I am investigating whether implementing GSCMP (green practices) and EMS (environmental management systems) simultaneously positively influences the relationship between GSCMP and firm performance. Therefore, I added an interaction term in my model to test if there is a moderating effect. However, I'm not sure how to interpret the outcome; Can I interpret the GSCMP and EMS variables separately, or should I only look at the interaction coefficient? In my example, the coefficient is negative, would that imply a negative moderating effect? However, the effect is not significant does this mean that there is no support for a moderating effect at all? The variable EMS can be either 1 (=implemented EMS) or 0(=no EMS)
Thank you in advance!
Model (1) without interaction term
Code:
xtreg TobinsQ_w laggedGSCMP Firmrisk_w Firmsize_w i.Industry i.year, re
Random-effects GLS regression Number of obs = 3704
Group variable: ID Number of groups = 463
R-sq: within = 0.1526 Obs per group: min = 8
between = 0.1937 avg = 8.0
overall = 0.1788 max = 8
Wald chi2(15) = 682.88
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
------------------------------------------------------------------------------
TobinsQ_w | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
laggedGSCMP | -.0372598 .0850066 -0.44 0.661 -.2038697 .12935
Firmrisk_w | -.4236717 .1316738 -3.22 0.001 -.6817476 -.1655957
Firmsize_w | -.2410935 .0255676 -9.43 0.000 -.2912051 -.1909818
|
Industry |
2 | -.558424 .2066768 -2.70 0.007 -.9635031 -.1533448
3 | .632852 .1774375 3.57 0.000 .285081 .9806231
4 | -.3235721 .2409018 -1.34 0.179 -.795731 .1485868
5 | -.5079201 .1782467 -2.85 0.004 -.8572772 -.1585631
6 | -.283431 .1584239 -1.79 0.074 -.5939362 .0270742
|
year |
2008 | -.6494589 .0436928 -14.86 0.000 -.7350951 -.5638226
2009 | -.4367351 .0436323 -10.01 0.000 -.5222528 -.3512174
2010 | -.2601445 .0436846 -5.96 0.000 -.3457647 -.1745243
2011 | -.3809431 .0442207 -8.61 0.000 -.467614 -.2942722
2012 | -.2867888 .0444518 -6.45 0.000 -.3739128 -.1996647
2013 | .1048752 .0444915 2.36 0.018 .0176735 .1920769
2014 | .1389063 .044855 3.10 0.002 .0509922 .2268205
|
_cons | 4.23682 .2465329 17.19 0.000 3.753625 4.720016
-------------+----------------------------------------------------------------
sigma_u | .86607694
sigma_e | .65909614
rho | .63325553 (fraction of variance due to u_i)
------------------------------------------------------------------------------
Code:
xtreg TobinsQ_w c.laggedGSCMP##i.laggedEMS Firmrisk_w Firmsize_w i.Industry i.year, re
Random-effects GLS regression Number of obs = 3704
Group variable: ID Number of groups = 463
R-sq: within = 0.1527 Obs per group: min = 8
between = 0.1935 avg = 8.0
overall = 0.1787 max = 8
Wald chi2(17) = 682.62
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
-----------------------------------------------------------------------------------------
TobinsQ_w | Coef. Std. Err. z P>|z| [95% Conf. Interval]
------------------------+----------------------------------------------------------------
laggedGSCMP | -.0258513 .1159604 -0.22 0.824 -.2531295 .2014269
1.laggedEMS | -.0137214 .0542992 -0.25 0.801 -.1201458 .0927031
|
laggedEMS#c.laggedGSCMP |
1 | -.0117569 .1467287 -0.08 0.936 -.2993398 .2758261
|
Firmrisk_w | -.424106 .1317266 -3.22 0.001 -.6822853 -.1659267
Firmsize_w | -.2401096 .0258406 -9.29 0.000 -.2907562 -.1894631
|
Industry |
2 | -.5561707 .2070759 -2.69 0.007 -.9620319 -.1503094
3 | .6347316 .1777639 3.57 0.000 .2863208 .9831425
4 | -.3226728 .2412485 -1.34 0.181 -.7955111 .1501655
5 | -.5064302 .1785666 -2.84 0.005 -.8564144 -.156446
6 | -.2812923 .158771 -1.77 0.076 -.5924778 .0298932
|
year |
2008 | -.6490783 .0437131 -14.85 0.000 -.7347544 -.5634021
2009 | -.4359109 .0437189 -9.97 0.000 -.5215983 -.3502235
2010 | -.2593825 .0437514 -5.93 0.000 -.3451336 -.1736313
2011 | -.3797915 .0443713 -8.56 0.000 -.4667576 -.2928254
2012 | -.2856803 .0445877 -6.41 0.000 -.3730705 -.1982901
2013 | .1056095 .044617 2.37 0.018 .0181619 .1930571
2014 | .1419346 .0457825 3.10 0.002 .0522025 .2316667
|
_cons | 4.229072 .2482444 17.04 0.000 3.742522 4.715622
------------------------+----------------------------------------------------------------
sigma_u | .86731741
sigma_e | .65914867
rho | .63388308 (fraction of variance due to u_i)
-----------------------------------------------------------------------------------------
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