If the bcuse command is not available, install it with the Stata command

ssc install bcuse

After loading the data into Stata, use

- 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,
`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,
`bcuse barium` - BEVERIDGE: N=135, time-series data on unemployment and vacancies,
`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,
`bcuse catholic` - CEMENT: N=312, time-series data for 1964-1989,
`bcuse cement` - CEOSAL1: N=209, cross-sectional firm-level data,
`bcuse ceosal1` - 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,
`bcuse cps78_85` - CPS91: N=1084, pooled CS data
`bcuse cps91` - CRIME1: N=2725, cross-sectional individual data,
`bcuse crime1` - CRIME2: N=92, cross-sectional individual data,
`bcuse crime2` - CRIME3: N=106, cross-sectional individual data,
`bcuse crime3` - CRIME4: N=630, cross-sectional county data,
`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,
`bcuse ezanders` - EZUNEM: N=198, time-series individual data,
`bcuse ezunem` - FAIR: N=21, quadrennial timeseries data for 1916-1992,
`bcuse fair` - FERTIL1: N=1129, cross-sectional family data,
`bcuse fertil1` - 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,
`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
`bcuse grogger` - HPRICE1: N=88, cross-sectional individual data,
`bcuse hprice1` - HPRICE2: N=506, cross-sectional individual data,
`bcuse hprice2` - HPRICE3: N=321, cross-sectional individual data,
`bcuse hprice3` - HSEINV: N=42, timeseries data on real housing invest,
`bcuse hseinv` - HTV: N=1,230, cross-sectional individual data,
`bcuse htv` - INFMRT: N=102, state-level panel data on infant mortality,
`bcuse infmrt` - INJURY: N=7150, cross-sectional individual data,
`bcuse injury` - INTDEF: N=49, cross-sectional individual data,
`bcuse intdef` - INTQRT: N=124, time-series quarter data on interest rates,
`bcuse intqrt` - INVEN: N=37, time-series data,
`bcuse inven` - JTRAIN: N=471, panel individual data on job training,
`bcuse jtrain` - JTRAIN2: N=445, cross-sectional individual data,
`bcuse jtrain2` - JTRAIN2: N=2675, cross-sectional individual data,
`bcuse jtrain3` - JTRAIN98: N=1130, cross-sectional individual data,
`bcuse jtrain98` - KEANE: N=12723, panel individual data,
`bcuse keane` - KIELMC: N=321, panel individual data,
`bcuse kielmc` - LABSUP: N=156, cross-sectional individual data,
`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,
`bcuse lowbrth` - MATHPNL: N=3850, cross-sectional data,
`bcuse mathpnl` - MEAP93 : N=408, cross-sectional school attainment test data,
`bcuse meap93` - MEAP01 : N=1823, cross-sectional school attainment test data,
`bcuse meap01` - MLB1 : N=353, cross-sectional major league baseball data,
`bcuse mlb1` - MROZ : N=753, cross-sectional labor force participation data,
`bcuse mroz` - MURDER : N=153, longitudinal state murder rate data,
`bcuse murder` - MURDERS : N=37349, longitudinal county-level murder rate data,
`bcuse murders` - NBASAL : N=269, cross-sectional individual data
`bcuse nbasal` - NCAA_RPI : N=336, cross-sectional individual data
`bcuse ncaa_rpi` - NLS80 : N=3710, cross-sectional individual data
`bcuse nls80` - NLS81_87 : N=3710, cross-sectional individual data
`bcuse nls81_87` - NORWAY : N=3710, cross-sectional district data
`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,
`bcuse pntsprd` - PRISON : N=714, state-level panel data on incarceration,
`bcuse prison` - PRMINWGE : N=38, timeseries data on Puerto Rican minimum wage,
`bcuse prminwge` - Q : N=2068, firm-level panel data
`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,
`bcuse rdtelec` - RECID : N=1445, cross-sectional data on recividism,
`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,
`bcuse traffic1` - TRAFFIC2 : N=108, state level timeseries data on traffic accidents,
`bcuse traffic2` - TWOYEAR : N=6,763, individual cross-sectional data,
`bcuse twoyear` - VOLAT : N=558, monthly timeseries data on S&P index,
`bcuse volat` - VOTE1 : N=173, cross-sectional individual data on Congressional campaign expenditures,
`bcuse vote1` - VOTE2 : N=186, panel data on Congressional campaign expenditures,
`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