# Personal Decision Calculator

## Personal Decision Earning Calculator.¶

### Given:¶

People that make different decisions earn different amounts of money.

### Find:¶

Mathematically show how personal decisions affect salary.

### Assumptions:¶

Alex, Peyton, Taylor and Drew are all peers.

Up through their 25$^{th}$ year of age they made 100% of the exact same personal decisions.

They all graduated from the same university. They all chose the same major. They all had the exact same GPA. They all started at the exact same company where they earned a starting salary of \$50,000.

Each additional year at \${COMPANY} they get a \$2,500 raise.

Each year they are *not* working their base salary goes down by \$2,500. This is due to skill and resume gap.

### Basic Equations & Functions:¶

```
import numpy
BASE_SALARY=50000
ANNUAL_RAISE=2500
def salary(year):
return year*ANNUAL_RAISE+BASE_SALARY
def starting_salary(old_salary,years_idle):
return max(BASE_SALARY,old_salary-years_idle*ANNUAL_RAISE)
```

### Solution / Graphs / Final Answers:¶

All of the individuals work for for \${COMPANY} for 5 years. Their annual incomes for the first 5 years are as follows:

```
for year in range(5):
print("Year {}: ${}".format(year,salary(year)))
```

During year 5 they all make separate personal decisions.

- Alex decides to continue working.
- Peyton leaves industry for 2 years.
- Taylor leaves industry for 5 years.
- Drew leaves industry for 10 years.

During year 6 their salaries are as follows:

```
year=5
print("Alex's Salary: ${}".format(salary(year)))
print("Peyton's Salary: ${}".format(0))
print("Taylor's Salary: ${}".format(0))
print("Drew's Salary: ${}".format(0))
```

At the end of year 6 each of the peers that left the industry will earn a percentage of what Alex did.

```
year6_alex= numpy.sum([salary(year) for i in range(6)])
year6_quitters=numpy.sum([salary(year) for i in range(5)])
print("Alex's 6 year salary: ${}".format(year6_alex))
print("Peer's 6 year salary: ${}".format(year6_quitters))
print("Peer's percentage earned: {:.2f}%".format(year6_quitters/year6_alex*100))
```

At the end of year 6 all of Alex's peers have only earned 83.33% of what Alex did. For no reason other than *their own* personal decisions. After year 7 it gets worse:

```
year7_alex= numpy.sum([salary(year) for i in range(7)])
year7_quitters=numpy.sum([salary(year) for i in range(5)])
print("Alex's 7 year salary: ${}".format(year7_alex))
print("Peer's 7 year salary: ${}".format(year7_quitters))
print("Peer's percentage earned: {:.2f}%".format(year7_quitters/year7_alex*100))
```

Good news. For year 8 Peyton goes back to work. Because Peyton was out of industry for 2 years ~~her~~ ~~his~~ its starting salary is:

```
peyton_starting_salary8=starting_salary(salary(5),2)
print("Peyton's (Re)Starting Salary: ${}".format(peyton_starting_salary8))
```

At the end of year 8 Taylor and Drew are still not working. Peyton has worked for a year again. Alex has been working for 8.

```
year8_alex =numpy.sum([salary(year) for i in range(8)])
year8_peyton =numpy.sum(numpy.sum([[salary(year) for i in range(5)],[peyton_starting_salary8]]))
year8_quitters=numpy.sum([salary(year) for i in range(5)])
```

At the end of year 8 cumulative salaries for the 4 peers is as follows:

```
print("Alex's 8 year cumulative salary: ${}".format(year8_alex))
print("Peyton's 8 year cumulative salary: ${}".format(year8_peyton))
print("Other's 8 year cumulative salary: ${}".format(year8_quitters))
print("Peyton's percentage earned: {:.2f}%".format(year8_peyton/year8_alex*100))
print("Other's percentage earned: {:.2f}%".format(year8_quitters/year8_alex*100))
```

Years 9+ are left as an exercise to the reader.

#### Bonus Question:¶

- What are Alex, Peyton, Taylor and Drew's genders?
- What function used their gender to generate cumulative earned income?