Part I:Wage, Gender, Education, and Experience:Consider the following model:wageBo+B₁female + B₂ (educ≥6) + 83 experience + u. (1)where wage is the hourly wage in U.S. dollars. (educ≥6] is a dummy variable that equals 1 if an individualhas 6 or more years of education and equals zero otherwise. For example, [educ≥6]=1 for an individualwith 7 years of education, and [educ≥6]=0 for an individual with 3 years of education. experience is thenumber of years of experience an individual has. female is a dummy variable that equals 1 for female andequals 0 for male.An OLS regression for the above model gives us:wage = 9.50 0.11 female + 0.08 * [educ> 6] +0.03 * experience.(2.75) (0.05)(0.03)(0.01)where the numbers in parentheses (below the coefficients) are the standard errors.DQuestion 1Obtain the predicted wage for a female with 7 years of education and 7 years of experience (round to 2 decimal places). Question 2What is the estimated wage difference between:• a female with 4 years of education and 9 years of experience, and• a male with 8 years of education and 10 years of experience? (round to 2 decimal places).

Question 1:To calculate the predicted wage for a female with 7 years of education and 7 years of…

Answered: Part I: Wage, Gender, Education, and…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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For each of the four models above calculate the elasticity of gas price to oil price; indicate what determines its value if it is not constant; compute its numerical value assuming both variables equal their sample average. The sample averages of each variable are the following gas = 4 . 2 0 8 oil = 5 7 . 9 4 Note:- Do not provide handwritten solution. Maintain accuracy and quality in your answer. Take care of plagiarism. Answer completely. You will get up vote for sure.
The following data give the hourly wage rates (Rands) for a sample of 20 workers selected from a largecompany.12.50 9.45 13.85 7.25 8.70 14.60 11.75 14.50 10.80 12.457.50 15.90 9.75 11.50 13.30 6.25 15.50 12.80 5.35 9.50 (vi) Draw an OGIVE curve corresponding to the data and use it to estimate the median.
23. An education economist estimated the wage equation to determine the rate of return to an additional year of education and obtained ln(WAGE) = -0.5220+0.1075×EDUC. it is anticipated that ability ( attributes, such as intelligence, creativity, perseverance and industriousness) is positively correlated with UDUC. If a measure of a bility is omitted from the model the estimated return to education A. Is biased downwards B. Is biased upwards C. Cannot be estimated D. All the above
Consider the population regression of log earnings [Y;, where Y,= In(Earnings,;)] against two binary variables: whether a worker is married (D₁, where D₁;= 1 if the th person is married) and the worker's gender (D2;, where D₂;= 1 if the th person is female), and the product of the two binary variables Y₁ = Po+B₁D₁+P₂D2i + P3 (D₁¡ × D₂i) + Hi- The interaction term: O A. indicates the effect of being married on log earnings. B. does not make sense since it could be zero for married males. C. allows the population effect on log earnings of being married to depend on gender. D. cannot be estimated without the presence of a continuous variable.
Suppose we are interested in evaluating the impact on wages of being a college graduate and of residing in California. To this end we define the following dummy variables if college graduate if not college graduate coll = ncoll = if not college graduate if college graduate 1 cali = if residing in California S 1 if not residing in California ncali = if not residing in California if residing in California and for wages denoting yearly wages, we estimate the following model of returns to education wages = B1 coll+ B2ncoll + B3 cali + B4(coll x cali) + e where E[e|coll, cali] = 0. Which parameter or combination of parameters measures the increase in expected wages from obtaining a college degree (comparing to not having a college degree) when residing California? O a. None of the other options is correct O b. Bi – B2 O c. B1 – B2 + ß4 O d. B4 O e. Bi + B3 – ßi – B4 O f. Bi + B4 – ß2 – B4 Clear my choice
The question is the last bolded test. You need the previous questions to answer the last. Thank you for your time and help. You are the manager of a firm that sells CR2032 batteries for car key fobs and other electronic goods. You typically sell the fobs for $7 for a four-pack and sell an average of 27,192 four-packs per month. Due to increased labor costs, you decide to raise the price to $9 per four-pack. When you do this your monthly sales fall to 22,248 four-packs Assuming that your firm’s demand function is linear (i.e., takes the form ), calculate the linear demand function 22,248-27,192/9-7= -4944/2= -2472 Demand equation as 27,192 = a-2472(7) => a=27192+17304 =>a=44,496 27192= -2472x7+b 27192= -17304+b 27192+17304=44,496 Demand function is Q= -2472P+44,496 . Using the demand function that you estimated in the previous problem Calculate demand and total revenue when the price is $7 Q= -2472x7+44,496= -17,304+44,496= 27,192 Total revenue=PxQ=7x27,192=…
Consider the following panel model to examine the effect of retirement on consumption expenditure, consit, of individual i over years t=1,…,3: (B1) log⁡(consit) = β0 + β1retiredit + β2ageit + β3marriedit + β4healthit + δ1Yr2t + δ2Yr3t + ai + uit Where: retiredit is a dummy variable equal to 1 if individual i is retired on year t and 0 otherwise ageit is the individual's age in years marriedit is an indicator variable for whether the individual is married (1) or not (0) in year t healthit is an indicator variable equal to 1 if the individual is in 'good health' and 0 otherwise Yr2 is a dummy variable equal to 1 in year t=2 and 0 otherwise Yr3 is a dummy variable equal to 1 in year t=3 and 0 otherwise We obtain the following results when we estimate Model (B1) using RE and FE methods: Variable Random Effects Fixed Effects retired -0.072*** (0.024) -0.054** (0.025) age -0.008*** (0.003) - married 0.031*** (0.010) 0.028 (0.022) health 0.050*** (0.021) 0.052***…
9
A tax on Cigarettes introduced in Mexico in 2012 increased the price of Cigarettes by about 10%. An evaluation of the first two years of this measure found that Cigarette consumption fell on average by 7.6%, with a larger decrease (11.7%) in low income households. The study also estimated an increase in consumption of alternatives to smoking, particularly Nicotine patches, of 2.1% (AYA, 2017) Explain factors that determine responsiveness to a price increase. Which of these factors might explain the difference in the economy and low income households?
1. Suppose output (Q) is related to labor (L) and capital (K) in the following nonlinear way: Q = albKc When taking log to this equation, it is transformed into a linear LnQ = Ina + b In(L) + c Ln (K) One hundred twenty-three observations are used to obtain the following regression results: Dependant Variable: Observations: Variable Intercept L K Q 123 5.5215 Parameter Standard Estimate error 0.650 R-square 0.350 0.7547 0.9750 0.2950 0.1450 F-ratio 184.56 t-ratio 5.66 2.20 2.41 p-value on F 0.00001 p-value 0.0001 0.0295 0.0173 a. Write the regression equation based on the output either in the transformed linear form or the original non-linear form.
A few minutes ago, Joe Doe learned that his company was having a bad year and thus merit salary increases will be smaller than expected. His division’s budget has been set and so he knows that he will receive a salary increase of $400 over his present salary. Joe Doe has also been offered a transfer to another division in the company. He is unsure of the salary situation in the other division. However, his best information leads him to conclude that if he took the new job, there is a 60% chance that he would receive a salary increase of $600 over his present salary, and a 40% chance that he would receive only a $150 increase over his present salary. To summarize, Joe Doe can either stick with his current job and get a salary increase of $400, or take a transfer to a new job and get a salary increase of either $600 with a 60% probability, or $150 with a 40% probability. Amount ($X) Value (v($X)) $0 -5 $150 -2.5 $200 -2 $300 0 $350 0.5 $400 1 $600 1.85…
How do you interpret the R-squared obtained from running this regression?

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