Econometrics exercises

7/27/2019 Econometrics exercises

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Giuseppe Ragusa

Department of Economics

Luiss Guido Carli

October 18, 2013

Econometrics

PS # 2

Name:

(Please type clearly)

Read carefully the questions and think before writing.

When you do write, please do so in a clear way.

Always, always, explain the reasoning behind your answer.

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1. You want to investigate determinants of car fatalities. You have traffic fatalities data on the lower 48 US states(i.e. excluding Alaska and Hawaii), for 1988. In particular, you have the following variables:

fatal vehicle fatalities per ten thousand people

unemp unemployment rate

income per capita personal income in 1987 (in thousands of dollars)

beertax tax on case of beer in 1987 dollar

miles average miles per driver

jail =1 if state has mandatory jail for drunk driving

service = 1 if state has mandatory community service

youngdrivers percent of drivers aged 15-24.

Here is the output of the R summary command:

## fatal unemp income beertax

## Min. :1.23 Min. : 2.40 Min. :10.7 Min. :0.0433

## 1st Qu.:1.63 1st Qu.: 3.95 1st Qu.:12.4 1st Qu.:0.1947

## Median :2.02 Median : 5.20 Median :14.5 Median :0.3465

## Mean :2.07 Mean : 5.46 Mean :14.8 Mean :0.4882

## 3rd Qu.:2.48 3rd Qu.: 6.55 3rd Qu.:16.0 3rd Qu.:0.6027

## M ax. :3.24 Max. :10.90 Max. :22.2 Max. :2.1944

## miles jail service youngdrivers

## Min. : 5790 Min. :0.000 Min. :0.000 Min. :0.0731

## 1st Qu.: 8043 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.1559

## Median : 8538 Median :0.000 Median :0.000 Median :0.1624

## Mean : 8618 Mean :0.298 Mean :0.213 Mean :0.1623

## 3rd Qu.: 9297 3rd Qu.:1.000 3rd Qu.:0.000 3rd Qu.:0.1712

## M ax. :11812 Max. :1.000 Max. :1.000 Max. :0.2207

You run the following regression:

fatal =0.5708(0.9333)

+ 0.0490(0.0363)

unemp 0.0573(0.0248)

income + 0.1442(0.1317)

beertax + 0.0002(0.0001)

miles

+ 0.1279(0.1622)

jail + 0.0973(0.1856)

service + 0.3288(2.6413)

youngdrivers

(a) What is the interpretation of the coefficient on unemp?

(b) What is the effect of an increase in income of$20, 000? Is this effect statistically significant at 5%?

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(c) Everything else being equal, how many more fatalities per 500 people would you expect if on average 10000

more miles were driven?

(d) There at least 2 errors in the following statement made by a politician who saw your regression analysis:

Increasing beertax by .43 will increase fatalities by at least 0.144. This is strong evidence that there

is no relationship between taxing beer and fatalities rate, thus, for the welfare of individuals, statesshould abolish taxes on alcoholic beverages.

Can you spot the errors?

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2. Earnings functions are one of the most investigated relationships in economics. These typically relate the logarithm

of earnings to a series of explanatory variables such as education, work experience, gender, race, etc.

Consider using data on a sample of Italian workers aged between 15 and 64. The sample is from the Labor Force

Survey from ISTAT which is the most comprehensive survey on labor related issues. The sample we use here is a

small subsample (3,000 observations) of the one provided by ISTAT

We estimate first a regression model linking lhourlywage, the logarithm of hourlywage, with age dummies and

education dummies and a dummy for gender. Here a brief summary of the variables:

hourlywage | hourly wage

male | =1 if individual is a male

educ | years of education

The summary is reported below.

## Min. 1st Qu. Median Mean 3rd Qu. Max.

## 1.00 3.00 5.00 4.87 5.00 12.00

## male educ hourlywage

## Min. :0.000 Min. : 0.0 Min. : 1.56

## 1st Qu.:0.000 1st Qu.: 8.0 1st Qu.: 6.88

## Median :1.000 Median :13.0 Median : 8.33

## Mean :0.541 Mean :11.5 Mean : 9.49

## 3rd Qu.:1.000 3rd Qu.:13.0 3rd Qu.: 10.53

## Max. :1.000 Max. :20.0 Max. :128.75

Consider the following regression:

##

## Coefficients:

## Estimate Std. Error t value Pr(>|t|)

## (Intercept) 4.3612 0.3367 12.95


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(b) What does this regression say about the returns on education in Italy? Has a big effect on wages?

(c) Based on the regression, can you say something definitive about female discrimination in Italy? (Explain)

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