Who Has More Economic Stability at the Close of Any Fiscal Year?

Date:  2021-09-02 02:30:11
7 pages  (1841 words)
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A close examination of the economic dimensions of drivers compel the rise of a strong argument on their stability. Stability, in this case, is the ability for the driver to live a comfortable life, provide for his or her family needs and have pension savings for later use. There is definitely two categories of drivers whether employed permanently, semi-permanently, or a casual worker. These categories are drivers who receive the wage at the close of the days business, or the driver who receives a monthly pay. This study is focused on determining who amongst these two categories has higher economic prowess and whoever can live a sustainable life at any social class.

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For whichever the category a driver lies, the economic dynamism and the ability to support their families is highly debatable. According to Ness, (2010) on Immigrant Unions & The New US Labor market, starting from the late 1990s there has been an influx of low wage drivers in the greengrocery, delivery workers and black-car drivers. In an excerpt from PayScale calculators, a delivery driver earns between $8 and $20 per hour with the average wage being $13.40. All over times are paid between $14.40 and $34.00. There are also bonuses and tips pay. On a monthly scale, wages are paid with the least earner pocketing $ 1208 and the highest paid member getting $5, 600. According to Indeed business magazine The average Delivery driver salary in the United States is approximately $ 2, 555 per month. This reporting utilized information obtained from over 206,000 employees in the previous two years.

To appropriately describe salary margins for both daily wage earners and the monthly paid drivers, the combination of all allowances is made and the average values calculated. The data is discrete in nature and its implications are directly tied to the economic benefits the drivers enjoy. For instance, the calculations attached to a daily wage driver are not company specific since the driver can be hired as many times within a day as possible by different companies. Note that, different companies have different payment scale for daily hired drivers and for monthly paid drivers. An example is Sysco that pays $22.57 per hour as compared to Coca-Cola that pays $16.65 per hour. This means that if a person works under these two companies in a certain day, they will earn at least $39.22 as wage for two hours only. The comparison to be drawn from a daily wage earner as opposed to monthly employed worker has an undeniable difference and can be used to strongly predict the economic power of the two categories of drivers.

Method of Data Collection

This study used both secondary data sources and primary data source to reinforce the authenticity of the collected data. A strong correlation was drawn between both the primary data source and the secondary sources. All data from the secondary sources was picked from internet sources on sites such as indeed.com and PayScale.com. Primary data collection utilized a number of platforms. First, was the use of social media in Facebook, Whatsapp groups and BBM groups on which I posted the question on a personal wall and on various Facebook groups that belong to drivers. My post read: Drivers: I have a statistical project and am answering the question, Who has more economic stability at the close of any Fiscal year- Day wage drivers or monthly paid drivers? I really need your help and so, please post into my inbox whether you are a daily wage earner or monthly paid worker and the amount you get as the pay. Also, if you are a day wage earner please post how many work stations hire you in a day and the average wage. Most of the responses that were received through the Facebook were from drivers who worked on a daily basis.

The second platform that was used to obtain data was through recruitment agencies alongside transport companies. From these agencies, most drivers were employed for a monthly salary. At least 18 workers recorded their names plus the monthly earnings. All non-monetary benefits that the workers enjoyed were evaluated and added to the monetary payment received since the main theme of this study was to establish the economic stability of either category of workers. The collected data was focused more on my spheres of influence with little-recorded data on other pieces of information that were irrelevant to the theme of the study.

There was a face to face interview with the recruitment agency workers and also the private companies employees. The data collected from these workers were entered into the data collection sheet and later transferred for analysis. The sheet also considered the age of the worker since it was understood the number of years an employee has worked would have influenced their economic stability. For instance, a driver who takes care of a nuclear family only and has worked for a long would focus more on making savings which implies that their economic power is stronger.

The sampling process was strict to ensure that only relevant data was utilized in the correlation tests. Furthermore, relevant data was sampled to ensure that equal sets of data are used for equality in the assessment process. The mean values were then computed for both sets of data. The variance was also computed that was transformed into the standard deviation. These two results were then used in the data analysis process to find the t-values for a general population since the group used in this study was a sample.

The sampling process was open and tried to consider all genders. However, there was a challenge in that most drivers who responded to my question were men. During the study 5 out of the 36 responses for both categories of drivers were women. In conclusion, as regards the representation of women in the transport industry, there was a strong conviction that not many women were involved in the truck driving industry. However, sticking to the theme of the study, lack of more women did bar, change or disable the computation process of the t-score.The collected valid data is shown on the table below.

Raw Data and Analysed on Drivers Wages

Name Income (in $) Name Income (in $)
Cooper K. 50.74 Megan S. 1,322
Vincent A. 147.62 Heather B. 1,234
Alexander F. 51.36 Paige H. 1,629
Peter K. 89.78 Derulo W. 2,327
Catherine K. 102.34 Redempta W. 3,216
Jason D. 89.64 Peter L. 1,433
Hofmann R. 132.79 Joseph M. 2,167
Richard H. 178.23 Jackson B. 4,301
Stanley F. 80.56 Leon M. 2,347
Antony K. 97.34 Kelvin N. 2,116
Mackenzie O. 101.98 Victor A. 4,214
Walter S. 67.76 Richard S. 2,882
Fredrick A. 59.23 George M. 1,242
Daniel K. 66.57 Duncan H. 2,330
Gabriel C. 87.43 Andrew K. 1,807
Erick N. 90.14 Stephen S. 3,462
Morrison C. 120.54 James C. 2,567
Antony A. 97.45 Japheth C. 3,018
Average day wage 89.52777767 Average day wage 2423
Standard Deviation 32.90126146 Standard Deviation 993.34393
Pieces of data 18 Pieces of data 18

Hypothesis Tests

Since the actual standard deviation of all drivers in the job market is not known, a t-value regression is done using the formula for unknown standard deviations. The formula is shown below and applied in the calculation process.

For this data, the following were the estimates for the points:

Average Hours of Sleep (x2): 2423

Standard Deviation (s2): 993.34

Pieces of Data (n2): 18

Average Hours of Sleep (x2): 2423

Standard Deviation (s2): 993.34

Pieces of Data (n2): 18

Average Hours of Sleep (x1): 89.53

Standard Deviation (s1): 32.90

Pieces of Data (n1): 18

Degrees of Freedom used: 15

Level of Significance: 0.01


The argument I am trying to prove in this study is that daily wage drivers have a higher economic empowerment than the monthly salary drivers.

Ho: m1 m2

Ha: m1 > m2


The basic understanding of the concept of economic power of any individual lies in the ability of the driver to live a sustainable living class. For instance, if the driver intends to live in the upper class of social status grouping, the driver should be able to sustain that kind of life style all through. The explanation to the t-values obtained was only satisfactory when a few other considerations were made on the net earnings by any single driver. The t-test value done in this study took into consideration that some benefits could be enjoyed by any worker if employed on a monthly basis. However, it was understood that some benefits like the medical cover for family members are contributed partly by the driver and partly by some of the companies. This factor implied that the calculation of t-value was to be based on the absolute earnings that any worker gains.

Another important consideration was the contribution citizens make as part of the tax. It was easy to acknowledge the fact that day wage drivers are more economically empowered as compared to the monthly ones due to the taxation imposed. It was the concrete knowledge that any deductions made before the net pay impacts on the overall earning of any individual. However, no salary adjustment was done for any reasons whatsoever since the study aimed at comparing what the driver earns as income regardless of whether imposed on taxation or not. The conclusion considered the t-test value obtained that created a certainty level that could erase any doubts about the fact that there is stronger economic empowerment on the side of day-wage drivers.

Finally, the results showered a higher value of t than that of ta. The statistical implication of t=9.972 greater than ta=3.994 is that the null hypothesis could not hold and region within which ta lies is outside the normal curves considerable range. The null hypothesis tried to prove that monthly salary is earning drivers enjoyed more economic power than the daily earning drivers. T-value computed allowed up to 15 spheres of influence considering that the general population of active drivers who fall under either day wage drivers or monthly salary drivers was could not be covered conveniently. The study also opens a wayward for other researchers to expand their spheres of influence beyond mine so that a more reasonable conclusion could be arrived at. Furthermore, the data contained here is open for inference since the respondents gave their consent through the Facebook inbox. The t-scores in this data can also be relied on for small-scale analyses.


Since the t-value is greater than ta, that is, t=9.972 while ta=3.994, the required point is within the unwanted region of the normal curve. This means that the null hypothesis can be certainly rejected. The certainty level is 99.98% which implies that daily wage drivers are more economically empowered as compared to monthly salary earning drivers. This is an implication that, due to the many agencies that can require casual drivers, then daily wage drivers are likely to spend more hours at different wage levels as contrasted to monthly salary drivers who are fixed to same rate of earning. From a generalized perspective, if a day wage driver takes an average of three hours to transport goods for each firm then in a day he or she can work for at least three companies. It means that the wage per hour should be multiplied by 9. A day hired driver also has flexibility and can extend working hours to beyond...

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