Are There Any Effective Ways to Curb Runaway Debt?

Modern-day sages and philosophers pose the question: If jealousy is the green-eyed monster, is debt the facilitator of everything we want? It’s an interesting topic to broach since so many of us are prone to the consumer-driven culture that dominates Western society. Our need for impulse satisfaction typically results in excessive spending and the concomitant debt which results. Many experts have weighed in on this highly contentious topic, with a variety of approaches put forth to manage debt effectively.

There is an age-old proverb that states there is nothing new under the sun. For many of us, we already understand the time-tested advice of financial gurus including the following:

  • If you want to cut your debt, cut your expenses
  • If you want to cut your debt, change your lifestyle
  • If you want to cut your debt, cut up your credit cards

Each approach to debt management has merit in it. Debt is a result of overspending, fueled in part by an emotional desire to live a certain way. Many of us are primed by our cultural zeitgeist to acquire the latest iPhone, iPad, MacBook, fashion accouterments, brand name products, and aspire towards the best exotic vacations. A life well managed is a life well lived.

In the US today, debt is intricately interwoven into the fabric of society. One of the leading experts in consumer debt in America, DebtConsolidation.com provided an interactive summary of mortgage debt, automobile loan debt, student loan debt, and credit card debt. The survey results are astonishing since they give credence to rising debt levels across the nation. Consider the following statistical data:

  • In 1999 credit card debt per borrower in the United States averaged around $2,370, but by 2016, that figure increased to $2,859. States with high levels of credit card debt include Alaska at $4,110 per borrower, Minnesota at $3,080, New York at $3,520, and Georgia at $2,890.
  • In 1999 automobile loans were hovering around $1,830 per borrower in the United States. By 2016, the average automobile loan debt spiked to $4,286 per borrower. States like Texas rank high at $6,370, New Mexico at $5,220, Wyoming at $5,070, and Georgia at $4,940.
  • In 1999 student loan debt averaged around $530 per borrower in the United States. Fast-forward to 2016 and that figure reads $4,985. States with high levels of student loan debt include Georgia at $6,340, Washington DC at $12,200, Maryland at $6,010, and New York at $5,550.
  • The biggest contributor to debt in the United States is mortgage debt. In 1999 the average mortgage debt per borrower in the US was $15,870. Fast-forward to 2016 and that figure rose to $30,602. States with high levels of mortgage debt include Hawaii at $52,380, Alaska at $40,940, California at $51,890, Colorado at $47,060, Washington state at $44,640, Virginia at $45,830, and Washington DC at $59,270.

It is a natural consequence that debt levels increase when interest rates decrease. However, the Federal Reserve Bank has embarked upon a policy of quantitative tightening, and this is the reason increasing debt is now a problem. Budgets remain the number #1 remedy for proper financial management and there are several options available including debt consolidation of high debt into low-interest debt repayments. Debt is a necessary evil, but it needs to be managed well to add value to your life.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $29 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.