Hello,

I have a question regarding different regression methods to proof my assumption. I want to know how ICT adoption influences the innovativeness of a firm. I have paneldata and two waves of observations. My simple OLS model is
Code:
 reg Inno dum_ICT firmage l10 c3 b2b i.a4a i.a1
where Inno is the innovation index (values from 1 to 4), dum_ICT is the ICT adoption dummy, l10 is a dummy if the firm offered formal training programmes last year, c3 is a dummy if the firm applied for a electrical connection last year, b2b is the percentage of the firm owned by foreigners, a4a is a dummy for the industry sector and a1 is a dummy for the country. In the model above, ICT is highly significant and it turns out that if a firm generally adopted ICT their innovativeness increases. Here you can see the outcome without all the dummies for the industry sector and the countries.

Code:
. regress Inno dum_ICT firmage l10 c3 b2b i.a4a i.a1

      Source |       SS           df       MS      Number of obs   =     5,167
-------------+----------------------------------   F(53, 5113)     =     56.83
       Model |  3378.29391        53  63.7413945   Prob > F        =    0.0000
    Residual |  5735.11813     5,113   1.1216738   R-squared       =    0.3707
-------------+----------------------------------   Adj R-squared   =    0.3642
       Total |  9113.41204     5,166  1.76411383   Root MSE        =    1.0591

------------------------------------------------------------------------------
        Inno |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     dum_ICT |   .2775897    .046647     5.95   0.000     .1861417    .3690376
     firmage |   -.000695   .0010344    -0.67   0.502    -.0027229    .0013329
         l10 |    .447571    .033803    13.24   0.000     .3813027    .5138393
          c3 |   .1906314   .0429038     4.44   0.000     .1065216    .2747412
         b2b |   .0007365   .0005533     1.33   0.183    -.0003482    .0018211
             |
But now I want to include different types of regressions with the same model in order to convince the reader that my hypothesis, that firms with higher ICT adoption perform better, is true. So I want further proof.
Therefore I adapted the model obove in order to see if firms which had zero ICT in year 1 but adapted ICT between year one and year 2 perform better:

Code:
.  xtset panelid year
       panel variable:  panelid (strongly balanced)
        time variable:  year, 1 to 2
                delta:  1 unit

.  regress Inno dum_ICT firmage l10 c3 b2b i.a4a i.a1 if year==2 & L.dum_ICT==0

      Source |       SS           df       MS      Number of obs   =     3,103
-------------+----------------------------------   F(52, 3050)     =     42.74
       Model |  2659.28191        52  51.1400367   Prob > F        =    0.0000
    Residual |  3649.46382     3,050  1.19654552   R-squared       =    0.4215
-------------+----------------------------------   Adj R-squared   =    0.4117
       Total |  6308.74573     3,102  2.03376716   Root MSE        =    1.0939

------------------------------------------------------------------------------
        Inno |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     dum_ICT |   .2845822   .0697638     4.08   0.000     .1477933     .421371
     firmage |  -.0010243   .0015315    -0.67   0.504    -.0040271    .0019784
         l10 |   .5705493   .0458074    12.46   0.000     .4807328    .6603659
          c3 |   .1032498   .0581832     1.77   0.076    -.0108325    .2173322
         b2b |    .000677   .0007668     0.88   0.377    -.0008264    .0021804
             |
         a4a |
          2  |  -.2885407   .2943741    -0.98   0.327    -.8657323    .2886509
          3  |  -.2833497   .3388527    -0.84   0.403    -.9477525    .3810531
          4  |   .0474887    .355741     0.13   0.894    -.6500278    .7450051
          5  |  -.4421766   .2829037    -1.56   0.118    -.9968779    .1125246
          6  |  -.4162328   .2702835    -1.54   0.124    -.9461891    .1137235
          7  |  -.0908338   .3143731    -0.29   0.773    -.7072384    .5255707
          8  |  -.3757523   .2803625    -1.34   0.180    -.9254708    .1739663
          9  |  -.5647735   .2802044    -2.02   0.044    -1.114182    -.015365
         10  |  -.5570642   .4010146    -1.39   0.165     -1.34335     .229222
         11  |   .0529539   .4912183     0.11   0.914    -.9101984    1.016106
         12  |  -.1009795   .3189997    -0.32   0.752    -.7264557    .5244966
         13  |   -.197411   .2702716    -0.73   0.465    -.7273438    .3325219
         14  |  -.6717909   .4492206    -1.50   0.135    -1.552597    .2090148
         15  |  -.1987418   .3154601    -0.63   0.529    -.8172777    .4197941
         16  |    .290117   .3185079     0.91   0.362    -.3343948    .9146287
         17  |   -1.44027   .8197693    -1.76   0.079    -3.047626    .1670859
         18  |   .5777043   .8202959     0.70   0.481    -1.030684    2.186093
         19  |  -.3818752   .2680905    -1.42   0.154    -.9075315    .1437811
         20  |  -.2772761   .2735206    -1.01   0.311    -.8135795    .2590272
         21  |  -.6216942   .3072893    -2.02   0.043    -1.224209   -.0191792
         23  |  -.4837853   .3432089    -1.41   0.159    -1.156729     .189159
         24  |  -.4641845   .2853611    -1.63   0.104    -1.023704     .095335
         25  |   -.971511   .3362687    -2.89   0.004    -1.630847   -.3121749
             |
          a1 |
         11  |   .1634681   .1675756     0.98   0.329    -.1651045    .4920408
         12  |  -.3999711   .1618933    -2.47   0.014     -.717402   -.0825401
         13  |  -.1681401   .2141821    -0.79   0.432     -.588096    .2518158
         14  |  -.2134868   .2321303    -0.92   0.358    -.6686344    .2416608
         15  |   .3120863   .1438212     2.17   0.030       .03009    .5940826
         16  |  -.2574673   .1790772    -1.44   0.151    -.6085916     .093657
         17  |  -.1769258   .1475234    -1.20   0.231    -.4661812    .1123295
         18  |   .4848021   .1341442     3.61   0.000       .22178    .7478242
         19  |   1.083772   .1396287     7.76   0.000     .8099967    1.357548
         20  |   1.801743   .2438499     7.39   0.000     1.323616     2.27987
         21  |   1.915563     .14788    12.95   0.000     1.625609    2.205518
         22  |  -.0225719   .1868979    -0.12   0.904    -.3890305    .3438867
         23  |   1.349221   .1709095     7.89   0.000     1.014112    1.684331
         25  |  -.0714195   .1505803    -0.47   0.635    -.3666687    .2238297
         27  |   1.790655   .1183697    15.13   0.000     1.558562    2.022747
         28  |   1.999689   .1823233    10.97   0.000       1.6422    2.357178
         29  |   .9985773   .1282099     7.79   0.000     .7471907    1.249964
         30  |   .6118559   .1740438     3.52   0.000     .2706009     .953111
         31  |  -.4342055   .1394508    -3.11   0.002    -.7076325   -.1607785
         32  |   2.301774   .1525156    15.09   0.000      2.00273    2.600817
         33  |   .7592134   .2025517     3.75   0.000     .3620618    1.156365
         34  |   1.854734   .1338328    13.86   0.000     1.592322    2.117146
         35  |   1.692545   .1409749    12.01   0.000      1.41613    1.968961
         36  |   .7192039   .1578082     4.56   0.000     .4097826    1.028625
             |
       _cons |   .2699117    .281987     0.96   0.339     -.282992    .8228155
------------------------------------------------------------------------------
So it turns out yes, they do.

But I want further proof so I include Treatment effects and here a propensity score model (logistic).
Outcome variable: Inno
Treatment variable: dum_ICT
Treatment independent: firmage l10 c3 b2b

Code:
. teffects psmatch (Inno) (dum_ICT firmage l10 c3 b2b)

Treatment-effects estimation                   Number of obs      =      6,460
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: logit                                        max =        206
------------------------------------------------------------------------------
             |              AI Robust
        Inno |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE          |
     dum_ICT |
   (1 vs 0)  |   .3910807   .0534788     7.31   0.000     .2862642    .4958973
------------------------------------------------------------------------------


Also that model shows that ICT adoption is significant and if a firm adopts ICT their innovativeness increases. My question now is, what else would you suggest to do in order to proof my assumption? Would you suggest to use another estimation technique? I also think about interaction terms in order to see why ICT adoption positively affects Innovation. Therefore I am curious about interesting interactions with ICT.
By the way, I will do all the regressions I showed above for the dependent variables:

Capacity Utilization
Export Rate
Gross Profit Margin
Labor Productivity and
Cost of Material Ratio

in order to show how ICT adoption affects overall firm performance.

Thank you very much.


Kind regards
Dominik