Can the Summer Youth Employment Program Potentially Rescue New York from its Economic Decline due to the COVID-19 pandemic? - Christopher Pineda
Economic Decline?
How has COVID-19 changed the economy of NYC?
Due to COVID-19, many have lost their jobs to the point where employment in NY declined by nearly 2 million from February to April 2020. With jobs declining, employers lose money for their businesses, and people losing their jobs are forced to seek employment elsewhere, and would be lucky to find employment at all. The struggle to live in NY only gets more difficult, and since employers and employees become devastated from the pandemic, we need a solution that can benefit both parties.
Solution Overview
To help NY come back from economic devastation, we should utilize programs such as the Summer Youth Employment Program, especially since it benefits both the employer and the employee while giving students work experience. This would especially help employers since the school pays the employees until the SYEP is over for the year, unless they want to hire the intern for their company. Although the solution sounds simple, the solution should not be implemented without showing proof that the SYEP did improve the amount of people becoming employed over time. Such proof should be shown with charts and excel data.
Data and Statistics
To find out if the SYEP improved economic activity in NY, we will be finding out from NYC open data to get accurate data regarding the program. The data consist of very large CSVs that will be filtered with python programming. One of the CSVs focuses on neighborhood locations in NY while the other CSV is centered towards the boroughs in NY, both CSVs displaying information over the years.
Narrowed Data from CSVs
This is information narrowed down by a program I created to filter information based off of columns, location, and year
Neighborhood Bar Graph
This bar graph is the total of each neighborhood location from 2018 compared to 2019. The bar graph here is specifically for "Enrolled in college readiness courses or participated in college readiness activities through the program", however the program I made can check any column for comparison.
Numeric Neighborhood Information
This is the narrowed excel manipulated by the program to help create the plot. It can find the total for any column for each year to help with comparison. 10,329 people in 2018 enrolled in college readiness courses or participated in college readiness activities through the program while in 2019 it increased to 21,286. Over the years, it is present that the SYEP is leading students into more opportunities, and this increase is also correlated with the increase of students applying for the SYEP.
Percent Increase
If people are curious as to how much of a change is present, I made a function that detects the percent increase from the older year to the newer year, and the function can take in any column that the user would like.
Filtered Borough Data
To organize the Borough CSV data, a function was made to organize the data so that the boroughs would be the column names, and the year as the row names since the originally data was assembled so that the user would have to manually look through the CSV to find the data. I added an extra column, Total, so that the user can see the total of all boroughs for that following year in case they want to get general NY population information. This is a pivot table that can help plot bar graphs. Using the total, we can see that each year has an increasing number of people enrolling in the SYEP.
Enrollment Bar Graph
Using the pivot table function, a bar graph function was made to accompany the pivot table, giving the user a visualization of the information they desire.
Overview
The data gathered using python has shown that if there is an existing economic decline, then a program (like the Summer Youth Employment Program) can help states like New York recover from such decline because the program benefits employers and employees which circulates the economy since money is being spent and received. This was approachable with libraries such as pandas and matplotlib to visually demonstrate increases as well as numeric data.