What is Economic Mobility and Why Does it Matter?
Economic mobility is defined as "the ability of an individual, family or some other group to improve (or lower) their economic status—usually measured in income" and is a key factor in the creation of a vibrant society and a healthy economy. It is also a globally universal issue, which most countries are far from solving - some who even exacerbate the problem. This project aims to investigate whether the United States falls into that category of countries that discourage economic mobility, primarily focusing on wealth, housing, and education.
An Introduction: Wealth Inequality
As defined, economic mobility is often measured in terms of income and wealth - seen an upwards trend in an individual’s lifetime, if achieved. As demonstrated by the two below graphs, this is not the case. We can draw from these that the share of the total net worth held by the top 1% has rarely dipped over the last 30 years and, in general, has been increasing steadily. Compared to this, the net worth held by the bottom 50% has been volatile, facing a substantial downfall after 2007 - the year of the Financial Crisis.
Net Worth held by the Top 1%
Net Worth held by the Bottom 50%
These charts were constructed using FRED APIs. Click here for the first chart's data and here for the second.
The Beginning: Residential Housing
There is a well-versed idea that economic mobility stems from an individual’s background - i.e. the home and state in which they are born. After observing the House Price Index, it is clear that states are respectively affected by usual house market fluctuations. However, since 1980, New York has seen a significant rise in residential house pricing and currently is over 2.5 times higher than that of Mississippi. Such significant differences between states, as further evidenced by the map chart, would prevent movements of economic mobility. This deepens the belief that an individual may be economically immobile from the outset.
US Median House Price (2020)
US House Price Index
The interactive map was made using Datawrapper with data that I scraped from Wikipedia using Python Pandas.
US House Price Index data was collected through locally downloading CSV data files of a select few states from FRED before merging them via Python Pandas. The data for this chart can be found here.
The Quintessential Driver of Economic Mobility: Education
In America, school funding is based on the real estate taxes of the houses within that school district. The higher the house prices are, the more funding goes to the school, producing higher-quality education. Inversely, poorer neighborhoods pool less funding for schools in their area.
4 of 10 people residing in Massachusetts have a Bachelor's degree or higher; this is twice the amount of people for those in West Virginia. When we refer back to the map, West Virginia also has the lowest median house price (MHP). The second chart below supports this positive causal relationship between MHP and state education funding. It should be noted, however, that the relationship is weak and dissipates after MHPs of $450,000. California and the District of Columbia are outliers, likely caused by substantial income inequalities that resulted in a higher number of poorer neighborhoods. Interestingly, Hawaii (another outlier) is one of the only school districts not funded through property taxes, justifying why it does not fit the regression.
US Education Attainment
Relationship between House Prices and State Funding on Education
The first chart was produced by scraping data from Wikipedia using Python. This data can be found here.
For the second graph, I regressed MHP on the education funding for all 50 states - for more representative results. The data was collected by scraping two websites (Wikipedia, Education Data Initiative), and then merged through Python Pandas. The data for this graph may be found here.
Project Challenges
Unable to access data directly from API calls, I used a CORS reverse proxy to provide automation. Another challenge was during the scraping process. Often data was not formatted in the usual HTML table structure, which I solved by using pandas to extract the data before cleaning commas and currencies. Data preparation also entailed merging datasets from different sources in Python by reindexing tables for a smooth merge of region-specific data. Repetitive work was performed through loops to save time also.
The Conclusion
With the current regressive tax system held in place, the US is systematically powering the rich and forbidding the poor from moving up the economic ladder. By selectively enabling the same states to consistently have access to higher real estate assets, greater community funds, and better schools, the US is - and will continue to - disenable its citizens of economic mobility, provoking severe implications for the economy in the future.
While most of the factors illustrated above provide apparent correlations to economic mobility, there is not adequate evidence to conclude direct causality. However, the research provided some beneficial insight and motivation for assessing potential policy reformations.
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