wooldridge: 115 Data Sets from 'Introductory Econometrics ...

1 Package wooldridge October 12, 2022 TypePackageTitle115 Data Sets from ``Introductory Econometrics : A ModernApproach, 7e'' by Jeffrey M. learning both Econometrics and R may find the introductionto both challenging. The wooldridge data package aims to lighten the task by efficientlyloading any data set found in the text with a single command. Data sets have beencompressed to a fraction of their original size. Documentation files contain page numbers,the original source, time of publication, and notes from the author suggesting avenues forfurther analysis and research. If one needs an introduction to R model syntax, avignette contains solutions to examples from chapters of the sets are from the 7th edition ( wooldridge 2020, ISBN-13: 978-1-337-55886-0),and are backwards compatible with all previous versions of the (>= ) , quantmod, rmarkdown, stargazer, tinytest, xts, M.

2 Shea [aut, cre],Kennth H. Brown [ctb]MaintainerJustin M. 07:50:04 UTC12 Rtopics documented:Rtopics documented:admnrev ..4affairs ..5airfare ..6alcohol ..7apple ..9approval .. 10athlet1 .. 11athlet2 .. 13attend .. 14audit .. 15barium .. 16beauty .. 17benefits .. 19beveridge .. 20big9salary .. 21bwght .. 22bwght2 .. 23campus .. 25card .. 26catholic .. 27cement .. 28census2000 .. 30ceosal1 .. 31ceosal2 .. 32charity .. 33consump .. 34corn .. 35countymurders .. 36cps78_85 .. 37cps91 .. 38crime1 .. 39crime2 .. 41crime3 .. 42crime4 .. 43discrim .. 46driving .. 47earns .. 50econmath .. 51elem94_95 .. 52engin .. 53expendshares .. 54ezanders .. 55ezunem .. 57fair .. 58fertil1 .. 60fertil2 .. 61 Rtopics documented:3fertil3 .. 63fish .. 64fringe.

3 65gpa1 .. 67gpa2 .. 69gpa3 .. 70happiness .. 71hprice1 .. 72hprice2 .. 73hprice3 .. 75hseinv .. 76htv .. 77infmrt .. 78injury .. 79intdef .. 81intqrt .. 82inven .. 83jtrain .. 84jtrain2 .. 85jtrain3 .. 87jtrain98 .. 88k401k .. 89k401ksubs .. 90kielmc .. 91labsup .. 92lawsch85 .. 94loanapp .. 95lowbrth .. 97mathpnl .. 99meap00_01 .. 101meap01 .. 102meap93 .. 103meapsingle .. 104minwage .. 105mlb1 .. 107mroz .. 109murder .. 111nbasal .. 112ncaa_rpi .. 113nyse .. 114okun .. 115openness .. 116pension .. 117phillips .. 118pntsprd .. 119prison .. 120prminwge .. 122rdchem .. 1234admnrevrdtelec .. 124recid .. 125rental .. 126return .. 127saving .. 128school93_98 .. 129sleep75 .. 130slp75_81 .. 132smoke .. 133traffic1 .. 134traffic2 .. 135twoyear.

4 137volat .. 138vote1 .. 140vote2 .. 141voucher .. 142wage1 .. 143wage2 .. 145wagepan .. 146wageprc .. 148wine .. 149 Index150admnrevadmnrevDescriptionWooldri dge Source: Data from the National Highway Traffic Safety Administration: A Digest ofState Alcohol-Highway Safety Related Legislation, Department of Transportation, used the third (1985), eighth (1990), and 13th (1995) editions. Data loads ('admnrev')FormatA with 153 observations on 5 variables: state:state postal code year:85, 90, or 95 admnrev:=1 if admin. revoc. law daysfrst:days suspended, first offense daysscnd:days suspended, second offenseaffairs5 NotesThis is not so much a data set as a summary of so-called administrative per se laws atthe statelevel, for three different years. It could be supplemented with drunk-driving fatalities for a niceeconometric analysis. In addition, the data for 2000 or later years can be added, forming the basisfor a term project.

5 Many other explanatory variables could be included. Unemployment rates,state-level tax rates on alcohol, and membership in MADD are just a few in Text: not (admnrev)affairsaffairsDescriptionWooldr idge Source: Fair (1978), A Theory of Extramarital Affairs, Journal of Political Econ-omy 86, 45-61, 1978. I collected the data from Professor Fair s web cite at the Yale UniversityDepartment of Economics. He originally obtained the data from a survey by Psychology loads ('affairs')FormatA with 601 observations on 19 variables: id:identifier male:=1 if male age:in years yrsmarr:years married kids:=1 if have kids relig:5 = very relig., 4 = somewhat, 3 = slightly, 2 = not at all, 1 = anti educ:years schooling occup:occupation, reverse Hollingshead scale ratemarr:5 = vry hap marr, 4 = hap than avg, 3 = avg, 2 = smewht unhap, 1 = vry unhap6airfare naffairs:number of affairs within last year affair:=1 if had at least one affair vryhap:ratemarr == 5 hapavg:ratemarr == 4 avgmarr:ratemarr == 3 unhap:ratemarr == 2 vryrel:relig == 5 smerel:relig == 4 slghtrel:relig == 3 notrel:relig == 2 NotesThis is an interesting data set for problem sets starting in Chapter 7.

6 Even though naffairs (numberof extramarital affairs a woman reports) is a count variable, a linear model can be used to modelits conditional mean as an approximation. Or, you could ask the students to estimate a linearprobability model for the binary indicator affair, equal to one of the woman reports having anyextramarital affairs. One possibility is to test whether putting the single marriage rating variable,ratemarr, is enough, against the alternative that a full set of dummy variables is needed; see pages239-240 for a similar example. This is also a good data set to illustrate Poisson regression (usingnaffairs) in Section or probit and logit (using affair) in Section in Text: not (affairs)airfareairfareDescriptionWooldr idge Source: Jiyoung Kwon, a former doctoral student in economics at MSU, kindly pro-vided these data, which she obtained from the Domestic Airline Fares Consumer Report by the of Transportation.

7 Data loads ('airfare')alcohol7 FormatA with 4596 observations on 14 variables: year:1997, 1998, 1999, 2000 id:route identifier dist:distance, in miles passen:avg. passengers per day fare:avg. one-way fare, $ bmktshr:fraction market, biggest carrier ldist:log(distance) y98:=1 if year == 1998 y99:=1 if year == 1999 y00:=1 if year == 2000 lfare:log(fare) ldistsq:ldist^2 concen:= bmktshr lpassen:log(passen)NotesThis data set nicely illustrates the different estimates obtained when applying pooled OLS, randomeffects, and fixed in Text: pages 506-507, 581 (airfare)alcoholalcoholDescriptionWooldr idge Source: Terza, (2002), Alcohol Abuse and Employment: A Second Look, Journalof Applied Econometrics 17, 393-404. I obtained these data from the Journal of Applied Econo-metrics data archive at Data loads ('alcohol')FormatA with 9822 observations on 33 variables: abuse:=1 if abuse alcohol status:out of workforce = 1.

8 Unemployed = 2, employed = 3 unemrate:state unemployment rate age:age in years educ:years of schooling married:=1 if married famsize:family size white:=1 if white exhealth:=1 if in excellent health vghealth:=1 if in very good health goodhealth:=1 if in good health fairhealth:=1 if in fair health northeast:=1 if live in northeast midwest:=1 if live in midwest south:=1 if live in south centcity:=1 if live in central city of MSA outercity:=1 if in outer city of MSA qrt1:=1 if interviewed in first quarter qrt2:=1 if interviewed in second quarter qrt3:=1 if interviewed in third quarter beertax:state excise tax, $ per gallon cigtax:state cigarette tax, cents per pack ethanol:state per-capita ethanol consumption mothalc:=1 if mother an alcoholic fathalc:=1 if father an alcoholic livealc:=1 if lived with alcoholic inwf:=1 if status > 1 employ:=1 if employed agesq:age^2 beertaxsq:beertax^2 cigtaxsq:cigtax^2 ethanolsq:ethanol^2 educsq:educ^2apple9 Used in Textpage 629 (alcohol)appleappleDescriptionWooldridge Source: These data were used in the doctoral dissertation of Jeffrey Blend, Departmentof Agricultural Economics, Michigan State University, 1998.

9 The thesis was supervised by Profes-sor Eileen van Ravensway. Drs. Blend and van Ravensway kindly provided the data, which wereobtained from a telephone survey conducted by the Institute for Public Policy and Social Researchat MSU. Data loads ('apple')FormatA with 660 observations on 17 variables: id:respondent identifier educ:years schooling date:date: month/day/year state:home state regprc:price of regular apples ecoprc:price of ecolabeled apples inseason:=1 if interviewed in Nov. hhsize:household size male:=1 if male faminc:family income, thousands age:in years reglbs:quantity regular apples, pounds ecolbs:quantity ecolabeled apples, lbs10approval numlt5:# in household younger than 5 num5_17:# in household 5 to 17 num18_64:# in household 18 to 64 numgt64:# in household older than 64 NotesThis data set is close to a true experimental data set because the price pairs facing a family wererandomly determined.

10 In other words, the family head was presented with prices for the eco-labeledand regular apples, and then asked how much of each kind of apple the family would buy at the givenprices. As predicted by basic economics, the own price effect is negative (and strong) and the crossprice effect is positive (and strong). While the main dependent variable, ecolbs, piles up at zero,estimating a linear model is still worthwhile. Interestingly, because the survey design induces astrong positive correlation between the prices of eco-labeled and regular apples, there is an omittedvariable problem if either of the price variables is dropped from the demand equation. A goodexam question is to show a simple regression of ecolbs on ecoprc and then a multiple regression onboth prices, and ask students to decide whether the price variables must be positively or in Text: pages 201, 223, 266, 626-627 (apple)approvalapprovalDescriptionWooldr idge Source: Harbridge, L.