Trying to find answers please.
Exercise 33 Table 3 .3 is an extract from a (hypothetical) select life table with a select period of two years. Note carefully the layout each row relates to a xed age at selection. Use this table to calculate (a) the probability that a life currently aged 75 who has just been selected will survive to age 35. (b) the probability that a life currently aged 76 who was selected one year ago will die between ages 35 and 3?, and (C) 4|24m1+1 - Table 3.3. Extrectfrom a (hypothetical) select life table. I In] low 31+: I + 2 25 15 930 15663 15 236 T? 26 15503 15224 14316 T3 '1'? 15050 14244 14310 '19 30 12 576 32 31 11923 33 32 11 250 34 33 10 542 35 34 9 312 36 35 9 064 3'? Exercise 3.4 CMI (Table A23) is based on UK data from 1999 to 2002 for female non-smokers who are term insurance policyholders. It has a select period of ve years. An extract from this table, showing values of q[x_;]+;, is given in Table 3.9. Use this survival model to calculate (3) 2pm] 1 03) sq[73]+2, c. From the model above, what is the variable that allows the effect of a worker's age to depend on the gender? What is its parameter? What sign do you expect for that parameter if getting one year older is more valuable for women than men? (2pts) d. For workers of the same age and education, what is the percentage earning gap between male and female?(Ipt) e. If f, > 0, then does the earning gap between male and female worker get larger for older people or for younger people? (2pts) E. Provide a correct interpretation for the coefficient of the variable age female (1pt) g. Provide a reason for the presence of the variable female edu in this model i.e what can this variable in this model allow us to investigate? (Ipt) Ill. (15pts) You are given the following economic model: log(wage) =0.18+0.093edi + 0.044 exp+0.043 female -0.016edu. female -0.010exp. female - 0.00068 exp Sid errors (0.132) (0.009) (0.005) (0.196) (0.014) (0.003) (0.0001) 1 526 R-Squared - 0.4160 10612 With all the variables described as follows: log wage) = log of average hourly wage; female is a dummy variable equal to I if the observed person is a female, and 0 if male; edu female is an interaction variable equal to education female; edu is the number of years of schooling: exp is the number of years of experience exp.female is an interaction variable equal to experience female exp - experience "experience. a. Is the parameter of the variable exp statistically significant at 5%? (Ipt) b. Provide a possible reason for the presence of the variable exp* in the model estimated above (Ipt) c. What is the estimated return to an additional year of experience for a female worker? For a male worker? (2pts) d. What is the optimal number of experience years for a female worker? For a male worker? (2pts) e. Calculate the estimated effect of the 5" year of experience on a female's wage, and on a male's wage. (2pts). f. From the estimated equation above, provide an appropriate interpretation for each coefficient associated to the following variables: female, education, and female. edu? (3pts) g. Is education more valuable for male or for female workers? What about experience? (1 pt) h. If a male and a female have 10 years of experience each, how many years of education would it take for their wages to be the same? (1pt) In order to test whether gender does significantly affect the wage earned by workers, the following restricted model has been estimated. log(wage) = 0.128 +0.090edu + 0.041exp-0.0007 exp' Sid errors (0.106) (0.0075) (0.0052) (0.00012) 1=526 R-Squared = 0.3003 i. At 5%, test whether the gender is a statistically significant in explaining workers' wages. State clearly your null and alternative hypothesis, and explain the implications of your test conclusion. (2pts)
