Pooled logit with Clustered standard errors a substitute for a panel model with high variation of panelid?

[Marcel Issa]

By exploring my dataset, I noticed great Variation among my Groups of the panelid. I thus want to know if this alone justifies to run a fixed effects model to Control for all the heterogeneity of my panelid or if a pooled model or random effects model with clustered Standard Errors, which tells Stata that the observations are correlated within the Group, will also account for the heterogeneity as much?

[Marcel Issa]

Sry I forgot to Display my dataset:
input float Infodummy int Age byte Numberofemployees long Totalassets float Corporationdummy long Grossprofit float(Profitability_pct Leverage_pct) long Loansize byte Maturity double Duration byte Housebank str6 Loantype float Banks
1 8 28 1500 1 1600 6.25 95 475 10 0 0 "Credit" 7
1 8 28 1500 1 1600 6.25 95 475 10 0 0 "Credit" 1
1 6 15 500 1 800 8.75 50 150 10 5.75 1 "Credit" 8
1 6 15 500 1 800 8.75 50 30 1 5.75 1 "LC" 8
1 6 15 500 1 800 8.75 50 20 1 6 1 "LC" 8
1 23 10 387 0 815 3.435583 72 80 1 10 1 "LC" 8
1 24 10 415 0 830 5.060241 77 80 1 11 1 "LC" 8
1 25 10 400 0 850 3.529412 90 120 1 12 1 "LC" 8
0 24 10 415 0 830 5.060241 77 60 6 1 0 "Credit" 7
1 15 25 800 1 3500 3.4285715 20 100 1 4.666666666666667 0 "LC" 3
1 15 25 800 1 3500 3.4285715 20 620 20 0 0 "Credit" 6
1 15 25 800 1 3500 3.4285715 20 230 3 5 0 "LC" 5
0 7 8 130 0 300 23.333334 40 50 10 4.75 1 "Credit" 1
0 1 3 60 0 190 0 0 20 10 0 1 "Credit" 1
0 7 8 130 0 300 23.333334 40 15 3 3 0 "LC" 3
1 20 12 450 1 800 8.125 26 50 10 10.083333333333334 0 "Credit" 8
1 18 12 462 1 830 8.192771 32 125 5 8 0 "Credit" 8
1 19 12 438 1 755 7.549669 30 100 5 0 0 "Credit" 4
1 20 12 450 1 800 8.125 26 15 1 10 0 "LC" 8
1 19 12 438 1 755 7.549669 30 15 1 9 0 "LC" 8
1 18 12 462 1 830 8.192771 32 15 1 8 0 "LC" 8
1 19 12 438 1 755 7.549669 30 120 1 10 0 "LC" 5
1 18 12 462 1 830 8.192771 32 120 1 9 0 "LC" 5
1 20 12 450 1 800 8.125 26 10 1 10.583333333333334 0 "LC" 5
1 15 10 320 1 1000 8 55 70 6 7 0 "Credit" 5
1 15 10 320 1 1000 8 55 100 5 5.166666666666667 0 "Credit" 4
1 10 12 277 1 800 9.375 60 150 4 5.083333333333333 1 "Credit" 5
1 18 25 720 1 1800 11.38889 45 350 3 12 1 "Credit" 5
0 20 25 695 1 2000 10.5 45 300 6 14 1 "Credit" 5
1 3 3 248 1 500 11 44 30 4 0 0 "Credit" 3
1 4 3 250 1 600 8.333333 50 50 5 1.33 0 "Credit" 4
1 3 3 248 1 500 11 44 8 1 0 0 "LC" 7
1 4 3 250 1 600 8.333333 50 8 1 1 0 "LC" 7
1 4 3 250 1 600 8.333333 50 10 3 1.083 0 "LC" 9
1 2 25 462 1 1750 2.2857144 45 100 1 0 0 "LC" 9
1 3 29 450 1 1900 2.710526 50 200 3 .5833333333333334 0 "LC" 9
1 3 29 450 1 1900 2.710526 50 100 1 1 0 "LC" 9
1 2 25 462 1 1750 2.2857144 45 250 5 0 0 "Credit" 4
1 4 29 440 1 2000 2.5 50 200 5 1.4166666666666667 0 "Credit" 4
1 7 9 360 1 415 18.795181 25 15 1 5 1 "LC" 7
1 8 9 350 1 435 18.62069 25 25 1 6 1 "LC" 7
1 9 9 345 1 430 18.60465 30 15 1 7 1 "LC" 7
1 45 14 1000 0 1450 7.931035 60 350 7 15 1 "Credit" 7
0 50 15 1050 0 1500 6.666667 70 300 10 20 1 "Credit" 7
1 45 14 1000 0 1450 7.931035 60 150 1 15 1 "LC" 7
1 46 15 970 0 1400 6.785714 70 150 1 16.5 1 "LC" 7
1 47 15 960 0 1475 6.779661 70 150 1 17.75 1 "LC" 7
1 7 3 350 0 400 12.5 50 20 1 7 1 "LC" 6
1 7 3 350 0 400 12.5 50 15 5 7 1 "Credit" 6
0 25 25 500 1 1100 18.181818 80 150 10 15 1 "Credit" 5
0 25 25 500 1 1100 18.181818 80 400 15 15 1 "Credit" 5
0 25 25 500 1 1100 18.181818 80 50 1 15 1 "LC" 5
0 40 25 620 0 2000 15 20 150 10 20 1 "Credit" 5
0 40 25 620 0 2000 15 20 50 1 20 1 "LC" 5
0 35 12 380 1 1500 6.666667 30 25 5 15 1 "Credit" 5
1 4 7 400 0 950 13.68421 25 300 5 3 1 "Credit" 7
0 7 9 425 0 1000 12.3 20 250 7 6 1 "Credit" 7
1 4 7 400 0 950 13.68421 25 50 1 3 1 "LC" 7
1 5 8 415 0 975 14.358974 20 80 1 4.333333333333333 1 "LC" 7
1 6 9 410 0 935 13.368984 20 80 1 5.333333333333333 1 "LC" 7
1 7 9 425 0 1000 12.3 20 80 1 6 1 "LC" 7
1 102 6 370 0 427 14.285714 42 80 5 23 1 "Credit" 5
1 102 6 370 0 427 14.285714 42 30 1 8 0 "LC" 6
1 103 6 375 0 430 13.953488 45 45 1 8.75 0 "LC" 6
1 102 6 370 0 427 14.285714 42 80 5 0 0 "Credit" 2
1 17 28 3500 1 2875 5.495652 38 500 10 14 1 "Credit" 5
1 22 30 3625 1 3000 5 40 400 7 4 0 "Credit" 3
1 22 30 3625 1 3000 5 40 60 2 5 0 "LC" 4
1 22 30 3625 1 3000 5 40 50 2 .16666666666666666 0 "LC" 4
1 18 15 3100 1 2600 6.538462 50 150 3 5 0 "Credit" 3
1 18 15 3100 1 2600 6.538462 50 130 4 4 0 "Credit" 4
1 18 15 3100 1 2600 6.538462 50 50 2 4 0 "LC" 4
0 26 35 2650 1 2300 9 21 300 5 22 1 "Credit" 7
0 27 35 2710 1 2425 9.278351 28 250 7 23 1 "Credit" 7
0 29 33 2665 1 2400 8.75 25 50 9 25.25 1 "Credit" 7
0 30 33 2700 1 2350 8.297873 25 80 10 26.333333333333332 1 "Credit" 7
0 27 34 2710 1 2425 9.278351 28 80 1 23.166666666666668 1 "LC" 7
0 17 26 1980 1 1650 8.939394 26 325 10 16 1 "Credit" 7
0 19 26 2050 1 1700 8.941176 31 150 8 18.333333333333332 1 "Credit" 7
0 20 26 1930 1 1750 8.857142 33 220 5 19.166666666666668 1 "Credit" 7
0 19 26 2050 1 1700 8.941176 31 80 1 18.166666666666668 1 "LC" 7
end
[/CODE]

I want to run a Regression using Collateraldummy as dependent variable and one that uses Infodummy as dependent variable.