Read the analyst's analysis (see below) of the relationship between the minimum wage and employment across states, study the table of regression results (See below), and answer the following questions: a. Interpret the coefficient on Minimum Wage in Column 1. Table 1: Minimum Wage and Teenage EmploymentDep. Variable: % of Teens Employed(3)-0.23|(4)-0.35(2)(5)(1)-0.42***(0.10)Minimum Wage-0.31*-0.16(0.15)(0.14)(0.25)(0.19)Implied Elasticity-0.12-0.09-0.06State-level Unemployment Rate-5.14***-5.81**-4.69**(1.80)(2.86)(2.31)Constant43.0***(2.4)Average Teen Emp. Rate40%40%40%27%47%State Fixed Effects:XXXXSampleAllAllAllHigh Unemp. Low Unemp.N1,0201,0201,020351669Notes: This table presents estimates of the relationship between the state minimum wage (in real2021 dollars) and teen employment rates during the period 2000-2019. The unit of observation isthe individual. Teen employment and state unemployment rates are measured in percentage points,scaled 0 to 100. Columns 2 through 5 omit the constant due to the inclusion of state fixed effects.Columns 4 and 5 split the sample based on annual state unemployment rates in the year of thesurvey using a cutoff of 8%. The implied elasticity presents the elasticity of teen employment withrespect to the minimum wage, as implied by the estimate in each column. The average teenemployment rate provides the fraction of teens employed in the sample for each column. Standarderrors in parenthesis, clustered by state-year. Significance levels: * p < 0.1, ** p < 0.05,***p<0.01.

In minimum wage law there is a minimum wage that needs to be paid to the labors, it is generally…

Read the analyst's analysis (see below) of the relationship between the minimum wage and employment across states, study the table of regression results (See below), and answer the following questions: a. Interpret the coefficient on Minimum Wage in Column 1.

Read the analyst's analysis (see below) of the relationship between the minimum wage and employment across states, study the table of regression results (See below), and answer the following questions: a. Interpret the coefficient on Minimum Wage in Column 1.

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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Suppose you have the following wage and education data for a sample with 3 observations: obs. no. 1 2 3 wage educ ($/hr) (years) 10.73 11 13.44 12 18.05 16 Calculate the coefficient for the regression of wage on education through the origin (i.e. ₁ in the model wage = B₁ educ). Express your answer out to B1 two (2) decimal points.
The following data from a random sample of individuals that are 25 years old. Note, wage is the hourly earnings in US dollars and educ is years of education. i 1 2 3 4 wage 12.18 12.18 20.09 7.39 educ 13 11 16 12 Consider the following model: In(wage)-Bo+B₁educ+u. Calculate the estimate of B₁. Round your answer to two decimal places.
Using data from a random sample of 1000 working adults, we obtain the following estimated regression to study the effect of experience (exper) on log of wage (log(wage)). log(wage) = 5.423 -0.034ezper +0.009ezper² + 0.082educ + 0.157male What other regression do you need to run to test the null hypothesis that, holding other factors fixed, experience has no effect on log(wage)? Explain what test you would perform.
The tables to the right give price-demand and price-supply data for the sale of soybeans at a grain market, where x is the number of bushels of soybeans (in thousands of bushels) and p is the price per bushel (in dollars). Use quadratic regression to model the price-demand data and linear regression to model the price-supply data. Complete parts (A) and (B) below. (A) Find the equilibrium quantity and equilibrium price. The equilibrium quantity is thousand bushels. (Round to three decimal places as needed.) (...) Price-Demand p=D(x) 6.67 6.56 6.47 6.36 6.34 X 0 10 20 30 40 Price-Supply p= S(x) 6.46 6.54 6.60 6.61 6.68 X 0 10 20 30 40
INSUR A life insurance company wishes to examine the relationship between the amount of life insurance held by a family and family income. From a random sample of households, the company collected the accompanying data. The data are in units of thousands of dollars. INCOME 97 38 141 29 y = x = Let INSUR 280 75 INCOME 303 81 453 137 The numerator of the slope coefficient formula for the estimated regression equation is: 357 77 a 400,201.10 199 43 b 392,354.02 251 53 384,660.80 807 184 d 377,118.44 147 45 272 70 537 128 527 117 245 55 483 116 673 204 194 46 154 51 163 48 280 69 507 140 464 136 321 71 873 206 476 144 574 111 251 65 497 130 826 171 133 32 259 82 281 73 446 146 332 77 219 48 208 55 180 48 169 42 273 69 502 127 547 126 281 80 428 143 370 77 221 49 214 51
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.
7. The Stata data set "college_gpa" has data on students' college GPAS, high school GPAS, ACT scores, lectures skipped during the academic year, and other characteristics. We wish to examine the predictors of college GPAS. You must submit your do-file (commands). a) Regress college GPA on high school GPA and write the estimated regression line. b) Interpret the intercept of your regression in a sentence. Does this coefficient make sense?n Explain c) Interpret the coefficient on high school GPA in a sentence. Does this coefficient make sense? Explain. reg colGPA hsGPA Source df MS Number of obs 141 %3D F(1, 139) 33.80 Model 5.58478164 1 5.58478164 Prob > F e.0000 Residual 22.9668501 139 .165229138 R-squared Adj R-squared 0.1956 %3D 0.1898 %3D Total 28.5516318 140 .203940227 Root MSE .40648 colGPA Coef. Std. Err. t P>|t| [95% Conf. Interval] hsGPA .6242948 .1073816 5.81 0.000 .4119822 .8366073 _cons 890254 3669263 2.43 0.017 647755
Suppose the researcher considers the following model : Wage = Bo+B,Female + u, and runs OLS, using wage data on 250 randomly selected male workers and 280 female workers. The researcher has obtained the estimated equation as Wage = 15 (1.00) 3 Female, R = 0.05. (0.5) In the equation, Wage is measured in dollars per hour, Female is a binary variable that is equal to 1 if the person is a female and O if the person is a male. The numbers in the parentheses are the standard errors of the coefficients. Which Statement is NOT correct? The coefficient of Female, -3.00, is statistically significant at 5%. O The p-value for the test that Ho: B = 0, H : B 0 is less than 0.05. This regression may suffer omitted variable bias. R Since insignificant. is too low, the wage difference between men and women is The researcher can increase R- by including more regressors in the model.
You are given a sample of 600 working individuals, with data on their location and earnings. You construct the following model: wage=Bo+Binorth+u, where the dependent variable is the annual earnings (wage) in $1,000. The explanatory variable is north (which equals one if a worker lives in the north, and equals zero otherwise). An OLS regression yields B 24.8. B₁ = 2.1. = As we have learned in class, education matters a lot in explaining wages-more years of education lead to higher wages. In the above econometric model, education belongs to the error term, u. We also know that in our population of interest, workers in the north have lower drop-out rates than workers in the south. These facts imply that B₁ is biased and inconsistent because the estimation is subject to omitted variable bias. The correct model should be: wage Bo+Binorth + B₂educ + e, where educ denotes the number of years of education. The OLS estimate of B1 from our incorrect model presented at the beginning of this…