Impact Of Demographic Profile On Income Level Of Senior Managers In Unicorns

Variables Collected

The income senior level managers has been one of the controversial area in the recent time, especially with the growing unicorn which do not hesitate to pay higher compensation for the right candidate. Unicorn are those start-ups whose valuation is more than $ 1 billon. With increasing number of unicorns in last few years, and the higher compensation of the managers, it would be interesting to analyze the factors affecting their income(Business Insider, 2016). Also, there has been limited research on this area, which focus on factors affecting the income level of the senior level managers in unicorn. One of the most important factors are the demographic profile of the managers. So, the current research will only focus on the demographic factors and its impact on income level.

The main aim of the current study is examine the impact of demographic profile on the income level of the senior level managers in unicorns.

Null hypothesis: Demographic profile do not have significant impact on the income level  of the senior managers in unicorn.

Alternative hypothesis. Demographic profile have significant impact on the income level of the senior managers in unicorn.

The variables collected for the current research are as follows: 

The income level of the senior managers have been collected in four different categories.

Age – Measured in terms of years and it is a continuous variable

Education- Highest degree held by the managers and it is a categorical variable

IQ level – IQ score of the managers and it is a continuous variable

Marital status- Marital status of the managers and it is categorical variable

Work experience – Total work experience of the managers in the related industry and it is continuous variable 

Primary data was collected from 50 senior managers from 5 different unicorns. However for some managers the data was not complete, so the final sample size for the current study is 45. 

The descriptive statistics for the continuous variable have been shown in the table below. Descriptive statistics help the research to get the overview of the data. This also shows whether the collected data is as per the requirement of the research paper or not(Wooldridge, 2002). 

On the basis of the results it can be concluded that the average age of the managers in the unicorn is 46 years. This is because most of the unicorn hire the managers from the other established companies who have enough knowledge and experience about the industry and who can guide the unicorn in right direction.  The minimum age is 28 years whereas the maximum age is 60 years. Managers who are young might be the founders of the unicorn. 

Statistics
	Age	IQ_score	Experience
N	Valid	44	44	44
Missing	0	0	0
Mean	46.1591	105.9545	17.3864
Median	45.5000	104.5000	20.0000
Mode	44.00a	96.00a	22.00
Std. Deviation	10.04164	9.39806	8.24400
Variance	100.835	88.323	67.964
Skewness	-.275	-.049	-.339
Std. Error of Skewness	.357	.357	.357
Kurtosis	-1.104	-1.281	-.942
Std. Error of Kurtosis	.702	.702	.702
Minimum	28.00	90.00	2.00
Maximum	60.00	120.00	31.00
a. Multiple modes exist. The smallest value is shown

Descriptive Statistics

Similarly the descriptive statistics also show that the average IQ score of the managers is 105.95 with standard deviation of 9.3. This shows that the average IQ level of the managers in not significantly higher than the average IQ level of the normal human being. In fact the maximum IQ score in the current data set is 120 only whereas the minimum is 90. Furthermore the descriptive statistics for work experience is the 17 years. This shows that the senior managers in the unicorn are highly experienced. This can be one of the reason for higher compensation in the unicorns as the managers have to leave their stable job and the trade off to leave the stable job with the venerable unicorn is with the higher income. 

For the categorical variable the graphical presentation of the descriptive analysis have been shown,

The graphical results as shown in the figure above shows that most of the senior managers in the unicorn hold the post graduation, which is logical also. Only 11 % of the managers have bachelor degree. The close analysis of the data indicates that the senior managers who have only the bachelor degree have vast work experience. Furthermore only around 9 % of the managers have Phd. 

Results from the income level distribution shows that around 48 % of the managers have income level between $ 100000 – $ 150000 followed by 30 % of the managers having income level of $50000 and $10000. The proportion of managers having very low level of income and very level of income is comparatively low for the collected sample. Since the data was collected only for the 44 managers, the results cannot be easily generalized. However on the basis of the results it can be said the senior managers are earning higher income as compare to their counterparts in other companies. 

Results from the marital status show that more than 90 % of the managers are married and only 9 % of the managers are unmarried or are divorced. High proportion of the married managers may be because of the higher age group data being collected, as the descriptive statistics for age shows that average age is 46 years. 

Results from the cross tabulation has been discussed in this secion. Cross tabulation has been performed to examine whether there is significant difference in the income level on the basis of different demographic factors.

Cross Tabulation Results

The first cross tabulation results are to examine if there is significant difference in the income level of the managers for different education level. In other words, cross tabulation along with the chi square test help to examine whether the senior manager with post-graduation degree have significantly different income level from that of the senior managers holding bachelor degree of the Phd degree. 

Income level * Educational level Crosstabulation
% of Total
	Educational level	Total
Bachelor degree/Diploma	Post Graduation	Phd
Income level	0 – $50000		4.5%		4.5%
$50001 -$ 100000		25.0%	4.5%	29.5%
$100001 – $150000	6.8%	38.6%	2.3%	47.7%
above $150000	4.5%	11.4%	2.3%	18.2%
Total	11.4%	79.5%	9.1%	100.0%

Symmetric Measures
	Value	Approx. Sig.
Nominal by Nominal	Contingency Coefficient	.317	.557
N of Valid Cases	44

Results from the Pearson chi-square test show that the chi square value of 4.89 with 6 degree of freedom is not statistically significant at 5 % significance level. This is because the p value is more than the 0.05. On the basis of these results it can be concluded that there is no significant difference in income level on the basis of the education level of the senior managers. 

Similarly the cross tabulation has been performed to examine whether there is statistically significant difference in the income level of the senior managers for different marital status. Results from the cross tabulation and chi square test are shown in the table below. 

Income level * Marital Status Crosstabulation
% of Total
	Marital Status	Total
Married	Unmarried/Divorsed
Income level	0 – $50000	2.3%	2.3%	4.5%
$50001 -$ 100000	27.3%	2.3%	29.5%
$100001 – $150000	43.2%	4.5%	47.7%
above $150000	18.2%		18.2%
Total	90.9%	9.1%	100.0%

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	4.886a	3	.180
Likelihood Ratio	3.776	3	.287
Linear-by-Linear Association	2.073	1	.150
N of Valid Cases	44
a. 5 cells (62.5%) have expected count less than 5. The minimum expected count is .18.

Symmetric Measures
	Value	Approx. Sig.
Nominal by Nominal	Contingency Coefficient	.316	.180
N of Valid Cases	44

In this case also the pearson chi square value is not statistically significant at 5 %. So, there is no statistically significant difference in the income level for managers with different marital status. In other words married managers are also earning similar income as the unmarried/divorced managers. 

The third cross tabulation is to check the statistical difference on the basis of the age of the senior managers. Results from the chi square are shown in the table below and the results shows that the chi square value of 85.21 with 75 degrees of freedom is not statistically significant. On the basis of the results it can be said that there is no difference in the income level for managers in different age group. 

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	85.216a	75	.197
Likelihood Ratio	72.588	75	.557
Linear-by-Linear Association	17.422	1	.000
N of Valid Cases	44
a. 104 cells (100.0%) have expected count less than 5. The minimum expected count is .05.

Symmetric Measures
	Value	Approx. Sig.
Nominal by Nominal	Contingency Coefficient	.812	.197
N of Valid Cases	44

Results from the contingency coefficient is also insignificant indicating the relationship is not strong. 

Similarly the chi square test for income level and the IQ score has also been conducted and in this case also the chi square value of 72.877 is not statistically significant as the  p value is higher than 0.05. So, there is no statistically significant difference in the income level for managers with different IQ score. 

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	72.877a	63	.185
Likelihood Ratio	58.725	63	.629
Linear-by-Linear Association	.191	1	.662
N of Valid Cases	44
a. 88 cells (100.0%) have expected count less than 5. The minimum expected count is .05.

Symmetric Measures
	Value	Approx. Sig.
Nominal by Nominal	Contingency Coefficient	.790	.185
N of Valid Cases	44

Lastly the cross tabulation for the income level and the work experience has been conducted. As shown in the table below the chi square value is statistically insignificant indicating there is no statistically significant difference in the income level for managers with different work experience 

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	79.345a	69	.185
Likelihood Ratio	63.398	69	.668
Linear-by-Linear Association	12.596	1	.000
N of Valid Cases	44
a. 96 cells (100.0%) have expected count less than 5. The minimum expected count is .05.

Conclusion

Symmetric Measures
	Value	Approx. Sig.
Nominal by Nominal	Contingency Coefficient	.802	.185
N of Valid Cases	44

In addition the contingency coefficient is also insignificant which indicates weak relationship between the two variables. 

In this section results from the inferential analysis has been presented.  For the inferential analysis the One way ANOVA, correlation and the multiple regression analysis has been performed and the results are discussed below.

The first inferential analysis is the one Way ANOVA table. Results from the ANOVA shows that there is statistically significant difference in the group mean in the age for different income level. This is because the F statistic of age, 9.288 is statistically significant at 5 %. 

ANOVA
	Sum of Squares	df	Mean Square	F	Sig.
Age	Between Groups	1780.203	3	593.401	9.288	.000
Within Groups	2555.683	40	63.892
Total	4335.886	43
IQ_score	Between Groups	131.532	3	43.844	.478	.699
Within Groups	3666.377	40	91.659
Total	3797.909	43
Experience	Between Groups	914.930	3	304.977	6.077	.002
Within Groups	2007.502	40	50.188
Total	2922.432	43

Similarly the results for experience is also significant as the p value is 0.002 which is less than 5 %. However the significance of IQ score is statistically insignificant. 

The correlation analysis is conducted to examine the relationship between the two variable and also the direction of the relationship. In this case also the correlation was performed and the results are shown in the table below. 

Correlations
	Income level	Age	Educational level	IQ_score	Marital Status	Experience
Income level	Pearson Correlation	1	.637**	-.205	-.067	-.220	.541**
Sig. (2-tailed)		.000	.181	.667	.152	.000
N	44	44	44	44	44	44
Age	Pearson Correlation	.637**	1	-.116	-.053	-.443**	.893**
Sig. (2-tailed)	.000		.454	.732	.003	.000
N	44	44	44	44	44	44
Educational level	Pearson Correlation	-.205	-.116	1	-.282	.016	-.109
Sig. (2-tailed)	.181	.454		.064	.918	.482
N	44	44	44	44	44	44
IQ_score	Pearson Correlation	-.067	-.053	-.282	1	-.203	.023
Sig. (2-tailed)	.667	.732	.064		.187	.880
N	44	44	44	44	44	44
Marital Status	Pearson Correlation	-.220	-.443**	.016	-.203	1	-.481**
Sig. (2-tailed)	.152	.003	.918	.187		.001
N	44	44	44	44	44	44
Experience	Pearson Correlation	.541**	.893**	-.109	.023	-.481**	1
Sig. (2-tailed)	.000	.000	.482	.880	.001
N	44	44	44	44	44	44
**. Correlation is significant at the 0.01 level (2-tailed).

Results from the correlation analysis show that the dependent variable income level is positively and significantly correlated with age and experience, whereas there exist negative relationship between income level with level of education, IQ score, and marital status. Correlation between other variables can be interpreted from the above table. 

Results from the regression analysis have been discussed in this section. Regression analysis allows that researcher to examine the impact of the independent variables on the dependent variable. The regression coefficient tells whether the impact is positive or negative and the significance value or the p value shows whether the impact is statistically significant or not. In this case the income level is the dependent variable whereas the demographic factors age, education level, marital status, IQ score and work experience are the independent variables. 

Model Summaryb
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Durbin-Watson
1	.657a	.432	.358	.63699	2.356
a. Predictors: (Constant), Experience, IQ_score, Educational level, Marital Status, Age
b. Dependent Variable: Income level

Model summary the results for R squared and the Durbin Watson test. The R squared value in this case is 0.35 which means that 35 % of the variance is explained by the demographic factors whereas other variation is due to some other factors. Furthermore the value of Durbin Watson is 2.36 which is close to 2, so there is no problem of autocorrelation in the data set. 

ANOVAa
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	11.741	5	2.348	5.787	.000b
Residual	15.419	38	.406
Total	27.159	43
a. Dependent Variable: Income level
b. Predictors: (Constant), Experience, IQ_score, Educational level, Marital Status, Age

Similarly the results from the ANOVA test show that the F statics  of 5.787 which indicates the cumulative effect of the independent variable on the dependent variable  is statistically significant as the p value is less than 0.05. 

Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.	Collinearity Statistics
B	Std. Error	Beta	Tolerance	VIF
1	(Constant)	1.225	1.868		.656	.516
Age	.057	.022	.726	2.610	.013	.193	5.175
Educational level	-.260	.225	-.150	-1.154	.256	.890	1.124
IQ_score	-.005	.011	-.059	-.440	.662	.833	1.200
Marital Status	.120	.394	.044	.305	.762	.718	1.392
Experience	-.010	.027	-.101	-.360	.721	.190	5.251
a. Dependent Variable: Income level

Finally the results for the regression coefficient are presented in the table above. As table indicates that age, and marital status have positive coefficient indicating that these variable have positive impact on the income level. The coefficient of age is statistically significant also. The coefficient can be interpreted as, with one year increase in the age, the income level increases by 0.057 units holding all other factors constant.

However on the other hand the coefficient of educational level, IQ score and experience shows negative coefficient. This indicates that those variables have negative impact on the income level. However the coefficient of experience, educational level and IQ score were expected to be positive as shown by the previous results(Gavett, 2015; Gensowski, 2018; Heckman, Humphries, & Veramendi, 2016; Ours & Stoeldraijer, 2010; Wannakrairoj, 2013). In this case the results are opposite. This may be because of the small sample size and the biasness in the data, as data was only collected from the senior managers in the unicorns. Furthermore the results for the multicollinearity shows that there is no problem of mulitcollinearity as the VIF are between 1 and 10. 

Hypothesis testing

Since there are 5 different demographic variables, 5 sub hypothesis has been tested. 

Null hypothesis: There is no significant relationship between the age and the income level of the senior managers in unicorn.

Alternative hypothesis: There is significant relationship between the age and the income level of the senior managers in unicorn.

Since the regression coefficient of age is statistically significant the null hypothesis can be rejected and alternative hypothesis can be accepted.

Hypothesis 1b.

Null hypothesis: There is no significant relationship between educational level and the income level of the senior managers in unicorn.

Alternative hypothesis: There is significant relationship between education level and the income level of the senior managers in unicorn.

Since the regression coefficient is not statistically significant we fail to reject the null hypothesis.

Hypothesis 1c.

Null hypothesis: There is no significant relationship between marital status and the income level of the senior managers in unicorn.

Alternative hypothesis: There is significant relationship between marital status and the income level of the senior managers in unicorn.

Since the regression coefficient is not statistically significant we fail to reject the null hypothesis. 

Hypothesis 1d.

Null hypothesis: There is no significant relationship between IQ score and the income level of the senior managers in unicorn.

Alternative hypothesis: There is significant relationship between IQ score and the income level of the senior managers in unicorn.

Since the regression coefficient is not statistically significant we fail to reject the null hypothesis. 

Hypothesis 1e.

Null hypothesis: There is no significant relationship between work experience and the income level of the senior managers in unicorn.

Alternative hypothesis: There is significant relationship between work experience and the income level of the senior managers in unicorn.

Since the regression coefficient is not statistically significant we fail to reject the null hypothesis.  

References

Business Insider. (2016, May 24). The 10 highest paid CEOs of 2016. Business Insider. Retrieved from https://www.businessinsider.in/The-10-highest-paid-CEOs-of-2016/articleshow/58827319.cms

Gavett, G. (2015). The Factors That Lead to High CEO Pay. Harvard Business Review. Washington D. C.: Harvard Business School Press.

Gensowski, M. (2018). Personality, IQ, and Lifetime Earnings. Copenhagen.

Heckman, J. J., Humphries, J. E., & Veramendi, G. (2016). Returns to Education: The Causal Effects of Education on Earnings, Health and Smoking (No. 9957). Germany.

Ours, J. C. van, & Stoeldraijer, L. (2010). Age, Wage and Productivity. Germany.

Wannakrairoj, W. (2013). he Effect of Education and Experience on Wages: The Case Study of Thailand in 2012. Southeast Asian Journal of Economics, 1(1), 27–48.

Wooldridge, J. M. (2002). Econometric Analysis of Cross Section and Panel Data. booksgooglecom (Vol. 58). MIT Press. https://doi.org/10.1515/humr.2003.021