Wooldridge datasets

Wooldridge data sets

Each of these data sets is readable by Stata--running on the desktop, apps.bc.edu or on a Unix server--over the Web. You need only copy the line given below each dataset into your Stata command window or Stata do-file.
If the bcuse command is not available, install it with the Stata command

ssc install bcuse

After loading the data into Stata, use save to make a copy of the data on your own machine if you wish. The link from each dataset's name gives you the codebook of variable names and definitions. Please report any problems accessing these data to baum.

401K: N=1534, cross-sectional data on pensions,
```
bcuse 401k
```
401K-50: N=767, 50% sample of 401K dataset,
```
bcuse 401k-50
```
401KSUBS: N=9275, cross-sectional data on pensions
```
bcuse 401ksubs
```
ADMNREV: N=153, timeseries data on offenses,
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bcuse admnrev
```
AFFAIRS: N=601, cross-sectional individual data
```
bcuse affairs
```
AIRFARE: N=4596, cross-sectional data on airfares
```
bcuse airfare
```
APPLE: N=660, cross-sectional individual data on consumers,
```
bcuse apple
```
ATHLET1: N=118, cross-sectional individual data on schools' athletic programs,
```
bcuse athlet1
```
ATHLET2: N=30, cross-sectional individual data on schools' athletic programs,
```
bcuse athlet2
```
ATTEND: N=680, cross-sectional individual data on classes attended,
```
bcuse attend
```
AUDIT: N=241, cross-sectional individual data on job offers,
```
bcuse audit
```
BARIUM: N=131, time-series data on barium export,
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bcuse barium
```
BEVERIDGE: N=135, time-series data on unemployment and vacancies,
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bcuse beveridge
```
BWGHT: N=1388, cross-sectional individual data on birth weights,
```
bcuse bwght
```
BWGHT50: N=694, cross-sectional individual data on birth weights (50% sample),
```
bcuse bwght50
```
BWGHT2: N=1832, cross-sectional individual data on birth weights,
```
bcuse bwght2
```
CAMPUS: N=97, cross-sectional data on crime in colleges,
```
bcuse campus
```
CARD: N=3010, cross-sectional individual data on consumers,
```
bcuse card
```
CATHOLIC: N=7430, cross-sectional individual data on schooling,
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bcuse catholic
```
CEMENT: N=312, time-series data for 1964-1989,
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bcuse cement
```
CEOSAL1: N=209, cross-sectional firm-level data,
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bcuse ceosal1
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CEOSAL2: N=177, cross-sectional firm-level data,
```
bcuse ceosal2
```
CONSUMP: N=37, time-series data on consumption,
```
bcuse consump
```
CORN: N=37, cross-sectional individual data on consumers,
```
bcuse corn
```
CORNWELL: N=630, country panel data,
```
bcuse cornwell
```
CPS78_85: N=1084, pooled CS data for two years,
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bcuse cps78_85
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CPS91: N=1084, pooled CS data
```
bcuse cps91
```
CRIME1: N=2725, cross-sectional individual data,
```
bcuse crime1
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CRIME2: N=92, cross-sectional individual data,
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bcuse crime2
```
CRIME3: N=106, cross-sectional individual data,
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bcuse crime3
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CRIME4: N=630, cross-sectional county data,
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bcuse crime4
```
DISCRIM: N=410, cross-sectional firm level data,
```
bcuse discrim
```
EARNS: N=41, cross-sectional individual data,
```
bcuse earns
```
ENGIN: N=403, cross-sectional individual data,
```
bcuse engin 
```
EZANDERS: N=108, time-series individual data,
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bcuse ezanders
```
EZUNEM: N=198, time-series individual data,
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bcuse ezunem
```
FAIR: N=21, quadrennial timeseries data for 1916-1992,
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bcuse fair
```
FERTIL1: N=1129, cross-sectional family data,
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bcuse fertil1
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FERTIL2: N=4361, cross-sectional family data,
```
bcuse fertil2
```
FERTIL3: N=72, cross-sectional family data,
```
bcuse fertil3
```
FISH: N=616, cross-sectional data on fish sales,
```
bcuse fish
```
FRINGE: N=616, cross-sectional family data,
```
bcuse fringe
```
GPA1: N=141, cross-sectional individual data,
```
bcuse gpa1
```
GPA2: N=4137, cross-sectional individual data,
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bcuse gpa2
```
GPA2-20: N=827, cross-sectional individual data, 20% sample of GPA2
```
bcuse gpa2-20
```
GPA3: N=732, cross-sectional individual data,
```
bcuse gpa3
```
GROGGER: N=2725, cross-sectional individual data
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bcuse grogger
```
HPRICE1: N=88, cross-sectional individual data,
```
bcuse hprice1
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HPRICE2: N=506, cross-sectional individual data,
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bcuse hprice2
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HPRICE3: N=321, cross-sectional individual data,
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bcuse hprice3
```
HSEINV: N=42, timeseries data on real housing invest,
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bcuse hseinv
```
HTV: N=1,230, cross-sectional individual data,
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bcuse htv
```
INFMRT: N=102, state-level panel data on infant mortality,
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bcuse infmrt
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INJURY: N=7150, cross-sectional individual data,
```
bcuse injury
```
INTDEF: N=49, cross-sectional individual data,
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bcuse intdef
```
INTQRT: N=124, time-series quarter data on interest rates,
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bcuse intqrt
```
INVEN: N=37, time-series data,
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bcuse inven
```
JTRAIN: N=471, panel individual data on job training,
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bcuse jtrain
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JTRAIN2: N=445, cross-sectional individual data,
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bcuse jtrain2
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JTRAIN2: N=2675, cross-sectional individual data,
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bcuse jtrain3
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JTRAIN98: N=1130, cross-sectional individual data,
```
bcuse jtrain98
```
KEANE: N=12723, panel individual data,
```
bcuse keane
```
KIELMC: N=321, panel individual data,
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bcuse kielmc
```
LABSUP: N=156, cross-sectional individual data,
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bcuse labsup
```
LAWSCH85: N=156, cross-sectional individual data,
```
bcuse lawsch85
```
LOANAPP: N=1989, cross-sectional individual data,
```
bcuse loanapp
```
LOWBRTH: N=1989, cross-sectional individual data,
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bcuse lowbrth
```
MATHPNL: N=3850, cross-sectional data,
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bcuse mathpnl
```
MEAP93 : N=408, cross-sectional school attainment test data,
```
bcuse meap93
```
MEAP01 : N=1823, cross-sectional school attainment test data,
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bcuse meap01
```
MLB1 : N=353, cross-sectional major league baseball data,
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bcuse mlb1
```
MROZ : N=753, cross-sectional labor force participation data,
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bcuse mroz
```
MURDER : N=153, longitudinal state murder rate data,
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bcuse murder
```
MURDERS : N=37349, longitudinal county-level murder rate data,
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bcuse murders
```
NBASAL : N=269, cross-sectional individual data
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bcuse nbasal
```
NCAA_RPI : N=336, cross-sectional individual data
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bcuse ncaa_rpi
```
NLS80 : N=3710, cross-sectional individual data
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bcuse nls80
```
NLS81_87 : N=3710, cross-sectional individual data
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bcuse nls81_87
```
NORWAY : N=3710, cross-sectional district data
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bcuse norway
```
NYSE : N=691, time-series NYSE stock price and returns data,
```
bcuse nyse
```
OPENNESS : N=114, cross-sectional country data on openness to trade,
```
bcuse openness
```
PATENT : N=2260, cross-sectional individual data
```
bcuse patent
```
PENSION : N=226, cross-sectional individual data
```
bcuse pension
```
PHILLIPS : N=49, time-series Phillips curve data,
```
bcuse phillips
```
PNTSPRD : N=553, cross-sectional gambling point spread data,
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bcuse pntsprd
```
PRISON : N=714, state-level panel data on incarceration,
```
bcuse prison
```
PRMINWGE : N=38, timeseries data on Puerto Rican minimum wage,
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bcuse prminwge
```
Q : N=2068, firm-level panel data
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bcuse q
```
RDCHEM : N=32, cross-sectional data on chemical firms' R&D expenditures,
```
bcuse rdchem
```
RDTELEC : N=29, cross-sectional firm data on R&D,
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bcuse rdtelec
```
RECID : N=1445, cross-sectional data on recividism,
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bcuse recid
```
RENTAL : N=128, city-level panel data on rental housing,
```
bcuse rental
```
RETURN : N=142, cross-sectional data on CEO salaries,
```
bcuse return
```
SAVING : N=100, cross-sectional individual data on consumption and saving,
```
bcuse saving
```
SAVING : N=10668, cross-sectional individual data on consumption and saving,
```
bcuse school93_98
```
SLEEP75 : N=706, cross-sectional individual data on sleep patterns,
```
bcuse sleep75
```
SLP75_81 : N=239, panel individual data on sleep patterns,
```
bcuse slp75_81
```
SMOKE : N=807, cross-sectional individual data on smoking,
```
bcuse smoke
```
TRAFFIC1 : N=51, state level cross-sectional data on traffic deaths,
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bcuse traffic1
```
TRAFFIC2 : N=108, state level timeseries data on traffic accidents,
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bcuse traffic2
```
TWOYEAR : N=6,763, individual cross-sectional data,
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bcuse twoyear
```
VOLAT : N=558, monthly timeseries data on S&P index,
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bcuse volat
```
VOTE1 : N=173, cross-sectional individual data on Congressional campaign expenditures,
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bcuse vote1
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VOTE2 : N=186, panel data on Congressional campaign expenditures,
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bcuse vote2
```
WAGE1 : N=526, cross-sectional data on wages,
```
bcuse wage1
```
WAGE2 : N=935, cross-sectional data on wages,
```
bcuse wage2
```
WAGEPAN : N=4360, individual panel data on wages,
```
bcuse wagepan
```
WAGEPRC : N=286, macro timeseries data on wages and prices,
```
bcuse wageprc
```
WINE : N=21, cross-sectional individual data,
```
bcuse wine
```

Last updated: 2022/02/12