The datasets in the table below are referenced in the Wooldridge undergraduate econometrics textbook with explanations and models described in-text. The original list of data is compiled by Boston College, and can be found here.

Download data and export to Excel

Using the wine dataset as an example, the following code chunks will export the data to a CSV file, which you can open with Excel.

Stata:

cd "<directory_path>"
ssc install bcuse
bcuse wine  
outsheet using wine.csv, comma replace

setwd("<directory_path>")
install.packages("wooldridge")
library(wooldridge)
data('wine')
write.csv(wine, "wine.csv")

Note that you need to replace the text in the <brackets> above. If you want to clean the data before exporting to Excel, do so after importing the data, but before exporting to CSV.

List of available data sets

Name	Sample Size	Description
401K	N=1534	cross-sectional data on pensions
401K-50	N=767	50% sample of 401K dataset
401KSUBS	N=9275	cross-sectional data on pensions
ADMNREV	N=153	timeseries data on offenses
AFFAIRS	N=601	cross-sectional individual data
AIRFARE	N=4596	cross-sectional data on airfares
APPLE	N=660	cross-sectional individual data on consumers
ATHLET1	N=118	cross-sectional individual data on schools’ athletic programs
ATHLET2	N=30	cross-sectional individual data on schools’ athletic programs
ATTEND	N=680	cross-sectional individual data on classes attended
AUDIT	N=241	cross-sectional individual data on job offers
BARIUM	N=131	time-series data on barium export
BEVERIDGE	N=135	time-series data on unemployment and vacancies
BWGHT	N=1388	cross-sectional individual data on birth weights
BWGHT50	N=694	cross-sectional individual data on birth weights (50% sample)
BWGHT2	N=1832	cross-sectional individual data on birth weights
CAMPUS	N=97	cross-sectional data on crime in colleges
CARD	N=3010	cross-sectional individual data on consumers
CEMENT	N=312	time-series data for 1964-1989
CEOSAL1	N=209	cross-sectional firm-level data
CEOSAL2	N=177	cross-sectional firm-level data
CONSUMP	N=37	time-series data on consumption
CORN	N=37	cross-sectional individual data on consumers
CORNWELL	N=630	country panel data
CPS78_85	N=1084	pooled CS data for two years
CPS91	N=1084	pooled CS data
CRIME1	N=2725	cross-sectional individual data
CRIME2	N=92	cross-sectional individual data
CRIME3	N=106	cross-sectional individual data
CRIME4	N=630	cross-sectional county data
DISCRIM	N=410	cross-sectional firm level data
EARNS	N=41	cross-sectional individual data
ENGIN	N=403	cross-sectional individual data
EZANDERS	N=108	time-series individual data
EZUNEM	N=198	time-series individual data
FAIR	N=21	quadrennial timeseries data for 1916-1992
FERTIL1	N=1129	cross-sectional family data
FERTIL2	N=4361	cross-sectional family data
FERTIL3	N=72	cross-sectional family data
FISH	N=616	cross-sectional data on fish sales
FRINGE	N=616	cross-sectional family data
GPA1	N=141	cross-sectional individual data
GPA2	N=4137	cross-sectional individual data
GPA2-20	N=827	cross-sectional individual data
GPA3	N=732	cross-sectional individual data
GROGGER	N=2725	cross-sectional individual data
HPRICE1	N=88	cross-sectional individual data
HPRICE2	N=506	cross-sectional individual data
HPRICE3	N=321	cross-sectional individual data
HSEINV	N=42	timeseries data on real housing invest
HTV	N=1230	cross-sectional individual data
INFMRT	N=102	state-level panel data on infant mortality
INJURY	N=7150	cross-sectional individual data
INTDEF	N=49	cross-sectional individual data
INTQRT	N=124	time-series quarter data on interest rates
INVEN	N=37	time-series data
JTRAIN	N=471	panel individual data on job training
JTRAIN2	N=445	cross-sectional individual data
KEANE	N=12723	panel individual data
KIELMC	N=321	panel individual data
LABSUP	N=156	cross-sectional individual data
LAWSCH85	N=156	cross-sectional individual data
LOANAPP	N=1989	cross-sectional individual data
LOWBRTH	N=1989	cross-sectional individual data
MATHPNL	N=3850	cross-sectional data
MEAP93	N=408	cross-sectional school attainment test data
MEAP01	N=1823	cross-sectional school attainment test data
MEAP01_40	N=729	cross-sectional school attainment test data
MLB1	N=353	cross-sectional major league baseball data
MROZ	N=753	cross-sectional labor force participation data
MURDER	N=153	longitudinal state murder rate data
NBASAL	N=269	cross-sectional individual data
NLS80	N=3710	cross-sectional individual data
NLS81_87	N=3710	cross-sectional individual data
NORWAY	N=3710	cross-sectional district data
NYSE	N=691	time-series NYSE stock price and returns data
OPENNESS	N=114	cross-sectional country data on openness to trade
PATENT	N=2260	cross-sectional individual data
PENSION	N=226	cross-sectional individual data
PHILLIPS	N=49	time-series Phillips curve data
PNTSPRD	N=553	cross-sectional gambling point spread data
PRISON	N=714	state-level panel data on incarceration
PRMINWGE	N=38	timeseries data on Puerto Rican minimum wage
Q	N=2068	firm-level panel data
RDCHEM	N=32	cross-sectional data on chemical firms’ R&D expenditures
RDTELEC	N=29	cross-sectional firm data on R&D
RECID	N=1445	cross-sectional data on recividism
RENTAL	N=128	city-level panel data on rental housing
RETURN	N=142	cross-sectional data on CEO salaries
SAVING	N=100	cross-sectional individual data on consumption and saving
SLEEP75	N=706	cross-sectional individual data on sleep patterns
SLP75_81	N=239	panel individual data on sleep patterns
SMOKE	N=807	cross-sectional individual data on smoking
TRAFFIC1	N=51	state level cross-sectional data on traffic deaths
TRAFFIC2	N=108	state level timeseries data on traffic accidents
TWOYEAR	N=6763	individual cross-sectional data
VOLAT	N=558	monthly timeseries data on S&P index
VOTE1	N=173	cross-sectional individual data on Congressional campaign expenditures
VOTE2	N=186	panel data on Congressional campaign expenditures
WAGE1	N=526	cross-sectional data on wages
WAGE2	N=935	cross-sectional data on wages
WAGEPAN	N=4360	individual panel data on wages
WAGEPRC	N=286	macro timeseries data on wages and prices
WINE	N=21	cross-sectional individual data