st: comparing coefficients accross models

[Traci Schlesinger <tracias@Princeton.EDU>]

hi stata users!

I have two models (and many other similar pairs).  The first examines 
the effects of changes in sentencing policies on black men's admission's 
to prison for violent crimes.  The second examines the effects of 
changes in sentencing policies on white men's admission's to prison for 
violent crimes.  Here are the models and their results:

. newey2 bmv_rate mt_b ca il ne nj tx moreharsh lfp perblack adol 
violent,  lag(1) t(mergeyear) force


Regression with Newey-West standard errors          Number of obs  
=        61
maximum lag : 1                                     F( 10,    50)  =     
53.18
                                                  Prob > F       =    
0.0000

------------------------------------------------------------------------------ 
           |             Newey-West
  bmv_rate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
Interval]
-------------+---------------------------------------------------------------- 
      mt_b |   25.63707   7.820457     3.28   0.002     9.929216    
41.34492
        ca |  -1683.497   553.7605    -3.04   0.004    -2795.758   
-571.2367
        il |  -1180.779   335.2558    -3.52   0.001     -1854.16   
-507.3985
        nj |  -1722.034   499.4906    -3.45   0.001     -2725.29   
-718.7775
        tx |  -1444.673   409.6565    -3.53   0.001    -2267.492   
-621.8537
 moreharsh |  -334.9321   134.9855    -2.48   0.016    -606.0585   
-63.80571
       lfp |   234.4752   99.71753     2.35   0.023     34.18662    
434.7637
  perblack |  -11910.15   3144.488    -3.79   0.000    -18226.04   
-5594.259
      adol |  -4938.191   1418.736    -3.48   0.001    -7787.806   
-2088.577
violent_ar~s|   .2775444   .0685935     4.05   0.000     .1397702    
.4153186
     _cons |   3497.981   896.4067     3.90   0.000     1697.496    
5298.467
------------------------------------------------------------------------------ 

. newey2 wmv_rate mt_b ca il ne nj tx moreharsh lfp perblack adol 
violent,  lag(1) t(mergeyear) force


Regression with Newey-West standard errors          Number of obs  
=        61
maximum lag : 1                                     F( 10,    50)  =     
22.22
                                                  Prob > F       =    
0.0000

------------------------------------------------------------------------------ 
           |             Newey-West
  wmv_rate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
Interval]
-------------+---------------------------------------------------------------- 
      mt_b |   2.133085   1.140938     1.87   0.067    -.1585574    
4.424727
        ca |  -218.6071   78.00666    -2.80   0.007    -375.2881    
-61.9261
        il |   -158.486   48.60826    -3.26   0.002    -256.1185   
-60.85343
        nj |  -226.1264   72.99826    -3.10   0.003    -372.7477   
-79.50511
        tx |  -176.1477   60.56243    -2.91   0.005    -297.7909   
-54.50448
 moreharsh |  -68.57535   26.88301    -2.55   0.014    -122.5715   
-14.57924
       lfp |   52.18322   12.67309     4.12   0.000     26.72858    
77.63787
  perblack |  -1466.577   462.0384    -3.17   0.003    -2394.608   
-538.5452
      adol |  -755.8335   258.1447    -2.93   0.005    -1274.332   
-237.3346
violent_ar~s|  -.0159294   .0164146    -0.97   0.336    -.0488991    
.0170404
     _cons |   489.9222   145.1734     3.37   0.001     198.3329    
781.5115
------------------------------------------------------------------------------ 

From the results of these models, it seems changes in sentencing 
polices (variable mt_b) increase admission rates more for black men than 
for white men.  I want a way to test that this difference is 'real'.  I 
was told that suest would be the way to go, but none of the examples in 
the help file quite fit.

i had thought that the chow test example was relevant, but realize i am 
incorrect. in that example, the same model is run, once on men and once 
on women.  in contrast, my dependent variable (rather than my sample) 
changes -- from blacks' admission rates to whites'.  this is necessary 
since the i'm looking at state-year level rather than individual level 
outcomes. at this point, i am not even sure suest can address my question.

does anyone have any suggestions?

thanks,
traci


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