105th and 106th Congressional District Data (HDMG106.DTA)-
Download the following Excel file:
2000 Census Data For Congressional Districts (CD2000CENSUS.XLS)and merge it into HDMG106.DTA. (Note that this will require some ingenuity on your part!) Use the following variable names and definitions (note that the variable district also appears in the Excel file but it will be one of the variables you will need to use to do the merge):
statenmlong str20 %20s Name of state (long) total_pop double %10.0g Total population of CD 2000 white00 float %9.0g Percent White 2000 black00 float %9.0g Percent Black 2000 asian00 float %9.0g Percent Asian 2000 hispanic00 float %9.0g Percent Hispanic 2000 owner00 float %9.0g Percent Owner-Occupied Housing Units 2000Do the d and summ commands, and report the results.
In STATA run the regressions:
regress bush00 black00 south hispanic00 income owner00 dwnom1n dwnom2n dole96
regress gore00 black00 south hispanic00 income owner00 dwnom1n dwnom2n clint96
Analyze these two regressions. What do you think accounts for the differences between them. Be specific.
In STATA compute the correlation matrix for the independent variables:
correlate black00 hispanic00 income owner00 dwnom1n dwnom2n
Examine the entries of the correlation matrix. Do you see anything that strikes you as odd? Be explicit.
To obtain the eigenvalues and eigenvectors of the correlation matrix, use the STATA command:
factor black00 hispanic00 income owner00 dwnom1n dwnom2n, pc
To obtain a graph of the eigenvalues, use the STATA command:
greigen, xlabel(1,2,3,4,5,6)
Does this graph lead you to believe that there is a significant problem with multicollinearity with these independent variables? Why? Why not?
set mem 20m
which allocates 20 meg of memory for Stata to work with.
Congressional Elections Data
From Lublin and Jacobson (Stata Dataset)Download the dataset and bring it up in Stata. If you issue the d command you will see:
. d
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)
-------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------
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
-------------------------------------------------------------------------------
Sorted by:
If you issue the summ command you will see:
. summ
Variable | Obs Mean Std. Dev. Min Max
-------------+-----------------------------------------------------
year | 7832 1976.996 10.37915 1960 1994
congress | 7832 95.49783 5.189574 87 104
icpsrid | 7832 12325.2 7208.363 2 95120
icpsrst | 7832 36.75447 21.00158 1 82
cdist1 | 7832 9.979443 10.88324 1 99
statenm | 0
cdist2 | 7832 9.566394 9.151734 1 52
dempct | 7832 56.98605 23.56704 0 100
blkpct | 7595 11.12508 14.51647 .0194025 95.50033
whpct | 7595 85.85531 15.74295 3.862633 99.89686
forpct | 6723 5.732316 6.60551 .116483 58.52188
south | 7832 .2858784 .4518606 0 1
incomewh | 5859 17896.74 11820.14 2088.375 78717
incomebl | 5856 12378.12 8885.767 1213 66320
hs25 | 6723 57.11782 15.5113 14.8 92.3
college | 6723 12.88006 7.004294 1.9 51.4
party1 | 7830 140.3649 49.22029 100 329
blackrep | 7832 .0390705 .1937751 0 1
latinorp | 7832 .020429 .1716504 0 3
womanrep | 7832 .046859 .2113504 0 1
incumb1 | 7817 1.242548 .6364613 0 3
votesd | 6382 83320.55 53081.79 0 1872351
votesr | 6443 71985.16 57503.79 0 1786018
demvshr | 7832 57.00632 23.59968 0 100
whowon | 7832 .601762 .525787 0 9
incshr | 7832 71.56447 18.54318 20.6 99.999
incshrl | 7832 69.86711 17.08056 22.1 99.999
redist | 7832 .2893258 .4534784 0 1
incumbst | 7832 .8476762 .8639607 0 9
challeng | 7832 1.135981 1.803747 0 9
challenh | 7832 1.124362 2.017193 0 9
icpsrid2 | 7832 12325.2 7208.363 2 95120
party2 | 7832 140.4164 49.26602 100 329
name | 0
dwnom1 | 7832 -.0354424 .3335639 -1.07 1.37
dwnom2 | 7832 .0107231 .5186352 -1.83 1.43
partynm | 0
xincome | 6723 15494.69 10600.03 1968 64199
xhispct | 4780 6.610872 11.38954 .0137409 83.71677
order | 7832 3916.5 2261.048 1 7832
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Your assignment is to build a model of the Democratic Vote Share.
That is, use demvshr as your dependent variable (note, do not
use dempct -- it has some errors in it!). You are free to use
any independent variables you want but you must include median family income
(xincome) in your specification. Whatever other independent
variables you use, you must have a reasonable explanation for your specification!
Note that xincome is in nominal dollars! To see the distribution of xincome use the graph command in Stata; namely:
graph xincome congress
To correct the xincome variable as well as the incomewh and incomebl variables, you need to apply a price deflator. For congress 88 - 91 use 100/90.6, for 93 - 97 use 100/125.3, for 98 - 102 use 100/289.1, and for 103 - 104 use 100/420.3. These transformations will correct the income variables to 1967 dollars.
When you have settled on your specification and have finished your analysis using Stata, paste the variables that you settled on into EVIEWS and replicate your analysis using EVIEWS.