# Data and Business Decision Making

VerifiedAdded on 2022/11/01

|15

|1220

|381

AI Summary

This document discusses data and business decision making. It covers topics such as the final dataset, chosen numerical and categorical variables, scatter plots, hypothesis testing, and regression models.

## Contribute Materials

Your contribution can guide someone’s learning journey. Share your
documents today.

Running head: DATA AND BUSINESS DECISION MAKING

Data and Business Decision Making

Name of the Student

Name of the University

Course ID

Data and Business Decision Making

Name of the Student

Name of the University

Course ID

## Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.

1DATA AND BUSINESS DECISION MAKING

Table of Contents

Answer to question 1.......................................................................................................................2

Answer to question 2.......................................................................................................................2

Answer to question 3.......................................................................................................................3

Answer to question 4.1....................................................................................................................5

Answer to question 4.2....................................................................................................................6

Answer to question 4.3....................................................................................................................6

Answer to question 4.4....................................................................................................................7

Answer to question 4.5....................................................................................................................7

Answer to question 5.1....................................................................................................................8

Answer to question 6.1..................................................................................................................10

Answer to question 6.2..................................................................................................................10

Answer to question 6.3..................................................................................................................11

Answer to question 6.4..................................................................................................................11

Answer to question 6.5..................................................................................................................12

Answer to question 6.6..................................................................................................................13

Answer to question 6.7..................................................................................................................13

Reference.......................................................................................................................................14

Table of Contents

Answer to question 1.......................................................................................................................2

Answer to question 2.......................................................................................................................2

Answer to question 3.......................................................................................................................3

Answer to question 4.1....................................................................................................................5

Answer to question 4.2....................................................................................................................6

Answer to question 4.3....................................................................................................................6

Answer to question 4.4....................................................................................................................7

Answer to question 4.5....................................................................................................................7

Answer to question 5.1....................................................................................................................8

Answer to question 6.1..................................................................................................................10

Answer to question 6.2..................................................................................................................10

Answer to question 6.3..................................................................................................................11

Answer to question 6.4..................................................................................................................11

Answer to question 6.5..................................................................................................................12

Answer to question 6.6..................................................................................................................13

Answer to question 6.7..................................................................................................................13

Reference.......................................................................................................................................14

2DATA AND BUSINESS DECISION MAKING

Answer to question 1

The Final dataset is prepared by removing all the missing observations in order to get

consistent and unbiased results. The final dataset has 111 observations.

Answer to question 2

The chosen numerical variables are tenure and age. The average value of tenure is 7.76

years and the tenure of respondent lies between 0 and 19 years. The histogram of the tenure

shows the right skewed distribution (Godoey and Reich 2019).

The average value of age is 33.37 years and the age of respondents lies between 10 and

38 years. The histogram of the age shows the right skewed distribution.

tenure age

Mean 7.7568 33.3694

Standard Error 0.4462 0.2867

Median 8 33

Mode 7 38

Standard Deviation 4.7005 3.0209

Sample Variance 22.0948 9.1260

Kurtosis -0.9044 -1.2466

Skewness 0.1318 0.0635

Range 19 10

Minimum 0 28

Maximum 19 38

Sum 861 3704

Count 111 111

The chosen categorical variables are marriage and gender. There is 86% of married and

14% single respondents. There is approximately 42% female and 58% of male respondent.

Answer to question 1

The Final dataset is prepared by removing all the missing observations in order to get

consistent and unbiased results. The final dataset has 111 observations.

Answer to question 2

The chosen numerical variables are tenure and age. The average value of tenure is 7.76

years and the tenure of respondent lies between 0 and 19 years. The histogram of the tenure

shows the right skewed distribution (Godoey and Reich 2019).

The average value of age is 33.37 years and the age of respondents lies between 10 and

38 years. The histogram of the age shows the right skewed distribution.

tenure age

Mean 7.7568 33.3694

Standard Error 0.4462 0.2867

Median 8 33

Mode 7 38

Standard Deviation 4.7005 3.0209

Sample Variance 22.0948 9.1260

Kurtosis -0.9044 -1.2466

Skewness 0.1318 0.0635

Range 19 10

Minimum 0 28

Maximum 19 38

Sum 861 3704

Count 111 111

The chosen categorical variables are marriage and gender. There is 86% of married and

14% single respondents. There is approximately 42% female and 58% of male respondent.

3DATA AND BUSINESS DECISION MAKING

Marriage Frequency Frequency Percentage

married 96 86%

single 15 14%

Grand Total 111 100%

Gender Frequency Frequency Percentage

Female 47 42%

Male 64 58%

Grand Total 111 100%

Answer to question 3

The scatter plot shows a strong relationship between IQ and education (Walker and Zhu

2017). The regression result shows that the education has significant impact on IQ as the p-value

of the coefficient is significant at 5% significance level.

Marriage Frequency Frequency Percentage

married 96 86%

single 15 14%

Grand Total 111 100%

Gender Frequency Frequency Percentage

Female 47 42%

Male 64 58%

Grand Total 111 100%

Answer to question 3

The scatter plot shows a strong relationship between IQ and education (Walker and Zhu

2017). The regression result shows that the education has significant impact on IQ as the p-value

of the coefficient is significant at 5% significance level.

## Paraphrase This Document

Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser

4DATA AND BUSINESS DECISION MAKING

9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9

0

20

40

60

80

100

120

140

160

IQ vs Educati on

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.65637241

R Square 0.43082474

Adjusted R Square 0.425602949

Standard Error 11.55835342

Observations 111

ANOVA

df SS MS F Significance F

Regression 1 11022.32106 11022.32106 82.50516134 5.25971E-15

Residual 109 14561.91317 133.5955337

Total 110 25584.23423

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 50.13766342 6.233462772 8.043308391 0.00 37.78314286 62.49218398

educ 3.991431845 0.439428438 9.08323518 0.00 3.120498968 4.862364723

The scatter plot shows a moderate positive correlation between KW and age. The

regression result shows that the age has significant impact on KW as the p-value of the

coefficient of age is significant at 5% significance level.

9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9

0

20

40

60

80

100

120

140

160

IQ vs Educati on

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.65637241

R Square 0.43082474

Adjusted R Square 0.425602949

Standard Error 11.55835342

Observations 111

ANOVA

df SS MS F Significance F

Regression 1 11022.32106 11022.32106 82.50516134 5.25971E-15

Residual 109 14561.91317 133.5955337

Total 110 25584.23423

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 50.13766342 6.233462772 8.043308391 0.00 37.78314286 62.49218398

educ 3.991431845 0.439428438 9.08323518 0.00 3.120498968 4.862364723

The scatter plot shows a moderate positive correlation between KW and age. The

regression result shows that the age has significant impact on KW as the p-value of the

coefficient of age is significant at 5% significance level.

5DATA AND BUSINESS DECISION MAKING

25 27 29 31 33 35 37 39

0

10

20

30

40

50

60

KW vs Age

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.3827247

R Square 0.146478196

Adjusted R Square 0.13864772

Standard Error 6.604072379

Observations 111

ANOVA

df SS MS F Significance F

Regression 1 815.8466008 815.8466008 18.70616926 3.38941E-05

Residual 109 4753.901147 43.61377199

Total 110 5569.747748

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 8.088487633 6.983620782 1.158208311 0.249 -5.752821718 21.92979698

age 0.901505905 0.208437639 4.32506292 0.000 0.48838928 1.31462253

Answer to question 4.1

wage

Mean 1100.7477

Standard Error 41.5113

Median 1027

Mode 1000

Standard Deviation 437.3485

901

1134

1368

667

1602

1836

More

433

2070

2303

2537

0

5

10

15

20

25

30

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

Histogram

Bin

Frequency

25 27 29 31 33 35 37 39

0

10

20

30

40

50

60

KW vs Age

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.3827247

R Square 0.146478196

Adjusted R Square 0.13864772

Standard Error 6.604072379

Observations 111

ANOVA

df SS MS F Significance F

Regression 1 815.8466008 815.8466008 18.70616926 3.38941E-05

Residual 109 4753.901147 43.61377199

Total 110 5569.747748

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 8.088487633 6.983620782 1.158208311 0.249 -5.752821718 21.92979698

age 0.901505905 0.208437639 4.32506292 0.000 0.48838928 1.31462253

Answer to question 4.1

wage

Mean 1100.7477

Standard Error 41.5113

Median 1027

Mode 1000

Standard Deviation 437.3485

901

1134

1368

667

1602

1836

More

433

2070

2303

2537

0

5

10

15

20

25

30

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

Histogram

Bin

Frequency

6DATA AND BUSINESS DECISION MAKING

The threshold value is decided by using the median as the distribution is right skewed not

normally distributed.

Answer to question 4.2

The below graph depicts that the number of male respondents is higher than the female in

both the cases of high and low wage. The number of high and low wage earners are

approximately same across male and female.

Answer to question 4.3

The threshold value is decided by using the median as the distribution is right skewed not

normally distributed.

Answer to question 4.2

The below graph depicts that the number of male respondents is higher than the female in

both the cases of high and low wage. The number of high and low wage earners are

approximately same across male and female.

Answer to question 4.3

## Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.

7DATA AND BUSINESS DECISION MAKING

Female Male Grand Total

High Wage 22 33 55

Low Wage 25 31 56

Total 47 64 111

Female Male Grand Total

High Wage 0.198 0.297 0.495

Low Wage 0.225 0.279 0.505

Total 0.423 0.577 1.000

Female Male Grand Total

High Wage 0.468 0.516 0.495

Low Wage 0.532 0.484 0.505

Total 1.000 1.000 1.000

Gender

Wage

Joint Probability

Wage

Gender

Marginal Probability

Gender

Wage

Answer to question 4.4

Probability of female or low wage is 47+56−25

111 =0.7027.

Probability of being a female earning a low wage is 25

111=0.22523.

Answer to question 4.5

Female Male Grand Total

High Wage 22 33 55

Low Wage 25 31 56

Total 47 64 111

Female Male Grand Total

High Wage 0.198 0.297 0.495

Low Wage 0.225 0.279 0.505

Total 0.423 0.577 1.000

Female Male Grand Total

High Wage 0.468 0.516 0.495

Low Wage 0.532 0.484 0.505

Total 1.000 1.000 1.000

Gender

Wage

Joint Probability

Wage

Gender

Marginal Probability

Gender

Wage

Answer to question 4.4

Probability of female or low wage is 47+56−25

111 =0.7027.

Probability of being a female earning a low wage is 25

111=0.22523.

Answer to question 4.5

8DATA AND BUSINESS DECISION MAKING

Female Male Grand Total

High Wage 22 33 55

Low Wage 25 31 56

Total 47 64 111

Female Male Grand Total

High Wage 23.288 31.712 55

Low Wage 23.712 32.288 56

Total 47 64 111

Female Male Grand Total

High Wage 0.071 0.052 0.124

Low Wage 0.070 0.051 0.121

Total 0.141 0.104 0.245

H_0 = Ther is no association between male and high wage

H_1 = Ther is an association between male and high wage

degrees of freedom= (rows-1)*(columns-1) 1*1

Crtical value at alpha=0.05 3.841

sig No

Gender

Wage

Expected

Gender

Wage

(O-E)^2/E

Gender

Wage

The above test shows that the null hypothesis cannot be rejected which says males do not

tend to earn higher wage than the females.

Answer to question 5.1

Hypothesis Test for μ (Mean)

Hypotheses

Null Hypothesis μ = 900

Alternative Hypothesis μ ≠ 900

Level of significance α 0.05

Female Male Grand Total

High Wage 22 33 55

Low Wage 25 31 56

Total 47 64 111

Female Male Grand Total

High Wage 23.288 31.712 55

Low Wage 23.712 32.288 56

Total 47 64 111

Female Male Grand Total

High Wage 0.071 0.052 0.124

Low Wage 0.070 0.051 0.121

Total 0.141 0.104 0.245

H_0 = Ther is no association between male and high wage

H_1 = Ther is an association between male and high wage

degrees of freedom= (rows-1)*(columns-1) 1*1

Crtical value at alpha=0.05 3.841

sig No

Gender

Wage

Expected

Gender

Wage

(O-E)^2/E

Gender

Wage

The above test shows that the null hypothesis cannot be rejected which says males do not

tend to earn higher wage than the females.

Answer to question 5.1

Hypothesis Test for μ (Mean)

Hypotheses

Null Hypothesis μ = 900

Alternative Hypothesis μ ≠ 900

Level of significance α 0.05

9DATA AND BUSINESS DECISION MAKING

Critical Value (s) 1.9600

Population Standard Deviation 437.3485

Sample Data

Sample Mean 1100.748

Sample Size 111

Standard Error of the Mean 41.51

Z Sample Statistic 4.835981

p-value 0.000003

The z-stat for the sample is 4.836 and the critical value of z-stat is 1.96 at 0.05

significance level. The test rule says the greater value of z-stat of the sample than the critical

value at a given significance level is enough to reject the null hypothesis (Johnes 2018). Hence,

the alternative hypothesis is accepted here which says the sample mean is not equal to 900.

Confidence Interval for mean (m)

Data

Population Standard Deviation

437.348

5

Sample mean

1100.74

8

Sample Size 111

Confidence Level 95%

Intermediate Calculations

Standard error of the mean 41.5113

Z Value 1.9600

Sampling Error/Margin of Error 81.3621

Confidence Interval

Interval Lower Limit 1019.39

Interval Upper Limit 1182.11

The 95% confidence interval of the mean value is (1019.39, 1182.11). This says that the

average monthly wage is not same for the current and the previous year.

Critical Value (s) 1.9600

Population Standard Deviation 437.3485

Sample Data

Sample Mean 1100.748

Sample Size 111

Standard Error of the Mean 41.51

Z Sample Statistic 4.835981

p-value 0.000003

The z-stat for the sample is 4.836 and the critical value of z-stat is 1.96 at 0.05

significance level. The test rule says the greater value of z-stat of the sample than the critical

value at a given significance level is enough to reject the null hypothesis (Johnes 2018). Hence,

the alternative hypothesis is accepted here which says the sample mean is not equal to 900.

Confidence Interval for mean (m)

Data

Population Standard Deviation

437.348

5

Sample mean

1100.74

8

Sample Size 111

Confidence Level 95%

Intermediate Calculations

Standard error of the mean 41.5113

Z Value 1.9600

Sampling Error/Margin of Error 81.3621

Confidence Interval

Interval Lower Limit 1019.39

Interval Upper Limit 1182.11

The 95% confidence interval of the mean value is (1019.39, 1182.11). This says that the

average monthly wage is not same for the current and the previous year.

## Paraphrase This Document

Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser

10DATA AND BUSINESS DECISION MAKING

Answer to question 6.1

The null hypothesis, H0: The mean coefficient is 0.

The alternative hypothesis, H0: The mean coefficient is not 0

Regression Statistics

Multiple R 0.9405

R Square 0.8846

Adjusted R Square 0.8755

Standard Error 403.9371

Observations 111

ANOVA

df SS MS F Significance F

Regression 1 137584600.6 137584600.6 843.2230 0.0000

Residual 110 17948166.42 163165.1493

Total 111 155532767

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 0.0000 #N/A #N/A #N/A #N/A #N/A

educ 78.4842 2.7028 29.0383 0.0000 73.1279 83.8405

The p-value is significant for the coefficient of education with 0.00 p-value. The

education has a significant positive impact on wage (Bottema‐Beutel 2016). One unit rise in the

value of education will raise the wage by 78.4842.

The regression model:

wages=78.4842∗educ

Answer to question 6.2

Answer to question 6.1

The null hypothesis, H0: The mean coefficient is 0.

The alternative hypothesis, H0: The mean coefficient is not 0

Regression Statistics

Multiple R 0.9405

R Square 0.8846

Adjusted R Square 0.8755

Standard Error 403.9371

Observations 111

ANOVA

df SS MS F Significance F

Regression 1 137584600.6 137584600.6 843.2230 0.0000

Residual 110 17948166.42 163165.1493

Total 111 155532767

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 0.0000 #N/A #N/A #N/A #N/A #N/A

educ 78.4842 2.7028 29.0383 0.0000 73.1279 83.8405

The p-value is significant for the coefficient of education with 0.00 p-value. The

education has a significant positive impact on wage (Bottema‐Beutel 2016). One unit rise in the

value of education will raise the wage by 78.4842.

The regression model:

wages=78.4842∗educ

Answer to question 6.2

11DATA AND BUSINESS DECISION MAKING

Regression Statistics

Multiple R 0.9414

R Square 0.8861

Adjusted R Square 0.8771

Standard Error 401.2297

Observations 111

ANOVA

df SS MS F Significance F

Regression 1 137824386.7 137824386.69 856.13 0.00

Residual 110 17708380.31 160985.28

Total 111 155532767

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 0 #N/A #N/A #N/A #N/A #N/A

IQ 10.4182 0.3561 29.2597 0.0000 9.7126 11.1238

The p-value is less than 0.05 for the coefficient of IQ. The IQ is significant and has a

positive impact on wage. One unit rise in the value of IQ will raise the wage by 10.4182.

The regression model:

wages=10.4182∗IQ

Answer to question 6.3

The value of R2 is the goodness of fit for the model. Here in case of education as an

explanatory variable, R2 is 0.8846 which indicates that the education can explain 88.46% of the

variance in wage (Edo and Rapoport 2019). On the other hand, the model with IQ as an

explanatory variable, R2 is 0.8861 which indicates that the IQ can explain 88.61% of the variance

in wage. Here, the value of R2 is higher for IQ which means IQ is the better predictor of wage

(Wang 2016).

Answer to question 6.4

Regression Statistics

Multiple R 0.9414

R Square 0.8861

Adjusted R Square 0.8771

Standard Error 401.2297

Observations 111

ANOVA

df SS MS F Significance F

Regression 1 137824386.7 137824386.69 856.13 0.00

Residual 110 17708380.31 160985.28

Total 111 155532767

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 0 #N/A #N/A #N/A #N/A #N/A

IQ 10.4182 0.3561 29.2597 0.0000 9.7126 11.1238

The p-value is less than 0.05 for the coefficient of IQ. The IQ is significant and has a

positive impact on wage. One unit rise in the value of IQ will raise the wage by 10.4182.

The regression model:

wages=10.4182∗IQ

Answer to question 6.3

The value of R2 is the goodness of fit for the model. Here in case of education as an

explanatory variable, R2 is 0.8846 which indicates that the education can explain 88.46% of the

variance in wage (Edo and Rapoport 2019). On the other hand, the model with IQ as an

explanatory variable, R2 is 0.8861 which indicates that the IQ can explain 88.61% of the variance

in wage. Here, the value of R2 is higher for IQ which means IQ is the better predictor of wage

(Wang 2016).

Answer to question 6.4

12DATA AND BUSINESS DECISION MAKING

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.1461

R Square 0.0213

Adjusted R Square 0.0124

Standard Error 434.6367

Observations 111

ANOVA

df SS MS F Significance F

Regression 1 449017.82 449017.82 2.38 0.13

Residual 109 20591087.12 188909.0562

Total 110 21040104.94

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 1278.2647 122.3095 10.4511 0.0000 1035.8513 1520.6780

exper -15.7509 10.2165 -1.5417 0.1260 -35.9996 4.4978

The p-value of the coefficient of work experience is greater than 0.05 which indicates

that it has no significant impact on wage.

The regression model:

wages=1278.647−15.7509∗exper

Answer to question 6.5

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.1461

R Square 0.0213

Adjusted R Square 0.0124

Standard Error 434.6367

Observations 111

ANOVA

df SS MS F Significance F

Regression 1 449017.82 449017.82 2.38 0.13

Residual 109 20591087.12 188909.0562

Total 110 21040104.94

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 1278.2647 122.3095 10.4511 0.0000 1035.8513 1520.6780

exper -15.7509 10.2165 -1.5417 0.1260 -35.9996 4.4978

The p-value of the coefficient of work experience is greater than 0.05 which indicates

that it has no significant impact on wage.

The regression model:

wages=1278.647−15.7509∗exper

Answer to question 6.5

## Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.

13DATA AND BUSINESS DECISION MAKING

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.940

R Square 0.884

Adjusted R Square 0.875

Standard Error 405.201

Observations 111

ANOVA

df SS MS F Significance F

Regression 1 137472089.60 137472089.60 837.28 0.00

Residual 110 18060677.40 164187.98

Total 111 155532767.00

CoefficientsStandard Error t Stat P-value Lower 95%

Intercept 0 #N/A #N/A #N/A #N/A

KW 28.67 0.99 28.94 0.00 26.70

The p-value of the coefficient of KW scores is less than 0.05 which implies that it has

significant impact on wage.

The regression model:

wages=28.67∗KW

Answer to question 6.6

The KW score is the better predictor as the coefficient of this variable is significant and

the value of R2 is better.

Answer to question 6.7

The link between wage and education is strong. The goodness of fit is better in case of

the model of wage and education compared to the model of wage and work experience.

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.940

R Square 0.884

Adjusted R Square 0.875

Standard Error 405.201

Observations 111

ANOVA

df SS MS F Significance F

Regression 1 137472089.60 137472089.60 837.28 0.00

Residual 110 18060677.40 164187.98

Total 111 155532767.00

CoefficientsStandard Error t Stat P-value Lower 95%

Intercept 0 #N/A #N/A #N/A #N/A

KW 28.67 0.99 28.94 0.00 26.70

The p-value of the coefficient of KW scores is less than 0.05 which implies that it has

significant impact on wage.

The regression model:

wages=28.67∗KW

Answer to question 6.6

The KW score is the better predictor as the coefficient of this variable is significant and

the value of R2 is better.

Answer to question 6.7

The link between wage and education is strong. The goodness of fit is better in case of

the model of wage and education compared to the model of wage and work experience.

14DATA AND BUSINESS DECISION MAKING

Reference

Bottema‐Beutel, K., 2016. Associations between joint attention and language in autism spectrum

disorder and typical development: A systematic review and meta‐regression analysis. Autism

Research, 9(10), pp.1021-1035.

Edo, A. and Rapoport, H., 2019. Minimum wages and the labor market effects of immigration.

Labour Economics, 61, p.101753.

Godoey, A. and Reich, M., 2019. Minimum Wage Effects in Low-Wage Areas. Working Paper#

106-19.

Johnes, G., 2018. A sporting chance: on the impact of sports participation on subsequent

earnings. Economics Bulletin, 38(1), pp.146-151.

Walker, I. and Zhu, Y., 2017. University selectivity and the graduate wage premium: Evidence

from the UK.

Wang, Z., 2016. Wage growth, ability sorting, and location choice at labor-force entry: New

evidence from US Census data. Journal of Urban Economics, 96, pp.112-120.

Reference

Bottema‐Beutel, K., 2016. Associations between joint attention and language in autism spectrum

disorder and typical development: A systematic review and meta‐regression analysis. Autism

Research, 9(10), pp.1021-1035.

Edo, A. and Rapoport, H., 2019. Minimum wages and the labor market effects of immigration.

Labour Economics, 61, p.101753.

Godoey, A. and Reich, M., 2019. Minimum Wage Effects in Low-Wage Areas. Working Paper#

106-19.

Johnes, G., 2018. A sporting chance: on the impact of sports participation on subsequent

earnings. Economics Bulletin, 38(1), pp.146-151.

Walker, I. and Zhu, Y., 2017. University selectivity and the graduate wage premium: Evidence

from the UK.

Wang, Z., 2016. Wage growth, ability sorting, and location choice at labor-force entry: New

evidence from US Census data. Journal of Urban Economics, 96, pp.112-120.

1 out of 15

### Related Documents

Your All-in-One AI-Powered Toolkit for Academic Success.

##### +13062052269

##### info@desklib.com

Available 24*7 on WhatsApp / Email

Unlock your academic potential

© 2024 | Zucol Services PVT LTD | All rights reserved.