labour being one of the resources that are available. Ehrenberg, Smith (2003) notes that marginal product of labour is a theoretical concept whose estimation at enterprise level pauses a great challenge. These leads to looking for indices that can be applied in determination of wages one of which involve taking average productivity minus cost plus profit target (Sjørup, K. 2004). The wages of an employee may be increased or reduced depending on level of education, experience and some other features of the employee. The two parties, employer and employee have interest in knowing the level of impact of the affecting factors before settling for a decision such as making recruitment and wage determination for the case of employer; being ready to offer services and accept the wages for the case of an employee (Esping-Anderson, 1990). Describing the effects education, level of experience and other features involving wages acts as an important information source for those involved in state policy making as it gives a chance of identifying possible problems with regards to wages and productivity, as well as in making decisions with regards to future state policies with the aim of facilitating employment and welfare of those in employment (Oz, 2000). This study looks at the various factors that influnce the level of income earned. Some of the factors that will be lloked into include gender, level of education, place of education among others. Results 1.1. Descriptive Statistics First the analysis is to give a brief summary concerning the focal variables by use of descriptive statistics. The variables that feature in this part are the scale varibles which include the age, manthly income and house hold income, number of under 18 year in the house hold and number of over 18 year old (adults) in the house hold which are all scale variables. Running the descriptive statisctics for the five variables it can be seen that the average age was 32.1884 , the mean monthly income was was 11678.34 while the mean household income was 32582.502. The mean number of chidren and adults in the household were 0.75 and 2.06 respectively. 1.2. T-tests T-tests are useful in discovering differences between two groups of respondents regarding a continuous dependent variable. In this study there were several gender-related hypotheses and this made it appropriate for independent samples t-tests analysis to be used. H1: Male and female respondents signicantly different monthly earnings. The T-test showed that there was significant difference between male and female with regards to their monthly income where it can be seen from table 4.2 that the mean for female was lower at 8493.26 while that of male was higher at 14516.47. table 4.3 clearly show that the difference was statistically significant t(1003)= -2.146 at p =0.032. H2: The house hold eanings of Male and female are not significantly different It may occur that female who are at the same point in life may becoming from totally different set up from that of their male counter parts. This may be due to the fact that in some cultuaral set ups female are not given same opportunities as their male counter parts. Thus for a female to be at certain level like being in a university then the female may have a much well of background. Also it is my nview that when situations becomes tough at household level women are likely to give up first, a typical case being where girls are married off in some cultures so as to have fee to further education of the boys. In the present world the researcher believes that things have significantly changed and both male and female are almost getting equal opportunities. From table 4.4 the house hold earning for males are seen to be than that of female but from table 4.5 the different is not statistically significant t(1017)= -1.557 (p>0.05). H3:The ages of male and female are signicantly different Age may affect one productivity level and it was important to investigate if there was difference in age of the male and females. Table 4.6 shows that the mean age of female was higher at 33.9 while that of male was about 31.2 and the difference in age was statistically significant t(1017)=3.879 (p=0.000) as can be observed from table 4.7. H4: The monthly income has with type of family in the respondents household The respondent either came from house hold where the members were close relatives are non-relatives. Having close relatives may may be good for ones productivity because of the moral support one is likely to receive. In order to investigate how the nature of house hold affect ones income a t-test was done with the results being as shown in table 4.8 and 4.9. From table 4.8 it can be seen that those who came from close relative house holds had a higher monthly income of 13615.23 while those from household with non-close relatives had a lower mean monthly income of 3977.04 and from table 4.9 it can be seen that this difference was statistically significant t(1003)=-2.440 (p=0.015). 1.3. One-Way ANOVA In the next section ANOVA is used in analysis of various aspects that have effect on productivity at house hold and individual level. H5: Monthly income is significantly different for respondents with different employment statuses. My hypothesis implies that people who are unemployed or only work part-time will have different earnings because of the difference in time input. In order to investigate if different employment statuses resulted to difference in earning level a univariate analysis of variance test was done and the results were as shown in table 4.10 and 4.11. From table 4.10 it can be seen that those respondents who were full time employed had the highest earning with a mean monthly income of 18333.67 (SD=57030.417) while full time students with part time jobs had the least monthly income mean of 6554.12 (SD=22481.72). From table 4.11 it can be seen that the difference in earnings was statistically significant F(6)= 2.722 (p=0.013). It can also be seen that the interaction of the two variables is statistically (intercept result) F(1)= 24.433 (p=0.000). H6: Monthly income is different for different age groups The hypothesis implies that age may affect the level of productivity and efficiency at work and this will inturn dictate the level of earning that one can get. In order to investigate if difference in age resulted to difference in earning level a univariate analysis of variance test was done with agegroup being the fixed factor and monthly income (salary) being the dependant variable and the results were as shown in table 4.12 and 4.13. From able 4.12 it can be seen that that the age group with the highest monthly income was 31-40 years with a mean of 18314.78 the age group of 41-50 with mean monthly income of 12775.88. The mean of those 30 years and below was the lowest at 9747.35. From table 4.12 it can be seen that the main effect of age was not statistically significant F(4)= 1.860 (p=0.115). The interaction of the two variables (intercept result) was however, statistically significant F(1)= 26.987 (p=0.000). H7: Monthly income is diffrent depending on where one acquired there higher education By performing a univariate analysis of variance test it is possible to establish is there is a relationship between monthly incomes and where one acquired there higher education. From table 4.14 it can be seen that most of the respondents either acquired their highest level education in United States or in India with the remaining few acquiring their higher education from other countries. The respondents who indicated that they acquired higher education from India had the highest monthly income with a mean of 20509.53 while those who acquired there higher education from United States had the least monthly income with a mean of 5368.91. From table 4.15 it can be seen that the main effect of high education was statistically significant F(2)= 15.818 (p=0.000). The interaction of the two variables (intercept result) was also statistically significant F(1)= 11.035 (p=0.001). 1.4. Regression models and correlation For me, the most interesting part of a quantitative research project is building regression models. Examining correlations between two metric variables can give valuable insight and be very useful when creating a regression model. In this analysis, the focus is limited to linear relationships between two or more metric variables to explain the variation of a dependent variable caused by independent variables and to predict the values of Y. Firstly, the correlations between some of the focal variables has to be explored. To do this, there was use of bivariate correlations and the results are as summarized in table 4.16. From the table it can be seen that the strongest correlation was betwwen monthly income and house hold in come with a Pearson correlation of r= 0.734 at p=0.000. rom raw 1 of the table i can also be seen that age and education level variables had a signifact but weak correlation of r=0.117 (p=0.000) while the correlation betwwen age and the variable age negative and weak but statistically significant.in the table it can be seen from raw 2 that there was a positive significant correlation between education level and age with Pearson correlation value of r=0.117 (p=0.000). From the table it can also be seen that there was no significant correlation between age and monthly income, between age and house hold income and also between child and monthly income. Regression analysis The model summary (Table 4.17.) it seen that the model’s Adjusted R2 value is ratherstrong at 0.67 where R2 signifies the proportion of variance in Y accounted for by X (Malhotra 2010, p. 574). Adjusted R2, the coefficient of multiple regression is adjusted for the number of variables included in the model and the sample size and hence useful in comparing multiple regression models (Malhotra 2010, p. 578). From table 4.19 it can be seen that the model is statistically significant F(5)= 322.352 at p=0.000. From table 4.20 which gives a summary of coefficients it can be seen that the significant predictors on monthly income are the constant with a value of -7961.530, and education level and house hold income with coefficients of 1229.095 and 0.337 respectively. The unstandardized coefficient for educational level can be interpreted in the following way: if respondent A moves 1 point higher on the education scale than respondent B, then respondent A’s monthly income moves 1229.095 point higher than that of respondent B. This shows a strong positive relationship between the two variables. The variables that had to be excluded from the model were age , adult and child. 1. Conclusion and discussion The research has yielded interesting results with some any of the hypotheses being proven to be incorrect and only some of them were supported by the analysis of the data. In terms of gender it was found that female drew relatively low monthly income than their male counter parts. This could be attributed to the fact that in most cultures women are expected to take care of the home and there success is measured in how much they have succeeded in keeping their homes in order. On the other hand male are expected to be bread winners providing all the financial need of the home. It is due to some of these cultural setting that makes male to be more hard working and hence being able to generate more income than women. Also male having more monthly income may be attributed to some unfairness at work place where males are given more opportunities than famale. Men could also be aggressive and the female may choose to let them have there way and thus resulting to the diffrence in the earnings. In terms of house hold income there was no significant diffrence for the case of male and female even though I had anticipated otherwise because I would expect that sampling men and women at the same place like say a university you would expect that the woman could have come from a better off house hold than the males. This results therefore shows that women are able to make it just like their male counter parts when placed in the same conditions. In terms of age it was found that the age of women was higher than that of the male. It was important to know if there was a difference in age since it was my expectation that those who are older may have much more experience at work place that those who are younger. This believe on level of earnings being dependant on the age was dissapproved since it was found that there was no way age was related to the level of earning. This could be attributed to the fact than those who are older are not technology servy as those who are middle aged. With the modern work place being full of modern technology the past experience of the old generation may be on no use. On the other hand it was seen that those who were 30 years and below had relatively low level of pay than their counter parts in the other age groups. This could be attributed to the facts most of thos aged below 30 years are not fully involved in working because there are still at school acquiring skills and may only work on part time basis. References Ehrenberg, R.G., & Smith, R. S. (2003) Modern Labor Economics. Theory and Public Policy. 8th edition. Pearson Education, Inc. Esping-Anderson, Gøsta (1990) The Three Worlds of Welfare Capitalism. Princeton University Press Hazans, M. (2005a) Unemployment and the Earnings Structure in Latvia, World Bank Policy Research Working Paper (No. 3504). Washington DC, World Bank. Haws, KL, Bearden, WO and Nenkov, GY 2012, ’Consumer spending self-control effectiveness and outcome elaboration prompts’, Journal of the Academy of Marketing Science vol. 40 no.5 p. 695-710. Hazans, M. (2006, will be published) Latvia: Sharing the High Growth Dividend. A Living Standards Assessment, Background paper for World Bank. Kasser, T and Ryan RM, 1993, ’A Dark Side of the American Dream: Correlates of Financial Success as a Central Life Aspiration’, Journal of Personality and Social Psychology vol. 65 no. 2 Malhotra, NK 2010, ’Marketing Research: An Applied Orientation’ Global Edition, Pearson Education, New Jersey Oz, E. (2000) Management Information Systems (2nd ed.). Course Technology Rutkowski, J., & Scarpetta, S. (2005) Enhancing Job Opportunities in Eastern Europe and the Former Soviet Union. The World Bank Sachs, J. D., &, Larrain, F. (1993) Macroeconomics in The Global Economy. Prentice-Hall, Inc. Samuel, L. (1997) Fundamental social rights Case law of the European Social Charter. Council of Europe Publishing Sjørup, K. (Ed.). (2004) The European Employment Strategy and national employment policies. Addressing the employment and gender challenges of the Knowledge Based Society. (Report no. I), WELLKNOW Project Appendix Table 4.1 Descriptive Statistics N Mean Std. Deviation Monthly income 1005 12310.95 42937.911 Household income 1019 32668.981 105700.0310 adult 1020 2.06 1.428 child 1020 .75 1.101 age 1019 32.1884 10.89934 Valid N (listwise) 1000 Table 4.2 Group Statistics Gender of respondent N Mean Std. Deviation Std. Error Mean Monthly income Female 368 8493.26 33319.730 1736.911 Male 637 14516.47 47502.595 1882.121 Table 4.3 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper Monthly income Equal variances assumed 7.390 .007 -2.146 1003 .032 -6023.214 2806.414 -11530.330 -516.098 Equal variances not assumed -2.352 966.175 .019 -6023.214 2561.101 -11049.177 -997.251 Table 4.4 Group Statistics Gender of respondent N Mean Std. Deviation Std. Error Mean Household income Female 375 25917.352 54527.9856 2815.8131 Male 644 36600.443 126162.0249 4971.4802 Table 4.5 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper Household income Equal variances assumed 3.276 .071 -1.557 1017 .120 -10683.0905 6861.1992 -24146.81 2780.63 Equal variances not assumed -1.870 953.090 .062 -10683.0905 5713.5295 -21895.64 529.46 Table 4.6 Group Statistics Gender of respondent N Mean Std. Deviation Std. Error Mean age Female 375 33.9120 10.96081 .56601 Male 644 31.1848 10.74510 .42342 Table 4.7 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper age Equal variances assumed 5.621 .018 3.879 1017 .000 2.72722 .70316 1.34741 4.10703 Equal variances not assumed 3.858 769.536 .000 2.72722 .70686 1.33961 4.11482 Table 4.8 Group Statistics People in house hold family N Mean Std. Deviation Std. Error Mean Monthly income No 136 3977.04 11227.202 962.725 Yes 869 13615.23 45829.504 1554.659 Table 4.9 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper Monthly income Equal variances assumed 8.165 .004 -2.440 1003 .015 -9638.181 3949.805 -17389.011 -1887.352 Equal variances not assumed -5.271 853.95 .000 -9638.181 1828.607 -13227.273 -6049.090 Table 4.10 Descriptive Statistics Dependent Variable: Monthly income Employment status Mean Std. Deviation N Full time employment (total over 25 hours a week) 18333.67 57030.417 341 Full time student/education with part time employment 6554.12 22481.720 282 Part time student/education with full time employment 11027.37 27204.585 67 Part time employment (total under 25 hours a week) 12894.20 36534.943 117 Retired 15500.46 57772.757 110 Other 7052.21 8463.124 14 9 2989.57 8235.328 74 Total 12310.95 42937.911 1005 Table 4.11 Tests of Between-Subjects Effects Dependent Variable: Monthly income Source Type III Sum of Squares df Mean Square F Sig. Noncent. Parameter Observed Powerb Corrected Model 29801064557.862a 6 4966844092.977 2.722 .013 16.330 .875 Intercept 44587690726.881 1 44587690726.881 24.433 .000 24.433 .999 emplsta 29801064557.864 6 4966844092.977 2.722 .013 16.330 .875 Error 1821237808772.033 998 1824887583.940 Total 2003356265325.000 1005 Corrected Total 1851038873329.895 1004 a. R Squared = .016 (Adjusted R Squared = .010) b. Computed using alpha = .05 Table 4. 12 Descriptive Statistics Dependent Variable: Monthly income age group of respondent Mean Std. Deviation N 30 and below 9747.35 33554.417 590 31-40 18314.78 64384.385 261 41-50 12775.88 27858.921 83 51-60 12448.30 23098.559 44 61 and above 8640.44 19455.539 27 Total 12310.95 42937.911 1005 Table 4. 13 Tests of Between-Subjects Effects Dependent Variable: Monthly income Source Type III Sum of Squares df Mean Square F Sig. Noncent. Parameter Observed Powerb Corrected Model 13668028125.795a 4 3417007031.449 1.860 .115 7.439 .567 Intercept 49585982288.950 1 49585982288.950 26.987 .000 26.987 .999 agegp 13668028125.788 4 3417007031.447 1.860 .115 7.439 .567 Error 1837370845204.100 1000 1837370845.204 Total 2003356265325.000 1005 Corrected Total 1851038873329.895 1004 a. R Squared = .007 (Adjusted R Squared = .003) b. Computed using alpha = .05 Table 4.14 Descriptive Statistics Dependent Variable: Monthly income Highest education country Mean Std. Deviation N others 7597.95 9850.034 19 United states 5368.91 19453.925 528 India 20509.53 59044.061 458 Total 12310.95 42937.911 1005 Table 4. 15 Tests of Between-Subjects Effects Dependent Variable: Monthly income Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 56652648736.421a 2 28326324368.210 15.818 .000 Intercept 19761763643.929 1 19761763643.929 11.035 .001 highedu 56652648736.422 2 28326324368.211 15.818 .000 Error 1794386224593.475 1002 1790804615.363 Total 2003356265325.000 1005 Corrected Total 1851038873329.895 1004 a. R Squared = .031 (Adjusted R Squared = .029) 4.16 Correlation between variables Correlations age Education level Household income Monthly income adult child age Pearson Correlation 1 .117** -.034 .019 -.145** .157** Sig. (2-tailed) .000 .284 .546 .000 .000 N 1019 1019 1019 1005 1016 1016 Education level Pearson Correlation .117** 1 .102** .120** -.004 -.050 Sig. (2-tailed) .000 .001 .000 .908 .108 N 1019 1019 1019 1005 1016 1016 Household income Pearson Correlation -.034 .102** 1 .734** .105** .025 Sig. (2-tailed) .284 .001 .000 .001 .426 N 1019 1019 1019 1005 1016 1016 Monthly income Pearson Correlation .019 .120** .734** 1 .062* .036 Sig. (2-tailed) .546 .000 .000 .049 .254 N 1005 1005 1005 1005 1002 1002 adult Pearson Correlation -.145** -.004 .105** .062* 1 .161** Sig. (2-tailed) .000 .908 .001 .049 .000 N 1016 1016 1016 1002 1020 1018 child Pearson Correlation .157** -.050 .025 .036 .161** 1 Sig. (2-tailed) .000 .108 .426 .254 .000 N 1016 1016 1016 1002 1018 1020 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). Table 4.18 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .786a .619 .617 26640.796 a. Predictors: (Constant), child, Household income, Education level , adult, age Table 4.19 ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 1143918440551.824 5 228783688110.365 322.352 .000b Residual 705473604055.892 994 709731996.032 Total 1849392044607.716 999 a. Dependent Variable: Monthly income b. Predictors: (Constant), child, Household income, Education level , adult, age Table 4.20 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -7961.530 4087.127 -1.948 .052 age 115.368 80.751 .029 1.429 .153 Education level 1229.095 618.831 .040 1.986 .047 adult -584.258 610.526 -.019 -.957 .339 Household income .337 .009 .783 39.513 .000 child 1234.328 799.728 .031 1.543 .123 a. Dependent Variable: Monthly income Read More