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Weekly Update of "Common Space" DW-NOMINATE Scores
(Joint House and Senate Scaling)

Jeff Lewis, Nolan McCarty, Keith Poole, and Howard Rosenthal

Updated 12 December 2016 [Minor update 20 December 2016] (This is the Final Release for Congresses 1 - 114.)

Beginning 1 January 2017 Jeff Lewis (UCLA) is in charge of the NOMINATE project (now nearly 35 years old). At some point voteview.com will be assigned to a UCLA server. This server, K7MOA.COM, will be a repository for all estimations done by Poole and Rosenthal, and Poole separately (OC, BasicSpace), from 1991 to date. Health permitting, Poole will maintain this repository until 2020. Keith Poole and Howard Rosenthal would like to thank the countless researchers who have sent us corrections to our data base. Keith Poole will be happy to answer questions on the data and programs of the project.

This is the thirty-first release of Weekly Common Space DW-NOMINATE scores for the House and Senate and the eleventh for the 2nd session of the 114^th Congress. The House and Senate were scaled as if they were one legislature using the 650 Legislators who served in both the House and Senate as "glue" (bridge observations). That is, we estimated a single ideal point for each member of Congress based upon his/her entire record of service in Congress. In the Poole-Rosenthal framework we used the Constant model so that each unique legislator has the same ideal point throughout his or her career.

In order to easily update the Common Space DW-NOMINATE scores when new roll calls are cast in Congress we had to write a new DW-NOMINATE program that required as input only the roll call matrix from Congresses 1 to 114 and the previous Legislator and Roll Call output files for Congresses 1 - 113 from the former program. Jeff Lewis wrote a batch file that combines PERL and Python scripts to combine all the roll call vote matrices together and then run the program. When we have everything completed these scores will be posted at the new voteview website at UCLA and the links below will go there with updated numbers of roll calls and legislators.

The New DW-NOMINATE program uses LBFGS to simultaneously estimate the roll call paraments and to simultaneously estimate the legislator parameters. Beta and the 2nd dimension weight are estimated using the Brent local minimization algorithm (Brent, Richard. 2002. Algorithms for Minimization Without Derivatives. New York: Dover). Legislators and the Roll Call Midpoints are constrained to lie in the unit circle.

As of 12 December 2016 there were a total of 104,635 roll calls of which 93,727 were scalable. The number of unique legislators is 12,046 (this counts two new members, Evans (D-PA), and Comer (R-KY), and one former member, Hanabusa (D-HI), all three were elected in Special Elections on 8 November 2016) producing a total of 17,492,427 choices. The second dimension weight is 0.4153 and Beta is 7.6912. The correct classification is 87.42 percent with an APRE of 0.6294 and a geometric mean probability of 0.7568.

In order to calculate distances from these Common Space DW-NOMINATE scores you must multiply the second dimension by the weight parameter. To calculate the choice probabilities you must apply both the second dimension weight and the Beta parameter. Use the Yea and Nay outcome coordinates with considerable caution because, as we explain in Congress: A Political Economic History of Roll Call Voting, they are poorly identified. However, the cutting line is identified and can be used safely.

Please note that these files contain scores for most Presidents. For Presidents prior to Eisenhower these are based on roll calls corresponding to Presidential requests. These roll calls were compiled by an NSF project headed by Elaine Swift ( Study No. 3371, Database of Congressional Historical Statistics, 1789-1989). Many of these scores are based upon a small number of roll calls so use them with caution!

In the files below the House Coordinates for each Congress are stacked on top of the Senate coordinates. If you have questions or need help with these files please send us e-mail at jblewis_at_ucla.edu (Jeff Lewis) or ktpoole_at_uga.edu (Keith Poole).

Please note that at the end of each Congress we will post a final set of coordinates with bootstrapped standard errors on our Common Space DW-NOMINATE download page.

The format of the legislator files is:


 1.  Congress Number
 2.  ICPSR ID Number:  5 digit code assigned by the ICPSR as
                       corrected by Howard Rosenthal and myself.
 3.  State Code:  2 digit ICPSR State Code.
 4.  Congressional District Number (0 if Senate or President)
 5.  State Name
 6.  Party Code:  100 = Dem., 200 = Repub. (See PARTY3.DAT)
 7.  Occupancy:  ICPSR Occupancy Code -- 0=only occupant; 1=1st occupant; 2=2nd occupant; etc.
 8.  Last Means of Attaining Office:  ICPSR Attain-Office Code -- 1=general election;
                2=special election; 3=elected by state legislature; 5=appointed
 9.  Name
10.  1st Dimension Coordinate
11.  2nd Dimension Coordinate
12.  Log-Likelihood
13.  Number of Votes
14.  Number of Classification Errors
15.  Geometric Mean Probability

The format of the roll call files is:

 1.  Congress Number
 2.  Roll Call Number
 3.  Log-Likelihood
 4.  Spread on 1st Dimension    -- if the roll call was not scaled, there
 5.  Midpoint on 1st Dimension  -- are 0.000's in all four fields
 6.  Spread on 2nd Dimension    --
 7.  Midpoint on 2nd Dimension  --

Legislator Estimates 1^st to 114^th Houses and Senates (Text File, 47,045 lines, 12 December 2016)

Legislator Estimates 1^st to 114^th Houses and Senates (STATA 14 File, 47,045 lines, 12 December 2016)

Legislator Estimates 1^st to 114^th Houses and Senates (STATA 12 File, 47,045 lines, 12 December 2016)

Legislator Estimates 1^st to 114^th Houses and Senates (STATA 9 File, 47,045 lines, 12 December 2016)

Legislator Estimates 1^st to 114^th Houses and Senates (EVIEWS 9 File, 47,045 lines, 12 December 2016)

Roll Call Estimates 1^st to 114^th Houses and Senates (Text File, 104,635 lines, 12 December 2016)

Roll Call Estimates 1^st to 114^th Houses and Senates (STATA 14 File, 104,635 lines, 12 December 2016)

Roll Call Estimates 1^st to 114^th Houses and Senates (STATA 12 File, 104,635 lines, 12 December 2016)

Roll Call Estimates 1^st to 114^th Houses and Senates (STATA 9 File, 104,635 lines, 12 December 2016)

Roll Call Estimates 1^st to 114^th Houses and Senates (EVIEWS 9 File, 104,635 lines, 12 December 2016)

Below is STATA output showing regressions of these new coordinates onto the old coordinates for Congresses 1 - 113. All the r-squares are greater than 0.95 so that the new program is producing essentially the same coordinates as the old program. However, note that as roll calls are added (1,829 -- 2015-16, total 114^th Congress) that will slightly change the scores for Represenatives/Senators who served in previous Congresses.




. regress dwnom1new dwnom1

      Source |       SS           df       MS      Number of obs   =    46,506
-------------+----------------------------------   F(1, 46504)     >  99999.00
       Model |  6337.23422         1  6337.23422   Prob > F        =    0.0000
    Residual |  15.4389598    46,504  .000331992   R-squared       =    0.9976
-------------+----------------------------------   Adj R-squared   =    0.9976
       Total |  6352.67318    46,505  .136601939   Root MSE        =    .01822

------------------------------------------------------------------------------
   dwnom1new |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dwnom1 |   .9756203   .0002233  4369.04   0.000     .9751827     .976058
       _cons |  -.0017729   .0000845   -20.98   0.000    -.0019385   -.0016072
------------------------------------------------------------------------------

. regress dwnom2new dwnom2

      Source |       SS           df       MS      Number of obs   =    46,506
-------------+----------------------------------   F(1, 46504)     >  99999.00
       Model |  10091.1505         1  10091.1505   Prob > F        =    0.0000
    Residual |  250.073645    46,504  .005377465   R-squared       =    0.9758
-------------+----------------------------------   Adj R-squared   =    0.9758
       Total |  10341.2242    46,505  .222368007   Root MSE        =    .07333

------------------------------------------------------------------------------
   dwnom2new |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dwnom2 |   1.009491   .0007369  1369.88   0.000     1.008046    1.010935
       _cons |    .008995   .0003401    26.45   0.000     .0083284    .0096616
------------------------------------------------------------------------------

. regress spread1new spread1 if (vardum==1)

      Source |       SS           df       MS      Number of obs   =    92,182
-------------+----------------------------------   F(1, 92180)     >  99999.00
       Model |  10512.7982         1  10512.7982   Prob > F        =    0.0000
    Residual |  56.4450622    92,180  .000612335   R-squared       =    0.9947
-------------+----------------------------------   Adj R-squared   =    0.9947
       Total |  10569.2432    92,181  .114657502   Root MSE        =    .02475

------------------------------------------------------------------------------
  spread1new |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     spread1 |   1.029077   .0002484  4143.47   0.000      1.02859    1.029564
       _cons |  -.0000282   .0000816    -0.35   0.730     -.000188    .0001317
------------------------------------------------------------------------------

. regress mid1new mid1 if (vardum==1)

      Source |       SS           df       MS      Number of obs   =    92,182
-------------+----------------------------------   F(1, 92180)     >  99999.00
       Model |  11859.8461         1  11859.8461   Prob > F        =    0.0000
    Residual |  140.163429    92,180  .001520541   R-squared       =    0.9883
-------------+----------------------------------   Adj R-squared   =    0.9883
       Total |  12000.0095    92,181  .130178774   Root MSE        =    .03899

------------------------------------------------------------------------------
     mid1new |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        mid1 |   .9820736   .0003516  2792.80   0.000     .9813844    .9827629
       _cons |   .0000695   .0001284     0.54   0.589    -.0001823    .0003212
------------------------------------------------------------------------------

. regress spread2new spread2 if (vardum==1)

      Source |       SS           df       MS      Number of obs   =    92,182
-------------+----------------------------------   F(1, 92180)     >  99999.00
       Model |  23803.1281         1  23803.1281   Prob > F        =    0.0000
    Residual |  1170.16423    92,180   .01269434   R-squared       =    0.9531
-------------+----------------------------------   Adj R-squared   =    0.9531
       Total |  24973.2924    92,181  .270915833   Root MSE        =    .11267

------------------------------------------------------------------------------
  spread2new |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     spread2 |   1.060118   .0007742  1369.34   0.000     1.058601    1.061636
       _cons |  -.0007929   .0003711    -2.14   0.033    -.0015203   -.0000655
------------------------------------------------------------------------------

. regress mid2new mid2 if (vardum==1)

      Source |       SS           df       MS      Number of obs   =    92,182
-------------+----------------------------------   F(1, 92180)     >  99999.00
       Model |  29244.8144         1  29244.8144   Prob > F        =    0.0000
    Residual |  318.065259    92,180   .00345048   R-squared       =    0.9892
-------------+----------------------------------   Adj R-squared   =    0.9892
       Total |  29562.8796    92,181  .320704696   Root MSE        =    .05874

------------------------------------------------------------------------------
     mid2new |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        mid2 |   .9959004   .0003421  2911.28   0.000       .99523    .9965709
       _cons |  -.0001018   .0001935    -0.53   0.599    -.0004811    .0002775
------------------------------------------------------------------------------