FIRST PART: The Stata file ceosalary.dta contains data on the characteristics of 177 chief executive o?cers,...

FIRST PART: The Stata file ceosalary.dta contains data on the characteristics of 177 chief executive o?cers, which we will use to examine the e?ects of firm performance on CEO salary. The variables in the dataset include 1. salary (1990 compensation, $1000s), 2. age (in years), 3. college (=1 if attended college), 4. grad (=1 if attended graduate school), 5. comten (years with company), 6. ceoten (years as ceo with company), 7. sales (1990 firm sales, millions), 8. profits (1990 profits, millions), 9. mktval (market value, end 1990, millions). a. Load the dataset & inspect characteristics of it: What is the total count of observations in the dataset, how many variables there are in the dataset? b. Generate a summary table with sample means, standard deviations, and Minimum/maximum values for all the variables. c. How many dummy variables are in the data set? d. Are years the person stays with the company as ceo correlated with the salary? e. Is there any relationship between salary and years stayed with the company as ceo? Provide a two-way graph of the variables showing the relationship between the two. f. Run a single regression of salary over years the worker stays with the company as ceo. Is the result supporting your answer to the previous part? g. Do differences in years workers stayed with the company as CEO explain a lot of variation of their salaries? h. Imagine “age” is the variable missing in your single linear regression model above. Is this variable causing omitted variable bias? If yes, is it upward or downward bias? i. Include age in the regression and run a MLRM with age and years as ceo. Did the coefficient of the previous regression change substantially? j. Generate a dummy variable which takes value of 0 if worker is less than 50 years old and 1 otherwise. k. Run a regression of salary on the dummy you created and interpret the coefficient. l. Provide the scatter plot and the regression line for part k. m. Estimate a regression of salary on firm sales and market value. Interpret the e?ects of sales and market value on salaries of CEOs n. Add profits and ceoten to the model. Which firm characteristics are significant determinant of salaries? Interpret the e?ect of an additional year of CEO tenure on salaries. o. Did the coefficient or firm sales change dramatically from part G to part H by adding more regressors? Was the coefficient suffering from omitted variable bias? p. EXTRA CREDIT: Check for heteroscedasticity for the regression of salary and ceoten, what would be the issue if there exist heteroscedasticity? what do you suggest statistically to correct for it? salary age college grad comten ceoten sales profits mktval

salary	age	college	grad	comten	ceoten	sales	profits	mktval
1161	49	1	1	9	2	6200	966	23200
600	43	1	1	10	10	283	48	1100
379	51	1	1	9	3	169	40	1100
651	55	1	0	22	22	1100	-54	1000
497	44	1	1	8	6	351	28	387
1067	64	1	1	7	7	19000	614	3900
945	59	1	0	35	10	536	24	623
1261	63	1	1	32	8	4800	191	2100
503	47	1	1	4	4	610	7	454
1094	64	1	1	39	5	2900	230	3900
601	54	1	1	26	7	1200	34	533
355	66	1	0	39	8	560	8	477
1200	72	1	0	37	37	796	35	678
697	51	1	0	25	1	8200	234	5700
1041	63	1	1	21	11	4300	91	1400
245	44	1	1	7	7	135	24	558
817	68	1	0	38	4	1300	55	847
1675	71	0	0	31	12	674	115	1200
971	72	1	1	33	24	1400	69	609
609	58	1	0	36	1	1100	69	880
470	60	1	1	20	6	2300	210	2200
867	59	1	0	36	14	884	81	1500
752	54	1	0	32	4	1600	193	3200
246	51	1	0	8	8	78	13	458
825	56	1	1	4	4	10700	295	5900
358	50	1	1	23	4	99	25	2300
1162	58	1	0	24	6	3800	226	1800
270	43	1	0	15	2	150	28	713
829	56	1	0	14	8	2200	184	1500
300	77	0	0	45	26	6900	483	4700
1627	62	1	1	13	4	8300	596	9100
1237	63	1	1	37	9	4600	108	6200
540	61	1	1	37	1	5200	549	5600
1798	66	1	1	21	14	24300	338	12500
474	40	1	0	18	1	2700	117	2000
1336	60	1	1	21	13	4500	562	4300
541	51	1	0	30	4	1400	82	1200
129	66	1	1	4	4	59	28	412
1700	54	1	1	21	5	6800	1200	20400
1750	66	1	1	31	24	16200	1400	17900
624	61	1	1	21	13	1100	109	934
791	66	1	0	14	8	2300	-60	487
1487	51	1	0	3	3	22200	182	2800
2021	56	1	0	34	3	51300	2700	42900
1550	47	1	1	19	3	1100	120	4900
401	64	1	0	44	8	571	57	670
1295	62	1	0	8	8	10700	1300	16400
449	56	1	0	31	1	661	37	538
456	56	1	1	9	3	381	34	6700
1142	53	1	1	30	1	28000	1900	26300
577	64	1	0	26	2	3000	287	5700
600	56	1	1	18	7	11700	-40	4000
649	44	1	1	4	4	336	17	475
822	60	1	0	22	20	896	77	752
1080	52	1	0	18	5	388	55	1600
1738	54	0	0	34	12	10700	842	15400
581	54	1	0	19	19	408	23	403
912	54	1	0	9	9	2600	239	2400
650	69	1	0	37	13	261	40	817
2199	52	1	1	8	8	5600	475	6300
609	53	1	1	15	15	567	34	498
1946	73	1	0	25	21	7800	484	8000
552	52	1	0	30	1	2800	308	3500
481	59	1	1	26	4	611	90	667
526	45	1	0	8	7	2400	106	2000
471	60	1	0	3	2	160	7	425
630	56	1	0	29	1	1700	-55	420
622	57	1	0	35	4	2500	143	1200
999	52	1	0	28	17	159	21	398
585	60	1	1	36	10	1700	33	449
1107	57	1	0	17	6	2200	149	1100
1099	59	1	0	34	10	8600	182	1800
425	86	1	1	13	13	36	11	644
2792	40	1	0	11	11	534	35	888
350	54	1	0	31	4	1000	46	812
363	58	1	1	36	6	717	80	880
2265	63	1	1	35	6	18000	1700	18800
377	45	1	0	7	5	238	57	1200
879	63	1	1	21	9	1700	212	4900
720	49	1	0	12	12	672	23	1400
950	63	1	0	27	14	2600	6	1500
1143	67	0	0	23	3	1800	56	918
1064	58	1	0	27	3	3500	195	2600
1253	60	1	1	36	5	2600	142	3700
462	58	1	1	23	0	1400	50	769
174	69	1	0	13	13	29	6	390
474	63	1	0	41	4	2200	175	2600
1248	48	1	1	21	7	3500	423	7300
1101	62	1	1	32	3	954	96	1200
348	43	1	1	12	10	586	79	1400
650	55	1	1	28	5	5700	-438	817
875	58	1	1	32	10	5300	308	2200
1600	61	1	0	4	1	12300	877	9400
1500	55	1	1	30	4	7900	665	4800
323	39	1	1	15	3	637	63	517
459	59	1	0	33	3	785	40	1400
925	56	1	1	26	12	3300	67	2200
375	46	1	1	4	4	599	20	501
447	53	1	0	4	1	143	16	527
1340	55	1	0	13	10	1400	131	2900
1749	57	1	1	26	11	8100	40	10000
491	43	1	1	21	2	561	54	521
5299	64	1	0	42	13	2400	119	1500
431	58	1	1	33	3	815	36	550
729	50	1	1	15	3	2000	182	2600
1284	54	1	1	32	3	12300	1300	19600
1373	57	1	0	36	8	14300	1600	23600
989	40	1	0	18	5	439	30	582
515	52	1	1	27	1	1100	51	889
1301	50	1	1	19	15	1800	130	1600
834	58	1	0	35	1	4400	63	890
849	46	1	1	24	24	538	36	473
100	61	1	1	26	26	2700	394	10100
679	62	1	0	40	6	4900	-463	1400
567	56	1	0	31	10	597	65	1700
559	54	1	0	22	2	2100	13	686
704	52	1	1	6	6	50	8	903
308	45	1	1	14	14	210	39	1900
1392	48	1	1	6	6	4800	51	1100
389	55	1	0	29	4	478	38	420
790	69	1	0	45	37	1200	140	3200
396	80	1	0	58	28	513	53	963
398	54	1	0	4	4	633	69	1800
707	46	1	1	6	1	130	26	1200
984	60	1	0	7	4	1500	135	1700
410	55	1	0	36	20	501	34	590
1095	60	1	1	33	5	27600	1400	17100
694	61	1	1	35	19	4200	75	1000
834	61	1	1	32	0	7600	364	5300
1630	39	1	1	8	8	227	27	822
493	55	1	1	4	1	1300	80	834
625	57	0	0	36	9	1400	87	979
483	52	1	1	18	14	1000	35	548
733	60	1	0	8	8	347	18	778
2102	67	1	1	41	20	10300	1700	45400
853	58	1	0	34	34	818	33	411
345	54	1	1	33	0	994	56	781
800	57	1	1	12	9	1800	32	479
764	55	1	1	31	16	1100	145	2100
806	59	1	1	3	3	3000	257	3900
310	40	1	0	18	1	2400	60	1300
1119	61	1	0	34	9	2500	71	1200
1287	59	1	1	4	3	4700	222	2700
1170	57	1	1	9	3	1900	208	5600
880	62	1	0	36	12	5300	229	4000
1091	33	1	0	9	9	181	36	1300
1100	65	1	0	18	6	563	-271	544
650	53	1	1	5	4	1482	40	557
607	38	1	1	7	3	231	38	599
1133	63	1	0	9	9	870	37	686
393	58	1	1	36	6	285	40	956
605	53	1	0	16	4	422	30	505
1444	59	1	1	2	2	6204	401	10700
1033	62	1	1	30	1	5400	478	7300
1142	50	1	1	11	3	2600	166	2200
537	46	1	1	20	20	1200	17	669
693	42	1	0	17	12	1400	206	3000
439	57	1	1	26	12	2600	280	3800
358	64	1	0	43	11	584	45	423
1276	64	1	0	41	17	635	52	1300
873	41	1	1	2	2	149	21	567
537	57	1	1	35	1	11400	210	4800
713	57	1	1	12	2	1600	55	1300
1350	68	1	1	5	5	3300	92	2100
1268	47	1	0	20	4	1100	47	2500
465	64	1	1	31	3	2400	326	2400
693	46	1	1	7	3	2200	44	533
369	49	1	1	4	1	65	-132	1200
381	54	1	0	30	2	2700	386	4500
467	49	1	1	13	0	513	49	534
559	57	1	1	34	16	605	56	653
218	57	1	1	33	5	504	41	421
264	63	1	0	42	3	334	43	480
185	58	1	0	39	1	766	49	560
387	71	1	1	32	13	432	28	477
2220	63	1	1	18	18	277	-80	540
445	69	1	0	23	0	249	31	828

Expert Solution

Part 1) There are 177 observations in the dataset.

The total number of variables in the dataset is 9

Part 2)

Variable	Observations	Mean	Std. Dev.	Min	Max
salary	177	865.86	587.59	100	5,299
age	177	56.43	8.42	33	86
college	177	0.97	0.17	0	1
grad	177	0.53	0.50	0	1
comten	177	22.50	12.29	2	58
ceoten	177	7.95	7.15	0	37
sales	177	3529.46	6088.65	29	51,300
profits	177	207.83	404.45	-463	2,700
mktval	177	3600.32	6442.28	387	45,400

Part 3) There are two dummy variables in the dataset.

Part 4)

	ceoten	salary
ceoten	1
salary	0.1429	1

As can be seen from the above table the correlation coefficient between the tenure of the CEO in the company and salary is very low (0.14) so one can say that the number of years a person stays with the company as CEO is not correlated with the salary.

Rahul Sunny answered 2 years ago

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