Final Project

Texas is the Best Place Overall For A Recent College Graduate to Live

As my journey as an undergraduate student comes to a close, I am plagued with one question- where should I start this new chapter in my life?

This is a difficult question which requires many considerations, mostly involving personal preferences. Besides personal preferences, what are the factors that determine how ‘good’ a place is?

World Measures

To start my investigation, I turned to existing world measures to examine which factors were included and which were not. Both the World Happiness Report and Human Development Index are measured each year for every country in the world using a number of different variables in their methodology. The World Happiness Report primarily uses survey data from the Gallup World poll, which is administered annually in each country in the world. From a couple of questions, as well as economic and demographic measures, they development their ‘Happiness Score’. These factors include: GDP per Capita, Life Expectancy, and survey results from questions about social support, freedom to make choices, generosity, corruption, and institutional trust.
The Human Development Index uses fewer and more objective measures. They consider Life Expectancy at Birth, Expected Years of Schooling, Mean Years of Schooling, and Gross National Income Per Capita. They describe these factors as “a summary measure of average achievement in key dimensions of human development”. The graphs below outline the data from each measure from 2022. The HDI graph is color coded by different levels of development: “Very High”, “High”, “Medium”, and “Low”. The graph is hoverable and will show the exact HDI score and country name. Also, the legend allows a user to isolate each level of development on the graph. The WHR graph is similar. However, the levels shown are “Above Median” and “Below Median”, which are coded according to if the country’s score was above or below the median score for 2022.

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My Investigation

After exploring how much variability there was globally in those measures, I decided to come up with factors of my own to explore and choose the ‘best’ place to live in the US. The United States is the country I chose because I realistically will not be immigrating out of the United States, and all these factors can be applied to each US State. I wanted to make my own ‘index’ specifically according to factors important to new graduates. Economic opportunity, being close to peers, affordability, and labor measures were all factors I took into account. The measures I chose to reflect these factors were GDP, Population Demographics, Average Rent Price, Labor Measures and Rural/Urban Classification.

Economic Opportunity

To represent economic opportunity, I chose to use GDP by US state. The two shiny apps below present GDP Data by US State from 2019-2022. These are both interactive plots. The first plot shows GDP in millions for every US state. The year for the data can be chosen from the dropdown. From this plot alone, there are clear front runners in GDP. Most notably, California has the highest GDP out of all the states for all four years. Other states that are clear standouts are New York, Texas, Florida, and Illinois. Those states also saw a positive up trend in GDP up to 2022. The hover feature also shows the actual GDP number for every bar, The second plot shows the percent change in GDP from the previous year for every state. To show a certain state, the state can be selected from the drop down in the sidebar. Most states had significant dips in GDP from 2019-2020, largely due to the COVID-19 pandemic recession. This should be considered while viewing this data as a sign of economic opportunity in each state.

Population Demographics

The next factor I decided to investigate was population demographics. If I move to a new city, I want to be around peers, or people my age.First, I imposed the proportion of people ages 18-24 on a US Map, showing the mainland 48 states and they were colored accordingly. This map is shown below. Utah and North Dakota both seemingly have the highest proportion of 18-24 year olds on the map, while Nevada has the lowest. Then, I realized that I should also be taking GDP into account simultaneously. So, to narrow down from 50 states, and the include GDP, I then examined data from the states that had the top 10 highest GDPs from the data I showed before.

The chart below includes 2022 population data from the top 10 states with the highest average GDP from 2019-2022. I wanted to emphasize these states because I am taking all of these factors into account simultaneously, not just one by one. I was comfortable with narrowing down states just based on GDP because economic opportunity is one of the most important factors when deciding where to live. Also, I only included metropolitan areas in this data. A metropolitan area is defined by a Rural/Urban Continuum Code than equals 1,2, or 3. Rural Urban Continuum Codes are developed by the USDA and assign values to counties with certain demographic attributes. A code of ‘1’ indicates the county is a part of a metro area with more than 1 million people, a code of ‘2’ indicates a population of 250,000 to 1 million people, and ‘3’ indicates a population below 250,000. All of these codes indicate that the county in question is in a ‘metro’ area. This bar graph below shows raw metropolitan population numbers, in 100,000s, by state. Then, they are color coded according to proportion of people aged 18-24. The standouts, to me, include Texas, with the highest proportion out of the 10 states, and Georgia, in second place.

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Affordability

While GDP and population demographics are important to take into account, any job suited for a new graduate will not be on the high end of a salary range. So, considering the cost of living in a place is important before moving there. One of the highest categories for living expenses is the cost of shelter- whether that be a mortgage or rent. Recent graduates are less likely to have a mortgage or buy a house, so I chose rent expenses as my metric for affordability. Again, for this data, I only included areas that were classified as metropolitan (RUCC = 1,2, or 3).
The series of box plots below shows the distribution of median rents for a 1 bedroom apartment in each state (with top 10 GDP). This data is from the US Department of Housing and Urban Development. There is quite a lot of variability in this visualization. California is easily a standout, with the highest median value, and the largest IQR, showing variation between areas. New Jersey has a smaller IQR, but its median is right behind California’s. Illinois has the lowest median, but had several outliers (which probably represent the state’s largest metropolitan areas). New York has a similar pattern, where the overall distribution is lower but it has several outliers above the distribution.

Unemployment Rate

Although GDP is a great measure of the economic health and opportunity of an area, it does not mean that the job market is viable. So, I wanted to include a secondary measure of economic opportunity: Unemployment Rate. For each of the top 10 GDP states, I gathered unemployment data from 2000-2022. The unemployment rates were pulled from state metropolitan areas, so they show the average unemployment rate in metropolitan areas for each state. These vales are plotted against the national unemployment rate for each year. The points are also sized relative to their ratio with national unemployment rate (if they get bigger, state unemployment rate is higher relative to national). For 22 years, the most notable spikes in unemployment were after the 2008 crash, 2011, and in 2020, during the COVID-19 pandemic. California and Washington consistently have the highest unemployment rates over this period. The other states tend to stay in a cluster, generally increasing as national unemployment increases.

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My Index

My investigation was thorough and informative regarding the metropolitan areas of each state. In order to effectively rank states, I must first rank each variable I explored. These are seen below.

• 1. Population Demographics: how many young people live there?
• 2. Affordability: could I afford rent?
• 3. Unemployment: How do these rates vary?
• 4. GDP: what is the economic health of the state?

After I ranked each factor, I then ranked each state I analyzed according to how they land in these categories. The overall score was calculated by assigning points to each category (more points = more important) and using those proportions (out of 10 total points) to calculate an overall score. For example, my number one important factor was Population Demographics. This was allocated 4 points. So, for each state, after ranking them, I multiplied the rank by .4 (4/10 = .4), and that was the score for that category. I did this to eac category and summed up all of their points, then ranked them accordingly.
The map below shows each individual category ranked, as well as the overall score displayed on a map for each top 10 state. The drop down allows you to see each category’s individual ranking as well as the overall results. Using my method and weighted rank system, the overall winner was Texas.