Dear all,
I am analysing what are the determinants for the adoption of bonus compensation for a firm (i) headquartered in a country (j). I have more than 700 firms headquartered in 16 countries; my dependent variables is a dichotomous indicating whether bonus compensation is adopted (1) or not (0), and my key independent variables are the first two in the output of the model displayed below.
The first one indicates whether the country in which the firm is headquartered discourage (-1), encourage (1) or does not have a clear position on bonus compensation (0). The second one indicates whether the firm is listed in US.
As you can see, these two variables are measured on two different levels. Therefore, similarly to what happens with continuous dependent variable with mixed command, I'd want to understand how much of my variance is explained by firm- and country-level variables.
Thank you for your time,
Luigi
I am analysing what are the determinants for the adoption of bonus compensation for a firm (i) headquartered in a country (j). I have more than 700 firms headquartered in 16 countries; my dependent variables is a dichotomous indicating whether bonus compensation is adopted (1) or not (0), and my key independent variables are the first two in the output of the model displayed below.
The first one indicates whether the country in which the firm is headquartered discourage (-1), encourage (1) or does not have a clear position on bonus compensation (0). The second one indicates whether the firm is listed in US.
As you can see, these two variables are measured on two different levels. Therefore, similarly to what happens with continuous dependent variable with mixed command, I'd want to understand how much of my variance is explained by firm- and country-level variables.
Code:
melogit FIRM_INCENTIVE_POLICY CodesIncentivesRECOMM2 USListing *** all remaining indep vars *** || CountryC:, or
Fitting fixed-effects model:
Iteration 0: log likelihood = -163.67629
Iteration 1: log likelihood = -136.00745
Iteration 2: log likelihood = -135.00212
Iteration 3: log likelihood = -134.96207
Iteration 4: log likelihood = -134.962
Iteration 5: log likelihood = -134.962
Refining starting values:
Grid node 0: log likelihood = -139.52018
Fitting full model:
Iteration 0: log likelihood = -139.52018 (not concave)
******I omitted all iteration for the sake of brevity******
Iteration 54: log likelihood = -134.9648
Iteration 55: log likelihood = -134.96271
Iteration 56: log likelihood = -134.96218
Iteration 57: log likelihood = -134.962
Iteration 58: log likelihood = -134.962
Mixed-effects logistic regression Number of obs = 727
Group variable: CountryCODED Number of groups = 16
Obs per group:
min = 3
avg = 45.4
max = 365
Integration method: mvaghermite Integration pts. = 7
Wald chi2(23) = 200.88
Log likelihood = -134.962 Prob > chi2 = 0.0000
----------------------------------------------------------------------------------------------
FIRM_INCENTIVE_POLICY | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
CodesIncentivesRECOMM2 | 9.843344 3.403505 6.61 0.000 4.998332 19.38475
USListing | 10.54212 6.53276 3.80 0.000 3.129346 35.5142
FAMILY | 2.478101 1.416788 1.59 0.112 .8081043 7.599248
Outsider5_LN | 1.005343 .1950676 0.03 0.978 .6873139 1.47053
MEETINGS_LN | .4335576 .2472911 -1.47 0.143 .1417559 1.326027
RepRisk_1Y_AVG | 1.003941 .0181223 0.22 0.827 .9690433 1.040096
BETA9802 | .7564532 .3403387 -0.62 0.535 .313195 1.827045
ASSETSUSD_LN | 1.074118 .225251 0.34 0.733 .7121118 1.620151
TOTALDEBTTOTALASSETS8 | 2.979734 3.803166 0.86 0.392 .2442032 36.3583
TSR | 1.001388 .0037582 0.37 0.712 .994049 1.008781
RETURNONASSETS8326 | .3602494 1.129024 -0.33 0.745 .0007743 167.5997
|
SectorENCODED |
Consumer Discretionary | 2.762074 1.600434 1.75 0.080 .8872009 8.599013
Consumer Staples | 2.734421 1.905945 1.44 0.149 .6975269 10.71938
Energy | 2.054449 1.460637 1.01 0.311 .5099447 8.276902
Health Care | 1.901764 1.115238 1.10 0.273 .6025489 6.002343
Information Technology | 7.974622 5.946515 2.78 0.005 1.84921 34.39014
Materials | 1.237006 .7601254 0.35 0.729 .3709527 4.12501
Telecommunication Services | 2.581441 2.960618 0.83 0.408 .2726662 24.43954
Utilities | 1.341628 1.07053 0.37 0.713 .2808211 6.409653
|
Prot_min_inv_Ext_Dir_Liab_LN | 12.63175 17.98257 1.78 0.075 .7756964 205.7003
SayONPaySwitzNB | .9826529 .9250025 -0.02 0.985 .1552877 6.218179
RuleOfLaw | 3.383666 5.149616 0.80 0.423 .1713722 66.80896
CountryMKTCap2GDP | 1.004822 .0069745 0.69 0.488 .9912444 1.018585
_cons | .0000205 .0001294 -1.71 0.087 8.75e-11 4.81151
-----------------------------+----------------------------------------------------------------
CountryCODED |
var(_cons)| 1.37e-34 5.45e-18 . .
----------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline odds (conditional on zero random effects).
LR test vs. logistic model: chi2(0) = 5.7e-14 Prob > chi2 = .
Note: LR test is conservative and provided only for reference.
Thank you for your time,
Luigi
