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A published paper on the determinants of income inequalities in different, a mix of developed and developing, countries reports the following table: Explaining income inequality (GINI coefficient) asing expenditure on different bpes of education, controlling for GDP per capita; linear (OLS) regression Coefficient t 1) Public spending on higher education (% of GDP) 0.21 3.59 2) Public spending on primary education (% of GDP) -0.28 -3.83 3) GDP per capita -0.0003 -2.51 Constant 44.2 10.15 N=48 (country) R-squared 0.49; (3, 44) 13.07. Explain and fully discuss the results in the table a) Are the regression coefficients for public speniding on higher/primary education statistically significant; why? Show how you calculated all the relevant statistics b) How would you interpret the three regression coefficients? c) Can we say that public spending on primary education is a more important determinant of income inequality than public spending on higher education? If so, why? d) How to interpret the constant? e) What does the F-statistic show? f) How to interpret the R-squared? Would you say that it is a large R-squared? g) Overall, do these results make any sense? We are interested in the associations between individuals' own education, their father's education and their occupational status. For this purpose, we have a representative sample of individuals who were bom in the early 1970s in Britain The sample size is 5000. Occupational status is measured around age 35 by a so- called occupational earnings score on a scale of 1-100 (higher values indicate more favourable occupational earnings positions). We perform a linear regression (OLS) analysis. Our dependent variable 15 the occupational earnings score. We have two explanatory variables, respondent's highest level of education (measured on a scale of 1-8) and parental education (measured on a scale of 0-5). The results are summarised by the following table Regression coefficients [standard errors in brackets] Model 1 Model 2 Model 3 Own education 6.525 - 5.981 [0.1604] [0.1689] Father's education -- 4.696 3.104 [0.2282] [0.5096] Intercept 27.100 46.425 25.935 Explain and fully discuss the results: a) What is the difference between Model 1. /Model 2 and Model 3? b) Why do we see reduction in the sizes of the regression coefficients between Model 1 and Model 3 and between Model 2 and Model 3? c) How would you interpret the standard errors in Model 3? d) Do these regression coefficients significantly differ from 0, and if so, why? Show how you calculated all the relevant statistics e) Would you say that own education is a more important predictor than father's education? If so, why?

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