Contains data from D:\statadat\lublin5.dta
obs: 7,832
vars: 39 1 Nov 2001 11:35
size: 1,057,320 (98.7% of memory free)
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storage display value
variable name type format label variable label
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year int %8.0g year
congress byte %8.0g congress (87-104)
icpsrid long %12.0g icpsr id #
icpsrst byte %8.0g icpsr state code
cdist1 byte %8.0g cong. district (p&r)
statenm str7 %9s state name
cdist2 byte %8.0g cong. district (lublin)
dempct float %9.0g demo. % two party vote
blkpct float %9.0g black percent of pop.
whpct float %9.0g white percent of pop.
forpct double %10.0g foreign born % of pop.
south byte %8.0g south (1=confederacy + KY +OK,
0=north)
incomewh float %9.0g white median family income
incomebl long %12.0g black median family income
hs25 float %9.0g percent 25 and older completing
high school or more
college float %9.0g percent 25 or older completed 4
yrs college or more
party1 int %8.0g party code (100=Dem, 200=Rep)
blackrep byte %8.0g blackrep =1 if black
representative, 0 otherwise
latinorp byte %8.0g latinorp=1 if mexican, 2=PR,
3=Cuban, 0 otherwise
womanrep byte %8.0g woman representative (1=woman,
0=man)
incumb1 byte %8.0g incumbency (0=repub, 1=demo.,
2=open)
votesd long %12.0g number of votes for democrat
votesr long %12.0g number votes for republican
demvshr float %9.0g democrats share two-party vote
whowon byte %8.0g 0 = repub won, 1= demo. won,
99=3rd party won
incshr float %9.0g incumbents share 2-party vote,
99.9=unopposed
incshrl float %9.0g incumbents share 2-party vote
last elect, 99.9=unpposed
redist byte %8.0g redistricted: 0=district
unchange, 1=re-districting
incumbst byte %8.0g incumbency status:
0 = republican incumbent
1 = democratic incumbent
2 = open seat formerly held by democrat
3 = open seat formerly held by republican
4 = open seat, new (from redistricting)
5 = two incumbents (from redistricting)
9 = third-party incumbent
challeng byte %8.0g challenger quality
0 = challenger has not held elective office
1 = challenger has held elective office
2 = only Democratic candidate for open seat has held office
3 = only Republican candidate for open seat has held office
4 = both candidates for open seat have held office
5 = no challenger
6 = no Democrat candidate (open)
7 = no Republican candidate (open)
challenh byte %8.0g challenger misc. information
0 = Nothing special (ignore)
1 = At Large or multi-candidate race
2 = unopposed
3 = incumbent switched parties since last election
4 = challenger was state legislator
5 = only Democrat was state legislator (open seat)
6 = only Republican was state legislator (open seat)
7 = both candidates for open seat were state legislators
8 = challenger is former U.S. Representative
9 = odd race, third party; in general, DO NOT USE
icpsrid2 long %12.0g icpsr id number
party2 int %8.0g party id (100=Dem, 200=Repub)
name str11 %11s member name
dwnom1 float %9.0g dwnominate 1st dimension
dwnom2 float %9.0g dwnominate 2nd dimension
(multiply by .3)
partynm str13 %13s name of political party
xincome long %12.0g median family income
xhispct float %9.0g percent hispanic
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-
Using EVIEWS, augment your model of the
Democratic Vote Share with indicator variables
that control for Challenger Quality and
Incumbency Status.
You are free to use
any independent variables you want but you must include median family income in 1967
dollars
in your specification. Defend the reasonableness of your specification.
Using EVIEWS, test for the presence of heteroskedasticity and autocorrelation. Estimate your model with AR(1), AR(2), and AR(3) terms.
In EVIEWS, interpret the results from the HIST RESID command.
Party Identification: 0=strong democrat
1=weak democrat
2=lean democrat
3=independent
4=lean republican
5=weak republican
6=strong republican
Family Income: Raw Data (we will not use this variable)
Family Income Quintile: 1 is the lowest quintile, 5 is the highest
Race: 0 = White
1 = Black
Sex: 0 = Male
1 = Female
South: 0 = North
1 = South
Education: 1 = High School or less
2 = Some College
3 = College degree
Age: In Years
Presidential Vote: 0 = Did Not Vote
1 = Voted for Democratic Candidate For President
2 = Voted for Republican Candidate For President
3 = Voted for 3rd Party Candidate for President
The data are in the text files:
1956 Data
1960 Data
1964 Data
1968 Data
1972 Data
1976 Data
1980 Data
1984 Data
1988 Data
1992 Data
1996 Data
2000 Data-
Download these files and
load them into Stata. Specifically,
stack the elections on top of one another and add a
variable called year
that is equal to the year of the election for every respondent in that NES
survey. When you are finished you should have 19,449 observations for the
combined 1952 - 2000 election dataset. Turn in the
d and
summ commands for this dataset.
In Stata, a binary dependent variable is always defined as 0 being the "negative" outcome with all other nonmissing values being the "positive" outcome. Use Presidential Vote as a dependent variable with the remaining variables as independent variables; that is, run the following model on the combined dataset:
probit voted party income race sex south education age year
What is your interpretation of the coefficients (what do they tell you about American Politics)? Be Specific.
The above model is clearly mis-specified because one would expect the the propensity to vote to vary with the absolute strength of party id. Create a variable called strongparty from party (report the steps you used to create strongparty from party) and run the following model on the combined dataset:
probit voted strongparty income race sex south education age year
What is your interpretation of the coefficients (what do they tell you about American Politics -- note the changes from the mis-specified model)? Be Specific.
In Stata, create a dependent variable from Presidential Vote where 0 = Voted for the Democratic Party Candidate and 1 = Voted for Republican Party Candidate (note that non-voters and 3rd party voters are missing data!). Run the following probit model on the combined dataset:
probit y party income race sex south education age year
What is your interpretation of the coefficients (what do they tell you about American Politics)? Be Specific.
The above model is somewhat mis-specified because we know that the "gender gap" opened up dramatically in just the past 20 years. To account for this try interacting sex with year -- sexyear -- and run the following model on the combined dataset:
probit y party income race sex sexyear south education age year
What is your interpretation of the coefficients (what do they tell you about American Politics -- note the changes from the mis-specified model)? Be Specific.
A better specification for this model is to replace the variable year with indicator variables for each election. This has the effect of controlling for the unique circumstances of each election that are not captured by our independent variables -- for example, Lyndon Johnson's landslide victory over Barry Goldwater in 1964. To generate these indicator variables use the command:
tabulate year, gen(elec)
This creates 12 indicator variables named elec1 (equals 1 if 1956, 0 otherwise) to elec12 (equals 1 if 2000, 0 otherwise). Re-run the model in 2.e. only replace year with elec2, elec3, ... , elec12; specifically:
probit y party income race sex sexyear south education age elec2 elec3 elec4 elec5 elec6 elec7 elec8 elec9 elec10 elec11 elec12
Interpret the coefficients on the election indicators and interpret the coefficient for the intercept term.
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In
Stata run the specification used in 2.f only with
party as the dependent variable:
oprobit party income race sex sexyear south education age elec2 elec3 elec4 elec5 elec6 elec7 elec8 elec9 elec10 elec11 elec12
What is your interpretation of the coefficients? Be Specific.