Weekly Update of "Common Space" DW-NOMINATE Scores

(Joint House and Senate Scaling)

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


Updated 26 June 2016 (Updates only occur when Congress is in Session)


This is the twenty-eigth release of WeeklyCommon Space DW-NOMINATE scores for the House and Senate and the eighth for the second session of the 114th 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 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 26 June 2016 there were a total of 104,302 roll calls of which 93,456 were scalable. The number of unique legislators is 12,044 (this counts Warren Davidson (R-OH) who was elected to replace former Speaker John Boehner) producing a total of 17,392,378 choices. The second dimension weight is 0.4158 and Beta is 7.7793. The correct classification is 87.37 percent with an APRE of 0.6277 and a geometric mean probability of 0.7561.

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).Updated 26 June 2016 (Updates only occur when Congress is in Session)


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.  Name
 8.  1st Dimension Coordinate
 9.  2nd Dimension Coordinate
10.  Log-Likelihood
11.  Number of Votes
12.  Number of Classification Errors
13.  Geometric Mean Probability
The format of the roll call files is:
 1.  Congress Number
 2.  Roll Call Number
 3.  Spread on 1st Dimension    -- if the roll call was not scaled, there
 4.  Midpoint on 1st Dimension  -- are 0.000's in all four fields
 5.  Spread on 2nd Dimension    --
 6.  Midpoint on 2nd Dimension  --

Legislator Estimates 1st to 114th Houses and Senates (Text File, 47,042 lines, 26 June 2016)
Legislator Estimates 1st to 114th Houses and Senates (STATA 14 File, 47,042 lines, 26 June 2016)
Legislator Estimates 1st to 114th Houses and Senates (STATA 12 File, 47,042 lines, 26 June 2016)
Legislator Estimates 1st to 114th Houses and Senates (STATA 9 File, 47,042 lines, 26 June 2016)
Legislator Estimates 1st to 114th Houses and Senates (EVIEWS 9 File, 47,042 lines, 26 June 2016)

Roll Call Estimates 1st to 114th Houses and Senates (Text File, 104,302 lines, 26 June 2016)
Roll Call Estimates 1st to 114th Houses and Senates (STATA 14 File, 104,302 lines, 26 June 2016)
Roll Call Estimates 1st to 114th Houses and Senates (STATA 12 File, 104,302 lines, 26 June 2016)
Roll Call Estimates 1st to 114th Houses and Senates (STATA 9 File, 104,302 lines, 26 June 2016)
Roll Call Estimates 1st to 114th Houses and Senates (EVIEWS 9 File, 104,302 lines, 26 June 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 0.96 so that the new program is producing essentially the same coordinates as the old program. However, note that as roll calls are added (1,496 -- 2015-16) that will slightly change the scores for Represenatives/Senators who served in the 114th and previous Congresses.


-------------------------------------------------------------------------------

. regress dwnom1new dwnom1

      Source |       SS           df       MS      Number of obs   =    46,506
-------------+----------------------------------   F(1, 46504)     >  99999.00
       Model |  6345.70352         1  6345.70352   Prob > F        =    0.0000
    Residual |  14.8732055    46,504  .000319826   R-squared       =    0.9977
-------------+----------------------------------   Adj R-squared   =    0.9977
       Total |  6360.57672    46,505   .13677189   Root MSE        =    .01788

------------------------------------------------------------------------------
   dwnom1new |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dwnom1 |    .976272   .0002192  4454.33   0.000     .9758425    .9767016
       _cons |  -.0019001   .0000829   -22.91   0.000    -.0020626   -.0017375
------------------------------------------------------------------------------


. regress dwnom2new dwnom2

      Source |       SS           df       MS      Number of obs   =    46,506
-------------+----------------------------------   F(1, 46504)     >  99999.00
       Model |  10043.9766         1  10043.9766   Prob > F        =    0.0000
    Residual |  252.298837    46,504  .005425315   R-squared       =    0.9755
-------------+----------------------------------   Adj R-squared   =    0.9755
       Total |  10296.2755    46,505  .221401473   Root MSE        =    .07366

------------------------------------------------------------------------------
   dwnom2new |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dwnom2 |   1.007128   .0007402  1360.63   0.000     1.005678    1.008579
       _cons |   .0105437   .0003416    30.86   0.000     .0098741    .0112133
------------------------------------------------------------------------------


. regress spread1new spread1 if (vardum==1)

      Source |       SS           df       MS      Number of obs   =    92,182
-------------+----------------------------------   F(1, 92180)     >  99999.00
       Model |  10377.4062         1  10377.4062   Prob > F        =    0.0000
    Residual |  46.1624947    92,180  .000500786   R-squared       =    0.9956
-------------+----------------------------------   Adj R-squared   =    0.9956
       Total |  10423.5687    92,181  .113077193   Root MSE        =    .02238

------------------------------------------------------------------------------
  spread1new |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     spread1 |   1.022429   .0002246  4552.17   0.000     1.021988    1.022869
       _cons |  -.0000352   .0000738    -0.48   0.634    -.0001797    .0001094
------------------------------------------------------------------------------

. regress mid1new mid1 if (vardum==1)

      Source |       SS           df       MS      Number of obs   =    92,182
-------------+----------------------------------   F(1, 92180)     >  99999.00
       Model |  11907.1705         1  11907.1705   Prob > F        =    0.0000
    Residual |  116.576043    92,180  .001264657   R-squared       =    0.9903
-------------+----------------------------------   Adj R-squared   =    0.9903
       Total |  12023.7465    92,181  .130436278   Root MSE        =    .03556

------------------------------------------------------------------------------
     mid1new |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        mid1 |   .9840311   .0003207  3068.44   0.000     .9834025    .9846596
       _cons |  -.0000832   .0001171    -0.71   0.478    -.0003128    .0001464
------------------------------------------------------------------------------

. regress spread2new spread2 if (vardum==1)

      Source |       SS           df       MS      Number of obs   =    92,182
-------------+----------------------------------   F(1, 92180)     >  99999.00
       Model |  23216.6893         1  23216.6893   Prob > F        =    0.0000
    Residual |  951.684925    92,180  .010324202   R-squared       =    0.9606
-------------+----------------------------------   Adj R-squared   =    0.9606
       Total |  24168.3743    92,181  .262183902   Root MSE        =    .10161

------------------------------------------------------------------------------
  spread2new |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     spread2 |   1.046978   .0006982  1499.59   0.000     1.045609    1.048346
       _cons |  -.0005859   .0003347    -1.75   0.080    -.0012419    .0000701
------------------------------------------------------------------------------

. regress mid2new mid2 if (vardum==1)

      Source |       SS           df       MS      Number of obs   =    92,182
-------------+----------------------------------   F(1, 92180)     >  99999.00
       Model |  29231.7209         1  29231.7209   Prob > F        =    0.0000
    Residual |  265.905226    92,180   .00288463   R-squared       =    0.9910
-------------+----------------------------------   Adj R-squared   =    0.9910
       Total |  29497.6261    92,181  .319996811   Root MSE        =    .05371

------------------------------------------------------------------------------
     mid2new |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        mid2 |   .9956775   .0003128  3183.33   0.000     .9950644    .9962905
       _cons |  -.0001382   .0001769    -0.78   0.435     -.000485    .0002086
------------------------------------------------------------------------------

 


 

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