Data package for Lectures on Econometrics

Description

This package contains data sets for Lectures on Econometrics by Chirok Han

Author(s)

NA

See Also

help(package="loedata")

Boyle data set

Description

Robert Boyle's data set

Usage

data(Boyle)

Format

A data frame with 25 rows and 2 variables:

volume

the number of equal spaces in the shorter leg, that contained the same parcel of air diversely extended

pressure

the pressure sustained by the included air

Author(s)

NA

Source

https://www.chemteam.info/GasLaw/Gas-Boyle-Data.html

Death rate and related variables for Korean districts

Description

Death rate and related variables for Korean districts for 2008-2010

Usage

data(Death)

Format

A data frame with 258 rows and 9 variables:

region

region ID

year

year

regpop

registered population (end of year)

death

number of registered deaths

drink

percentage of drinkers (more than once in a month)

smoke

percentage of smokers (smoker = has smoked 100+ cigarettes and currently smoking)

aged

percentage of those aged 65 and over

vehipc

number of vehicles per person

deathrate

= death/regpop*1000

Author(s)

NA

Source

Statistics Korea

CO2 emissions

Description

CO2 emissions per capita and GDP per capita in 2005

Usage

data(Ekc)

Format

A data frame with 183 rows and 4 variables:

ccode

country code

cname

country name

gdppcppp

GDP per capital, ppp adjusted (USD)

co2pc

CO2 emissions per capita (ton)

Author(s)

NA

Source

http://wdi.worldbank.org

Card and Krueger (1994) fastfood data set

Description

Card and Krueger (1994) fastfood data set

Usage

data(Fastfood)

Format

A data frame with 820 rows and 35 variables:

id

ID of fastfood restaurant [+]

sheet

sheet number (unique store id)

after

1 if second interview [+]

chain

chain 1=bk; 2=kfc; 3=roys; 4=wendys

co_owned

1 if company owned

nj

1 if NJ; 0 if Pa

southj

1 if in southern NJ

centralj

1 if in central NJ

northj

1 if in northern NJ

pa1

1 if in PA, northeast suburbs of Philadelphia

pa2

1 if in PA, Easton etc

shore

1 if on NJ shore

type2

type 2nd interview 1=phone; 2=personal

status2

status of second interview; see details

date2

date of second interview MMDDYY format

ncalls

number of call-backs*

empft

# full-time employees

emppt

# part-time employees

nmgrs

# managers/assistant managers

fte

full time equivalent, FTE = empft + nmgrs + 0-.5*emppt [+]

dfte

FTE for after - FTE for before [+]

wage_st

starting wage ($/hr)

inctime

months to usual first raise

firstinc

usual amount of first raise ($/hr)

bonus

1 if cash bounty for new workers

pctaff

% employees affected by new minimum

meals

free/reduced price code (see details)

open

hour of opening

hrsopen

number hrs open per day

psoda

price of medium soda, including tax

pfry

price of small fries, including tax

pentree

price of entree, including tax

nregs

number of cash registers in store

nregs11

number of registers open at 11:00 am

balanced

1 if empft, nmgrs and emppt observed both periods [+]

Details

See attr(Fastfood, "desc"). [+] are added by Chirok Han.

Author(s)

NA

Source

https://davidcard.berkeley.edu/data_sets.html

References

Card, D., and A. Krueger (1994). Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania, American Economic Review 84, 772-793.

Open DART firm data

Description

Korean firm data for 2018 in KOSPI and KOSDAQ

Usage

data(Firmdata)

Format

A data frame with 2073 rows and 24 variables:

corpcode

Firm code

market

"KOSPI" or "KOSDAQ"

kospi

=1 if KOSPI

kosdaq

=1 if KOSDAQ

indcode

industry code

sic0

one of A, C, GHI, DEF, JK, and Others

sic1

A, B, ..., U (top SIC categories)

sic2

2-digit SIC

sic3

3-digit SIC

estdate

establishment date in yyyymmdd

estyear

establishment year

age

=2018-estyear

inkorea

=1 if the firm operates in Korea

status

="000" if firm information is available

nemp

number of employees

totsal

total annual salary paid (sum)

avgten

average tenure in years

avgsal

=totsal/nemp

fstype

CFS or OFS

accstatus

="000" if account information is available

sales

sales in KRW

oprofit

operating profit in KRW

netinc

net income in KRW

Author(s)

NA

Source

opendart.fss.or.kr

Galton family data

Description

Parent-level version of Galton's family data

Usage

data(Galtonpar)

Format

A data frame with 205 rows of 10 variables:

id

parent ID, a factor with levels 001-204

father

height of father

mother

height of mother

midparht

mid-parent height, calculated as father + 1.08*mother)/2

numchild

number of children

numson

number of sons

numdtr

number of daughters

avgchildht

average height of children

avgsonht

average height of sons

avgdtrht

average height of daughters

Author(s)

NA

Source

GaltonFamilies data in HistData package

See Also

HistData::GaltonFamilies

Household consumption shares

Description

Household consumption shares of communication and recreation sector in Korean Household Income and Expenditure Survey 2014

Usage

data(Hcons)

Format

A data frame with 6723 rows of 3 variables:

age

age of household head

comm

share of consumption for communication in %

rec

share of consumption for recreation in %

Author(s)

NA

Source

Korea Household Income and Expenditure Survey 2014 http://kostat.go.kr/portal/eng/surveyOutline/6/1/index.static

See Also

Hies

Household Income and Expenditure Survey 2016

Description

A subset (30 <= age <= 39) of Korea Household Income and Expenditure Survey 2016

Usage

data(Hies)

Format

A data frame with 1368 rows of 26 variables:

year

year of survey, =2016

famsize

number of family members

empnum

number of employed members

age

age of household head

emp

1 if head is employed

ownhouse

1 if own house

weight

cross sectional weight

inc

household monthly income

haspinc

1 if has income from properties

totexp

household total monthly expenditure

cons

household monthly consumption

cons01

household monthly consumption in section 01

cons02

household monthly consumption in section 02

cons03

household monthly consumption in section 03

cons04

household monthly consumption in section 04

cons05

household monthly consumption in section 05

cons06

household monthly consumption in section 06

cons07

household monthly consumption in section 07

cons08

household monthly consumption in section 08

cons09

household monthly consumption in section 09

cons10

household monthly consumption in section 10

cons11

household monthly consumption in section 11

cons12

household monthly consumption in section 12

propens

propensity to consume (=cons/inc)

educ

years of head's education

female

1 if head is female

Author(s)

NA

Source

http://kostat.go.kr/portal/eng/surveyOutline/6/1/index.static

See Also

Hcons

The Boston HMDA data set

Description

The Boston HMDA data set in the Ecdat package, with yes/no converted to 1/0

Usage

data(Hmda)

Format

A data frame with 2381 rows of 13 variables:

dir

debt payments to total income ratio

hir

housing expenses to income ratio

lvr

ratio of size of loan to assessed value of propensity

ccs

consumer credit score from 1 to 6 (a low value being a good score)

mcs

mortgage credit score from 1 to 4 (a low value being a good score)

pbcr

1 if public bad credit score

dmi

1 if denied mortgage insurance

self

1 if self employed

single

1 if the applicant is single

uria

1989 Massachusetts unemployment rate in the applicant's industry

condominium

1 if unit is a condominium

black

1 if the applicant is black

deny

1 if mortgage application denied

Author(s)

NA

Source

Hmda data in the Ecdat package

Artificial data for studying IV estimation

Description

Artificial data for studying IV estimation

Usage

data(Ivdata)

Format

A data frame with 100 rows of 5 variables:

y

y variable

x1

x1 variable

x2

x2 variable

z2a

z2a variable

z2b

z2b variable

Author(s)

NA

Subset of 2011 KLIPS

Description

Subset (30 <= age <= 39, nonzero income, 9 <= educ < 20) of 2011 KLIPS

Usage

data(Klips)

Format

A data frame with 646 rows of 8 variables:

age

age

educ

years of education

tenure

tenure

regular

1 if regular, 0 if irregular

hours

hours worked per week

earn

monthly earning in 10,000 KRW

labinc

annual labor income after tax

married

1 if married

Author(s)

NA

Source

Korea Labor Institute https://www.kli.re.kr/klips/index.do

KLoSA wave 4

Description

Korea Longitudinal Study of Aging wave 4 (2012)

Usage

data(Klosa)

Format

A data frame with 2153 rows of 45 variables:

pid

personal ID

wave

= (year-2006)/2 + 1

male

1 if male

educ

years of education

age

age

married

1 if married, 0 otherwise

childnum

number of children

hsize

number of housemates

region

region type, one of "big city", "small city", and "town"

htype

type of residential facility, either "dwelling" or "apartment"

religion

1 if has religion

meeting1

1 if in religious meeting groups

meeting2

1 if in social gathering groups

meeting3

1 if in leisure/sports groups, etc.

meeting4

1 if in union/fraternity groups, etc.

meeting5

1 if in volunteer service groups

meeting6

1 if in political/civic/interest groups

health

health conditions, one of "excellent", "above average", "average", "below average", and "poor"

hlth

1=poor, 2=below average, 3=average, 4=above average, 5=excellent

hlth3

1=health above average, 0=average, -1=below average

height

height in cm

weight

weight in kg

exercise

period of regular exercise; 0=do not regularly exercise, 1=0~3mo, 2=4~6mo, 3=7mo~1yr, 4=1~2yr, 5=3~4yr, 6=5~6yr, 7=7+yr

bmi

BMI

smoke

# of cigarettes smoked per day

working

1 if working

jobtype

job type; one of waged employee, self-employed, unemployed, unpaid family worker

jobseeking

1 if seeking a job

receive

amount received from children last year (10k KRW)

give

amount given to children last year (10k KRW)

poketm

regular pocket money received from children (10k KRW)

satisfy1

satisfaction about health conditions

satisfy2

satisfaction about economic conditions

satisfy3

satisfaction about relationship with spouse

satisfy4

satisfaction about relationship with children

satisfy5

satisfaction in comparison to others in the same age group (out of 100)

travel1

number of travels last year

travel2

expenditure on travel (10k KRW)

culture1

number of cultural activities

culture2

expenditure on cultural activities

hobby1

hours for hobbies, per month

hobby2

expenditure on hobbies (10k KRW)

training1

hours for self development, per month

training2

expenditure on self development (10k KRW)

voluntary

hours of volunteer service

Author(s)

Goeun Lee, NA

Source

https://survey.keis.or.kr/klosa/klosa01.jsp

Average salary

Description

Average salary for Korean firms in 2012

Usage

data(Ksalary)

Format

A data frame with 1636 rows and 10 variables:

seqno

sequential number

market

"kospi" or "kosdaq"

sales

sales in Bil. KRW

profit

profit in Bil. KRW

sector

sector (character)

emp

number of employees

avgsal

average salary in Mil. KRW

avgtenure

average years of tenure

kospi

=1 if KOSPI

kosdaq

=1 if KOSDAQ

Author(s)

NA

Source

https://blog.naver.com/naamoo01/130185489128

Public servants and financial independence

Description

Korean regional public servants and financial independence in 2010

Usage

data(Pubserv)

Format

A data frame with 86 rows of 3 variables:

gun

name of gun

servpc

number of public servants per 1000 pop

finind

financial independence index, = (local tax + other income)/budget * 100

Author(s)

NA

Source

http://kostat.go.kr/

Korean regional data (2014-2016 averages)

Description

Korean regional data for 2014-2016 average

Usage

data(Regko)

Format

A data frame with 264 rows of 23 variables:

id

ID of region

metro

Metropolitan region name (metro cities and provinces)

region

Region name

type

1=si (non-metropolitan cities), 2=gun, 3=gu in metro cities and provinces

grdp

gross regional GDP

regpop

population

popgrowth

population growth

eq5d

the EQ-5D health index

deaths

number of registered deaths

drink

% of drinkers

hdrink

% of high-risk drinkers

smoke

% of smokers

aged

% of aged 65 and over

divorce

# of divorces per 1000 pop

medrate

# of medical beds per 1000 pop

gcomp

gender composition # men / 100 women

vehipc

# of vehicles per person

accpv

# of accidents per 1000 vehicles

dumppc

waste dump per person, kg/day

stratio

# of students per teacher

deathrate

# of deaths per 100,000 pop

pctmale

=gcmp/(gcomp+100)*100, % of male

accpc

=vehipc*accpv, # of accidents per 1000 pop

Author(s)

NA

Source

http://kostat.go.kr/

Korean regional panel data (2014-2016)

Description

Korean regional panel data (2014-2016)

Usage

data(RegkoPanel)

Format

A data frame with 792 rows of 24 variables:

id

ID of region

metro

Metropolitan region name (metro cities and provinces)

region

Region name

type

1=si (non-metropolitan cities), 2=gun, 3=gu in metro cities and provinces

year

Year

grdp

gross regional GDP

regpop

population

popgrowth

population growth (=100*(regpop/regpop[-1]-1))

eq5d

the EQ-5D health index

deaths

number of deaths

drink

% of drinkers

hdrink

% of high-risk drinkers

smoke

% of smokers

aged

% of aged 65 and over

divorce

# of divorces per 1000 pop

medrate

# of medical beds per 1000 pop

gcomp

gender composition # men / 100 women

vehipc

# of vehicles per person

accpv

# of accidents per 1000 vehicles

dumppc

waste dump per person, kg/day

stratio

# of students per teacher

deathrate

# of deaths per 100,000 pop

pctmale

=gcmp/(gcomp+100)*100, % of male

accpc

=vehipc*accpv, # of accidents per 1000 pop

Author(s)

NA

Source

http://kostat.go.kr/